Diagnosing multiscale entrainment in density-driven flows in the ocean

It is well-known in ocean modelling that small scale mixing processes in density-driven flows that take place in the ocean are crucial for the global ocean circulation (and hence climate), but the scale of mixing is much smaller than one grid cell in global ocean circulation models (where the number of cells is constrained by the limits of computer power). These processes include the rapid sinking of dense (cold/salty) water due to storms and ice formation at the ocean surface (such as takes place in the Labrador Sea), and the flow of dense water over steep slopes in the ocean floor. The solution to this is to apply physical knowledge of these processes so that their effects can be prescribed in the global models (this is called parameterisation). A key quantity which must be accurately predicted is the entrainment into these flows. If one considers hot air rising from a factor chimney, then it is possible to predict how high and how fast the air will rise based on the difference between the density of the hot air and the lower density of the surroundings. As the air rises, the flow becomes turbulent, and air from the surroundings is mixing into the hot air, changing the density: this is called entrainment. In the case of many small scale mixing processes in the ocean, it is hard to predict the entrainment (because the effect of the rotation of the Earth is important, and the dynamics is rather complicated). The aim of this project is to build up a complete picture of small scale mixing in these density-driven flows by studying data from idealised models of these flows. We will use data obtained from the Imperial College Ocean Model (ICOM), which has a dynamically changing grid that is very suitable for studying these problems. The key concept will be Lagrangian particle trajectories: the paths that fluid particles take as they move with the flow. Following these paths reveals how water from the surroundings is mixed into the flow. The first part of the project will be to build a computational tool for computing Lagrangian particle trajectories on large datasets obtained from ocean models. The main challenge is to produce a code which can be run on a computer with many processors, although the basic strategy for this is well-developed and so one can make use of existing software to take care of the parallel aspects. The second part of the project is to apply a range of techniques to studying the particle trajectories to identify the key mixing processes in the density-driven flows. A complete picture of the mixing will be produced which can then be used to develop parameterisations for use in global ocean models.