A clustering-based approach to ocean model–data comparison around Antarctica
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...
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ftosti:oai:osti.gov:1807859 2023-07-30T03:56:06+02:00 A clustering-based approach to ocean model–data comparison around Antarctica Sun, Qiang Little, Christopher M. Barthel, Alice M. Padman, Laurie 2023-07-04 application/pdf http://www.osti.gov/servlets/purl/1807859 https://www.osti.gov/biblio/1807859 https://doi.org/10.5194/os-17-131-2021 unknown http://www.osti.gov/servlets/purl/1807859 https://www.osti.gov/biblio/1807859 https://doi.org/10.5194/os-17-131-2021 doi:10.5194/os-17-131-2021 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.5194/os-17-131-2021 2023-07-11T10:05:26Z 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 ... Other/Unknown Material Amundsen Sea Antarc* Antarctic Antarctica Ross Sea SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Antarctic The Antarctic Ross Sea Amundsen Sea Ocean Science 17 1 131 145 |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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language |
unknown |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Sun, Qiang Little, Christopher M. Barthel, Alice M. Padman, Laurie A clustering-based approach to ocean model–data comparison around Antarctica |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
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 ... |
author |
Sun, Qiang Little, Christopher M. Barthel, Alice M. Padman, Laurie |
author_facet |
Sun, Qiang Little, Christopher M. Barthel, Alice M. Padman, Laurie |
author_sort |
Sun, Qiang |
title |
A clustering-based approach to ocean model–data comparison around Antarctica |
title_short |
A clustering-based approach to ocean model–data comparison around Antarctica |
title_full |
A clustering-based approach to ocean model–data comparison around Antarctica |
title_fullStr |
A clustering-based approach to ocean model–data comparison around Antarctica |
title_full_unstemmed |
A clustering-based approach to ocean model–data comparison around Antarctica |
title_sort |
clustering-based approach to ocean model–data comparison around antarctica |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1807859 https://www.osti.gov/biblio/1807859 https://doi.org/10.5194/os-17-131-2021 |
geographic |
Antarctic The Antarctic Ross Sea Amundsen Sea |
geographic_facet |
Antarctic The Antarctic Ross Sea Amundsen Sea |
genre |
Amundsen Sea Antarc* Antarctic Antarctica Ross Sea |
genre_facet |
Amundsen Sea Antarc* Antarctic Antarctica Ross Sea |
op_relation |
http://www.osti.gov/servlets/purl/1807859 https://www.osti.gov/biblio/1807859 https://doi.org/10.5194/os-17-131-2021 doi:10.5194/os-17-131-2021 |
op_doi |
https://doi.org/10.5194/os-17-131-2021 |
container_title |
Ocean Science |
container_volume |
17 |
container_issue |
1 |
container_start_page |
131 |
op_container_end_page |
145 |
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1772810909735976960 |