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dc.rights.licenseopenen_US
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSGUBIN, Giovanni
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSWINGEDOUW, Didier
hal.structure.identifierOcéan et variabilité du climat [LOCEAN-VARCLIM]
dc.contributor.authorBORCHERT, Leonard F.
hal.structure.identifierOcéan et variabilité du climat [LOCEAN-VARCLIM]
dc.contributor.authorMENARY, Matthew
hal.structure.identifierthe Climate Data Factory
dc.contributor.authorNOEL, Thomas
hal.structure.identifierthe Climate Data Factory
dc.contributor.authorLOUKOS, Harilaos
hal.structure.identifierOcéan et variabilité du climat [LOCEAN-VARCLIM]
dc.contributor.authorMIGNOT, Juliette
dc.date.accessioned2024-02-05T10:31:11Z
dc.date.available2024-02-05T10:31:11Z
dc.date.issued2021-07
dc.identifier.issn0930-7575en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/187796
dc.description.abstractEnDecadal Climate Predictions (DCP) have gained considerable attention for their potential utility in promoting optimised plans of adaptation to climate change and variability. Their effective applicability to a targeted problem is nevertheless conditional on a detailed evaluation of their ability to simulate the near-term climate evolution under specific conditions. Here we explore the performance of the IPSL-CM5A-LR DCP system in predicting air temperature over Europe, by proposing a systematic assessessment of the prediction skill for different time windows (periods of the calendar time, forecast years and months/seasons). In this framework, we also compare raw and de-biased hindcasts, in which the temperature outputs have been corrected using a quantile matching method. The systematic analysis allows to discern certain conditions conferring larger predictability, which we find to be intermittent in time. The predictions appear more skilful around the 1960s and after the 1980s, in coincidence with large shifts of the Atlantic Multidecadal Variability, which are well reproduced in the hindcasts. Averages on longer forecast periods also generally imply better prediction skill, while the best predicted months appear to be mainly those between late spring and early autumn. Moreover, we find an overall added value due to initialisation, while de-biased predictions significantly outperform raw predictions only for a few specific time windows. Finally, we discuss the potential implications of the proposed systematic exploration of skill opportunities in DCPs for integrated applications in climate sensitive sectors.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enClimate Variability
dc.subject.enDecadal Climate Predictions
dc.subject.enDe-biasing
dc.subject.enAtlantic Multidecadal Variability
dc.subject.enClimate Service
dc.title.enSystematic investigation of skill opportunities in decadal prediction of air temperature over Europe
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s00382-021-05863-0en_US
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Climatologieen_US
dc.description.sponsorshipEuropeEuropean Climate Prediction systemen_US
bordeaux.journalClimate Dynamicsen_US
bordeaux.page3245-3263en_US
bordeaux.volume57en_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.issue11-12en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.teamPALEOen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-03318273
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccCC BYen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Climate%20Dynamics&rft.date=2021-07&rft.volume=57&rft.issue=11-12&rft.spage=3245-3263&rft.epage=3245-3263&rft.eissn=0930-7575&rft.issn=0930-7575&rft.au=SGUBIN,%20Giovanni&SWINGEDOUW,%20Didier&BORCHERT,%20Leonard%20F.&MENARY,%20Matthew&NOEL,%20Thomas&rft.genre=article


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