Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2017-2024
© Author(s) 2024. 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-18-2017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the sensitivity of sea ice deformation statistics to plastic damage
Antoine Savard
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC, Canada
Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC, Canada
Related authors
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Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, and Lekima Yakuden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-227, https://doi.org/10.5194/essd-2024-227, 2024
Revised manuscript accepted for ESSD
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Sea ice forms a thin boundary between the ocean and the atmosphere, with a 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 01 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
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.
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.
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
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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.
Damien Ringeisen, L. Bruno Tremblay, and Martin Losch
The Cryosphere, 15, 2873–2888, https://doi.org/10.5194/tc-15-2873-2021, https://doi.org/10.5194/tc-15-2873-2021, 2021
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Deformations in the Arctic sea ice cover take the shape of narrow lines. High-resolution sea ice models recreate these deformation lines. Recent studies have shown that the most widely used sea ice model creates fracture lines with intersection angles larger than those observed and cannot create smaller angles. In our work, we change the way sea ice deforms post-fracture. This change allows us to understand the link between the sea ice model and intersection angles and create more acute angles.
Shihe Ren, Xi Liang, Qizhen Sun, Hao Yu, L. Bruno Tremblay, Bo Lin, Xiaoping Mai, Fu Zhao, Ming Li, Na Liu, Zhikun Chen, and Yunfei Zhang
Geosci. Model Dev., 14, 1101–1124, https://doi.org/10.5194/gmd-14-1101-2021, https://doi.org/10.5194/gmd-14-1101-2021, 2021
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Sea ice plays a crucial role in global energy and water budgets. To get a better simulation of sea ice, we coupled a sea ice model with an atmospheric and ocean model to form a fully coupled system. The sea ice simulation results of this coupled system demonstrated that a two-way coupled model has better performance in terms of sea ice, especially in summer. This indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
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Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Mathieu Plante, Bruno Tremblay, Martin Losch, and Jean-François Lemieux
The Cryosphere, 14, 2137–2157, https://doi.org/10.5194/tc-14-2137-2020, https://doi.org/10.5194/tc-14-2137-2020, 2020
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We study the formation of ice arches between two islands using a model that resolves crack initiation and propagation. This model uses a damage parameter to parameterize the presence or absence of cracks in the ice. We find that the damage parameter allows for cracks to propagate in the ice but in a different orientation than predicted by theory. The results call for improvement in how stress relaxation associated with this damage is parameterized.
Angela Cheng, Barbara Casati, Adrienne Tivy, Tom Zagon, Jean-François Lemieux, and L. Bruno Tremblay
The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, https://doi.org/10.5194/tc-14-1289-2020, 2020
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Sea ice charts by the Canadian Ice Service (CIS) contain visually estimated ice concentration produced by analysts. The accuracy of manually derived ice concentrations is not well understood. The subsequent uncertainty of ice charts results in downstream uncertainties for ice charts users, such as models and climatology studies, and when used as a verification source for automated sea ice classifiers. This study quantifies the level of accuracy and inter-analyst agreement for ice charts by CIS.
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
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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.
