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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Cited articles

Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., 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., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat. Commun., 12, 5124,, 2021. a, b
Andrews, J., Babb, D., Barber, D. G., and Ackley, S. F.: Climate change and sea ice: Shipping in Hudson Bay, Hudson Strait, and Foxe Basin (1980–2016), Elementa: Science of the Anthropocene, 6, 19,, 2018. a, b, c, d
Askenov, Y., Popova, E., Yool, A., Nurser, A., Williams, T., Bertino, L., and Bergh, J.: On the future navigability of Arctic sea ice routes: High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy, 75, 300–317,, 2017. a, b
Bruneau, J., Babb, D., Chan, W., Kirillov, S., Ehn, J., Hanesiak, J., and Barber, D.: The ice factory of Hudson Bay: Spatiotemporal variability of the Kivalliq polynya, Elementa: Science of the Anthropocene, 9, 00168,, 2021. a, b
Bushuk, M., Msadek, R., Winton, M., Vecchi, G., Gudget, R., Rosati, A., and Yang, X.: Skillful regional prediction of Arctic sea ice on seasonal time scales, Geophys. Res. Lett., 44, 4953–4964,, 2017. a, b
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.