Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1013-2024
© 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-18-1013-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Mar 2024
Research article |
| 04 Mar 2024
| 04 Mar 2024
Smoothed particle hydrodynamics implementation of the standard viscous–plastic sea-ice model and validation in simple idealized experiments
Oreste Marquis
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
Jean-François Lemieux
Recherche en prévision numérique environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Mohammed Islam
The Ocean, Coastal and River Engineering Research Centre, National Research Council, St. John’s, Newfoundland, Canada
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Noemie Planat, Carolina Olivia Dufour, Camille Lique, Jan Klaus Rieck, Claude Talandier, and L. Bruno Tremblay
EGUsphere, https://doi.org/10.5194/egusphere-2025-3527, https://doi.org/10.5194/egusphere-2025-3527, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
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We detect and track mesoscale eddies in the Canadian Basin of the Arctic Ocean and describe their spatio-temporal characteristics in a high resolution pan-Arctic model. Results show eddies of typical size 12 km, lasting 10 days and travelling 11 km, with roughly an equal number of cyclones and anticyclones detected. Seasonal, decadal and interannual changes of the number of eddies detected show strong correlations with the ice cover, and with the mean circulation of the basin.
Jean-Francois Lemieux, Mathieu Plante, Nils Hutter, Damien Ringeisen, Bruno Tremblay, Francois Roy, and Philippe Blain
EGUsphere, https://doi.org/10.5194/egusphere-2024-3831, https://doi.org/10.5194/egusphere-2024-3831, 2025
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Sea ice models simulate angles between cracks that are too wide compared to observations. Ringeisen et al. argue that this is due to the flow rule which defines the fracture deformations. We implemented a non-normal flow rule. This flow rule also leads to angles that are too wide. This is a consequence of deformations that tend to align with the grid. Nevertheless, this flow rule could be used to optimize deformations while other parameters could be used to modify landfast ice and ice drift.
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
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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.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
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.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
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.
Gregory C. Smith, Yimin Liu, Mounir Benkiran, Kamel Chikhar, Dorina Surcel Colan, Audrey-Anne Gauthier, Charles-Emmanuel Testut, Frederic Dupont, Ji Lei, François Roy, Jean-François Lemieux, and Fraser Davidson
Geosci. Model Dev., 14, 1445–1467, https://doi.org/10.5194/gmd-14-1445-2021, https://doi.org/10.5194/gmd-14-1445-2021, 2021
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Canada's coastlines include diverse ocean environments. In response to the strong need to support marine activities and security, we present the first pan-Canadian operational regional ocean analysis system. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach. Innovations are compared to those from the Canadian global analysis system. Particular improvements are found near the Gulf Stream due to the higher model grid resolution.
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.
Jean-François Lemieux and Frédéric Dupont
Geosci. Model Dev., 13, 1763–1769, https://doi.org/10.5194/gmd-13-1763-2020, https://doi.org/10.5194/gmd-13-1763-2020, 2020
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Sea ice dynamics plays an important role in shaping the sea cover in polar regions. Winds and ocean currents exert large stresses on the sea ice cover. This can lead to the formation of long cracks and ridges, which strongly impact the exchange of heat, momentum and moisture between the atmosphere and the ocean. It is therefore crucial for a sea ice model to be able to represent these features. This article describes how internal sea ice stresses should be diagnosed from model simulations.
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.
Frédéric Laliberté, Stephen E. L. Howell, Jean-François Lemieux, Frédéric Dupont, and Ji Lei
The Cryosphere, 12, 3577–3588, https://doi.org/10.5194/tc-12-3577-2018, https://doi.org/10.5194/tc-12-3577-2018, 2018
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Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.
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.
F. Dupont, S. Higginson, R. Bourdallé-Badie, Y. Lu, F. Roy, G. C. Smith, J.-F. Lemieux, G. Garric, and F. Davidson
Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, https://doi.org/10.5194/gmd-8-1577-2015, 2015
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1/12th degree resolution runs of Arctic--Atlantic were compared for the period 2003-2009. We found good representation of sea surface height and of its statistics; model temperature and salinity in general agreement with in situ measurements, but upper ocean properties in Beaufort Sea are challenging; distribution of concentration and volume of sea ice is improved when slowing down the ice and further improvements require better initial conditions and modifications to mixing.
