Articles | Volume 14, issue 1
https://doi.org/10.5194/tc-14-93-2020
© Author(s) 2020. 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-14-93-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jan 2020
Research article |
| 16 Jan 2020
| 16 Jan 2020
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Martin Losch
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
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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
This preprint is open for discussion and under review for The Cryosphere (TC).
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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.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
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.
Nils Hutter, Lorenzo Zampieri, and Martin Losch
The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, https://doi.org/10.5194/tc-13-627-2019, 2019
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Arctic sea ice is an aggregate of ice floes with various sizes. The different sizes result from constant deformation of the ice pack. If a floe breaks, open ocean is exposed in a lead. Collision of floes forms pressure ridges. Here, we present algorithms that detect and track these deformation features in satellite observations and model output. The tracked features are used to provide a comprehensive description of localized deformation of sea ice and help to understand its material properties.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
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.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
Short summary
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
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.
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.
Svetlana N. Losa, Stephanie Dutkiewicz, Martin Losch, Julia Oelker, Mariana A. Soppa, Scarlett Trimborn, Hongyan Xi, and Astrid Bracher
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-289, https://doi.org/10.5194/bg-2019-289, 2019
Manuscript not accepted for further review
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This study highlights recent advances and challenges of applying coupled physical-biogeochemical modeling for investigating the distribution of the key phytoplankton groups in the Southern Ocean. By leveraging satellite and in situ observations we define numerical ecological model requirements in the phytoplankton trait specification and level of physiological and morphological differentiation for capturing and explaining the observed biogeography of diatoms, coccolithophores and Phaeocystis.
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.
Nils Hutter, Lorenzo Zampieri, and Martin Losch
The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, https://doi.org/10.5194/tc-13-627-2019, 2019
Short summary
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Arctic sea ice is an aggregate of ice floes with various sizes. The different sizes result from constant deformation of the ice pack. If a floe breaks, open ocean is exposed in a lead. Collision of floes forms pressure ridges. Here, we present algorithms that detect and track these deformation features in satellite observations and model output. The tracked features are used to provide a comprehensive description of localized deformation of sea ice and help to understand its material properties.
Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, Lars Nerger, and Thomas Lavergne
The Cryosphere, 10, 761–774, https://doi.org/10.5194/tc-10-761-2016, https://doi.org/10.5194/tc-10-761-2016, 2016
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We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter. Comparing with the approach using a constant data uncertainty, the sea ice concentration estimates are further improved when the SICCI-provided uncertainty are taken into account, but the sea ice thickness cannot be improved. We find the data assimilation system cannot give a reasonable ensemble spread of sea ice concentration and thickness if the provided uncertainty are directly used.
T. Kurahashi-Nakamura, M. Losch, and A. Paul
Geosci. Model Dev., 7, 419–432, https://doi.org/10.5194/gmd-7-419-2014, https://doi.org/10.5194/gmd-7-419-2014, 2014
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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.
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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.
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
Antonov, J. I., Locarnini, R. A., Boyer, T. P., Mishonov, A. V., and Garcia,
H. E.: World Ocean Atlas 2005, Volume 2: Salinity, U.S. Government Printing Office, Washington, D.C., USA, 2006. a
Borradaile, G. J.: Statistics of Earth Science Data: Their Distribution in
Time, Space and Orientation, Springer-Verlag Berlin Heidelberg,
https://doi.org/10.1007/978-3-662-05223-5, 2003. a
Castellani, G., Losch, M., Ungermann, M., and Gerdes, R.: Sea-Ice Drag as
Function of Deformation and Ice Cover: Effects on Simulated Sea Ice and Ocean
Circulation in the Arctic., Ocean Model., 128, 48–66,
https://doi.org/10.1016/j.ocemod.2018.06.002, 2018. a, b
Clauset, A., Shalizi, C., and Newman, M.: Power-Law Distributions in Empirical
Data, SIAM Rev., 51, 661–703, https://doi.org/10.1137/070710111, 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
Cunningham, G. F., Kwok, R., and Banfield, J.: Ice lead orientation
characteristics in the winter Beaufort Sea, in: Proceedings of IGARSS '94 –
1994 IEEE International Geoscience and Remote Sensing Symposium, 8–12 August 1994, Pasadena, CA, USA, vol. 3, 1747–1749, https://doi.org/10.1109/IGARSS.1994.399553, 1994. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a, b, c
Dempsey, J., Xie, Y., Adamson, R., and Farmer, D.: Fracture of a ridged
multi-year Arctic sea ice floe, Cold Reg. Sci. Technol., 76–77,
63–68, https://doi.org/10.1016/j.coldregions.2011.09.012, 2012. a, b
Eguíluz, V. M., Fernández-Gracia, J., Irigoien, X., and Duarte, C. M.:
A quantitative assessment of Arctic shipping in 2010–2014, Sci.
Rep., 6, 30682, https://doi.org/10.1038/srep30682, 2016. a
Erlingsson, B.: Two-dimensional deformation patterns in sea ice, J.
Glaciol., 34, 301–308, 1988. 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, 1–15, https://doi.org/10.1029/2008JC005182, 2009. a, b
Haas, C.: Dynamics Versus Thermodynamics: The Sea Ice Thickness Distribution,
chap. 4, John Wiley & Sons, Ltd, 113–151,
https://doi.org/10.1002/9781444317145.ch4,
2010. a
Heorton, H. D. B. S., Feltham, D. L., and Tsamados, M.: Stress and deformation
characteristics of sea ice in a high-resolution, anisotropic sea ice model,
Philos. T. Roy. Soc. A, 376, 20170349, https://doi.org/10.1098/rsta.2017.0349,
2018. a
Horvat, C., Tziperman, E., and Campin, J.-M.: Interaction of sea ice floe size, ocean eddies, and sea ice melting, Geophys. Res. Lett., 43,
8083–8090, https://doi.org/10.1002/2016GL069742,
2016. a, b, c
Hutchings, J. K., Heil, P., and Hibler, W. D.: Modeling Linear Kinematic
Features in Sea Ice, Mon. Weather Rev., 133, 3481–3497,
https://doi.org/10.1175/MWR3045.1,
2005. a, b
Hutter, N.: Viscous-plastic sea-ice models at very high resolution, Master's
thesis, University of Bremen, Alfred Wegener Institute, Helmholtz Centre for
Polar and Marine research, https://doi.org/10013/epic.46129, 2015. a, b
Hutter, N.: lkf_tools: a code to detect and track Linear Kinematic Features
(LKFs) in sea-ice deformation data (Version v1.0), Zenodo,
https://doi.org/10.5281/zenodo.2560078,
2019a. a
Hutter, N.: Linear Kinematic Features (leads & pressure ridges)
detected and tracked in sea-ice deformation simulated in an Arctic
configuration of MITgcm using a 2-km horizontal grid spacing from 1997 to
2008, PANGAEA, https://doi.org/10.1594/PANGAEA.909636, 2019b. a, b
Hutter, N.: Linear Kinematic Features (leads & pressure ridges)
detected and tracked in sea-ice deformation simulated in an Arctic
configuration of MITgcm using a 2-km horizontal grid with an active 5-class
ice thickness distribution spacing from 1997 to 2008, PANGAEA,
https://doi.org/10.1594/PANGAEA.909632, 2019c. a, b
Hutter, N., Zampieri, L., and Losch, M.: Linear Kinematic Features
(leads & pressure ridges) detected and tracked in RADARSAT Geophysical
Processor System (RGPS) sea-ice deformation data from 1997 to 2008, PANGAEA,
https://doi.org/10.1594/PANGAEA.898114, 2019b. a, b, c
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day,
J. J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland,
M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A.,
Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G.,
Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction
Capabilities on Daily to Seasonal Time Scales, B. Am.
Meteorol. Soc., 97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1,
2016. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling,
H., Rakowsky, N., Scholz, P., Sein, D., Wang, Q., and Jung, T.: Fast EVP
solutions in a high-resolution sea ice model, J. Adv. Model.
