Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1685-2024
© 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-18-1685-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2024
Research article |
| 09 Apr 2024
| 09 Apr 2024
Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics
Recherche en prévision numérique environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Jean-François Lemieux
Recherche en prévision numérique environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
L. Bruno Tremblay
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada
Adrienne Tivy
Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada
Joey Angnatok
Nunatsiavut Research Centre, Nain, Labrador, Canada
François Roy
Recherche en prévision numérique environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Gregory Smith
Recherche en prévision numérique environnementale, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Frédéric Dupont
Service Météorologique Canadien, Environnement et Changement Climatique Canada, Dorval, Quebec, Canada
Adrian K. Turner
T-3 Fluid Dynamics and Solid Mechanics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Related authors
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, and Lekima Yakuden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-227, https://doi.org/10.5194/essd-2024-227, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with a complex crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 01 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Mathieu Plante 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.
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
Short summary
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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.
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.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, and Lekima Yakuden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-227, https://doi.org/10.5194/essd-2024-227, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with a complex crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 01 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
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.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
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We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Jean-Philippe Paquin, François Roy, Gregory C. Smith, Sarah MacDermid, Ji Lei, Frédéric Dupont, Youyu Lu, Stephanne Taylor, Simon St-Onge-Drouin, Hauke Blanken, Michael Dunphy, and Nancy Soontiens
EGUsphere, https://doi.org/10.5194/egusphere-2023-42, https://doi.org/10.5194/egusphere-2023-42, 2023
Preprint withdrawn
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This paper present the Coastal Ice-Ocean Prediction System implemented operationally at Environment and climate change Canada. The objective is to enhance the numerical guidance in coastal areas to support electronic navigation and response to environmental emergencies in the aquatic environment. Model evaluation against observations shows improvements for most surface ocean variables in the coastal system compared to current coarser-resolution operational systems.
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.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Milena Veneziani, Wieslaw Maslowski, Younjoo J. Lee, Gennaro D'Angelo, Robert Osinski, Mark R. Petersen, Wilbert Weijer, Anthony P. Craig, John D. Wolfe, Darin Comeau, and Adrian K. Turner
Geosci. Model Dev., 15, 3133–3160, https://doi.org/10.5194/gmd-15-3133-2022, https://doi.org/10.5194/gmd-15-3133-2022, 2022
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We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to better resolve the Arctic ocean and sea-ice system and low spatial resolution elsewhere. The configuration satisfactorily represents many aspects of the Arctic system and its interactions with the sub-Arctic, while keeping computational costs at a fraction of those necessary for global high-resolution ESMs. E3SM-Arctic can thus be an efficient tool to study Arctic processes on climate-relevant timescales.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
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We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
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
Short summary
Short summary
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
Short summary
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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.
Christopher Subich, Pierre Pellerin, Gregory Smith, and Frederic Dupont
Geosci. Model Dev., 13, 4379–4398, https://doi.org/10.5194/gmd-13-4379-2020, https://doi.org/10.5194/gmd-13-4379-2020, 2020
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This work presents a semi-Lagrangian advection module for the NEMO (OPA) ocean model. Semi-Lagrangian advection transports fluid properties (temperature, salinity, velocity) between time steps by following fluid motion and interpolating from upstream locations of fluid parcels.
This method is commonly used in atmospheric models to extend time step size, but it has not previously been applied to operational ocean models. Overcoming this required a new approach for solid boundaries (coastlines).
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
Short summary
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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
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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.
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.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
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.
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
EGUsphere, https://doi.org/10.5194/egusphere-2023-982, https://doi.org/10.5194/egusphere-2023-982, 2023
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Ice mélange is a mixture of sea ice and icebergs, which can have a strong influence on the sea-ice-ocean interaction. So far, ice mélange is not represented in climate models. We include icebergs into the most used sea-ice model by modifying the mathematical equations that describe the material law of sea ice. We show with three test cases that the modification is necessary to represent icebergs. Furthermore we suggest a numerical method to solve the ice mélange equations computational efficient.
