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.
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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 |
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