Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 13
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.
https://doi.org/10.5194/gmd-18-4045-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 13
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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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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