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The VALUE perfect predictor experiment: evaluation of temporal variability
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dc.contributor.authorMaraun, Douglases_ES
dc.contributor.authorHuth, Radanes_ES
dc.contributor.authorGutiérrez Llorente, José Manueles_ES
dc.contributor.authorSan-Martín, Danieles_ES
dc.contributor.authorDubrovsky, Martines_ES
dc.contributor.authorFischer, Andreas M.es_ES
dc.contributor.authorHertig, Elkees_ES
dc.contributor.authorSoares, Pedro M. M.es_ES
dc.contributor.authorBartholy, Judites_ES
dc.contributor.authorPongracz, R.es_ES
dc.contributor.authorWidmann, Martines_ES
dc.contributor.authorCasado Calle, María Jesúses_ES
dc.contributor.authorRamos Calzado, Petraes_ES
dc.contributor.authorBedia, Joaquínes_ES
dc.identifier.citationInternational Journal of Climatology. 2019, 39(9), p. 3786-3818es_ES
dc.description.abstractTemporal variability is an important feature of climate, comprising systematic vari-ations such as the annual cycle, as well as residual temporal variations such asshort-term variations, spells and variability from interannual to long-term trends.The EU-COST Action VALUE developed a comprehensive framework to evaluatedownscaling methods. Here we present the evaluation of the perfect predictorexperiment for temporal variability. Overall, the behaviour of the differentapproaches turned out to be as expected from their structure and implementation.The chosen regional climate model adds value to reanalysis data for most consid-ered aspects, for all seasons and for both temperature and precipitation. Bias cor-rection methods do not directly modify temporal variability apart from the annualcycle. However, wet day corrections substantially improve transition probabilitiesand spell length distributions, whereas interannual variability is in some cases dete-riorated by quantile mapping. The performance of perfect prognosis (PP) statisticaldownscaling methods varies strongly from aspect to aspect and method to method,and depends strongly on the predictor choice. Unconditional weather generatorstend to perform well for the aspects they have been calibrated for, but underrepre-sent long spells and interannual variability. Long-term temperature trends of thedriving model are essentially unchanged by bias correction methods. If precipita-tion trends are not well simulated by the driving model, bias correction furtherdeteriorates these trends. The performance of PP methods to simulate trendsdepends strongly on the chosen predictors.es_ES
dc.description.sponsorshipVALUE has been funded as EU COST Action ES1102.es_ES
dc.publisherRoyal Meteorological Societyes_ES
dc.subjectInterannual variabilityes_ES
dc.subjectLong-term trendses_ES
dc.subjectTemporal variabilityes_ES
dc.titleThe VALUE perfect predictor experiment: evaluation of temporal variabilityes_ES
Appears in Collections:Artículos científicos 2019-2022

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