From ff94dde35234fc36103390be652f5d8486d2c0bc Mon Sep 17 00:00:00 2001 From: Charles Doutriaux Date: Fri, 13 Apr 2018 09:59:49 -0700 Subject: [PATCH] Issue 531 json load (#533) * fix #531 for Jiwoo * testing ids are correctly read in --- tests/io/test_MC1_all.json | 11558 +++++++++++++++++++++++++++++++++++ tests/test_pmp_jsons.py | 11 +- 2 files changed, 11568 insertions(+), 1 deletion(-) create mode 100644 tests/io/test_MC1_all.json diff --git a/tests/io/test_MC1_all.json b/tests/io/test_MC1_all.json new file mode 100644 index 000000000..e9a3664ff --- /dev/null +++ b/tests/io/test_MC1_all.json @@ -0,0 +1,11558 @@ +{ + "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", + "REFERENCE": "The statistics in this file are based on Bellenger, H et al. Clim Dyn (2014) 42:1999-2018. doi:10.1007/s00382-013-1783-z", + "RESULTS": { + "model": { + "ACCESS1-0": { + "description_of_the_collection": "Describe which science question this collection is about", + "metrics": { + "EnsoAlphaLhf": { + "datasets": [ + "ACCESS1-0", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lhf_Tropflux": { + "nonlinearity": -0.9772199067582236, + "nonlinearity_error": -0.121535008706755, + "value": -1.8563408866312727, + "value_error": 0.025303792756335923 + }, + "ref_sst_Tropflux__lhf_Tropflux": { + "nonlinearity": -0.9769496270390065, + "nonlinearity_error": -0.12195536088477753, + "value": -1.8882982234421286, + "value_error": 0.02157680252798863 + } + }, + "name": "EnsoAlphaLhf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lhfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "ACCESS1-0", + "nonlinearity": -0.15537037002623855, + "nonlinearity_error": 0.7814683842637272, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 8.130198539862452, + "value_error": 0.16735477699456341 + }, + "name": "Latent feedback (alpha_lh)", + "observations": { + "sst_HadISST__lhf_Tropflux": { + "name": "sst_HadISST__lhf_Tropflux", + "nonlinearity": -6.820444867245091, + "nonlinearity_error": 2.0831539870003857, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -9.49411462980068, + "value_error": 0.4759691991297382 + }, + "sst_Tropflux__lhf_Tropflux": { + "name": "sst_Tropflux__lhf_Tropflux", + "nonlinearity": -6.740470980194552, + "nonlinearity_error": 1.7599796206299931, + "nyears": 156, + 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"sst_HadISST__lwr_Tropflux", + "nonlinearity": -3.031769834247151, + "nonlinearity_error": 0.7619025217405913, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.0717003477091378, + "value_error": 0.17343063925758676 + }, + "sst_Tropflux__lwr_Tropflux": { + "name": "sst_Tropflux__lwr_Tropflux", + "nonlinearity": -2.5351479356121907, + "nonlinearity_error": 0.6763952005160798, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.2149657778997496, + "value_error": 0.15526541110254435 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaShf": { + "datasets": [ + "ACCESS1-3", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__shf_Tropflux": { + 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"sst_HadISST__shf_Tropflux": { + "name": "sst_HadISST__shf_Tropflux", + "nonlinearity": 1.4632205439277906, + "nonlinearity_error": 0.25678170750848167, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -0.49616546838376474, + "value_error": 0.0633840713521507 + }, + "sst_Tropflux__shf_Tropflux": { + "name": "sst_Tropflux__shf_Tropflux", + "nonlinearity": 1.2599685687659758, + "nonlinearity_error": 0.2247028437558145, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -0.46024785893909487, + "value_error": 0.05486276726276936 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaSwr": { + "datasets": [ + "ACCESS1-3", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__swr_Tropflux": { + "nonlinearity": -0.9517293412147807, + "nonlinearity_error": 0.045713153893058245, + "value": -1.5147054641692161, + "value_error": 0.016848702520852278 + }, + "ref_sst_Tropflux__swr_Tropflux": { + "nonlinearity": -0.9453442466990021, + "nonlinearity_error": 0.051702636360985384, + "value": -1.5503056688706593, + "value_error": 0.015429352549311437 + } + }, + "name": "EnsoAlphaSwr", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 swrA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "ACCESS1-3", + "nonlinearity": 0.8167507222325594, + "nonlinearity_error": 0.6779837710133906, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 4.035269229211515, + "value_error": 0.1424579448126269 + }, + "name": "Longwave feedback 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[model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": -0.032424210166532914, + "value_error": 0.1586652190030101 + }, + "ref_Tropflux": { + "value": -0.18505932007550657, + "value_error": 0.19744830850310702 + } + }, + "name": "EnsoAmpl", + "raw_values": { + "method_to_compute_diagnostic": "Standard deviation of nino3 sstA, time series are linearly detrended", + "model": { + "name": "ACCESS1-3", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7574301649975974, + "value_error": 0.0606429469786739 + }, + "name": "ENSO amplitude", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7828122333734305, + "value_error": 0.06569214332935802 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.9294298145329737, + "value_error": 0.15077342535484006 