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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ftdoajarticles:oai:doaj.org/article:d9c9060000274bca842dcc1ddfc25873 2023-05-15T15:08:54+02:00 Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Chandra Rupa Rajulapati Amir AghaKouchak 2021-10-01T00:00:00Z https://doi.org/10.1029/2021EF002196 https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873 EN eng Wiley https://doi.org/10.1029/2021EF002196 https://doaj.org/toc/2328-4277 2328-4277 doi:10.1029/2021EF002196 https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873 Earth's Future, Vol 9, Iss 10, Pp n/a-n/a (2021) precipitation extremes CMIP6 climate change Environmental sciences GE1-350 Ecology QH540-549.5 article 2021 ftdoajarticles https://doi.org/10.1029/2021EF002196 2022-12-31T02:12:37Z 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. Article in Journal/Newspaper Arctic Climate change Directory of Open Access Journals: DOAJ Articles Arctic Earth's Future 9 10 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
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 |
topic_facet |
precipitation extremes CMIP6 climate change Environmental sciences GE1-350 Ecology QH540-549.5 |
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 in Journal/Newspaper |
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 |
Wiley |
publishDate |
2021 |
url |
https://doi.org/10.1029/2021EF002196 https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Earth's Future, Vol 9, Iss 10, Pp n/a-n/a (2021) |
op_relation |
https://doi.org/10.1029/2021EF002196 https://doaj.org/toc/2328-4277 2328-4277 doi:10.1029/2021EF002196 https://doaj.org/article/d9c9060000274bca842dcc1ddfc25873 |
op_doi |
https://doi.org/10.1029/2021EF002196 |
container_title |
Earth's Future |
container_volume |
9 |
container_issue |
10 |
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1766340174885683200 |