Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions
Name
2018EA000519.pdf
Description
Published version
Size
2.56 MB
Format
Unknown
Checksum (MD5)
14a25f7dace33084fd90ac600643bfab
Author(s)
Sonnewald, Maike
Wunsch, Carl
Heimbach, Patrick
Date Issued
May 2019
Journal
Earth and Space Science
Publisher
American Geophysical Union (AGU)
Citation
Sonnewald, Maike, Wunsch, Carl and Heimbach, Patrick. 2019. "Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions." Earth and Space Science, 6 (5).
Version
Final published version
Abstract
©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.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2018ea000519
