Seasonal Arctic sea ice forecasting with probabilistic deep learning

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 benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.

Details

Publication status:
Published
Author(s):
Authors: Andersson, Tom R. ORCIDORCID record for Tom R. Andersson, Hosking, J. Scott ORCIDORCID record for J. Scott Hosking, Pérez-Ortiz, María, Paige, Brooks, Elliott, Andrew, Russell, Chris, Law, Stephen, Jones, Daniel C. ORCIDORCID record for Daniel C. Jones, Wilkinson, Jeremy, Phillips, Tony ORCIDORCID record for Tony Phillips, Byrne, James ORCIDORCID record for James Byrne, Tietsche, Steffen, Sarojini, Beena Balan, Blanchard-Wrigglesworth, Eduardo, Aksenov, Yevgeny ORCIDORCID record for Yevgeny Aksenov, Downie, Rod, Shuckburgh, Emily ORCIDORCID record for Emily Shuckburgh

On this site: Dan Jones, Emily Shuckburgh, James Byrne, Scott Hosking, Jeremy Wilkinson, Tony Phillips, Tom Andersson
Date:
26 August, 2021
Journal/Source:
Nature Communications / 12
Page(s):
12pp
Link to published article:
https://doi.org/10.1038/s41467-021-25257-4