Related subject area
Discipline: Sea ice | Subject: Numerical Modelling
A hybrid ice-mélange model based on particle and continuum methods
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Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics
Understanding the influence of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere–ocean–wave–sea ice coupled model
Sea ice cover in the Copernicus Arctic Regional Reanalysis
Phase-field models of floe fracture in sea ice
The effect of partial dissolution on sea-ice chemical transport: a combined model–observational study using poly- and perfluoroalkylated substances (PFASs)
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology
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Toward a method for downscaling sea ice pressure for navigation purposes
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Wave energy attenuation in fields of colliding ice floes – Part 1: Discrete-element modelling of dissipation due to ice–water drag
Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
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Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
The Cryosphere, 19, 129–141, https://doi.org/10.5194/tc-19-129-2025, https://doi.org/10.5194/tc-19-129-2025, 2025
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Ice mélange, a mixture of sea ice and icebergs, can impact sea-ice–ocean interactions. But climate models do not yet represent it due to computational limits. To address this shortcoming and include ice mélange into climate models, we suggest representing icebergs as particles. We integrate their feedback into mathematical equations used to model the sea-ice motion in climate models. The setup is computationally efficient due to the iceberg particle usage and enables a realistic representation.
Polona Itkin and Glen E. Liston
EGUsphere, https://doi.org/10.5194/egusphere-2024-3402, https://doi.org/10.5194/egusphere-2024-3402, 2024
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The MOSAiC project provided a year of observations of Arctic snow and sea ice, though some data were interrupted, especially during summer melt onset. We developed a data-model fusion system to produce continuous, high-resolution time series of snow and sea ice parameters. On all three analyzed three ice types snow redistribution correlated with sea ice deformation and level ice thickness was governed by the thinnest fraction of snow cover.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
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We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
Christopher Riedel and Jeffrey Anderson
The Cryosphere, 18, 2875–2896, https://doi.org/10.5194/tc-18-2875-2024, https://doi.org/10.5194/tc-18-2875-2024, 2024
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Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
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.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
The Cryosphere, 18, 1215–1239, https://doi.org/10.5194/tc-18-1215-2024, https://doi.org/10.5194/tc-18-1215-2024, 2024
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We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Huy Dinh, Dimitrios Giannakis, Joanna Slawinska, and Georg Stadler
The Cryosphere, 17, 3883–3893, https://doi.org/10.5194/tc-17-3883-2023, https://doi.org/10.5194/tc-17-3883-2023, 2023
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We develop a numerical method to simulate the fracture in kilometer-sized chunks of floating ice in the ocean. Our approach uses a mathematical model that balances deformation energy against the energy required for fracture. We study the strength of ice chunks that contain random impurities due to prior damage or refreezing and what types of fractures are likely to occur. Our model shows that crack direction critically depends on the orientation of impurities relative to surrounding forces.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
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A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
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We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
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Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
The Cryosphere, 16, 4447–4472, https://doi.org/10.5194/tc-16-4447-2022, https://doi.org/10.5194/tc-16-4447-2022, 2022
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On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or floes, are more easily melted by the warming waters than a continuous ice cover, it is important to incorporate these floe sizes in climate models. These models simulate climate evolution at the century scale and are built by combining specialised modules. We study the statistical distribution of floe sizes under the impact of waves to better understand how to connect sea ice modules to wave modules.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
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We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
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Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
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Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arne Melsom
The Cryosphere, 15, 3785–3796, https://doi.org/10.5194/tc-15-3785-2021, https://doi.org/10.5194/tc-15-3785-2021, 2021
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This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.
