Seasonal Arctic sea ice forecasting with probabilistic deep learning

Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

Bibliographic Details
Published in:Nature Communications
Main Authors: Tom R. Andersson, J. Scott Hosking, María Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena Balan Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie, Emily Shuckburgh
Format: Article in Journal/Newspaper
Language:English
Published: Nature Portfolio 2021
Subjects:
Science
Q
Arctic
Global warming
Sea ice
Online Access:https://doi.org/10.1038/s41467-021-25257-4
https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
Description
Summary:Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

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