Seasonal Arctic sea ice forecasting with probabilistic deep learning

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 2 of this Preprint.

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Tom R. Andersson , J. Scott Hosking , Maria Pérez-Ortiz , Brooks Paige , Andrew Elliott , Chris Russell, Stephen Law, Dan Jones , Jeremy Wilkinson, Tony Phillips , Steffen Tietsche , Beena Sarojini , Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov , Rod Downie, Emily Shuckburgh 


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 models 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.



Artificial Intelligence and Robotics, Earth Sciences, Statistics and Probability


machine learning, Arctic, sea ice, AI


Published: 2021-02-02 14:26

Last Updated: 2021-05-21 15:57

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CC BY Attribution 4.0 International

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The data that support the findings of this study will be made available via a public repository upon publication.