Research article
20 Oct 2022
Research article
| 20 Oct 2022
Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
Related subject area
Discipline: Sea ice | Subject: Numerical Modelling
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The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
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New insight from CryoSat-2 sea ice thickness for sea ice modelling
Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
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
Short summary
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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
Short summary
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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
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.
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
Short summary
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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
Short summary
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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
Short summary
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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
Asplin, M. G., Galley, R., Barber, D. G., and Prinsenberg, S.: Fracture of
summer perennial sea ice by ocean swell as a result of Arctic storms, J. Geophys. Res.-Oceans, 117, C06025, https://doi.org/10.1029/2011jc007221,
2012. a
Azzalini, A.: Statistical inference: based on the likelihood, in: Monographs on
statistics and applied probability, 1st edn., 68, Chapman & Hall/CRC, Boca Raton,
New York, ISBN 9780412606502, 1996. a
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020. a, b, c
Bennetts, L. G. and Squire, V. A.: On the calculation of an attenuation
coefficient for transects of ice-covered ocean, P. Roy.
Soc. A-Math. Phy., 468, 136–162,
https://doi.org/10.1098/rspa.2011.0155, 2011. a
Bennetts, L. G., O'Farrell, S., and Uotila, P.: Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model, The Cryosphere, 11, 1035–1040, https://doi.org/10.5194/tc-11-1035-2017, 2017. a, b
Bonath, V., Zhaka, V., and Sand, B.: Field measurements on the behavior of
brash ice, in: Proceedings of the 25th International Conference on Port and
Ocean Engineering under Arctic Conditions, Delft, The Netherlands, 9–13 June 2019, ISSN 0376-6756, https://www.poac.com/Papers/2019/pdf/POAC19-106.pdf (last access: 12 September 2022), 2019. a
Boutin, G., Ardhuin, F., Dumont, D., Sévigny, C., Girard-Ardhuin, F., and
Accensi, M.: Floe Size Effect on Wave-Ice Interactions: Possible Effects,
Implementation in Wave Model, and Evaluation, J. Geophys.
Res.-Oceans, 123, 4779–4805, https://doi.org/10.1029/2017jc013622, 2018. a, b
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020. a, b, c
Boutin, G., Williams, T., Rampal, P., Olason, E., and Lique, C.: Wave–sea-ice interactions in a brittle rheological framework, The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, 2021. a, b, c
Castruccio, F. S., Ruprich-Robert, Y., Yeager, S. G., Danabasoglu, G., Msadek,
R., and Delworth, T. L.: Modulation of Arctic Sea Ice Loss by Atmospheric
Teleconnections from Atlantic Multidecadal Variability, J. Climate,
32, 1419–1441, https://doi.org/10.1175/jcli-d-18-0307.1, 2019. a
Clauset, A., Shalizi, C. R., and Newman, M. E. J.: Power-Law Distributions in
Empirical Data, SIAM Review, 51, 661–703, https://doi.org/10.1137/070710111, 2009. a, b, c
Collins, C. O., Rogers, W. E., Marchenko, A., and Babanin, A. V.: In situ
measurements of an energetic wave event in the Arctic marginal ice zone,
Geophys. Res. Lett., 42, 1863–1870, https://doi.org/10.1002/2015gl063063,
2015. a
Demmel, J. W., Eisenstat, S. C., Gilbert, J. R., Li, X. S., and Liu, J. W. H.:
A Supernodal Approach to Sparse Partial Pivoting, SIAM J. Matrix
Anal. A., 20, 720–755, https://doi.org/10.1137/s0895479895291765,
1999. a
Dolatshah, A., Nelli, F., Bennetts, L. G., Alberello, A., Meylan, M. H., Monty,
J. P., and Toffoli, A.: Letter: Hydroelastic interactions between water waves
and floating freshwater ice, Phys. Fluids, 30, 091702,
https://doi.org/10.1063/1.5050262, 2018. a
Dumas-Lefebvre, E. and Dumont, D.: Aerial observations of sea ice break-up by ship waves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-328, in review, 2021. a, b, c
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
Fox, C. and Squire, V. A.: Reflection and transmission characteristics at the
edge of shore fast sea ice, J. Geophys. Res., 95, 11629,
https://doi.org/10.1029/jc095ic07p11629, 1990. a
Fox, C. and Squire, V. A.: On the oblique reflexion and transmission of ocean
waves at shore fast sea ice, Philos. T. Roy. Soc.
