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Articles | Volume 19, issue 11
Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-6043-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6043-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 11
Research article
 | 
21 Nov 2025
Research article |  | 21 Nov 2025

Automatic detection of Arctic polynyas using hybrid supervised-unsupervised deep learning

Céline Heuzé and Carmen Hau Man Wong

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Adams, S., Willmes, S., Schröder, D., Heinemann, G., Bauer, M., and Krumpen, T.: Improvement and Sensitivity Analysis of Thermal Thin-Ice Thickness Retrievals, IEEE Transactions on Geoscience and Remote Sensing, 51, 3306–3318, https://doi.org/10.1109/TGRS.2012.2219539, 2013. a
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., Balan Sarojini, B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R. and Shuckburgh, E. : Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nature Communications, 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021. a
Aslam, Z., Javaid, N., Ahmad, A., Ahmed, A., and Gulfam, S. M.: A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids, Energies, 13, 5599, https://doi.org/10.3390/en13215599, 2020. a
Athanase, M., Köhler, R., Heuzé, C., Lévine, X., and Williams, R.: The Arctic Beaufort Gyre in CMIP6 Models: Present and Future, Journal of Geophysical Research: Oceans, 130, e2024JC021873, https://doi.org/10.1029/2024JC021873, 2025. a
Bennett, M. G., Renfrew, I. A., Stevens, D. P., and Moore, G. W. K.: The Northeast Water Polynya, Greenland: Climatology, Atmospheric Forcing and Ocean Response, Journal of Geophysical Research: Oceans, 129, e2023JC020513, https://doi.org/10.1029/2023JC020513, 2024. a
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Polynyas are areas with no- or thin-ice within the ice pack. They play a crucial role for the Earth system, yet their monitoring in the Arctic is challenging because polynya detection is non-trivial. We here demonstrate that polynyas can successfully be detected with a novel, machine-learning based method. In fact, we argue that they are better detected than with traditional methods, which seem to fail as sea ice decreases because of climate change.
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