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoMu": { + "datasets": [ + "ACCESS1-3", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__taux_Tropflux": { + "nonlinearity": -1.512685697935589, + "nonlinearity_error": -0.15016010661785203, + "value": -0.6214510176642332, + "value_error": 0.03262248308716771 + }, + "ref_sst_Tropflux__taux_Tropflux": { + "nonlinearity": -1.793760827060615, + "nonlinearity_error": -0.49755760647395764, + "value": -0.6219871608649602, + "value_error": 0.030947860973598473 + } + }, + "name": "EnsoMu", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino4 tauxA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "ACCESS1-3", + "nonlinearity": -1.887428711556523, + "nonlinearity_error": 1.000671224007929, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 5.399635010258118, + "value_error": 0.2097296960210307 + }, + "name": "Bjerknes feedback (mu)", + "observations": { + "sst_HadISST__taux_Tropflux": { + "name": "sst_HadISST__taux_Tropflux", + "nonlinearity": 3.6814538013378506, + "nonlinearity_error": 3.0300800774838237, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.264032561759018, + "value_error": 0.6752057908137518 + }, + "sst_Tropflux__taux_Tropflux": { + "name": "sst_Tropflux__taux_Tropflux", + "nonlinearity": 2.377830509154606, + "nonlinearity_error": 2.7511799603797837, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.2842635255814, + "value_error": 0.6146291385513142 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "10-3 N/m2/C" + } + }, + "EnsoRmse": { + "datasets": [ + "ACCESS1-3", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the statistical value between the model and the observations", + "metric_values": { + "ref_HadISST": { + "value": 0.7152198814167953, + "value_error": null + }, + "ref_Tropflux": { + "value": 0.7869503482954503, + "value_error": null + } + }, + "name": "EnsoRmse", + "raw_values": { + "method_to_compute_diagnostic": "Spatial root mean square error of tropical_pacific sst, time series are linearly detrended, model regridded to observations", + "model": { + "name": "ACCESS1-3", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": null, + "value_error": null + }, + "name": "ENSO RMSE", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 142, + "time_period": [ + "1870-1-1 0:0:0.0", + "2012-11-1 0:0:0.0" + ], + "value": null, + "value_error": null + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 38, + "time_period": [ + "1979-1-15 0:0:0.0", + "2017-7-15 12:0:0.0" + ], + "value": null, + "value_error": null + } + }, + "ref": "Using CDAT regridding and rms (uncentered and biased) calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoSeasonality": { + "datasets": [ + "ACCESS1-3", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": -0.41966810098487356, + "value_error": 0.1906502511275286 + }, + "ref_Tropflux": { + "value": -0.51444265846875, + "value_error": 0.23646954174388723 + } + }, + "name": "EnsoSeasonality", + "raw_values": { + "method_to_compute_diagnostic": "Ratio between NDJ and MAM standard deviation nino3 sstA, time series are linearly detrended", + "model": { + "name": "ACCESS1-3", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.9683389343058706, + "value_error": 0.15530801780032455 + }, + "name": "ENSO Seasonality", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.6685950504344595, + "value_error": 0.28054643880761876 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.9942833759904117, + "value_error": 0.6513736508033522 + } + }, + "ref": "Using CDAT std dev calculation", + "time_frequency": "monthly", + "units": "" + } + } + }, + "name": "Metrics Collection 1" + }, + "CCSM4": { + "description_of_the_collection": "Describe which science question this collection is about", + "metrics": { + "EnsoAlphaLhf": { + "datasets": [ + "CCSM4", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lhf_Tropflux": { + "nonlinearity": -1.4374787083672949, + "nonlinearity_error": 0.047437907696848976, + "value": -1.9158186036631244, + "value_error": 0.032317807462417907 + }, + "ref_sst_Tropflux__lhf_Tropflux": { + "nonlinearity": -1.4426692763428537, + "nonlinearity_error": 0.028380957963509947, + "value": -1.9499955582285466, + "value_error": 0.02852812212243746 + } + }, + "name": "EnsoAlphaLhf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lhfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CCSM4", + "nonlinearity": 2.983799411012728, + "nonlinearity_error": 0.5877878814992892, + 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"CCSM4", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lwr_Tropflux": { + "nonlinearity": -0.1464114209584479, + "nonlinearity_error": -0.30213313619162935, + "value": -0.6406766518917217, + "value_error": 0.11440072408874644 + }, + "ref_sst_Tropflux__lwr_Tropflux": { + "nonlinearity": 0.020802008609861353, + "nonlinearity_error": -0.37714265238787437, + "value": -0.6830470749774253, + "value_error": 0.09012389156718334 + } + }, + "name": "EnsoAlphaLwr", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lwrA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CCSM4", + "nonlinearity": -2.5878841047960677, + "nonlinearity_error": 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"monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaSwr": { + "datasets": [ + "CCSM4", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__swr_Tropflux": { + "nonlinearity": -0.21451982044971682, + "nonlinearity_error": 0.1334409982866434, + "value": -0.5024803458868652, + "value_error": -0.05723067188790537 + }, + "ref_sst_Tropflux__swr_Tropflux": { + "nonlinearity": -0.11061891433167058, + "nonlinearity_error": 0.1501595403568715, + "value": -0.46806881781418885, + "value_error": -0.05869069044226943 + } + }, + "name": "EnsoAlphaSwr", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 swrA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + 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calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaThf": { + "datasets": [ + "CCSM4", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__thf_Tropflux": { + "nonlinearity": 0.37417756235541155, + "nonlinearity_error": 0.5316433910697529, + "value": -1.3472190909425779, + "value_error": 0.0017623255661010013 + }, + "ref_sst_Tropflux__thf_Tropflux": { + "nonlinearity": 0.6922193189085827, + "nonlinearity_error": 0.6846852408334888, + "value": -1.3699079516572938, + "value_error": 0.0007427857176226033 + } + }, + "name": "EnsoAlphaThf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 thfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CCSM4", + "nonlinearity": 11.722860147838507, + "nonlinearity_error": 0.782071334543937, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 5.818876130931281, + "value_error": 0.19191699507285317 + }, + "name": "Heat flux feedback (alpha)", + "observations": { + "sst_HadISST__thf_Tropflux": { + "name": "sst_HadISST__thf_Tropflux", + "nonlinearity": 8.530819065146805, + "nonlinearity_error": 2.731293498505309, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -16.758514386795596, + "value_error": 0.6377844974635841 + }, + "sst_Tropflux__thf_Tropflux": { + "name": "sst_Tropflux__thf_Tropflux", + "nonlinearity": 6.927506391665181, + "nonlinearity_error": 2.3407663553703095, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -15.730605694906114, + "value_error": 0.5504111587771748 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAmpl": { + "datasets": [ + "CCSM4", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": 0.47867481925781014, + "value_error": 0.24247636877330003 + }, + "ref_Tropflux": { + "value": 0.24541382210543902, + "value_error": 0.30174570814637963 + } + }, + "name": "EnsoAmpl", + "raw_values": { + "method_to_compute_diagnostic": "Standard deviation of nino3 sstA, time series are linearly detrended", + "model": { + "name": "CCSM4", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.15752473769626, + "value_error": 0.09267614961550993 + }, + "name": "ENSO amplitude", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + 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"time_frequency": "monthly", + "units": "" + } + } + }, + "name": "Metrics Collection 1" + }, + "CESM1-BGC": { + "description_of_the_collection": "Describe which science question this collection is about", + "metrics": { + "EnsoAlphaLhf": { + "datasets": [ + "CESM1-BGC", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lhf_Tropflux": { + "nonlinearity": -1.4536967610467268, + "nonlinearity_error": 0.04032180463511273, + "value": -1.8711967362014195, + "value_error": 0.028151625961512616 + }, + "ref_sst_Tropflux__lhf_Tropflux": { + "nonlinearity": -1.4590797518836816, + "nonlinearity_error": 0.02045308655169631, + "value": -1.9037084706776632, + "value_error": 0.024449874741888457 + } + }, + "name": "EnsoAlphaLhf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lhfA over nino3 sstA, time series 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"method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__taux_Tropflux": { + "nonlinearity": -0.6298904287249417, + "nonlinearity_error": 0.48798142620973284, + "value": -1.5334452606535665, + "value_error": -0.015050104035375914 + }, + "ref_sst_Tropflux__taux_Tropflux": { + "nonlinearity": -0.4269813248520761, + "nonlinearity_error": 0.9468702354594117, + "value": -1.5326897361037586, + "value_error": -0.01273404668248025 + } + }, + "name": "EnsoMu", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino4 tauxA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CESM1-WACCM", + "nonlinearity": 1.3625412880820855, + "nonlinearity_error": 0.6750194380954535, + "nyears": 156, + "time_period": [ + 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"Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the statistical value between the model and the observations", + "metric_values": { + "ref_HadISST": { + "value": 2.1017971023766115, + "value_error": null + }, + "ref_Tropflux": { + "value": 2.107881198906762, + "value_error": null + } + }, + "name": "EnsoRmse", + "raw_values": { + "method_to_compute_diagnostic": "Spatial root mean square error of tropical_pacific sst, time series are linearly detrended, model regridded to observations", + "model": { + "name": "CESM1-WACCM", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": null, + "value_error": null + }, + "name": "ENSO RMSE", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 142, + "time_period": [ + "1870-1-1 0:0:0.0", + "2012-11-1 0:0:0.0" + ], + "value": null, + "value_error": null + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 38, + "time_period": [ + "1979-1-15 0:0:0.0", + "2017-7-15 