Ice shelves, which are the floating parts surrounding 75% of Antarctica’s coastline, play an important role in Antarctica’s contribution to sea level rise. Ice shelves act as border control: by providing resistance to the ice, they prevent the land ice to enter the ocean and as such slow down sea level rise. When these ice shelves are weakened, for example due to surface/ocean melt or due to weakened shear zones as a result of damage, they become potentially unstable. This can result in ice shelf collapse, causing accelerated glacier flow and increasing sea level contributions.
Yet, assessing how much, how fast Antarctica’s ice shelves are weakened remains a major uncertainty in projections of sea level rise as many of the processes that control ice shelf instability are not well understood or quantified. For example, the weakening of ice shelves due to damage in the shear zones is considered key for the
collapse of Larsen-B ice shelf and the retreat of Pine Island Glacier and Thwaites Glacier, whereas at the same time it has been identified as one of the least understood processes in marine ice sheet dynamics.
The recent availability of frequent, high resolution, multi-source satellite imagery across all Antarctic ice shelves and the advent of big data and machine learning approaches offer now the opportunity to develop approaches to assess and understand the impact of these damage (changes) on ice shelf instability. The objective of this project
is to use remote sensing and big data approaches i) to develop damage indicators across all Antarctic ice shelves and ii) to assess the potential impact of these damage areas on future ice shelf stability. This will allow evaluating the role of damage areas and their impact on ice shelf stability on future sea level rise under different scenarios.
The objective of this project is to use the remote sensing i) to develop damage indicators across all Antarctic ice shelves and ii) to assess the potential impact of these damage areas on future ice shelf stability. Within this framework, we will focus for the first time on the development of techniques that combine the potential of frequent, high resolution, multi-source satellite imagery across all Antarctic ice shelves (e.g. Sentinel-1 & -2, Landsat archive) with the potential of improved image processing, artificial intelligence and machine learning.
The proposed research aims to answer the following overall research questions:
1) What is the present state of mechanical weakening of Antarctic ice shelves and how is it changing over time?
2) How can we use remote sensing to improve the representation of the mechanical weakening due to damage in dynamic ice sheet models over Antarctica?
3) What is the importance of the mechanical weakening due to damage on ice shelf instability?
The results of this project will allow to assess the importance of ice shelf weakening due to damage on the future of Antarctic ice shelves. This will generate important insights into the sensitivity of Antarctica to climate change and climate variability, and on the role of damage on Antarctic ice shelf stability. Moreover, it will strongly support the (coupled) ice sheet/climate model communities as it will enable a more accurate representation of ice shelf processes in their models and reduce the uncertainties due to mechanical weakening in the next generation of these models for projecting sea level rise.