Earth Syst., 11, 1269–1284, https://doi.org/10.1029/2018MS001485,
2019. a, b, c
Kwok, R.: The RADARSAT Geophysical Processor System, in: Analysis of SAR Data
of the Polar Oceans, Springer Berlin Heidelberg, 235–257,
https://doi.org/10.1007/978-3-642-60282-5_11, 1998. a
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. and Shen, H., vol. 94
of Solid Mechanics and Its Applications, Springer
Netherlands, 315–322, https://doi.org/10.1007/978-94-015-9735-7_26, 2001. a, b, c
Kwok, R. and Cunningham, G. F.: Radarsat Geophysical Processor System: Data
User'S Handbook (Version 2.0), National Aeronautics and Space Administration, Pasadena, California, USA, 2014. a
Laherrère, J. and Sornette, D.: Stretched exponential distributions in
nature and economy: “fat tails” with characteristic scales, Eur.
Phys. J. B, 2, 525–539,
https://doi.org/10.1007/s100510050276, 1998. a
Lemieux, J.-F., Tremblay, L. B., Dupont, F., Plante, M., Smith, G. C., and
Dumont, D.: A basal stress parameterization for modeling landfast ice,
J. Geophys. Res.-Oceans, 120, 3157–3173,
https://doi.org/10.1002/2014JC010678,
2015. a, b
Levy, G., Coon, M., Nguyen, G., and Sulsky, D.: Metrics for evaluating linear
features, Geophys. Res. Lett., 35, L21705, https://doi.org/10.1029/2008GL035086,
2008. a
Lindsay, R. W. and Stern, H. L.: The RADARSAT geophysical processor system:
quality of sea ice trajectory and deformation estimates, J. Atmos. Ocean.
Tech., 20, 1333–1347,
https://doi.org/10.1175/1520-0426(2003)020<1333:TRGPSQ>2.0.CO;2,
2003. a, b
Linow, S. and Dierking, W.: Object-Based Detection of Linear Kinematic Features
in Sea Ice, Remote Sensing, 9, 493, https://doi.org/10.3390/rs9050493, 2017. a, b, c
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging,
strength, and stability in high-resolution sea ice models, J.
Geophys. Res.-Oceans, 112, C03S91, https://doi.org/10.1029/2005JC003355,
2007. a
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., and Garcia,
H. E.: World Ocean Atlas 2005, Volume 1: Temperature, U.S. Government Printing Office, Washington, D.C., USA, 2006. 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
Lüpkes, C. and Gryanik, V.: Parameterization of drag coefficients over
polar sea ice for climate models, Mercator Ocean Quarterly Newsletter –
Special Issue, 51, 29–34, 2015. a
Mahoney, A. R., Eicken, H., Shapiro, L. H., Heinrichs, T., Meyer, F. J., and
Gaylord, A. G.: Mapping and Characterization of Recurring Spring Leads and
Landfast Ice in the Beaufort and Chukchi Seas, final Report: OCS
Study BOEM 2012-067, available at:
https://www.boem.gov/sites/default/files/boem-newsroom/Library/Publications/2012/BOEM-2012-067.pdf (last access: 14 January 2020), 2012. a, b
Manucharyan, G. E. and Thompson, A. F.: Submesoscale Sea Ice-Ocean Interactions
in Marginal Ice Zones, J. Geophys. Res.-Oceans, 122,
9455–9475, https://doi.org/10.1002/2017JC012895,
2017. a
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
Finite-Volume, Incompressible Navier Stokes Model for Studies of the
Ocean on Parallel Computers, J. Geophys. Res., 102, 5753–5766,
https://doi.org/10.1029/96JC02775, 1997. a
Massonnet, F., Goosse, H., Fichefet, T., and Counillon, F.: Calibration of sea
ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman
filter, J. Geophys. Res.-Oceans, 119, 4168–4184,
https://doi.org/10.1002/2013JC009705,
2014. a
Menemenlis, D., Campin, J., Heimbach, P., Hill, C., Lee, T., Nguyen, A.,
Schodlok, M., and Zhang, H.: ECCO2: High Resolution Global Ocean and Sea
Ice Data Synthesis, Mercator Ocean Quaterly Newsletter, 31, 13–21, 2008. a
Miles, M. W. and Barry, R. G.: A 5-year satellite climatology of winter sea ice
leads in the western Arctic, J. Geophys. Res.-Oceans, 103,
21723–21734, https://doi.org/10.1029/98JC01997, 1998. a, b
MITgcm Group: MITgcm User Manual, Online documentation, MIT/EAPS,
Cambridge, MA 02139, USA, available at: http://mitgcm.org/public/docs.html (last access: 14 January 2020), 2017. a
Mohammadi-Aragh, M., Goessling, H. F., Losch, M., Hutter, N., and Jung, T.:
Predictability of Arctic sea ice on weather time scales, Sci.