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.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
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The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
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The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
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Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
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Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Evelyn Jäkel, Johannes Stapf, Manfred Wendisch, Marcel Nicolaus, Wolfgang Dorn, and Annette Rinke
The Cryosphere, 13, 1695–1708, https://doi.org/10.5194/tc-13-1695-2019, https://doi.org/10.5194/tc-13-1695-2019, 2019
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The sea ice surface albedo parameterization of a coupled regional climate model was validated against aircraft measurements performed in May–June 2017 north of Svalbard. The albedo parameterization was run offline from the model using the measured parameters surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes. An adjustment of the variables and additionally accounting for cloud cover reduced the root-mean-squared error.
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.
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
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The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Ann Keen and Ed Blockley
The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, https://doi.org/10.5194/tc-12-2855-2018, 2018
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As the climate warms during the 21st century, our model shows extra melting at the top and the base of the Arctic sea ice. The reducing ice cover affects the impact these processes have on the sea ice volume budget, where the largest individual change is a reduction in the amount of growth at the base of existing ice. Using different forcing scenarios we show that, for this model, changes in the volume budget depend on the evolving ice area but not on the speed at which the ice area declines.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
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Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O., Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M., Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S., Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels, B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B., Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features, J. Adv. Model. Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019ms001726, 2019. a
Beatty, C. and Holland, D.: Modeling landfast sea ice by adding tensile strength, J. Phys. Oceanogr., 40, 185–198, https://doi.org/10.1175/2009JPO4105.1, 2010. a
Belytschko, T., Krongauz, Y., Dolbow, J., and Gerlach, C.: On the completeness of meshfree particle methods, Int. J. Numer. Meth. Eng., 43, 785–819, https://doi.org/10.1002/(sici)1097-0207(19981115)43:5<785::aid-nme420>3.0.co;2-9, 1998. a, b
Bouchat, A. and Tremblay, B.: Using sea-ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice, J. Geophys. Res.-Oceans, 122, 5802–5825, https://doi.org/10.1002/2017jc013020, 2017. 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, Q.: 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
Cavelan, A., Cabezón, R. M., Korndorfer, J. H. M., and Ciorba, F. M.: Finding Neighbors in a Forest: A b-tree for Smoothed Particle Hydrodynamics Simulations, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.02639, 7 October 2019. a
Chalk, C., Pastor, M., Peakall, J., Borman, D., Sleigh, P., Murphy, W., and Fuentes, R.: Stress-Particle Smoothed Particle Hydrodynamics: An application to the failure and post-failure behaviour of slopes, Comput. Meth. Appl. M., 366, 113034, https://doi.org/10.1016/j.cma.2020.113034, 2020. a, b