A, 347, 185–218,
https://doi.org/10.1098/rsta.1994.0044, 1994. a, b
Herman, A.: Wave-induced stress and breaking of sea ice in a coupled hydrodynamic discrete-element wave–ice model, The Cryosphere, 11, 2711–2725, https://doi.org/10.5194/tc-11-2711-2017, 2017. a, b, c, d
Herman, A., Evers, K.-U., and Reimer, N.: Floe-size distributions in laboratory ice broken by waves, The Cryosphere, 12, 685–699, https://doi.org/10.5194/tc-12-685-2018, 2018. a
Herman, A., Wenta, M., and Cheng, S.: Sizes and Shapes of Sea Ice Floes Broken
by Waves – A Case Study From the East Antarctic Coast, Front.
Earth Sci., 9, 655977, https://doi.org/10.3389/feart.2021.655977, 2021. a, b, c, d
Horvat, C. and Roach, L. A.: WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture, Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, 2022. a, b
Horvat, C. and Tziperman, E.: The evolution of scaling laws in the sea ice floe
size distribution, J. Geophys. Res.-Oceans, 122, 7630–7650,
https://doi.org/10.1002/2016jc012573, 2017. 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
Huang, H.-P.: Ice formation in frequently transited navigation channels, PhD
thesis, The University of Iowa, ISBN 9798207503257, 1988. a
Hunke, E., Allard, R., Bailey, D. A., Blain, P., Craig, A., Dupont, F.,
DuVivier, A., Grumbine, R., Hebert, D., Holland, M., Jeffery, N.,
Jean-Francois Lemieux, Osinski, R., Rasmussen, T., Ribergaard, M., Roberts,
A., Francois Roy, Turner, M., and Worthen, D.: CICE-Consortium/CICE: CICE
Version 6, Zenodo [code], https://doi.org/10.5281/zenodo.1205674, 2021. a
Inoue, J.: Ice floe distribution in the Sea of Okhotsk in the period when
sea-ice extent is advancing, Geophys. Res. Lett., 31, L20303,
https://doi.org/10.1029/2004gl020809, 2004. a
Keller, J. B.: Gravity waves on ice-covered water, J. Geophys.
Res.-Oceans, 103, 7663–7669, https://doi.org/10.1029/97jc02966, 1998. a
Kish, L.: Survey sampling, John Wiley New York, ISBN 9780471109495, 1965. a
Kohout, A. and Williams, M.: Waves in-ice observations made during the SIPEX II
voyage of the Aurora Australis, 2012, Ver. 1, Australian Antarctic Data Centre [data set], https://doi.org/10.4225/15/53266BEC9607F, 2015. a
Kolmogoroff, A.: The logarithmically normal law of distribution of
dimensions of particles when broken into small parts, in: CR (Doklady) Acad. Sci.
URSS (NS), vol. 31, pp. 99–101, 1941. a
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses
and coupled variability (1958–2018), Environ. Res.
Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi,
D.: Thinning and volume loss of the Arctic Ocean sea ice cover:
2003–2008, J. Geophys. Res., 114, C07005,
https://doi.org/10.1029/2009jc005312, 2009. a
Lilliefors, H. W.: On the Kolmogorov-Smirnov Test for Normality with Mean and
Variance Unknown, J. Am. Stat. Assoc., 62,
399–402, https://doi.org/10.1080/01621459.1967.10482916, 1967. a
Massey, F. J.: The Kolmogorov-Smirnov Test for Goodness of Fit, J.
