Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5843–5861, 2021
Geosci. Model Dev., 14, 5843–5861, 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.

Related authors

Modelling seasonal meltwater forcing of the velocity of land-terminating margins of the Greenland Ice Sheet
Conrad P. Koziol and Neil Arnold
The Cryosphere, 12, 971–991,,, 2018
Short summary
Incorporating modelled subglacial hydrology into inversions for basal drag
Conrad P. Koziol and Neil Arnold
The Cryosphere, 11, 2783–2797,,, 2017
Short summary

Related subject area

SNICAR-ADv3: a community tool for modeling spectral snow albedo
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704,,, 2021
Short summary
STEMMUS-UEB v1.0.0: integrated modeling of snowpack and soil water and energy transfer with three complexity levels of soil physical processes
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376,,, 2021
Short summary
A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343,,, 2021
Short summary
Ice Algae Model Intercomparison Project phase 2 (IAMIP2)
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861,,, 2021
Short summary
A Gaussian process emulator for simulating ice sheet–climate interactions on a multi-million-year timescale: CLISEMv1.0
Jonas Van Breedam, Philippe Huybrechts, and Michel Crucifix
Geosci. Model Dev., 14, 6373–6401,,, 2021
Short summary

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,, 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,, 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,, 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,, 2021. a
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.