Attribution of temperature changes in Western China
Annual mean temperature anomalies (°C, relative to 1961–1990) for the WC region from observations (black line) during 1958–2015 and multi‐model ensembles under ALL (red line), GHG (green line), ANT (brown line) and NAT (blue line) forcings during 1958–2012. Pink and blue shadings show the 5–95% rang...
Gespeichert in:
Veröffentlicht in: | International journal of climatology 2018-02, Vol.38 (2), p.742-750 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 750 |
---|---|
container_issue | 2 |
container_start_page | 742 |
container_title | International journal of climatology |
container_volume | 38 |
creator | Wang, Yujie Sun, Ying Hu, Ting Qin, Dahe Song, Lianchun |
description | Annual mean temperature anomalies (°C, relative to 1961–1990) for the WC region from observations (black line) during 1958–2015 and multi‐model ensembles under ALL (red line), GHG (green line), ANT (brown line) and NAT (blue line) forcings during 1958–2012. Pink and blue shadings show the 5–95% ranges of the individual model simulations from ALL and NAT experiments, respectively.
ABSTRACT
Western China (WC) is located in the arid and semi‐arid belt of the mid‐latitudes. Understanding the causes of the large temperature increases experienced in this region is of great importance because both the fragile ecological environment and the human societies in WC are highly vulnerable to changes in climate. We conducted a detection and attribution analysis on the annual mean temperature using an optimal fingerprinting method by comparing observations based on homogenized station data with simulations conducted using the models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results show that the increase in the mean temperature can be attributed to human activities. The effect of anthropogenic (ANT) forcing can be detected in single‐signal detection analyses when the response to the ANT forcing is compared directly with the observations. This effect can also be separated from the influence of the natural external forcing in two‐signal analyses. The warming attributable to the ANT forcing explains most of the observed regional warming. The models appear to have underestimated the observed warming, suggesting that the projected future temperature increases based on raw output from the model simulations may have also been underestimated. |
doi_str_mv | 10.1002/joc.5206 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1992833784</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1992833784</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2936-6dfc09ba26616a8f753c81f866f6fe5f391c8bbe42a785d473d522d88be0d3833</originalsourceid><addsrcrecordid>eNp10LFOwzAQBmALgUQoSDxCJBaWlLOdOOexiqCAKnUBMVqOY9NEbRLsRKhvT0pYmW757r_TT8gthSUFYA9NZ5YZA3FGIgoyTwAQz0kEKGWCKcVLchVCAwBSUhERvhoGX5fjUHdt3Ll4sIfeej2M3sZmp9tPG-K6jT9sGKxv42JXt_qaXDi9D_bmby7I-9PjW_GcbLbrl2K1SQyTXCSicgZkqZkQVGh0ecYNUodCOOFs5rikBsvSpkznmFVpzquMsQqxtFBx5HxB7ubc3ndf4_SBarrRt9NJRaVkk8gxndT9rIzvQvDWqd7XB-2PioI6VTJtGXWqZKLJTL_rvT3-69Trtvj1PyGMYOM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1992833784</pqid></control><display><type>article</type><title>Attribution of temperature changes in Western China</title><source>Wiley Online Library - AutoHoldings Journals</source><creator>Wang, Yujie ; Sun, Ying ; Hu, Ting ; Qin, Dahe ; Song, Lianchun</creator><creatorcontrib>Wang, Yujie ; Sun, Ying ; Hu, Ting ; Qin, Dahe ; Song, Lianchun</creatorcontrib><description>Annual mean temperature anomalies (°C, relative to 1961–1990) for the WC region from observations (black line) during 1958–2015 and multi‐model ensembles under ALL (red line), GHG (green line), ANT (brown line) and NAT (blue line) forcings during 1958–2012. Pink and blue shadings show the 5–95% ranges of the individual model simulations from ALL and NAT experiments, respectively.
