The potential added value of Regional Climate Models in South America using a multiresolution approach
This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distingu...
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description | This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent. |
doi_str_mv | 10.1007/s00382-019-05073-9 |
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X. ; Cabrelli, Carlos ; Menéndez, Claudio G.</creator><creatorcontrib>Falco, Magdalena ; Carril, Andrea F. ; Li, Laurent Z. X. ; Cabrelli, Carlos ; Menéndez, Claudio G.</creatorcontrib><description>This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. 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All Rights Reserved.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-a6d6c2825e446dc5b523db7693abcd4d7099da1c96f5d866fa740c89dd63494a3</citedby><cites>FETCH-LOGICAL-c458t-a6d6c2825e446dc5b523db7693abcd4d7099da1c96f5d866fa740c89dd63494a3</cites><orcidid>0000-0002-2480-3209 ; 0000-0002-3855-3976</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/s00382-019-05073-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00382-019-05073-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://insu.hal.science/insu-03727004$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Falco, Magdalena</creatorcontrib><creatorcontrib>Carril, Andrea F.</creatorcontrib><creatorcontrib>Li, Laurent Z. X.</creatorcontrib><creatorcontrib>Cabrelli, Carlos</creatorcontrib><creatorcontrib>Menéndez, Claudio G.</creatorcontrib><title>The potential added value of Regional Climate Models in South America using a multiresolution approach</title><title>Climate dynamics</title><addtitle>Clim Dyn</addtitle><description>This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent.</description><subject>Air temperature</subject><subject>Analysis</subject><subject>Atmospheric models</subject><subject>Atmospheric precipitations</subject><subject>Climate</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Coasts</subject><subject>Comparative analysis</subject><subject>Computer simulation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Extreme weather</subject><subject>Fields</subject><subject>Filtration</subject><subject>Geophysics/Geodesy</subject><subject>Oceanography</subject><subject>Precipitation</subject><subject>Precipitation variability</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rainfall</subject><subject>Regional climate models</subject><subject>Regional climates</subject><subject>Regions</subject><subject>Sciences of the Universe</subject><subject>Spatial distribution</subject><subject>Spatial variability</subject><subject>Spatial variations</subject><subject>Surface temperature</subject><subject>Surface-air temperature relationships</subject><subject>TRMM satellite</subject><subject>Tropical climate</subject><subject>Tropical Rainfall Measuring Mission (TRMM)</subject><subject>Wavelengths</subject><subject>Wavelet analysis</subject><issn>0930-7575</issn><issn>1432-0894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</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>eNp9kt1rFDEUxQdRcK3-Az4FBEFhajL5mjwuS20LK0Jbn8PdJLOTkp2sSabof9_o-LUvkofA4Xcu91xO07wm-JxgLD9kjGnftZioFnMsaaueNCvCaJV6xZ42K6wobiWX_HnzIud7jAkTsls1w93o0DEWNxUPAYG1zqIHCLNDcUA3bu_jVPVN8AcoDn2K1oWM_IRu41xGtD645A2gOftpjwAd5lB8cjmGuVQnguMxRTDjy-bZACG7V7_-s-bLx4u7zVW7_Xx5vVlvW8N4X1oQVpiu77hjTFjDd7yjdieForAzllmJlbJAjBIDt70QA0iGTa-sFZQpBvSseb_MHSHoY6pLp-86gtdX6632U541prKTGLMHUuE3C1x3_Dq7XPR9nFONm3VHOSG4nquv1PlC7SG4OmOIJYGpz7qDN3Fyg6_6WhDKO1FXr4Z3J4bKFPet7GHOWV_f3pyyb_9hRwehjL-Pl0_BbgFNijknN_xJR7D-UQG9VEDXCuifFdCqmuhiyhWe9i79Dfgf1yNMPrGm</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Falco, Magdalena</creator><creator>Carril, Andrea F.</creator><creator>Li, Laurent Z. X.</creator><creator>Cabrelli, Carlos</creator><creator>Menéndez, Claudio G.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><general>Springer Verlag</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>AEUYN</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><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-2480-3209</orcidid><orcidid>https://orcid.org/0000-0002-3855-3976</orcidid></search><sort><creationdate>20200201</creationdate><title>The potential added value of Regional Climate Models in South America using a multiresolution approach</title><author>Falco, Magdalena ; Carril, Andrea F. ; Li, Laurent Z. 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X.</creatorcontrib><creatorcontrib>Cabrelli, Carlos</creatorcontrib><creatorcontrib>Menéndez, Claudio G.</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 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>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><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Climate dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Falco, Magdalena</au><au>Carril, Andrea F.</au><au>Li, Laurent Z. X.</au><au>Cabrelli, Carlos</au><au>Menéndez, Claudio G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The potential added value of Regional Climate Models in South America using a multiresolution approach</atitle><jtitle>Climate dynamics</jtitle><stitle>Clim Dyn</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>54</volume><issue>3-4</issue><spage>1553</spage><epage>1569</epage><pages>1553-1569</pages><issn>0930-7575</issn><eissn>1432-0894</eissn><abstract>This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00382-019-05073-9</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-2480-3209</orcidid><orcidid>https://orcid.org/0000-0002-3855-3976</orcidid></addata></record> |
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subjects | Air temperature Analysis Atmospheric models Atmospheric precipitations Climate Climate models Climatology Coasts Comparative analysis Computer simulation Earth and Environmental Science Earth Sciences Extreme weather Fields Filtration Geophysics/Geodesy Oceanography Precipitation Precipitation variability Rain Rain and rainfall Rainfall Regional climate models Regional climates Regions Sciences of the Universe Spatial distribution Spatial variability Spatial variations Surface temperature Surface-air temperature relationships TRMM satellite Tropical climate Tropical Rainfall Measuring Mission (TRMM) Wavelengths Wavelet analysis |
title | The potential added value of Regional Climate Models in South America using a multiresolution approach |
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