An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea

Abstract

Using in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results show that the estimation error of the MG model is still more than one order of magnitude in the NSCS. Also, the importance of parameters obtained from machine learning indicates that the normalized depth (D) is one of the most relevant parameters to the turbulent kinetic energy dissipation rate ε. Therefore, in this study, D is introduced into the MG model to obtain an improved MG model (IMG). The IMG model has an average correlation (r) between the estimated and observed log10ε of 0.79, which is at least 49% higher than the MG model, and an average root mean square error (RMSE) of 0.25, which is at least 42% lower than that of the MG model. The IMG model accurately estimates the multi-year turbulent mixing observed in the NSCS, including before and after tropical cyclone passages. This provides a new perspective to study the physical principles and spatial and temporal distribution of turbulent mixing.

Keywords

• turbulent mixing parameterization
• MG model
• northwestern South China Sea
• machine learning