The Antarctic ice sheet is ringed with floating extensions known as ice shelves, which dam the flow of grounded ice into the ocean. Their removal can cause acceleration of ice flow and subsequent sea level rise. For example, in the decade following the collapse of the Larsen B Ice Shelf in 2002, acceleration of its tributary glaciers led to 9 Gtyr-1 of ice loss – one-third of ice loss from the entire Antarctic Peninsula during that period. The Larsen C ice shelf (LCIS) is an order of magnitude larger than the area lost from Larsen B and concerns have been raised about its long-term viability, in the context of increased episodes of extreme melt events associated with atmospheric rivers.
Firn is a porous material (like a sponge) formed as snow compacts into ice. It is a key component of the ice shelf system and it is thought that for ice shelf collapse to occur, its firn layer needs to become saturated with melt water. Whilst the firn layer on LCIS remains unsaturated at present, it is not clear when saturation (and potential ice shelf collapse) will occur. This PhD project will combine a novel set of field observations with a physically based model to predict the saturation of the ice shelf firn layer and provide valuable new insight into its likelihood of collapse over the next century. The project will be supervised by Dr Katie Miles, Dr Amber Leeson and Prof Mal McMillan at Lancaster University and by Dr Vincent Verjans at the University of Busan in South Korea. Training will be given in glaciology, data handling and process-based modelling. The student will be based at Lancaster but will have the opportunity to spend a three-month research visit at the University of Busan during their PhD.
Candidates shall be good honours graduates in appropriate subject areas, of a recognised university or comparable university, or persons holding equivalent qualifications who show evidence of exceptional ability, or who have demonstrated their ability in graduate studies.
Students should have an interest in Glaciology, especially of the ice sheets. Coding experience (ideally in Python), or at least a willingness to learn, would be an advantage.
Email address for enquiries.