Global observational constraints on surface ocean kinetic energy

Shane Elipot, University of Miami, Rosenstiel School of Marine and Atmospheric Science, Miami, FL, United States, Jonathan M Lilly, Theiss Research, La Jolla, CA, United States, Rick Lumpkin, NOAA/AOML, Miami, FL, United States and Edward Zaron, Portland State University, Portland, OR, United States
Abstract:
The latest version of the high resolution global surface drifter dataset comprises over 153 million estimates of near surface ocean velocity and nearly as many accompanying SST estimates, at hourly resolution. This dataset resolves fine details of the velocity field (the typical horizontal excursion within one-hour sampling interval of a surface drifter is about 600 m), as well as SST temporal and spatial evolution along trajectories (down to diurnal variations at hourly time steps). As such, drifter hourly measurements constitute a unique in situ dataset for science validation and understanding the velocity and SST signals associated with the finescale SSH measurements that will be acquired by SWOT. We present here a study describing, characterizing, and modeling partly the oceanic surface kinetic energy field by analyzing the high-resolution global hourly drifter dataset. Drifter data are analyzed jointly with ancillary global datasets to determine how much of the variance can be accounted for by different types of motions: hourly atmospheric reanalysis data and Argo data are used to estimate the momentum transfer function and identify the broadband wind-driven velocity component (from the zero frequency to superinertial frequencies); altimeter-derived velocity fields are linearly regressed on drifter velocities to identify the low-frequency geostrophic component; barotropic and baroclinic tide model velocity outputs are also regressed onto drifter velocities, this time to extract the stationnary component of tides. We find that the results of these comparisons, as well as the unexplained residual motions (non stationary tides, internal waves, submesoscale motions), are heterogeneous in space and time, exhibiting as an example the signatures of wind stress variance patterns and buoyancy fluxes, the geography of large-scale ocean currents, seasonal ocean stratification as observed from Argo, and bottom topography.