Abstract
Air–sea momentum and scalar fluxes are strongly influenced by the coupling dynamics between turbulent winds and a spectrum of waves. Because direct field observations are difficult, particularly in high winds, many modeling and laboratory studies have aimed to elucidate the impacts of the sea state and other surface wave features on momentum and energy fluxes between wind and waves as well as on the mean wind profile and drag coefficient. Opposing wind is common under transient winds, for example, under tropical cyclones, but few studies have examined its impacts on air–sea fluxes. In this study, we employ a large-eddy simulation for wind blowing over steep sinusoidal waves of varying phase speeds, both following and opposing wind, to investigate impacts on the mean wind profile, drag coefficient, and wave growth/decay rates. The airflow dynamics and impacts rapidly change as the wave age increases for waves following wind. However, there is a rather smooth transition from the slowest waves following wind to the fastest waves opposing wind, with gradual enhancement of a flow perturbation identified by a strong vorticity layer detached from the crest despite the absence of apparent airflow separation. The vorticity layer appears to increase the effective surface roughness and wave form drag (wave attenuation rate) substantially for faster waves opposing wind.
Significance Statement
Surface waves increase friction at the sea surface and modify how wind forces upper-ocean currents and turbulence. Therefore, it is important to include effects of different wave conditions in weather and climate forecasts. We aim to inform more accurate forecasts by investigating wind blowing over waves propagating in the opposite direction using large-eddy simulation. We find that when waves oppose wind, they decay as expected, but also increase the surface friction much more drastically than when waves follow wind. This finding has important implications for how waves opposing wind are represented as a source of surface friction in forecast models.
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