Seminar: Toward a robust method to estimate submesoscale fluxes from SWOT: a few pieces of a challenging puzzle

Abstract

The ocean's submesoscale consists of vortices, fronts and filaments occurring at scales well below the internal deformation radius, with O(1) Rossby and Richardson numbers. They affect Earth's climate in (at least) three major ways: (1) mixed-layer instability restratifies upper-ocean horizontal gradients; (2) the energy released through restratification generates submesoscale turbulence that stengthens the mesoscale via an inverse cascade; and (3) submesoscale processes drive intense fronts and filaments with associated vertical velocities that punch through the mixed-layer, creating conduits for oxygen, carbon and heat fluxes between the atmosphere and ocean's interior.

The Ka-band Radar Interferometer (KaRIn) aboard NASA's recently launched Surface Water Ocean Topography (SWOT) satellite measures sea surface height (SSH) in parallel 50 km wide swaths, resolving SSH features at scales of a few kilometers. This presents a landmark opportunity to quantify upper ocean submesoscale fluxes on a global scale.  However, inferring submesoscale surface velocities from this data presents a challenge. While geostrophic balance provides accurate estimates of surface velocities at scales seen by traditional nadir altimetry, it is insufficient at SWOT scales for two reasons: (1) submesoscale dynamics is characterized by O(1) Rossby number and exhibit a wide range ageostrophic motions like convergent fronts and strong vortical asymmetry; and (2) inertia-gravity waves (IGWs) strongly influence SSH at these scales. These wave motions are ageostrophic, and moreover, their O(1 day) timescales make it infeasible to remove them from the 21-day repeat cycle SWOT data using temporal filtering. Alternate methods, not relying on geostrophy or temporal filtering, need to be developed if we wish to quantify submesoscale processes from SWOT.

Our SWOT Science Team is focused on estimating the transport-active velocity field from SWOT, using a mix of dynamical, statistical, and machine-learning approaches.  In this talk, I will discuss a number of results achieved by our team* over the past eight years, emphasizing how they fit together, providing parts of the solution to this multifaceted puzzle. Finally, I'll discuss some of our current work and plans going forward, with new collaborators joining the effort.

*Ryan Abernathey (formerly Columbia/LDEO), Dhruv Balwada (Columbia/LDEO), Spencer Jones (TAMU), Qiyu Xiao (former PhD student at NYU), Abigail Bodner (MIT), Leah Johnson (UW/APL), Tatsu Monkman (new postdoc at NYU), Ryan Du (former PhD student at NYU)