Andrea Klus, Matthias Prange, Vidya Varma, Louis Bruno Tremblay, and Michael Schulz
Clim. Past, 14, 1165–1178, https://doi.org/10.5194/cp-14-1165-2018, https://doi.org/10.5194/cp-14-1165-2018, 2018
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Numerous proxy records from the northern North Atlantic suggest substantial climate variability including the occurrence of multi-decadal-to-centennial cold events during the Holocene. We analyzed two abrupt cold events in a Holocene simulation using a comprehensive climate model. It is shown that the events were ultimately triggered by prolonged phases of positive North Atlantic Oscillation causing changes in ocean circulation followed by severe cooling, freshening, and expansion of sea ice.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Vincent Le Fouest, Atsushi Matsuoka, Manfredi Manizza, Mona Shernetsky, Bruno Tremblay, and Marcel Babin
Biogeosciences, 15, 1335–1346, https://doi.org/10.5194/bg-15-1335-2018, https://doi.org/10.5194/bg-15-1335-2018, 2018
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Climate warming could enhance the load of terrigenous dissolved organic carbon (tDOC) of Arctic rivers. We show that tDOC concentrations simulated by an ocean–biogeochemical model in the Canadian Beaufort Sea compare favorably with their satellite counterparts. Over spring–summer, riverine tDOC contributes to 35 % of primary production and an equivalent of ~ 10 % of tDOC is exported westwards with the potential for fueling the biological production of the eastern Alaskan nearshore waters.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Related subject area
Discipline: Sea ice | Subject: Rheology
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology
Non-normal flow rules affect fracture angles in sea ice viscous–plastic rheologies
Behavior of saline ice under cyclic flexural loading
Parameter optimization in sea ice models with elastic–viscoplastic rheology
Landfast sea ice material properties derived from ice bridge simulations using the Maxwell elasto-brittle rheology
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
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We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Damien Ringeisen, L. Bruno Tremblay, and Martin Losch
The Cryosphere, 15, 2873–2888, https://doi.org/10.5194/tc-15-2873-2021, https://doi.org/10.5194/tc-15-2873-2021, 2021
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Deformations in the Arctic sea ice cover take the shape of narrow lines. High-resolution sea ice models recreate these deformation lines. Recent studies have shown that the most widely used sea ice model creates fracture lines with intersection angles larger than those observed and cannot create smaller angles. In our work, we change the way sea ice deforms post-fracture. This change allows us to understand the link between the sea ice model and intersection angles and create more acute angles.
Andrii Murdza, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2415–2428, https://doi.org/10.5194/tc-15-2415-2021, https://doi.org/10.5194/tc-15-2415-2021, 2021
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It has been suggested that the observed sudden breakup of Arctic and Antarctic floating ice covers may be due to fatigue failure associated with cyclic loading from ocean swells that can penetrate deeply into an ice pack. To investigate this possibility, we measured the flexural strength of saline ice after cyclic loading. Contrary to expectations, we find that the flexural strength of saline ice increases upon cycling, similar to the behavior of laboratory-grown ice and natural lake ice.
Gleb Panteleev, Max Yaremchuk, Jacob N. Stroh, Oceana P. Francis, and Richard Allard
The Cryosphere, 14, 4427–4451, https://doi.org/10.5194/tc-14-4427-2020, https://doi.org/10.5194/tc-14-4427-2020, 2020
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In the CICE6 community model, rheology and landfast grounding/arching effects are simulated by functions of sea ice thickness and concentration with a set of fixed parameters empirically adjusted to optimize model performance. In this study we consider a spatially variable extension for representing these parameters in the two-dimensional elastic–viscoplastic (EVP) sea ice model and analyze the feasibility of the optimization of these parameters through the 4D-Var data assimilation approach.
Mathieu Plante, Bruno Tremblay, Martin Losch, and Jean-François Lemieux
The Cryosphere, 14, 2137–2157, https://doi.org/10.5194/tc-14-2137-2020, https://doi.org/10.5194/tc-14-2137-2020, 2020
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We study the formation of ice arches between two islands using a model that resolves crack initiation and propagation. This model uses a damage parameter to parameterize the presence or absence of cracks in the ice. We find that the damage parameter allows for cracks to propagate in the ice but in a different orientation than predicted by theory. The results call for improvement in how stress relaxation associated with this damage is parameterized.
