Articles | Volume 19, issue 7
https://doi.org/10.5194/tc-19-2583-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-2583-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2025
Research article |
| 18 Jul 2025
| 18 Jul 2025
Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland
Younghyun Koo
Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA 18015, USA
National Snow and Ice Data Center (NSIDC), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80309, USA
Gong Cheng
Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
Mathieu Morlighem
Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
Maryam Rahnemoonfar
CORRESPONDING AUTHOR
Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA 18015, USA
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Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
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Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, https://doi.org/10.5194/gmd-14-2545-2021, 2021
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Jowan M. Barnes, Thiago Dias dos Santos, Daniel Goldberg, G. Hilmar Gudmundsson, Mathieu Morlighem, and Jan De Rydt
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Xiangbin Cui, Hafeez Jeofry, Jamin S. Greenbaum, Jingxue Guo, Lin Li, Laura E. Lindzey, Feras A. Habbal, Wei Wei, Duncan A. Young, Neil Ross, Mathieu Morlighem, Lenneke M. Jong, Jason L. Roberts, Donald D. Blankenship, Sun Bo, and Martin J. Siegert
Earth Syst. Sci. Data, 12, 2765–2774, https://doi.org/10.5194/essd-12-2765-2020, https://doi.org/10.5194/essd-12-2765-2020, 2020
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We present a topographic digital elevation model (DEM) for Princess Elizabeth Land (PEL), East Antarctica. The DEM covers an area of approximately 900 000 km2 and was built from radio-echo sounding data collected in four campaigns since 2015. Previously, to generate the Bedmap2 topographic product, PEL’s bed was characterised from low-resolution satellite gravity data across an otherwise large (>200 km wide) data-free zone.
Eric Larour, Lambert Caron, Mathieu Morlighem, Surendra Adhikari, Thomas Frederikse, Nicole-Jeanne Schlegel, Erik Ivins, Benjamin Hamlington, Robert Kopp, and Sophie Nowicki
Geosci. Model Dev., 13, 4925–4941, https://doi.org/10.5194/gmd-13-4925-2020, https://doi.org/10.5194/gmd-13-4925-2020, 2020
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Martin Rückamp, Angelika Humbert, Thomas Kleiner, Mathieu Morlighem, and Helene Seroussi
Geosci. Model Dev., 13, 4491–4501, https://doi.org/10.5194/gmd-13-4491-2020, https://doi.org/10.5194/gmd-13-4491-2020, 2020
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Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
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In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century.
The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Hélène Seroussi, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, https://doi.org/10.5194/tc-14-3033-2020, 2020
Short summary
Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
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
Bondzio, J. H., Seroussi, H., Morlighem, M., Kleiner, T., Rückamp, M., Humbert, A., and Larour, E. Y.: Modelling calving front dynamics using a level-set method: application to Jakobshavn Isbræ, West Greenland, The Cryosphere, 10, 497–510, https://doi.org/10.5194/tc-10-497-2016, 2016. a
Brinkerhoff, D., Aschwanden, A., and Fahnestock, M.: Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference, J. Glaciol., 67, 385–403, https://doi.org/10.1017/jog.2020.112, 2021. a, b
Cheng, G., Morlighem, M., and Gudmundsson, G. H.: Numerical stabilization methods for level-set-based ice front migration, Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, 2024. a
Choi, Y., Morlighem, M., Rignot, E., and Wood, M.: Ice dynamics will remain a primary driver of Greenland ice sheet mass loss over the next century, Communications Earth and Environment, 2, 26, https://doi.org/10.1038/s43247-021-00092-z, 2021. a, b, c
Choi, Y., Seroussi, H., Morlighem, M., Schlegel, N.-J., and Gardner, A.: Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland, The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, 2023. a
