Parameterization of Langmuir circulation under geostrophic effects using the data-driven approach

Abstract

Langmuir circulation (LC) and geostrophic effects are crucial physical processes that affect upper-ocean mixing. This study investigates the impact of LC on ocean mixing with a particular focus on geostrophic effects. By combining feedforward neural network (FNN) and Large Eddy Simulation (LES), this study simulated the interaction between varying intensities of LC and different geostrophic effects. The results revealed that the eddy viscosity coefficient in high-latitude areas exceeded that in mid-latitude areas, with this difference being most pronounced in the surface layer and gradually diminishing with depth. Analysis of the vertical momentum flux, upper mixed layer depth, and horizontal velocity shear characteristics demonstrates that geostrophic effects influence high-latitude ocean turbulence and mixing processes. Based on these findings, an improved LC parameterization scheme (KPPLT-FNN) incorporating geostrophic effects was developed, which relies on friction velocity, geostrophic effect, turbulent Langmuir number, and seawater depth. In GOTM, comparative analysis with observational data from COREII and the Ocean Climate Station Papa indicates that KPPLT-FNN demonstrates superior performance in simulating summer ocean temperature, ocean salinity, and winter mixed layer depth. Statistical analysis confirms that the simulation results incorporating geostrophic effects outperform those without such considerations. This study provides valuable insights into improving the accuracy of ocean model simulations.