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Cambridge NERC Doctoral Training Partnerships

Graduate Research Opportunities

Lead Supervisor: Colm-cille P. Caulfield, Applied Mathematics and Theoretical Physics

Co-Supervisor: Ali Mashayek, Imperial College

Brief summary: 
This project will advance our understanding of deep ocean turbulent mixing mediated by wave-breaking (a key area of uncertainty for the climate) through an iterative combination of observational data analysis, high resolution numerical simulations, physical modelling and machine learning.
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. Observations of such turbulence, and our ability to model it numerically, have both been limited historically. However, 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 now also available to allow flow simulation at adequately high resolution. An exciting opportunity is emerging to use simulations and observations in tandem to understand the physics of such turbulence, and thus to represent the key effects of this wave-induced turbulence in climate models, which inevitably have too coarse a resolution to describe the intricate and fascinating wave-driven dynamics directly. Theoretical advances in "artificial intelligence" also promise a way to "mine" efficiently the vast amount of accumulated data to machine-learn the "best" way to model ocean mixing.
Project summary : 
This project will advance our understanding of deep ocean turbulence mediated by wave-breaking through an iterative combination of observational data analysis, high resolution numerical simulations and mathematical modelling. 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 other research groups with whom the co-supervisors have long-standing collaborations, including groups based at Scripps Institution of Oceanography (UC San Diego), Woods Hole Oceanographic Institution (Massachusetts), the Met Office, and the National Oceanographic Centre, Southampton.
What will the student do?: 
The student will interact with oceanographers to understand and interpret recent ocean turbulence observations. Armed with this knowledge informed directly by observations, the student will then construct mathematical theoretical models to describe the underlying physics. The student will simulate idealisations of the observed flows (which nevertheless capture the key physics) to gain detailed understanding of deep ocean turbulence driven by wave breaking. The student will then connect this understanding 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. A key challenge is to manipulate, analyse and extract useful information from enormous datasets (both from numerical simulation and observation). To address this challenge, the student will apply various recently developed automated "artificial intelligence" techniques. 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
Cimoli, L., Caulfield, C.P., Johnson, H.L., Marshall, D.P., Mashayek, A., Naveira Garabato, A.C. & Vic, C. 2019. Sensitivity of deep ocean mixing to local internal tide breaking and mixing efficiency. Geophysical Research Letters, vol. 46, pp. 14622-14633, DOI: 10.1029/2019GL085056
Howland, C. J., Taylor, J. R., & Caulfield, C. P. (2021). Shear-induced breaking of internal gravity waves. Journal of Fluid Mechanics, 921, A24. DOI:10.1017/jfm.2021.506
You can find out about applying for this project on the Department of Applied Mathematics and Theoretical Physics (DAMTP) page.
Prof Colm-cille Caulfield
Department of Applied Mathematics and Theoretical Physics PhD Admissions