Seasonal Arctic sea ice forecasting with probabilistic deep learning

Journal article

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

Authors: Andersson, TR; Hosking, JS; Pérez-Ortiz, M; Paige, B; Elliott, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 12
• Issue: 1
• Pages: 5124-5124
• Article number: 5124
• Publication date: 2021-08-26
• DOI: 10.1038/s41467-021-25257-4
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Oceanography; Meteorology; Antarctic sea ice; Geology; Sea ice concentration; Climate change; Arctic sea ice decline; Sea ice thickness; Sea ice; Arctic; Arctic ice pack; Geography; Environmental science; Climatology