Articles | Volume 19, issue 7
https://doi.org/10.5194/tc-19-2583-2025
https://doi.org/10.5194/tc-19-2583-2025
Research article
 | 
18 Jul 2025
Research article |  | 18 Jul 2025

Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland

Younghyun Koo, Gong Cheng, Mathieu Morlighem, and Maryam Rahnemoonfar

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Cited articles

Aschwanden, A., Fahnestock, M. A., Truffer, M., Brinkerhoff, D. J., Hock, R., Khroulev, C., Mottram, R., and Khan, S. A.: Contribution of the Greenland Ice Sheet to sea level over the next millennium, Science Advances, 5, eaav9396, https://doi.org/10.1126/sciadv.aav9396, 2019. a
Bassis, J. N. and Jacobs, S.: Diverse calving patterns linked to glacier geometry, Nat. Geosci., 6, 833–836, https://doi.org/10.1038/ngeo1887, 2013. a
Bevan, S. L., Luckman, A., Khan, S. A., and Murray, T.: Seasonal dynamic thinning at Helheim Glacier, Earth Planet. Sc. Lett., 415, 47–53, https://doi.org/10.1016/j.epsl.2015.01.031, 2015. a
Black, N. and Najafi, A. R.: Learning finite element convergence with the multi-fidelity graph neural network, Comput. Method. Appl. M., 397, 115120, https://doi.org/10.1016/j.cma.2022.115120, 2022. a, b
Bondzio, J., Morlighem, M., Seroussi, H., Kleiner, T., Ruckamp, M., Mouginot, J., Moon, T., Larour, E., and Humbert, A.: The mechanisms behind Jakobshavn Isbræ's acceleration and mass loss: a 3-D thermomechanical model study, Geophys. Res. Lett., 44, 6252–6260, https://doi.org/10.1002/2017GL073309, 2017. a
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Short summary
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in the mass losses of Greenland ice sheets. However, calving parameters have been poorly understood because of the intensive computational demands of traditional numerical models. To address this issue and find the optimal calving parameter that best represents real observations, we develop deep-learning emulators based on graph neural network architectures.
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