Articles | Volume 15, issue 4
https://doi.org/10.5194/tc-15-1731-2021
https://doi.org/10.5194/tc-15-1731-2021
Research article
 | 
09 Apr 2021
Research article |  | 09 Apr 2021

Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty

Olalekan Babaniyi, Ruanui Nicholson, Umberto Villa, and Noémi Petra

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (10 Dec 2020) by Olivier Gagliardini
AR by Noemi Petra on behalf of the Authors (14 Dec 2020)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to revisions (further review by editor and referees) (15 Dec 2020) by Olivier Gagliardini
ED: Referee Nomination & Report Request started (16 Dec 2020) by Olivier Gagliardini
RR by Anonymous Referee #2 (27 Dec 2020)
RR by Douglas Brinkerhoff (29 Dec 2020)
ED: Publish subject to minor revisions (review by editor) (05 Jan 2021) by Olivier Gagliardini
AR by Noemi Petra on behalf of the Authors (22 Jan 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (03 Feb 2021) by Olivier Gagliardini
AR by Noemi Petra on behalf of the Authors (09 Feb 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 Feb 2021) by Olivier Gagliardini
AR by Noemi Petra on behalf of the Authors (20 Feb 2021)  Manuscript 
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Short summary
We consider the problem of inferring unknown parameter fields under additional uncertainty for an ice sheet model from synthetic surface ice flow velocity measurements. Our results indicate that accounting for model uncertainty stemming from the presence of nuisance parameters is crucial. Namely our findings suggest that using nominal values for these parameters, as is often done in practice, without taking into account the resulting modeling error can lead to overconfident and biased results.