Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4045-2025
https://doi.org/10.5194/gmd-18-4045-2025
Model description paper
 | 
03 Jul 2025
Model description paper |  | 03 Jul 2025

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman

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Cited articles

Andrews, L. C., Catania, G. A., Hoffman, M. J., Gulley, J. D., Lüthi, M. P., Ryser, C., Hawley, R. L., and Neumann, T. A.: Direct observations of evolving subglacial drainage beneath the Greenland Ice Sheet, Nature, 514, 80–83, https://doi.org/10.1038/nature13796, 2014. a
Berdahl, M., Leguy, G., Lipscomb, W. H., and Urban, N. M.: Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties, The Cryosphere, 15, 2683–2699, https://doi.org/10.5194/tc-15-2683-2021, 2021. a
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. a
Bolibar, J., Sapienza, F., Maussion, F., Lguensat, R., Wouters, B., and Pérez, F.: Universal differential equations for glacier ice flow modelling, Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, 2023. a
Brinkerhoff, D., Meyer, C. R., Bueler, E., Truffer, M., and Bartholomaus, T. C.: Inversion of a glacier hydrology model, Ann. Glaciol., 57, 84–95, https://doi.org/10.1017/aog.2016.3, 2016. a
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Short summary
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian process (GP) emulators to accelerate subglacial drainage modelling by ~ 1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for > 90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
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