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Damage development on Antarctic ice shelves sensitive to climate warming

Nature Climate Change volume 15, pages 1333–1339 (2025)Cite this article

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Abstract

Damage features, such as rifts and crevasses, are the first signs of a weakened ice shelf and the precursor for retreat. Yet, damage changes are not widely quantified on Antarctic ice shelves, leaving future ice shelf weakening poorly understood. Here we use satellite imagery to detect both long-term (24-year) and short-term (annual, 2015–2021) Antarctic-wide damage changes, revealing a multiyear damage development cycle strongly correlated to ice shelf area changes, and a net decline in damaged area from 1997 to 2021. We establish a data-driven link between damage and ice flow characteristics, which shows that ice flow acceleration, strain rate increases and thinning lead to more damage development, in particular under high-emission climate scenarios. This sensitivity to warming suggests that without quantification of damage impacts by detailed physical models the (timing of) ice shelf retreat and Antarctic mass loss may currently be underestimated.

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Fig. 1: Long-term damage change between 1997 and 2021.
Fig. 2: Relationship between damaged area changes and ice shelf area changes (advance/retreat).
Fig. 3: Damage impact on ice shelf weakening and retreat.
Fig. 4: Projections of damage change (%) by 2100.

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Data availability

The RAMP mosaic is available at the Alaska Satellite Facility via https://asf.alaska.edu/data-sets/derived-data-sets/ramp/ramp-get-ramp-data/. Sentinel-1 orbits are publicly available, and were accessed through the Google Earth Engine (GEE), for which the code is included in the repository accompanying this manuscript at https://github.com/mizeboud/antarctic-damage-change (ref. 36). ITS_LIVE velocity composites40 are available through the data portal at https://nsidc.org/apps/itslive/, the REMA mosaic39 was accessed as GEE asset ‘UMN/PGC/REMA/V1_1/8m’. Ice sheet model output data of models participating in the ISMIP-6 project are available through a Globus endpoint49; instructions and information are available at GHub https://theghub.org/groups/ismip6/wiki. The produced annual damage maps can be viewed in GEE via https://code.earthengine.google.com/1984c17a29c3720e7ad3327a79a81527, or downloaded as NetCDF/GeoTiff format at the 4TU research data repository via https://doi.org/10.4121/70f914ee-b20d-4682-b2ec-54eddcc8569d (ref. 50). Annual ice-front positions were obtained from https://github.com/chadagreene/ice-shelf-geometry (ref. 31), and intersected with the MEaSUREs grounding line32, available at the National Snow and Ice Data Center (NSIDC) via https://nsidc.org/data/nsidc-0709/versions/2. The adjusted annual ice shelf polygons, and other supplementary data such as a list of all processed orbits of Sentinel-1, supporting shapefiles and NetCDF files of projected damage values, are available at the 4TU research data repository via https://doi.org/10.4121/911e8799-f0dc-42e3-82b4-766ad680a71e (ref. 51).

Code availability

The NeRD method is available at https://doi.org/10.5281/zenodo.16759609 (ref. 52). The code used in this work to pre- and postprocess data, and to develop and apply the random forest model, is available at Zenodo via https://doi.org/10.5281/zenodo.16759664 (ref. 36).

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Acknowledgements

M.I. and S.R.H. were funded by the Dutch Research Council (NWO) under grant numbers ALWGO.2018.043 and OCENW.GROOT.2019.091, respectively.

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Authors and Affiliations

  1. Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands

    Maaike Izeboud, Bert Wouters, Sophie de Roda Husman & Stef Lhermitte

  2. Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium

    Maaike Izeboud

  3. Department of Earth & Environmental Sciences, KU Leuven, Leuven, Belgium

    Stef Lhermitte

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Contributions

M.I. and S.L. conceived and designed the study. M.I. carried out the data processing, applied the detection method, developed the RF model and performed the analysis. S.R.H. provided guidance on the development of the random forest model and B.W. contributed to ice sheet model data analysis. All authors actively participated in scientific discussions, and contributed to the analysis and interpretation of the results. M.I. wrote the manuscript with contributions from all coauthors.

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Correspondence to Maaike Izeboud.

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Extended data

Extended Data Table 1 Overview of features used in the Random Forest regression model
Full size table

Extended Data Fig. 1 Overview of used datasets to produce damage assessments.

(a) RAMP mosaic from 199734, processed with NeRD only for data within the red tiles that cover all ice shelves. (b) Data coverage of Sentinel-1 EW ascending orbit images in one assessment year (2021), showing data gaps on the Filchner–Ronne, Ross and Amery Ice Shelves. (c) Example of annual calving front positions at the Pine Island Glacier, adapted from Greene et al.31 with manual corrections to fit the used Sentinel-1 images. Grounding line from32 in black. Grounding line shapefiles in a–c from ref. 32 under a Creative Commons licence CC BY. Ice-front shapefile in c from ref. 31 under a Creative Commons license CC BY.

