Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2429-2024
© 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-18-2429-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 May 2024
Research article |
| 16 May 2024
| 16 May 2024
A large-scale high-resolution numerical model for sea-ice fragmentation dynamics
Jan Åström
CORRESPONDING AUTHOR
CSC – IT Center for Science Ltd., P.O. Box 405, 02101 Espoo, Finland
Fredrik Robertsen
CSC – IT Center for Science Ltd., P.O. Box 405, 02101 Espoo, Finland
Jari Haapala
Marine Research Unit, Finnish Meteorological Institute, PL 503, 00101 Helsinki, Finland
Arttu Polojärvi
Department of Mechanical Engineering, School of Engineering, Aalto University, P.O. Box 14100, 00076 Aalto, Finland
Rivo Uiboupin
Department of Marine Systems, Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, Estonia
Ilja Maljutenko
Department of Marine Systems, Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, Estonia
Related authors
Matias Uusinoka, Jari Haapala, Jan Åström, Mikko Lensu, and Arttu Polojärvi
EGUsphere, https://doi.org/10.5194/egusphere-2025-311, https://doi.org/10.5194/egusphere-2025-311, 2025
Short summary
Short summary
We tracked sea ice deformation over a nine-month period using high-resolution ship radar data and a state-of-the-art deep learning technique. We observe that the typically consistent scale-invariant pattern in sea ice deformation has a lower limit of about 102 meters in winter, but this behavior disappears during summer. Our findings provide critical insights for considering current modeling assumptions and for connecting the scales of interest in sea ice dynamics.
Douglas I. Benn, Adrian Luckman, Jan A. Åström, Anna J. Crawford, Stephen L. Cornford, Suzanne L. Bevan, Thomas Zwinger, Rupert Gladstone, Karen Alley, Erin Pettit, and Jeremy Bassis
The Cryosphere, 16, 2545–2564, https://doi.org/10.5194/tc-16-2545-2022, https://doi.org/10.5194/tc-16-2545-2022, 2022
Short summary
Short summary
Thwaites Glacier (TG), in West Antarctica, is potentially unstable and may contribute significantly to sea-level rise as global warming continues. Using satellite data, we show that Thwaites Eastern Ice Shelf, the largest remaining floating extension of TG, has started to accelerate as it fragments along a shear zone. Computer modelling does not indicate that fragmentation will lead to imminent glacier collapse, but it is clear that major, rapid, and unpredictable changes are underway.
Amirhossein Barzandeh, Matjaž Ličer, Marko Rus, Matej Kristan, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, and Rivo Uiboupin
Ocean Sci., 21, 1315–1327, https://doi.org/10.5194/os-21-1315-2025, https://doi.org/10.5194/os-21-1315-2025, 2025
Short summary
Short summary
We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast and compared it to traditional numerical models. HIDRA2 performed better overall, offering faster forecasts and valuable uncertainty estimates using ensemble predictions.
Igor V. Polyakov, Andrey V. Pnyushkov, Eddy C. Carmack, Matthew Charette, Kyoung-Ho Cho, Steven Dykstra, Jari Haapala, Jinyoung Jung, Lauren Kipp, and Eun Jin Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2316, https://doi.org/10.5194/egusphere-2025-2316, 2025
Short summary
Short summary
The Siberian Arctic Ocean greatly influences the Arctic climate system. Moreover, the region is experiencing some of the most notable Arctic climate change. In the summer, strong near-inertial currents in the upper (<30m) ocean account for more than half of the current speed and shear. In the winter, upper ocean ventilation due to atlantification distributes wind energy to far deeper (>100m) layers. Understanding the implications for mixing and halocline weakening depends on these findings.
Matias Uusinoka, Jari Haapala, Jan Åström, Mikko Lensu, and Arttu Polojärvi
EGUsphere, https://doi.org/10.5194/egusphere-2025-311, https://doi.org/10.5194/egusphere-2025-311, 2025
Short summary
Short summary
We tracked sea ice deformation over a nine-month period using high-resolution ship radar data and a state-of-the-art deep learning technique. We observe that the typically consistent scale-invariant pattern in sea ice deformation has a lower limit of about 102 meters in winter, but this behavior disappears during summer. Our findings provide critical insights for considering current modeling assumptions and for connecting the scales of interest in sea ice dynamics.
