Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.790 IF 4.790
  • IF 5-year value: 5.921 IF 5-year
    5.921
  • CiteScore value: 5.27 CiteScore
    5.27
  • SNIP value: 1.551 SNIP 1.551
  • IPP value: 5.08 IPP 5.08
  • SJR value: 3.016 SJR 3.016
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 63 Scimago H
    index 63
  • h5-index value: 51 h5-index 51
TC | Volume 12, issue 7
The Cryosphere, 12, 2229–2248, 2018
https://doi.org/10.5194/tc-12-2229-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 12, 2229–2248, 2018
https://doi.org/10.5194/tc-12-2229-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Jul 2018

Research article | 11 Jul 2018

A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions

Giri Gopalan et al.
Related authors  
Annual and interannual variability and trends of albedo for Icelandic glaciers
Andri Gunnarsson, Sigurdur M. Gardarsson, Finnur Pálsson, Tómas Jóhannesson, and Óli G. B. Sveinsson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-328,https://doi.org/10.5194/tc-2019-328, 2020
Manuscript under review for TC
Short summary
Future evolution and uncertainty of river flow regime change in a deglaciating river basin
Jonathan D. Mackay, Nicholas E. Barrand, David M. Hannah, Stefan Krause, Christopher R. Jackson, Jez Everest, Guðfinna Aðalgeirsdóttir, and Andrew R. Black
Hydrol. Earth Syst. Sci., 23, 1833–1865, https://doi.org/10.5194/hess-23-1833-2019,https://doi.org/10.5194/hess-23-1833-2019, 2019
Short summary
The Open Global Glacier Model (OGGM) v1.1
Fabien Maussion, Anton Butenko, Nicolas Champollion, Matthias Dusch, Julia Eis, Kévin Fourteau, Philipp Gregor, Alexander H. Jarosch, Johannes Landmann, Felix Oesterle, Beatriz Recinos, Timo Rothenpieler, Anouk Vlug, Christian T. Wild, and Ben Marzeion
Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019,https://doi.org/10.5194/gmd-12-909-2019, 2019
Short summary
Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection
Jonathan D. Mackay, Nicholas E. Barrand, David M. Hannah, Stefan Krause, Christopher R. Jackson, Jez Everest, and Guðfinna Aðalgeirsdóttir
The Cryosphere, 12, 2175–2210, https://doi.org/10.5194/tc-12-2175-2018,https://doi.org/10.5194/tc-12-2175-2018, 2018
Short summary
Modelling debris transport within glaciers by advection in a full-Stokes ice flow model
Anna Wirbel, Alexander H. Jarosch, and Lindsey Nicholson
The Cryosphere, 12, 189–204, https://doi.org/10.5194/tc-12-189-2018,https://doi.org/10.5194/tc-12-189-2018, 2018
Short summary
Related subject area  
Discipline: Ice sheets | Subject: Numerical Modelling
Simulated retreat of Jakobshavn Isbræ during the 21st century
Xiaoran Guo, Liyun Zhao, Rupert M. Gladstone, Sainan Sun, and John C. Moore
The Cryosphere, 13, 3139–3153, https://doi.org/10.5194/tc-13-3139-2019,https://doi.org/10.5194/tc-13-3139-2019, 2019
Parameter sensitivity analysis of dynamic ice sheet models-Numerical computations
Gong Cheng and Per Lötstedt
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-151,https://doi.org/10.5194/tc-2019-151, 2019
Revised manuscript accepted for TC
Short summary
Development of physically based liquid water schemes for Greenland firn-densification models
Vincent Verjans, Amber A. Leeson, C. Max Stevens, Michael MacFerrin, Brice Noël, and Michiel R. van den Broeke
The Cryosphere, 13, 1819–1842, https://doi.org/10.5194/tc-13-1819-2019,https://doi.org/10.5194/tc-13-1819-2019, 2019
Short summary
Regional grid refinement in an Earth system model: impacts on the simulated Greenland surface mass balance
Leonardus van Kampenhout, Alan M. Rhoades, Adam R. Herrington, Colin M. Zarzycki, Jan T. M. Lenaerts, William J. Sacks, and Michiel R. van den Broeke
The Cryosphere, 13, 1547–1564, https://doi.org/10.5194/tc-13-1547-2019,https://doi.org/10.5194/tc-13-1547-2019, 2019
Short summary
initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6
Hélène Seroussi, Sophie Nowicki, Erika Simon, Ayako Abe-Ouchi, Torsten Albrecht, Julien Brondex, Stephen Cornford, Christophe Dumas, Fabien Gillet-Chaulet, Heiko Goelzer, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Thomas Kleiner, Eric Larour, Gunter Leguy, William H. Lipscomb, Daniel Lowry, Matthias Mengel, Mathieu Morlighem, Frank Pattyn, Anthony J. Payne, David Pollard, Stephen F. Price, Aurélien Quiquet, Thomas J. Reerink, Ronja Reese, Christian B. Rodehacke, Nicole-Jeanne Schlegel, Andrew Shepherd, Sainan Sun, Johannes Sutter, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, and Tong Zhang
The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019,https://doi.org/10.5194/tc-13-1441-2019, 2019
Short summary
Cited articles  
Berliner, L. M.: Hierarchical Bayesian Time Series Models, in: Maximum Entropy and Bayesian Methods, edited by: Hanson, K. M. and Silver, R. N., Springer Netherlands, Dordrecht, 15–22, 1996. a
Berliner, L. M.: Physical-statistical modeling in geophysics, J. Geophys. Res.-Atmos., 108, 8776, https://doi.org/10.1029/2002JD002865, 2003. a, b
Berliner, L. M., Jezek, K., Cressie, N., Kim, Y., Lam, C. Q., and van der Veen, C. J.: Modeling dynamic controls on ice streams: a Bayesian statistical approach, J. Glaciol., 54, 705–714, https://doi.org/10.3189/002214308786570917, 2008. a
Brinkerhoff, D. J., Aschwanden, A., and Truffer, M.: Bayesian Inference of Subglacial Topography Using Mass Conservation, Front. Earth Sci., 4, 8, https://doi.org/10.3389/feart.2016.00008, 2016. a
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, b, c
Publications Copernicus
Download
Short summary
Geophysical systems can often contain scientific parameters whose values are uncertain, complex underlying dynamics, and field measurements with errors. These components are naturally modeled together within what is known as a Bayesian hierarchical model (BHM). This paper constructs such a model for shallow glaciers based on an approximation of the underlying dynamics. The evaluation of this model is aided by the use of exact analytical solutions from the literature.
Geophysical systems can often contain scientific parameters whose values are uncertain, complex...
Citation