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
Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., and Turner, A. K.: Impact of a New Sea Ice Thermodynamic Formulation in the CESM2 Sea Ice Component, J. Adv. Model. Earth Sy., 12, e2020MS002154, https://doi.org/10.1029/2020MS002154, 2020. a, b, c
Barber, D., Hanesiak, J., Chan, W., and Piwowar, J.: Sea‐ice and meteorological conditions in Northern Baffin Bay and the North Water polynya between 1979 and 1996, Atmos. Ocean, 39, 343–359, https://doi.org/10.1080/07055900.2001.9649685, 2001. a
Buehner, M., Morneau, J., and Charette, C.: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction, Nonlin. Processes Geophys., 20, 669–682, https://doi.org/10.5194/npg-20-669-2013, 2013. a
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L., Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J., and Zadra, A.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part I: The Global System, Mon. Weather Rev., 143, 2532–2559, https://doi.org/10.1175/MWR-D-14-00354.1, 2015. a, b
Caixin, W., Bin, C., Keguang, W., Sebastian, G., and Olga, P.: Modelling snow ice and superimposed ice on landfast sea ice in Kongsfjorden, Svalbard, Polar Res., 34, https://doi.org/10.3402/polar.v34.20828, 2015. a
Carmack, E. C. and Macdonald, R.: Oceanography of the Canadian Shelf of the Beaufort Sea: A Setting for Marine Life, Arctic, 55, 29–45, 2002. a
Cheng, Y., Cheng, B., Zheng, F., Vihma, T., Kontu, A., Yang, Q., and Liao, Z.: Air/snow, snow/ice and ice/water interfaces detection from high-resolution vertical temperature profiles measured by ice mass-balance buoys on an Arctic lake, Ann. Glaciol., 61, 309–319, https://doi.org/10.1017/aog.2020.51, 2020. a, b, c, d, e, f, g
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.: The Community Climate System Model Version 3 (CCSM3), J. Climate, 19, 2122–2143, https://doi.org/10.1175/JCLI3761.1, 2006. a
Cooley, S. W., Ryan, J. C., Smith, L. C., Horvat, C., Pearson, B., Dale, B., and Lynch, A. H.: Coldest Canadian Arctic communities face greatest reductions in shorefast sea ice, Nat. Clim. Change, 10, 533–538, https://doi.org/10.1038/s41558-020-0757-5, 2020. a
Côté, J., Desmarais, J.-G., Gravel, S., Méthot, A., Patoine, A., Roch, M., and Staniforth, A.: The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part II: Results, Mon. Weather Rev., 126, 1397–1418, https://doi.org/10.1175/1520-0493(1998)126<1397:TOCMGE>2.0.CO;2, 1998a. a
Côté, J., Gravel, S., Méthot, A., Patoine, A., Roch, M., and Staniforth, A.: The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation, Mon. Weather Rev., 126, 1373–1395, https://doi.org/10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2, 1998b. a
Dammann, D. O., Eriksson, L. E. B., Mahoney, A. R., Eicken, H., and Meyer, F. J.: Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry, The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, 2019. a
Divine, D. V., Korsnes, R., and Makshtas, A. P.: Temporal and spatial variation of shore-fast ice in the Kara Sea, Cont. Shelf Res., 24, 1717–1736, https://doi.org/10.1016/j.csr.2004.05.010, 2004. a
Dumont, D., Gratton, Y., and Arbetter, T. E.: Modeling the Dynamics of the North Water Polynya Ice Bridge, J. Phys. Oceanogr., 39, 1448–1461, https://doi.org/10.1175/2008JPO3965.1, 2009. a
Dumont, D., Gratton, Y., and Arbetter, T. E.: Modeling Wind-Driven Circulation and Landfast Ice-Edge Processes during Polynya Events in Northern Baffin Bay, J. Phys. Oceanogr., 40, 1356–1372, https://doi.org/10.1175/2010JPO4292.1, 2010. a, b
