A document by Elizabeth Yankovsky. Click on the document to view its contents.

We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low-pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially-varying and anisotropic filters. The new diffusion-based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open-source python package implementing this algorithm, called gcm-filters, is currently under development.

We develop a parameterization for representing the effects of submesoscale symmetric instability (SI) in the ocean interior. SI is an important contributor to water mass modification and mesoscale energy dissipation throughout the World Ocean. Dense gravity currents forced by surface buoyancy loss over shallow shelves are a particularly compelling test case, as they are characterized by density fronts and shears susceptible to a wide range of submesoscale instabilities. We present idealized experiments of Arctic shelf overflows employing the GFDL-MOM6 in z* and isopycnal coordinates. At the highest resolutions, the dense flow undergoes geostrophic adjustment and forms bottom- and surface-intensified jets. The density front along the topography combined with geostrophic shear initiates SI, leading to the onset of secondary shear instability, dissipation of geostrophic energy, and turbulent mixing. We explore the impact of vertical coordinate, resolution, and parameterization of shear-driven mixing on the representation of water mass transformation. We find that in isopycnal and low-resolution z* simulations, limited vertical resolution leads to inadequate representation of diapycnal mixing. This motivates our development of a parameterization for SI-driven turbulence. The parameterization is based on identifying unstable regions through a balanced Richardson number criterion and slumping isopycnals towards a balanced state. The potential energy extracted from the large-scale flow is assumed to correspond to the kinetic energy of SI which is dissipated through shear mixing. Parameterizing submesoscale instabilities by combining isopycnal slumping with diapycnal mixing becomes crucial as ocean models move towards resolving mesoscale eddies and fronts but not the submesoscale phenomena they host.

We describe an idealized primitive equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to make eddy-resolving calculations on the finest grids more affordable. The model has intermediate complexity, incorporating basin-scale geometry with idealized Atlantic and Southern oceans, and with non-uniform ocean depth to allow for mesoscale eddy interactions with topography. The model is perfectly adiabatic and spans the equator, and thus fills a gap between quasi-geostrophic models, which cannot span two hemispheres, and idealized general circulation models, which generally have diabatic processes and buoyancy forcing. We show that the model solution is approaching convergence in mean kinetic energy for the ocean mesoscale processes of interest, and has a rich range of dynamics with circulation features that emerge only due to resolving mesoscale turbulence.