Refined Assessment and Future Projections of Indian Summer Monsoon Rainfall Using CMIP6 Models
Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Tay...
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Veröffentlicht in: | Water (Basel) 2023-12, Vol.15 (24), p.4305 |
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description | Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region. |
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Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w15244305</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Atmospheric circulation ; Climate models ; Climate science ; Datasets ; Precipitation ; Rain and rainfall ; Simulation ; Simulation methods ; Standard deviation ; Wind</subject><ispartof>Water (Basel), 2023-12, Vol.15 (24), p.4305</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c291t-643a82773efe1234fe33960a92db65941b089bccfe055b71963e7c2a05aae1913</cites><orcidid>0009-0002-6631-5665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Li, Jiahao</creatorcontrib><creatorcontrib>Fan, Lingli</creatorcontrib><creatorcontrib>Chen, Xuzhe</creatorcontrib><creatorcontrib>Lin, Chunqiao</creatorcontrib><creatorcontrib>Song, Luchi</creatorcontrib><creatorcontrib>Xu, Jianjun</creatorcontrib><title>Refined Assessment and Future Projections of Indian Summer Monsoon Rainfall Using CMIP6 Models</title><title>Water (Basel)</title><description>Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region.</description><subject>Analysis</subject><subject>Atmospheric circulation</subject><subject>Climate models</subject><subject>Climate science</subject><subject>Datasets</subject><subject>Precipitation</subject><subject>Rain and rainfall</subject><subject>Simulation</subject><subject>Simulation methods</subject><subject>Standard deviation</subject><subject>Wind</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUE1Lw0AQXUTBUnvwHyx48pC6n0n2WIrVQIul2qths5mUlGS37iaI_96VijhzmOHNvPl4CN1SMudckYdPKpkQnMgLNGEk44kQgl7-y6_RLIQjiSZUnksyQe87aFoLNV6EACH0YAesbY1X4zB6wFvvjmCG1tmAXYMLW7fa4tex78HjTUSds3inW9vorsP70NoDXm6KbRqLNXThBl3FSoDZb5yi_erxbfmcrF-eiuVinRim6JCkguucZRmHBijjooH4T0q0YnWVSiVoRXJVGdMAkbLKqEo5ZIZpIrUGqiiforvz3JN3HyOEoTy60du4smQqPsvyNGowRfNz10F3UMaj3eC1iV5D3xpnoxQRX2SZ4kLyVEbC_ZlgvAvBQ1OefNtr_1VSUv5IXv5Jzr8BfyNxQA</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Li, Jiahao</creator><creator>Fan, Lingli</creator><creator>Chen, Xuzhe</creator><creator>Lin, Chunqiao</creator><creator>Song, Luchi</creator><creator>Xu, Jianjun</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0009-0002-6631-5665</orcidid></search><sort><creationdate>20231201</creationdate><title>Refined Assessment and Future Projections of Indian Summer Monsoon Rainfall Using CMIP6 Models</title><author>Li, Jiahao ; Fan, Lingli ; Chen, Xuzhe ; Lin, Chunqiao ; Song, Luchi ; Xu, Jianjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-643a82773efe1234fe33960a92db65941b089bccfe055b71963e7c2a05aae1913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis</topic><topic>Atmospheric circulation</topic><topic>Climate models</topic><topic>Climate science</topic><topic>Datasets</topic><topic>Precipitation</topic><topic>Rain and rainfall</topic><topic>Simulation</topic><topic>Simulation methods</topic><topic>Standard deviation</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Jiahao</creatorcontrib><creatorcontrib>Fan, Lingli</creatorcontrib><creatorcontrib>Chen, Xuzhe</creatorcontrib><creatorcontrib>Lin, Chunqiao</creatorcontrib><creatorcontrib>Song, Luchi</creatorcontrib><creatorcontrib>Xu, Jianjun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Jiahao</au><au>Fan, Lingli</au><au>Chen, Xuzhe</au><au>Lin, Chunqiao</au><au>Song, Luchi</au><au>Xu, Jianjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Refined Assessment and Future Projections of Indian Summer Monsoon Rainfall Using CMIP6 Models</atitle><jtitle>Water (Basel)</jtitle><date>2023-12-01</date><risdate>2023</risdate><volume>15</volume><issue>24</issue><spage>4305</spage><pages>4305-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w15244305</doi><orcidid>https://orcid.org/0009-0002-6631-5665</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Atmospheric circulation Climate models Climate science Datasets Precipitation Rain and rainfall Simulation Simulation methods Standard deviation Wind |
title | Refined Assessment and Future Projections of Indian Summer Monsoon Rainfall Using CMIP6 Models |
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