Articles | Volume 13, issue 4
https://doi.org/10.5194/tc-13-1167-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-1167-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2019
Research article |
| 09 Apr 2019
| 09 Apr 2019
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar und Meeresforschung (AWI), Bremerhaven, Germany
Martin Losch
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar und Meeresforschung (AWI), Bremerhaven, Germany
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Nils Hutter
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar und Meeresforschung (AWI), Bremerhaven, Germany
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Jean-Francois Lemieux, Damien Ringeisen, Martin Losch, William Lipscomb, and Jinro Ukita
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This preprint is open for discussion and under review for The Cryosphere (TC).
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The sea ice cover in the Arctic and in the Southern Ocean strongly varies spatially due to the formation of leads and pressure ridges. Leads and pressure ridges are formed when sea ice fails because forces inside the ice cover reach critical values. This work describes how the representation of leads and ridges in sea ice models should be modified when different critical forces are used.
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Sea ice moves and deforms as wind and ocean currents push it around, creating narrow fracture lines on the surface. To accurately reproduce these fracture patterns in computer models used for climate predictions and navigation, we tested a mathematical approach to sea ice dynamics, common in other fields but rare in sea ice modeling, to see if it could better capture these patterns. Our results show this approach could improve fracture representation, though it requires more computing power.
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In sea ice models, the flow rule determines how sea ice deforms (opening, closing and shearing) when critical stresses are reached. We implemented in CICE a novel approach to define the flow rule. This is a useful capability as it allows to independently optimize critical stresses and deformations. As opposed to the standard flow rule, the novel approach can lead to a thicker and more active sea ice cover with narrower deformations.
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Clara Burgard, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anna Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
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The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle
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Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Damien Ringeisen, Nils Hutter, and Luisa von Albedyll
The Cryosphere, 17, 4047–4061, https://doi.org/10.5194/tc-17-4047-2023, https://doi.org/10.5194/tc-17-4047-2023, 2023
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When sea ice is put into motion by wind and ocean currents, it deforms following narrow lines. Our two datasets at different locations and resolutions show that the intersection angle between these lines is often acute and rarely obtuse. We use the orientation of narrow lines to gain indications about the mechanical properties of sea ice and to constrain how to design sea-ice mechanical models for high-resolution simulation of the Arctic and improve regional predictions of sea-ice motion.
Florence L. Beaudry and L. Bruno Tremblay
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Sea ice in the Arctic Ocean moves under the action of waves and currents, causing it to deform along narrow lines where the ice opens or piles up. A standard way to evaluate whether models reproduce this behavior relies on how deformation changes with scale. Using synthetic ice fields, we show that this metric responds to how much opening and closing is happening overall, rather than to the organization of the deformation itself, raising caution to its use as a model evaluation tool.
Jan P. Gärtner, Martin Losch, Suvarchal K. Cheedela, Markus Jochum, and Roman Nuterman
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Climate simulations help us understand the Earth system and its evolution. The models used to perform these simulations are highly complex, require significant programming expertise to build and consume a lot of energy. A key component of climate models is their sea ice components. In this work, we present a sea ice model that offers an easier development process while maintaining strong performance. The model is able to run on a computer's graphics card, which greatly reduces its energy usage.
Jean-Francois Lemieux, Damien Ringeisen, Martin Losch, William Lipscomb, and Jinro Ukita
EGUsphere, https://doi.org/10.5194/egusphere-2026-1362, https://doi.org/10.5194/egusphere-2026-1362, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The sea ice cover in the Arctic and in the Southern Ocean strongly varies spatially due to the formation of leads and pressure ridges. Leads and pressure ridges are formed when sea ice fails because forces inside the ice cover reach critical values. This work describes how the representation of leads and ridges in sea ice models should be modified when different critical forces are used.
Damien Ringeisen, Bruno Tremblay, Jean-Francois Lemieux, and Martin Losch
EGUsphere, https://doi.org/10.5194/egusphere-2026-1845, https://doi.org/10.5194/egusphere-2026-1845, 2026
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Sea ice moves and deforms as wind and ocean currents push it around, creating narrow fracture lines on the surface. To accurately reproduce these fracture patterns in computer models used for climate predictions and navigation, we tested a mathematical approach to sea ice dynamics, common in other fields but rare in sea ice modeling, to see if it could better capture these patterns. Our results show this approach could improve fracture representation, though it requires more computing power.
