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This is a Preprint and has not been peer reviewed.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: https://doi.org/10.1038/s41467-021-25257-4. This is version 2 of this Preprint.

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Authors

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 

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 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.

DOI

https://doi.org/10.31223/X5430P

Subjects

Artificial Intelligence and Robotics, Earth Sciences, Statistics and Probability

Keywords

machine learning, Arctic, sea ice, AI

Dates

Published: 2021-02-02 14:26

Last Updated: 2021-05-21 15:57

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The data that support the findings of this study will be made available via a public repository upon publication.

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