Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment

VALUE is a network that developed a framework to evaluate statistical downscaling methods including model output statistics such as simple bias correction and quantile mapping; perfect prognosis methods such as regression models and analog methods; and weather generators. The first experiment addres...

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Detalles Bibliográficos
Autores: Maraun, Douglas, Widmann, Martin, Gutiérrez, José M.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/213840
Acceso en línea:http://hdl.handle.net/10261/213840
Access Level:acceso abierto
Palabra clave:Bias correction
Evaluation
Regional climate
Statistical downscaling
Validation
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spelling Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experimentMaraun, DouglasWidmann, MartinGutiérrez, José M.Bias correctionEvaluationRegional climateStatistical downscalingValidationVALUE is a network that developed a framework to evaluate statistical downscaling methods including model output statistics such as simple bias correction and quantile mapping; perfect prognosis methods such as regression models and analog methods; and weather generators. The first experiment addresses the downscaling performance in present climate with perfect predictors. This paper presents a synthesis of the VALUE special issue, with a focus on the results of this first experiment. This paper presents a synthesis of the results. Model output statistics performs mostly well, but requires predictors at a resolution close to the target one. Perfect prog performance depends crucially on model structure and predictor choice. Weather generators perform in principle well for all aspects that can be expressed by the available model structure. Inter-annual variability is underrepresented by both perfect prog and weather generator approaches. Spatial variability is poorly represented by almost all participating methods (inherited by model output statistics from the driving model, not represented by the perfect prog and weather generator methods). Further studies are required to systematically assess (a) the role of predictor choice for perfect prog; (b) the performance of spatial weather generators, to study the performance based on GCM predictors; (c) downscaling skill in simulated future climates; and (d) the credibility of simulated predictors in a future climate.VALUE was funded from 2012 to 2015 as EU COST Action ES1102.John Wiley & SonsEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/213840reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1002/joc.5877Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2138402026-05-22T06:33:51Z
dc.title.none.fl_str_mv Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
title Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
spellingShingle Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
Maraun, Douglas
Bias correction
Evaluation
Regional climate
Statistical downscaling
Validation
title_short Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
title_full Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
title_fullStr Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
title_full_unstemmed Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
title_sort Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment
dc.creator.none.fl_str_mv Maraun, Douglas
Widmann, Martin
Gutiérrez, José M.
author Maraun, Douglas
author_facet Maraun, Douglas
Widmann, Martin
Gutiérrez, José M.
author_role author
author2 Widmann, Martin
Gutiérrez, José M.
author2_role author
author
dc.contributor.none.fl_str_mv European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Bias correction
Evaluation
Regional climate
Statistical downscaling
Validation
topic Bias correction
Evaluation
Regional climate
Statistical downscaling
Validation
description VALUE is a network that developed a framework to evaluate statistical downscaling methods including model output statistics such as simple bias correction and quantile mapping; perfect prognosis methods such as regression models and analog methods; and weather generators. The first experiment addresses the downscaling performance in present climate with perfect predictors. This paper presents a synthesis of the VALUE special issue, with a focus on the results of this first experiment. This paper presents a synthesis of the results. Model output statistics performs mostly well, but requires predictors at a resolution close to the target one. Perfect prog performance depends crucially on model structure and predictor choice. Weather generators perform in principle well for all aspects that can be expressed by the available model structure. Inter-annual variability is underrepresented by both perfect prog and weather generator approaches. Spatial variability is poorly represented by almost all participating methods (inherited by model output statistics from the driving model, not represented by the perfect prog and weather generator methods). Further studies are required to systematically assess (a) the role of predictor choice for perfect prog; (b) the performance of spatial weather generators, to study the performance based on GCM predictors; (c) downscaling skill in simulated future climates; and (d) the credibility of simulated predictors in a future climate.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/213840
url http://hdl.handle.net/10261/213840
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1002/joc.5877
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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