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dc.contributor.authorGarcía Manzanas, Rodrigo 
dc.contributor.authorLucero, A.
dc.contributor.authorWeisheimer, A.
dc.contributor.authorGutiérrez Llorente, José Manuel
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2019-12-16T12:11:17Z
dc.date.available2019-12-16T12:11:17Z
dc.date.issued2018-02-01
dc.identifier.issn0930-7575
dc.identifier.issn1432-0894
dc.identifier.otherCGL2015-66583-Res_ES
dc.identifier.urihttp://hdl.handle.net/10902/17571
dc.description.abstractStatistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.es_ES
dc.description.sponsorshipThis study was partially supported by the SPECS and EUPORIAS projects, funded by the European Commission through the Seventh Framework Programme for Research under grant agreements 308378 and 308291, respectively. JMG acknowledges partial support from the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER).es_ES
dc.format.extent25 p.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rights© Springer. This is a post-peer-review, pre-copyedit version of an article published in Climate Dynamics. The final authenticated version is available online at: https://doi.org/10.1007/s00382-017-3668-z*
dc.sourceClimate Dynamics, 2018, 50(3-4), 1161-1176es_ES
dc.subject.otherStatistical downscalinges_ES
dc.subject.otherPerfect prognosises_ES
dc.subject.otherBias correctiones_ES
dc.subject.otherSeasonal forecastinges_ES
dc.subject.otherPrecipitationes_ES
dc.subject.otherSkilles_ES
dc.subject.otherCorrelationes_ES
dc.subject.otherReliability categorieses_ES
dc.titleCan bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1007/s00382-017-3668-zes_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/308291/EU/EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale/EUPORIAS/es_ES
dc.identifier.DOI10.1007/s00382-017-3668-z
dc.type.versionacceptedVersiones_ES


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