Am. Stat. Assoc., 46, 68–78,
https://doi.org/10.1080/01621459.1951.10500769, 1951. a
Massom, R. A., Scambos, T. A., Bennetts, L. G., Reid, P., Squire, V. A., and
Stammerjohn, S. E.: Antarctic ice shelf disintegration triggered by sea ice
loss and ocean swell, Nature, 558, 383–389, https://doi.org/10.1038/s41586-018-0212-1,
2018. a
Meylan, M. and Squire, V. A.: The response of ice floes to ocean waves, J. Geophys. Res., 99, 891–900, https://doi.org/10.1029/93jc02695, 1994. a
Meylan, M. H., Bennetts, L. G., and Kohout, A. L.: In situ measurements and
analysis of ocean waves in the Antarctic marginal ice zone, Geophys.
Res. Lett., 41, 5046–5051, https://doi.org/10.1002/2014gl060809, 2014. a
Mokus, N. and Montiel, F.: Model code and simulation results for the
investigation of a wave-generated floe size distribution, Figshare [code and data set],
https://doi.org/10.6084/m9.figshare.17303927, 2021. a
Mokus, N. and Montiel, F.: Floe size distributions in irregular sea states, in:
Proceedings of the 37th International Workshop on Water Waves and Floating
Bodies, Giardini Naxos, Italy, 10–13 April 2022, 106–109, ISBN 9788876170539, http://www.iwwwfb.org/Abstracts/iwwwfb37/IWWWFB37GLOBAL027.pdf (last access: 12 September 2022), 2022a. a
Montiel, F. and Mokus, N.: Theoretical framework for the emergent floe size distribution in the marginal ice zone: the case for lognormality,
Phil. Trans. R. Soc. A, 380, 20210257, https://doi.org/10.1098/rsta.2021.0257,
2022b. a, b
Montiel, F., Bennetts, L., and Squire, V.: The transient response of floating
elastic plates to wavemaker forcing in two dimensions, J. Fluid.
Struct., 28, 416–433, https://doi.org/10.1016/j.jfluidstructs.2011.10.007, 2012. a
Montiel, F., Squire, V. A., and Bennetts, L. G.: Attenuation and directional
spreading of ocean wave spectra in the marginal ice zone, J. Fluid
Mech., 790, 492–522, https://doi.org/10.1017/jfm.2016.21, 2016. a, b
Montiel, F., Squire, V. A., Doble, M., Thomson, J., and Wadhams, P.:
Attenuation and Directional Spreading of Ocean Waves During a Storm Event in
the Autumn Beaufort Sea Marginal Ice Zone, J. Geophys. Res.-Oceans, 123, 5912–5932, https://doi.org/10.1029/2018jc013763, 2018. a
Montiel, F., Kohout, A. L., and Roach, L. A.: Physical Drivers of Ocean Wave
Attenuation in the Marginal Ice Zone, J. Phys. Oceanogr., 52,
889–906, https://doi.org/10.1175/jpo-d-21-0240.1, 2022. a
Mosig, J. E. M.: Contemporary wave–ice interaction models, PhD thesis,
University of Otago, http://hdl.handle.net/10523/7958, 2018. a
Mosig, J. E. M., Montiel, F., and Squire, V. A.: Comparison of
viscoelastic-type models for ocean wave attenuation in ice-covered seas,
J. Geophys. Res.-Oceans, 120, 6072–6090,
https://doi.org/10.1002/2015jc010881, 2015. a, b
Ochi, M. K.: Ocean waves: the Stochastic Approach, vol. 6, Cambridge University
Press, ISBN 9780521017671, 2005. a
Paget, M., Worby, A. P., and Michael, K. J.: Determining the floe-size
distribution of East Antarctic sea ice from digital aerial photographs,
Ann. Glaciol., 33, 94–100, https://doi.org/10.3189/172756401781818473, 2001. a
Parkinson, C. L. and Comiso, J. C.: On the 2012 record low Arctic sea ice
cover: Combined impact of preconditioning and an August storm, Geophys.
