More Like this

1. Ocean Surface Boundary Layer Response to Abruptly Turning Winds

   https://doi.org/10.1175/JPO-D-20-0198.1  

   Wang, Xingchi; Kukulka, Tobias (March 2021, Journal of Physical Oceanography)

   null (Ed.)

   Abstract Turbulence driven by wind and waves controls the transport of heat, momentum, and matter in the ocean surface boundary layer (OSBL). For realistic ocean conditions, winds and waves are often neither aligned nor constant, for example, when winds turn rapidly. Based on a Large Eddy Simulation (LES) method, which captures shear-driven turbulence (ST) and Langmuir turbulence (LT) driven by the Craik-Leibovich vortex force, we investigate the OSBL response to abruptly turning winds. We design idealized LES experiments, whose winds are initially constant to equilibrate OSBL turbulence before abruptly turning 90° either cyclonically or anticyclonically. The transient Stokes drift for LT is estimated from a spectral wave model. The OSBL response includes three successive stages that follow the change in direction. During stage 1, turbulent kinetic energy (TKE) decreases due to reduced TKE production. Stage 2 is characterized by TKE increasing with TKE shear production recovering and exceeding TKE dissipation. Transient TKE levels may exceed their stationary values due to inertial resonance and non-equilibrium turbulence. Turbulence relaxes to its equilibrium state at stage 3, but LT still adjusts due to slowly developing waves. During stages 1 and 2, greatly misaligned wind and waves lead to Eulerian TKE production exceeding Stokes TKE production. A Reynolds stress budget analysis and Reynolds-averaged Navier-Stokes equation models indicate that Stokes production furthermore drives the OSBL response. The Coriolis effects result in asymmetrical OSBL responses to wind turning directions. Our results suggest that transient wind conditions play a key role in understanding realistic OSBL dynamics. 

   more » « less
2. The K‐Profile Parameterization Augmented by Deep Neural Networks (KPP_DNN) in the General Ocean Turbulence Model (GOTM)

   https://doi.org/10.1029/2024MS004405  

   Yuan, Jianguo; Liang, Jun‐Hong; Chassignet, Eric P; Zavala‐Romero, Olmo; Wan, Xiaoliang; Cronin, Meghan F (September 2024, Journal of Advances in Modeling Earth Systems)

   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. 

   more » « less
3. The performance of subgrid-scale models in large-eddy simulation of Langmuir circulation in shallow water with the finite volume method

   Zeidi, S; Sarvepalli, L S; Tejada-Martinez, A E (August 2024, Computers fluids)

   Langmuir turbulence consists of Langmuir circulation (LC) generated at the surface of rivers, lakes, bays, and oceans by the interaction between the wind-driven shear and surface gravity waves. In homogeneous shallow water, LC can extend to the bottom of the water column and interact with the bottom boundary layer. Large-eddy simulation (LES) of LC in shallow water was performed with the finite volume method and various forms of subgrid-scale (SGS) model characterized by different near-wall treatments of the SGS eddy viscosity. The wave forcing relative to wind forcing in the LES was set following the field measurements of full-depth LC during the presence of LC engulfing a water column 15 m in depth in the coastal ocean, reported in the literature. It is found that the SGS model can greatly impact the structure of LC in the lower half of the water column. Results are evaluated in terms of (1) the Langmuir turbulence velocity statistics and (2) the lateral (crosswind) length scale and overall cell structure of LC. LES with an eddy viscosity with velocity scale in terms of S and Ω (where S is the norm of the strain rate tensor and Ω is the norm of the vorticity tensor) and a Van Driest wall damping function (referred to as the S-Omega model) is found to provide best agreement with pseudo-spectral LES in terms of the lateral length scale and overall cell structure of LC. Two other SGS models, namely the dynamic Smagorinsky model and the wall-adapting local-eddy viscosity model are found to provide less agreement with pseudo- spectral LES, for example, as they lead to less coherent bottom convergence of the cells and weaker associ ated upward transport of slow downwind moving fluid. Finally, LES with the S-Omega SGS model is also found to lead to good agreement with physical measurements of LC in the coastal ocean in terms of Langmuir turbulence decay during periods of surface heating 

   more » « less
4. How Well Do Large‐Eddy Simulations and Global Climate Models Represent Observed Boundary Layer Structures and Low Clouds Over the Summertime Southern Ocean?

   https://doi.org/10.1029/2020MS002205  

   Atlas, R. L.; Bretherton, C. S.; Blossey, P. N.; Gettelman, A.; Bardeen, C.; Lin, Pu; Ming, Yi (November 2020, Journal of Advances in Modeling Earth Systems)

   Abstract Climate models struggle to accurately represent the highly reflective boundary layer clouds overlying the remote and stormy Southern Ocean. We use in situ aircraft observations from the Southern Ocean Clouds, Radiation and Aerosol Transport Experimental Study (SOCRATES) to evaluate Southern Ocean clouds in a cloud‐resolving large‐eddy simulation (LES) and two coarse resolution global atmospheric models, the CESM Community Atmosphere Model (CAM6) and the GFDL Atmosphere Model (AM4), run in a nudged hindcast framework. We develop six case studies from SOCRATES data which span the range of observed cloud and boundary layer properties. For each case, the LES is run once forced purely using reanalysis data (fifth generation European Centre for Medium‐Range Weather Forecasts atmospheric reanalysis, “ERA5 based”) and once strongly nudged to an aircraft profile(“Obs based”). The ERA5‐based LES can be compared with the global models, which are also nudged to reanalysis data and are better for simulating cumulus. The Obs‐based LES closely matches an observed cloud profile and is useful for microphysical comparisons and sensitivity tests and simulating multilayer stratiform clouds. We use two‐moment Morrison microphysics in the LES and find that it simulates too few frozen particles in clouds occurring within the Hallett‐Mossop temperature range. We tweak the Hallett‐Mossop parameterization so that it activates within boundary layer clouds, and we achieve better agreement between observed and simulated microphysics. The nudged global climate models (GCMs) simulate liquid‐dominated mixed‐phase clouds in the stratiform cases but excessively glaciate cumulus clouds. Both GCMs struggle to represent two‐layer clouds, and CAM6 has low droplet concentrations in all cases and underpredicts stratiform cloud‐driven turbulence. 

   more » « less
5. Langmuir Mixing Schemes Based on a Modified K‐Profile Parameterization

   https://doi.org/10.1029/2024MS004729  

   Wang, Peng; McWilliams, James C; Yuan, Jianguo; Liang, Jun‐Hong (April 2025, Journal of Advances in Modeling Earth Systems)

   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. 

   more » « less