Search for: All records

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 Enhancement of currents in Earth's ionosphere adversely impacts systems and technologies, and one example of extreme enhancement is supersubstorms. Despite the name, whether a supersubstorm is a substorm remains an open question, because studies suggest that unlike substorms, supersubstorms sometimes affect all local times including the dayside. The spectacular May 2024 storm contains signatures of two supersubstorms that occurred successively in time with similar magnitude and duration, and we explore the nature of them by examining the morphology of the auroral electrojet, the corresponding disturbances in the magnetosphere, and the solar wind driving conditions. The results show that the two events exhibit distinctly different features. The first event was characterized by a locally intensified electrojet followed by a rapid expansion in latitude and local time. Auroral observations showed poleward expansion of auroras (or aurorae), and geosynchronous observations showed thickening of the plasma sheet, magnetic field dipolarization, and energetic particle injections. The second event was characterized by an instantaneous intensification of the electrojet over broad latitude and local time. Auroras did not expand but brightened simultaneously across the sky. Radar and LEO observations showed enhancement of the ionospheric electric field. Therefore, the first event is a substorm, whereas the second event is enhancement of general magnetospheric convection driven by a solar wind pressure increase. These results illustrate that the so‐called supersubstorms have more than one type of driver, and that internal instability in the magnetotail and external driving of the solar wind are equally important in driving extreme auroral electrojet activity. 

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.