Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5843–5861, 2021
https://doi.org/10.5194/gmd-14-5843-2021
Geosci. Model Dev., 14, 5843–5861, 2021
https://doi.org/10.5194/gmd-14-5843-2021

Model description paper 24 Sep 2021

Model description paper | 24 Sep 2021

fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models

Conrad P. Koziol et al.

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Cited articles

Alexanderian, A., Petra, N., Stadler, G., and Ghattas, O.: A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized _0-Sparsification, SIAM J. Sci. Comp., 36, A2122–A2148, https://doi.org/10.1137/130933381, 2014. a
Alnæs, M. S., Logg, A., Ølgaard, K. B., Rognes, M. E., and Wells, G. N.: Unified Form Language: A Domain-Specific Language for Weak Formulations of Partial Differential Equations, ACM T. Math. Softw., 40, 1–37, https://doi.org/10.1145/2566630, 2014. a
Arthern, R. J., Hindmarsh, R. C. A., and Williams, C. R.: Flow speed within the Antarctic ice sheet and its controls inferred from satellite observations, J. Geophys. Res.-Earth, 120, 1171–1188, https://doi.org/10.1002/2014JF003239, 2015. a
Babaniyi, O., Nicholson, R., Villa, U., and Petra, N.: Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty, The Cryosphere, 15, 1731–1750, https://doi.org/10.5194/tc-15-1731-2021, 2021. a
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Short summary
Sea level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depends strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent or how it will affect forecasts. We provide a framework for quantifying how this initialization uncertainty affects ice loss forecasts.