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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-321', Anonymous Referee #1, 29 Oct 2021
  • RC2: 'Comment on gmd-2021-321', Anonymous Referee #2, 18 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Kevin Bulthuis on behalf of the Authors (01 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Dec 2021) by Alexander Robel
RR by Anonymous Referee #2 (23 Dec 2021)
ED: Publish subject to technical corrections (06 Jan 2022) by Alexander Robel
AR by Kevin Bulthuis on behalf of the Authors (10 Jan 2022)  Author's response    Manuscript
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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.