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Identifying and comparing Antarctic continental shelf water masses in models and observations
  • Christopher Little,
  • Qiang Sun,
  • Alice Barthel
Christopher Little
Atmospheric and Environmental Research Lexington

Corresponding Author:clittle@aer.com

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Qiang Sun
University of Connecticut
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Alice Barthel
Los Alamos National Laboratory
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A wealth of new climate model simulations have recently become available through the Coupled Model Intercomparison Project, Phase 6 (CMIP6). Evaluation of the representation of the Antarctic ocean across CMIP6 models is critical: projections of near-ice sheet temperature change will be used as input into sea level projections, and previous CMIP ensembles show substantial biases with a wide inter-model and inter-region spread. However, the ocean over the Antarctic continental shelf remains sparsely sampled, posing challenges for model-data comparison. Here, we assess a new clustering-based, grid-independent, methodology to identify and compare regional water masses, focusing on the Pacific sector of the Antarctic continental shelf. We find that temperature is insufficient to differentiate water masses, given the complexity and diversity of hydrographic profiles on the continental shelf. In contrast, clustering approaches applied to World Ocean Atlas 2018 temperature and salinity profiles identify “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 western Ross Sea, emerge naturally from the algorithm. We compare the location and properties of observed regimes to those found in the modern hydrographic state of the Community Earth System Model, version 2. Although CESM2 biases can be substantial, the locations of distinct regimes, and inter-cluster differences in water mass properties, are relatively consistent with observations. Differences in the locations and properties of hydrographic regimes are consistent with those expected from missing or poorly-represented physical processes (e.g. katabatic winds, ice shelf basal melting). We note other applications of this method, including the assessment of seasonal variability, and model-data comparison with different CMIP6 simulations and higher resolution regional ocean models.