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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-857-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jun 2023
Research article |
| 22 Jun 2023
| 22 Jun 2023
Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Maike Sonnewald
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
School of Oceanography, University of Washington, Seattle, WA, USA
Shenjie Zhou
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Ute Hausmann
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Andrew J. S. Meijers
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Isabella Rosso
Scripps Institution of Oceanography, UCSD, La Jolla, CA, USA
GeoOptics Switzerland SA, Lausanne, Switzerland
Lars Boehme
SMRU, University of St. Andrews, St. Andrews, UK
Michael P. Meredith
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Alberto C. Naveira Garabato
Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK
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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.
Machine learning is transforming oceanography. For example, unsupervised classification...
