Evaluation of multiple regional climate models for summer climate extremes over East Asia
In this study, five regional climate models (RCMs) participating in the CORDEX-East Asia project (HadGEM3-RA, RegCM4, SNU-MM5, SNU-WRF, and YSU-RSM) are evaluated in terms of their performances in simulating the climatology of summer extremes in East Asia. Seasonal maxima of daily mean temperature a...
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creator | Park, Changyong Min, Seung-Ki Lee, Donghyun Cha, Dong-Hyun Suh, Myoung-Seok Kang, Hyun-Suk Hong, Song-You Lee, Dong-Kyou Baek, Hee-Jeong Boo, Kyung-On Kwon, Won-Tae |
description | In this study, five regional climate models (RCMs) participating in the CORDEX-East Asia project (HadGEM3-RA, RegCM4, SNU-MM5, SNU-WRF, and YSU-RSM) are evaluated in terms of their performances in simulating the climatology of summer extremes in East Asia. Seasonal maxima of daily mean temperature and precipitation are analyzed using the generalized extreme value method. RCMs show systematic bias patterns in both seasonal means and extremes. A cold bias is located along the coast, whereas a warm bias occurs in northern China. Overall, wet bias occurs in East Asia, but with a substantial dry bias centered in South Korea. This dry bias appears to be related to the colder ocean surface around South Korea, positioning the monsoonal front further south compared to observations. Taylor diagram analyses reveal that the models simulate temperature means more accurately compared to extremes because of the higher spatial correlation, whereas precipitation extremes are simulated better than their means because of the higher spatial variability. The latter implies that extreme rainfall events can be captured more accurately by RCMs compared to the driving GCM despite poorer simulation of mean rainfall. Inter-RCM analysis indicates a close relationship between the means and extremes in terms of model skills, but it does not show a clear relationship between temperature and precipitation. Sub-regional analysis largely supports the mean–extreme skill relationship. Analyses of frequency and intensity distributions of daily data for three selected sub-regions suggest that overall shifts of temperature distribution and biases in moderate–heavy precipitations contribute importantly to the seasonal mean biases. |
doi_str_mv | 10.1007/s00382-015-2713-z |
format | Article |
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Seasonal maxima of daily mean temperature and precipitation are analyzed using the generalized extreme value method. RCMs show systematic bias patterns in both seasonal means and extremes. A cold bias is located along the coast, whereas a warm bias occurs in northern China. Overall, wet bias occurs in East Asia, but with a substantial dry bias centered in South Korea. This dry bias appears to be related to the colder ocean surface around South Korea, positioning the monsoonal front further south compared to observations. Taylor diagram analyses reveal that the models simulate temperature means more accurately compared to extremes because of the higher spatial correlation, whereas precipitation extremes are simulated better than their means because of the higher spatial variability. The latter implies that extreme rainfall events can be captured more accurately by RCMs compared to the driving GCM despite poorer simulation of mean rainfall. Inter-RCM analysis indicates a close relationship between the means and extremes in terms of model skills, but it does not show a clear relationship between temperature and precipitation. Sub-regional analysis largely supports the mean–extreme skill relationship. Analyses of frequency and intensity distributions of daily data for three selected sub-regions suggest that overall shifts of temperature distribution and biases in moderate–heavy precipitations contribute importantly to the seasonal mean biases.