Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models
Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Progr...
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description | Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments.
(left top) Annual cycle of precipitation for a NAM region in Arizona for NOAA observed (in histogram), NARCCAP 20th century Phase II simulations (red thin lines), and NARCCAP ensemble mean (red thick line). (right top) Same as the left plot but for selected NARCCAP models that best represent the abrupt pre‐ and post‐monsoon transition in precipitation as distinguished in colours with the black case for the ensemble mean among these RCM–GCM pairs: CRCM(cgcm3), HRM3(hadcm3), RCM3(cgcm3), and WRFG(cgcm3 |
doi_str_mv | 10.1002/joc.5561 |
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(left top) Annual cycle of precipitation for a NAM region in Arizona for NOAA observed (in histogram), NARCCAP 20th century Phase II simulations (red thin lines), and NARCCAP ensemble mean (red thick line). (right top) Same as the left plot but for selected NARCCAP models that best represent the abrupt pre‐ and post‐monsoon transition in precipitation as distinguished in colours with the black case for the ensemble mean among these RCM–GCM pairs: CRCM(cgcm3), HRM3(hadcm3), RCM3(cgcm3), and WRFG(cgcm3). The identical histograms (bars) are superimposed to show comparisons with NOAA‐observed precipitation; these look different, because the scale has been doubled. The average is over the region defined between 30° and 37.5°N and 115° and 107.5°W (see box in Figure 2). (bottom) Same as the top panel but for the 21st century without the observed precipitation (bars) superimposed.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.5561</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Annual variations ; Atmospheric models ; Climate change ; Climate models ; Climate variability ; Climatology ; Computer simulation ; Confidence ; El Nino ; El Nino phenomena ; El Nino-Southern Oscillation event ; ENSO‐PDV ; Environmental assessment ; Environmental impact ; Future climates ; Global climate ; Global climate models ; Hydrologic data ; Monsoon precipitation ; Monsoons ; MTM‐SVD ; NAM ; NARCCAP ; North American monsoon ; Physical climatology ; Precipitation ; Precipitation data ; Quality assessment ; Regional analysis ; Regional climate models ; Regional climates ; Sea surface ; Sea surface temperature ; Simulation ; Southern Oscillation ; Statistical analysis ; Statistical methods ; Statistics ; Summer precipitation ; Surface temperature ; Teleconnections ; Variability ; Warm seasons</subject><ispartof>International journal of climatology, 2018-09, Vol.38 (11), p.4189-4210</ispartof><rights>2018 Royal Meteorological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2931-2fb675ce3e3e73abb4fa1c61bc042b3ca5e0696dcb941ea8a57c021882134c1a3</citedby><cites>FETCH-LOGICAL-c2931-2fb675ce3e3e73abb4fa1c61bc042b3ca5e0696dcb941ea8a57c021882134c1a3</cites><orcidid>0000-0002-0045-1595</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.5561$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.5561$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Carrillo, Carlos M.</creatorcontrib><creatorcontrib>Castro, Christopher L.</creatorcontrib><creatorcontrib>Garfin, Gregg</creatorcontrib><creatorcontrib>Chang, Hsin‐I</creatorcontrib><creatorcontrib>Bukovsky, Melissa S.</creatorcontrib><creatorcontrib>Mearns, Linda O.</creatorcontrib><title>Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models</title><title>International journal of climatology</title><description>Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments.
(left top) Annual cycle of precipitation for a NAM region in Arizona for NOAA observed (in histogram), NARCCAP 20th century Phase II simulations (red thin lines), and NARCCAP ensemble mean (red thick line). (right top) Same as the left plot but for selected NARCCAP models that best represent the abrupt pre‐ and post‐monsoon transition in precipitation as distinguished in colours with the black case for the ensemble mean among these RCM–GCM pairs: CRCM(cgcm3), HRM3(hadcm3), RCM3(cgcm3), and WRFG(cgcm3). The identical histograms (bars) are superimposed to show comparisons with NOAA‐observed precipitation; these look different, because the scale has been doubled. The average is over the region defined between 30° and 37.5°N and 115° and 107.5°W (see box in Figure 2). (bottom) Same as the top panel but for the 21st century without the observed precipitation (bars) superimposed.</description><subject>Annual variations</subject><subject>Atmospheric models</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climate variability</subject><subject>Climatology</subject><subject>Computer simulation</subject><subject>Confidence</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>El Nino-Southern Oscillation event</subject><subject>ENSO‐PDV</subject><subject>Environmental assessment</subject><subject>Environmental impact</subject><subject>Future climates</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Hydrologic data</subject><subject>Monsoon precipitation</subject><subject>Monsoons</subject><subject>MTM‐SVD</subject><subject>NAM</subject><subject>NARCCAP</subject><subject>North American monsoon</subject><subject>Physical climatology</subject><subject>Precipitation</subject><subject>Precipitation data</subject><subject>Quality assessment</subject><subject>Regional analysis</subject><subject>Regional climate models</subject><subject>Regional