Defining Southern Ocean fronts using unsupervised classification

• Published in: Ocean Science
• Main Authors: Thomas, Simon D. A., Jones, Daniel C., Faul, Anita, Mackie, Erik, Pauthenet, Etienne
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2021
• Subjects: article; Verlagsveröffentlichung; Sobel; Southern Ocean
• Online Access: https://doi.org/10.5194/os-17-1545-2021; https://noa.gwlb.de/receive/cop_mods_00058606; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00058233/os-17-1545-2021.pdf; https://os.copernicus.org/articles/17/1545/2021/os-17-1545-2021.pdf

Oceanographic fronts are transitions between thermohaline structures with different characteristics. Such transitions are ubiquitous, and their locations and properties affect how the ocean operates as part of the global climate system. In the Southern Ocean, fronts have classically been defined using a small number of continuous, circumpolar features in sea surface height or dynamic height. Modern observational and theoretical developments are challenging and expanding this traditional framework to accommodate a more complex view of fronts. Here, we present a complementary new approach for calculating fronts using an unsupervised classification method called Gaussian mixture modelling (GMM) and a novel inter-class parameter called the I-metric. The I-metric approach produces a probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The I-metric approach uses thermohaline information from a range of depth levels, making it more general than approaches that only use near-surface properties. We train the GMM using an observationally constrained state estimate in order to have more uniform spatial and temporal data coverage. The probabilistic boundaries defined by the I-metric roughly coincide with several classically defined fronts, offering a novel view of this structure. The I-metric fronts appear to be relatively sharp in the open ocean and somewhat diffuse near large topographic features, possibly highlighting the importance of topographically induced mixing. For comparison with a more localised method, we also use an edge detection approach for identifying fronts. We find a strong correlation between the edge field of the leading principal component and the zonal velocity; the edge detection method highlights the presence of jets, which are supported by thermal wind balance. This more localised method highlights the complex, multiscale structure of Southern Ocean fronts, complementing and contrasting with the more domain-wide view offered by the I-metric. The Sobel edge detection method may be useful for defining and tracking smaller-scale fronts and jets in model or reanalysis data. The I-metric approach may prove to be a useful method for inter-model comparison, as it uses the thermohaline structure of those models instead of tracking somewhat ad hoc values of sea surface height and/or dynamic height, which can vary considerably between models. In addition, the general I-metric approach allows front definitions to shift with changing temperature and salinity structures, which may be useful for characterising fronts in a changing climate.