Lead supervisor: Colm-cille Caulfield, DAMTP and IEEF
Co-supervisor: Ali Mashayek, Earth Sciences
Brief summary:
This project will use physically-informed data-driven methods to construct improved parameterizations for the transport of heat in the world's oceans, a key, and still poorly-understood, component of the changing global climate system.
Importance of the area of research concerned:
Evidence is accumulating that deep ocean turbulence exerts a leading order control on the global climate system through regulating the oceanic uptake and redistribution of heat, carbon, nutrients and other tracers. Recently, major international field programmes have shed light on deep ocean turbulence through state-of-the-art observations of turbulence generated by deep ocean waves that can have amplitudes measured in the tens to hundreds of metres. Computational resources are just now also becoming available to allow us to simulate such flows at adequately high resolution with appropriate parameters, in particular the correct Prandtl number (ie the ratio between the fluid's kinematic viscosity and thermal diffusivity). Furthermore, there has been an explosion of innovation in various data-driven methods. An exciting opportunity is emerging to use simulations and observations in tandem to understand the physics of such turbulence, and thus to construct physically-informed yet data-driven parameterizations of such turbulence in climate models, which inevitably have too coarse a resolution to describe the intricate and fascinating dynamics directly.
Project summary :
This project will advance our understanding of deep ocean turbulent mixing through an iterative combination of observational data analysis, high resolution numerical simulations and physics-informed data-driven methods. Improving the parameterisation of such turbulence is a key challenge for larger-scale predictive climate modelling, and is central to understanding transport of heat, carbon and nutrients in the oceans. The project will involve close collaborations with various observational and numerical research groups both in the UK and the US, with whom the supervisors have long-standing research collaborations.
What will the student do?:
The student will interact with oceanographers to understand and correctly interpret recent ocean turbulence observations, and with numerical scientists to understand and correctly analyse large-scale numerical simulations of stratified turbulent mixing processes. Armed with this knowledge informed directly by observations and simulations, the student will then construct a range of data-driven models to describe the underlying physics, utilising the various datasets to which they have access to "train" and "test" their models, which will always be directly constrained by physical insights and principles. The student will then connect these models to the "big picture" ocean circulation and climate system by interacting with centres of research excellence (both in the UK and the US) that focus on large scale ocean modelling. The core objective of the student will be not only to enhance our theoretical understanding of ocean turbulence but to transfer that understanding to climate models, through a highly collaborative and multi-disciplinary Ph.D. project.
References - references should provide further reading about the project:
Mashayek, A., Salehipour, H., Bouffard, D., Caulfield, C. P., Ferrari, R., Nikurashin, M., Peltier & W. R., Smyth, W. D. 2017.
Efficiency of turbulent mixing in the abyssal ocean circulation.
Geophysical Research Letters, vol. 44, pp. 6296-6306, DOI: 10.1002/2016GL072452
Mashayek, A., Reynard, N., Zhai, F. M., Srinivasan, K., Jelley, A., Naveira Garabato, A., Caulfield, C. P. 2022
Deep Ocean Learning of Small Scale Turbulence.
Geophysical Research Letters, vol. 49. e2022GL098039, DOI: 10.1029/2022GL098039
Couchman, M. M. P., De Bruyn Kops, S., Caulfield, C. P. 2023
Mixing across stable density interfaces in forced stratified turbulence.
Journal of Fluid Mechanics, vol 961, A20, DOI:10.1017/JFM.2023.253
Applying
You can find out about applying for this project on the Department of Applied Mathematics and Theoretical Physics (DAMTP) page.