Preprints
https://doi.org/10.5194/egusphere-2023-1920
https://doi.org/10.5194/egusphere-2023-1920
06 Sep 2023
 | 06 Sep 2023

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.

Journal article(s) based on this preprint

30 Jan 2024
A new approach to understanding fluid mixing in process-study models of stratified fluids
Samuel George Hartharn-Evans, Marek Stastna, and Magda Carr
Nonlin. Processes Geophys., 31, 61–74, https://doi.org/10.5194/npg-31-61-2024,https://doi.org/10.5194/npg-31-61-2024, 2024
Short summary
Samuel George Hartharn-Evans, Marek Stastna, and Magda Carr

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1920', Anonymous Referee #1, 01 Oct 2023
  • RC2: 'Comment on egusphere-2023-1920', Anonymous Referee #2, 12 Nov 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1920', Anonymous Referee #1, 01 Oct 2023
  • RC2: 'Comment on egusphere-2023-1920', Anonymous Referee #2, 12 Nov 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Sam Hartharn-Evans on behalf of the Authors (04 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (17 Dec 2023) by Kateryna Terletska
AR by Sam Hartharn-Evans on behalf of the Authors (20 Dec 2023)  Manuscript 

Journal article(s) based on this preprint

30 Jan 2024
A new approach to understanding fluid mixing in process-study models of stratified fluids
Samuel George Hartharn-Evans, Marek Stastna, and Magda Carr
Nonlin. Processes Geophys., 31, 61–74, https://doi.org/10.5194/npg-31-61-2024,https://doi.org/10.5194/npg-31-61-2024, 2024
Short summary
Samuel George Hartharn-Evans, Marek Stastna, and Magda Carr

Model code and software

USP Matlab Code Samuel Hartharn-Evans https://github.com/HartharnSam/SPINS_USP

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

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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.