IceNet is a probabilistic, deep learning sea ice forecasting system developed by an international team and led by British Antarctic Survey and The Alan Turing Institute [Andersson et al., 2021]. IceNet has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. 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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Maps showing IceNet’s 1-month-ahead forecasts for Arctic sea ice over the 2018 melting season (June to August). Overlaid are the predicted and true ice edges, and the error between them.

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IceNet’s U-Net neural network architecture.


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British Antarctic Survey | The Alan Turing Institute | others [to do]


[1] Andersson et al. Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nat Commun 12, 5124 (2021).