Structural decomposition of decadal climate prediction errors: A Bayesian approach

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2045-2322

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Structural decomposition of decadal climate prediction errors: A Bayesian approach ; volume:7 ; number:1 ; day:9 ; month:10 ; year:2017 ; pages:1-11 ; date:12.2017
Scientific reports ; 7, Heft 1 (9.10.2017), 1-11, 12.2017

Classification: Geowissenschaften

Creator: Zanchettin, Davide
Gaetan, Carlo
Arisido, Maeregu Woldeyes
Modali, Kameswarrao
Toniazzo, Thomas
Keenlyside, Noel S.
Rubino, Angelo

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-017-13144-2

URN: urn:nbn:de:101:1-2019102019154284545021

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:27 AM CEST

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Zanchettin, Davide
Gaetan, Carlo
Arisido, Maeregu Woldeyes
Modali, Kameswarrao
Toniazzo, Thomas
Keenlyside, Noel S.
Rubino, Angelo
SpringerLink (Online service)

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Structural decomposition of decadal climate prediction errors: A Bayesian approach

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Other Objects (12)

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Skillful prediction of length of day one year ahead in multiple decadal prediction systems

Exploiting residual errors in nonlinear online prediction

Growth of finite errors in ensemble prediction

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The CMCC Decadal Prediction System

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The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

Mnemonic prediction errors bias hippocampal states

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Bayesian prediction for stochastic processes

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Skillful prediction of length of day one year ahead in multiple decadal prediction systems

Exploiting residual errors in nonlinear online prediction

Growth of finite errors in ensemble prediction

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Assessing the skill of the Pacific Decadal Oscillation (PDO) in a decadal prediction experiment

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Growth of finite errors in ensemble prediction

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