Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19

Bulthuis, Kevin; Larour, Eric

doi:https://doi.org/10.5194/gmd-15-1195-2022

Articles | Volume 15, issue 3

https://doi.org/10.5194/gmd-15-1195-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-1195-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 3

Development and technical paper

|

10 Feb 2022

Development and technical paper |

| 10 Feb 2022

Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19

Kevin Bulthuis and Eric Larour

Data sets

Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 -- Datasets and results Kevin Bulthuis and Eric Larour https://doi.org/10.5281/zenodo.5532710

Model code and software

Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 -- Software Kevin Bulthuis and Eric Larour https://doi.org/10.5281/zenodo.5532775

Short summary

We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.