Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3907-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3907-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Jul 2023
Model description paper |
| 13 Jul 2023
| 13 Jul 2023
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
Institute of Analysis and Numerics, Otto von Guericke University Magdeburg, Magdeburg, Germany
Véronique Dansereau
Institut des Sciences de la Terre, Université Grenoble Alpes, CNRS (UMR5275), Gières, France
Christian Lessig
Institute of Simulation and Graphics, Otto von Guericke University Magdeburg, Magdeburg, Germany
Piotr Minakowski
Institute of Analysis and Numerics, Otto von Guericke University Magdeburg, Magdeburg, Germany
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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.
Cited articles
Bouchat, A. and Tremblay, B. L.: Using sea-ice deformation fields to constrain
the mechanical strength parameters of geophysical sea ice, J. Geophys. Res.-Oceans, 122, 5802–5825, 2017. a
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G.,
Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers,
P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay,
B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 1. Scaling and
Statistical Properties of Sea-Ice Deformation Fields, J. Geophysical
Res.-Oceans, 127, e2021JC017667,
https://doi.org/10.1029/2021JC017667, 2022. a
Bouillon, S., Fichefet, T., Legat, V., and Madec, G.: The
elastic–viscous–plastic method revisited, Ocean Model., 71, 2–12, https://doi.org/10.1016/j.ocemod.2013.05.013, 2013. a, b
Braack, M., Becker, R., Meidner, D., Richter, T., and Vexler, B.: The Finite
Element Toolkit Gascoigne, Zenodo [code], https://doi.org/10.5281/ZENODO.5574969,
2021. a
Burov, E. B.: Rheology and strength of the lithosphere, Marine Petrol.
Geol., 28, 1402–1443,
https://doi.org/10.1016/j.marpetgeo.2011.05.008, 2011. a
Chalmers, N. and Krivodonova, L.: A robust CFL condition for the discontinuous
Galerkin method on triangular meshes, J. Comput. Phys., 403,
109095, https://doi.org/10.1016/j.jcp.2019.109095, 2020. a
Coon, M.: A review of AIDJEX modeling, in: Sea Ice Processes and Models:
Symposium Proceedings, 12–27, Univ. of Wash. Press, Seattle, 1980. a
Coon, M. D., Maykut, G. A., Pritchard, R. S., Rothrock, D. A., and Thorndike,
A. S.: Modeling the pack ice as an elastic plastic material, AIDJEX Bull.,
24, 1–105, 1974. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. a
Dansereau, V., Weiss, J., Saramito, P., and Lattes, P.: A Maxwell elasto-brittle rheology for sea ice modelling, The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, 2016. a, b, c, d
Dansereau, V., Weiss, J., Saramito, P., Lattes, P., and Coche, E.: Ice bridges and ridges in the Maxwell-EB sea ice rheology, The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, 2017. a
Dansereau, V., Weiss, J., and Saramito, P.: A Continuum
Viscous-Elastic-Brittle, Finite Element DG Model for the Fracture and Drift
of Sea Ice, in: Challenges and Innovations in Geomechanics, edited by: Barla,
M., Di Donna, A., and Sterpi, D., 125–139, Springer International
Publishing, https://doi.org/10.1007/978-3-030-64514-4_8, 2021. a
Di Pietro, D. and Ern, A.: Mathematical Aspects of Discontinuous Galerkin
Methods, Springer Berlin Heidelberg, https://doi.org/10.1007/978-3-642-22980-0, 2012. a, b, c
Ern, A. and Guermond, J.-L.: Finite Elements II, Springer International
Publishing, https://doi.org/10.1007/978-3-030-56923-5, 2021. a, b
Flato, G.: A particle-in-cell sea-ice model, Atmosphere-Ocean, 31,
339–358, https://doi.org/10.1080/07055900.1993.9649475, 1993. a
Goosse, H., Kay, J. E., Armour, K. C., Bodas-Salcedo, A., Chepfer, H.,
Docquier, D., Jonko, A., Kushner, P. J., Lecomte, O., Massonnet, F., Park,
H.-S., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate
feedbacks in polar regions, Nat. Commun., 9, 1–13, 2018. a
Hibler, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys.
