Parameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model

Abstract

Representing mesoscale turbulence in eddy-permitting ocean models raises challenges for climate simulations; in such models, eddies and their associated energy and transport effects are resolved either marginally or only over parts of the domain. Kinetic energy backscatter parameterizations have recently shown promise as both a momentum \textit{and} a buoyancy closure for partially resolved mesoscale turbulence—energizing eddies which can themselves maintain accurate large-scale stratification by slumping steep isopycnals. However, it has not been systematically explored whether such backscatter parameterizations can also serve as a closure for tracer mixing along isopycnals. Here, we present simulations using GFDL-MOM6 in an idealized basin-scale configuration to assess whether isopycnal mixing is improved, at 1/2$^\circ$ and 1/4$^\circ$ eddy-permitting resolutions, through the addition of a backscatter parameterization. We assess the representation of isopycnal mixing principally through diagnosing the three-dimensional structure of isopycnal diffusivities via a multiple tracer inversion method. Isopycnal mixing via backscatter alone shows significant improvement and closely resembles a 1/32$^\circ$ eddy-resolving simulation. Backscatter-parameterized mixing also outperforms simulations with no mesoscale parameterization or with an isopycnal diffusion parameterization alone, with the latter damping the tracer signature of partially resolved eddy variability. Simulations that vary the magnitude of backscatter show that increases in isopycnal diffusivities largely track increases in eddy energy. Our results suggest that parameterizing backscatter can plausibly capture key mesoscale physics in a unified framework: the inverse cascade of kinetic energy, the slumping of steep isopycnals, and the along-isopycnal mixing of tracers.

Laure Zanna

Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics; Professor of Mathematics and Data Science

My research interests include Climate Dynamics, Physical Oceanography, Applied Math, and Data Science.