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Abstract Langmuir turbulence, a dominant process in the ocean surface boundary layer, drives substantial vertical mixing that influences temperature, salinity, mixed layer depth, and biogeochemical tracer distributions. While direct resolution of Langmuir turbulence in ocean and climate models remains computationally prohibitive, its effects are commonly parameterized, frequently within established turbulent mixing frameworks like the K‐profile parameterization (KPP). This study utilizes a modified KPP that determines boundary layer depth through an integral criterion, diverging from the conventional KPP's dependence on the bulk Richardson number. The modified KPP demonstrates markedly lower sensitivity to model vertical resolution than its conventional counterpart. Building upon this modified KPP framework, we introduce an innovative parameterization scheme for Langmuir mixing effects. We evaluate the performance of this new scheme against existing approaches using a one‐dimensional (1D) column model across four different scenarios, incorporating validation against both large eddy simulation (LES) results and field measurements. Our analysis reveals that the new Langmuir mixing scheme, explicitly designed for the modified KPP framework, performs competitively while maintaining reduced sensitivity to vertical resolution. 

Abstract This study utilizes Deep Neural Networks (DNN) to improve the K‐Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11‐year turbulence‐resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN‐augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long‐term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first‐order and second‐moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing. 

Abstract Large-eddy simulations are used to investigate the influence of a horizontal frontal zone, represented by a stationary uniform background horizontal temperature gradient, on the wind- and wave-driven ocean surface boundary layers. In a frontal zone, the temperature structure, the ageostrophic mean horizontal current, and the turbulence in the ocean surface boundary layer all change with the relative angle among the wind and the front. The net heating and cooling of the boundary layer could be explained by the depth-integrated horizontal advective buoyancy flux, called the Ekman Buoyancy Flux (or the Ekman-Stokes Buoyancy Flux if wave effects are included). However, the detailed temperature profiles are also modulated by the depth-dependent advective buoyancy flux and submesoscale eddies. The surface current is deflected less (more) to the right of the wind and wave when the depth-integrated advective buoyancy flux cools (warms) the ocean surface boundary layer. Horizontal mixing is greatly enhanced by submesoscale eddies. The eddy-induced horizontal mixing is anisotropic and is stronger to the right of the wind direction. Vertical turbulent mixing depends on the superposition of the geostrophic and ageostrophic current, the depth-dependent advective buoyancy flux, and submesoscale eddies.