Articles | Volume 15, issue 4
https://doi.org/10.5194/tc-15-1731-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-1731-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2021
Research article |
| 09 Apr 2021
| 09 Apr 2021
Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty
Olalekan Babaniyi
School of Mathematical Sciences, Rochester Institute of Technology,
Rochester, NY 14623, USA
Ruanui Nicholson
Department of Engineering Science, University of Auckland, Auckland 1010, New Zealand
Umberto Villa
Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Department of Applied Mathematics, University of California, Merced, Merced, CA 95343, USA
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Cited
18 citations as recorded by crossref.
- Coefficient identification of the regularized p-Stokes equations N. Schmidt 10.1016/j.nonrwa.2024.104197
- Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty A. Alexanderian et al. 10.1088/1361-6420/ad602e
- Improvements on the discretisation of boundary conditions to the momentum balance for glacial ice C. Berends et al. 10.1017/jog.2024.45
- Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network K. Wu et al. 10.1007/s10915-023-02145-1
- Validating ensemble historical simulations of Upernavik Isstrøm (1985–2019) using observations of surface velocity and elevation E. Jager et al. 10.1017/jog.2024.10
- Learning physics-based models from data: perspectives from inverse problems and model reduction O. Ghattas & K. Willcox 10.1017/S0962492921000064
- Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 K. Bulthuis & E. Larour 10.5194/gmd-15-1195-2022
- HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS W. Reese et al. 10.1615/Int.J.UncertaintyQuantification.2023047605
- Multifidelity uncertainty quantification for ice sheet simulations N. Aretz et al. 10.1007/s10596-024-10329-3
- An augmented lagrangian algorithm for recovery of ice thickness in unidirectional flow using the shallow ice approximation E. McGeorge et al. 10.1016/j.apm.2022.03.001
- On global normal linear approximations for nonlinear Bayesian inverse problems R. Nicholson et al. 10.1088/1361-6420/acc129
- Compensating errors in inversions for subglacial bed roughness: same steady state, different dynamic response C. Berends et al. 10.5194/tc-17-1585-2023
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- Variational inference at glacier scale D. Brinkerhoff 10.1016/j.jcp.2022.111095
- Variational inference of ice shelf rheology with physics-informed machine learning B. Riel & B. Minchew 10.1017/jog.2023.8
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- A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams B. Recinos et al. 10.5194/tc-17-4241-2023
18 citations as recorded by crossref.
- Coefficient identification of the regularized p-Stokes equations N. Schmidt 10.1016/j.nonrwa.2024.104197
- Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty A. Alexanderian et al. 10.1088/1361-6420/ad602e
- Improvements on the discretisation of boundary conditions to the momentum balance for glacial ice C. Berends et al. 10.1017/jog.2024.45
- Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network K. Wu et al. 10.1007/s10915-023-02145-1
- Validating ensemble historical simulations of Upernavik Isstrøm (1985–2019) using observations of surface velocity and elevation E. Jager et al. 10.1017/jog.2024.10
- Learning physics-based models from data: perspectives from inverse problems and model reduction O. Ghattas & K. Willcox 10.1017/S0962492921000064
- Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 K. Bulthuis & E. Larour 10.5194/gmd-15-1195-2022
- HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS W. Reese et al. 10.1615/Int.J.UncertaintyQuantification.2023047605
- Multifidelity uncertainty quantification for ice sheet simulations N. Aretz et al. 10.1007/s10596-024-10329-3
- An augmented lagrangian algorithm for recovery of ice thickness in unidirectional flow using the shallow ice approximation E. McGeorge et al. 10.1016/j.apm.2022.03.001
- On global normal linear approximations for nonlinear Bayesian inverse problems R. Nicholson et al. 10.1088/1361-6420/acc129
- Compensating errors in inversions for subglacial bed roughness: same steady state, different dynamic response C. Berends et al. 10.5194/tc-17-1585-2023
- The Bayesian Approach to Inverse Robin Problems A. Rasmussen et al. 10.1137/23M1620624
- Variational inference at glacier scale D. Brinkerhoff 10.1016/j.jcp.2022.111095
- Variational inference of ice shelf rheology with physics-informed machine learning B. Riel & B. Minchew 10.1017/jog.2023.8
- fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models C. Koziol et al. 10.5194/gmd-14-5843-2021
- A mixed, unified forward/inverse framework for earthquake problems: fault implementation and coseismic slip estimate S. Puel et al. 10.1093/gji/ggac050
- A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams B. Recinos et al. 10.5194/tc-17-4241-2023
Latest update: 06 May 2025
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.
We consider the problem of inferring unknown parameter fields under additional uncertainty for...
