Ocean Science
Ocean Science
OS
Articles | Volume 19, issue 3
Articles | Volume 19, issue 3
https://doi.org/10.5194/os-19-857-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-19-857-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 3
Research article
 | 
22 Jun 2023
Research article |  | 22 Jun 2023

Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region

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

Data sets

SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints Dani Jones and Shenjie Zhou https://doi.org/10.5281/zenodo.7468656

South Atlantic Ocean profile dataset: identification of near-Antarctic profiles using unsupervised classification Dani Jones and Shenjie Zhou https://doi.org/10.5281/zenodo.7465132

Model code and software

so-wise/weddell_gyre_clusters: Third release Dani Jones https://doi.org/10.5281/zenodo.7465388

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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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Special issue
The Weddell Sea and the ocean off Dronning Maud Land: unique...