Noémie Planat, Carolina Olivia Dufour, Camille Lique, Jan Klaus Rieck, Claude Talandier, and Louis Bruno Tremblay
Ocean Sci., 22, 653–678, https://doi.org/10.5194/os-22-653-2026, https://doi.org/10.5194/os-22-653-2026, 2026
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Nils Hutter and Cecilia M. Bitz
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Polar oceans are covered by many pieces of floating ice, called floes, that vary in size, shape and thickness. This study focuses on how fracturing caused by stresses from wind and ocean currents changes floe sizes. Using satellite images, we track ice motion and floe sizes, and study floe shapes and how deformation alters the distribution of floe sizes. We find that stronger deformation creates more small floes and present a formula to include this effect in sea-ice models.
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In sea ice models, the flow rule determines how sea ice deforms (opening, closing and shearing) when critical stresses are reached. We implemented in CICE a novel approach to define the flow rule. This is a useful capability as it allows to independently optimize critical stresses and deformations. As opposed to the standard flow rule, the novel approach can lead to a thicker and more active sea ice cover with narrower deformations.
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Clara Burgard, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anna Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
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To measure Arctic ice thickness, we often check how much ice sticks out of the water. This method depends on knowing the ice's density, which drops significantly in summer. Our study, validated with sonar and laser data, shows that these seasonal changes in density can complicate melt measurements. We stress the importance of considering these density changes for more accurate ice thickness readings.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle
Earth Syst. Sci. Data, 17, 423–434, https://doi.org/10.5194/essd-17-423-2025, https://doi.org/10.5194/essd-17-423-2025, 2025
Short summary
Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Antoine Savard and Bruno Tremblay
The Cryosphere, 18, 2017–2034, https://doi.org/10.5194/tc-18-2017-2024, https://doi.org/10.5194/tc-18-2017-2024, 2024
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We include a suitable plastic damage parametrization in the standard viscous–plastic (VP) sea ice model to disentangle its effect from resolved model physics (visco-plastic with and without damage) on its ability to reproduce observed scaling laws of deformation. This study shows that including a damage parametrization in the VP model improves its performance in simulating the statistical behavior of fracture patterns. Therefore, a damage parametrization is a powerful tuning knob.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
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We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Damien Ringeisen, Nils Hutter, and Luisa von Albedyll
The Cryosphere, 17, 4047–4061, https://doi.org/10.5194/tc-17-4047-2023, https://doi.org/10.5194/tc-17-4047-2023, 2023
Short summary
Short summary
When sea ice is put into motion by wind and ocean currents, it deforms following narrow lines. Our two datasets at different locations and resolutions show that the intersection angle between these lines is often acute and rarely obtuse. We use the orientation of narrow lines to gain indications about the mechanical properties of sea ice and to constrain how to design sea-ice mechanical models for high-resolution simulation of the Arctic and improve regional predictions of sea-ice motion.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
Short summary
Short summary
Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Charles Brunette, L. Bruno Tremblay, and Robert Newton
The Cryosphere, 16, 533–557, https://doi.org/10.5194/tc-16-533-2022, https://doi.org/10.5194/tc-16-533-2022, 2022
Short summary
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Sea ice motion is a versatile parameter for monitoring the Arctic climate system. In this contribution, we use data from drifting buoys, winds, and ice thickness to parameterize the motion of sea ice in a free drift regime – i.e., flowing freely in response to the forcing from the winds and ocean currents. We show that including a dependence on sea ice thickness and taking into account a climatology of the surface ocean circulation significantly improves the accuracy of sea ice motion estimates.
Mathieu Plante and L. Bruno Tremblay
The Cryosphere, 15, 5623–5638, https://doi.org/10.5194/tc-15-5623-2021, https://doi.org/10.5194/tc-15-5623-2021, 2021
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We propose a generalized form for the damage parameterization such that super-critical stresses can return to the yield with different final sub-critical stress states. In uniaxial compression simulations, the generalization improves the orientation of sea ice fractures and reduces the growth of numerical errors. Shear and convergence deformations however remain predominant along the fractures, contrary to observations, and this calls for modification of the post-fracture viscosity formulation.