Cited articles
Aagaard, K., Coachman, L., and Carmack, E.: On the halocline of the Arctic Ocean, Deep-Sea Res. Pt. A, 28, 529–545, https://doi.org/10.1016/0198-0149(81)90115-1, 1981. a
Akima, H.: Algorithm 760: rectangular-grid-data surface fitting that has the accuracy of a bicubic polynomial, ACM T. Math. Softw., 22, 357–361, 1996. a
Amitrano, D. and Helmstetter, A.: Brittle creep, damage, and time to failure in rocks, J. Geophys. Res.-Sol. Ea., 111, B11201, https://doi.org/10.1029/2005JB004252, 2006. a
Amitrano, D., Grasso, J.-R., and Hantz, D.: From diffuse to localised damage through elastic interaction, Geophys. Res. Lett., 26, 2109–2112, 1999. a
Bouchat, A. and Tremblay, B.: Reassessing the quality of sea-ice deformation estimates derived from the RADARSAT Geophysical Processor System and its impact on the spatiotemporal scaling statistics, J. Geophys. Res.-Oceans, 125, e2019JC015944, https://doi.org/10.1029/2019JC015944, 2020. a
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, W: Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea-Ice Deformation Fields, J. Geophys. Res.-Oceans, 127, e2021JC017667, https://doi.org/10.1029/2021JC017667, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m
Bouillon, S. and Rampal, P.: Presentation of the dynamical core of neXtSIM, a new sea ice model, Ocean Model., 91, 23–37, 2015. a
Bouillon, S., Fichefet, T., Legat, V., and Madec, G.: The elastic–viscous–plastic method revisited, Ocean Model., 71, 2–12, 2013. a
Brown, R. A.: Planetary boundary layer modeling for AIDJEX, in: Proc. ICSI/AIDJEX Symp. on Sea Ice Processes and Models, University of Washington, 1979. a
Clauset, A., Shalizi, C. R., and Newman, M. E.: Power-law distributions in empirical data, SIAM Rev., 51, 661–703, 2009. 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
Cowie, P. A., Vanneste, C., and Sornette, D.: Statistical physics model for the spatiotemporal evolution of faults, J. Geophys. Res.-Sol. Ea., 98, 21809–21821, https://doi.org/10.1029/93JB02223, 1993. a
Flato, G. M. and Hibler, W. D.: Modeling pack ice as a cavitating fluid, J. Phys. Oceanogr., 22, 626–651, 1992. a
Friedlein, J., Mergheim, J., and Steinmann, P.: Efficient gradient enhancements for plasticity with ductile damage in the logarithmic strain space, Eur. J. Mech., 99, 104946, https://doi.org/10.1016/j.euromechsol.2023.104946, 2023. 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, b
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, b, c, d
Hafezolghorani, M., Hejazi, F., Vaghei, R., Jaafar, M. S. B., and Karimzade, K.: Simplified Damage Plasticity Model for Concrete, Struct. Eng. Int., 27, 68–78, https://doi.org/10.2749/101686616X1081, 2017. a
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
Hoek, E.: Brittle fracture of rock, Rock mechanics in engineering practice, J. Wiley, London, 130, 9–124, 1968. a
Hoffman, J. P., Ackerman, S. A., Liu, Y., and Key, J. R.: The detection and characterization of Arctic Sea ice leads with satellite imagers, Remote Sens., 11, 521, https://doi.org/10.3390/rs11050521, 2019. a
Hunke, E. C.: Viscous–plastic sea ice dynamics with the EVP model: Linearization issues, J. Comput. Phys., 170, 18–38, 2001. a
Hunke, E. C. and Dukowicz, J. K.: An elastic–viscous–plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997. a
Hutter, N., Losch, M., and Menemenlis, D.: 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, 2018. a
Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D. S., Koldunov, N. V., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 2. Evaluating linear kinematic features in high-resolution sea ice simulations, J. Geophys. Res.-Oceans, 127, e2021JC017666, https://doi.org/10.1029/2021JC017666, 2022. a