climates</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Simulation</subject><subject>Southern Oscillation</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Summer precipitation</subject><subject>Surface temperature</subject><subject>Teleconnections</subject><subject>Variability</subject><subject>Warm seasons</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kdtKBDEMhosouB7ARyh4481oO6dtL5fBI6Iiej1kspndLjPTse0gvo5PanW9E8lFSPLlJ-Fn7ESKcylEerGxeF4UpdxhMyn0PBFCqV02E0rrROVS7bMD7zdCCK1lOWOfT4CmNcg9AfeTawGJB-pHchAmR9xRB4GW3AxtN9GA5Lkd-IN1Yc0XPTmDMPDeDt7G9ugIzWgCBBOrdxPW5g_7TKs4hI5XnemjNK_WMKyIL7wn73saAn9yduWgj7JL6vwR22uh83T8mw_Z69XlS3WT3D9e31aL-wRTnckkbZtyXiBlMeYZNE3egsRSNijytMkQChKlLpfY6FwSKCjmKFKpVCqzHCVkh-x0qzs6-zaRD_XGTi5e6utUaKWLQhcyUmdbCp313lFbjy7-4T5qKepvB-IW1t8ORDTZou-mo49_ufrusfrhvwDy5YtI</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Carrillo, Carlos M.</creator><creator>Castro, Christopher L.</creator><creator>Garfin, Gregg</creator><creator>Chang, Hsin‐I</creator><creator>Bukovsky, Melissa S.</creator><creator>Mearns, Linda O.</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/0000-0002-0045-1595</orcidid></search><sort><creationdate>201809</creationdate><title>Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models</title><author>Carrillo, Carlos M. ; Castro, Christopher L. ; Garfin, Gregg ; Chang, Hsin‐I ; Bukovsky, Melissa S. ; Mearns, Linda O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2931-2fb675ce3e3e73abb4fa1c61bc042b3ca5e0696dcb941ea8a57c021882134c1a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Annual variations</topic><topic>Atmospheric models</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climate variability</topic><topic>Climatology</topic><topic>Computer simulation</topic><topic>Confidence</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>El Nino-Southern Oscillation event</topic><topic>ENSO‐PDV</topic><topic>Environmental assessment</topic><topic>Environmental impact</topic><topic>Future climates</topic><topic>Global climate</topic><topic>Global climate models</topic><topic>Hydrologic data</topic><topic>Monsoon precipitation</topic><topic>Monsoons</topic><topic>MTM‐SVD</topic><topic>NAM</topic><topic>NARCCAP</topic><topic>North American monsoon</topic><topic>Physical climatology</topic><topic>Precipitation</topic><topic>Precipitation data</topic><topic>Quality assessment</topic><topic>Regional analysis</topic><topic>Regional climate models</topic><topic>Regional climates</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Simulation</topic><topic>Southern Oscillation</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Summer precipitation</topic><topic>Surface temperature</topic><topic>Teleconnections</topic><topic>Variability</topic><topic>Warm seasons</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Carrillo, Carlos M.</creatorcontrib><creatorcontrib>Castro, Christopher L.</creatorcontrib><creatorcontrib>Garfin, Gregg</creatorcontrib><creatorcontrib>Chang, Hsin‐I</creatorcontrib><creatorcontrib>Bukovsky, Melissa S.</creatorcontrib><creatorcontrib>Mearns, Linda O.</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>Carrillo, Carlos M.</au><au>Castro, Christopher L.</au><au>Garfin, Gregg</au><au>Chang, Hsin‐I</au><au>Bukovsky, Melissa S.</au><au>Mearns, Linda O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models</atitle><jtitle>International journal of climatology</jtitle><date>2018-09</date><risdate>2018</risdate><volume>38</volume><issue>11</issue><spage>4189</spage><epage>4210</epage><pages>4189-4210</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments.
(left top) Annual cycle of precipitation for a NAM region in Arizona for NOAA observed (in histogram), NARCCAP 20th century Phase II simulations (red thin lines), and NARCCAP ensemble mean (red thick line). (right top) Same as the left plot but for selected NARCCAP models that best represent the abrupt pre‐ and post‐monsoon transition in precipitation as distinguished in colours with the black case for the ensemble mean among these RCM–GCM pairs: CRCM(cgcm3), HRM3(hadcm3), RCM3(cgcm3), and WRFG(cgcm3). The identical histograms (bars) are superimposed to show comparisons with NOAA‐observed precipitation; these look different, because the scale has been doubled. The average is over the region defined between 30° and 37.5°N and 115° and 107.5°W (see box in Figure 2). (bottom) Same as the top panel but for the 21st century without the observed precipitation (bars) superimposed.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.5561</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-0045-1595</orcidid></addata></record> |
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subjects | Annual variations Atmospheric models Climate change Climate models Climate variability Climatology Computer simulation Confidence El Nino El Nino phenomena El Nino-Southern Oscillation event ENSO‐PDV Environmental assessment Environmental impact Future climates Global climate Global climate models Hydrologic data Monsoon precipitation Monsoons MTM‐SVD NAM NARCCAP North American monsoon Physical climatology Precipitation Precipitation data Quality assessment Regional analysis Regional climate models Regional climates Sea surface Sea surface temperature Simulation Southern Oscillation Statistical analysis Statistical methods Statistics Summer precipitation Surface temperature Teleconnections Variability Warm seasons |
title | Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models |
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