ABSTRACT
Western China (WC) is located in the arid and semi‐arid belt of the mid‐latitudes. Understanding the causes of the large temperature increases experienced in this region is of great importance because both the fragile ecological environment and the human societies in WC are highly vulnerable to changes in climate. We conducted a detection and attribution analysis on the annual mean temperature using an optimal fingerprinting method by comparing observations based on homogenized station data with simulations conducted using the models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results show that the increase in the mean temperature can be attributed to human activities. The effect of anthropogenic (ANT) forcing can be detected in single‐signal detection analyses when the response to the ANT forcing is compared directly with the observations. This effect can also be separated from the influence of the natural external forcing in two‐signal analyses. The warming attributable to the ANT forcing explains most of the observed regional warming. The models appear to have underestimated the observed warming, suggesting that the projected future temperature increases based on raw output from the model simulations may have also been underestimated.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.5206</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Anthropogenic factors ; Aridity ; Climate change ; Climate models ; Computer simulation ; Detection ; detection and attribution ; Fingerprinting ; Human influences ; Intercomparison ; mean temperature changes ; Mean temperatures ; Signal detection ; Temperature ; Temperature changes ; Temperature effects ; Temperature rise ; Western China</subject><ispartof>International journal of climatology, 2018-02, Vol.38 (2), p.742-750</ispartof><rights>2017 Royal Meteorological Society</rights><rights>2018 Royal Meteorological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2936-6dfc09ba26616a8f753c81f866f6fe5f391c8bbe42a785d473d522d88be0d3833</citedby><cites>FETCH-LOGICAL-c2936-6dfc09ba26616a8f753c81f866f6fe5f391c8bbe42a785d473d522d88be0d3833</cites><orcidid>0000-0003-2335-3485</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.5206$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.5206$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27929,27930,45579,45580</link.rule.ids></links><search><creatorcontrib>Wang, Yujie</creatorcontrib><creatorcontrib>Sun, Ying</creatorcontrib><creatorcontrib>Hu, Ting</creatorcontrib><creatorcontrib>Qin, Dahe</creatorcontrib><creatorcontrib>Song, Lianchun</creatorcontrib><title>Attribution of temperature changes in Western China</title><title>International journal of climatology</title><description>Annual mean temperature anomalies (°C, relative to 1961–1990) for the WC region from observations (black line) during 1958–2015 and multi‐model ensembles under ALL (red line), GHG (green line), ANT (brown line) and NAT (blue line) forcings during 1958–2012. Pink and blue shadings show the 5–95% ranges of the individual model simulations from ALL and NAT experiments, respectively.
ABSTRACT
Western China (WC) is located in the arid and semi‐arid belt of the mid‐latitudes. Understanding the causes of the large temperature increases experienced in this region is of great importance because both the fragile ecological environment and the human societies in WC are highly vulnerable to changes in climate. We conducted a detection and attribution analysis on the annual mean temperature using an optimal fingerprinting method by comparing observations based on homogenized station data with simulations conducted using the models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results show that the increase in the mean temperature can be attributed to human activities. The effect of anthropogenic (ANT) forcing can be detected in single‐signal detection analyses when the response to the ANT forcing is compared directly with the observations. This effect can also be separated from the influence of the natural external forcing in two‐signal analyses. The warming attributable to the ANT forcing explains most of the observed regional warming. The models appear to have underestimated the observed warming, suggesting that the projected future temperature increases based on raw output from the model simulations may have also been underestimated.</description><subject>Anthropogenic factors</subject><subject>Aridity</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Computer simulation</subject><subject>Detection</subject><subject>detection and attribution</subject><subject>Fingerprinting</subject><subject>Human influences</subject><subject>Intercomparison</subject><subject>mean temperature changes</subject><subject>Mean temperatures</subject><subject>Signal detection</subject><subject>Temperature</subject><subject>Temperature changes</subject><subject>Temperature effects</subject><subject>Temperature rise</subject><subject>Western