Isaksson, P. and Ståhle, P.: Mode II crack paths under compression in brittle solids–a theory and experimental comparison, Int. J. Solids Struct., 39, 2281–2297, 2002a. a
Isaksson, P. and Ståhle, P.: Prediction of shear crack growth direction under compressive loading and plane strain conditions, Int. J. Fract., 113, 175–194, 2002b. a
Jason, L., Huerta, A., Pijaudier-Cabot, G., and Ghavamian, S.: An elastic plastic damage formulation for concrete: Application to elementary tests and comparison with an isotropic damage model, Comput. Method. Appl. M., 195, 7077–7092, https://doi.org/10.1016/j.cma.2005.04.017, 2006. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–472, 1996. a
Kreyscher, M., Harder, M., and Lemke, P.: First results of the Sea-Ice Model Intercomparison Project (SIMIP), Ann. Glaciol., 25, 8–11, 1997. a
Kreyscher, M., Harder, M., Lemke, P., and Flato, G. M.: Results of the Sea Ice Model Intercomparison Project: Evaluation of sea ice rheology schemes for use in climate simulations, J. Geophys. Res.-Oceans, 105, 11299–11320, 2000. a
Krupnik, I.: SIKU: knowing our ice: documenting Inuit sea ice knowledge and use, edited by: Krupnik, I., Aporta, C., Gearheard, S., Laidler, G. J., and Holm, K. L., Springer Dordrecht, 501 pp., https://doi.org/10.1007/978-90-481-8587-0, 2010. a
Kwok, R.: RADARSAT-1 data 1997–2008 (CSA), Dataset: Three-day Gridded Sea-Ice Kinematics Data Retrieved from ASF DAAC [data set], https://doi.org/10.5067/GWQU7WKQZBO4, 1997. a, b
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., Springer Netherlands, Dordrecht, 315–322, ISBN 978-94-015-9735-7, 2001. a
Kwok, R., Schweiger, A., Rothrock, D. A., Pang, S., and Kottmeier, C.: Sea ice motion from satellite passive microwave imagery assessed with ERS SAR and buoy motions, J. Geophys. Res.-Oceans, 103, 8191–8214, https://doi.org/10.1029/97JC03334, 1998. 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, b
Lemieux, J.-F., Tremblay, B., Thomas, S., Sedláček, J., and Mysak, L. A.: Using the preconditioned Generalized Minimum RESidual (GMRES) method to solve the sea-ice momentum equation, J. Geophys. Res.-Oceans, 113, C10004, https://doi.org/10.1029/2007JC004680, 2008. a, b, c
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, b, c, d
Lindsay, R. and Stern, H.: The RADARSAT geophysical processor system: Quality of sea ice trajectory and deformation estimates, J. Atmos. Ocean. Tech., 20, 1333–1347, 2003. a
Lovejoy, S. and Schertzer, D.: Scaling and multifractal fields in the solid earth and topography, Nonlin. Processes Geophys., 14, 465–502, https://doi.org/10.5194/npg-14-465-2007, 2007. a, b
Lubliner, J., Oliver, J., Oller, S., and Oñate, E.: A plastic-damage model for concrete, Int. J. Solids Struct., 25, 299–326, https://doi.org/10.1016/0020-7683(89)90050-4, 1989. a
Luccioni, B., Oller, S., and Danesi, R.: Coupled plastic-damaged model, Comput. Method. Appl. M., 129, 81–89, https://doi.org/10.1016/0045-7825(95)00887-X, 1996. a
Marsan, D. and Weiss, J.: Space/time coupling in brittle deformation at geophysical scales, Earth Planet. Sc. Lett., 296, 353–359, https://doi.org/10.1016/j.epsl.2010.05.019, 2010. a, b
McPhee, M. G.: Ice-ocean momentum transfer for the aidjex ice model, AIDJEX Bull., 29, 93–111, 1975. a
McPhee, M. G.: The Upper Ocean, Springer US, Boston, MA, 339–394, ISBN 978-1-4899-5352-0, https://doi.org/10.1007/978-1-4899-5352-0_5, 1986. a
McPhee, M. G., Kwok, R., Robins, R., and Coon, M.: Upwelling of Arctic pycnocline associated with shear motion of sea ice, Geophys. Res. Lett., 32, L10616, https://doi.org/10.1029/2004GL021819, 2005. a
Murdza, A., Schulson, E., and Renshaw, C.: Relaxation of flexure-induced strengthening of ice, Geophys. Res. Lett., 49, e2021GL096559, https://doi.org/10.1029/2021GL096559, 2022. a, b, c
