Probabilistic Evaluation of Drought in CMIP6 Simulations

Abstract As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations,...

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Autores principales:	Simon Michael Papalexiou, Chandra Rupa Rajulapati, Konstantinos M. Andreadis, Efi Foufoula‐Georgiou, Martyn P. Clark, Kevin E. Trenberth
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	CMIP6 droughts reliability of climate models climate change Hellinger distance precipitation Environmental sciences GE1-350 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/89f8410d8ac34b0d9b10affa28111950
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spelling	oai:doaj.org-article:89f8410d8ac34b0d9b10affa281119502021-11-23T22:36:10ZProbabilistic Evaluation of Drought in CMIP6 Simulations2328-427710.1029/2021EF002150https://doaj.org/article/89f8410d8ac34b0d9b10affa281119502021-10-01T00:00:00Zhttps://doi.org/10.1029/2021EF002150https://doaj.org/toc/2328-4277Abstract As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments.Simon Michael PapalexiouChandra Rupa RajulapatiKonstantinos M. AndreadisEfi Foufoula‐GeorgiouMartyn P. ClarkKevin E. TrenberthAmerican Geophysical Union (AGU)articleCMIP6droughtsreliability of climate modelsclimate changeHellinger distanceprecipitationEnvironmental sciencesGE1-350EcologyQH540-549.5ENEarth's Future, Vol 9, Iss 10, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CMIP6 droughts reliability of climate models climate change Hellinger distance precipitation Environmental sciences GE1-350 Ecology QH540-549.5
spellingShingle	CMIP6 droughts reliability of climate models climate change Hellinger distance precipitation Environmental sciences GE1-350 Ecology QH540-549.5 Simon Michael Papalexiou Chandra Rupa Rajulapati Konstantinos M. Andreadis Efi Foufoula‐Georgiou Martyn P. Clark Kevin E. Trenberth Probabilistic Evaluation of Drought in CMIP6 Simulations
description	Abstract As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments.
format	article
author	Simon Michael Papalexiou Chandra Rupa Rajulapati Konstantinos M. Andreadis Efi Foufoula‐Georgiou Martyn P. Clark Kevin E. Trenberth
author_facet	Simon Michael Papalexiou Chandra Rupa Rajulapati Konstantinos M. Andreadis Efi Foufoula‐Georgiou Martyn P. Clark Kevin E. Trenberth
author_sort	Simon Michael Papalexiou
title	Probabilistic Evaluation of Drought in CMIP6 Simulations
title_short	Probabilistic Evaluation of Drought in CMIP6 Simulations
title_full	Probabilistic Evaluation of Drought in CMIP6 Simulations
title_fullStr	Probabilistic Evaluation of Drought in CMIP6 Simulations
title_full_unstemmed	Probabilistic Evaluation of Drought in CMIP6 Simulations
title_sort	probabilistic evaluation of drought in cmip6 simulations
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/89f8410d8ac34b0d9b10affa28111950
work_keys_str_mv	AT simonmichaelpapalexiou probabilisticevaluationofdroughtincmip6simulations AT chandraruparajulapati probabilisticevaluationofdroughtincmip6simulations AT konstantinosmandreadis probabilisticevaluationofdroughtincmip6simulations AT efifoufoulageorgiou probabilisticevaluationofdroughtincmip6simulations AT martynpclark probabilisticevaluationofdroughtincmip6simulations AT kevinetrenberth probabilisticevaluationofdroughtincmip6simulations
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Probabilistic Evaluation of Drought in CMIP6 Simulations

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