Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study
The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–...
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| Veröffentlicht in: | Theoretical and applied climatology 2021-08, Vol.145 (3-4), p.1385-1406 |
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| creator | Kamruzzaman, Mohammad Shahid, Shamsuddin Islam, ARM Towfiqul Hwang, Syewoon Cho, Jaepil Zaman, Md. Asad Uz Ahmed, Minhaz Rahman, Md. Mizanur Hossain, Md. Belal |
| description | The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community. |
| doi_str_mv | 10.1007/s00704-021-03691-0 |
| format | Article |
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Asad Uz ; Ahmed, Minhaz ; Rahman, Md. Mizanur ; Hossain, Md. Belal</creator><creatorcontrib>Kamruzzaman, Mohammad ; Shahid, Shamsuddin ; Islam, ARM Towfiqul ; Hwang, Syewoon ; Cho, Jaepil ; Zaman, Md. Asad Uz ; Ahmed, Minhaz ; Rahman, Md. Mizanur ; Hossain, Md. Belal</creatorcontrib><description>The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-021-03691-0</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Analysis ; Annual rainfall ; Annual variations ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate ; Climate change ; Climate change research ; Climate models ; Climate science ; Climatology ; Cold season ; Decision analysis ; Decision-making ; Earth and Environmental Science ; Earth Sciences ; Environmental risk ; Geographical distribution ; Global climate ; Global climate models ; Global temperature changes ; Historic temperatures ; Interannual variability ; Intercomparison ; Meteorological observations ; Minimum temperatures ; Monsoon rainfall ; Multiple criterion ; Original Paper ; Performance evaluation ; Precipitation ; Rain ; Rain and rainfall ; Rainfall ; Rainfall simulators ; Risk assessment ; Seasonal rainfall ; Seasonal variability ; Simulation ; Spatial variability ; Spatial variations ; Temperature ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather ; Winter</subject><ispartof>Theoretical and applied climatology, 2021-08, Vol.145 (3-4), p.1385-1406</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021</rights><rights>COPYRIGHT 2021 Springer</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-a973c72e7d03869330c872a5f4afcbe1e6c2af75fb05fcf11d223e1d0fe614973</citedby><cites>FETCH-LOGICAL-c458t-a973c72e7d03869330c872a5f4afcbe1e6c2af75fb05fcf11d223e1d0fe614973</cites><orcidid>0000-0001-6640-8082</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-021-03691-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-021-03691-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Kamruzzaman, Mohammad</creatorcontrib><creatorcontrib>Shahid, Shamsuddin</creatorcontrib><creatorcontrib>Islam, ARM Towfiqul</creatorcontrib><creatorcontrib>Hwang, Syewoon</creatorcontrib><creatorcontrib>Cho, Jaepil</creatorcontrib><creatorcontrib>Zaman, Md. Asad Uz</creatorcontrib><creatorcontrib>Ahmed, Minhaz</creatorcontrib><creatorcontrib>Rahman, Md. Mizanur</creatorcontrib><creatorcontrib>Hossain, Md. Belal</creatorcontrib><title>Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.</description><subject>Analysis</subject><subject>Annual rainfall</subject><subject>Annual variations</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate change research</subject><subject>Climate models</subject><subject>Climate science</subject><subject>Climatology</subject><subject>Cold season</subject><subject>Decision analysis</subject><subject>Decision-making</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental risk</subject><subject>Geographical distribution</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Global temperature changes</subject><subject>Historic temperatures</subject><subject>Interannual variability</subject><subject>Intercomparison</subject><subject>Meteorological observations</subject><subject>Minimum temperatures</subject><subject>Monsoon rainfall</subject><subject>Multiple criterion</subject><subject>Original Paper</subject><subject>Performance evaluation</subject><subject>Precipitation</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rainfall</subject><subject>Rainfall simulators</subject><subject>Risk assessment</subject><subject>Seasonal rainfall</subject><subject>Seasonal variability</subject><subject>Simulation</subject><subject>Spatial variability</subject><subject>Spatial variations</subject><subject>Temperature</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Weather</subject><subject>Winter</subject><issn>0177-798X</issn><issn>1434-4483</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kcuKFDEUhoMo2La-gKuAKxc15lZJlbux8dIwongBdyGTOqnJUJWUSQqcR_CtTXcJMhsJnITk-05CfoSeU3JBCVGvci1ENITRhnDZ1_oA7ajgohGi4w_RjlClGtV3Px6jJznfEkKYlGqHfh_ivJjkcww4Onz4ePwssQnDedXiOQ4w4QWSi2k2wQL2AWc_r5MpPoz4xucSk7emQgmsX3ypB7XXqUWBuZqmrOmsvTFhnMwA-eY1Nid88rMPJt3hXNbh7il65MyU4dnfeY--v3v77fChufr0_ni4vGqsaLvSmF5xqxiogfBO9pwT2ylmWieMs9dAQVpmnGrdNWmddZQOjHGgA3EgqajyHr3Y-i4p_lwhF30b1xTqlZq1reh6RYWo1MVGjWYC7YOLJRlbxwCztzGA83X_UspeyrZlsgov7wmVKfCrjGbNWR-_frnPso21KeacwOkl-bn-hKZEn_LUW5665qnPeda6R3yTcoXDCOnfu_9j_QFnjaPE</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Kamruzzaman, Mohammad</creator><creator>Shahid, Shamsuddin</creator><creator>Islam, ARM Towfiqul</creator><creator>Hwang, Syewoon</creator><creator>Cho, Jaepil</creator><creator>Zaman, Md. 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Asad Uz</au><au>Ahmed, Minhaz</au><au>Rahman, Md. Mizanur</au><au>Hossain, Md. Belal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study</atitle><jtitle>Theoretical and applied climatology</jtitle><stitle>Theor Appl Climatol</stitle><date>2021-08-01</date><risdate>2021</risdate><volume>145</volume><issue>3-4</issue><spage>1385</spage><epage>1406</epage><pages>1385-1406</pages><issn>0177-798X</issn><eissn>1434-4483</eissn><abstract>The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00704-021-03691-0</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0001-6640-8082</orcidid></addata></record> |
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| ispartof | Theoretical and applied climatology, 2021-08, Vol.145 (3-4), p.1385-1406 |
| issn | 0177-798X 1434-4483 |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Analysis Annual rainfall Annual variations Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate Climate change Climate change research Climate models Climate science Climatology Cold season Decision analysis Decision-making Earth and Environmental Science Earth Sciences Environmental risk Geographical distribution Global climate Global climate models Global temperature changes Historic temperatures Interannual variability Intercomparison Meteorological observations Minimum temperatures Monsoon rainfall Multiple criterion Original Paper Performance evaluation Precipitation Rain Rain and rainfall Rainfall Rainfall simulators Risk assessment Seasonal rainfall Seasonal variability Simulation Spatial variability Spatial variations Temperature Waste Water Technology Water Management Water Pollution Control Weather Winter |
| title | Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study |
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