An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

Jakeman, John D.; Perego, Mauro; Seidl, D. Thomas; Hartland, Tucker A.; Hillebrand, Trevor R.; Hoffman, Matthew J.; Price, Stephen F.

doi:https://doi.org/10.5194/esd-16-513-2025

Articles | Volume 16, issue 2

https://doi.org/10.5194/esd-16-513-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Theoretical and computational aspects of ensemble design,...

https://doi.org/10.5194/esd-16-513-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 2

Research article

|

03 Apr 2025

Research article |

| 03 Apr 2025

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

John D. Jakeman, Mauro Perego, D. Thomas Seidl, Tucker A. Hartland, Trevor R. Hillebrand, Matthew J. Hoffman, and Stephen F. Price

Model code and software

PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling (https://github.com/sandialabs/pyapprox) J. Jakeman https://doi.org/10.1016/j.envsoft.2023.105825

Short summary

This study investigated the computational benefits of using multiple models of varying cost and accuracy to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. Despite some models being incapable of capturing the local features of the ice-flow fields, using multiple models reduced the error in the estimated statistics by over an order of magnitude when compared to an approach that only used a single accurate model.