Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 3
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

Viewed

Total article views: 3,744 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,714 886 144 3,744 113 114
  • HTML: 2,714
  • PDF: 886
  • XML: 144
  • Total: 3,744
  • BibTeX: 113
  • EndNote: 114
Views and downloads (calculated since 29 Sep 2021)
Cumulative views and downloads (calculated since 29 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,744 (including HTML, PDF, and XML) Thereof 3,564 with geography defined and 180 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jun 2025
Download
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.
Read more
Share