Seasonal Arctic sea ice forecasting with probabilistic deep learning

Andersson, T. R. et al. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124. (doi: 10.1038/s41467-021-25257-4) (PMID:34446701)

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Abstract

Abstract: 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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Elliott, Dr Andrew
Authors: 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., and Shuckburgh, E.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Nature Communications
Publisher:Nature Research
ISSN:2041-1723
ISSN (Online):2041-1723
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Nature Communications 12: 5124
Publisher Policy:Reproduced under a Creative Commons License
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