Nature Communications (Aug 2021)

Seasonal Arctic sea ice forecasting with probabilistic deep learning

  • 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

DOI
https://doi.org/10.1038/s41467-021-25257-4
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 12

Abstract

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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.