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Predicting global patterns of long-term climate change from short-term simulations using machine learning

Mansfield Laura, Nowack Peer, Kasoar Matthew, Everitt Richard, Collins William, Voulgarakis Apostolos

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URIhttp://purl.tuc.gr/dl/dias/8AFEFC3A-355D-47E5-B5B7-168942BC8F01-
Identifierhttps://doi.org/10.1038/s41612-020-00148-5-
Identifierhttps://www.nature.com/articles/s41612-020-00148-5-
Languageen-
TitlePredicting global patterns of long-term climate change from short-term simulations using machine learningen
CreatorMansfield Lauraen
CreatorNowack Peeren
CreatorKasoar Matthewen
CreatorEveritt Richarden
CreatorCollins Williamen
CreatorVoulgarakis Apostolosen
CreatorΒουλγαρακης Αποστολοςel
PublisherSpringer Natureen
Content SummaryUnderstanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-term and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability, and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-04-20-
Date of Publication2020-
SubjectAtmospheric scienceen
SubjectClimate changeen
SubjectClimate-change impactsen
SubjectClimate-change mitigationen
SubjectProjection and predictionen
Bibliographic CitationL. A. Mansfield, P. J. Nowack, M. Kasoar, R. G. Everitt, W. J. Collins and A. Voulgarakis, “Predicting global patterns of long-term climate change from short-term simulations using machine learning,” npj Clim. Atmos. Sci., vol. 3, no. 1, Nov. 2020. doi: 10.1038/s41612-020-00148-5en

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