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Global climatology of submesoscale restratification using machine learning.
1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK.
Scientific Reports
|March 21, 2026
Summary
Submesoscale eddies significantly impact ocean stratification. A new machine learning method using Argo float data reveals global patterns of submesoscale restratification (SR), peaking in spring.
Area of Science:
- Oceanography
- Physical Oceanography
- Climate Science
Background:
- Submesoscale eddies influence ocean surface mixed layer stratification and energy transfer.
- Global-scale studies are limited by a lack of comprehensive, long-term observational data.
Purpose of the Study:
- To develop the first global observational climatology of submesoscale restratification.
- To utilize unsupervised machine learning for analyzing ocean density profiles.
Main Methods:
- Applied an unsupervised machine learning method (profile classification model - PCM) to Argo float density profiles (2000-2021).
- Classified vertical profiles based on ocean surface mixed layer density shape.
- Defined the submesoscale restratification (SR) index as the fraction of profiles exhibiting submesoscale characteristics.
Main Results:
- The SR index exhibits seasonal peaks in spring across both hemispheres.
- SR index maxima lag mixed layer depth maxima by approximately one month.
- Identified "hotspots" of high SR index in the Norwegian Sea and Drake Passage during spring.
Conclusions:
- Submesoscale eddies play a crucial role in restratifying the ocean mixed layer.
- The developed method enables global-scale analysis of submesoscale restratification patterns.
- Findings advance understanding of ocean mixing and energy transfer processes.
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