de Juan, J., Elósegui, P., Nettles, M., Larsen, T. B., Davis, J. L., Hamilton, G. S., Stearns, L. A., Andersen, M. L., Ekström, G., Ahlstrøm, A. P., Stenseng, L., Khan, S. A., and Forsberg, R.: Sudden increase in tidal response linked to calving and acceleration at a large Greenland outlet glacier, Geophys. Res. Lett., 37, L12501, https://doi.org/10.1029/2010GL043289, 2010. a
dos Santos, T. D., Morlighem, M., and Seroussi, H.: Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18, Geosci. Model Dev., 14, 2545–2573, https://doi.org/10.5194/gmd-14-2545-2021, 2021. a
Edwards, T., Nowicki, S., Goelzer, H., Seroussi, H., Marzeion, B., Smith, C. E., Jourdain, N. C., Slater, D., McKenna, C., Simon, E., Abe-Ouchi, A., Gregory, J., Hock, R., Larour, E., Lipscomb, W., Payne, A., Shepherd, A., Agosta, C., Alexander, P., Albrecht, T., Anderson, B., Asay-Davis, X., Aschwanden, A., Barthel, A., Calov, R., Chambers, C., Golledge, N., Greve, R., Hatterman, T., Hoffman, M., Humbert, A., Huss, M., Huybrechts, P., Immerzeel, W., Kleiner, T., Kraaijenbrink, P., Le Clec'h, S., Lee, V., Leguy, G., Little, C., Lowry, D., Malles, J.-H., Maussion, F., Morlighem, M., Nias, I., Pattyn, F., Pelle, T., Price, S., Quiquet, A., Radic, V., Reese, R., Rounce, D., Rückamp, M., Sakai, A., Schlegel, N., Trusel, L. D., Van Breedam, J., Van De Wal, R. S. W., van den Broeke, M., Winkelmann, R., Zhao, C., Zhang, T., Zekollari, H., Zwinger, T., Champollion, N., Choi, Y., Cullather, R., Cuzzone, J., Dumas, C., Felikson, D., Fettweis, X., Fujita, S., and Gladstone, R.: Quantifying uncertainties in the land ice contribution to sea level rise this century, Nature, 593, 74–82, https://doi.org/10.1038/s41586-021-03302-y, 2021. a
Everett, A., Murray, T., Selmes, N., Holland, D., and Reeve, D. E.: The impacts of a subglacial discharge plume on calving, submarine melting, and mélange mass loss at Helheim Glacier, South East Greenland, J. Geophys. Res.-Earth, 126, e2020JF005910, https://doi.org/10.1029/2020JF005910, 2021. a
Fu, X., Zhou, F., Peddireddy, D., Kang, Z., Jun, M. B.-G., and Aggarwal, V.: An finite element analysis surrogate model with boundary oriented graph embedding approach for rapid design, Journal of Computational Design and Engineering, 10, 1026–1046, https://doi.org/10.1093/jcde/qwad025, 2023. a, b
Gagliardini, O., Zwinger, T., Gillet-Chaulet, F., Durand, G., Favier, L., de Fleurian, B., Greve, R., Malinen, M., Martín, C., Råback, P., Ruokolainen, J., Sacchettini, M., Schäfer, M., Seddik, H., and Thies, J.: Capabilities and performance of Elmer/Ice, a new-generation ice sheet model, Geosci. Model Dev., 6, 1299–1318, https://doi.org/10.5194/gmd-6-1299-2013, 2013. a
Greene, C. A., Gardner, A. S., Wood, M., and Cuzzone, J. K.: Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022, Nature, 625, 523–528, https://doi.org/10.1038/s41586-023-06863-2, 2024. a, b
Jiang, C. and Chen, N.-Z.: Graph Neural Networks (GNNs) based accelerated numerical simulation, Eng. Appl. Artif. Intel., 123, 106370, https://doi.org/10.1016/j.engappai.2023.106370, 2023. a, b
Jouvet, G.: Inversion of a Stokes glacier flow model emulated by deep learning, J. Glaciol., 69, 13–26, https://doi.org/10.1017/jog.2022.41, 2023. a, b, c
Jouvet, G. and Cordonnier, G.: Ice-flow model emulator based on physics-informed deep learning, J. Glaciol., 69, 1941–1955, https://doi.org/10.1017/jog.2023.73, 2023. a, b, c
Jouvet, G., Picasso, M., Rappaz, J., and Blatter, H.: A new algorithm to simulate the dynamics of a glacier: theory and applications, J. Glaciol., 54, 801–811, https://doi.org/10.3189/002214308787780049, 2008. a
Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.: Deep learning speeds up ice flow modelling by several orders of magnitude, J. Glaciol., 68, 651–664, https://doi.org/10.1017/jog.2021.120, 2022. a, b, c
King, M. D., Howat, I. M., Candela, S. G., Noh, M. J., Jeong, S., Noël, B. P., van den Broeke, M. R., Wouters, B., and Negrete, A.: Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat, Communications Earth and Environment, 1, 1, https://doi.org/10.1038/s43247-020-0001-2, 2020. a, b