Chen, J. and Beraun, J.: A generalized smoothed particle hydrodynamics method for nonlinear dynamic problems, Comput. Meth. Appl. M., 190, 225–239, https://doi.org/10.1016/s0045-7825(99)00422-3, 2000. 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., 112, C11S90, https://doi.org/10.1029/2005jc003393, 2007. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. 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
Dansereau, V., Weiss, J., Saramito, P., Lattes, P., and Coche, E.: Ice bridges and ridges in the Maxwell-EB sea ice rheology, The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, 2017. a, b, c, d
Dehnen, W. and Aly, H.: Improving convergence in smoothed particle hydrodynamics simulations without pairing instability, Mon. Not. R. Astron. Soc., 425, 1068–1082, https://doi.org/10.1111/j.1365-2966.2012.21439.x, 2012. a, b
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, c
Dyka, C. and Ingel, R.: An approach for tension instability in smoothed particle hydrodynamics (SPH), Comput. Struct., 57, 573–580, https://doi.org/10.1016/0045-7949(95)00059-p, 1995. a
Flato, G. M.: A particle-in-cell sea-ice model, Atmos. Ocean, 31, 339–358, https://doi.org/10.1080/07055900.1993.9649475, 1993. a
Fleissner, F., Gaugele, T., and Eberhard, P.: Applications of the discrete element method in mechanical engineering, Multibody Syst. Dyn., 18, 81–94, https://doi.org/10.1007/s11044-007-9066-2, 2007. a
Fraga Filho, C. A.: Smoothed Particle Hydrodynamics: Fundamentals and Basic Applications in Continuum Mechanics, Springer, ISBN 978-3-030-00772-0, https://doi.org/10.1007/978-3-030-00773-7, 2019. a
Gingold, R. A. and Monaghan, J. J.: Smoothed particle hydrodynamics: theory and application to non-spherical stars, Mon. Not. R. Astron. Soc., 181, 375–389, https://doi.org/10.1093/mnras/181.3.375, 1977. 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
Gray, J. and Morland, L.: A Two-Dimensional Model for the Dynamics of Sea Ice, Philos. T. Roy. Soc. B, 347, 219–290, https://doi.org/10.1098/rsta.1994.0045, 1994. a, b
Gray, J. M. N. T.: Loss of Hyperbolicity and Ill-posedness of the Viscous–Plastic Sea Ice Rheology in Uniaxial Divergent Flow, J. Phys. Oceanogr., 29, 2920–2929, https://doi.org/10.1175/1520-0485(1999)029<2920:lohaip>2.0.co;2, 1999. a
Gutfraind, R. and Savage, S. B.: Smoothed Particle Hydrodynamics for the Simulation of Broken-Ice Fields: Mohr–Coulomb-Type Rheology and Frictional Boundary Conditions, J. Comput. Phys., 134, 203–215, https://doi.org/10.1006/jcph.1997.5681, 1997. 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
Hibler, W., Hutchings, J., and Ip, C.: sea-ice arching and multiple flow States of Arctic pack ice, Ann. Glaciol., 44, 339–344, https://doi.org/10.3189/172756406781811448, 2006. a
Hopkins, M. A. and Thorndike, A. S.: Floe formation in Arctic sea ice, J. Geophys. Res., 111, C11S23, https://doi.org/10.1029/2005jc003352, 2006. a
Hosseini, K., Omidvar, P., Kheirkhahan, M., and Farzin, S.: Smoothed particle hydrodynamics for the interaction of Newtonian and non-Newtonian fluids using the μ(I) model, Powder Technol., 351, 325–337, https://doi.org/10.1016/j.powtec.2019.02.045, 2019. 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
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
Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D., Koldunov, N., 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, https://doi.org/10.1029/2021jc017666, 2022. a, b, c
Johnson, G. R. and Beissel, S. R.: Normalized smoothing functions for sph impact computations, Int. J. Numer. Meth. Eng., 39, 2725–2741, https://doi.org/10.1002/(SICI)1097-0207(19960830)39:16<2725::AID-NME973>3.0.CO;2-9, 1996. 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, https://doi.org/10.1029/1999jc000016, 2000. a
Lahiri, S. K., Bhattacharya, K., Shaw, A., and Ramachandra, L. S.: A stable SPH with adaptive B-spline kernel, ArXiv [preprint], https://doi.org/10.48550/arXiv.2001.03416, 4 January 2020. a
