Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia
The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Gro...
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description | The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.
The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. |
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The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.7744</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Climate action ; Climate adaptation ; Climate change ; Climate change adaptation ; climate extreme ; Climate models ; Climatology ; CMIP6 ; Dry season ; Environmental hazards ; extreme precipitation ; Extreme weather ; Global climate ; Global climate models ; Indonesia ; Interannual variability ; Intercomparison ; Mitigation ; model evaluation ; Modelling ; Performance evaluation ; Precipitation ; Rainy season ; Resolution ; Seasons ; Simulation ; Wet season</subject><ispartof>International journal of climatology, 2023-01, Vol.43 (1), p.174-196</ispartof><rights>2022 The Authors. published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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><citedby>FETCH-LOGICAL-c3934-9218b4b4c17e13f857b0b10cb8aebbc4c543d402e356818fc48f9420ef71b9603</citedby><cites>FETCH-LOGICAL-c3934-9218b4b4c17e13f857b0b10cb8aebbc4c543d402e356818fc48f9420ef71b9603</cites><orcidid>0000-0002-7503-7989 ; 0000-0003-0233-1690 ; 0000-0002-7454-9827 ; 0000-0002-6749-010X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.7744$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.7744$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Kurniadi, Ari</creatorcontrib><creatorcontrib>Weller, Evan</creatorcontrib><creatorcontrib>Kim, Yeon‐Hee</creatorcontrib><creatorcontrib>Min, Seung‐Ki</creatorcontrib><title>Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia</title><title>International journal of climatology</title><description>The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.
The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014.</description><subject>Climate action</subject><subject>Climate adaptation</subject><subject>Climate change</subject><subject>Climate change adaptation</subject><subject>climate extreme</subject><subject>Climate models</subject><subject>Climatology</subject><subject>CMIP6</subject><subject>Dry season</subject><subject>Environmental hazards</subject><subject>extreme precipitation</subject><subject>Extreme weather</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Indonesia</subject><subject>Interannual variability</subject><subject>Intercomparison</subject><subject>Mitigation</subject><subject>model evaluation</subject><subject>Modelling</subject><subject>Performance evaluation</subject><subject>Precipitation</subject><subject>Rainy season</subject><subject>Resolution</subject><subject>Seasons</subject><subject>Simulation</subject><subject>Wet season</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp10LFOwzAUBVALgUQpSHyCJRaWlOfESewRVQWKitoB5sh2XkSiJA52AnTjE_hGvoSUdmV6y7n3So-QSwYzBhDeVNbM0pTzIzJhINMAQIhjMgEhZSA4E6fkzPsKAKRkyYS4xbuqB9WXtqW2oHM7dDXm9MnmWNNl26MztumUK_0INs5WaHq6eVUeaUKbnfr5-vZlM9SqH3P42TtskHYOTdmV_aH4Hd1YltsWfanOyUmhao8XhzslL3eL5_lDsFrfL-e3q8BEMuKBDJnQXHPDUmRRIeJUg2ZgtFCoteEm5lHOIcQoTgQTheGikDwELFKmZQLRlFzteztn3wb0fVbZwbXjZBamSTxuACSjut4r46z3Dousc2Wj3DZjkO0-OqZMtvvoSIM9_Shr3P7rssf1_M__AhateYs</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Kurniadi, Ari</creator><creator>Weller, Evan</creator><creator>Kim, Yeon‐Hee</creator><creator>Min, Seung‐Ki</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-7503-7989</orcidid><orcidid>https://orcid.org/0000-0003-0233-1690</orcidid><orcidid>https://orcid.org/0000-0002-7454-9827</orcidid><orcidid>https://orcid.org/0000-0002-6749-010X</orcidid></search><sort><creationdate>202301</creationdate><title>Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia</title><author>Kurniadi, Ari ; Weller, Evan ; Kim, Yeon‐Hee ; Min, Seung‐Ki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3934-9218b4b4c17e13f857b0b10cb8aebbc4c543d402e356818fc48f9420ef71b9603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Climate action</topic><topic>Climate adaptation</topic><topic>Climate change</topic><topic>Climate change adaptation</topic><topic>climate extreme</topic><topic>Climate models</topic><topic>Climatology</topic><topic>CMIP6</topic><topic>Dry season</topic><topic>Environmental hazards</topic><topic>extreme precipitation</topic><topic>Extreme weather</topic><topic>Global climate</topic><topic>Global climate models</topic><topic>Indonesia</topic><topic>Interannual variability</topic><topic>Intercomparison</topic><topic>Mitigation</topic><topic>model evaluation</topic><topic>Modelling</topic><topic>Performance evaluation</topic><topic>Precipitation</topic><topic>Rainy season</topic><topic>Resolution</topic><topic>Seasons</topic><topic>Simulation</topic><topic>Wet season</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kurniadi, Ari</creatorcontrib><creatorcontrib>Weller, Evan</creatorcontrib><creatorcontrib>Kim, Yeon‐Hee</creatorcontrib><creatorcontrib>Min, Seung‐Ki</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kurniadi, Ari</au><au>Weller, Evan</au><au>Kim, Yeon‐Hee</au><au>Min, Seung‐Ki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia</atitle><jtitle>International journal of climatology</jtitle><date>2023-01</date><risdate>2023</risdate><volume>43</volume><issue>1</issue><spage>174</spage><epage>196</epage><pages>174-196</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.
The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.7744</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-7503-7989</orcidid><orcidid>https://orcid.org/0000-0003-0233-1690</orcidid><orcidid>https://orcid.org/0000-0002-7454-9827</orcidid><orcidid>https://orcid.org/0000-0002-6749-010X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Climate action Climate adaptation Climate change Climate change adaptation climate extreme Climate models Climatology CMIP6 Dry season Environmental hazards extreme precipitation Extreme weather Global climate Global climate models Indonesia Interannual variability Intercomparison Mitigation model evaluation Modelling Performance evaluation Precipitation Rainy season Resolution Seasons Simulation Wet season |
title | Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia |
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