Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions

Abstract

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.

Keywords

• machine learning
• global patterns
• ocean dynamics
• big data
• ocean modeling
• physical oceanography