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 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.

Recommended citation: Andersson, T., Hosking, J.S., Perez-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. (2021). "Seasonal Arctic sea ice forecasting with probabilistic deep learning." Nature Communications, 12, 5124. https://doi.org/10.1038/s41467-021-25257-4
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