</description><identifier>ISSN: 0930-7575</identifier><identifier>EISSN: 1432-0894</identifier><identifier>DOI: 10.1007/s00382-015-2713-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Analysis ; Atmospheric models ; Climate change ; Climate models ; Climatology ; Earth and Environmental Science ; Earth Sciences ; Extreme weather ; Geophysics/Geodesy ; Marine ; Oceanography ; Precipitation ; Regional analysis ; Regional planning ; Summer ; Temperature ; Temperature distribution</subject><ispartof>Climate dynamics, 2016-04, Vol.46 (7-8), p.2469-2486</ispartof><rights>Springer-Verlag Berlin Heidelberg 2015</rights><rights>COPYRIGHT 2016 Springer</rights><rights>Springer-Verlag Berlin Heidelberg 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-1d63dae921cdfa96756d17edfc2026314706f4a5c7eb4b747f08128ca98e6ea43</citedby><cites>FETCH-LOGICAL-c453t-1d63dae921cdfa96756d17edfc2026314706f4a5c7eb4b747f08128ca98e6ea43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00382-015-2713-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00382-015-2713-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Park, Changyong</creatorcontrib><creatorcontrib>Min, Seung-Ki</creatorcontrib><creatorcontrib>Lee, Donghyun</creatorcontrib><creatorcontrib>Cha, Dong-Hyun</creatorcontrib><creatorcontrib>Suh, Myoung-Seok</creatorcontrib><creatorcontrib>Kang, Hyun-Suk</creatorcontrib><creatorcontrib>Hong, Song-You</creatorcontrib><creatorcontrib>Lee, Dong-Kyou</creatorcontrib><creatorcontrib>Baek, Hee-Jeong</creatorcontrib><creatorcontrib>Boo, Kyung-On</creatorcontrib><creatorcontrib>Kwon, Won-Tae</creatorcontrib><title>Evaluation of multiple regional climate models for summer climate extremes over East Asia</title><title>Climate dynamics</title><addtitle>Clim Dyn</addtitle><description>In this study, five regional climate models (RCMs) participating in the CORDEX-East Asia project (HadGEM3-RA, RegCM4, SNU-MM5, SNU-WRF, and YSU-RSM) are evaluated in terms of their performances in simulating the climatology of summer extremes in East Asia. Seasonal maxima of daily mean temperature and precipitation are analyzed using the generalized extreme value method. RCMs show systematic bias patterns in both seasonal means and extremes. A cold bias is located along the coast, whereas a warm bias occurs in northern China. Overall, wet bias occurs in East Asia, but with a substantial dry bias centered in South Korea. This dry bias appears to be related to the colder ocean surface around South Korea, positioning the monsoonal front further south compared to observations. Taylor diagram analyses reveal that the models simulate temperature means more accurately compared to extremes because of the higher spatial correlation, whereas precipitation extremes are simulated better than their means because of the higher spatial variability. The latter implies that extreme rainfall events can be captured more accurately by RCMs compared to the driving GCM despite poorer simulation of mean rainfall. Inter-RCM analysis indicates a close relationship between the means and extremes in terms of model skills, but it does not show a clear relationship between temperature and precipitation. Sub-regional analysis largely supports the mean–extreme skill relationship. Analyses of frequency and intensity distributions of daily data for three selected sub-regions suggest that overall shifts of temperature distribution and biases in moderate–heavy precipitations contribute importantly to the seasonal mean biases.</description><subject>Analysis</subject><subject>Atmospheric models</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Extreme weather</subject><subject>Geophysics/Geodesy</subject><subject>Marine</subject><subject>Oceanography</subject><subject>Precipitation</subject><subject>Regional analysis</subject><subject>Regional planning</subject><subject>Summer</subject><subject>Temperature</subject><subject>Temperature distribution</subject><issn>0930-7575</issn><issn>1432-0894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</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>eNp1kU9r3DAQxUVpodu0H6A3QaGkB6f6Z8s-LmGbBAKFtjnkJBR5tHGQrY1GDk0-fbU4lGyhzEHw9HvDzDxCPnJ2whnTX5Ex2YqK8boSmsvq6RVZcSWL0nbqNVmxTrJK17p-S94h3jHGVaPFilxvHmyYbR7iRKOn4xzysAtAE2yLZAN1YRhtBjrGHgJSHxPFeRwh_f2B3znBCEjjQ1E3FjNd42DfkzfeBoQPz-8Rufq2-XV6Xl1-P7s4XV9WTtUyV7xvZG-hE9z13naNrpuea-i9E0w0kivNGq9s7TTcqButtGctF62zXQsNWCWPyPHSd5fi_QyYzTiggxDsBHFGw3XLaqG0lAX99A96F-dUttxTuhEta5ku1MlCbW0AM0w-5mRdqR7GwcUJ_FD0tVK6TCNEVwxfDgyFyeUoWzsjmoufPw7Zzy_YW7Ah32IM8z4APAT5AroUERN4s0vl3unRcGb2kZslclMiN_vIzVPxiMWDhZ22kF7s91_TH3KgrQQ</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Park, Changyong</creator><creator>Min, Seung-Ki</creator><creator>Lee, Donghyun</creator><creator>Cha, Dong-Hyun</creator><creator>Suh, Myoung-Seok</creator><creator>Kang, Hyun-Suk</creator><creator>Hong, Song-You</creator><creator>Lee, Dong-Kyou</creator><creator>Baek, Hee-Jeong</creator><creator>Boo, Kyung-On</creator><creator>Kwon, Won-Tae</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M1Q</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope></search><sort><creationdate>20160401</creationdate><title>Evaluation