Cited articles
Aksenov, Y. and Hibler, W. D.: Failure Propagation Effects in an
Anisotropic Sea Ice Dynamics Model, in: IUTAM Symposium on
Scaling Laws in Ice Mechanics and Ice Dynamics, edited by:
Dempsey, J. P. and Shen, H. H., Solid Mechanics and Its Applications,
363–372, UTAM Symposium, Fairbanks, Alaska, USA, 13–16 June 2000, Kluwer Academic Publishers, 2001. a
Babić, M., Shen, H. H., and Shen, H. T.: The stress tensor in granular
shear
flows of uniform, deformable disks at high solids concentrations, J.
Fluid Mech., 219, 81–118, https://doi.org/10.1017/S0022112090002877,
1990. a
Balendran, B. and Nemat-Nasser, S.: Double sliding model for cyclic
deformation of granular materials, including dilatancy effects, J. Mech.
Phys. Solids, 41, 573–612, https://doi.org/10.1016/0022-5096(93)90049-L, 1993. a, b
Bouchat, A. and Tremblay, B.: Energy dissipation in viscous-plastic sea-ice
models, J. Geophys. Res.-Oceans, 119, 976–994,
https://doi.org/10.1002/2013JC009436,
2014. a
Bröhan, D. and Kaleschke, L.: A Nine-Year Climatology of Arctic
Sea
Ice Lead Orientation and Frequency from AMSR-E, Remote Sensing,
6, 1451–1475, https://doi.org/10.3390/rs6021451, 2014. a
Coon, M., Kwok, R., Levy, G., Pruis, M., Schreyer, H., and Sulsky, D.: Arctic
Ice Dynamics Joint Experiment (AIDJEX) assumptions revisited and
found inadequate, J. Geophys. Res.-Oceans, 112, C11S90,
https://doi.org/10.1029/2005JC003393,
2007. a, b
Cox, G. F. N. and Richter-Menge, J. A.: Tensile Strength of Multi-Year
Pressure Ridge Sea Ice Samples, J. Energ. Resour.-ASME, 107,
375–380, https://doi.org/10.1115/1.3231204, 1985. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell
elasto-brittle rheology for sea ice modelling, The Cryosphere, 10,
1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a, b, c, d
Dansereau, V., Démery, V., Berthier, E., Weiss, J., and Ponson, L.: Fault
orientation in damage failure under compression, arXiv:1712.08530 [cond-mat,
physics:physics], available at: http://arxiv.org/abs/1712.08530 (last access: 4 April 2019), 2017. a
Dempsey, J. P., Xie, Y., Adamson, R. M., and Farmer, D. M.: Fracture of a
ridged multi-year Arctic sea ice floe, Cold Reg. Sci. Technol.,
76–77, 63–68, https://doi.org/10.1016/j.coldregions.2011.09.012,
2012. a
Dethloff, K., Rex, M., and Shupe, M.: Multidisciplinary drifting
Observatory for the Study of Arctic Climate (MOSAiC), EGU General
Assembly Conference Abstracts, 18, available at:
https://ui.adsabs.harvard.edu/#abs/2016EGUGA..18.3064D/abstract (last
access: 4 April 2019), 2016. a
Dumont, D., Gratton, Y., and Arbetter, T. E.: Modeling the Dynamics of the
North Water Polynya Ice Bridge, J. Phys. Oceanogr.,
39, 1448–1461, https://doi.org/10.1175/2008JPO3965.1,
2009. a, b
Feltham, D. L.: Sea Ice Rheology, Annu. Rev. Fluid Mech., 40,
91–112, https://doi.org/10.1146/annurev.fluid.40.111406.102151,
2008. a
Girard, L., Weiss, J., Molines, J. M., Barnier, B., and Bouillon, S.:
Evaluation of high-resolution sea ice models on the basis of statistical and
scaling properties of Arctic sea ice drift and deformation, J.
