Articles | Volume 14, issue 5
Geosci. Model Dev., 14, 2545–2573, 2021
https://doi.org/10.5194/gmd-14-2545-2021
Geosci. Model Dev., 14, 2545–2573, 2021
https://doi.org/10.5194/gmd-14-2545-2021
Development and technical paper
07 May 2021
Development and technical paper | 07 May 2021

Assessment of numerical schemes for transient, finite-element ice flow models using ISSM v4.18

Thiago Dias dos Santos et al.

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Cited articles

Akin, J. and Tezduyar, T. E.: Calculation of the advective limit of the SUPG stabilization parameter for linear and higher-order elements, Comput. Method. Appl. M., 193, 1909–1922, https://doi.org/10.1016/j.cma.2003.12.050, 2004. a
Almeida, R. C. and Silva, R. S.: A stable Petrov–Galerkin method for convection-dominated problems, Comput. Method. Appl. M., 140, 291–304, https://doi.org/10.1016/S0045-7825(96)01108-5, 1997. a
Arnold, D. N., Brezzi, F., Cockburn, B., and Marini, L. D.: Unified Analysis of Discontinuous Galerkin Methods for Elliptic Problems, SIAM J. Numer. Anal., 39, 1749–1779, https://doi.org/10.1137/S0036142901384162, 2002. a
Aschwanden, A., Fahnestock, M. A., Truffer, M., Brinkerhoff, D. J., Hock, R., Khroulev, C., Mottram, R., and Khan, S. A.: Contribution of the Greenland Ice Sheet to sea level over the next millennium, Science Advances, 5, eaav9396, https://doi.org/10.1126/sciadv.aav9396, 2019. a
Babuška, I., Baumann, C., and Oden, J.: A discontinuous hp finite element method for diffusion problems: 1-D analysis, Comput. Math. Appl., 37, 103–122, https://doi.org/10.1016/S0898-1221(99)00117-0, 1999. a
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Numerical models are routinely used to understand the past and future behavior of ice sheets in response to climate evolution. As is always the case with numerical modeling, one needs to minimize biases and numerical artifacts due to the choice of numerical scheme employed in such models. Here, we assess different numerical schemes in time-dependent simulations of ice sheets. We also introduce a new parameterization for the driving stress, the force that drives the ice sheet flow.