Kipf, T. N. and Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1609.02907, 2017. a, b
Kondo, K. and Sugiyama, S.: Calving, ice flow, and thickness of outlet glaciers controlled by land-fast sea ice in Lützow-Holm Bay, East Antarctica, J. Glaciol., 69, 1751–1763, https://doi.org/10.1017/jog.2023.59, 2023. a
Koo, Y.: Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland, Zenodo [code and data set], https://doi.org/10.5281/zenodo.11392220, 2024. a
Koo, Y. and Rahnemoonfar, M.: Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling, J. Glaciol., 71, e15, https://doi.org/10.1017/jog.2024.93, 2025. a, b
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM), J. Geophys. Res.-Earth, 117, F01022, https://doi.org/10.1029/2011JF002140, 2012. a, b
Lippert, E., Morlighem, M., Cheng, G., and Khan, S.: Modeling a century of change: Kangerlussuaq Glacier's mass loss from 1933 to 2021, Geophys. Res. Lett., 51, e2023GL106286, https://doi.org/10.1029/2023GL106286, 2024. a
MacAyeal, D. R.: Large-scale ice flow over a viscous basal sediment: theory and application to ice stream B, Antarctica, J. Geophys. Res.-Sol. Ea., 94, 4071–4087, https://doi.org/10.1029/JB094iB04p04071, 1989. a
Maurizi, M., Gao, C., and Berto, F.: Predicting stress, strain and deformation fields in materials and structures with graph neural networks, Sci. Rep.-UK, 12, 21834, https://doi.org/10.1038/s41598-022-26424-3, 2022. a, b
Melton, S. M., Alley, R. B., Anandakrishnan, S., Parizek, B. R., Shahin, M. G., Stearns, L. A., LeWinter, A. L., and Finnegan, D. C.: Meltwater drainage and iceberg calving observed in high-spatiotemporal resolution at Helheim Glacier, Greenland, J. Glaciol., 68, 812–828, https://doi.org/10.1017/jog.2021.141, 2022. a
Meng, Y., Lai, C.-Y., Culberg, R., Shahin, M. G., Stearns, L. A., Burton, J. C., and Nissanka, K.: Seasonal changes of mélange thickness coincide with Greenland calving dynamics, Nat. Commun., 16, 1–16, https://doi.org/10.1038/s41467-024-55241-7, 2025. a
Miles, V. V., Miles, M. W., and Johannessen, O. M.: Satellite archives reveal abrupt changes in behavior of Helheim Glacier, southeast Greenland, J. Glaciol., 62, 137–146, https://doi.org/10.1017/jog.2016.24, 2016. a
Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., Catania, G., Chauché, N., Dowdeswell, J. A., Dorschel, B., Fenty, I., Hogan, K., Howat, I., Hubbard, A., Jakobsson, M., Jordan, T. M., Kjeldsen, K. K., Millan, R., Mayer, L., Mouginot, J., Noël, B. P. Y., O'Cofaigh, C., Palmer, S., Rysgaard, S., Seroussi, H., Siegert, M. J., Slabon, P., Straneo, F., van den Broeke, M. R., Weinrebe, W., Wood, M., and Zinglersen, K. B.: BedMachine v3: complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation, Geophys. Res. Lett., 44, 11,051–11,061, https://doi.org/10.1002/2017GL074954, 2017. a
Morlighem, M., Rignot, E., Binder, T., Blankenship, D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W. S., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., Broeke, M. R. v. d., Ommen, T. D. v., Wessem, M. v., and Young, D. A.: Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, Nat. Geosci., 13, 132–137, https://doi.org/10.1038/s41561-019-0510-8, 2020. a
Mouginot, J., Rignot, E., Scheuchl, B., and Millan, R.: Comprehensive annual ice sheet velocity mapping using Landsat-8, Sentinel-1, and RADARSAT-2 data, Remote Sens.-Basel, 9, 364, https://doi.org/10.3390/rs9040364, 2017. a, b, c
Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R., Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018, P. Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.1073/pnas.1904242116, 2019. a, b, c, d
O'Neel, S., Echelmeyer, K. A., and Motyka, R. J.: Short-term variations in calving of a tidewater glacier: LeConte Glacier, Alaska, U.S.A., J. Glaciol., 49, 587–598, https://doi.org/10.3189/172756503781830430, 2003. a
Osher, S. and Sethian, J. A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79, 12–49, https://doi.org/10.1016/0021-9991(88)90002-2, 1988. a
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023. a, b
Perera, R., Guzzetti, D., and Agrawal, V.: Graph neural networks for simulating crack coalescence and propagation in brittle materials, Comput. Method. Appl. M., 395, 115021, https://doi.org/10.1016/j.cma.2022.115021, 2022. a, b
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W.: Learning Mesh-Based Simulation with Graph Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.03409, 2021. a
Rignot, E., Xu, Y., Menemenlis, D., Mouginot, J., Scheuchl, B., Li, X., Morlighem, M., Seroussi, H., den Broeke, M. v., Fenty, I., Cai, C., An, L., and de Fleurian, B.: Modeling of ocean-induced ice melt rates of five west Greenland glaciers over the past two decades, Geophys. Res. Lett., 43, 6374–6382, 2016. a
Salehi, Y. and Giannacopoulos, D.: PhysGNN: a physics–driven graph neural network based model for predicting soft tissue deformation in image–guided neurosurgery, arXiv [preprint], https://doi.org/10.48550/arXiv.2109.04352 2022. a, b
Satorras, V. G., Hoogeboom, E., and Welling, M.: E(n) Equivariant Graph Neural Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2102.09844, 2022. a, b, c
Shivaditya, M. V., Alves, J., Bugiotti, F., and Magoulès, F.: Graph Neural Network-based Surrogate Models for Finite Element Analysis, in: 2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Chizhou, China, 14–18 October 2022, IEEE, https://doi.org/10.1109/DCABES57229.2022.00035, 54–57, 2022. a, b
Slater, D. and Straneo, F.: Submarine melting of glaciers in Greenland amplified by atmospheric warming, Nat. Geosci., 15, 794–799, https://doi.org/10.1038/s41561-022-01035-9, 2022. a
Tarasov, L., Dyke, A. S., Neal, R. M., and Peltier, W.: A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling, Earth Planet. Sc. Lett., 315–316, 30–40, https://doi.org/10.1016/j.epsl.2011.09.010, 2012. a, b
Tedesco, M. and Fettweis, X.: Unprecedented atmospheric conditions (1948–2019) drive the 2019 exceptional melting season over the Greenland ice sheet, The Cryosphere, 14, 1209–1223, https://doi.org/10.5194/tc-14-1209-2020, 2020. a
Todd, J. and Christoffersen, P.: Are seasonal calving dynamics forced by buttressing from ice mélange or undercutting by melting? Outcomes from full-Stokes simulations of Store Glacier, West Greenland, The Cryosphere, 8, 2353–2365, https://doi.org/10.5194/tc-8-2353-2014, 2014. a
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y.: Graph Attention Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1710.10903, 2018. a, b, c
Verjans, V. and Robel, A.: Accelerating subglacial hydrology for ice sheet models with deep learning methods, Geophys. Res. Lett., 51, e2023GL105281, https://doi.org/10.1029/2023GL105281, 2024. a
Wehrlé, A., Lüthi, M. P., and Vieli, A.: The control of short-term ice mélange weakening episodes on calving activity at major Greenland outlet glaciers, The Cryosphere, 17, 309–326, https://doi.org/10.5194/tc-17-309-2023, 2023. a, b
Wilner, J. A., Morlighem, M., and Cheng, G.: Evaluation of four calving laws for Antarctic ice shelves, The Cryosphere, 17, 4889–4901, https://doi.org/10.5194/tc-17-4889-2023, 2023. a, b, c
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726, https://doi.org/10.5194/tc-5-715-2011, 2011. a
Wood, M., Rignot, E., Fenty, I., An, L., Bjørk, A., Broeke, M. v. d., Cai, C., Kane, E., Menemenlis, D., Millan, R., Morlighem, M., Mouginot, J., Noël, B., Scheuchl, B., Velicogna, I., Willis, J. K., and Zhang, H.: Ocean forcing drives glacier retreat in Greenland, Science Advances, 7, eaba7282, https://doi.org/10.1126/sciadv.aba7282, 2021. a, b
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S.: A comprehensive survey on graph neural networks, IEEE T. Neur. Net. Lear., 32, 4–24, https://doi.org/10.1109/TNNLS.2020.2978386, 2021. a
Xie, S., Dixon, T. H., Holland, D. M., Voytenko, D., and Vaňková, I.: Rapid iceberg calving following removal of tightly packed pro-glacial mélange, Nat. Commun., 10, 3250, https://doi.org/10.1038/s41467-019-10908-4, 2019. a, b
Zhang, S., Tong, H., Xu, J., and Maciejewski, R.: Graph convolutional networks: a comprehensive review, Comput. Soc. Netw., 6, 1–23, https://doi.org/10.1186/s40649-019-0069-y, 2019. a, b
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.
Calving, the breaking of ice bodies from the terminus of a glacier, plays an important role in...
