Towards the Upper-Ocean Unbalanced Submesoscale Motions in the Oleander Observations

Haijin Cao aKey Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China
bCollege of Oceanography, Hohai University, Nanjing, China

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Baylor Fox-Kemper cDepartment of Earth, Environmental, and Planetary Sciences, Brown University, Providence, Rhode Island

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Zhiyou Jing dState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

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Xiangzhou Song aKey Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China
bCollege of Oceanography, Hohai University, Nanjing, China

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Yuyi Liu dState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

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Abstract

Oceanic submesoscale dynamics with horizontal scales < 20 km have similar temporal and spatial scales as internal gravity waves (IGWs), but they differ dynamically and have distinct impacts on the ocean. Separating unbalanced submesoscale motions (USMs), quasi-balanced submesoscale motions (QBMs), and IGWs in observations remains a great challenge. Based on the wave–vortex decomposition and the vertical scale separation approach for distinguishing IGWs and USMs, the long-term repeat Oleander observations in the Gulf Stream region provide an opportunity to quantify these processes separately. Here in this study, the role of USMs in the divergence is emphasized, which has confounded the wave–vortex decomposition of wintertime data in previous analyses. We also adopt the vertical filtering approach to identify the USMs by applying a high-pass filter to the vertical scales, as USMs are characterized by smaller vertical scales. This approach is tested with submesoscale-permitting model data to confirm its effectiveness in filtering the submesoscale velocity perturbations, before being applied to the compiled velocity data of the Oleander dataset (years 2005–18). The results show that the averaged submesoscale eddy kinetic energy by USMs can reach ∼1 × 10−3 m2 s−2 at z = −30 m in winter, much stronger than found in other seasons. Importantly, this study exemplifies the possibility of obtaining USMs from existing ADCP observations and reveals the seasonal dynamical regimes for the submesoscales.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haijin Cao, h.cao@hhu.edu.cn

Abstract

Oceanic submesoscale dynamics with horizontal scales < 20 km have similar temporal and spatial scales as internal gravity waves (IGWs), but they differ dynamically and have distinct impacts on the ocean. Separating unbalanced submesoscale motions (USMs), quasi-balanced submesoscale motions (QBMs), and IGWs in observations remains a great challenge. Based on the wave–vortex decomposition and the vertical scale separation approach for distinguishing IGWs and USMs, the long-term repeat Oleander observations in the Gulf Stream region provide an opportunity to quantify these processes separately. Here in this study, the role of USMs in the divergence is emphasized, which has confounded the wave–vortex decomposition of wintertime data in previous analyses. We also adopt the vertical filtering approach to identify the USMs by applying a high-pass filter to the vertical scales, as USMs are characterized by smaller vertical scales. This approach is tested with submesoscale-permitting model data to confirm its effectiveness in filtering the submesoscale velocity perturbations, before being applied to the compiled velocity data of the Oleander dataset (years 2005–18). The results show that the averaged submesoscale eddy kinetic energy by USMs can reach ∼1 × 10−3 m2 s−2 at z = −30 m in winter, much stronger than found in other seasons. Importantly, this study exemplifies the possibility of obtaining USMs from existing ADCP observations and reveals the seasonal dynamical regimes for the submesoscales.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haijin Cao, h.cao@hhu.edu.cn

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