Abstract

Turbulent mixing in the stably stratified ocean interior is of fundamental importance for the vertical transport of heat, carbon, nutrients, and other properties that influence global energy budgets and biological activity. Motivated by the uncertainties associated with existing mixing parameterisations that are used widely in oceanographic practice, this talk will look at two key issues in the field of stratified turbulence. Firstly, how is mixing modified by the environment in which flow instabilities leading to turbulence first develop, and in what way does the history of the turbulent flow matter to its subsequent evolution? Secondly, how can we use data-driven tools such as machine learning to better understand the structure and evolution of stratified turbulent flows, and what, if any, physical insight can be gained from such `black box’ models? We investigate these questions using data from a variety of direct numerical simulations designed to reproduce small-scale mixing processes in an ocean environment.