Understanding uncertainties in the measurement and simulation of Antarctic ice sheet dynamics
Background
The Antarctic Ice Sheet is the largest reservoir of freshwater on Earth. If it were to melt entirely, global sea levels would rise by an average of 58 metres. Over recent decades, it has become clear that the ice sheet is losing mass, and that this ice loss is driven by changes to the speed at which ice flows towards the sea – a process referred to as ice dynamics. Due to the complicated physical processes involved, the largest component of our uncertainty in future sea level rise (SLR) comes from the future dynamics of the Antarctic Ice Sheet.
The main methods by which we measure ice flow use satellite images to track the movement of features across the ice sheet surface. However, despite the importance and ubiquity of these methods, a proper quantification of uncertainties in the data they produce remains elusive. This leads, via the data-assimilation methods that make use of ice velocity data, to large but unquantified uncertainties in projections of SLR.
PhD opportunity
This project will tackle the problem of quantifying uncertainty in offset tracking procedures, using synthetic aperture radar (SAR) data from the European Space Agency’s Sentinel-1 satellites, and translating these into the first estimates of parametric uncertainty in continent-wide Antarctic mass loss. You will investigate existing and novel, machine-learning based methods of offset tracking, and dive into technical work around the processing of Sentinel-1 data, modifying established processing chains to operationalise your methods. You will use recent advancements in data assimilation, making use of the automatic differentiation and Bayesian parameter estimation capabilities of recently developed numerical frameworks, to estimate distributions of ice loss in Antarctica given uncertainties in ice speed measurement. There will be significant scope to take the project in a number of directions, for example focussing on observational or numerical techniques, investigating how the introduction of additional data changes uncertainties, or diving into the poorly understood physics of certain ice dynamic processes.
During your PhD you will be part of the world-leading Institute for Climate and Atmospheric Science, based in the School of Earth and Environment at the University of Leeds. Through supervision by Dr Surawy-Stepney (University of Leeds) you will learn to process Sentinel-1 data, model glacier flow and use machine learning to enhance these activities. Co-supervision by Dr Goldberg (University of Edinburgh) will provide expertise in finite-element frameworks for data assimilation and uncertainty quantification. Through co-supervision by Professor Hooper (University of Leeds) you will learn the fundamentals of satellite radar imagining and co-supervision by Professor Hogg (University of Leeds) will provide expertise in ice velocity as well as access to a decade of observational data across the Antarctic Ice Sheet.
Other information
Enthusiasm and aptitude for mathematics is essential. An understanding of programming is highly valued, as coding will be central to the project, but is not essential, as is any previous experience geographical/climate/environmental science.
