Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-1053-2021
© Author(s) 2021. 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-15-1053-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2021
Research article |
| 26 Feb 2021
| 26 Feb 2021
On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Pierre Rampal
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
now at: Institut de Géophysique de l'Environnement, CNRS, Grenoble, France
Véronique Dansereau
Nansen Environmental Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
now at: Institut des Sciences de la Terre, CNRS, Grenoble, France
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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.
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
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Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
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Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
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It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
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.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Heather Regan, Pierre Rampal, Einar Ólason, Guillaume Boutin, and Anton Korosov
The Cryosphere, 17, 1873–1893, https://doi.org/10.5194/tc-17-1873-2023, https://doi.org/10.5194/tc-17-1873-2023, 2023
Short summary
Short summary
Multiyear ice (MYI), sea ice that survives the summer, is more resistant to changes than younger ice in the Arctic, so it is a good indicator of sea ice resilience. We use a model with a new way of tracking MYI to assess the contribution of different processes affecting MYI. We find two important years for MYI decline: 2007, when dynamics are important, and 2012, when melt is important. These affect MYI volume and area in different ways, which is important for the interpretation of observations.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
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Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
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
Short summary
Short summary
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.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, https://doi.org/10.5194/tc-15-431-2021, 2021
Short summary
Short summary
In this study, we investigate the interactions of surface ocean waves with sea ice. We focus on the evolution of sea ice after it has been fragmented by the waves. Fragmented sea ice is expected to experience less resistance to deformation. We reproduce this evolution using a new coupling framework between a wave model and the recently developed sea ice model neXtSIM. We find that waves can significantly increase the mobility of compact sea ice over wide areas in the wake of storm events.
Cited articles
Aagaard, K., Coachman, L., and Carmack, E.: On the halocline of the Arctic
Ocean, Deep-Sea Res. Pt. I, 28, 529–545, https://doi.org/10.1016/0198-0149(81)90115-1, 1981. a
Andreas, E. and Murphy, B.: Bulk transfer coefficients for heat and momentum
over leads and polynyas, J. Phys. Ocean., 16, 1875–1883, 1986. a
Andreas, E. L. and Cash, B. A.: Convective heat transfer over wintertime leads
and polynyas, J. Geophys. Res.-Oceans, 104, 25721–25734,
https://doi.org/10.1029/1999JC900241, 1999. a, b, c
Andreas, E. L., Paulson, C. A., William, R. M., Lindsay, R. W., and
Businger, J. A.: The turbulent heat flux from arctic leads,
Bound.-Lay. Meteorol., 17, 57–91, https://doi.org/10.1007/BF00121937, 1979. a
Barthélemy, A., Fichefet, T., Goosse, H., and Madec, G.: Modeling the
interplay between sea ice formation and the oceanic mixed layer: Limitations
of simple brine rejection parameterizations, Ocean Modell., 86, 141–152,
https://doi.org/10.1016/j.ocemod.2014.12.009, 2015. a
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, 2015a. a, b, c
Bouillon, S. and Rampal, P.: Presentation of the dynamical core of neXtSIM, a
