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Cambridge NERC Doctoral Landscape Awards (Training Partnerships)

Graduate Research Opportunities

Lead supervisor: David Al-Attar, Earth Sciences

Co-supervisor: Ali Mashayek, Earth SciencesJerry Mitrovica, Harvard

Brief summary: 
This project will develop and apply novel mathematical techniques to more accurately determine changes within ice sheets and the oceans from available observations.
Importance of the area of research concerned: 
As the climate warms, ice sheets and glaciers are melting, while large-scale ocean circulation is being modified. These changes can be monitored using observational techniques including tide gauges, GPS, ocean and ice altimetry, and satellite gravity. Such observations do not, in general, tell us directly what we wish to know. For example, a tide gauge provides a record of sea level change at its location, but data from many such instruments (along with other observations) must be appropriately combined to estimate global mean sea level change. Going from the available data to the parameters of interest constitutes an inverse problem. Improving the methods for solving such problems will directly contribute to understanding of how the Earth’s climate is changing and will aid in the development of more accurate future projections.
Project summary : 
This project will develop and apply methods for quantitatively monitoring ice sheets and the oceans. Problems of particular interest and importance are (i) how to best estimate the rate of global mean sea level change and determine its relative contributions, (ii) mapping out mass loss in Antarctica and Greenland as a function of space and time, and (iii) monitoring changes in large-scale ocean circulation. While such questions have been addressed in the past, this project will take a new approach built on cutting-edge physical, mathematical and statistical methods. In particular, inverse problems will be rigorously posed and solved within a function-space setting. This avoids issues in past work associated with low-dimensional parametrisation of the physical system, and will lead to more accurate uncertainty estimates that are crucial for those who use the results.
What will the student do?: 
The student will develop and apply techniques from inverse theory and non-parametric statistics to analyse the relevant geophysical data sets. In doing this, they will gain an understanding of relevant aspects of ocean, ice sheet and solid Earth dynamics. The project will involve a substantial component of theoretical and computational work. The data sets involved are large, and the methods will involve the solution of high-dimensional non-linear optimisation problems. As a result, the student will develop expertise in high perform computing and contribute to the development of open source software for the wider scientific community.
References - references should provide further reading about the project: 
Hay, C.C., Morrow, E., Kopp, R.E. and Mitrovica, J.X., 2015. Probabilistic reanalysis of twentieth-century sea-level rise. Nature, 517(7535), pp.481-484.
Al-Attar, D., Syvret, F., Crawford, O., Mitrovica, J.X. and Lloyd, A.J., 2023. Reciprocity and sensitivity kernels for sea level fingerprints. arXiv preprint arXiv:2307.10835.
You can find out about applying for this project on the Department of Earth Sciences page.