Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6827-2025
https://doi.org/10.5194/tc-19-6827-2025
Research article
 | 
17 Dec 2025
Research article |  | 17 Dec 2025

Gravity inversion for sub-ice shelf bathymetry: strengths, limitations, and insights from synthetic modeling

Matthew Davis Tankersley, Huw Horgan, Fabio Caratori Tontini, and Kirsty Tinto

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Cited articles

Adusumilli, S., Fricker, H. A., Medley, B., Padman, L., and Siegfried, M. R.: Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves, Nature Geoscience, 13, 616–620, https://doi.org/10.1038/s41561-020-0616-z, 2020. a
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, Association for Computing Machinery, New York, NY, USA, 2623–2631, ISBN 978-1-4503-6201-6, https://doi.org/10.1145/3292500.3330701, 2019. a, b
Alley, R. B., Anandakrishnan, S., Christianson, K., Horgan, H. J., Muto, A., Parizek, B. R., Pollard, D., and Walker, R. T.: Oceanic Forcing of Ice-Sheet Retreat: West Antarctica and More, Annual Review of Earth and Planetary Sciences, 43, 207–231, https://doi.org/10.1146/annurev-earth-060614-105344, 2015. a
An, L., Rignot, E., Chauche, N., Holland, D. M., Holland, D., Jakobsson, M., Kane, E., Wood, M., Klaucke, I., Morlighem, M., Velicogna, I., Weinrebe, W., and Willis, J. K.: Bathymetry of Southeast Greenland From Oceans Melting Greenland (OMG) Data, Geophysical Research Letters, 46, 11197–11205, https://doi.org/10.1029/2019GL083953, 2019a. a
An, L., Rignot, E., Millan, R., Tinto, K., and Willis, J.: Bathymetry of Northwest Greenland Using “Ocean Melting Greenland” (OMG) High-Resolution Airborne Gravity and Other Data, Remote Sensing, 11, 131, https://doi.org/10.3390/rs11020131, 2019b. a, b
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Short summary
We studied how gravity data can be used to estimate the shape of the seafloor beneath Antarctica’s floating ice shelves, where direct measurements are difficult. Using computer models based on real data, we tested when this method works well and where it has limits. We found that it could greatly improve seafloor maps for most ice shelves with high-quality gravity data. Better models of the seafloor will help us understand how ocean water melts ice from below, affecting future sea level rise.
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