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Ocean Science An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/os-2020-51
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2020-51
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Jun 2020

04 Jun 2020

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This preprint is currently under review for the journal OS.

A clustering-based approach to ocean model-data comparison around Antarctica

Qiang Sun1, Christopher M. Little1, Alice M. Barthel2, and Laurie Padman3 Qiang Sun et al.
  • 1Atmospheric and Environmental Research, Inc., Lexington, MA 02421, USA
  • 2Los Alamos National Laboratory, Los Alamos, NM 87545, USA
  • 3Earth and Space Research, 3350 SW Cascade Ave., Corvallis, OR 97333, USA

Abstract. The Antarctic Continental Shelf Seas (ACSS) are a critical, rapidly-changing element of the Earth system. Analyses of global-scale general circulation model (GCM) simulations, including those available through the Coupled Model Intercomparison Project, Phase 6 (CMIP6), can help reveal the origins of observed changes and predict the future evolution of the ACSS. However, an evaluation of ACSS hydrography in GCMs is vital: previous CMIP ensembles exhibit substantial mean-state biases (reflecting, for example, misplaced water masses) with a wide inter-model spread. Here, we demonstrate the utility of clustering tools for the identification and model-data comparison of hydrographic regimes. In this proof-of-concept analysis, we apply K-means clustering to hydrographic metrics from one GCM (Community Earth System Model version 2; CESM2) and one observation-based product (World Ocean Atlas 2018; WOA), focusing on the Amundsen, Bellingshausen, and Ross Seas. When applied to WOA temperature and salinity profiles, clustering identifies source and mixed regimes that have a physically interpretable basis. For example, meltwater-freshened coastal currents in the Amundsen Sea, and high salinity shelf water formation regions in the southwestern Ross Sea, emerge naturally from the algorithm. Both regions also exhibit clearly differentiated inner- and outer-shelf regimes. The same analysis applied to CESM2 demonstrates that, although mean-state model bias can be substantial, using a clustering approach highlights that the relative differences between regimes, and the locations where each regime dominates, are well represented in the model. CESM2 is generally fresher and warmer than WOA and lacks a clearly defined fresh-water-enriched coastal current. Given the sparsity of observations on the ACSS, this technique is a promising tool for the evaluation of a larger model ensemble (e.g., CMIP6) on a circum-Antarctic basis.

Qiang Sun et al.

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Qiang Sun et al.

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