Res. Lett., 40, 1356–1361, https://doi.org/10.1002/grl.50349, 2013. a
Passerotti, G., Bennetts, L. G., von Bock und Polach, F., Alberello, A.,
Puolakka, O., Dolatshah, A., Monbaliu, J., and Toffoli, A.: Interactions
between irregular wave fields and sea ice: A physical model for wave
attenuation and ice break up, J. Phys. Oceanogr., 52, 1431–1446, https://doi.org/10.1175/jpo-d-21-0238.1, 2022. a
Passerotti, G., Bennetts, L. G., von Bock und Polach, F., Alberello, A.,
Puolakka, O., Dolatshah, A., Monbaliu, J., and Toffoli, A.: Interactions
between Irregular Wave Fields and Sea Ice: A Physical Model for Wave
Attenuation and Ice Breakup in an Ice Tank, J. Phys. Oceanogr.,
52, 1431–1446, https://doi.org/10.1175/jpo-d-21-0238.1, 2022. a
Perovich, D. K. and Jones, K. F.: The seasonal evolution of sea ice floe size
distribution, J. Geophys. Res.-Oceans, 119, 8767–8777,
https://doi.org/10.1002/2014jc010136, 2014. a
Pierson, W. J. and Moskowitz, L.: A proposed spectral form for fully developed
wind seas based on the similarity theory of S. A. Kitaigorodskii, J.
Geophys. Res., 69, 5181–5190, https://doi.org/10.1029/jz069i024p05181, 1964. a
Roach, L. A., Bitz, C. M., Horvat, C., and Dean, S. M.: Advances in Modeling
Interactions Between Sea Ice and Ocean Surface Waves, J. Adv.
Model.Earth Sy., 11, 4167–4181, https://doi.org/10.1029/2019ms001836, 2019. a, b, c
Robinson, N. and Palmer, S.: A modal analysis of a rectangular plate floating
on an incompressible liquid, J. Sound Vib. 142, 453–460,
https://doi.org/10.1016/0022-460x(90)90661-i, 1990. a
Rothrock, D. A. and Thorndike, A. S.: Measuring the sea ice floe size
distribution, J. Geophys. Res., 89, 6477–6486,
https://doi.org/10.1029/jc089ic04p06477, 1984. a, b, c
Santi, F. D. and Olla, P.: Effect of small floating disks on the propagation of
gravity waves, Fluid Dyn. Res., 49, 025512,
https://doi.org/10.1088/1873-7005/aa59e1, 2017. a
Squire, V. and Fox, C.: On ice coupled waves: a comparison of data and theory,
in: Advances in ice technology: Proc. 3rd Int. Conf. on Ice Technology, Computational Mechanics Publications Cambridge, MA,
269–280, 1992. a
Squire, V. A.: Ocean Wave Interactions with Sea Ice: A Reappraisal, Annu.
Review Fluid Mech., 52, 37–60,
https://doi.org/10.1146/annurev-fluid-010719-060301, 2020. a
Squire, V. A. and Montiel, F.: Evolution of Directional Wave Spectra in the
Marginal Ice Zone: A New Model Tested with Legacy Data, J. Phys.
