Stability of marine basins in the Antarctic Peninsula Ice Sheet from remote sensing

This project will assess the current and future stability of marine ice-sheet basins that collectively underlie Ellsworth Land, the region that connects the West Antarctic and Antarctic Peninsula Ice Sheets. It is well known that marine ice-sheets basins, defined as the parts of the ice sheets that are grounded below sea level, are particularly vulnerable to retreat and therefore have significant potential to contribute to future global sea-level rise (DeConto and Pollard, 2016; Mercer, 1978), and these processes are much studied in West Antarctica's Amundsen Sea Embayment (Bingham et al., 2017; Scambos et al., 2017). Despite some studies over the last decade also drawing attention to West Antarctica's Ellsworth Land also being vulnerable to marine ice-sheet instability (Bingham et al., 2012) and currently showing signs of this being underway (Hogg et al., 2017; Wouters et al., 2015), the region remains relatively data-poor for good estimates of its future retreat rates and sea-level contributions. Addressing this fundamental knowledge gap and elucidating the dangers to near-future sea-level rise posed by potential retreat of ice from Ellsworth Land form the focus of this project.

Aim, objectives and methods of the proposed research

The main aim of the project is to assess the near-term (200 years into the future) stability of the Ellsworth Land marine basin lying between West Antarctica and the Antarctic Peninsula. This comprises the following objectives:

1. To compile for Ellsworth Land time series of contemporary glaciological change (changes to ice-surface velocity, surface elevation and the grounding line) from Earth Observation datasets;

2. To track englacial horizons (treated as isochrones, or surfaces of equal age) across Ellsworth Land, and thereby place age constraints on its deep ice, using age constraints from adjacent layer-tracking and links to West Antarctica's ice core age-depth profiles - with the ultimate aim of constraining what is the oldest ice in the region, and hence give clues to its past stability;

3. To explore the application of machine-learning methods for tracking englacial layers in geophysical data and/or compiling time series of contemporary changes from Earth Observation data.