Articles | Volume 14, issue 9
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, Joe A. Todd, Daniel N. Goldberg, and James R. Maddison

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

Universal differential equations for glacier ice flow modelling
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687,,, 2023
Short summary
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823,,, 2023
Short summary
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652,,, 2023
Short summary
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322,,, 2023
Short summary
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550,,, 2023
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.