Seasonal Arctic sea ice forecasting with probabilistic deep learning

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 m...

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Bibliographic Details
Main Authors: Andersson, TR, Hosking, JS, Pérez-Ortiz, M, Paige, B, Elliott, A, Russell, C, Law, S, Jones, DC, Wilkinson, J, Phillips, T, Byrne, J, Tietsche, S, Sarojini, BB, Blanchard-Wrigglesworth, E, Aksenov, Y, Downie, R, Shuckburgh, E
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Computer science
Cryospheric science
Environmental impact
Statistics
Arctic
Sea ice
Online Access:https://discovery.ucl.ac.uk/id/eprint/10134451/1/s41467-021-25257-4.pdf
https://discovery.ucl.ac.uk/id/eprint/10134451/
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Summary: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.

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