Seasonal Arctic sea ice forecasting with probabilistic deep learning

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Authors

Tom R. Andersson , J. Scott Hosking , Maria Pérez-Ortiz , Brooks Paige , Andrew Elliott , Chris Russell, Stephen Law, Daniel C. 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 and calibrating their forecasts can be challenging. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations covering 1850-2100 and observational data from 1979-2011 to forecast the next 6 months of monthly-averaged sea ice concentration maps. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice. It also demonstrates a greater ability to predict anomalous pan-Arctic sea ice extents than the models submitted to the Sea Ice Outlook programme. In addition, IceNet’s well-calibrated probabilistic forecasts mean it can reliably bound the ice edge between two contours. IceNet’s accuracy and reliability represent a step-change in sea ice forecasting, providing a robust framework to build early-warning systems and 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 08:26

Last Updated: 2021-02-02 16:26

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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