{"refrec":{"BRefID":289946,"RR":"<b>Zanchettin, D.; Gaetan, C.; Arisido, M.W.; Modali, K.; Toniazzo, T.; Keenlyside, N.; Rubino, A.</b> (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. <i>NPG Scientific Reports 7(1)</i>: 11 pp. <a href=\"https://dx.doi.org/10.1038/s41598-017-13144-2\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-017-13144-2</a>","BEntID":281985,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 7(1)</i>: 11 pp. <a href=\"https://dx.doi.org/10.1038/s41598-017-13144-2\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-017-13144-2</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Zanchettin, D.; Gaetan, C.; Arisido, M.W.; Modali, K.; Toniazzo, T.; Keenlyside, N.; Rubino, A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Zanchettin, D. <i>et al.</i>","Englishabstract":"Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Structural decomposition of decadal climate prediction errors: A Bayesian approach","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-10 01:32:10.726527","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2017,"MonPub":null,"DateUpdate":"2018-02-13","DateCreate":"2017-10-11","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000412657900002","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-017-13144-2"},"refs":null,"anarec":{"AnaID":289946,"PubliDate":2017,"Pagination":"11 pp","XtraPublOfAnaID":null,"ISBN":null,"Volume":"7","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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