The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 9
Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3753-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3753-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 9
Research article
 | 
22 Sep 2022
Research article |  | 22 Sep 2022

Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region

Nazanin Asadi, Philippe Lamontagne, Matthew King, Martin Richard, and K. Andrea Scott

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Latest update: 18 Apr 2024
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Short summary
Machine learning approaches are deployed to provide accurate daily spatial maps of sea ice presence probability based on ERA5 data as input. Predictions are capable of predicting freeze-up/breakup dates within a 7 d period at specific locations of interest to shipping operators and communities. Forecasts of the proposed method during the breakup season have skills comparing to Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.
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