Rep., 8, 6514, https://doi.org/10.1038/s41598-018-24660-0, 2018. a
Mourre, B., Aguiar, E., Juza, M., Hernandez-Lasheras, J., Reyes, E., Heslop,
E., Escudier, R., Cutolo, E., Ruiz, S., Mason, E., Pascual, A., and
Tintoré, J.: Assessment of High-Resolution Regional Ocean Prediction
Systems Using Multi-Platform Observations: Illustrations in the Western
Mediterranean Sea, in: New Frontiers in Operational Oceanography, edited by:
Chassignet, E., Pascual, A., Tintoré, J., and Verron, J.,
GODAE Ocean View, 663–694, https://doi.org/10.17125/gov2018.ch24, 2018. a
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Arctic ice-ocean simulation with
optimized model parameters: approach and assessment, J. Geophys. Res., 116,
C04025, https://doi.org/10.1029/2010JC006573, 2011. a, b
Nguyen, A. T., Kwok, R., and Menemenlis, D.: Source and Pathway of the Western
Arctic Upper Halocline in a Data-Constrained Coupled Ocean and Sea Ice Model,
J. Phys. Oceanogr., 42, 802–823, https://doi.org/10.1175/JPO-D-11-040.1,
2012. a
Oikkonen, A., Haapala, J., Lensu, M., Karvonen, J., and Itkin, P.: Small-scale
sea ice deformation during N-ICE2015: From compact pack ice to marginal ice
zone, J. Geophys. Res.-Oceans, 122, 5105–5120,
https://doi.org/10.1002/2016JC012387, 2017. a
Pritchard, R. S.: Mathematical characteristics of sea ice dynamics models,
J. Geophys. Res.-Oceans, 93, 15609–15618,
https://doi.org/10.1029/JC093iC12p15609,
1988. 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, 1–12, https://doi.org/10.1029/2007JC004143, 2008. 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, c
Rothrock, D. A.: The energetics of the plastic deformation of pack ice by
ridging, J. Geophys. Res., 80, 4514–4519,
https://doi.org/10.1029/JC080i033p04514,
1975. a, b, c
Schaffer, J. and Timmermann, R.: Greenland and Antarctic ice sheet
topography, cavity geometry, and global bathymetry (RTopo-2), links to NetCDF
files, https://doi.org/10.1594/PANGAEA.856844, 2016. a
Schulson, E. M.: Compressive shear faults within arctic sea ice: Fracture on
scales large and small, J. Geophys. Res.-Oceans, 109, C07016,
https://doi.org/10.1029/2003JC002108, 2004. a
Schulson, E. M., Fortt, A., Iliescu, D., and Renshaw, C.: On the role of
frictional sliding in the compressive fracture of ice and granite: Terminal
vs. post-terminal failure, Acta Mater., 54, 3923–3932,
https://doi.org/10.1016/j.actamat.2006.04.024,
2006. a
Stamoulis, C. and Dyer, I.: Acoustically derived ice-fracture velocity in
central Arctic pack ice, J. Acoust. Soc. Am.,
108, 96–104, https://doi.org/10.1121/1.429448, 2000. a
Stern, H., Schweiger, A., Zhang, J., and Steele, M.: On reconciling disparate
studies of the sea-ice floe size distribution, Elementa Science of the
Anthropocene, 6, 1–16, https://doi.org/10.1525/elementa.304, 2018. a
Sumata, H., Kauker, F., Karcher, M., and Gerdes, R.: Simultaneous Parameter
Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm,
Mon. Weather Rev., 147, 1899–1926, https://doi.org/10.1175/MWR-D-18-0360.1, 2019. a
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80,
4501–4513, https://doi.org/10.1029/JC080i033p04501,
1975. a
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
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz,
