Seasonal Arctic sea ice forecasting with probabilistic deep learning

• Published in: Nature Communications
• Main Authors: Andersson, Tom R., Hosking, J. Scott, Pérez-Ortiz, María, Paige, Brooks, Elliott, Andrew, Russell, Chris, Law, Stephen, Jones, Daniel C., Wilkinson, Jeremy, Phillips, Tony, Byrne, James, Tietsche, Steffen, Sarojini, Beena Balan, Blanchard-Wrigglesworth, Eduardo, Aksenov, Yevgeny, Downie, Rod, Shuckburgh, Emily
• Other Authors: Alan Turing Institute
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: General Physics and Astronomy; General Biochemistry, Genetics and Molecular Biology; General Chemistry; Arctic; Sea ice
• Online Access: http://dx.doi.org/10.1038/s41467-021-25257-4; https://www.nature.com/articles/s41467-021-25257-4.pdf; https://www.nature.com/articles/s41467-021-25257-4

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.