05 Jan 2023
05 Jan 2023
Status: this preprint is open for discussion.

Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region

Dan(i) Jones1, Maike Sonnewald2,3,4, Shenjie Zhou1, Ute Hausmann1, Andrew J. S. Meijers1, Isabella Rosso5,6, Lars Boehme7, Michael P. Meredith1, and Alberto C. Naveira Garabato8 Dan(i) Jones et al.
  • 1British Antarctic Survey, NERC, UKRI, Cambridge, UK
  • 2Princeton University, Princeton, NJ, USA
  • 3NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
  • 4University of Washington, Seattle, WA, USA
  • 5Scripps Institution of Oceanography, UCSD, La Jolla, CA, USA
  • 6GeoOptics Switzerland SA, Lausanne, Switzerland
  • 7SMRU, University of St. Andrews, UK
  • 8University of Southampton, Southampton, UK

Abstract. The Weddell Gyre is a major feature of the Southern Ocean and an important component of the planetary climate system; it regulates air-sea exchanges, controls the formation of deep and bottom waters, and hosts upwelling of relatively warm subsurface waters. It is characterized by extremely low sea surface temperatures, ubiquitous sea ice formation, and widespread salt stratification that stabilises the water column. Observing the Weddell Gyre is challenging, as it is extremely remote and largely covered with sea ice. At present, it is one of the most poorly-sampled regions of the global ocean, highlighting the need to extract as much value as possible from existing observations. Here, we apply a profile classification model (PCM), which is an unsupervised classification technique, to a Weddell Gyre profile dataset to identify coherent regimes in temperature and salinity. We find that, despite not being given any positional information, the PCM identifies four spatially coherent thermohaline domains that can be described as follows: (1) a circumpolar class, (2) a transition region between the circumpolar waters and the Weddell Gyre, (3) a gyre edge class with northern and southern branches, and (4) a gyre core class. PCM highlights, in an objective and interpretable way, both expected and under-appreciated structures in the Weddell Gyre dataset. For instance, PCM identifies the inflow of Circumpolar Deep Water (CDW) across the eastern boundary, the presence of the Weddell-Scotia Confluence waters, and structured spatial variability in mixing between Winter Water and CDW. PCM offers a useful complement to existing expertise-driven approaches for characterising the physical configuration and variability of the Weddell Gyre and surrounding regions.

Dan(i) Jones et al.

Status: open (until 02 Mar 2023)

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Dan(i) Jones et al.

Data sets

SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints Dani Jones, Shenjie Zhou

South Atlantic Ocean profile dataset: identification of near-Antarctic profiles using unsupervised classification Dani Jones, Shenjie Zhou

Model code and software

so-wise/weddell_gyre_clusters: First release Dani Jones

Dan(i) Jones et al.


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Short summary
Machine learning is transforming oceanography. For example, unsupervised classification approaches help researchers identify under-appreciated 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.