Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island

Minghao HU; Lingling XIE; Mingming LI; Quanan ZHENG

doi:10.1007/s00343-024-3266-y

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Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island

Physics | Updated：2025-05-27

- Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island
- Journal of Oceanology and Limnology Vol. 43, Issue 3, Pages: 657-675(2025)
- Affiliations：
  
  1.Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
  2.Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Ocean (LCRE), Zhanjiang 524088, China
  3.Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Zhanjiang 524088, China
  4.Department of Atmospheric and Oceanic Science, University of Maryland, College Park 20742, USA
- Author bio：
  
  xiell@gdou.edu.cn
- Funds：
- DOI：10.1007/s00343-024-3266-y
  CLC：
- Received：08 December 2023，
  
  Accepted：29 March 2024，
  
  Online First：21 May 2024，
  
  Published：01 May 2025
- Accepted：
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HU Minghao,XIE Lingling,LI Mingming,et al.Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island[J].Journal of Oceanology and Limnology,2025,43(03):657-675.
DOI：

HU Minghao,XIE Lingling,LI Mingming,et al.Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island[J].Journal of Oceanology and Limnology,2025,43(03):657-675. DOI： 10.1007/s00343-024-3266-y.

摘要

Abstract

The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island

in South China Sea (SCS). The deep neural network model is used and incorporates the Richardson number

the normalized depth

the horizontal velocity speed

the shear

the stratification

and the density

as input parameters. Comparing to the scheme without parameter

and region division

the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate

. The correlation coefficient (

) between predicted and observed lg

increases from 0.49 to 0.62

and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes

such as the G89 and MG03

the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations (SHAP) framework analysis reveals the importance descending order of the input parameters as:

and

in the whole depth

while

is most important in the upper and bottom boundary laye

rs (

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=81358373&type=

2.28600001

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=81358814&type=

1.60866666

0.3

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=81358375&type=

2.28600001

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=81358816&type=

1.60866666

0.65) and least important in middle layer (0.3

0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.

关键词

Keywords

references

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