Marek Muchow and Arttu Polojärvi
The Cryosphere, 18, 4765–4774, https://doi.org/10.5194/tc-18-4765-2024, https://doi.org/10.5194/tc-18-4765-2024, 2024
Short summary
Short summary
We present the first explicit three-dimensional simulations of sea-ice ridge formation, which enables us to observe failure in several locations simultaneously. Sea-ice ridges are formed when ice converges and fails due to wind and ocean currents, so broken ice accumulates in a ridge. Previous two-dimensional models could not capture this behavior. We conclude that non-simultaneous failure is necessary to simulate ridging forces to assess how ridging forces relate to other ice properties.
Jüri Elken, Ilja Maljutenko, Priidik Lagemaa, Rivo Uiboupin, and Urmas Raudsepp
State Planet, 4-osr8, 9, https://doi.org/10.5194/sp-4-osr8-9-2024, https://doi.org/10.5194/sp-4-osr8-9-2024, 2024
Short summary
Short summary
Baltic deep water is generally warmer than surface water during winter when district heating is required. Depending on the location, depth, and oceanographic situation, bottom water of Tallinn Bay can be used as an energy source for seawater heat pumps until the end of February, covering the major time interval when heating is needed. Episodically, there are colder-water events when seawater heat extraction has to be complemented by other sources of heating energy.
Anja Lindenthal, Claudia Hinrichs, Simon Jandt-Scheelke, Tim Kruschke, Priidik Lagemaa, Eefke M. van der Lee, Ilja Maljutenko, Helen E. Morrison, Tabea R. Panteleit, and Urmas Raudsepp
State Planet, 4-osr8, 16, https://doi.org/10.5194/sp-4-osr8-16-2024, https://doi.org/10.5194/sp-4-osr8-16-2024, 2024
Short summary
Short summary
In 2022, large parts of the Baltic Sea experienced the third-warmest to warmest summer and autumn temperatures since 1997 and several marine heatwaves (MHWs). Using remote sensing, reanalysis, and in situ data, this study characterizes regional differences in MHW properties in the Baltic Sea in 2022. Furthermore, it presents an analysis of long-term trends and the relationship between atmospheric warming and MHW occurrences, including their propagation into deeper layers.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Urmas Raudsepp, Ilja Maljutenko, Priidik Lagemaa, and Karina von Schuckmann
State Planet Discuss., https://doi.org/10.5194/sp-2024-19, https://doi.org/10.5194/sp-2024-19, 2024
Revised manuscript accepted for SP
Short summary
Short summary
Over the last three decades, the Baltic Sea has experienced rising temperature and salinity, reflecting broader atmospheric warming. Heat content fluctuations are driven by subsurface temperature changes in the upper 100 meters, including the thermocline and halocline, influenced by air temperature, evaporation, and wind stress. Freshwater content changes mainly result from salinity shifts in the halocline, with saline water inflow, precipitation, and wind stress as key factors.
Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin
EGUsphere, https://doi.org/10.5194/egusphere-2024-1701, https://doi.org/10.5194/egusphere-2024-1701, 2024
Short summary
Short summary
The sea ice statistics study highlights the bias in model estimations compared to satellite data and provides a simple approach to minimise that. During the study period, the model estimates sea ice forming slightly earlier but aligns well with the satellite data for ice season's end. Rapid decrease in the sea ice parameters is observed across the Baltic Sea, especially the ice thickness in the Bothnian Bay sub-basin. These statistics could be crucial for regional adaptation strategies.
Urmas Raudsepp, Ilja Maljutenko, Amirhossein Barzandeh, Rivo Uiboupin, and Priidik Lagemaa
State Planet, 1-osr7, 7, https://doi.org/10.5194/sp-1-osr7-7-2023, https://doi.org/10.5194/sp-1-osr7-7-2023, 2023
Short summary
Short summary
The freshwater content in the Baltic Sea has wide sub-regional variability characterized by the local climate dynamics. The total freshwater content trend is negative due to the recent increased inflows of saltwater, but there are also regions where the increase in runoff and decrease in ice content have led to an increase in the freshwater content.