Dupont, F., Dumont, D., Lemieux, J.-F., Dumas-Lefebvre, E., and Caya, A.: A probabilistic seabed–ice keel interaction model, The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, 2022. a
DuVivier, A. K., Holland, M. M., Landrum, L., Singh, H. A., Bailey, D. A., and Maroon, E. A.: Impacts of Sea Ice Mushy Thermodynamics in the Antarctic on the Coupled Earth System, Geophys. Res. Lett., 48, e2021GL094287, https://doi.org/10.1029/2021GL094287, 2021. a, b
Eicken, H., Fischer, H., and Lemke, P.: Effects of the snow cover on Antarctic sea ice and potential modulation of its response to climate change, Ann. Glaciol., 21, 369–376, https://doi.org/10.3189/S0260305500016086, 1995. a
Eicken, H., Jones, J., Meyer, F., Mahoney, A., Druckenmiller, M. L., Rohith, M., and Kambhamettu, C.: Environmental Security in Arctic Ice-Covered Seas: From Strategy to Tactics of Hazard Identification and Emergency Response, Mar. Technol. Soc. J., 45, 37–48, https://doi.org/10.4031/MTSJ.45.3.1, 2011. a
Feltham, D. L., Untersteiner, N., Wettlaufer, J. S., and Worster, M. G.: Sea ice is a mushy layer, Geophys. Res. Lett., 33, L14501, https://doi.org/10.1029/2006GL026290, 2006. a, b, c
Flocco, D., Feltham, D. L., and Turner, A. K.: Incorporation of a physically based melt pond scheme into the sea ice component of a climate model, J. Geophys. Res.-Oceans, 115, C08012, https://doi.org/10.1029/2009JC005568, 2010. a
Galley, R. J., Else, B. G. T., Howell, S. E. L., Lukovich, J. V., and Barber, D. G.: Landfast Sea Ice Conditions in the Canadian Arctic: 1983–2009, Arctic, 65, 133–144, 2012. a
Gearheard, S., Matumeak, W., Angutikjuaq, I., Maslanik, J., Huntington, H. P., Leavitt, J., Kagak, D. M., Tigullaraq, G., and Barry, R. G.: “It's Not that Simple”: A Collaborative Comparison of Sea Ice Environments, Their Uses, Observed Changes, and Adaptations in Barrow, Alaska, USA, and Clyde River, Nunavut, Canada, AMBIO, 35, 203–211, https://doi.org/10.1579/0044-7447(2006)35[203:INTSAC]2.0.CO;2, 2006. a
Gough, A. J., Mahoney, A. R., Langhorne, P. J., Williams, M. J., Robinson, N. J., and Haskell, T. G.: Signatures of supercooling: McMurdo Sound platelet ice, J. Glaciol., 58, 38–50, https://doi.org/10.3189/2012JoG10J218, 2012. a
Granskog, M. A., Rösel, A., Dodd, P. A., Divine, D., Gerland, S., Martma, T., and Leng, M. J.: Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard, J. Geophys. Res.-Oceans, 122, 2539–2549, https://doi.org/10.1002/2016JC012398, 2017. a
Gultepe, I., Isaac, G. A., Williams, A., Marcotte, D., and Strawbridge, K. B.: Turbulent heat fluxes over leads and polynyas, and their effects on arctic clouds during FIRE.ACE: Aircraft observations for April 1998, Atmos. Ocean, 41, 15–34, https://doi.org/10.3137/ao.410102, 2003. a
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 815–846, 1979. a
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.: Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012. a
Howell, S. E. L., Wohlleben, T., Dabboor, M., Derksen, C., Komarov, A., and Pizzolato, L.: Recent changes in the exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago, J. Geophys. Res.- Oceans, 118, 3595–3607, https://doi.org/10.1002/jgrc.20265, 2013. 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
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: Cice: the los alamos sea ice model documentation and software user’s manual version 4.1 la-cc-06-012, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, 675, 500, 2010. a
Hunke, E. C., Hebert, D. A., and Lecomte, O.: Level-ice melt ponds in the Los Alamos sea ice model, CICE, Ocean Model., 71, 26–42, https://doi.org/10.1016/j.ocemod.2012.11.008, 2013. a