of multiple regional climate models for summer climate extremes over East Asia</title><author>Park, Changyong ; Min, Seung-Ki ; Lee, Donghyun ; Cha, Dong-Hyun ; Suh, Myoung-Seok ; Kang, Hyun-Suk ; Hong, Song-You ; Lee, Dong-Kyou ; Baek, Hee-Jeong ; Boo, Kyung-On ; Kwon, Won-Tae</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-1d63dae921cdfa96756d17edfc2026314706f4a5c7eb4b747f08128ca98e6ea43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Analysis</topic><topic>Atmospheric models</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatology</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Extreme weather</topic><topic>Geophysics/Geodesy</topic><topic>Marine</topic><topic>Oceanography</topic><topic>Precipitation</topic><topic>Regional analysis</topic><topic>Regional planning</topic><topic>Summer</topic><topic>Temperature</topic><topic>Temperature distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Changyong</creatorcontrib><creatorcontrib>Min, Seung-Ki</creatorcontrib><creatorcontrib>Lee, Donghyun</creatorcontrib><creatorcontrib>Cha, Dong-Hyun</creatorcontrib><creatorcontrib>Suh, Myoung-Seok</creatorcontrib><creatorcontrib>Kang, Hyun-Suk</creatorcontrib><creatorcontrib>Hong, Song-You</creatorcontrib><creatorcontrib>Lee, Dong-Kyou</creatorcontrib><creatorcontrib>Baek, Hee-Jeong</creatorcontrib><creatorcontrib>Boo, Kyung-On</creatorcontrib><creatorcontrib>Kwon, Won-Tae</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Military Database</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Climate dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Park, Changyong</au><au>Min, Seung-Ki</au><au>Lee, Donghyun</au><au>Cha, Dong-Hyun</au><au>Suh, Myoung-Seok</au><au>Kang, Hyun-Suk</au><au>Hong, Song-You</au><au>Lee, Dong-Kyou</au><au>Baek, Hee-Jeong</au><au>Boo, Kyung-On</au><au>Kwon, Won-Tae</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of multiple regional climate models for summer climate extremes over East Asia</atitle><jtitle>Climate dynamics</jtitle><stitle>Clim Dyn</stitle><date>2016-04-01</date><risdate>2016</risdate><volume>46</volume><issue>7-8</issue><spage>2469</spage><epage>2486</epage><pages>2469-2486</pages><issn>0930-7575</issn><eissn>1432-0894</eissn><abstract>In this study, five regional climate models (RCMs) participating in the CORDEX-East Asia project (HadGEM3-RA, RegCM4, SNU-MM5, SNU-WRF, and YSU-RSM) are evaluated in terms of their performances in simulating the climatology of summer extremes in East Asia. Seasonal maxima of daily mean temperature and precipitation are analyzed using the generalized extreme value method. RCMs show systematic bias patterns in both seasonal means and extremes. A cold bias is located along the coast, whereas a warm bias occurs in northern China. Overall, wet bias occurs in East Asia, but with a substantial dry bias centered in South Korea. This dry bias appears to be related to the colder ocean surface around South Korea, positioning the monsoonal front further south compared to observations. Taylor diagram analyses reveal that the models simulate temperature means more accurately compared to extremes because of the higher spatial correlation, whereas precipitation extremes are simulated better than their means because of the higher spatial variability. The latter implies that extreme rainfall events can be captured more accurately by RCMs compared to the driving GCM despite poorer simulation of mean rainfall. Inter-RCM analysis indicates a close relationship between the means and extremes in terms of model skills, but it does not show a clear relationship between temperature and precipitation. Sub-regional analysis largely supports the mean–extreme skill relationship. Analyses of frequency and intensity distributions of daily data for three selected sub-regions suggest that overall shifts of temperature distribution and biases in moderate–heavy precipitations contribute importantly to the seasonal mean biases.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00382-015-2713-z</doi><tpages>18</tpages></addata></record> |
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subjects | Analysis Atmospheric models Climate change Climate models Climatology Earth and Environmental Science Earth Sciences Extreme weather Geophysics/Geodesy Marine Oceanography Precipitation Regional analysis Regional planning Summer Temperature Temperature distribution |
title | Evaluation of multiple regional climate models for summer climate extremes over East Asia |
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