Geophys. Res.-Oceans, 114, C08015, https://doi.org/10.1029/2008JC005182,
2009. a
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat,
V.:
A new modeling framework for sea-ice mechanics based on elasto-brittle
rheology, Ann. Glaciol., 52, 123–132,
https://doi.org/10.3189/172756411795931499, 2011. a
Heorton, H. D. B. S., Feltham, D. L., and Tsamados, M.: Stress and
deformation
characteristics of sea ice in a high-resolution, anisotropic sea ice model,
Philos. T. R. Soc. A, 376, 20170349, https://doi.org/10.1098/rsta.2017.0349,
2018. a, b
Herman, A.: Discrete-Element bonded-particle Sea Ice model DESIgn, version
1.3a – model description and implementation, Geosci. Model Dev., 9,
1219–1241, https://doi.org/10.5194/gmd-9-1219-2016, 2016. a, b
Hibler, W. D.: A viscous sea ice law as a stochastic average of plasticity,
J. Geophys. Res., 82, 3932–3938, https://doi.org/10.1029/JC082i027p03932, 1977. a
Hibler, W. D. and Schulson, E. M.: On modeling sea-ice fracture and flow in
numerical investigations of climate, Ann. Glaciol., 25, 26–32,
https://doi.org/10.3189/S0260305500190019,
1997. a
Hibler, W. D., Hutchings, J. K., and Ip, C. F.: sea-ice arching and multiple
flow States of Arctic pack ice, Ann. Glaciol., 44, 339–344,
https://doi.org/10.3189/172756406781811448,
2006. a
Hundsdorfer, W., Koren, B., vanLoon, M., and Verwer, J. G.: A Positive
Finite-Difference Advection Scheme, J. Comput. Phys.,
117, 35–46, https://doi.org/10.1006/jcph.1995.1042,
1995. a
Hunke, E. C.: Viscous–Plastic Sea Ice Dynamics with the EVP
Model: Linearization Issues, J. Comput. Phys., 170,
18–38, https://doi.org/10.1006/jcph.2001.6710,
2001. a
Hunke, E. C. and Dukowicz, J. K.: An Elastic–Viscous–Plastic Model
for Sea Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2,
1997. a, b
Hutchings, J. K., Jasak, H., and Laxon, S. W.: A strength implicit correction
scheme for the viscous-plastic sea ice model, Ocean Model., 7, 111–133,
https://doi.org/10.1016/S1463-5003(03)00040-4,
2004. a
Hutchings, J. K., Heil, P., and Hibler, W. D.: Modeling Linear Kinematic
Features in Sea Ice, Mon. Weather Rev., 133, 3481–3497,
https://doi.org/10.1175/MWR3045.1, 2005. a, b, c, d
Hutter, K. and Rajagopal, K. R.: On flows of granular materials, Continuum
Mech. Therm., 6, 81–139, https://doi.org/10.1007/BF01140894, 1994. a, b
Hutter, N., Martin, L., and Dimitris, M.: Scaling Properties of Arctic
Sea Ice Deformation in a High-Resolution Viscous-Plastic
Sea Ice Model and in Satellite Observations, J. Geophys.
Res.-Oceans, 123, 672–687, https://doi.org/10.1002/2017JC013119,
2018. a, b, c, d
Kwok, R.: Deformation of the Arctic Ocean Sea Ice Cover between
November 1996 and April 1997: A Qualitative Survey, in: IUTAM
Symposium on Scaling Laws in Ice Mechanics and Ice Dynamics,
edited by: Dempsey, J. P. and Shen, H. H., no. 94 in Solid Mechanics and
Its Applications, Springer Netherlands, 315–322,
https://doi.org/10.1007/978-94-015-9735-7_26,
2001. a, b
Lemieux, J.-F. and Tremblay, B.: Numerical convergence of viscous-plastic sea
ice models, J. Geophys. Res.-Oceans, 114, C05009, https://doi.org/10.1029/2008JC005017,
2009. a
Lemieux, J.-F., Tremblay, B., Sedláček, J., Tupper, P., Thomas, S.,
Huard,
D., and Auclair, J.-P.: Improving the numerical convergence of
viscous-plastic sea ice models with the Jacobian-free Newton–Krylov
method, J. Comput. Phys., 229, 2840–2852,
https://doi.org/10.1016/j.jcp.2009.12.011,
2010. a
Lindsay, R. W. and Rothrock, D. A.: Arctic sea ice leads from advanced very
high resolution radiometer images, J. Geophys. Res.-Oceans,
100, 4533–4544, https://doi.org/10.1029/94JC02393,
1995. a
Linow, S. and Dierking, W.: Object-Based Detection of Linear