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"method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": 0.20419292599380084, + "value_error": 0.19746622056094082 + }, + "ref_Tropflux": { + "value": 0.014231455748346276, + "value_error": 0.24573357337703317 + } + }, + "name": "EnsoAmpl", + "raw_values": { + "method_to_compute_diagnostic": "Standard deviation of nino3 sstA, time series are linearly detrended", + "model": { + "name": "CNRM-CM5", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.9426569538096934, + "value_error": 0.07547295884253676 + }, + "name": "ENSO amplitude", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7828122333734305, + "value_error": 0.06569214332935802 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + 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sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CNRM-CM5", + "nonlinearity": -3.588912928899469, + "nonlinearity_error": 0.9430633798131347, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 8.615837988062582, + "value_error": 0.2079849629767291 + }, + "name": "Bjerknes feedback (mu)", + "observations": { + "sst_HadISST__taux_Tropflux": { + "name": "sst_HadISST__taux_Tropflux", + "nonlinearity": 3.6814538013378506, + "nonlinearity_error": 3.0300800774838237, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.264032561759018, + "value_error": 0.6752057908137518 + }, + "sst_Tropflux__taux_Tropflux": { + "name": "sst_Tropflux__taux_Tropflux", + "nonlinearity": 2.377830509154606, + "nonlinearity_error": 2.7511799603797837, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.2842635255814, + "value_error": 0.6146291385513142 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "10-3 N/m2/C" + } + }, + "EnsoRmse": { + "datasets": [ + "CNRM-CM5", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the statistical value between the model and the observations", + "metric_values": { + "ref_HadISST": { + "value": 1.9114929262986853, + "value_error": null + }, + "ref_Tropflux": { + "value": 1.9985745517841664, + "value_error": null + } + }, + "name": "EnsoRmse", + "raw_values": { + "method_to_compute_diagnostic": "Spatial root mean square error of tropical_pacific sst, time series are linearly detrended, model regridded to observations", + "model": { + "name": "CNRM-CM5", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": null, + "value_error": null + }, + "name": "ENSO RMSE", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 142, + "time_period": [ + "1870-1-1 0:0:0.0", + "2012-11-1 0:0:0.0" + ], + "value": null, + "value_error": null + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 38, + "time_period": [ + "1979-1-15 0:0:0.0", + "2017-7-15 12:0:0.0" + ], + "value": null, + "value_error": null + } + }, + "ref": "Using CDAT regridding and rms (uncentered and biased) calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoSeasonality": { + "datasets": [ + "CNRM-CM5", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": 0.1867234081546302, + "value_error": 0.3898615881145965 + }, + "ref_Tropflux": { + "value": -0.007081526667126319, + "value_error": 0.48355766929116356 + } + }, + "name": "EnsoSeasonality", + "raw_values": { + "method_to_compute_diagnostic": "Ratio between NDJ and MAM standard deviation nino3 sstA, time series are linearly detrended", + "model": { + "name": "CNRM-CM5", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.980160805081529, + "value_error": 0.31759009027511187 + }, + "name": "ENSO Seasonality", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.6685950504344595, + "value_error": 0.28054643880761876 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.9942833759904117, + "value_error": 0.6513736508033522 + } + }, + "ref": "Using CDAT std dev calculation", + "time_frequency": "monthly", + "units": "" + } + } + }, + "name": "Metrics Collection 1" + }, + "CNRM-CM5-2": { + "description_of_the_collection": "Describe which science question this collection is about", + "metrics": { + "EnsoAlphaLhf": { + "datasets": [ + "CNRM-CM5-2", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lhf_Tropflux": { + "nonlinearity": -0.9773224387912962, + "nonlinearity_error": -0.11628889019575799, + "value": -2.006274553074066, + "value_error": 0.03295577237332275 + }, + "ref_sst_Tropflux__lhf_Tropflux": { + "nonlinearity": -0.9770533755872537, + "nonlinearity_error": -0.11665159687578958, + "value": -2.0438271858150836, + "value_error": 0.028696463023482676 + } + }, + "name": "EnsoAlphaLhf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lhfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + 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calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaLwr": { + "datasets": [ + "CNRM-CM5-2", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lwr_Tropflux": { + "nonlinearity": -0.31162540995616606, + "nonlinearity_error": -0.3135191595059278, + "value": 3.5310166981629894, + "value_error": 0.8229051561027719 + }, + "ref_sst_Tropflux__lwr_Tropflux": { + "nonlinearity": -0.1767765157053151, + "nonlinearity_error": -0.3876965103749478, + "value": 2.996731643990272, + "value_error": 0.5898477259888761 + } + }, + "name": "EnsoAlphaLwr", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lwrA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "CNRM-CM5-2", + "nonlinearity": -2.086993316757145, + "nonlinearity_error": 