Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions
Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the sta...
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ftpubmed:oai:pubmedcentral.nih.gov:6686691 2023-05-15T18:24:54+02:00 Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions Sonnewald, Maike Wunsch, Carl Heimbach, Patrick 2019-05-21 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686691/ https://doi.org/10.1029/2018EA000519 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686691/ http://dx.doi.org/10.1029/2018EA000519 ©2019. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. CC-BY-NC-ND Research Articles Text 2019 ftpubmed https://doi.org/10.1029/2018EA000519 2019-08-18T00:49:03Z Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Text Southern Ocean PubMed Central (PMC) Southern Ocean Earth and Space Science 6 5 784 794 |
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Research Articles Sonnewald, Maike Wunsch, Carl Heimbach, Patrick Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
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Research Articles |
description |
Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. |
format |
Text |
author |
Sonnewald, Maike Wunsch, Carl Heimbach, Patrick |
author_facet |
Sonnewald, Maike Wunsch, Carl Heimbach, Patrick |
author_sort |
Sonnewald, Maike |
title |
Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
title_short |
Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
title_full |
Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
title_fullStr |
Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
title_full_unstemmed |
Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
title_sort |
unsupervised learning reveals geography of global ocean dynamical regions |
publisher |
John Wiley and Sons Inc. |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686691/ https://doi.org/10.1029/2018EA000519 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686691/ http://dx.doi.org/10.1029/2018EA000519 |
op_rights |
©2019. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1029/2018EA000519 |
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Earth and Space Science |
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