Abstract
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. Because the ACSS are also a sparely sampled region, grid-point-based model assessments are of limited value. Our goal is to demonstrate the utility of clustering tools for identifying hydrographic regimes that are common to different source fields (model or data), while allowing for biases in other metrics (e.g., water mass core properties) and shifts in region boundaries. We apply K-means clustering to hydrographic metrics based on the stratification 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 “primary” and “mixed” regimes that have physically interpretable bases. For example, meltwater-freshened coastal currents in the Amundsen Sea and a region of high-salinity shelf water formation 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 biases in water mass T–S characteristics 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 has a limited fresh-water-enriched coastal regimes. Given the sparsity of observations of the ACSS, this technique is a promising tool for the evaluation of a larger model ensemble (e.g., CMIP6) on a circum-Antarctic basis.
Highlights
The Antarctic Continental Shelf seas (ACSS, defined here as the ocean regions adjacent to Antarctica with water depth shallower than 2500 m) are critical components of the climate system, playing an essential role in ice sheet mass balance, sea ice formation and ocean circulation (Rignot et al, 2008; Hobbs et al, 2016; Bindoff et al, 2000)
Salinity poorly represents the vertical composition of water masses since it increases monotonically with water depth over most of the ACSS (Fig. 1); salinity alone is insufficient to identify regimes with sub-surface heat reservoirs that are characteristic of regions with high ice shelf basal melt rates (Rignot et al, 2013; Dinniman et al, 2016; Adusumilli et al, 2020)
We have demonstrated the utility and sensitivity of a clustering-based approach for assessing hydrographic regimes and their water properties on the Antarctic continental shelf, using the World Ocean Atlas objective analysis product (WOA) and numerical model output from the Community Earth System Model version2 (CESM2)
Summary
The Antarctic Continental Shelf seas (ACSS, defined here as the ocean regions adjacent to Antarctica with water depth shallower than 2500 m) are critical components of the climate system, playing an essential role in ice sheet mass balance, sea ice formation and ocean circulation (Rignot et al, 2008; Hobbs et al, 2016; Bindoff et al, 2000). In the Amundsen–Bellingshausen seas sectors, the atmosphere (Bromwich et al, 2013) and subsurface ocean (Schmidtko et al, 2014) are warming, the sea-icefree period is rapidly increasing (Stammerjohn et al, 2012), ice shelves are thinning (Rignot et al, 2013; Paolo et al, 2015; Adusumilli et al, 2020) and the grounded portion of the ice sheet is losing mass at an accelerating rate (Shepherd et al, 2018; Sutterley et al, 2014; Gardner et al, 2018). Sun et al.: A clustering-based approach to ocean model–data comparison around Antarctica
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