Seasonal Arctic sea ice forecasting with probabilistic deep learning

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.

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Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Science
Q
Acceso en línea:https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
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https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149

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