Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning
Abstract Submesoscale eddies are important features in the upper ocean where they mediate air‐sea exchanges, convey heat and tracer fluxes into ocean interior, and enhance biological production. However, due to their small size (0.1–10 km) and short lifetime (hours to days), directly observing subme...
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American Geophysical Union (AGU)
2025-01-01
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| Series: | Earth and Space Science |
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| Online Access: | https://doi.org/10.1029/2022EA002618 |
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| author | Leyu Yao John R. Taylor Dani C. Jones Scott D. Bachman |
| author_facet | Leyu Yao John R. Taylor Dani C. Jones Scott D. Bachman |
| author_sort | Leyu Yao |
| collection | DOAJ |
| description | Abstract Submesoscale eddies are important features in the upper ocean where they mediate air‐sea exchanges, convey heat and tracer fluxes into ocean interior, and enhance biological production. However, due to their small size (0.1–10 km) and short lifetime (hours to days), directly observing submesoscales in the field generally requires targeted high resolution surveys. Submesoscales increase the vertical density stratification of the upper ocean and qualitatively modify the vertical density profile. In this paper, we propose an unsupervised machine learning algorithm to identify submesoscale activity using vertical density profiles. The algorithm, based on the profile classification model (PCM) approach, is trained and tested on two model‐based data sets with vastly different resolutions. One data set is extracted from a large‐eddy simulation (LES) in a 4 km by 4 km domain and the other from a regional model for a sector in the Southern Ocean. We show that the adapted PCM can identify regions with high submesoscale activity, as characterized by the vorticity field (i.e., where surface vertical vorticity ζ is similar to Coriolis frequency f and Rossby number Ro=ζ/f∼O(1)), using solely the vertical density profiles, without any additional information on the velocity, the profile location, or horizontal density gradients. The results of this paper show that the adapted PCM can be applied to data sets from different sources and provides a method to study submesoscale eddies using global data sets (e.g., CTD profiles collected from ships, gliders, and Argo floats). |
| format | Article |
| id | doaj-art-112453b8c4184467bbbde375fe7f6907 |
| institution | Kabale University |
| issn | 2333-5084 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-112453b8c4184467bbbde375fe7f69072025-01-28T11:08:39ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842025-01-01121n/an/a10.1029/2022EA002618Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine LearningLeyu Yao0John R. Taylor1Dani C. Jones2Scott D. Bachman3Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UKDepartment of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UKCooperative Institute for Great Lakes Research (CIGLR) University of Michigan Ann Arbor MI USANational Center for Atmospheric Research Boulder CO USAAbstract Submesoscale eddies are important features in the upper ocean where they mediate air‐sea exchanges, convey heat and tracer fluxes into ocean interior, and enhance biological production. However, due to their small size (0.1–10 km) and short lifetime (hours to days), directly observing submesoscales in the field generally requires targeted high resolution surveys. Submesoscales increase the vertical density stratification of the upper ocean and qualitatively modify the vertical density profile. In this paper, we propose an unsupervised machine learning algorithm to identify submesoscale activity using vertical density profiles. The algorithm, based on the profile classification model (PCM) approach, is trained and tested on two model‐based data sets with vastly different resolutions. One data set is extracted from a large‐eddy simulation (LES) in a 4 km by 4 km domain and the other from a regional model for a sector in the Southern Ocean. We show that the adapted PCM can identify regions with high submesoscale activity, as characterized by the vorticity field (i.e., where surface vertical vorticity ζ is similar to Coriolis frequency f and Rossby number Ro=ζ/f∼O(1)), using solely the vertical density profiles, without any additional information on the velocity, the profile location, or horizontal density gradients. The results of this paper show that the adapted PCM can be applied to data sets from different sources and provides a method to study submesoscale eddies using global data sets (e.g., CTD profiles collected from ships, gliders, and Argo floats).https://doi.org/10.1029/2022EA002618oceanographymachine learning |
| spellingShingle | Leyu Yao John R. Taylor Dani C. Jones Scott D. Bachman Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning Earth and Space Science oceanography machine learning |
| title | Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning |
| title_full | Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning |
| title_fullStr | Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning |
| title_full_unstemmed | Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning |
| title_short | Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning |
| title_sort | identifying ocean submesoscale activity from vertical density profiles using machine learning |
| topic | oceanography machine learning |
| url | https://doi.org/10.1029/2022EA002618 |
| work_keys_str_mv | AT leyuyao identifyingoceansubmesoscaleactivityfromverticaldensityprofilesusingmachinelearning AT johnrtaylor identifyingoceansubmesoscaleactivityfromverticaldensityprofilesusingmachinelearning AT danicjones identifyingoceansubmesoscaleactivityfromverticaldensityprofilesusingmachinelearning AT scottdbachman identifyingoceansubmesoscaleactivityfromverticaldensityprofilesusingmachinelearning |