How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana
The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative wea...
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description | The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings. |
doi_str_mv | 10.1007/s00704-014-1175-2 |
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Examples from Georgia and Louisiana</title><source>SpringerLink Journals - AutoHoldings</source><creator>Allard, Jason ; Thompson, Clint ; Keim, Barry D.</creator><creatorcontrib>Allard, Jason ; Thompson, Clint ; Keim, Barry D.</creatorcontrib><description>The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-014-1175-2</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Analysis ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate change ; Climate science ; Climate studies ; Climatic data ; Climatology ; Data analysis ; Data centers ; Earth and Environmental Science ; Earth Sciences ; Global temperature changes ; Original Paper ; Precipitation (Meteorology) ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather forecasting</subject><ispartof>Theoretical and applied climatology, 2015-04, Vol.120 (1-2), p.323-330</ispartof><rights>Springer-Verlag Wien 2014</rights><rights>COPYRIGHT 2015 Springer</rights><rights>Springer-Verlag Wien 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c407t-385973d733de442384b6f7deb54d365a62ae5931886b86c20265d22f43e8a8b23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-014-1175-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-014-1175-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Allard, Jason</creatorcontrib><creatorcontrib>Thompson, Clint</creatorcontrib><creatorcontrib>Keim, Barry D.</creatorcontrib><title>How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.</description><subject>Analysis</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate change</subject><subject>Climate science</subject><subject>Climate studies</subject><subject>Climatic data</subject><subject>Climatology</subject><subject>Data analysis</subject><subject>Data centers</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Global temperature changes</subject><subject>Original Paper</subject><subject>Precipitation (Meteorology)</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Weather forecasting</subject><issn>0177-798X</issn><issn>1434-4483</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</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>eNp1kc9qFTEUxoMoeK0-gLuAG12k5t8kmZWU29oWLgr-AXchM3PmmjIzuSYZrV35EH1Cn8SM48IKEkgOh9_3cU4-hJ4yeswo1S9TuagklEnCmK4Iv4c2TApJpDTiPtpQpjXRtfn0ED1K6YpSypXSG3RzEb7hGJo5ZewTzp8BHyIQVguG37jsw-QGvB38WOoWn7rs8BamDPHnj9v2dxtw57_6tJJdARLkV_js2o2HARLuYxjxOYS49w67qcO7MBfaTe4xetC7IcGTP-8R-vj67MP2guzenl9uT3aklVRnIkxVa9FpITqQkgsjG9XrDppKdkJVTnEHVRnXGNUY1fKyWNVx3ksBxpmGiyP0fPU9xPBlhpTt6FMLw-AmCHOyTBXvmilVF_TZP-hVmGNZbKGULJxii-HxSu3dANZPfcjRteV0MPo2TND70j-RnJqqqrgoghd3BIXJcJ33bk7JXr5_d5dlK9vGkFKE3h5i-eb43TJql6jtGrUtUdslarsMxFdNKuy0h_jX2P8V_QJ2iqns</recordid><startdate>20150401</startdate><enddate>20150401</enddate><creator>Allard, Jason</creator><creator>Thompson, Clint</creator><creator>Keim, Barry D.</creator><general>Springer Vienna</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</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>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7ST</scope><scope>7U6</scope></search><sort><creationdate>20150401</creationdate><title>How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana</title><author>Allard, Jason ; Thompson, Clint ; Keim, Barry D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-385973d733de442384b6f7deb54d365a62ae5931886b86c20265d22f43e8a8b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Analysis</topic><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Atmospheric Sciences</topic><topic>Climate change</topic><topic>Climate science</topic><topic>Climate studies</topic><topic>Climatic data</topic><topic>Climatology</topic><topic>Data analysis</topic><topic>Data centers</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Global temperature changes</topic><topic>Original Paper</topic><topic>Precipitation (Meteorology)</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Allard, Jason</creatorcontrib><creatorcontrib>Thompson, Clint</creatorcontrib><creatorcontrib>Keim, Barry D.</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</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>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><jtitle>Theoretical and applied climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Allard, Jason</au><au>Thompson, Clint</au><au>Keim, Barry D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana</atitle><jtitle>Theoretical and applied climatology</jtitle><stitle>Theor Appl Climatol</stitle><date>2015-04-01</date><risdate>2015</risdate><volume>120</volume><issue>1-2</issue><spage>323</spage><epage>330</epage><pages>323-330</pages><issn>0177-798X</issn><eissn>1434-4483</eissn><abstract>The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00704-014-1175-2</doi><tpages>8</tpages></addata></record> |
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subjects | Analysis Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate change Climate science Climate studies Climatic data Climatology Data analysis Data centers Earth and Environmental Science Earth Sciences Global temperature changes Original Paper Precipitation (Meteorology) Waste Water Technology Water Management Water Pollution Control Weather forecasting |
title | How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana |
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