Uncertainty resulting from multiple data usage in statistical downscaling
Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalys...
Gespeichert in:
| Veröffentlicht in: | Geophysical research letters 2014-06, Vol.41 (11), p.4013-4019 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4019 |
|---|---|
| container_issue | 11 |
| container_start_page | 4013 |
| container_title | Geophysical research letters |
| container_volume | 41 |
| creator | Kannan, S. Ghosh, Subimal Mishra, Vimal Salvi, Kaustubh |
| description | Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
Key Points
Data uncertainty is higher than that due to multiple GCMs in downscaling
Downscaled results show disparities comparing to original GCM projections
Statistical downscaling suffers from the assumption of stationarity |
| doi_str_mv | 10.1002/2014GL060089 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1692401279</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3545547211</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4767-c0a17dae9ee9a65805e62822cdc87fa7baa52065a753d08f5ffebf985540b6aa3</originalsourceid><addsrcrecordid>eNqF0U9LwzAYBvAgCs7pzQ9Q8OLB6pu0SZqjiM7BnKAOvYV3bTo6-2cmLXPf3oyKiId5yhvyewLJQ8gphUsKwK4Y0Hg0AQGQqD0yoCqOwwRA7pMBgPIzk-KQHDm3BIAIIjog41mdGttiUbebwBrXlW1RL4LcNlVQbTer0gQZthh0DhcmKOrAtdgWri1SLIOsWdfODz5zTA5yLJ05-V6HZHZ3-3JzH04eR-Ob60mYxlLIMAWkMkOjjFEoeALcCJYwlmZpInOUc0TOQHCUPMogyXmem3muEs5jmAvEaEjO-3tXtvnojGt1VbjUlCXWpumcpkKxGCiT6n_KuZL-Z5jw9OwPXTadrf1DNFUUPJNC7VQiZsAZ5bFXF71KbeOcNble2aJCu9EU9LYo_bsoz1nP10VpNjutHj1NOBVC-lDYh3wT5vMnhPZd-1PJ9et0pAV_Sx6Ymurn6AsuZKGa</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1642052154</pqid></control><display><type>article</type><title>Uncertainty resulting from multiple data usage in statistical downscaling</title><source>Wiley Online Library AGU Free Content</source><source>Wiley Free Content</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Wiley Online Library All Journals</source><creator>Kannan, S. ; Ghosh, Subimal ; Mishra, Vimal ; Salvi, Kaustubh</creator><creatorcontrib>Kannan, S. ; Ghosh, Subimal ; Mishra, Vimal ; Salvi, Kaustubh</creatorcontrib><description>Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
Key Points
Data uncertainty is higher than that due to multiple GCMs in downscaling
Downscaled results show disparities comparing to original GCM projections
Statistical downscaling suffers from the assumption of stationarity</description><identifier>ISSN: 0094-8276</identifier><identifier>EISSN: 1944-8007</identifier><identifier>DOI: 10.1002/2014GL060089</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Calibration ; Circulation ; Climate ; climate change ; Computer simulation ; Data ; Data processing ; General circulation ; General circulation models ; Indian monsoon ; Mathematical models ; Meteorology ; Monsoon rainfall ; Monsoons ; Products ; Projection ; Rain ; Rainfall ; Regional climates ; Simulation ; Statistical downscaling ; Statistics ; Summer ; Summer monsoon ; Training ; Uncertainty</subject><ispartof>Geophysical research letters, 2014-06, Vol.41 (11), p.4013-4019</ispartof><rights>2014. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4767-c0a17dae9ee9a65805e62822cdc87fa7baa52065a753d08f5ffebf985540b6aa3</citedby><cites>FETCH-LOGICAL-c4767-c0a17dae9ee9a65805e62822cdc87fa7baa52065a753d08f5ffebf985540b6aa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2014GL060089$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2014GL060089$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,11514,27924,27925,45574,45575,46409,46468,46833,46892</link.rule.ids></links><search><creatorcontrib>Kannan, S.</creatorcontrib><creatorcontrib>Ghosh, Subimal</creatorcontrib><creatorcontrib>Mishra, Vimal</creatorcontrib><creatorcontrib>Salvi, Kaustubh</creatorcontrib><title>Uncertainty resulting from multiple data usage in statistical downscaling</title><title>Geophysical research letters</title><addtitle>Geophys. Res. Lett</addtitle><description>Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
Key Points
Data uncertainty is higher than that due to multiple GCMs in downscaling
Downscaled results show disparities comparing to original GCM projections
