Defining Southern Ocean fronts using unsupervised classification
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 usi...
| Main Authors: | , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/os-2021-40 https://os.copernicus.org/preprints/os-2021-40/ |
| Summary: | 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 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 statistical model on data from an observationally-constrained state estimate for more uniform spatial and temporal coverage. The probabilistic boundaries 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 use edge detection in principal component space and correlate the edges with surface velocities. 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. |
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