1. Seasonal Arctic sea ice forecasting with probabilistic deep learning.
- Author
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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, and Shuckburgh E
- 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., (© 2021. The Author(s).)
- Published
- 2021
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