Kinematic Features in Sea Ice, Remote Sensing, 9, 493,
https://doi.org/10.3390/rs9050493, 2017. a
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the
formulation of sea-ice models. Part 1: Effects of different solver
implementations and parameterizations, Ocean Model., 33, 129–144,
https://doi.org/10.1016/j.ocemod.2009.12.008,
2010. a, b
Marko, J. R. and Thomson, R. E.: Rectilinear leads and internal motions in
the ice pack of the western Arctic Ocean, J. Geophys. Res., 82, 979–987,
https://doi.org/10.1029/JC082i006p00979, 1977. a
Marsan, D., Weiss, J., Larose, E., and Métaxian, J.-P.: Sea-ice thickness
measurement based on the dispersion of ice swell, J. Acoust. Soc. Am., 131,
80–91, https://doi.org/10.1121/1.3662051, 2012. a
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the
ocean on parallel computers, J. Geophys. Res.-Oceans, 102,
5753–5766, https://doi.org/10.1029/96JC02775,
1997. a
Menge, J. A. R. and Jones, K. F.: The tensile strength of first-year sea ice,
J. Glaciol., 39, 609–618, https://doi.org/10.3189/S0022143000016506,
1993. a
Miller, P. A., Laxon, S. W., and Feltham, D. L.: Improving the spatial
distribution of modeled Arctic sea ice thickness, Geophys. Res.
Lett., 32, L18503, https://doi.org/10.1029/2005GL023622,
2005. a
Pritchard, R. S.: An Elastic-Plastic Constitutive Law for Sea
Ice, J. Appl. Mech., 42, 379–384, https://doi.org/10.1115/1.3423585, 1975. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new
Lagrangian sea ice model, The Cryosphere, 10, 1055–1073,
https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b
Rothrock, D. A.: The steady drift of an incompressible Arctic ice cover,
J. Geophys. Res., 80, 387–397, https://doi.org/10.1029/JC080i003p00387,
1975. a
Rothrock, D. A. and Thorndike, A. S.: Measuring the sea ice floe size
distribution, J. Geophys. Res.-Oceans, 89, 6477–6486,
https://doi.org/10.1029/JC089iC04p06477,
1984. a
Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L.,
Aleinov,
I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V., Chen, Y.-H.,
Cheng, Y., Clune, T. L., Genio, A. D., Fainchtein, R. d., Faluvegi, G.,
Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A. A., LeGrande,
A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S., Miller, R. L.,
Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J., Putman, W. M., Rind,
D., Romanou, A., Sato, M., Shindell, D. T., Sun, S., Syed, R. A., Tausnev,
N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao, M.-S., and Zhang, J.:
Configuration and assessment of the GISS ModelE2 contributions to the
CMIP5 archive, J. Adv. Model. Earth Syst., 6, 141–184,
https://doi.org/10.1002/2013MS000265,
2014. a
Schreyer, H. L., Sulsky, D. L., Munday, L. B., Coon, M. D., and Kwok, R.:
Elastic-decohesive constitutive model for sea ice, J. Geophys.
Res.-Oceans, 111, C11S26, https://doi.org/10.1029/2005JC003334,
2006. a
Schulson, E. M.: Compressive shear faults within arctic sea ice: Fracture
on scales large and small, J. Geophys. Res.-Oceans, 109, C07016,
https://doi.org/10.1029/2003JC002108, 2004. a, b, c, d
Sirven, J. and Tremblay, B.: Analytical Study of an Isotropic
Viscoplastic Sea Ice Model in Idealized Configurations, J. Phys.
Oceanogr., 45, 331–354, https://doi.org/10.1175/JPO-D-13-0109.1, 2014. a
Spreen, G., Kwok, R., Menemenlis, D., and Nguyen, A. T.: Sea-ice deformation
in a coupled ocean–sea-ice model and in satellite remote sensing data, The
Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, 2017. a
Stern, H. L., Rothrock, D. A., and Kwok, R.: Open water production in
Arctic
sea ice: Satellite measurements and model parameterizations, J.