Olason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz, M., Dansereau, V., and Samaké, A.: A New Brittle Rheology and Numerical Framework for Large-Scale Sea-Ice Models, J. Adv. Model. Earth Sy., 14, e2021MS002685, https://doi.org/10.1029/2021MS002685, 2022. a, b, c
Parisio, F. and Laloui, L.: Plastic-damage modeling of saturated quasi-brittle shales, Int. J. Rock Mech. Min., 93, 295–306, https://doi.org/10.1016/j.ijrmms.2017.01.016, 2017. a
Proshutinsky, A. and Johnson, M.: Arctic Ocean Oscillation Index (AOO): interannual and decadal changes of the Arctic climate, in: Geophys. Research Abstracts, Vienna, Austria, 3–8 April 2011, EGU2011-7850 13, 2011. a
Rampal, P., Weiss, J., Marsan, D., Lindsay, R., and Stern, H.: Scaling properties of sea ice deformation from buoy dispersion analysis, J. Geophys. Res.-Oceans, 113, C03002, https://doi.org/10.1029/2007JC004143, 2008. a
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a, b, c
Rigor, I. G., Wallace, J. M., and Colony, R. L.: Response of sea ice to the Arctic Oscillation, J. Climate, 15, 2648–2663, 2002. a
Savard, A.: McGill SIM plotting tools, Zenodo [code], https://doi.org/10.5281/zenodo.10798930, 2024a. a
Savard, A.: Plastic damage in sea ice – model outputs and observations, Zenodo [data set], https://doi.org/10.5281/zenodo.10830260, 2024b. a
Schmitt, F., Lovejoy, S., and Schertzer, D.: Multifractal analysis of the Greenland ice-core project climate data, Geophys. Res. Lett., 22, 1689–1692, 1995. a
Schreyer, H., Sulsky, D., Munday, L., Coon, M., 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. Geophy. Res.-Oceans, 109, C07016, https://doi.org/10.1029/2003JC002108, 2004. a
Steele, M., Morley, R., and Ermold, W.: PHC: A global ocean hydrography with a high-quality Arctic Ocean, J. Climate, 14, 2079–2087, 2001. a
Sulsky, D. and Peterson, K.: Toward a new elastic–decohesive model of Arctic sea ice, Phys. D, 240, 1674–1683, 2011. a
Tang, C.: Numerical simulation of progressive rock failure and associated seismicity, Int. J. Rock Mech. Min., 34, 249–261, https://doi.org/10.1016/S0148-9062(96)00039-3, 1997. a
Thompson, D. W. and Wallace, J. M.: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields, Geophys. Res. Lett., 25, 1297–1300, 1998. 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
Tsamados, M., Feltham, D. L., and Wilchinsky, A.: Impact of a new anisotropic rheology on simulations of Arctic sea ice, J. Geophys. Res.-Oceans, 118, 91–107, 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 A rctic, J. Geophys. Res.-Oceans, 122, 2090–2107, 2017. a
Voyiadjis, G. Z., Taqieddin, Z. N., and Kattan, P. I.: Anisotropic damage–plasticity model for concrete, Int. J. Plasticity, 24, 1946–1965, https://doi.org/10.1016/j.ijplas.2008.04.002, 2008. a
Weiss, J.: Intermittency of principal stress directions within Arctic sea ice, Phys. Rev. E, 77, 056106, https://doi.org/10.1103/PhysRevE.77.056106, 2008. a
Weiss, J.: Drift, Deformation, and Fracture of Sea Ice: A Perspective Across Scales, SpringerBriefs in Earth Sciences, Springer Netherlands, Dordrecht, ISBN 978-94-007-6201-5 978-94-007-6202-2, https://doi.org/10.1007/978-94-007-6202-2, 2013. a
Weiss, J.: Exploring the “solid turbulence” of sea ice dynamics down to unprecedented small scales, J. Geophys. Res.-Oceans, 122, 6071–6075, 2017. a
Weiss, J. and Dansereau, V.: Linking scales in sea ice mechanics, Philos. T. Roy. Soc. A, 375, 20150352, https://doi.org/10.1098/rsta.2015.0352, 2017. a
Wilchinsky, A. V. and Feltham, D. L.: Modelling the rheology of sea ice as a collection of diamond-shaped floes, J. Non-Newtonian Fluid Mech., 138, 22–32, 2006. 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
Short summary
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.
We include a suitable plastic damage parametrization in the standard viscous–plastic (VP) sea...