Lemieux, J.-F. and Tremblay, B.: Numerical convergence of viscous-plastic sea ice models, J. Geophys. Res., 114, C05009, https://doi.org/10.1029/2008jc005017, 2009. a
Li, B., Li, H., Liu, Y., Wang, A., and Ji, S.: A modified discrete element model for sea ice dynamics, Acta Oceanol. Sin., 33, 56–63, https://doi.org/10.1007/s13131-014-0428-3, 2014. a
Lilja, V.-P., Polojärvi, A., Tuhkuri, J., and Paavilainen, J.: Finite-discrete element modelling of sea ice sheet fracture, Int. J. Solids Struct., 217-218, 228–258, https://doi.org/10.1016/j.ijsolstr.2020.11.028, 2021. a
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging, strength, and stability in high-resolution sea ice models, J. Geophys. Res., 112, C03S91, https://doi.org/10.1029/2005jc003355, 2007. a
Liu, M. B., Liu, G. R., and Lam, K. Y.: A one-dimensional meshfree particle formulation for simulating shock waves, Shock Waves, 13, 201–211, https://doi.org/10.1007/s00193-003-0207-0, 2003. 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
Lucy, L. B.: A numerical approach to the testing of the fission hypothesis, Astron. J., 82, 1013, https://doi.org/10.1086/112164, 1977. a
Marquis, O.: McGill-sea-ice/SIMP: Sea Ice Modelling Particles (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.10714497, 2024. a
Marquis, O., Tremblay, B., Lemieux, J.-F., and Islam, M.: Smoothed Particle Hydrodynamics Implementation of the Standard Viscous-Plastic Sea-Ice Model and Validation in Simple Idealized Experiments, Zenodo [data set], https://doi.org/10.5281/zenodo.6950156, 2022. a
McPhee, M. G.: The Effect of the Oceanic Boundary Layer on the Mean Drift of Pack Ice: Application of a Simple Model, J. Phys. Oceanogr., 9, 388–400, https://doi.org/10.1175/1520-0485(1979)009<0388:teotob>2.0.co;2, 1979. a
Mehlmann, C., Danilov, S., Losch, M., Lemieux, J. F., Hutter, N., Richter, T., Blain, P., Hunke, E. C., and Korn, P.: Simulating Linear Kinematic Features in Viscous-Plastic Sea Ice Models on Quadrilateral and Triangular Grids With Different Variable Staggering, J. Adv. Model. Earth Sy., 13, e2021MS002523, https://doi.org/10.1029/2021ms002523, 2021. a, b
Monaghan, J.: SPH without a Tensile Instability, J. Comput. Phys., 159, 290–311, https://doi.org/10.1006/jcph.2000.6439, 2000. a
Monaghan, J.: Smoothed Particle Hydrodynamics and Its Diverse Applications, Annu. Rev. Fluid Mech., 44, 323–346, https://doi.org/10.1146/annurev-fluid-120710-101220, 2012. a, b
Monaghan, J. and Kajtar, J.: SPH particle boundary forces for arbitrary boundaries, Comput. Phys. Commun., 180, 1811–1820, https://doi.org/10.1016/j.cpc.2009.05.008, 2009. a
Monaghan, J. J.: Smoothed particle hydrodynamics, Rep. Prog. Phys., 68, 1703–1759, https://doi.org/10.1088/0034-4885/68/8/r01, 2005. a
Morland, L. W. and Staroszczyk, R.: A material coordinate treatment of the sea–ice dynamics equations, P. Roy. Soc. Lond. A, 454, 2819–2857, https://doi.org/10.1098/rspa.1998.0283, 1998. a
Morris, J. P., Fox, P. J., and Zhu, Y.: Modeling Low Reynolds Number Incompressible Flows Using SPH, J. Computa. Phys., 136, 214–226, https://doi.org/10.1006/jcph.1997.5776, 1997. a
Natanson, I. P.: Theory of Functions of a Real Variable, vol. 2, Frederick Ungar Publishing Co., New York, ISBN 9780486806433, 1961. a
Overland, J. E., McNutt, S. L., Salo, S., Groves, J., and Li, S.: Arctic sea ice as a granular plastic, J. Geophys. Res.-Oceans, 103, 21845–21867, https://doi.org/10.1029/98jc01263, 1998. a
Peiró, J. and Sherwin, S.: Finite difference, finite element and finite volume methods for partial differential equations, in: Handbook of materials modeling, 2415–2446, Springer, https://doi.org/10.1007/978-1-4020-3286-8_127, 2005. a
Rabatel, M., Labbé, S., and Weiss, J.: Dynamics of an assembly of rigid ice floes, J. Geophys. Res.-Oceans, 120, 5887–5909, https://doi.org/10.1002/2015jc010909, 2015. 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