Oceanogr., 46, 3121–3137, https://doi.org/10.1175/jpo-d-16-0118.1, 2016. a
Squire, V. A. and Moore, S. C.: Direct measurement of the attenuation of ocean
waves by pack ice, Nature, 283, 365–368, https://doi.org/10.1038/283365a0, 1980. a
Steele, M.: Sea ice melting and floe geometry in a simple ice-ocean model,
J. Geophys. Res.-Oceans, 97, 17729–17738,
https://doi.org/10.1029/92jc01755, 1992. a
Steer, A., Worby, A., and Heil, P.: Observed changes in sea-ice floe size
distribution during early summer in the western Weddell Sea, Deep-Sea
Res. Pt. II, 55, 933–942,
https://doi.org/10.1016/j.dsr2.2007.12.016, 2008. a
Stern, H. L., Schweiger, A. J., 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, b, c
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic
sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501,
https://doi.org/10.1029/2007gl029703, 2007. a
Thomson, J. and Rogers, W. E.: Swell and sea in the emerging Arctic Ocean,
Geophys. Res. Lett., 41, 3136–3140, https://doi.org/10.1002/2014gl059983,
2014. 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
Toyota, T., Takatsuji, S., and Nakayama, M.: Characteristics of sea ice floe
size distribution in the seasonal ice zone, Geophys. Res. Lett., 33, L02616,
https://doi.org/10.1029/2005gl024556, 2006. a
Toyota, T., Haas, C., and Tamura, T.: Size distribution and shape properties of
relatively small sea-ice floes in the Antarctic marginal ice zone in late
winter, Deep-Sea Res. Pt. II, 58,
1182–1193, https://doi.org/10.1016/j.dsr2.2010.10.034, 2011. a
Vaughan, G. L. and Squire, V. A.: Scattering of ice coupled waves by a sea-ice
sheet with random thickness, Wave. Random Complex, 17, 357–380,
https://doi.org/10.1080/17455030701250467, 2007. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
a
Wadhams, P.: Attenuation of swell by sea ice, J. Geophys. Res.,
78, 3552–3563, https://doi.org/10.1029/jc078i018p03552, 1973. a
Wadhams, P., Squire, V. A., Goodman, D. J., Cowan, A. M., and Moore, S. C.: The
attenuation rates of ocean waves in the marginal ice zone, J.
Geophys. Res., 93, 6799–6818, https://doi.org/10.1029/jc093ic06p06799, 1988. a
Wang, R. and Shen, H. H.: Gravity waves propagating into an ice-covered ocean:
A viscoelastic model, J. Geophys. Res., 115, C06024,
https://doi.org/10.1029/2009jc005591, 2010. a
Wang, Y., Holt, B., Rogers, W. E., Thomson, J., and Shen, H. H.: Wind and wave
influences on sea ice floe size and leads in the Beaufort and Chukchi Seas
during the summer-fall transition 2014, J. Geophys. Res.-Oceans, 121, 1502–1525, https://doi.org/10.1002/2015jc011349, 2016. a
Williams, T. and Porter, R.: The effect of submergence on the scattering by the
interface between two semi-infinite sheets, J. Fluid. Struct.,
25, 777–793, https://doi.org/10.1016/j.jfluidstructs.2009.02.001, 2009. a, b
Williams, T. D., Rampal, P., and Bouillon, S.: Wave–ice interactions in the neXtSIM sea-ice model, The Cryosphere, 11, 2117–2135, https://doi.org/10.5194/tc-11-2117-2017, 2017. a, b
Williams, T. D. C.: Reflections on ice: scattering of flexural gravity waves
by irregularities in Arctic and Antarctic ice sheets, PhD thesis,
University of Otago, http://hdl.handle.net/10523/8154 (last access: 12 September 2022), 2006. a
Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Sea ice floe size
distribution in the marginal ice zone: Theory and numerical experiments,
J. Geophys. Res.-Oceans, 120, 3484–3498,
https://doi.org/10.1002/2015jc010770, 2015. a
Zhang, L., Delworth, T. L., Cooke, W., and Yang, X.: Natural variability of
Southern Ocean convection as a driver of observed climate trends, Nat.
Clim. Change, 9, 59–65, https://doi.org/10.1038/s41558-018-0350-3, 2018. a
Short summary
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.
On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or...