Seasonal Arctic sea ice forecasting with probabilistic deep learning

Tom R. Andersson (), J. Scott Hosking, María Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena Balan Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie and Emily Shuckburgh
Additional contact information
Tom R. Andersson: NERC, UKRI
J. Scott Hosking: NERC, UKRI
María Pérez-Ortiz: University College London
Brooks Paige: The Alan Turing Institute
Andrew Elliott: The Alan Turing Institute
Chris Russell: Amazon Web Services
Stephen Law: The Alan Turing Institute
Daniel C. Jones: NERC, UKRI
Jeremy Wilkinson: NERC, UKRI
Tony Phillips: NERC, UKRI
James Byrne: NERC, UKRI
Steffen Tietsche: European Centre for Medium-Range Weather Forecasts (ECMWF)
Beena Balan Sarojini: European Centre for Medium-Range Weather Forecasts (ECMWF)
Eduardo Blanchard-Wrigglesworth: University of Washington
Yevgeny Aksenov: National Oceanography Centre
Rod Downie: WWF
Emily Shuckburgh: NERC, UKRI

Nature Communications, 2021, vol. 12, issue 1, 1-12

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.

Date: 2021
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DOI: 10.1038/s41467-021-25257-4

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