Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-513-2025
https://doi.org/10.5194/esd-16-513-2025
Research article
 | 
03 Apr 2025
Research article |  | 03 Apr 2025

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

John D. Jakeman, Mauro Perego, D. Thomas Seidl, Tucker A. Hartland, Trevor R. Hillebrand, Matthew J. Hoffman, and Stephen F. Price

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (12 Nov 2024) by Francisco de Melo Viríssimo
AR by John D. Jakeman on behalf of the Authors (12 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Nov 2024) by Francisco de Melo Viríssimo
RR by Vincent Verjans (20 Nov 2024)
RR by Douglas Brinkerhoff (28 Nov 2024)
RR by Dan Goldberg (13 Dec 2024)
ED: Publish subject to minor revisions (review by editor) (13 Dec 2024) by Francisco de Melo Viríssimo
AR by John D. Jakeman on behalf of the Authors (19 Dec 2024)  Author's response   Manuscript 
EF by Daria Karpachova (15 Jan 2025)  Author's tracked changes 
ED: Publish as is (15 Jan 2025) by Francisco de Melo Viríssimo
AR by John D. Jakeman on behalf of the Authors (16 Jan 2025)  Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by John D. Jakeman on behalf of the Authors (11 Mar 2025)   Author's adjustment   Manuscript
EA: Adjustments approved (14 Mar 2025) by Francisco de Melo Viríssimo
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Short summary
This study investigated the computational benefits of using multiple models of varying cost and accuracy to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. Despite some models being incapable of capturing the local features of the ice-flow fields, using multiple models reduced the error in the estimated statistics by over an order of magnitude when compared to an approach that only used a single accurate model.
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