N., Laxon, S. W., and Bacon, S.: Impact of Variable Atmospheric and Oceanic
Form Drag on Simulations of Arctic Sea Ice, J. Phys. Oceanogr.,
44, 1329–1353, https://doi.org/10.1175/JPO-D-13-0215.1, 2014. a, b
Ukita, J. and Moritz, R. E.: Yield curves and flow rules of pack ice, J. Geophys. Res.-Oceans, 100, 4545–4557, https://doi.org/10.1029/94JC02202,
1995. a
Ungermann, M. and Losch, M.: An Observationally Based Evaluation of Subgrid
Scale Ice Thickness Distributions Simulated in a Large-Scale Sea Ice-Ocean
Model of the Arctic Ocean, J. Geophys. Res.-Oceans, 123,
8052–8067, https://doi.org/10.1029/2018JC014022,
2018. a, b, c, d
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the Ice
Strength Formulation on the Performance of a Sea Ice Thickness Distribution
Model in the Arctic, J. Geophys. Res., 122, 2090–2107,
https://doi.org/10.1002/2016JC012128, 2017. a
Van Dyne, M., Tsatsoulis, C., and Fetterer, F.: Analyzing lead information from
SAR images, IEEE T. Geosci. Remote, 36, 647–660,
https://doi.org/10.1109/36.662745, 1998. a
Wadhams, P.: Sea ice thickness distribution in the Greenland Sea and Eurasian
Basin, May 1987, J. Geophys. Res.-Oceans, 97, 5331–5348,
https://doi.org/10.1029/91JC03137,
1992. a
Walter, B. A. and Overland, J. E.: The response of lead patterns in the
Beaufort Sea to storm-scale wind forcing, Ann. Glaciol., 17,
219–226, https://doi.org/10.3189/S0260305500012878, 1993. a
Wang, K.: Observing the yield curve of compacted pack ice, J.
Geophys. Res.-Oceans, 112, C05015, https://doi.org/10.1029/2006JC003610, 2007. a, b, c
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice leads
in the Arctic Ocean: Model assessment, interannual variability and trends,
Geophys. Res. Lett., 43, 7019–7027, https://doi.org/10.1002/2016GL068696,
2016. a, b
Weiss, J.: Scaling of Fracture and Faulting of Ice on Earth, Surv.
Geophys., 24, 185–227, https://doi.org/10.1023/A:1023293117309, 2003. a
Weiss, J.: Sea Ice Deformation, in: Drift, Deformation, and Fracture of Sea
Ice, SpringerBriefs in Earth Sciences, Springer Netherlands, 31–51,
https://doi.org/10.1007/978-94-007-6202-2_3, 2013. a
Wernecke, A. and Kaleschke, L.: Lead detection in Arctic sea ice from CryoSat-2: quality assessment, lead area fraction and width distribution, The Cryosphere, 9, 1955–1968, https://doi.org/10.5194/tc-9-1955-2015, 2015.
a, b
Willmes, S. and Heinemann, G.: Sea-Ice Wintertime Lead Frequencies and Regional
Characteristics in the Arctic, 2003-2015, Remote Sensing, 8,
https://doi.org/10.3390/rs8010004,
2016. a, b, c
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
Zhang, J., Thomas, D. R., Rothrock, D. A., Lindsay, R. W., Yu, Y., and Kwok,
R.: Assimilation of ice motion observations and comparisons with submarine
ice thickness data, J. Geophys. Res.-Oceans, 108, 3170,
https://doi.org/10.1029/2001JC001041, 2003. a
Zhao, M., Timmermans, M.-L., Cole, S., Krishfield, R., Proshutinsky, A., and
Toole, J.: Characterizing the eddy field in the Arctic Ocean halocline,
J. Geophys. Res.-Oceans, 119, 8800–8817,
https://doi.org/10.1002/2014JC010488,
2014. a
Short summary
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.
Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean...