Extended Data Fig. 2 Annual damage assessments obtained from NeRD algorithm.

Panels a, b show damage maps for 1997, 2000 respectively and c-i show every year from 2015 to 2021. No-data areas are shown in grey; red tiles in the 1997 and 2000 RAMP mosaic were excluded to match Sentinel-1 Coverage (2015–2021). The pie charts show aggregated sector values by pixel count: no-data (light grey), no-damage (light blue, \(\hat{D}=0\)) and damaged (purple, \(\hat{D} > 0\)). Percentages are provided for the no-damage and damaged class. The total ice shelf area varies per year based on annual calving front positions. Grounded ice32 is visualized with Antarctic DEM33. Basemaps in a–i from ref. 33 under a Creative Commons licence CC BY. Grounding line shapefiles in a–i from ref. 32 under a Creative Commons licence CC BY.

Extended Data Fig. 3 Damage maps of ice shelves in Amundsen Sea Embayment for assessed years.

Detected damage by NeRD on RAMP AMM and MAMM mosaics (1997, 2000) at 1000 m spatial resolution, and Sentinel-1 SAR imagery obtained in September-Oktober-November each year between 2015–2021 at 400 m resolution. Data is clipped to annual ice shelf front positions and static grounding line31,32; background is shaded Antarctic DEM33. Uniform grey areas indicate no-data coverage for that year. Basemaps in a–i from ref. 33 under a Creative Commons licence CC BY. Grounding line shapefiles in a–i from ref. 32 under a Creative Commons licence CC BY. Ice-front shapefiles in a–i from ref. 31 under a Creative Commons license CC BY.

Extended Data Fig. 4 Random Forest input feature information.

Box-plot of input features used for training the RF model on (a) pan-Antarctic scale and (b) in the Amundsen Sea Embayments. showing interquantile range. Damage signal values are binned by low to high signal values, to favour visualization of the minority (high damage signal) class (low: \(\hat{D}\in\) (0,0.0125], medium: \(\hat{D}\in\) (0.0125,0.0625], high: \(\hat{D}\in\) (0.0625,0.1625] and very high: \(\hat{D}\in\) (0.1625,0.5]). Boxes show inter-quartile range (IQR, Q1-Q3) and whiskers extend from the box to the farthest data point lying within 1.5x the IQR.

Extended Data Fig. 5 Evaluation of the Random Forest regression model.

Performance for training (a1-c1) and testing dataset, split in the spatial test set (a2-c2) and temporal test set (a3-c3). a1-a3 show the Structural Similarity Index calculated for each ice shelf, b1-b3 the Mean Absolute Error calculated for each ice shelf, and c1-c3 the regression of observed versus predicted values, weighted for the area of each ice shelf.

Extended Data Fig. 6 Random Forest prediction related to input parameters.

Panels for (a) maximum principal strain rate, (b) effective strain rate, (c) shear strain rate, (d) longitudinal strain rate, (e) transverse strain rate, (f) velocity, (g) principal strain rate change, (h) velocity change, (i) surface elevation. Each panel shows damage predictions made with the RF model, varying one parameter at a time from their 5 to 95 percentile value within the training dataset. All other parameters were set to their median value within the training dataset, adding small random Gaussian noise perturbations (< 5% of respective standard deviation). Each correlation plot is made with 750 samples (grey dots). A moving average of 50 samples is plotted in blue. Shaded in pink are the data distributions of each parameter within the training dataset and their median in dashed grey line.

Extended Data Fig. 7 Model ensembles of predicted damage change between 2015-2100.

Shown for each climate forcing experiment, experiment 05 (a), 06 (b), 07 (c) and experiment 08 (d). Each panel shows predicted values of damage change (%) with respect to the control simulation of each model, with the ensemble mean in black, smoothed with a 5-year running mean.

Extended Data Fig. 8 Spatial plots of predicted damage change (%) at year 2100.

Damage change is calculated with respect to the control simulation, shown for each climate forcing experiment and ice sheet model. Grounding line added in black32. Grounding line shapefiles from ref. 32 under a Creative Commons licence CC BY.

Extended Data Fig. 9 Spatial plots of projected change of ice parameters at year 2100.

Panels show the parameters used as input to generate damage prediction, obtained from ISMIP-6 model data. Shown are model ensemble means of absolute change values of each parameter for the selected experiment with respect to the control simulation. Grounding line added in black32. Grounding line shapefiles from ref. 32 under a Creative Commons licence CC BY.

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Izeboud, M., Wouters, B., de Roda Husman, S. et al. Damage development on Antarctic ice shelves sensitive to climate warming. Nat. Clim. Chang. 15, 1333–1339 (2025). https://doi.org/10.1038/s41558-025-02453-4

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