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Tom R. Andersson, J. Scott Hosking, María Pérez‐Ortiz, Brooks Paige, Andrew J. Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy Wilkinson, Tony Phillips and 7 more
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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Andersson, T. R., Hosking, J. S., Pérez‐Ortiz, M., Paige, B., Elliott, A. J., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., & Shuckburgh, E. F. (2021). Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12(1), 5124. https://doi.org/10.1038/s41467-021-25257-4
Andersson, Tom R., et al. "Seasonal Arctic sea ice forecasting with probabilistic deep learning." Nature Communications, vol. 12, no. 1, 2021, pp. 5124, https://doi.org/10.1038/s41467-021-25257-4.
Andersson, Tom R., J. Scott Hosking, María Pérez‐Ortiz, Brooks Paige, Andrew J. Elliott, Chris Russell, Stephen Law, et al. "Seasonal Arctic sea ice forecasting with probabilistic deep learning." Nature Communications 12, no. 1 (2021): 5124. https://doi.org/10.1038/s41467-021-25257-4.
Andersson T. R., Hosking J. S., Pérez‐Ortiz M. [и др.] Seasonal Arctic sea ice forecasting with probabilistic deep learning // Nature Communications. — 2021. — Vol. 12, № 1. — P. 5124. — URL: https://doi.org/10.1038/s41467-021-25257-4
@article{andersson2021seasonal,
title = {{Seasonal Arctic sea ice forecasting with probabilistic deep learning}},
author = {Tom R. Andersson and J. Scott Hosking and María Pérez‐Ortiz and Brooks Paige and Andrew J. Elliott and Chris Russell and Stephen Law and Daniel C. Jones and Jeremy Wilkinson and Tony Phillips and James Byrne and Steffen Tietsche and Beena Balan Sarojini and Eduardo Blanchard-Wrigglesworth and Yevgeny Aksenov and Rod Downie and Emily F. Shuckburgh},
journal = {Nature Communications},
year = {2021},
volume = {12},
number = {1},
pages = {5124},
doi = {10.1038/s41467-021-25257-4},
url = {https://doi.org/10.1038/s41467-021-25257-4}
}TY - JOUR AU - Andersson, Tom R. AU - Hosking, J. Scott AU - Pérez‐Ortiz, María AU - Paige, Brooks AU - Elliott, Andrew J. AU - Russell, Chris AU - Law, Stephen AU - Jones, Daniel C. AU - Wilkinson, Jeremy AU - Phillips, Tony AU - Byrne, James AU - Tietsche, Steffen AU - Sarojini, Beena Balan AU - Blanchard-Wrigglesworth, Eduardo AU - Aksenov, Yevgeny AU - Downie, Rod AU - Shuckburgh, Emily F. TI - Seasonal Arctic sea ice forecasting with probabilistic deep learning JO - Nature Communications VL - 12 IS - 1 SP - 5124 PY - 2021 DA - 2021/08/26 DO - 10.1038/s41467-021-25257-4 UR - https://doi.org/10.1038/s41467-021-25257-4 ER -
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