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Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0

hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorMICHEL, Simon
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSWINGEDOUW, Didier
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierNCAS-Climate [Reading]
dc.contributor.authorORTEGA, Pablo
hal.structure.identifierOcéan et variabilité du climat [VARCLIM]
dc.contributor.authorMIGNOT, Juliette
hal.structure.identifierOcéan et variabilité du climat [VARCLIM]
dc.contributor.authorKHODRI, Myriam
dc.date.accessioned2024-04-04T02:54:59Z
dc.date.available2024-04-04T02:54:59Z
dc.date.issued2020
dc.identifier.issn1991-9603
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192322
dc.description.abstractEnModes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.
dc.language.isoen
dc.publisherEuropean Geosciences Union
dc.title.enReconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0
dc.typeArticle de revue
dc.identifier.doi10.5194/gmd-13-841-2020
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre
dc.description.sponsorshipEuropeBlue-Action: Arctic impact on weather and climat
bordeaux.journalGeoscientific Model Development
bordeaux.page841 - 858
bordeaux.volume13
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02556996
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02556996v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Geoscientific%20Model%20Development&rft.date=2020&rft.volume=13&rft.issue=2&rft.spage=841%20-%20858&rft.epage=841%20-%20858&rft.eissn=1991-9603&rft.issn=1991-9603&rft.au=MICHEL,%20Simon&SWINGEDOUW,%20Didier&CHAVENT,%20Marie&ORTEGA,%20Pablo&MIGNOT,%20Juliette&rft.genre=article


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