EGU22-7594, updated on 24 Oct 2023
https://doi.org/10.5194/egusphere-egu22-7594
EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

What can high resolution ice surface observations tell us about the bed topography of Pine Island Glacier, West Antarctica?

Helen Ockenden, Rob Bingham, Andrew Curtis, Daniel Goldberg, and Antonios Giannopoulos
Helen Ockenden et al.
  • University of Edinburgh, Edinburgh, United Kingdom

The West Antarctic Ice Sheet has the potential to contribute up to 3m of sea level rise over the next few centuries. There is considerable uncertainty over the rate at which ice loss will occur, caused in part by a lack of knowledge about the bed topography beneath the ice sheet, which influences ice flow and retreat. Since direct bed topography observations are often further apart than ice sheet models require, we explore instead what we can learn about bed topography from high resolution observations of the ice surface, which are openly available. We apply an inversion methodology based on linear perturbation theory and developed by Gudmundsson (2003, 2008) to ice surface data from Pine Island Glacier, and present the bed topography results. Comparison to high-resolution radar sounding of the bed topography of Pine Island Glacier from the iSTAR 2013-14 ground surveys allows us to assess the success of the inversion methodology. We identify regions of the glacier where the landforms we see are likely to be artefacts, and regions where unknown and interesting landforms are likely to exist. This methodology has the potential to be extremely useful in regions where direct observations of bed topography are sparse, and for identifying areas where more observations would be of particularly high benefit.   

How to cite: Ockenden, H., Bingham, R., Curtis, A., Goldberg, D., and Giannopoulos, A.: What can high resolution ice surface observations tell us about the bed topography of Pine Island Glacier, West Antarctica?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7594, https://doi.org/10.5194/egusphere-egu22-7594, 2022.