Speaker

Shafer Smith, Ph.D., Professor of Mathematics and Atmosphere/Ocean Science, New York University

Sensing Sub-Mesoscale Structures from SWOT

Abstract

The Ka-band Radar Interferometer (KaRIn) aboard NASA’s recently launched Surface Water Ocean Topography (SWOT) satellite measures sea surface height (SSH) in 120 km wide swaths. Far exceeding its engineering expectations, the instrument is resolving SSH features at scales of a few kilometers — more than an order of magnitude improvement over the previous generation of altimeters — providing the first direct global observations of submesoscale dynamical structures.

Submesocale flows play a significant role in Earth’s climate in three major ways: (1) boundary layer baroclinic turbulence (“mixed layer instability”) rapidly restratifies horizontal gradients in the mixed layer; (2) the energy released through restratification generates submesoscale turbulence that undergoes a cascade to larger scales, charging up mesoscale eddies; and (3) submesoscale processes drive intense fronts and filaments with associated vertical velocities that punch through the mixed-layer, acting as transport conduits for tracer fluxes between the atmosphere and ocean’s interior. SWOT presents a landmark opportunity to characterize and quantify these upper ocean submesoscale processes on a global scale.

However, inferring submesoscale surface velocities from SWOT observations presents an enormous unsolved challenge. While geostrophic balance provides accurate estimates of surface velocities at the 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) are ubiquitous at these scales. These wave motions are ageostrophic, and moreover, their order 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, with an emphasis on developing ways to directly estimate vertical fluxes from instantaneous snapshots of SSH. In this talk, I will discuss our team’s approach to this problem, covering a number of results achieved with collaborators [1] over the past six years, emphasizing how they fit together and provide parts of the solution to this puzzle. Finally, I’ll discuss some of our current work and plans going forward, with new collaborators [2] joining the effort.

[1] Ryan Abernathey (formerly Columbia/LDEO), Dhruv Balwada (Columbia/LDEO), Spencer Jones (TAMU), Qiyu Xiao (former PhD student at NYU)
[2] Abigail Bodner (MIT), Leah Johnson (UW/APL), Tatsu Monkman (new postdoc at NYU), Ryan Du and Tea Susskind (both current PhD students at NYU)