Douglas I. Benn, Adrian Luckman, Jan A. Åström, Anna J. Crawford, Stephen L. Cornford, Suzanne L. Bevan, Thomas Zwinger, Rupert Gladstone, Karen Alley, Erin Pettit, and Jeremy Bassis
The Cryosphere, 16, 2545–2564, https://doi.org/10.5194/tc-16-2545-2022, https://doi.org/10.5194/tc-16-2545-2022, 2022
Short summary
Short summary
Thwaites Glacier (TG), in West Antarctica, is potentially unstable and may contribute significantly to sea-level rise as global warming continues. Using satellite data, we show that Thwaites Eastern Ice Shelf, the largest remaining floating extension of TG, has started to accelerate as it fragments along a shear zone. Computer modelling does not indicate that fragmentation will lead to imminent glacier collapse, but it is clear that major, rapid, and unpredictable changes are underway.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, https://doi.org/10.5194/esd-13-457-2022, 2022
Short summary
Short summary
Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge about the effects of global warming on past and future changes in the climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere.
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
Short summary
Short summary
A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
Short summary
Short summary
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
Short summary
Short summary
Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
Short summary
Short summary
We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
Andrii Murdza, Arttu Polojärvi, Erland M. Schulson, and Carl E. Renshaw
The Cryosphere, 15, 2957–2967, https://doi.org/10.5194/tc-15-2957-2021, https://doi.org/10.5194/tc-15-2957-2021, 2021
Short summary
Short summary
The strength of refrozen floes or piles of ice rubble is an important factor in assessing ice-structure interactions, as well as the integrity of an ice cover itself. The results of this paper provide unique data on the tensile strength of freeze bonds and are the first measurements to be reported. The provided information can lead to a better understanding of the behavior of refrozen ice floes and better estimates of the strength of an ice rubble pile.
Jukka-Pekka Jalkanen, Lasse Johansson, Magda Wilewska-Bien, Lena Granhag, Erik Ytreberg, K. Martin Eriksson, Daniel Yngsell, Ida-Maja Hassellöv, Kerstin Magnusson, Urmas Raudsepp, Ilja Maljutenko, Hulda Winnes, and Jana Moldanova
Ocean Sci., 17, 699–728, https://doi.org/10.5194/os-17-699-2021, https://doi.org/10.5194/os-17-699-2021, 2021
Short summary
Short summary
This modelling study describes a methodology for describing pollutant discharges from ships to the sea. The pilot area used is the Baltic Sea area and discharges of bilge, ballast, sewage, wash water as well as stern tube oil are reported for the year 2012. This work also reports the release of SOx scrubber effluents. This technique may be used by ships to comply with tight sulfur limits inside Emission Control Areas, but it also introduces a new pollutant stream from ships to the sea.
Iman E. Gharamti, John P. Dempsey, Arttu Polojärvi, and Jukka Tuhkuri
The Cryosphere, 15, 2401–2413, https://doi.org/10.5194/tc-15-2401-2021, https://doi.org/10.5194/tc-15-2401-2021, 2021
Short summary
Short summary
We study the creep and fracture behavior of 3 m × 6 m floating edge-cracked rectangular plates of warm columnar freshwater S2 ice under creep/cyclic-recovery loading and monotonic loading to fracture. Under the testing conditions, the ice response was elastic–viscoplastic; no significant viscoelasticity or major recovery was detected. There was no clear effect of the creep/cyclic loading on the fracture properties: failure load and crack opening displacements at crack growth initiation.
