06 Sep 2023
 | 06 Sep 2023
Status: this preprint is open for discussion.

A new approach to understanding fluid mixing in process-study models of stratified fluids

Samuel George Hartharn-Evans, Marek Stastna, and Magda Carr

Abstract. While well established energy-based methods of quantifying diapycnal mixing in process-study numerical models are often used to provide information about when mixing occurs, and how much much mixing has occurred, describing how and where this mixing has taken place remains a challenge. Moreover, methods based on sorting the density field struggle with under resolution and uncertainty as to the definition of the reference density when bathymetry is present. Here, an alternative method of understanding mixing is proposed. Paired histograms of user selected variables (which we abbreviate USP) are employed to identify mixing fluid, and are then used to identify regions of fluid in physical space that are undergoing mixing. This paper presents two case studies showcasing this method: shoaling internal solitary waves and a shear instability in cold water influenced by the nolinearity of the equation of state. The USP method identifies differences in the mixing processes associated with different internal solitary wave breaking types, including differences in the horizontal extent and advection of mixed fluid. The method is also used to identify how density, and passive tracers are mixed within the core of the cold-water Kelvin-Helmholtz instability.

Samuel George Hartharn-Evans et al.

Status: open (until 08 Nov 2023)

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  • RC1: 'Comment on egusphere-2023-1920', Anonymous Referee #1, 01 Oct 2023 reply

Samuel George Hartharn-Evans et al.

Model code and software

USP Matlab Code Samuel Hartharn-Evans

Samuel George Hartharn-Evans et al.


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Short summary
Across much of the ocean, and the world's lakes, less dense water (either because it is warm, or fresh) overlays denser water, forming stratification. The mixing of these layers affects the distribution of heat, nutrients, plankton, and sediment, and buoyancy, so is crucial to understand. We use small scale numerical experiments to better understand these processes, and here we propose a new analysis tool for understanding mixing within those models, looking at where two variables intersect.