Hunke, E. C., Allard, R., Bailey, D. A., Blain, P., Craig, A., Dupont, F., DuVivier, A. K., Grumbine, R., Hebert, D. A., Holland, M. M., Jeffery, N., Lemieux, J.-F., Osinski, R., Rasmussen, T., Ribergaard, M., Roach, L., Roberts, A., Turner, M., and Winton, M.: CICE-Consortium/Icepack: Icepack 1.4.0 (1.4.0), Zenodo [code], https://doi.org/10.5281/zenodo.10056496, 2023. a, b
Huwald, H., Tremblay, L.-B., and Blatter, H.: A multilayer sigma-coordinate thermodynamic sea ice model: Validation against Surface Heat Budget of the Arctic Ocean (SHEBA)/Sea Ice Model Intercomparison Project Part 2 (SIMIP2) data, J. Geophys. Res.-Oceans, 110, C05010, https://doi.org/10.1029/2004JC002328, 2005. a
Jackson, K., Wilkinson, J., Maksym, T., Meldrum, D., Beckers, J., Haas, C., and Mackenzie, D.: A Novel and Low-Cost Sea Ice Mass Balance Buoy, J. Atmos. Ocean. Tech., 30, 2676–2688, https://doi.org/10.1175/JTECH-D-13-00058.1, 2013. a, b, c
King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., and Beckers, J.: Local-scale variability of snow density on Arctic sea ice, The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, 2020. a
Kirillov, S., Babb, D. G., Komarov, A. S., Dmitrenko, I., Ehn, J. K., Worden, E., Candlish, L., Rysgaard, S., and Barber, D. G.: On the Physical Settings of Ice Bridge Formation in Nares Strait, J. Geophys. Res.-Oceans, 126, e2021JC017331, https://doi.org/10.1029/2021JC017331, 2021. a
Kwok, R.: Exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago, Geophys.Res. Lett., 33, L16501, https://doi.org/10.1029/2006GL027094, 2006. 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
Lemieux, J.-F., Dupont, F., Blain, P., Roy, F., Smith, G. C., and Flato, G. M.: Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges, J. Geophys. Res.-Oceans, 121, 7354–7368, https://doi.org/10.1002/2016JC012006, 2016. a, b, c
Liao, Z., Cheng, B., Zhao, J., Vihma, T., Jackson, K., Yang, Q., Yang, Y., Zhang, L., Li, Z., Qiu, Y., and Cheng, X.: Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm, Int. J. Digit. Earth, 12, 962–979, https://doi.org/10.1080/17538947.2018.1545877, 2019. a, b, c, d, e, f
Liu, Y., Losch, M., Hutter, N., and Mu, L.: A New Parameterization of Coastal Drag to Simulate Landfast Ice in Deep Marginal Seas in the Arctic, J. Geophys. Res.-Oceans, 127, e2022JC018413, https://doi.org/10.1029/2022JC018413, 2022. a, b
Lüpkes, C., Vihma, T., Birnbaum, G., and Wacker, U.: Influence of leads in sea ice on the temperature of the atmospheric boundary layer during polar night, Geophys. Res. Lett., 35, L03805, https://doi.org/10.1029/2007GL032461, 2008. a
Madec, G. and the NEMO team: NEMO ocean engine, Note du Pole de modélisation 27, Institut Pierre-Simon Laplace (IPSL), France, ISSN 1288-1619, 2008. a
Madec, G., Delecluse, P., Imbard, M., and Levy, C.: OPA 8 Ocean General Circulation Model – Reference Manual, Tech. rep., LODYC/IPSL Note 11, 1998. a
Maksym, T. and Jeffries, M. O.: A one-dimensional percolation model of flooding and snow ice formation on Antarctic sea ice, J. Geophys. Res.-Oceans, 105, 26313–26331, https://doi.org/10.1029/2000JC900130, 2000. a
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, https://doi.org/10.1029/JC076i006p01550, 1971. a
Melling, H.: Sea ice of the northern Canadian Arctic Archipelago, J. Geophys. Res.-Oceans, 107, 2-1–2-21, https://doi.org/10.1029/2001JC001102, 2002. a
Melling, H., Gratton, Y., and Ingram, G.: Ocean circulation within the North Water polynya of Baffin Bay, Atmos. Ocean, 39, 301–325, https://doi.org/10.1080/07055900.2001.9649683, 2001. a
