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

Related authors

Inversion of geothermal heat flux in a thermomechanically coupled nonlinear Stokes ice sheet model
Hongyu Zhu, Noemi Petra, Georg Stadler, Tobin Isaac, Thomas J. R. Hughes, and Omar Ghattas
The Cryosphere, 10, 1477–1494, https://doi.org/10.5194/tc-10-1477-2016,https://doi.org/10.5194/tc-10-1477-2016, 2016
Short summary

Related subject area

Discipline: Ice sheets | Subject: Numerical Modelling
Analytical solutions for the advective–diffusive ice column in the presence of strain heating
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere, 18, 4215–4232, https://doi.org/10.5194/tc-18-4215-2024,https://doi.org/10.5194/tc-18-4215-2024, 2024
Short summary
Ice viscosity governs hydraulic fracture that causes rapid drainage of supraglacial lakes
Tim Hageman, Jessica Mejía, Ravindra Duddu, and Emilio Martínez-Pañeda
The Cryosphere, 18, 3991–4009, https://doi.org/10.5194/tc-18-3991-2024,https://doi.org/10.5194/tc-18-3991-2024, 2024
Short summary
Biases in ice sheet models from missing noise-induced drift
Alexander A. Robel, Vincent Verjans, and Aminat A. Ambelorun
The Cryosphere, 18, 2613–2623, https://doi.org/10.5194/tc-18-2613-2024,https://doi.org/10.5194/tc-18-2613-2024, 2024
Short summary
Sensitivity of Future Projections of the Wilkes Subglacial Basin Ice Sheet to Grounding Line Melt Parameterizations
Yu Wang, Chen Zhao, Rupert Gladstone, Thomas Zwinger, Ben Galton-Fenzi, and Poul Christoffersen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1005,https://doi.org/10.5194/egusphere-2024-1005, 2024
Short summary
Modeling the timing of Patagonian Ice Sheet retreat in the Chilean Lake District from 22–10 ka
Joshua Cuzzone, Matias Romero, and Shaun A. Marcott
The Cryosphere, 18, 1381–1398, https://doi.org/10.5194/tc-18-1381-2024,https://doi.org/10.5194/tc-18-1381-2024, 2024
Short summary

Cited articles

Arridge, S., Kaipio, J., Kolehmainen, V., Schweiger, M., Somersalo, E., Tarvainen, T., and Vauhkonen, M.: Approximation errors and model reduction with an application in optical diffusion tomography, Inverse Probl., 22, 175–195, https://doi.org/10.1088/0266-5611/22/1/010, 2006. a
Balay, S., Buschelman, K., Gropp, W. D., Kaushik, D., Knepley, M. G., McInnes, L. C., Smith, B. F., and Zhang, H.: PETSc Web page, available at: http://www.mcs.anl.gov/petsc (last access: 25 March 2021), 2009. a
Bons, P. D., Kleiner, T., Llorens, M.-G., Prior, D. J., Sachau, T., Weikusat, I., and Jansen, D.: Greenland Ice Sheet: Higher nonlinearity of ice flow significantly reduces estimated basal motion, Geophys. Res. Lett., 45, 6542–6548, 2018. a, b
Brondex, J., Gillet-Chaulet, F., and Gagliardini, O.: Sensitivity of centennial mass loss projections of the Amundsen basin to the friction law, The Cryosphere, 13, 177–195, https://doi.org/10.5194/tc-13-177-2019, 2019. a, b
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters: The importance of model discrepancy, Inverse Probl., 30, 114007, https://doi.org/10.1088/0266-5611/30/11/114007, 2014. a
Download
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.