Lasse Johansson, Erik Ytreberg, Jukka-Pekka Jalkanen, Erik Fridell, K. Martin Eriksson, Maria Lagerström, Ilja Maljutenko, Urmas Raudsepp, Vivian Fischer, and Eva Roth
Ocean Sci., 16, 1143–1163, https://doi.org/10.5194/os-16-1143-2020, https://doi.org/10.5194/os-16-1143-2020, 2020
Short summary
Short summary
Very little is currently known about the activities and emissions of private leisure boats. To change this, a new model was created (BEAM). The model was used for the Baltic Sea to estimate leisure boat emissions, also considering antifouling paint leach. When compared to commercial shipping, the modeled leisure boat emissions were seen to be surprisingly large for some pollutant species, and these emissions were heavily concentrated on coastal inhabited areas during summer and early autumn.
Cited articles
Acheson, D. J.: Elementary Fluid Dynamics, Oxford University Press, 205, ISBN 0-19-859679-0, 1990. a
Astrom, J.: A small portion of a Kvarken simulation: A large-scale high-resolution numerical model for sea-ice fragmentation dynamics, Zenodo [video], https://doi.org/10.5281/zenodo.10471034, 2024. a
Åström, J. A. and Benn, D. I.: Effective rheology across the fragmentation transition for sea ice and ice shelves, Geophys. Res. Lett., 46, 13099–13106, 2019.
Åström, J. A., Riikilä, T. I., Tallinen, T., Zwinger, T., Benn, D., Moore, J. C., and Timonen, J.: A particle based simulation model for glacier dynamics, The Cryosphere, 7, 1591–1602, https://doi.org/10.5194/tc-7-1591-2013, 2013. a
Babic, M., Shen, H., and Bjedov, G.:Discrete element simulations of river ice transport. InProc. of the 12th IAHR Int. Symposium on Ice, 1, 564–574, Espoo, Finland, 1990. a
Baltic Sea Hydrographic Commission: Baltic Sea Bathymetry Database version 0.9, Baltic Sea Hydrographic Commission [data set], https://www.bshc.pro/data/ (last access: 14 May 2024), 2013. a
Benn, D. I., Luckman, A., Åström, J. A., Crawford, A. J., Cornford, S. L., Bevan, S. L., Zwinger, T., Gladstone, R., Alley, K., Pettit, E., and Bassis, J.: Rapid fragmentation of Thwaites Eastern Ice Shelf, The Cryosphere, 16, 2545–2564, https://doi.org/10.5194/tc-16-2545-2022, 2022. a
Blockley, E., Vancoppenolle, M., Hunke, E., Bitz, C., Feltham, D., Lemieux, J.-F., Losch, M., Maisonnave, E., Notz, D., Rampal, P., Tietsche, S., Tremblay, B., Turner, A., Massonnet, F., Ólason, E., Roberts, A., Aksenov, Y., Fichefet, T., Garric, G., Iovino, D., Madec, G., Rousset, C., Salas y Melia, D., and Schroeder, D.: The future of sea ice modelling. Toward defining a cutting-edge future for sea ice modelling: An International workshop, Laugarvatn, Iceland, 23-26 September 2019, B. Am. Meterol. Soc., E1304–E1311, https://doi.org/10.1175/BAMS-D-20-0073.1, 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, 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
Damsgaard, A., Adcroft, A., and Sergienko, O.: Application of Discrete Element Methods to Approximate Sea Ice Dynamics, J. Adv. Model. Earth Sy., 9, 2228–2244, 2018. a
Damsgaard, A., Sergienko, O., and Adcroft, A.: The Effects of Ice Floe-Floe Interactions on Pressure Ridging in Sea Ice, J. Adv. Model. Earth Sy., 13, e2020MS002336, https://doi.org/10.1029/2020MS002336, 2021. 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
Denton, A. A. and Timmermans, M.-L.: Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery, The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, 2022. a, b
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., 114, C08015, https://doi.org/10.1029/2008JC005182, 2009. a
Hibler, W. D. I.: A viscous sea ice law as a stochastic average of plasticity, J. Geophys. Res., 82, 3932–3938, 1977. a