China</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp10LFOwzAQBmALgUQoSDxCJBaWlLOdOOexiqCAKnUBMVqOY9NEbRLsRKhvT0pYmW757r_TT8gthSUFYA9NZ5YZA3FGIgoyTwAQz0kEKGWCKcVLchVCAwBSUhERvhoGX5fjUHdt3Ll4sIfeej2M3sZmp9tPG-K6jT9sGKxv42JXt_qaXDi9D_bmby7I-9PjW_GcbLbrl2K1SQyTXCSicgZkqZkQVGh0ecYNUodCOOFs5rikBsvSpkznmFVpzquMsQqxtFBx5HxB7ubc3ndf4_SBarrRt9NJRaVkk8gxndT9rIzvQvDWqd7XB-2PioI6VTJtGXWqZKLJTL_rvT3-69Trtvj1PyGMYOM</recordid><startdate>201802</startdate><enddate>201802</enddate><creator>Wang, Yujie</creator><creator>Sun, Ying</creator><creator>Hu, Ting</creator><creator>Qin, Dahe</creator><creator>Song, Lianchun</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><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/0000-0003-2335-3485</orcidid></search><sort><creationdate>201802</creationdate><title>Attribution of temperature changes in Western China</title><author>Wang, Yujie ; Sun, Ying ; Hu, Ting ; Qin, Dahe ; Song, Lianchun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2936-6dfc09ba26616a8f753c81f866f6fe5f391c8bbe42a785d473d522d88be0d3833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Anthropogenic factors</topic><topic>Aridity</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Computer simulation</topic><topic>Detection</topic><topic>detection and attribution</topic><topic>Fingerprinting</topic><topic>Human influences</topic><topic>Intercomparison</topic><topic>mean temperature changes</topic><topic>Mean temperatures</topic><topic>Signal detection</topic><topic>Temperature</topic><topic>Temperature changes</topic><topic>Temperature effects</topic><topic>Temperature rise</topic><topic>Western China</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yujie</creatorcontrib><creatorcontrib>Sun, Ying</creatorcontrib><creatorcontrib>Hu, Ting</creatorcontrib><creatorcontrib>Qin, Dahe</creatorcontrib><creatorcontrib>Song, Lianchun</creatorcontrib><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>Wang, Yujie</au><au>Sun, Ying</au><au>Hu, Ting</au><au>Qin, Dahe</au><au>Song, Lianchun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Attribution of temperature changes in Western China</atitle><jtitle>International journal of climatology</jtitle><date>2018-02</date><risdate>2018</risdate><volume>38</volume><issue>2</issue><spage>742</spage><epage>750</epage><pages>742-750</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>Annual mean temperature anomalies (°C, relative to 1961–1990) for the WC region from observations (black line) during 1958–2015 and multi‐model ensembles under ALL (red line), GHG (green line), ANT (brown line) and NAT (blue line) forcings during 1958–2012. Pink and blue shadings show the 5–95% ranges of the individual model simulations from ALL and NAT experiments, respectively.
ABSTRACT
Western China (WC) is located in the arid and semi‐arid belt of the mid‐latitudes. Understanding the causes of the large temperature increases experienced in this region is of great importance because both the fragile ecological environment and the human societies in WC are highly vulnerable to changes in climate. We conducted a detection and attribution analysis on the annual mean temperature using an optimal fingerprinting method by comparing observations based on homogenized station data with simulations conducted using the models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results show that the increase in the mean temperature can be attributed to human activities. The effect of anthropogenic (ANT) forcing can be detected in single‐signal detection analyses when the response to the ANT forcing is compared directly with the observations. This effect can also be separated from the influence of the natural external forcing in two‐signal analyses. The warming attributable to the ANT forcing explains most of the observed regional warming. The models appear to have underestimated the observed warming, suggesting that the projected future temperature increases based on raw output from the model simulations may have also been underestimated.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.5206</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-2335-3485</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0899-8418 |
ispartof | International journal of climatology, 2018-02, Vol.38 (2), p.742-750 |
issn | 0899-8418 1097-0088 |
language | eng |
recordid | cdi_proquest_journals_1992833784 |
source | Wiley Online Library - AutoHoldings Journals |
subjects | Anthropogenic factors Aridity Climate change Climate models Computer simulation Detection detection and attribution Fingerprinting Human influences Intercomparison mean temperature changes Mean temperatures Signal detection Temperature Temperature changes Temperature effects Temperature rise Western China |
title | Attribution of temperature changes in Western China |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T03%3A58%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Attribution%20of%20temperature%20changes%20in%20Western%20China&rft.jtitle=International%20journal%20of%20climatology&rft.au=Wang,%20Yujie&rft.date=2018-02&rft.volume=38&rft.issue=2&rft.spage=742&rft.epage=750&rft.pages=742-750&rft.issn=0899-8418&rft.eissn=1097-0088&rft_id=info:doi/10.1002/joc.5206&rft_dat=%3Cproquest_cross%3E1992833784%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1992833784&rft_id=info:pmid/&rfr_iscdi=true |