Oceanogr., 9, 815–846,
https://doi.org/10.1175/1520-0485(1979)009<0815:adtsim>2.0.co;2, 1979. a, b, c
Horvat, C. and Tziperman, E.: Understanding Melting due to Ocean Eddy Heat
Fluxes at the Edge of Sea-Ice Floes, Geophys. Res. Lett., 45,
9721–9730, https://doi.org/10.1029/2018GL079363, 2018. a
Huang, Z. J. and Savage, S. B.: Particle-in-cell and finite difference
approaches for the study of marginal ice zone problems, Cold Reg. Sci. Technol., 28, 1–28,
https://doi.org/10.1016/S0165-232X(98)00008-1, 1998. a
Hunke, E.: Viscous–Plastic Sea Ice Dynamics with the EVP Model:
Linearization Issues, J. Comput. Phys., 170, 18–38,
https://doi.org/10.1006/jcph.2001.6710, 2001. 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, 117,
672–687, https://doi.org/10.1002/2017JC013119, 2018. a, b
Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627–645, https://doi.org/10.5194/tc-13-627-2019, 2019. a, b
Ip, C., Hibler, W., and Flato, G.: On the effect of rheology on seasonal
sea-ice simulations, Ann. Glaciol., 15, 17–25,
https://doi.org/10.3189/1991aog15-1-17-25, 1991. a
Kimmritz, M., Danilov, S., and Losch, M.: The adaptive EVP method for solving
the sea ice momentum equation, Ocean Model., 101, 59–67,
https://doi.org/10.1016/j.ocemod.2016.03.004, 2016. a, b
Kwok, R.: Deformation of the Arctic Ocean sea ice cover between November 1996
and April 1997: a qualitative survey, Solid Mech. Appl.,
94, 315–322, 2001. a
Lemieux, J.-F., Knoll, D., Tremblay, B., Holland, D., and Losch, M.: A
comparison of the Jacobian-free Newton–Krylov method and the
EVP model for solving the sea ice momentum equation with a viscous-plastic
formulation: A serial algorithm study, J. Comput. Phys., 231,
5926–5944, https://doi.org/10.1016/j.jcp.2012.05.024, 2012. a
Lindsay, R. W. and Stern, H. L.: The RADARSAT geophysical processor system:
Quality of sea ice trajectory and deformation estimates, J. Atmos.
Ocean. Tech., 20, 1333–1347, 2003. a
Lipscomb, W. and Hunke, E.: Modeling Sea Ice Transport Using Incremental
Remapping, Mon. Weather Rev., 132, 1341–1354,
https://doi.org/10.1175/1520-0493(2004)132<1341:MSITUI>2.0.CO;2, 2005. a
Losch, M., Fuchs, A., Lemieux, J.-F., and Vanselow, A.: A parallel
Jacobian-free Newton–Krylov solver for a coupled sea ice-ocean
model, J. Comput. Phys., 257, 901–911,
https://doi.org/10.1016/j.jcp.2013.09.026, 2014. a
Marcq, S. and Weiss, J.: Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere, The Cryosphere, 6, 143–156, https://doi.org/10.5194/tc-6-143-2012, 2012. a
Marsan, D., Stern, H. L., Lindsay, R. W., and Weiss, J.: Scale Dependence and
Localization of the Deformation of Arctic Sea Ice, Phys. Rev. Lett.,
93, 178501, https://doi.org/10.1103/PhysRevLett.93.178501, 2004. a
Mehlmann, C. and Korn, P.: Sea-ice dynamics on triangular grids, J. Comput. Phys., 428, 110086, https://doi.org/10.1016/j.jcp.2020.110086, 2021. a
Mehlmann, C., Danilov, S., Losch, M., Lemieux, J.-F., Hutter, N., Richter, T.,
Blain, P., Hunke, E., and Korn, P.: Sea Ice Numerical VP-Comparison
Benchmark, Mendeley Repository, https://doi.org/10.17632/KJ58Y3SDTK.1,
2021a. a, b
Mehlmann, C., Danilov, S., Losch, M., Lemieux, J. F., Hutter, N., Richter, T.,
Blain, P., Hunke, E. C., and Korn, P.: Simulating Linear Kinematic Features
in Viscous-Plastic Sea Ice Models on Quadrilateral and Triangular Grids With
Different Variable Staggering, J. Adv. Model. Earth Sy.,
13, e2021MS002523, https://doi.org/10.1029/2021ms002523, 2021b. a, b, c, d, e, f, g, h, i, j, k, l
Oikkonen, A., Haapala, J., Lensu, M., Karvonen, J., and Itkin, P.: Small-scale
sea ice deformation during N-ICE2015: From compact pack ice to marginal ice
zone, J. Geophys. Res.-Oceans, 122, 5105–5120,
https://doi.org/10.1002/2016JC012387, 2017. a
Ólason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz,
M., Dansereau, V., and Samaké, A.: A New Brittle Rheology and Numerical
Framework for Large-Scale Sea-Ice Models, J. Adv. Model.
