A clustering-based approach to ocean model–data comparison around Antarctica Article Swipe
Qiang Sun
,
Christopher M. Little
,
Alice Barthel
,
Laurie Padman
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/os-17-131-2021
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5194/os-17-131-2021
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.
Related Topics
Concepts
Hydrography
Water mass
Geology
Data assimilation
Climatology
Cluster analysis
Oceanography
Ocean current
Stratification (seeds)
Temperature salinity diagrams
Coupled model intercomparison project
Environmental science
Meteorology
Salinity
Climate change
General Circulation Model
Computer science
Geography
Seed dormancy
Dormancy
Botany
Machine learning
Germination
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/os-17-131-2021
- https://os.copernicus.org/articles/17/131/2021/os-17-131-2021.pdf
- OA Status
- gold
- Cited By
- 14
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3097296224
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3097296224Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/os-17-131-2021Digital Object Identifier
- Title
-
A clustering-based approach to ocean model–data comparison around AntarcticaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-19Full publication date if available
- Authors
-
Qiang Sun, Christopher M. Little, Alice Barthel, Laurie PadmanList of authors in order
- Landing page
-
https://doi.org/10.5194/os-17-131-2021Publisher landing page
- PDF URL
-
https://os.copernicus.org/articles/17/131/2021/os-17-131-2021.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://os.copernicus.org/articles/17/131/2021/os-17-131-2021.pdfDirect OA link when available
- Concepts
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Hydrography, Water mass, Geology, Data assimilation, Climatology, Cluster analysis, Oceanography, Ocean current, Stratification (seeds), Temperature salinity diagrams, Coupled model intercomparison project, Environmental science, Meteorology, Salinity, Climate change, General Circulation Model, Computer science, Geography, Seed dormancy, Dormancy, Botany, Machine learning, Germination, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 2, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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