Center for Ocean-Atmospheric Prediction Studies

Abstract: Five different parameterizations of Langmuir turbulence (LT) effect are investigated in a realistic model of the North Atlantic and Arctic using realistic wave forcing from a global wave hindcast. The parameterizations mainly apply an enhancement to the turbulence velocity scale, and/or to the entrainment buoyancy flux in the surface boundary layer. An additional run is also performed with other wave effects to assess the relative importance of Langmuir turbulence, namely the Coriolis-Stokes forcing, Stokes tracer advection and wave-modified momentum fluxes. The default model (without wave effects) underestimates the mixed layer depth in summer and overestimates it at high latitudes in the winter. The results show that adding LT mixing reduces shallow mixed layer depth (MLD) biases, particularly in the subtropics all year-around, and in the Nordic Seas in summer. There is overall a stronger relative impact on the MLD during winter than during summer. In particular, the parameterization with the most vigorous LT effect causes winter MLD increases by more than 50% relative to a control run without Langmuir mixing. On the contrary, the parameterization which assumes LT effects on the entrainment buoyancy flux and accounts for the Stokes penetration depth is able to enhance the mixing in summer more than in winter. This parametrization is also distinct from the others because it restrains the LT mixing in regions of deep MLD biases, so it is the preferred choice for our purpose. The different parameterizations do not change the amplitude or phase of the seasonal cycle of heat content but do influence its long-term trend, which means that the LT can influence the drift of ocean models. The combined impact on water mass properties from the Coriolis-Stokes force, the Stokes drift tracer advection, and the wave-dependent momentum fluxes is negligible compared to the effect from the parameterized Langmuir turbulence.

Abstract: Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 sigmao measurements include 1) direct Ku-band sigmao intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.