The Library
Predicting global patterns of long-term climate change from short-term simulations using machine learning
Tools
Mansfield, L. A., Nowack, P. J., Kasoar, M., Everitt, Richard G., Collins, W. J. and Voulgarakis, A. (2020) Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Climate and Atmospheric Science, 3 . 44. doi:10.1038/s41612-020-00148-5 ISSN 1758-6798.
|
PDF
WRAP-Predicting-global-patterns-long-term-climate-simulations-machine-learning-Everitt-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1697Kb) | Preview |
|
PDF
st-290920-wrap--main.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (347Kb) |
Official URL: https://doi.org/10.1038/s41612-020-00148-5
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: | Journal Article | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QC Physics | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||
Library of Congress Subject Headings (LCSH): | Climatic changes , Climatic changes -- Mathematical models, Climatic changes -- Detection -- Simulation methods | ||||||||||||
Journal or Publication Title: | npj Climate and Atmospheric Science | ||||||||||||
Publisher: | Nature Publishing Group | ||||||||||||
ISSN: | 1758-6798 | ||||||||||||
Official Date: | 19 November 2020 | ||||||||||||
Dates: |
|
||||||||||||
Volume: | 3 | ||||||||||||
Article Number: | 44 | ||||||||||||
DOI: | 10.1038/s41612-020-00148-5 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 8 October 2020 | ||||||||||||
Date of first compliant Open Access: | 27 November 2020 | ||||||||||||
RIOXX Funder/Project Grant: |
|
||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year