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
Volume 10, issue 3
The Cryosphere, 10, 1055–1073, 2016
https://doi.org/10.5194/tc-10-1055-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 10, 1055–1073, 2016
https://doi.org/10.5194/tc-10-1055-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 May 2016

Research article | 20 May 2016

neXtSIM: a new Lagrangian sea ice model

Pierre Rampal et al.

Related authors

On the statistical properties of sea ice lead fraction and heat fluxes in the Arctic
Einar Örn Ólason, Pierre Rampal, and Véronique Dansereau
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-13,https://doi.org/10.5194/tc-2020-13, 2020
Preprint under review for TC
Short summary
Wave–sea-ice interactions in a brittle rheological framework
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-19,https://doi.org/10.5194/tc-2020-19, 2020
Preprint under review for TC
Short summary
On the multi-fractal scaling properties of sea ice deformation
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019,https://doi.org/10.5194/tc-13-2457-2019, 2019
Short summary
Data assimilation using adaptive, non-conservative, moving mesh models
Ali Aydoğdu, Alberto Carrassi, Colin T. Guider, Chris K. R. T Jones, and Pierre Rampal
Nonlin. Processes Geophys., 26, 175–193, https://doi.org/10.5194/npg-26-175-2019,https://doi.org/10.5194/npg-26-175-2019, 2019
Short summary
Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-154,https://doi.org/10.5194/tc-2019-154, 2019
Preprint under review for TC
Short summary

Related subject area

Sea Ice
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020,https://doi.org/10.5194/tc-14-1083-2020, 2020
Short summary
Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice
Shiming Xu, Lu Zhou, and Bin Wang
The Cryosphere, 14, 751–767, https://doi.org/10.5194/tc-14-751-2020,https://doi.org/10.5194/tc-14-751-2020, 2020
Short summary
Sea ice volume variability and water temperature in the Greenland Sea
Valeria Selyuzhenok, Igor Bashmachnikov, Robert Ricker, Anna Vesman, and Leonid Bobylev
The Cryosphere, 14, 477–495, https://doi.org/10.5194/tc-14-477-2020,https://doi.org/10.5194/tc-14-477-2020, 2020
Short summary
Sea ice export through the Fram Strait derived from a combined model and satellite data set
Chao Min, Longjiang Mu, Qinghua Yang, Robert Ricker, Qian Shi, Bo Han, Renhao Wu, and Jiping Liu
The Cryosphere, 13, 3209–3224, https://doi.org/10.5194/tc-13-3209-2019,https://doi.org/10.5194/tc-13-3209-2019, 2019
Short summary
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology
M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh
The Cryosphere, 13, 2915–2934, https://doi.org/10.5194/tc-13-2915-2019,https://doi.org/10.5194/tc-13-2915-2019, 2019
Short summary

Cited articles

Amante, C. and Eakins, B. W.: ETOPO1 Global Relief Model converted to PanMap layer format, NOAA – National Geophysical Data Center, https://doi.org/10.1594/PANGAEA.769615, 2009.
Amitrano, D., Grasso, J. R., and Hantz, D.: From diffuse to localised damage through elastic interaction, Geophys. Res. Lett., 26, 2109–2112, 1999.
Batchelor, G. K.: Diffusion in a field of homogeneous turbulence, Math. Proc. Cambridge, 48, 345–362, https://doi.org/10.1017/S0305004100027687, 1952.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res., 104, 15669–15678, 1999.
Publications Copernicus
Download
Short summary
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in its dynamical regime, as seen by the increase of extreme fracturing events and the acceleration of sea ice drift. In this paper we present a new sea ice model, neXtSIM, that is capable of simulating both sea ice drift and deformation as observed from satellites, with similar spatial and temporal scaling properties. At the same time, the model reproduces sea ice area, extent, and volume correctly.
The Arctic sea ice cover has changed drastically over the last decades and undergone a shift in...
Citation