Articles | Volume 15, issue 3
Geosci. Model Dev., 15, 1195–1217, 2022
Geosci. Model Dev., 15, 1195–1217, 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

Abramowitz, M. and Stegun, I. A.: Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables, 9th edn., Dover Publications, New York, NY, 1970. a
Babaniyi, O., Nicholson, R., Villa, U., and Petra, N.: Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty, The Cryosphere, 15, 1731–1750,, 2021. a, b
Bakka, H., Krainski, E., Bolin, D., Rue, H., and Lindgren, F.: The diffusion-based extension of the Matérn field to space-time, arXiv [preprint], arXiv:2006.04917, 2020. a
Bamber, J. L., Gomez-Dans, J. L., and Griggs, J. A.: A new 1 km digital elevation model of the Antarctic derived from combined satellite radar and laser data – Part 1: Data and methods, The Cryosphere, 3, 101–111,, 2009. a
Beskos, A., Girolami, M., Lan, S., Farrell, P. E., and Stuart, A. M.: Geometric MCMC for infinite-dimensional inverse problems, J. Comput. Phys., 335, 327–351,, 2017. a
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.