Hibler, W. D. I.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 815–846, 1979. 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., 127, e2021JC017666, https://doi.org/10.1029/2021JC017666, 2022. a
Hopkins, M. and Hibler III, W. D.: Numerical simulations of a compact convergent system of ice floes, Ann. Glaciol., 15, 26–30, https://doi.org/10.3189/1991AoG15-1-26-30, 1991. a
Hopkins, M. and Thorndike, A. S.: Floe formation in Arctic sea ice, J. Geophys. Res.-Oceans, 111, C11, https://doi.org/10.1029/2005JC003352, 2006. a
Kärnä, T., Ljungemyr, P., Falahat, S., Ringgaard, I., Axell, L., Korabel, V., Murawski, J., Maljutenko, I., Lindenthal, A., Jandt-Scheelke, S., Verjovkina, S., Lorkowski, I., Lagemaa, P., She, J., Tuomi, L., Nord, A., and Huess, V.: Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea, Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, 2021. a
Manucharyan, G. E. and Montemuro, B. P.: SubZero: A sea ice model with an explicit representation of the floe life cycle, J. Adv. Model. Earth Sy., 14, e2022MS003247, https://doi.org/10.1029/2022MS003247, 2022. a
Meyers, M. A. and Chawla, K. K.: Mechanical behavior of Materials, Prentice Hall, Inc., 570–580, 2009. a
Moore, G. W. K., Howell, S. E. L., and Brady, M.: Evolving relationship of Nares Strait ice arches on sea ice along the Strait and the North Water, the Arctic’s most productive polynya, Sci. Rep., 13, 9809, https://doi.org/10.1038/s41598-023-36179-0, 2023. a
Ólason, 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, 8, https://doi.org/10.1029/2021MS002685, 2022. a
Pemberton, P., Löptien, U., Hordoir, R., Höglund, A., Schimanke, S., Axell, L., and Haapala, J.: Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea, Geosci. Model Dev., 10, 3105–3123, https://doi.org/10.5194/gmd-10-3105-2017, 2017. a
Prasanna, M., Polojärvi, A., Wei, M., and Åström, J.: Modeling ice block failure within drift ice and ice rubble, Phys. Rev. E, 105, 045001, https://doi.org/10.1103/PhysRevE.105.045001, 2022. a
Riikilä, T. I., Tallinen, T., Åström, J. A., and Timonen, J.: A discrete-element model for viscoelastic deformation and fracture of glacial ice, Comput. Phys. Commun., 195, 14–22, 2015. a
Röhrs, J., Gusdal, Y., Rikardsen, E. S. U., Durán Moro, M., Brændshøi, J., Kristensen, N. M., Fritzner, S., Wang, K., Sperrevik, A. K., Idžanović, M., Lavergne, T., Debernard, J. B., and Christensen, K. H.: Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard, Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, 2023. 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
Sulak, D. J., Sutherland, D. A., Enderlin, E. M., Stearns, L. A., and Hamilton, G. S.: Iceberg properties and distributions in three Greenlandic fjords using satellite imagery, Ann. Glaciol., 58, 92–106, https://doi.org/10.1017/aog.2017.5, 2017. a
Tarkka Syke: Satellite images, 8 March 2018, Tarkka Syke [data set], http://tarkka.syke.fi/eo-tarkka/map/?ver=0&time=2018-03-08&style=opt&bbox=17.33265,62.02316,30.02959,65.25324&data=d-bm-esri,d-s2,d-lc&coll=call&lang=en (last access: 14 May 2024), 2018a. a
Tarkka Syke: Satellite images, 23 March 2018, Tarkka Syke [data set], http://tarkka.syke.fi/eo-tarkka/map/?ver=0&time=2018-03-23&style=opt&bbox=17.33265,62.02316,30.02959,65.25324&data=d-bm-esri,d-s2,d-lc&coll=call&lang=en (last access: 14 May 2024), 2018b. a
Todd, J.: joeatodd/HiDEM: Initial Release of HiDEM (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.1252379, 2018. a
Weiss, J.: Fracture and fragmentation of ice: a fractal analysis of scale invariance, Eng. Fract. Mech., 68, 1975–2012, 2001. 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 Sy., 14, e2021MS002614, https://doi.org/10.1029/2021MS002614, 2022. a
Short summary
The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle...
