The Greenland and Antarctic Ice Sheets are responsible for about a third of global sea level rise as a result of increased melting and changes to ice flow. Supraglacial lakes (SGLs) are becoming more abundant as a result of increased melting, and can act to speed up ice flow when they drain (Leeson et al., 2015). SGLs may be a potential feedback in the ice sheet system, since faster ice flow leads to thinning, which leads to more melting and SGL formation. There are tens of thousands of SGLs on Greenland alone, and so understanding their evolution is important, especially with respect to their drainage behavior. Until recently it was thought that SGL drainage occurs only during the summer months, however we now have compelling evidence for a small number of winter time drainage events (Benedeck et al., 2021). Winter lake drainage is likely to have a larger impact on ice flow than summer lake drainage because it is more slippery under the ice in winter. SGLs are typically studied at scale using optical satellite imagery (e.g. Corr et al., 2021), however optical satellites are unable to image the ice sheet surface in winter due to the darkness of the polar night. Recent advances in machine learning and data science however mean that it is now possible to ‘see in the dark’ and map supraglacial lakes during winter using Synthetic Aperture Radar (SAR) data. This PhD will exploit these advances to perform the first wide scale inventory of supraglacial lakes on Greenland and Antarctica during winter, and will use this inventory to understand the scale of wintertime lake drainage and examine its impact on ice flow.
Postgraduate degree in a natural or data science preferred (e.g. Environmental Science, Physics, Computer Science etc).
Interested candidates are recommended to make an informal enquiry to Amber Leeson at email@example.com.