Articles | Volume 22, issue 4
https://doi.org/10.5194/os-22-2559-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-22-2559-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An automated method for polynya detection using a geomorphon algorithm
Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 8LB, United Kingdom
Lars Boehme
Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 8LB, United Kingdom
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Dani C. Jones, Maike Sonnewald, Shenjie Zhou, Ute Hausmann, Andrew J. S. Meijers, Isabella Rosso, Lars Boehme, Michael P. Meredith, and Alberto C. Naveira Garabato
Ocean Sci., 19, 857–885, https://doi.org/10.5194/os-19-857-2023, https://doi.org/10.5194/os-19-857-2023, 2023
Short summary
Short summary
Machine learning is transforming oceanography. For example, unsupervised classification approaches help researchers identify underappreciated structures in ocean data, helping to generate new hypotheses. In this work, we use a type of unsupervised classification to identify structures in the temperature and salinity structure of the Weddell Gyre, which is an important region for global ocean circulation and for climate. We use our method to generate new ideas about mixing in the Weddell Gyre.
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Short summary
Polynyas are areas of open water in sea ice that are important for ocean life and climate. We developed a new method to automatically find polynyas using morphological patterns in Antarctic sea ice data. The method reliably detected both large and small polynyas, produced results consistent with existing approaches, and remained accurate despite data uncertainty. This approach offers a promising way to build consistent long-term records and improve future studies of changing polar environments.
Polynyas are areas of open water in sea ice that are important for ocean life and climate. We...