Seasonal Arctic sea ice forecasting with probabilistic deep learning
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 d...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Nature Publishing Group UK
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.17863/CAM.74715 https://www.repository.cam.ac.uk/handle/1810/327266 |
| Summary: | 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. |
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