Geophys. Res.-Oceans, 100, 20601–20612, https://doi.org/10.1029/95JC02306,
1995. a
Stroeve, J., Barrett, A., Serreze, M., and Schweiger, A.: Using records from
submarine, aircraft and satellites to evaluate climate model simulations of
Arctic sea ice thickness, The Cryosphere, 8, 1839–1854,
https://doi.org/10.5194/tc-8-1839-2014, 2014. a, b
Sulsky, D., Schreyer, H., Peterson, K., Kwok, R., and Coon, M.: Using the
material-point method to model sea ice dynamics, J. Geophys.
Res.-Oceans, 112, C02S90, https://doi.org/10.1029/2005JC003329,
2007. a
Tremblay, L.-B. and Mysak, L. A.: Modeling Sea Ice as a Granular
Material, Including the Dilatancy Effect, J. Phys.
Oceanogr., 27, 2342–2360,
https://doi.org/10.1175/1520-0485(1997)027<2342:MSIAAG>2.0.CO;2,
1997. a, b, c, d
Tsamados, M., Feltham, D. L., and Wilchinsky, A. V.: Impact of a new
anisotropic rheology on simulations of Arctic sea ice, J.
Geophys. Res.-Oceans, 118, 91–107, https://doi.org/10.1029/2012JC007990,
2013. a
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the
Ice Strength Formulation on the Performance of a Sea Ice
Thickness Distribution Model in the Arctic, J. Geophys. Res.-Oceans,
122, 2090–2107, https://doi.org/10.1002/2016JC012128, 2017.
a, b
Walter, B. A. and Overland, J. E.: The response of lead patterns in the
Beaufort Sea to storm-scale wind forcing, Ann. Glaciol., 17,
219–226, https://doi.org/10.3189/S0260305500012878,
1993. a, b, c
Wang, K.: Observing the yield curve of compacted pack ice, J.
Geophys. Res.-Oceans, 112, C05015, https://doi.org/10.1029/2006JC003610,
2007. a, b, c
Wang, K., Leppäranta, M., and Kõuts, T.: A study of sea ice dynamic
events in a small bay, Cold Reg. Sci. Technol., 45, 83–94,
https://doi.org/10.1016/j.coldregions.2006.02.002, 2006. a, b
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice
leads in the Arctic Ocean: Model assessment, interannual variability
and trends, Geophys. Res. Lett., 43, 7019–7027, https://doi.org/10.1002/2016GL068696,
2016. a
Weiss, J., Schulson, E. M., and Stern, H. L.: Sea ice rheology from in-situ,
satellite and laboratory observations: Fracture and friction, Earth
Planet. Sc. Lett., 255, 1–8, https://doi.org/10.1016/j.epsl.2006.11.033,
2007. a, b, c, d
Wilchinsky, A. V. and Feltham, D. L.: Anisotropic model for granulated sea
ice
dynamics, J. Mech. Phys. Solids, 54, 1147–1185,
https://doi.org/10.1016/j.jmps.2005.12.006,
2006. a, b
Wilchinsky, A. V. and Feltham, D. L.: Modeling Coulombic failure of sea ice
with leads, J. Geophys. Res.-Oceans, 116, C08040, https://doi.org/10.1029/2011JC007071,
2011. a
Wilchinsky, A. V. and Feltham, D. L.: Rheology of Discrete Failure
Regimes of Anisotropic Sea Ice, J. Phys. Oceanogr., 42,
1065–1082, https://doi.org/10.1175/JPO-D-11-0178.1,
2012. a
Wilchinsky, A. V., Feltham, D. L., and Hopkins, M. A.: Effect of shear
rupture on aggregate scale formation in sea ice, J. Geophys. Res.-Oceans,
115, C10002, https://doi.org/10.1029/2009JC006043, 2010. a, b, c
Wilchinsky, A. V., Feltham, D. L., and Hopkins, M. A.: Modelling the
reorientation of sea-ice faults as the wind changes direction, Ann.
Glaciol., 52, 83–90, https://doi.org/10.3189/172756411795931831,
2011. a
Zhang, J. and Hibler, W. D.: On an efficient numerical method for modeling
sea ice dynamics, J. Geophys. Res.-Oceans, 102, 8691–8702,
https://doi.org/10.1029/96JC03744, 1997. a, b
Zhang, J. and Rothrock, D. A.: Effect of sea ice rheology in numerical
investigations of climate, J. Geophys. Res.-Oceans, 110, C08014,
https://doi.org/10.1029/2004JC002599, 2005. a
Short summary
We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal...
