气候变化领域集成服务门户(CCPortal): Seasonal Arctic sea ice forecasting with probabilistic deep learning

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DOI	10.1038/s41467-021-25257-4
	Seasonal Arctic sea ice forecasting with probabilistic deep learning
	Andersson T.R.; Hosking J.S.; Pérez-Ortiz M.; Paige B.; Elliott A.; Russell C.; Law S.; Jones D.C.; Wilkinson J.; Phillips T.; Byrne J.; Tietsche S.; Sarojini B.B.; Blanchard-Wrigglesworth E.; Aksenov Y.; Downie R.; Shuckburgh E.
发表日期	2021
ISSN	2041-1723
卷号	12 期号:1
英文摘要	Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. © 2021, The Author(s).
语种	英语
scopus关键词	global climate; map; sea ice; Arctic; article; climate; deep learning; forecasting; sea ice; simulation; summer; Arctic Ocean
来源期刊	Nature Communications
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/250632
作者单位	British Antarctic Survey, NERC, UKRI, Cambridge, United Kingdom; The Alan Turing Institute, London, United Kingdom; Department of Computer Science, University College London, London, United Kingdom; School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom; Amazon Web Services, Tübingen, Germany; Department of Geography, University College London, London, United Kingdom; European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom; Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; National Oceanography Centre, Southampton, United Kingdom; WWF, Woking, United Kingdom; University of Cambridge, Cambridge, United Kingdom
推荐引用方式 GB/T 7714	Andersson T.R.,Hosking J.S.,Pérez-Ortiz M.,et al. Seasonal Arctic sea ice forecasting with probabilistic deep learning[J],2021,12(1).
APA	Andersson T.R..,Hosking J.S..,Pérez-Ortiz M..,Paige B..,Elliott A..,...&Shuckburgh E..(2021).Seasonal Arctic sea ice forecasting with probabilistic deep learning.Nature Communications,12(1).
MLA	Andersson T.R.,et al."Seasonal Arctic sea ice forecasting with probabilistic deep learning".Nature Communications 12.1(2021).