Human influence on frequency of temperature extremes
We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm...
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Veröffentlicht in: | Environmental research letters 2020-06, Vol.15 (6), p.64014 |
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description | We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents. |
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We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.</description><identifier>ISSN: 1748-9326</identifier><identifier>EISSN: 1748-9326</identifier><identifier>DOI: 10.1088/1748-9326/ab8497</identifier><identifier>CODEN: ERLNAL</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Anthropogenic factors ; anthropogenic forcing ; CMIP6 models ; Continents ; Cooling effects ; detection and attribution ; Fingerprinting ; Greenhouse effect ; Greenhouse gases ; HadEX3 dataset ; Human influences ; Land area ; natural forcing ; temperature extremes</subject><ispartof>Environmental research letters, 2020-06, Vol.15 (6), p.64014</ispartof><rights>2020 The Author(s). Published by IOP Publishing Ltd</rights><rights>Copyright IOP Publishing Jun 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c501t-7ef1d30d456e4b3514faa822d91b156d8da0d43edf972ba06de6e9430b66aa753</citedby><cites>FETCH-LOGICAL-c501t-7ef1d30d456e4b3514faa822d91b156d8da0d43edf972ba06de6e9430b66aa753</cites><orcidid>0000-0002-6749-010X ; 0000-0003-4177-7011 ; 0000-0003-0233-1690 ; 0000-0002-6551-6249 ; 0000-0003-2335-3485</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1748-9326/ab8497/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,860,2096,27901,27902,38845,38867,53815,53842</link.rule.ids></links><search><creatorcontrib>Hu, Ting</creatorcontrib><creatorcontrib>Sun, Ying</creatorcontrib><creatorcontrib>Zhang, Xuebin</creatorcontrib><creatorcontrib>Min, Seung-Ki</creatorcontrib><creatorcontrib>Kim, Yeon-Hee</creatorcontrib><title>Human influence on frequency of temperature extremes</title><title>Environmental research letters</title><addtitle>ERL</addtitle><addtitle>Environ. Res. Lett</addtitle><description>We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.</description><subject>Anthropogenic factors</subject><subject>anthropogenic forcing</subject><subject>CMIP6 models</subject><subject>Continents</subject><subject>Cooling effects</subject><subject>detection and attribution</subject><subject>Fingerprinting</subject><subject>Greenhouse effect</subject><subject>Greenhouse gases</subject><subject>HadEX3 dataset</subject><subject>Human influences</subject><subject>Land area</subject><subject>natural forcing</subject><subject>temperature extremes</subject><issn>1748-9326</issn><issn>1748-9326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNp1kL1PwzAQxSMEEqWwM0ZiYCHU33FGVAGtVIkFZusSn1GqJA5OItH_noQgYIDJ9vm93929KLqk5JYSrVc0FTrJOFMryLXI0qNo8V06_nU_jc66bk-IFDLVi0hshhqauGxcNWBTYOyb2AV8mx6H2Lu4x7rFAP0QMMb3PmCN3Xl04qDq8OLrXEYvD_fP602ye3rcru92SSEJ7ZMUHbWcWCEVipxLKhyAZsxmNKdSWW1h_ORoXZayHIiyqDATnORKAaSSL6PtzLUe9qYNZQ3hYDyU5rPgw6uB0JdFhSa1lDEO1hZIhR7ZoDUnkBXUKgfSjayrmdUGP27X9Wbvh9CM4xsmhdZsdGWjisyqIviuC-i-u1JippzNFKSZgjRzzqPleraUvv1hYqgMlUYZogShwrR2GuHmD-W_4A_L8orI</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Hu, Ting</creator><creator>Sun, Ying</creator><creator>Zhang, Xuebin</creator><creator>Min, Seung-Ki</creator><creator>Kim, Yeon-Hee</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6749-010X</orcidid><orcidid>https://orcid.org/0000-0003-4177-7011</orcidid><orcidid>https://orcid.org/0000-0003-0233-1690</orcidid><orcidid>https://orcid.org/0000-0002-6551-6249</orcidid><orcidid>https://orcid.org/0000-0003-2335-3485</orcidid></search><sort><creationdate>20200601</creationdate><title>Human influence on frequency of temperature extremes</title><author>Hu, Ting ; Sun, Ying ; Zhang, Xuebin ; Min, Seung-Ki ; Kim, Yeon-Hee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c501t-7ef1d30d456e4b3514faa822d91b156d8da0d43edf972ba06de6e9430b66aa753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Anthropogenic factors</topic><topic>anthropogenic forcing</topic><topic>CMIP6 models</topic><topic>Continents</topic><topic>Cooling effects</topic><topic>detection and attribution</topic><topic>Fingerprinting</topic><topic>Greenhouse effect</topic><topic>Greenhouse gases</topic><topic>HadEX3 dataset</topic><topic>Human influences</topic><topic>Land area</topic><topic>natural forcing</topic><topic>temperature extremes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Ting</creatorcontrib><creatorcontrib>Sun, Ying</creatorcontrib><creatorcontrib>Zhang, Xuebin</creatorcontrib><creatorcontrib>Min, Seung-Ki</creatorcontrib><creatorcontrib>Kim, Yeon-Hee</creatorcontrib><collection>Open Access: IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</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><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Environmental research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Ting</au><au>Sun, Ying</au><au>Zhang, Xuebin</au><au>Min, Seung-Ki</au><au>Kim, Yeon-Hee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Human influence on frequency of temperature extremes</atitle><jtitle>Environmental research letters</jtitle><stitle>ERL</stitle><addtitle>Environ. Res. Lett</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>15</volume><issue>6</issue><spage>64014</spage><pages>64014-</pages><issn>1748-9326</issn><eissn>1748-9326</eissn><coden>ERLNAL</coden><abstract>We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1748-9326/ab8497</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6749-010X</orcidid><orcidid>https://orcid.org/0000-0003-4177-7011</orcidid><orcidid>https://orcid.org/0000-0003-0233-1690</orcidid><orcidid>https://orcid.org/0000-0002-6551-6249</orcidid><orcidid>https://orcid.org/0000-0003-2335-3485</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Anthropogenic factors anthropogenic forcing CMIP6 models Continents Cooling effects detection and attribution Fingerprinting Greenhouse effect Greenhouse gases HadEX3 dataset Human influences Land area natural forcing temperature extremes |
title | Human influence on frequency of temperature extremes |
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