new sea ice model, Ocean Modell., 91, 23–37, 2015b. a
Bröhan, D. and Kaleschke, L.: A Nine-Year Climatology of Arctic Sea Ice
Lead Orientation and Frequency from AMSR-E, Remote Sens., 6, 1451–1475,
2014. a
Bromwich, D. H., Wilson, A. B., Bai, L.-S., Moore, G. W. K., and Bauer, P.: A
comparison of the regional Arctic System Reanalysis and the global
ERA-Interim Reanalysis for the Arctic, Q. J. Roy. Meteor. Soc., 142, 644–658, https://doi.org/10.1002/qj.2527, 2016. a
Chechin, D. G., Makhotina, I. A., Lüpkes, C., and Makshtas, A. P.: Effect of
Wind Speed and Leads on Clear-Sky Cooling over Arctic Sea Ice during Polar
Night, J. Atmos. Sci., 76, 2481–2503,
https://doi.org/10.1175/JAS-D-18-0277.1, 2019. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a
Esau, I. N.: Amplification of turbulent exchange over wide Arctic leads:
Large-eddy simulation study, J. Geophys. Res.-Atmos.,
112, d08109, https://doi.org/10.1029/2006JD007225, 2007. a, b, c
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.:
A new modelling framework for sea ice mechanics based on elasto-brittle
rheology, Ann. Glaciol., 52, 123–132, 2011. a
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Ocean., 9,
817–846, 1979. a
Hutchings, J. K., Roberts, A., Geiger, C. A., and Richter-Menge, J.: Spatial
and temporal characterization of sea-ice deformation, Ann. Glaciol., 52,
360–368, 2011. a
Hutter, N., Losch, M., and Menemenlis, D.: Scaling Properties of Arctic Sea Ice
Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in
Satellite Observations, J. Geophys. Res.-Oceans, 123,
672–687, https://doi.org/10.1002/2017JC013119, 2018. a
Ivanova, N., Rampal, P., and Bouillon, S.: Error assessment of satellite-derived lead fraction in the Arctic, The Cryosphere, 10, 585–595, https://doi.org/10.5194/tc-10-585-2016, 2016. a, b
Kagan, Y. Y.: Fractal dimension of brittle fracture, J. Nonlinear
Sci., 1, 1–16, 1991. a
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling,
H., Rakowsky, N., Scholz, P., Sein, D., Wang, Q., and Jung, T.: Fast EVP
Solutions in a High-Resolution Sea Ice Model, J. Adv. Model. Earth Sy., 11, 1269–1284, https://doi.org/10.1029/2018MS001485, 2019. a
Kozo, T. L.: Initial model results for Arctic mixed layer circulation under a
refreezing lead, J. Geophys. Res.-Oceans, 88, 2926–2934,
https://doi.org/10.1029/JC088iC05p02926, 1983. 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.-Oceans, 114, c07005, https://doi.org/10.1029/2009JC005312,
2009. a
Leppäranta, M.: The Drift of Sea Ice, Springer, Helsinki, 2005. a
Li, X., Krueger, S. K., Strong, C., Mace, G. G., and Benson, S.: Midwinter
Arctic leads form and dissipate low clouds, Nat. Commun., 11, 206,
https://doi.org/10.1038/s41467-019-14074-5, 2020. a
Lüpkes, C., Gryanik, V. M., Witha, B., Gryschka, M., Raasch, S., and
Gollnik, T.: Modeling convection over arctic leads with LES and a
non-eddy-resolving microscale model, J. Geophys. Res., 113, C09028,
https://doi.org/10.1029/2007JC004099, 2008a. 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,
2008b. a, b
Matsushita, M.: Fractal Viewpoint of Fracture and Accretion, J. Phys. Soc. Jpn., 54, 857–860, https://doi.org/10.1143/JPSJ.54.857, 1985. a
Morales Maqueda, M. A., Willmott, A. J., and Biggs, N. R. T.: Polynya Dynamics:
a Review of Observations and Modeling, Rev. Geophys., 42, RG1004,
https://doi.org/10.1029/2002RG000116, 2004. a
Morison, J. H. and McPhee, M. G.: Lead convection measured with an autonomous
underwater vehicle, J. Geophys. Res.-Oceans, 103, 3257–3281,
https://doi.org/10.1029/97JC02264, 1998. a
Morison, J. H., McPhee, M. G., Curtin, T. B., and Paulson, C. A.: The
oceanography of winter leads, J. Geophys. Res.-Oceans, 97,
11199–11218, https://doi.org/10.1029/92JC00684, 1992. a
Nguyen, A. T., Menemenlis, D., and Kwok, R.: Improved modeling of the Arctic
halocline with a subgrid-scale brine rejection parameterization, J. Geophys. Res.-Oceans, 114, c11014, https://doi.org/10.1029/2008JC005121, 2009. a