Merkouriadi, I., Liston, G. E., Graham, R. M., and Granskog, M. A.: Quantifying the Potential for Snow-Ice Formation in the Arctic Ocean, Geophys. Res. Lett., 47, e2019GL085020, https://doi.org/10.1029/2019GL085020, 2020. a
Notz, D. and Worster, M. G.: Desalination processes of sea ice revisited, J. Geophys. Res.-Oceans, 114, C05006, https://doi.org/10.1029/2008JC004885, 2009. a, b, c
Planck, C. J., Whitlock, J., Polashenski, C., and Perovich, D.: The evolution of the seasonal ice mass balance buoy, Cold Reg. Sci. Technol., 165, 102792, https://doi.org/10.1016/j.coldregions.2019.102792, 2019. a
Plante, M., Tremblay, B., Losch, M., and Lemieux, J.-F.: Landfast sea ice material properties derived from ice bridge simulations using the Maxwell elasto-brittle rheology, The Cryosphere, 14, 2137–2157, https://doi.org/10.5194/tc-14-2137-2020, 2020. a
Plante, M., Lemieux, J.-F., Tremblay, L. B., Tivy, A., Angnatok, J., Roy, F., Smith, G., Dupont, F., and Turner, A. K.: Using Icepack to reproduce Ice Mass Balance buoy observations in landfast ice – data and codes, Zenodo [code, data set], https://doi.org/10.5281/zenodo.10578911, 2024a. a
Plante, M., Allard, R., Angnatok, J., Bailey, D. A., Blain, P., Craig, A., Dupont, F., DuVivier, A., Grumbine, R., Hebert, D., Holland, M., Hunke, E., Jeffery, N., Lemieux, J.-F., Osinski, R., Rassmussen, T., Ribergaard, M., Roach, L., Roberts, A., Roy, F., Turner, A. K., Turner, M., Winton, M., Tremblay, L. B., Tivy, A., and Smith, G.: mathieuslplante/Icepack: Modified Icepack v.1.1.0 code used in Plante et al. (2024), Zenodo [code], https://doi.org/10.5281/zenodo.10845358, 2024b. a
Pringle, D. J., Eicken, H., Trodahl, H. J., and Backstrom, L. G. E.: Thermal conductivity of landfast Antarctic and Arctic sea ice, J. Geophys. Res.-Oceans, 112, C04017, https://doi.org/10.1029/2006JC003641, 2007. a
Provost, C., Sennéchael, N., Miguet, J., Itkin, P., Rösel, A., Koenig, Z., Villacieros-Robineau, N., and Granskog, M. A.: Observations of flooding and snow-ice formation in a thinner Arctic sea-ice regime during the N-ICE2015 campaign: Influence of basal ice melt and storms, J. Geophys. Res.-Oceans, 122, 7115–7134, https://doi.org/10.1002/2016JC012011, 2017. a, b, c, d, e, f, g, h
Raddatz, R. L., Asplin, M. G., Candlish, L., and Barber, D. G.: General Characteristics of the Atmospheric Boundary Layer Over a Flaw Lead Polynya Region in Winter and Spring, Bound.-Lay. Meteorol., 138, 321–335, https://doi.org/10.1007/s10546-010-9557-1, 2011. 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
Richter, M. E., Leonard, G. H., Smith, I. J., Langhorne, P. J., Mahoney, A. R., and Parry, M.: Accuracy and precision when deriving sea-ice thickness from thermistor strings: a comparison of methods, J. Glaciol., 69, 879–898, https://doi.org/10.1017/jog.2022.108, 2023. a, b
Richter-Menge, J. A., Perovich, D. K., Elder, B. C., Claffey, K., Rigor, I., and Ortmeyer, M.: Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover, Ann. Glaciol., 44, 205–210, https://doi.org/10.3189/172756406781811727, 2006. a
Rösel, A., Itkin, P., King, J., Divine, D., Wang, C., Granskog, M. A., Krumpen, T., and Gerland, S.: Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N-ICE2015 North of Svalbard, J. Geophys. Res.-Oceans, 123, 1156–1176, https://doi.org/10.1002/2017JC012865, 2018. a, b
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, https://doi.org/10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2, 1976. a
