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Seasonal Arctic sea ice forecasting with probabilistic deep learning

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
Source :
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.5d37269ed2734f7bac2303b89ff00149
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-021-25257-4