0.42604359419204996, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 4.855892170897185, + "value_error": 0.09609061947777572 + }, + "name": "Longwave feedback (alpha_lwr)", + "observations": { + "sst_HadISST__lwr_Tropflux": { + "name": "sst_HadISST__lwr_Tropflux", + "nonlinearity": -3.031769834247151, + "nonlinearity_error": 0.7619025217405913, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.0717003477091378, + "value_error": 0.17343063925758676 + }, + "sst_Tropflux__lwr_Tropflux": { + "name": "sst_Tropflux__lwr_Tropflux", + "nonlinearity": -2.5351479356121907, + "nonlinearity_error": 0.6763952005160798, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.2149657778997496, + "value_error": 0.15526541110254435 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaShf": { + "datasets": [ + "CNRM-CM5-2", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__shf_Tropflux": { + "nonlinearity": -0.9166175886698052, + "nonlinearity_error": 0.05281058534341898, + "value": -0.6408093980483319, + "value_error": -0.07117061119889642 + }, + "ref_sst_Tropflux__shf_Tropflux": { + "nonlinearity": -0.9031667453577251, + "nonlinearity_error": 0.0616056345104493, + "value": -0.6127782676334415, + "value_error": -0.07341586065676202 + } + }, + "name": "EnsoAlphaShf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 shfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when 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"observations": { + "HadISST": { + "name": "HadISST", + "nyears": 142, + "time_period": [ + "1870-1-1 0:0:0.0", + "2012-11-1 0:0:0.0" + ], + "value": null, + "value_error": null + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 38, + "time_period": [ + "1979-1-15 0:0:0.0", + "2017-7-15 12:0:0.0" + ], + "value": null, + "value_error": null + } + }, + "ref": "Using CDAT regridding and rms (uncentered and biased) calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoSeasonality": { + "datasets": [ + "INMCM4", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": -0.41214592840182274, + "value_error": 0.19312143028279652 + }, + "ref_Tropflux": { + "value": -0.5081489390847839, + "value_error": 0.23953462347841042 + } + }, + "name": "EnsoSeasonality", + "raw_values": { + "method_to_compute_diagnostic": "Ratio between NDJ and MAM standard deviation nino3 sstA, time series are linearly detrended", + "model": { + "name": "INMCM4", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.980890394246463, + "value_error": 0.1573210963772702 + }, + "name": "ENSO Seasonality", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.6685950504344595, + "value_error": 0.28054643880761876 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.9942833759904117, + "value_error": 0.6513736508033522 + } + }, + "ref": "Using CDAT std dev calculation", + "time_frequency": "monthly", + "units": "" + } + } + }, + "name": "Metrics Collection 1" + }, + "IPSL-CM5A-LR": { + "description_of_the_collection": "Describe which science question this collection is about", + "metrics": { + "EnsoAlphaLhf": { + "datasets": [ + "IPSL-CM5A-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lhf_Tropflux": { + "nonlinearity": -1.1706327380006314, + "nonlinearity_error": -0.031330745848226205, + "value": -1.4278036306714368, + "value_error": 0.00885701566246544 + }, + "ref_sst_Tropflux__lhf_Tropflux": { + "nonlinearity": -1.172657249842027, + "nonlinearity_error": -0.039354913460759, + "value": -1.443768610188012, + "value_error": 0.006853902324862227 + } + }, + "name": "EnsoAlphaLhf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lhfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + 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calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaLwr": { + "datasets": [ + "IPSL-CM5A-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__lwr_Tropflux": { + "nonlinearity": -0.9440196713036504, + "nonlinearity_error": -0.12972948613234223, + "value": -4.354365380002129, + "value_error": -0.47417402400089564 + }, + "ref_sst_Tropflux__lwr_Tropflux": { + "nonlinearity": -0.9330534248243588, + "nonlinearity_error": -0.15618052329202003, + "value": -3.9588278200774143, + "value_error": -0.317561497717275 + } + }, + "name": "EnsoAlphaLwr", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 lwrA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "IPSL-CM5A-LR", + "nonlinearity": -0.16971947185283298, + "nonlinearity_error": 0.35065838906680347, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -3.594874544091776, + "value_error": 0.07357726576088625 + }, + "name": "Longwave feedback (alpha_lwr)", + "observations": { + "sst_HadISST__lwr_Tropflux": { + "name": "sst_HadISST__lwr_Tropflux", + "nonlinearity": -3.031769834247151, + "nonlinearity_error": 0.7619025217405913, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.0717003477091378, + "value_error": 0.17343063925758676 + }, + "sst_Tropflux__lwr_Tropflux": { + "name": "sst_Tropflux__lwr_Tropflux", + "nonlinearity": -2.5351479356121907, + "nonlinearity_error": 0.6763952005160798, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.2149657778997496, + "value_error": 0.15526541110254435 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaShf": { + "datasets": [ + "IPSL-CM5A-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__shf_Tropflux": { + "nonlinearity": -1.07816421168051, + "nonlinearity_error": 