Rampal, P., Weiss, J., Marsan, D., Lindsay, R., and Stern, H.: Scaling
properties of sea ice deformation from buoy dispersion analysis, J. Geophys. Res.-Oceans, 113, c03002, https://doi.org/10.1029/2007JC004143, 2008. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b, c
Röhrs, J. and Kaleschke, L.: An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery, The Cryosphere, 6, 343–352, https://doi.org/10.5194/tc-6-343-2012, 2012. a, b, c
Rothrock, D. A. and Thorndike, A. S.: Measuring the Sea Ice Floe Size
Distribution, J. Geophys. Res., 89, 6477–6486, 1984. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Schertzer, D. and Lovejoy, S.: Physical Modeling and Analysis of Rain and
Clouds by Anisotropic Scaling Multiplicative Processes, J. Geophys. Res., 92,
9693–9714, 1987. a
Smith, D. C. and Morison, J. H.: A numerical study of haline convection beneath
leads in sea ice, J. Geophys. Res.-Oceans, 98,
10069–10083, https://doi.org/10.1029/93JC00137, 1993. a
Smith, D. C., Lavelle, J. W., and Fernando, H. J. S.: Arctic Ocean mixed-layer
eddy generation under leads in sea ice, J. Geophys. Res.-Oceans, 107, 1–17, https://doi.org/10.1029/2001JC000822, 2002. a
Smith, J.: Oceanographic investigations during the AIDJEX lead experiment,
AIDJEX Bull., 27, 125–133, 1974. a
Spreen, G., Kwok, R., Menemenlis, D., and Nguyen, A. T.: Sea-ice deformation in a coupled ocean–sea-ice model and in satellite remote sensing data, The Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, 2017. a
Stern, H. L. and Lindsay, R. W.: Spatial scaling of Arctic sea ice deformation,
J. Geophys. Res.-Oceans, 114, c10017, https://doi.org/10.1029/2009JC005380,
2009. a
Stern, H. L., Stark, A. J., Zhang, J., Steele, M., and Hwang, B.: Seasonal
evolution of the sea-ice floe size distribution in the Beaufort and Chukchi
seas, Elementa Sci. Anthro., 6, 48, https://doi.org/10.1525/elementa.305,
2018. a
Tamura, T. and Ohshima, K. I.: Mapping of sea ice production in the Arctic
coastal polynyas, J. Geophys. Res., 116, C07030, https://doi.org/10.1029/2010JC006586, 2011. a
Tetzlaff, A., Lüpkes, C., and Hartmann, J.: Aircraft-based observations of
atmospheric boundary-layer modification over Arctic leads, Q. J. Roy. Meteor. Soc., 141, 2839–2856, https://doi.org/10.1002/qj.2568,
2015. a
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016. a
Vihma, T., Pirazzini, R., Fer, I., Renfrew, I. A., Sedlar, J., Tjernström, M., Lüpkes, C., Nygård, T., Notz, D., Weiss, J., Marsan, D., Cheng, B., Birnbaum, G., Gerland, S., Chechin, D., and Gascard, J. C.: Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review, Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, 2014. a
Wang, Q., Danilov, S., Jung, T., Kaleschke, L., and Wernecke, A.: Sea ice leads
in the Arctic Ocean: Model assessment, interannual variability and trends,
Geophys. Res. Lett., 43, 7019–7027, https://doi.org/10.1002/2016GL068696,
2016. a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin,
N. N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea Ice,
J. Climate, 12, 1814–1829,
https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999. a
Weiss, J.: Scaling of Fracture and Faulting of Ice on Earth, Surv. Geophys., 24, 185–227, https://doi.org/10.1023/A:1023293117309, 2003. a
Weiss, J. and Dansereau, V.: Linking scales in sea ice mechanics,
Philos. T. Roy. Soc. A, 375, 20150352, https://doi.org/10.1098/rsta.2015.0352, 2017.
a
Willmes, S. and Heinemann, G.: Pan-Arctic lead detection from MODIS thermal
infrared imagery, Ann. Glaciol., 56, 29–37,
https://doi.org/10.3189/2015AoG69A615, 2015. a
Winsor, P. and Björk, G.: Polynya activity in the Arctic Ocean from 1958 to
1997, J. Geophys. Res.-Oceans, 105, 8789–8803,
https://doi.org/10.1029/1999JC900305, 2000. a
Winton, M.: A reformulated three-layer sea ice model, J. Atmos. Ocean. Tech.,
17, 525–531, 2000. a
Short summary
We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
We analyse the fractal properties observed in the pattern of the long, narrow openings that form...
