Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures
For the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind s...
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| Veröffentlicht in: | Journal of applied meteorology and climatology 2012-09, Vol.51 (9), p.1670-1684 |
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| description | For the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable. |
| doi_str_mv | 10.1175/JAMC-D-11-0161.1 |
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Heinke</creator><creatorcontrib>Schoetter, Robert ; Hoffmann, Peter ; Rechid, Diana ; Schlünzen, K. Heinke</creatorcontrib><description>For the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.</description><identifier>ISSN: 1558-8424</identifier><identifier>EISSN: 1558-8432</identifier><identifier>DOI: 10.1175/JAMC-D-11-0161.1</identifier><identifier>CODEN: JOAMEZ</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Annual cycles ; Annual variations ; Arithmetic mean ; Bias ; Chlorofluorocarbons ; Climate change ; Climate models ; Climate variability ; Climatology ; Climatology. Bioclimatology. Climate change ; Cloud cover ; Computer centers ; Confidence intervals ; Data processing ; Datasets ; Earth, ocean, space ; Evaluation ; Exact sciences and technology ; External geophysics ; General circulation models ; Geophysics. Techniques, methods, instrumentation and models ; Global climate ; Global climate models ; Humidity ; Meteorological parameters ; Meteorology ; Metropolitan areas ; Modeling ; Modelling ; Precipitation ; Probability density functions ; Probability theory ; Regional analysis ; Regional climate models ; Regional climates ; Relative humidity ; Resampling ; Simulation ; Simulations ; Statistical analysis ; Studies ; Temperature ; Time series ; Wind ; Wind speed ; Wind velocity</subject><ispartof>Journal of applied meteorology and climatology, 2012-09, Vol.51 (9), p.1670-1684</ispartof><rights>2012 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Sep 2012</rights><rights>Copyright American Meteorological Society 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-291931fcf6fd71ea8b433ff66baae7cb350c0134e392d3bafe9fbc289774abc73</citedby><cites>FETCH-LOGICAL-c426t-291931fcf6fd71ea8b433ff66baae7cb350c0134e392d3bafe9fbc289774abc73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26175233$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26175233$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,778,782,801,3670,27907,27908,58000,58233</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26342027$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Schoetter, Robert</creatorcontrib><creatorcontrib>Hoffmann, Peter</creatorcontrib><creatorcontrib>Rechid, Diana</creatorcontrib><creatorcontrib>Schlünzen, K. Heinke</creatorcontrib><title>Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures</title><title>Journal of applied meteorology and climatology</title><description>For the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.</description><subject>Annual cycles</subject><subject>Annual variations</subject><subject>Arithmetic mean</subject><subject>Bias</subject><subject>Chlorofluorocarbons</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climate variability</subject><subject>Climatology</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Cloud cover</subject><subject>Computer centers</subject><subject>Confidence intervals</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Earth, ocean, space</subject><subject>Evaluation</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>General circulation models</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Global climate</subject><subject>Global climate models</subject><subject>Humidity</subject><subject>Meteorological parameters</subject><subject>Meteorology</subject><subject>Metropolitan areas</subject><subject>Modeling</subject><subject>Modelling</subject><subject>Precipitation</subject><subject>Probability density functions</subject><subject>Probability theory</subject><subject>Regional analysis</subject><subject>Regional climate models</subject><subject>Regional climates</subject><subject>Relative humidity</subject><subject>Resampling</subject><subject>Simulation</subject><subject>Simulations</subject><subject>Statistical analysis</subject><subject>Studies</subject><subject>Temperature</subject><subject>Time series</subject><subject>Wind</subject><subject>Wind speed</subject><subject>Wind velocity</subject><issn>1558-8424</issn><issn>1558-8432</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kUtLxDAUhYso-Ny7EQoiuKnm1aRdascnDoLoOt5mEukQG81tBf-9GWcQceEqh5PvHm5ysmyfkhNKVXl6ezZtiklBaUGopCd0LduiZVkVleBs_UczsZltI84JEUKpcit7vvgAP8LQhT6Hfpafd4B5E2K05tsLLn-wL0mBzxvfvcJg82mYWZ9sHP2A-RN2_cvK-xU2tYBjtLibbTjwaPdW5072dHnx2FwXd_dXN83ZXWEEk0PBalpz6oyTbqaohaoVnDsnZQtglWl5SQyhXFhesxlvwdnatYZVtVICWqP4Tna8zH2L4X20OOjXDo31HnobRtSU1FxIJdkCPfyDzsMY0wtRs4qVJSeMVf9RlIiSy7S3SBRZUiYGxGidfovpl-JngvSiGL0oRk-S1otiNE0jR6tgQAPeRehNhz9zTHLByPeaB0tujkOIv-5TKuOcfwFC_paf</recordid><startdate>20120901</startdate><enddate>20120901</enddate><creator>Schoetter, Robert</creator><creator>Hoffmann, Peter</creator><creator>Rechid, Diana</creator><creator>Schlünzen, K. 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Heinke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-291931fcf6fd71ea8b433ff66baae7cb350c0134e392d3bafe9fbc289774abc73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Annual cycles</topic><topic>Annual variations</topic><topic>Arithmetic mean</topic><topic>Bias</topic><topic>Chlorofluorocarbons</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climate variability</topic><topic>Climatology</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>Cloud cover</topic><topic>Computer centers</topic><topic>Confidence intervals</topic><topic>Data processing</topic><topic>Datasets</topic><topic>Earth, ocean, space</topic><topic>Evaluation</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>General circulation models</topic><topic>Geophysics. 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Heinke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures</atitle><jtitle>Journal of applied meteorology and climatology</jtitle><date>2012-09-01</date><risdate>2012</risdate><volume>51</volume><issue>9</issue><spage>1670</spage><epage>1684</epage><pages>1670-1684</pages><issn>1558-8424</issn><eissn>1558-8432</eissn><coden>JOAMEZ</coden><abstract>For the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/JAMC-D-11-0161.1</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Annual cycles Annual variations Arithmetic mean Bias Chlorofluorocarbons Climate change Climate models Climate variability Climatology Climatology. Bioclimatology. Climate change Cloud cover Computer centers Confidence intervals Data processing Datasets Earth, ocean, space Evaluation Exact sciences and technology External geophysics General circulation models Geophysics. Techniques, methods, instrumentation and models Global climate Global climate models Humidity Meteorological parameters Meteorology Metropolitan areas Modeling Modelling Precipitation Probability density functions Probability theory Regional analysis Regional climate models Regional climates Relative humidity Resampling Simulation Simulations Statistical analysis Studies Temperature Time series Wind Wind speed Wind velocity |
| title | Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures |
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