Unsupervised clustering of Southern Ocean Argo float temperature profiles

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2018JC014629. This is version 5 of this Preprint.


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Daniel C. Jones , Harry J. Holt, Andrew Meijers, Emily Shuckburgh


The Southern Ocean has complex spatial variability, characterized by sharp fronts, steeply tilted isopycnals, and deep seasonal mixed layers. Methods of defining Southern Ocean spatial structures traditionally rely on somewhat ad-hoc combinations of physical, chemical, and dynamic properties. As a step towards an alternative approach for describing spatial variability in temperature, here we apply an unsupervised classification technique (that is, Gaussian mixture modelling or GMM) to Southern Ocean Argo float temperature profiles. GMM, without using any latitude or longitude information, automatically identifies several spatially coherent circumpolar classes influenced by the Antarctic Circumpolar Current. In addition, GMM identifies classes that bear the imprint of mode/intermediate water formation and export, large-scale gyre circulation, and the Agulhas Current, among others. Because GMM is robust, standardized, and automated, it can potentially be used to identify structures (such as fronts) in both observational and model datasets, possibly making it a useful complement to existing classification techniques.




Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics


machine learning, Southern Ocean, Argo, Gaussian Mixture Modeling, unsupervised clustering


Published: 2018-04-19 15:03

Last Updated: 2018-11-26 11:38

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CC BY Attribution 4.0 International

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