Articles | Volume 17, issue 6
Ocean Sci., 17, 1545–1562, 2021
https://doi.org/10.5194/os-17-1545-2021
Ocean Sci., 17, 1545–1562, 2021
https://doi.org/10.5194/os-17-1545-2021

Research article 02 Nov 2021

Research article | 02 Nov 2021

Defining Southern Ocean fronts using unsupervised classification

Simon D. A. Thomas et al.

Data sets

B-SOSE iteration 106 A. Verdy and M. Mazloff http://sose.ucsd.edu/BSOSE6_iter106_solution.html

Model code and software

so-wise/so-fronts: Defining Southern Ocean fronts using unsupervised classification (Version v0.2) S. D. A. Thomas https://doi.org/10.5281/zenodo.5500666

MITgcm/MITgcm: checkpoint67z J.-M. Campin, P. Heimbach, M. Losch, G. Forget, edhill3, A. Adcroft, amolod, D. Menemenlis, dfer22, C. Hill, O. Jahn, J. Scott, stephdut, M. Mazloff, B. Fox-Kemper, antnguyen13, E. Doddridge, I. Fenty, M. Bates, AndrewEichmann-NOAA, T. Smith, T. Martin, J. Lauderdale, R. Abernathey, samarkhatiwala, hongandyan, B. Deremble, dngoldberg, P. Bourgault, and R. Dussin https://doi.org/10.5281/zenodo.4968496

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Short summary
We propose a probabilistic method and a new inter-class comparison metric for highlighting fronts in the Southern Ocean. We compare it with an image processing method that provides a more localised view of fronts that effectively highlights sharp jets. These two complementary approaches offer two views of Southern Ocean structure: the probabilistic method highlights boundaries between coherent thermohaline structures across the entire Southern Ocean, whereas edge detection highlights local jets.