Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4045-2025
© 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/gmd-18-4045-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jul 2025
Model description paper |
| 03 Jul 2025
| 03 Jul 2025
Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0
Tim Hill
CORRESPONDING AUTHOR
Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada
Derek Bingham
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada
Gwenn E. Flowers
Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada
Matthew J. Hoffman
Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
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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.
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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.
Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train...
