Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation

Abstract Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitat...

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Autores principales:	Hebatallah Mohamed Abdelmoaty, Simon Michael Papalexiou, Chandra Rupa Rajulapati, Amir AghaKouchak
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	precipitation extremes CMIP6 climate change Environmental sciences GE1-350 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873
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spelling	oai:doaj.org-article:d9c9060000274bca842dcc1ddfc258732021-11-23T22:36:10ZBiases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation2328-427710.1029/2021EF002196https://doaj.org/article/d9c9060000274bca842dcc1ddfc258732021-10-01T00:00:00Zhttps://doi.org/10.1029/2021EF002196https://doaj.org/toc/2328-4277Abstract Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions.Hebatallah Mohamed AbdelmoatySimon Michael PapalexiouChandra Rupa RajulapatiAmir AghaKouchakAmerican Geophysical Union (AGU)articleprecipitation extremesCMIP6climate changeEnvironmental sciencesGE1-350EcologyQH540-549.5ENEarth's Future, Vol 9, Iss 10, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	precipitation extremes CMIP6 climate change Environmental sciences GE1-350 Ecology QH540-549.5
spellingShingle	precipitation extremes CMIP6 climate change Environmental sciences GE1-350 Ecology QH540-549.5 Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Chandra Rupa Rajulapati Amir AghaKouchak Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
description	Abstract Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions.
format	article
author	Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Chandra Rupa Rajulapati Amir AghaKouchak
author_facet	Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Chandra Rupa Rajulapati Amir AghaKouchak
author_sort	Hebatallah Mohamed Abdelmoaty
title	Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
title_short	Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
title_full	Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
title_fullStr	Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
title_full_unstemmed	Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation
title_sort	biases beyond the mean in cmip6 extreme precipitation: a global investigation
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873
work_keys_str_mv	AT hebatallahmohamedabdelmoaty biasesbeyondthemeanincmip6extremeprecipitationaglobalinvestigation AT simonmichaelpapalexiou biasesbeyondthemeanincmip6extremeprecipitationaglobalinvestigation AT chandraruparajulapati biasesbeyondthemeanincmip6extremeprecipitationaglobalinvestigation AT amiraghakouchak biasesbeyondthemeanincmip6extremeprecipitationaglobalinvestigation
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Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation

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