Seasonal Arctic sea ice forecasting with probabilistic deep learning

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Seasonal Arctic sea ice forecasting with probabilistic deep learning

Andersson, TR; Hosking, JS; Pérez-Ortiz, M; Paige, B; Elliott, A; Russell, C; Law, S; ... Shuckburgh, E; + view all (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications , 12 (1) , Article 5124. 10.1038/s41467-021-25257-4. Green open access

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Abstract

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.

Type:	Article
Title:	Seasonal Arctic sea ice forecasting with probabilistic deep learning
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1038/s41467-021-25257-4
Publisher version:	https://doi.org/10.1038/s41467-021-25257-4
Language:	English
Additional information:	This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords:	Computer science, Cryospheric science, Environmental impact, Statistics
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10134451