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Mansfield, Laura A., Nowack, Peer J. 
ORCID: https://orcid.org/0000-0003-4588-7832, Kasoar, Matt, Everitt, Richard G., Collins, William J. and Voulgarakis, Apostolos
(2020)
Predicting global patterns of long-term climate change from short-term simulations using machine learning.
npj Climate and Atmospheric Science, 3.
ISSN 2397-3722
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Abstract
Understanding 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | climate change,machine learning,climate modelling,big data,statistics,atmosphere,earth sciences,sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Groups > Climatic Research Unit Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Oct 2020 23:56 |
| Last Modified: | 14 Jun 2023 23:55 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/77401 |
| DOI: | 10.1038/s41612-020-00148-5 |
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