Parameterization of turbulent mixing by deep learning in the continental shelf sea east of Hainan Island
摘要
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 Ri, the normalized depth D, the horizontal velocity speed U, the shear S2, the stratification N2, and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient (r) between predicted and observed lgε 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: ρ, D, U, N2, S2, and Ri in the whole depth, while D is most important in the upper and bottom boundary layers (D≤0.3&D≥0.65) and least important in middle layer (0.3<D<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.