0.05371880955587206, + "value": -1.272598819631072, + "value_error": -0.00716417683714681 + }, + "ref_sst_Tropflux__shf_Tropflux": { + "nonlinearity": -1.0907732805135442, + "nonlinearity_error": 0.062125828493562685, + "value": -1.2938723524643505, + "value_error": -0.010234463062226074 + } + }, + "name": "EnsoAlphaShf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 shfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "IPSL-CM5A-LR", + "nonlinearity": -0.11437148033084274, + "nonlinearity_error": 0.09867360547887224, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.13525412102311235, + "value_error": 0.020833040189995 + }, + "name": "Latent feedback (alpha_lh)", + "observations": { + "sst_HadISST__shf_Tropflux": { + "name": "sst_HadISST__shf_Tropflux", + "nonlinearity": 1.4632205439277906, + "nonlinearity_error": 0.25678170750848167, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -0.49616546838376474, + "value_error": 0.0633840713521507 + }, + "sst_Tropflux__shf_Tropflux": { + "name": "sst_Tropflux__shf_Tropflux", + "nonlinearity": 1.2599685687659758, + "nonlinearity_error": 0.2247028437558145, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 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"method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "IPSL-CM5A-LR", + "nonlinearity": 0.6052749964856865, + "nonlinearity_error": 0.4694466806347614, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 6.279623073047289, + "value_error": 0.09835186806723037 + }, + "name": "Longwave feedback (alpha_swr)", + "observations": { + "sst_HadISST__swr_Tropflux": { + "name": "sst_HadISST__swr_Tropflux", + "nonlinearity": 16.92023151924854, + "nonlinearity_error": 1.9782903058582835, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -7.839958015065618, + "value_error": 0.533413931456101 + }, + "sst_Tropflux__swr_Tropflux": { + "name": "sst_Tropflux__swr_Tropflux", + "nonlinearity": 14.943545242795997, + "nonlinearity_error": 1.7315087415892565, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -7.3327778677852224, + "value_error": 0.46446543104855337 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaThf": { + "datasets": [ + "IPSL-CM5A-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__thf_Tropflux": { + "nonlinearity": -0.825928138637578, + "nonlinearity_error": 0.1344933762967297, + "value": -1.410634207631612, + "value_error": 0.007203171812379408 + }, + "ref_sst_Tropflux__thf_Tropflux": { + "nonlinearity": -0.785640680836789, + "nonlinearity_error": 0.1694206156353208, + "value": -1.4374668979550604, + "value_error": 0.006331923312717133 + } + }, + "name": "EnsoAlphaThf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 thfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "IPSL-CM5A-LR", + "nonlinearity": 1.4849755536161409, + "nonlinearity_error": 0.6718973154362047, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 6.881619276304784, + "value_error": 0.141181673307364 + }, + "name": "Heat flux feedback (alpha)", + "observations": { + "sst_HadISST__thf_Tropflux": { + "name": "sst_HadISST__thf_Tropflux", + "nonlinearity": 8.530819065146805, + "nonlinearity_error": 2.731293498505309, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -16.758514386795596, + "value_error": 0.6377844974635841 + }, + "sst_Tropflux__thf_Tropflux": { + "name": "sst_Tropflux__thf_Tropflux", + "nonlinearity": 6.927506391665181, + "nonlinearity_error": 2.3407663553703095, + "nyears": 156, + 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"HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__shf_Tropflux": { + "nonlinearity": -1.3361972014426053, + "nonlinearity_error": 0.029040332981601476, + "value": -1.5370104064629444, + "value_error": 0.013647882503583123 + }, + "ref_sst_Tropflux__shf_Tropflux": { + "nonlinearity": -1.3904308918147466, + "nonlinearity_error": 0.032612537014745174, + "value": -1.5789185428560608, + "value_error": 0.009765988669489125 + } + }, + "name": "EnsoAlphaShf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 shfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "IPSL-CM5B-LR", + "nonlinearity": -0.4919306519618498, + "nonlinearity_error": 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11.72610589957453, + "nonlinearity_error": 1.0119669100379893, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -2.8774115178294166, + "value_error": 0.2303768477707155 + }, + "name": "Longwave feedback (alpha_swr)", + "observations": { + "sst_HadISST__swr_Tropflux": { + "name": "sst_HadISST__swr_Tropflux", + "nonlinearity": 16.92023151924854, + "nonlinearity_error": 1.9782903058582835, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -7.839958015065618, + "value_error": 0.533413931456101 + }, + "sst_Tropflux__swr_Tropflux": { + "name": "sst_Tropflux__swr_Tropflux", + "nonlinearity": 14.943545242795997, + "nonlinearity_error": 1.7315087415892565, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -7.3327778677852224, + "value_error": 0.46446543104855337 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAlphaThf": { + "datasets": [ + "IPSL-CM5B-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__thf_Tropflux": { + "nonlinearity": 0.028868074458512475, + "nonlinearity_error": 0.45150919156655994, + "value": -1.1872082447506025, + "value_error": -0.006566462720856735 + }, + "ref_sst_Tropflux__thf_Tropflux": { + "nonlinearity": 0.26699087505306374, + "nonlinearity_error": 0.5784667560355679, + "value": -1.199441275423721, + "value_error": -0.007607334077537794 + } + }, + "name": "EnsoAlphaThf", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino3 thfA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": 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"time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAmpl": { + "datasets": [ + "IPSL-CM5B-LR", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": -0.049818411449837056, + "value_error": 0.15581287938786442 + }, + "ref_Tropflux": { + "value": -0.19970958558390578, + "value_error": 0.19389876162808437 + } + }, + "name": "EnsoAmpl", + "raw_values": { + "method_to_compute_diagnostic": "Standard deviation of nino3 sstA, time series are linearly detrended", + "model": { + "name": "IPSL-CM5B-LR", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7438137714432671, + "value_error": 0.0595527629980047 + }, + "name": "ENSO amplitude", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7828122333734305, + "value_error": 0.06569214332935802 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.9294298145329737, + "value_error": 0.15077342535484006 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoMu": { + "datasets": [ + "IPSL-CM5B-LR", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__taux_Tropflux": { + "nonlinearity": -2.1270564621693655, + "nonlinearity_error": -0.6462175229416329, + "value": -0.4138658547081181, + "value_error": 0.04321052926417185 + }, + "ref_sst_Tropflux__taux_Tropflux": { + "nonlinearity": -2.744954605049194, + "nonlinearity_error": -1.5832216597422746, + "value": -0.41469600312054594, + 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"name": "Heat flux feedback (alpha)", + "observations": { + "sst_HadISST__thf_Tropflux": { + "name": "sst_HadISST__thf_Tropflux", + "nonlinearity": 8.530819065146805, + "nonlinearity_error": 2.731293498505309, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -16.758514386795596, + "value_error": 0.6377844974635841 + }, + "sst_Tropflux__thf_Tropflux": { + "name": "sst_Tropflux__thf_Tropflux", + "nonlinearity": 6.927506391665181, + "nonlinearity_error": 2.3407663553703095, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": -15.730605694906114, + "value_error": 0.5504111587771748 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "W/m2/C" + } + }, + "EnsoAmpl": { + "datasets": [ + "NorESM1-ME", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": 0.362016442783305, + "value_error": 0.22334647006526318 + }, + "ref_Tropflux": { + "value": 0.147158308023815, + "value_error": 0.27793982198259476 + } + }, + "name": "EnsoAmpl", + "raw_values": { + "method_to_compute_diagnostic": "Standard deviation of nino3 sstA, time series are linearly detrended", + "model": { + "name": "NorESM1-ME", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.0662031334665343, + "value_error": 0.08536456967159751 + }, + "name": "ENSO amplitude", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.7828122333734305, + "value_error": 0.06569214332935802 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 0.9294298145329737, + "value_error": 0.15077342535484006 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoMu": { + "datasets": [ + "NorESM1-ME", + "HadISST", + "Tropflux", + "Tropflux" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_sst_HadISST__taux_Tropflux": { + "nonlinearity": -0.9180460814581987, + "nonlinearity_error": 0.24980758969236114, + "value": -0.421971878045519, + "value_error": 0.03733610274614315 + }, + "ref_sst_Tropflux__taux_Tropflux": { + "nonlinearity": -0.8731156136702466, + "nonlinearity_error": 0.42913485290801356, + "value": -0.422790545805508, + "value_error": 0.03479669769522172 + } + }, + "name": "EnsoMu", + "raw_values": { + "method_to_compute_diagnostic": "Regression of nino4 tauxA over nino3 sstA, time series are linearly detrended", + "method_to_compute_nonlinearity": "The nonlinearity is the regression computed when sstA<0 minus the regression computed when sstA>0", + "model": { + "name": "NorESM1-ME", + "nonlinearity": 0.30170956495024726, + "nonlinearity_error": 0.6713281648307446, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 8.24501195317113, + "value_error": 0.14227545010330314 + }, + "name": "Bjerknes feedback (mu)", + "observations": { + "sst_HadISST__taux_Tropflux": { + "name": "sst_HadISST__taux_Tropflux", + "nonlinearity": 3.6814538013378506, + "nonlinearity_error": 3.0300800774838237, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.264032561759018, + "value_error": 0.6752057908137518 + }, + "sst_Tropflux__taux_Tropflux": { + "name": "sst_Tropflux__taux_Tropflux", + "nonlinearity": 2.377830509154606, + "nonlinearity_error": 2.7511799603797837, + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 14.2842635255814, + "value_error": 0.6146291385513142 + } + }, + "ref": "Using CDAT regression calculation", + "time_frequency": "monthly", + "units": "10-3 N/m2/C" + } + }, + "EnsoRmse": { + "datasets": [ + "NorESM1-ME", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the statistical value between the model and the observations", + "metric_values": { + "ref_HadISST": { + "value": 2.3213514247838694, + "value_error": null + }, + "ref_Tropflux": { + "value": 2.432378565062895, + "value_error": null + } + }, + "name": "EnsoRmse", + "raw_values": { + "method_to_compute_diagnostic": "Spatial root mean square error of tropical_pacific sst, time series are linearly detrended, model regridded to observations", + "model": { + "name": "NorESM1-ME", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": null, + "value_error": null + }, + "name": "ENSO RMSE", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 142, + "time_period": [ + "1870-1-1 0:0:0.0", + "2012-11-1 0:0:0.0" + ], + "value": null, + "value_error": null + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 38, + "time_period": [ + "1979-1-15 0:0:0.0", + "2017-7-15 12:0:0.0" + ], + "value": null, + "value_error": null + } + }, + "ref": "Using CDAT regridding and rms (uncentered and biased) calculation", + "time_frequency": "monthly", + "units": "C" + } + }, + "EnsoSeasonality": { + "datasets": [ + "NorESM1-ME", + "HadISST", + "Tropflux", + "" + ], + "method_to_compute_metric": "The metric is the relative difference between model and observations values (M = [model-obs] / obs)", + "metric_values": { + "ref_HadISST": { + "value": -0.14535871238827058, + "value_error": 0.28076618980216284 + }, + "ref_Tropflux": { + "value": -0.2849309984857832, + "value_error": 0.3482431932139641 + } + }, + "name": "EnsoSeasonality", + "raw_values": { + "method_to_compute_diagnostic": "Ratio between NDJ and MAM standard deviation nino3 sstA, time series are linearly detrended", + "model": { + "name": "NorESM1-ME", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.426050222405865, + "value_error": 0.22871850493580234 + }, + "name": "ENSO Seasonality", + "observations": { + "HadISST": { + "name": "HadISST", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.6685950504344595, + "value_error": 0.28054643880761876 + }, + "Tropflux": { + "name": "Tropflux", + "nyears": 156, + "time_period": [ + "1850-1-16 12:0:0.0", + "2005-12-16 12:0:0.0" + ], + "value": 1.9942833759904117, + "value_error": 0.6513736508033522 + } + }, + "ref": "Using CDAT std dev calculation", + "time_frequency": "monthly", + "units": "" + } + } + }, + "name": "Metrics Collection 1" + } + }, + "obs": { + "HadISST": { + "sst": { + "path + filename": "/clim_obs/obs/ocn/mo/tos/UKMETOFFICE-HadISST-v1-1/130122_HadISST_sst.nc", + "varname": "sst" + } + }, + "Tropflux": { + "lhf": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_lhf_mo.xml", + "varname": "lhf" + }, + "lwr": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_lwr_mo.xml", + "varname": "lwr" + }, + "shf": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_shf_mo.xml", + "varname": "shf" + }, + "sst": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_sst_mo.xml", + "varname": "sst" + }, + "swr": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_swr_mo.xml", + "varname": "swr" + }, + "taux": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_taux_mo.xml", + "varname": "taux" + }, + "thf": { + "path + filename": "/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_netflux_mo.xml", + "varname": "netflux" + } + } + } + }, + "json_structure": [ + "type", + "data", + "metric", + "item", + "value or description" + ], + "json_version": 3.0, + "provenance": { + "commandLine": "PMPdriver_EnsoMetrics.py -p my_Param.py", + "conda": { + "DefaultEnvironment": "/export_backup/lee1043/anaconda2/envs/pmp_nightly-20180124", + "IsPrivate": "False", + "Platform": "linux-64", + "PythonVersion": "2.7.13.final.0", + "RootEnvironment": "/export_backup/lee1043/anaconda2 (writable)", + "Version": "4.3.24", + "buildVersion": "not installed", + "envVersion": "4.3.24" + }, + "date": "2018-03-08 18:35:46", + "openGL": { + "GLX": { + "client": {}, + "server": {} + } + }, + "osAccess": false, + "packages": { + "CDP": "2017.09.07", + "PMP": "1.1.2.2017.06.30.19.40.66ce8fec99c4e1d404dc0e5f25d4e6f8e6339f66", + "PMPObs": null, + "blas": null, + "cdms": "2.12.2017.11.30.06.06.14b9b9380c14680498a0ba8e9aefb4c84d2756be", + "cdtime": "2018.01.24", + "cdutil": "2.12.2017.11.16.21.46.e717f782e07d0a187f2b616a408103af20ceb57f", + "clapack": "3.2.1", + "esmf": "7.0.0", + "esmpy": "7.0.0", + "genutil": "2017.11.15", + "lapack": "3.6.1", + "matplotlib": "2.1.2", + "mesalib": null, + "numpy": "1.13.1", + "python": "2.7.14", + "vcs": "2.12.2018.01.24.16.21.d67c83aa153d32062df5c20f0c68e5a6f2103e8f", + "vtk": "7.1.0.2.12" + }, + "platform": { + "Name": "crunchy.llnl.gov", + "OS": "Linux", + "Version": "2.6.32-696.13.2.el6.x86_64" + }, + "userId": "lee1043" + } +} \ No newline at end of file diff --git a/tests/test_pmp_jsons.py b/tests/test_pmp_jsons.py index 503f87913..78d0cda73 100644 --- a/tests/test_pmp_jsons.py +++ b/tests/test_pmp_jsons.py @@ -3,7 +3,7 @@ import inspect import os import numpy - +import json class TestJSONs(unittest.TestCase): def testVariability(self): @@ -42,3 +42,12 @@ def testVariability(self): season="JJA", statistic="rmsc_glo") assert(numpy.allclose(data, 0.7626659864144966)) + def testCustomStruct(self): + pth = os.path.dirname(inspect.getfile(self.__class__)) + json_pth = os.path.join(pth,"io","test_MC1_all.json") + J = pcmdi_metrics.io.base.JSONs([json_pth]) + jids = J.getAxisIds()[1:] + json_data = json.load(open(json_pth)) + json_struct = json_data[u'json_structure'] + self.assertTrue(jids == json_struct) +