Ranta, J., Polojärvi, A., and Tuhkuri, J.: Limit mechanisms for ice loads on inclined structures: Buckling, Cold Reg. Sci. Technol., 147, 34–44, https://doi.org/10.1016/j.coldregions.2017.12.009, 2018. a
Rhoades, C. E.: A fast algorithm for calculating particle interactions in smooth particle hydrodynamic simulations, Comput. Phys. Commun., 70, 478–482, https://doi.org/10.1016/0010-4655(92)90109-c, 1992. a
Ringeisen, D., Losch, M., Tremblay, L. B., and Hutter, N.: Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies, The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, 2019. a, b
Salehizadeh, A. M. and Shafiei, A. R.: Modeling of granular column collapses with μ(I) rheology using smoothed particle hydrodynamic method, Granul. Matter, 21, 32, https://doi.org/10.1007/s10035-019-0886-6, 2019. 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., 111, C11S26, https://doi.org/10.1029/2005jc003334, 2006. a, b
Schulson, E. M.: Compressive shear faults within arctic sea ice: Fracture on scales large and small, J. Geophys. Res., 109, C07016, https://doi.org/10.1029/2003jc002108, 2004. a
Sheikh, B., Qiu, T., and Ahmadipur, A.: Comparison of SPH boundary approaches in simulating frictional soil–structure interaction, Acta Geotech., 16, 2389–2408, https://doi.org/10.1007/s11440-020-01063-y, 2020. a
Shen, H. T., Shen, H., and Tsai, S.-M.: Dynamic transport of river ice, J. Hydraul. Res., 28, 659–671, https://doi.org/10.1080/00221689009499017, 1990. a
Staroszczyk, R.: Simulation of Sea-ice Thermodynamics by a Smoothed Particle Hydrodynamics Method, Archives of Hydro-Engineering and Environmental Mechanics, 65, 277–299, https://doi.org/10.1515/heem-2018-0017, 2018. a
Sulsky, D., Schreyer, H., Peterson, K., Kwok, R., and Coon, M.: Using the material-point method to model sea ice dynamics, J. Geophys. Res., 112, C02S90, https://doi.org/10.1029/2005jc003329, 2007. a
Sun, P., Colagrossi, A., Marrone, S., Antuono, M., and Zhang, A.: Multi-resolution Delta-plus-SPH with tensile instability control: Towards high Reynolds number flows, Comput. Phys. Commun., 224, 63–80, https://doi.org/10.1016/j.cpc.2017.11.016, 2018. a
Sutherland, P. and Dumont, D.: Marginal Ice Zone Thickness and Extent due to Wave Radiation Stress, J. Phys. Oceanogr., 48, 1885–1901, https://doi.org/10.1175/jpo-d-17-0167.1, 2018. a
Swegle, J., Hicks, D., and Attaway, S.: Smoothed Particle Hydrodynamics Stability Analysis, J. Comput. Phys., 116, 123–134, https://doi.org/10.1006/jcph.1995.1010, 1995. a, b
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
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
Wendland, H.: Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree, Adv. Comput. Math., 4, 389–396, https://doi.org/10.1007/bf02123482, 1995. a
West, B., O'Connor, D., Parno, M., Krackow, M., and Polashenski, C.: Bonded Discrete Element Simulations of Sea Ice With Non-Local Failure: Applications to Nares Strait, J. Adv. Model. Earth Syst., 14, e2021MS002614, https://doi.org/10.1029/2021ms002614, 2022. a, b
Williams, J. and Tremblay, L. B.: The dependence of energy dissipation on spatial resolution in a viscous-plastic sea-ice model, Ocean Model., 130, 40–47, https://doi.org/10.1016/j.ocemod.2018.08.001, 2018. a
Xia, X. and Liang, Q.: A GPU-accelerated smoothed particle hydrodynamics (SPH) model for the shallow water equations, Environ. Model. Softw., 75, 28–43, https://doi.org/10.1016/j.envsoft.2015.10.002, 2016. a
Yang, E., Bui, H. H., Sterck, H. D., Nguyen, G. D., and Bouazza, A.: A scalable parallel computing SPH framework for predictions of geophysical granular flows, Comput. Geotech., 121, 103474, https://doi.org/10.1016/j.compgeo.2020.103474, 2020. a, b
Zhang, N., Zheng, X., and Ma, Q.: Updated Smoothed Particle Hydrodynamics for Simulating Bending and Compression Failure Progress of Ice, Water, 9, 882, https://doi.org/10.3390/w9110882, 2017. a
Short summary
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.
We developed a standard viscous–plastic sea-ice model based on the numerical framework called...