Smith, G. C., Roy, F., Mann, P., Dupont, F., Brasnett, B., Lemieux, J.-F., Laroche, S., and Bélair, S.: A new atmospheric dataset for forcing ice–ocean models: Evaluation of reforecasts using the Canadian global deterministic prediction system, Q. J. Roy. Meteor. Soc., 140, 881–894, https://doi.org/10.1002/qj.2194, 2014. a, b
Smith, G. C., Bélanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu, D.: Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Weather Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018. a
Smith, G. C., Liu, Y., Benkiran, M., Chikhar, K., Surcel Colan, D., Gauthier, A.-A., Testut, C.-E., Dupont, F., Lei, J., Roy, F., Lemieux, J.-F., and Davidson, F.: The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis, Geosci. Model Dev., 14, 1445–1467, https://doi.org/10.5194/gmd-14-1445-2021, 2021. a
Stirling, I.: The Biological Importance of Polynyas in the Canadian Arctic, Arctic, 33, 303–315, 1980. a
Stirling, I.: The importance of polynyas, ice edges, and leads to marine mammals and birds, J. Mar. Syst., 10, 9–21, https://doi.org/10.1016/S0924-7963(96)00054-1, 1997. a
Tian, Z., Cheng, B., Zhao, J., Vihma, T., Zhang, W., Li, Z., and Zhang, Z.: Observed and modelled snow and ice thickness in the Arctic Ocean with CHINARE buoy data, Acta Oceanol. Sin., 36, 66–75, https://doi.org/10.1007/s13131-017-1020-4, 2017. a, b
Tremblay, J.-E., Gratton, Y., Carmack, E. C., Payne, C. D., and Price, N. M.: Impact of the large-scale Arctic circulation and the North Water Polynya on nutrient inventories in Baffin Bay, J. Geophys. Res.-Oceans, 107, 26-1–26-14, https://doi.org/10.1029/2000JC000595, 2002. a
Tremblay, L.-B. and Mysak, L. A.: Modeling Sea Ice as a Granular Material, Including the Dilatancy Effect, J. Phys. Oceanogr., 27, 2342–2360, https://doi.org/10.1175/1520-0485(1997)027<2342:MSIAAG>2.0.CO;2, 1997. a
Trodahl, H. J., McGuinness, M. J., Langhorne, P. J., Collins, K., Pantoja, A. E., Smith, I. J., and Haskell, T. G.: Heat transport in McMurdo Sound first-year fast ice, J. Geophys. Res.-Oceans, 105, 11347–11358, https://doi.org/10.1029/1999JC000003, 2000. a
Turner, A. K. and Hunke, E. C.: Impacts of a mushy-layer thermodynamic approach in global sea-ice simulations using the CICE sea-ice model, J. Geophys. Res.-Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358, 2015. a, b, c
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the ice strength formulation on the performance of a sea ice thickness distribution model in the Arctic, J. Geophys. Res.-Oceans, 122, 2090–2107, https://doi.org/10.1002/2016JC012128, 2017. a
West, A., Collins, M., and Blockley, E.: Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes, Geosci. Model Dev., 13, 4845–4868, https://doi.org/10.5194/gmd-13-4845-2020, 2020. a
Wilchinsky, A. V. and Feltham, D. L.: A continuum anisotropic model of sea-ice dynamics, P. Roy. Soc. Lond. A Mat., 460, 2105–2140, https://doi.org/10.1098/rspa.2004.1282, 2004. a
Wongpan, P., Hughes, K. G., Langhorne, P. J., and Smith, I. J.: Brine Convection, Temperature Fluctuations, and Permeability in Winter Antarctic Land-Fast Sea Ice, J. Geophys. Res.-Oceans, 123, 216–230, https://doi.org/10.1002/2017JC012999, 2018. a
Zuo, G., Dou, Y., and Lei, R.: Discrimination Algorithm and Procedure of Snow Depth and Sea Ice Thickness Determination Using Measurements of the Vertical Ice Temperature Profile by the Ice-Tethered Buoys, Sensors, 18, 4162, https://doi.org/10.3390/s18124162, 2018. a
Short summary
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.
We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal...
