Abstract
The stability and dynamics of Thwaites Glacier depend on the structural properties of its marine terminus; however, the relationship between these variables on the floating ice tongue is poorly understood. Here we present a six-year record of ice speed, derived from satellite observations starting in 2015, showing two large-magnitude (approximately 30–45%) and prolonged (approximately one to two years) cycles of speed variation across the ice tongue. Using an automated, deep learning-based method of extracting high-resolution fracture maps from satellite imagery, we detail periods of increasing fracture development and subsequent reconsolidation in the ice tongue shear margin that coincide with the observed speed changes. Inverse modelling using the BISICLES ice-sheet model indicates that the variation in ice speed can be accounted for by these observed changes to the spatial pattern of fracturing. This study provides further evidence of direct coupling between fracturing and dynamic variability in West Antarctica but indicates that increased fracturing and associated speed changes are reversible on one- to two-year timescales. We suggest that fracturing does not necessarily lead to positive feedback with glacier acceleration on these timescales and that damage process modelling is important for accurately predicting the evolution of the Antarctic Ice Sheet.
| Original language | English |
|---|---|
| Pages (from-to) | 37-43 |
| Number of pages | 7 |
| Journal | Nature Geoscience |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2023 |
Bibliographical note
Funding Information:This work was led by the School of Earth and Environment at the University of Leeds. We thank D. Hogg for valuable advice regarding the deep learning methods employed in this study and R. Rigby for advice on high-performance computing. Much of this work was undertaken on ARC4, part of the High-Performance Computing facilities at the University of Leeds. We gratefully acknowledge the European Space Agency and the European Commission for the acquisition of Sentinel-1 data. A.E.H. and B.J.D. were supported by the Natural Environment Research Council (NERC) DeCAdeS project (NE/T012757/1) and ESA Polar+ Ice Shelves project (ESA-IPLPOE-EF-cb-LE-2019-834). S.L.C. was supported by the European Union’s Horizon 2020 research and innovation programme, grant agreement number 869304, PROTECT.
Funding Information:
This work was led by the School of Earth and Environment at the University of Leeds. We thank D. Hogg for valuable advice regarding the deep learning methods employed in this study and R. Rigby for advice on high-performance computing. Much of this work was undertaken on ARC4, part of the High-Performance Computing facilities at the University of Leeds. We gratefully acknowledge the European Space Agency and the European Commission for the acquisition of Sentinel-1 data. A.E.H. and B.J.D. were supported by the Natural Environment Research Council (NERC) DeCAdeS project (NE/T012757/1) and ESA Polar+ Ice Shelves project (ESA-IPLPOE-EF-cb-LE-2019-834). S.L.C. was supported by the European Union’s Horizon 2020 research and innovation programme, grant agreement number 869304, PROTECT.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature Limited.
