Seasonal Arctic sea ice forecasting with probabilistic deep learning

• 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/

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.