Modifying the mixed layer eddy parameterization to include frontogenesis arrest by boundary layer turbulence

Current submesoscale restratification parameterizations, which help set mixed layer depth in global climate models, depend on a simplistic scaling of frontal width shown to be unreliable in several circumstances. Observations and theory indicate that frontogenesis is common, but stable frontal widths arise in the presence of turbulence and instabilities that participate in keeping fronts at the scale observed, the arrested scale. Here we propose a new scaling law for arrested frontal width as a function of turbulent fluxes via the turbulent thermal wind (TTW) balance. A variety of large-eddy simulations (LES) of strain-induced fronts and TTW-induced filaments are used to evaluate this scaling. Frontal width given by boundary layer parameters drawn from observations in the General Ocean Turbulence Model (GOTM) are found qualitatively consistent with the observed range in regions of active submesoscales. The new arrested front scaling is used to modify the mixed layer eddy restratification parameterization commonly used in coarse-resolution climate models. Results in CESM-POP2 reveal the climate model's sensitivity to the parameterization update and changes in model biases. A comprehensive multimodel study is in planning for further testing. Significance StatementThe ocean surface plays a major role in the climate system, primarily through exchange in properties, such as in heat and carbon, between the ocean and atmosphere. Accurate model representation of ocean surface processes is crucial for climate simulations, yet they tend to be too small, fast, or complex to be resolved. Significant efforts lie in approximating these small-scale processes using reduced expressions that are solved by the model. This study presents an improved representation of the ocean surface in climate models by capturing some of the synergy that has been missing between the processes that define it. Results encourage further testing across a wider range of models to comprehensively evaluate the effects of this adjustment in climate simulations.