Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions
©2019. The Authors. 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, objectiv...
Published in: | Earth and Space Science |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
American Geophysical Union (AGU)
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/1721.1/141225.2 |
id |
ftmit:oai:dspace.mit.edu:1721.1/141225.2 |
---|---|
record_format |
openpolar |
spelling |
ftmit:oai:dspace.mit.edu:1721.1/141225.2 2023-06-11T04:16:57+02:00 Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions Sonnewald, Maike Wunsch, Carl Heimbach, Patrick 2022-03-16T16:44:35Z application/octet-stream https://hdl.handle.net/1721.1/141225.2 en eng American Geophysical Union (AGU) http://dx.doi.org/10.1029/2018ea000519 Earth and Space Science 2333-5084 https://hdl.handle.net/1721.1/141225.2 Sonnewald, Maike, Wunsch, Carl and Heimbach, Patrick. 2019. "Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions." Earth and Space Science, 6 (5). Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ Wiley Article http://purl.org/eprint/type/JournalArticle 2022 ftmit https://doi.org/10.1029/2018ea000519 2023-05-29T08:43:12Z ©2019. The Authors. 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. Article in Journal/Newspaper Southern Ocean DSpace@MIT (Massachusetts Institute of Technology) Southern Ocean Earth and Space Science 6 5 784 794 |
institution |
Open Polar |
collection |
DSpace@MIT (Massachusetts Institute of Technology) |
op_collection_id |
ftmit |
language |
English |
description |
©2019. The Authors. 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 |
Article in Journal/Newspaper |
author |
Sonnewald, Maike Wunsch, Carl Heimbach, Patrick |
spellingShingle |
Sonnewald, Maike Wunsch, Carl Heimbach, Patrick Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions |
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 |
American Geophysical Union (AGU) |
publishDate |
2022 |
url |
https://hdl.handle.net/1721.1/141225.2 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Wiley |
op_relation |
http://dx.doi.org/10.1029/2018ea000519 Earth and Space Science 2333-5084 https://hdl.handle.net/1721.1/141225.2 Sonnewald, Maike, Wunsch, Carl and Heimbach, Patrick. 2019. "Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions." Earth and Space Science, 6 (5). |
op_rights |
Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1029/2018ea000519 |
container_title |
Earth and Space Science |
container_volume |
6 |
container_issue |
5 |
container_start_page |
784 |
op_container_end_page |
794 |
_version_ |
1768375680654901248 |