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 14, issue 2
The Cryosphere, 14, 565–584, 2020
https://doi.org/10.5194/tc-14-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 14, 565–584, 2020
https://doi.org/10.5194/tc-14-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Feb 2020

Research article | 13 Feb 2020

Deep learning applied to glacier evolution modelling

Jordi Bolibar et al.

Related authors

A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015
Jordi Bolibar, Antoine Rabatel, Isabelle Gouttevin, and Clovis Galiez
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-35,https://doi.org/10.5194/essd-2020-35, 2020
Preprint under review for ESSD
Short summary

Related subject area

Discipline: Glaciers | Subject: Numerical Modelling
Initialization of a global glacier model based on present-day glacier geometry and past climate information: an ensemble approach
Julia Eis, Fabien Maussion, and Ben Marzeion
The Cryosphere, 13, 3317–3335, https://doi.org/10.5194/tc-13-3317-2019,https://doi.org/10.5194/tc-13-3317-2019, 2019
Short summary
Contrasting thinning patterns between lake- and land-terminating glaciers in the Bhutanese Himalaya
Shun Tsutaki, Koji Fujita, Takayuki Nuimura, Akiko Sakai, Shin Sugiyama, Jiro Komori, and Phuntsho Tshering
The Cryosphere, 13, 2733–2750, https://doi.org/10.5194/tc-13-2733-2019,https://doi.org/10.5194/tc-13-2733-2019, 2019
Short summary
Impact of frontal ablation on the ice thickness estimation of marine-terminating glaciers in Alaska
Beatriz Recinos, Fabien Maussion, Timo Rothenpieler, and Ben Marzeion
The Cryosphere, 13, 2657–2672, https://doi.org/10.5194/tc-13-2657-2019,https://doi.org/10.5194/tc-13-2657-2019, 2019
Short summary
Modeling the response of Greenland outlet glaciers to global warming using a coupled flow line–plume model
Johanna Beckmann, Mahé Perrette, Sebastian Beyer, Reinhard Calov, Matteo Willeit, and Andrey Ganopolski
The Cryosphere, 13, 2281–2301, https://doi.org/10.5194/tc-13-2281-2019,https://doi.org/10.5194/tc-13-2281-2019, 2019
Short summary
Buoyant forces promote tidewater glacier iceberg calving through large basal stress concentrations
Matt Trevers, Antony J. Payne, Stephen L. Cornford, and Twila Moon
The Cryosphere, 13, 1877–1887, https://doi.org/10.5194/tc-13-1877-2019,https://doi.org/10.5194/tc-13-1877-2019, 2019
Short summary

Cited articles

Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Benn, D. I. and Evans, D. J. A.: Glaciers & glaciation, Routledge, New York, NY, USA, 2nd edn., available at: http://www.imperial.eblib.com/EBLWeb/patron/?target=patron&extendedid=P_615876_0 (last access: February 2020), oCLC: 878863282, 2014. a
Bolibar, J.: JordiBolibar/ALPGM: ALPGM v1.0, https://doi.org/10.5281/zenodo.3269678, 2019. a, b
Bolibar, J.: JordiBolibar/ALPGM: ALPGM v1.1, https://doi.org/10.5281/zenodo.3609136, 2020. a
Brun, F., Berthier, E., Wagnon, P., Kääb, A., and Treichler, D.: A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016, Nature Geosci., 10, 668–673, https://doi.org/10.1038/ngeo2999, 2017. a
Publications Copernicus
Download
Short summary
We introduce a novel approach for simulating glacier mass balances using a deep artificial neural network (i.e. deep learning) from climate and topographical data. This has been added as a component of a new open-source parameterized glacier evolution model. Deep learning is found to outperform linear machine learning methods, mainly due to its nonlinearity. Potential applications range from regional mass balance reconstructions from observations to simulations for past and future climates.
We introduce a novel approach for simulating glacier mass balances using a deep artificial...
Citation