Earth Sy., 14, e2021MS002685,
https://doi.org/10.1029/2021MS002685, 2022. a, b
Peng, Z. and Gomberg, J.: An integrated perspective of the continuum between
earthquakes and slow-slip phenomena, Nat. Geosci., 3, 599–607,
https://doi.org/10.1038/ngeo940, 2010. 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., Weiss, J., and Marsan, D.: Positive trend in the mean speed and
deformation rate of Arctic sea ice, 1979-2007, J. Geophys. Res.-Oceans, 114, c05013, https://doi.org/10.1029/2008JC005066, 2009. a
Rampal, P., Weiss, J., Dubois, C., and Campin, J.-M.: IPCC climate models do
not capture Arctic sea ice drift acceleration: Consequences in terms of
projected sea ice thinning and decline, J. Geophys. Res.-Oceans, 116, c00D07, https://doi.org/10.1029/2011JC007110, 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, b, c
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a
Richter, T., Dansereau, V., Lessig, C., and Minakowski, P.: A snippet from
neXtSIM_DG : next generation sea-ice model with DG, Zenodo [code],
https://doi.org/10.5281/zenodo.7688635, 2023. a
Schreyer, H. L., Sulsky, D. L., Munday, L. B., Coon, M. D., and Kwok, R.:
Elastic-decohesive constitutive model for sea ice, J. Geophys. Res.-Oceans, 111, C11S26, https://doi.org/10.1029/2005JC003334, 2006. a
Shih, Y., Mehlmann, C., Losch, M., and Stadler, G.: Robust and efficient primal-dual Newton-Krylov solvers for viscous-plastic sea-ice models, J. Comput. Phys., 474,
111802, https://doi.org/10.1016/j.jcp.2022.111802, 2023.
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
Sulsky, D. and Peterson, K.: Toward a new elastic–decohesive model of Arctic
sea ice, Physica D, 240, 1674–1683,
https://doi.org/10.1016/j.physd.2011.07.005, 2011. a, b
Taylor, P., Hegyi, B., Boeke, R., and Boisvert, L.: On the Increasing
Importance of Air-Sea Exchanges in a Thawing Arctic: A Review, Atmosphere,
9, 41–39, 2018. a
Vihma, T.: Effects of Arctic Sea Ice Decline on Weather and Climate: A
Review, Surv. Geophys., 35, 1175–1214, 2014. a
Zhong, S., Yuen, D., Moresi, L., and Knepley, M.: Numerical Methods for Mantle
Convection, in: Treatise on Geophysics (Second Edition), edited by: Schubert,
G., Elsevier, Oxford, 2nd edn., 197–222,
https://doi.org/10.1016/B978-0-444-53802-4.00130-5, 2015. a
Short summary
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.
Sea ice covers not only the pole regions but affects the weather and climate globally. For...
