Articles | Volume 15, issue 3
Geosci. Model Dev., 15, 1195–1217, 2022
https://doi.org/10.5194/gmd-15-1195-2022
Geosci. Model Dev., 15, 1195–1217, 2022
https://doi.org/10.5194/gmd-15-1195-2022
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

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Cited articles

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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.