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Episodic dynamic change linked to damage on the Thwaites Glacier Ice Tongue

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.

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Fig. 1: Ice speed anomalies in TG.
Fig. 2: Observed fractures on the TGIT.
Fig. 3: Time series of TGIT flow and fracture.
Fig. 4: Annual maps of damage inferred using the BISICLES ice-sheet model in January from 2016 to 2021.

Data availability

Ice speed data processed for and used in this study are available at http://www.cpom.ucl.ac.uk/csopr/iv/index.php. The BISICLES ice-sheet model is publicly available at https://commons.lbl.gov/display/bisicles/BISICLES. Fracture and calving front data can be found on Zenodo at https://doi.org/10.5281/zenodo.7254130.

Code availability

The authors have made public a GitHub repository (https://github.com/R-Wolfcastle/FractureS1.git) containing a collection of Python and shell scripts that can be used to make fracture maps according to the methods described in this article. Please note that this repository is in development so specific elements such as the network states and architectures are likely to be changeable.

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Acknowledgements

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.

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Authors

Contributions

T.S.-S. and A.E.H designed the work and wrote the manuscript. T.S.-S. processed the ice velocity observations, generated fracture maps and calving front locations and performed the analysis. T.S.-S and S.L.C. performed the modelling, and B.J.D. analysed the ocean data. All authors contributed to scientific discussion, interpretation of the results and the manuscript.

Corresponding author

Correspondence to Trystan Surawy-Stepney.

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Nature Geoscience thanks Jeremy Bassis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor(s): James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Fracture evolution on the TGIT.

Reproduction of Fig. 2, with corresponding SAR backscatter images above each with the MEaSUREs grounding line location shown on all maps (dashed black line) (31). The image acquisition dates from (a) to (f) are, respectively: 29/03/2016, 12/03/2017, 25/03/2018, 26/03/2019, 08/03/2020 and 27/03/2021.

Extended Data Fig. 2 TGIT calving front location through time.

Calving front positions of Fig. 3e, extracted using our neural network, overlayed on the corresponding SAR backscatter images with the 2011 grounding line location shown on all maps (dashed black line) (31).

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Discussion. Section 2: Neural nets–background, training, error calculation. Section 3: Temperature dependence of damage field. Section 4: External forcing mechanisms discussion.

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Surawy-Stepney, T., Hogg, A.E., Cornford, S.L. et al. Episodic dynamic change linked to damage on the Thwaites Glacier Ice Tongue. Nat. Geosci. 16, 37–43 (2023). https://doi.org/10.1038/s41561-022-01097-9

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