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Abstract Measurements collected by a Remote Environmental Monitoring Units (REMUS) 600 autonomous underwater vehicle (AUV) off the coast of southern California demonstrate large-scale coherent wave-driven vortices, consistent with Langmuir turbulence (LT), and played a dominant role in structuring turbulent dissipation within the oceanic surface boundary layer. During a 10-h period with sustained wind speeds of 10 m s⁻¹, Langmuir circulations were limited to the upper third of the surface mixed layer by persistent stratification within the water column. The ensemble-averaged circulation, calculated using conditional averaging of acoustic Doppler dual current profile (AD2CP) velocity profiles using elevated backscattering intensity associated with subsurface bubble clouds, indicates that LT vortex pairs were characterized by an energetic downwelling zone flanked by broader, weaker upwelling regions with vertical velocity magnitudes similar to previous numerical studies of LT. Horizontally distributed microstructure estimates of turbulent kinetic energy dissipation rates were lognormally distributed near the surface in the wave mixing layer with the majority of values falling between wall layer scaling and wave transport layer scaling. Partitioning dissipation rates between downwelling centers and ambient conditions suggests that LT may play a dominant role in elevating dissipation rates in the ocean surface boundary layer (OSBL) by redistributing wave-breaking turbulence. 

Abstract The capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions.