Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6671-2023
https://doi.org/10.5194/gmd-16-6671-2023
Development and technical paper
 | Highlight paper
 | 
16 Nov 2023
Development and technical paper | Highlight paper |  | 16 Nov 2023

Universal differential equations for glacier ice flow modelling

Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez

Related authors

Decadal re-forecasts of glacier climatic mass balance
Larissa Nora van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
The Cryosphere, 19, 3879–3896, https://doi.org/10.5194/tc-19-3879-2025,https://doi.org/10.5194/tc-19-3879-2025, 2025
Short summary
Assessing spatiotemporal variability in melt–refreeze patterns in firn over Greenland with CryoSat-2
Weiran Li, Stef Lhermitte, Bert Wouters, Cornelis Slobbe, Max Brils, and Xavier Fettweis
The Cryosphere, 19, 3419–3442, https://doi.org/10.5194/tc-19-3419-2025,https://doi.org/10.5194/tc-19-3419-2025, 2025
Short summary
The Open Global Glacier Data Assimilation Framework (AGILE) v0.1
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor
EGUsphere, https://doi.org/10.5194/egusphere-2025-3401,https://doi.org/10.5194/egusphere-2025-3401, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Brief communication: Estimating Antarctic surface melt rates using passive microwave data calibrated with weather station observations
Valeria Di Biase, Peter Kuipers Munneke, Bert Wouters, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2900,https://doi.org/10.5194/egusphere-2025-2900, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Global mapping of lake-terminating glaciers
Jakob Steiner, William Armstrong, Will Kochtitzky, Robert McNabb, Rodrigo Aguayo, Tobias Bolch, Fabien Maussion, Vibhor Agarwal, Iestyn Barr, Nathaniel R. Baurley, Mike Cloutier, Katelyn DeWater, Frank Donachie, Yoann Drocourt, Siddhi Garg, Gunjan Joshi, Byron Guzman, Stanislav Kutuzov, Thomas Loriaux, Caleb Mathias, Biran Menounos, Evan Miles, Aleksandra Osika, Kaleigh Potter, Adina Racoviteanu, Brianna Rick, Miles Sterner, Guy D. Tallentire, Levan Tielidze, Rebecca White, Kunpeng Wu, and Whyjay Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-315,https://doi.org/10.5194/essd-2025-315, 2025
Preprint under review for ESSD
Short summary

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1603.04467, 2016. a
Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437, 2021. a
Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, General circulation models of the atmosphere, 17, 173–265, 1977. a
Arendt, A. A., Hamman, J., Rocklin, M., Tan, A., Fatland, D. R., Joughin, J., Gutmann, E. D., Setiawan, L., and Henderson, S. T.: Pangeo: Community tools for analysis of Earth Science Data in the Cloud, in: AGU Fall Meeting Abstracts, vol. 2018, IN54A–05, 2018. a
Download
Executive editor
The integration of neural networks into PDE solvers to simulate systems for which the PDE models are incomplete is a key advance at the cutting edge of geoscientific modelling. The approach presented here is applicable far beyond the realm of ice modelling, and will be of interest to model developers and users across geoscience and beyond.
Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Share