气候变化领域集成服务门户(CCPortal): Predicting global patterns of long-term climate change from short-term simulations using machine learning

CCPortal

DOI	10.1038/s41612-020-00148-5
	Predicting global patterns of long-term climate change from short-term simulations using machine learning
	Mansfield L.A.; Nowack P.J.; Kasoar M.; Everitt R.G.; Collins W.J.; Voulgarakis A.
发表日期	2020
ISSN	23973722
卷号	3 期号:1
英文摘要	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. © 2020, CROWN.
语种	英语
scopus关键词	climate change; climate forcing; climate modeling; climate prediction; computer simulation; data set; global climate; machine learning
来源期刊	npj Climate and Atmospheric Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178021
作者单位	Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, United Kingdom; School of Mathematics and Statistics, University of Reading, Whiteknights, Berkshire, RG6 6AX, United Kingdom; Grantham Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; Climatic Research Unit, Data Science Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, United Kingdom; Leverhulme Centre for Wildfires, Environment and Society, Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, United Kingdom; Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; Department of Meteorology, University of Reading, Whiteknights, Berkshire, RG6 6ET, United Kingdom; School of Environmental Engineering, Technical University of Crete, Chania, Crete, 73100, Greece
推荐引用方式 GB/T 7714	Mansfield L.A.,Nowack P.J.,Kasoar M.,et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning[J],2020,3(1).
APA	Mansfield L.A.,Nowack P.J.,Kasoar M.,Everitt R.G.,Collins W.J.,&Voulgarakis A..(2020).Predicting global patterns of long-term climate change from short-term simulations using machine learning.npj Climate and Atmospheric Science,3(1).
MLA	Mansfield L.A.,et al."Predicting global patterns of long-term climate change from short-term simulations using machine learning".npj Climate and Atmospheric Science 3.1(2020).