Statistical downscaling suffers from the assumption of stationarity</description><subject>Calibration</subject><subject>Circulation</subject><subject>Climate</subject><subject>climate change</subject><subject>Computer simulation</subject><subject>Data</subject><subject>Data processing</subject><subject>General circulation</subject><subject>General circulation models</subject><subject>Indian monsoon</subject><subject>Mathematical models</subject><subject>Meteorology</subject><subject>Monsoon rainfall</subject><subject>Monsoons</subject><subject>Products</subject><subject>Projection</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Regional climates</subject><subject>Simulation</subject><subject>Statistical downscaling</subject><subject>Statistics</subject><subject>Summer</subject><subject>Summer monsoon</subject><subject>Training</subject><subject>Uncertainty</subject><issn>0094-8276</issn><issn>1944-8007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqF0U9LwzAYBvAgCs7pzQ9Q8OLB6pu0SZqjiM7BnKAOvYV3bTo6-2cmLXPf3oyKiId5yhvyewLJQ8gphUsKwK4Y0Hg0AQGQqD0yoCqOwwRA7pMBgPIzk-KQHDm3BIAIIjog41mdGttiUbebwBrXlW1RL4LcNlVQbTer0gQZthh0DhcmKOrAtdgWri1SLIOsWdfODz5zTA5yLJ05-V6HZHZ3-3JzH04eR-Ob60mYxlLIMAWkMkOjjFEoeALcCJYwlmZpInOUc0TOQHCUPMogyXmem3muEs5jmAvEaEjO-3tXtvnojGt1VbjUlCXWpumcpkKxGCiT6n_KuZL-Z5jw9OwPXTadrf1DNFUUPJNC7VQiZsAZ5bFXF71KbeOcNble2aJCu9EU9LYo_bsoz1nP10VpNjutHj1NOBVC-lDYh3wT5vMnhPZd-1PJ9et0pAV_Sx6Ymurn6AsuZKGa</recordid><startdate>20140616</startdate><enddate>20140616</enddate><creator>Kannan, S.</creator><creator>Ghosh, Subimal</creator><creator>Mishra, Vimal</creator><creator>Salvi, Kaustubh</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope></search><sort><creationdate>20140616</creationdate><title>Uncertainty resulting from multiple data usage in statistical downscaling</title><author>Kannan, S. ; Ghosh, Subimal ; Mishra, Vimal ; Salvi, Kaustubh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4767-c0a17dae9ee9a65805e62822cdc87fa7baa52065a753d08f5ffebf985540b6aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Calibration</topic><topic>Circulation</topic><topic>Climate</topic><topic>climate change</topic><topic>Computer simulation</topic><topic>Data</topic><topic>Data processing</topic><topic>General circulation</topic><topic>General circulation models</topic><topic>Indian monsoon</topic><topic>Mathematical models</topic><topic>Meteorology</topic><topic>Monsoon rainfall</topic><topic>Monsoons</topic><topic>Products</topic><topic>Projection</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Regional climates</topic><topic>Simulation</topic><topic>Statistical downscaling</topic><topic>Statistics</topic><topic>Summer</topic><topic>Summer monsoon</topic><topic>Training</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kannan, S.</creatorcontrib><creatorcontrib>Ghosh, Subimal</creatorcontrib><creatorcontrib>Mishra, Vimal</creatorcontrib><creatorcontrib>Salvi, Kaustubh</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Geophysical research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kannan, S.</au><au>Ghosh, Subimal</au><au>Mishra, Vimal</au><au>Salvi, Kaustubh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Uncertainty resulting from multiple data usage in statistical downscaling</atitle><jtitle>Geophysical research letters</jtitle><addtitle>Geophys. Res. Lett</addtitle><date>2014-06-16</date><risdate>2014</risdate><volume>41</volume><issue>11</issue><spage>4013</spage><epage>4019</epage><pages>4013-4019</pages><issn>0094-8276</issn><eissn>1944-8007</eissn><abstract>Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
Key Points
Data uncertainty is higher than that due to multiple GCMs in downscaling
Downscaled results show disparities comparing to original GCM projections
Statistical downscaling suffers from the assumption of stationarity</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2014GL060089</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-8276 |
| ispartof | Geophysical research letters, 2014-06, Vol.41 (11), p.4013-4019 |
| issn | 0094-8276 1944-8007 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1692401279 |
| source | Wiley Online Library AGU Free Content; Wiley Free Content; EZB-FREE-00999 freely available EZB journals; Wiley Online Library All Journals |
| subjects | Calibration Circulation Climate climate change Computer simulation Data Data processing General circulation General circulation models Indian monsoon Mathematical models Meteorology Monsoon rainfall Monsoons Products Projection Rain Rainfall Regional climates Simulation Statistical downscaling Statistics Summer Summer monsoon Training Uncertainty |
| title | Uncertainty resulting from multiple data usage in statistical downscaling |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T04%3A00%3A20IST&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=Uncertainty%20resulting%20from%20multiple%20data%20usage%20in%20statistical%20downscaling&rft.jtitle=Geophysical%20research%20letters&rft.au=Kannan,%20S.&rft.date=2014-06-16&rft.volume=41&rft.issue=11&rft.spage=4013&rft.epage=4019&rft.pages=4013-4019&rft.issn=0094-8276&rft.eissn=1944-8007&rft_id=info:doi/10.1002/2014GL060089&rft_dat=%3Cproquest_cross%3E3545547211%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=1642052154&rft_id=info:pmid/&rfr_iscdi=true |