Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-603-2021
© 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/gmd-14-603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
29 Jan 2021
Model experiment description paper |
| 29 Jan 2021
| 29 Jan 2021
Comparison of sea ice kinematics at different resolutions modeled with a grid hierarchy in the Community Earth System Model (version 1.2.1)
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science (DESS), Tsinghua University, Beijing, China
University Corporation for Polar Research (UCPR), Beijing, China
Jialiang Ma
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science (DESS), Tsinghua University, Beijing, China
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science (DESS), Tsinghua University, Beijing, China
Yan Zhang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science (DESS), Tsinghua University, Beijing, China
Jiping Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Bin Wang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science (DESS), Tsinghua University, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O.,
Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M.,
Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S.,
Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels,
B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B.,
Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0:
Model Description and Simulation Features, J. Adv. Model.
Earth Syst., 11, 3167–3211, https://doi.org/10.1029/2019MS001726, 2019. a
Bouchat, A. and Tremblay, B.: Using sea-ice deformation fields to constrain the
mechanical strength parameters of geophysical sea ice, J. Geophys.
Res.-Oceans, 122, 5802–5825, https://doi.org/10.1002/2017JC013020, 2017. a
Briegleb, B. P. and Light, B.: A Delta-Eddington Multiple Scattering
Parameterization for Solar Radiation in the Sea Ice Component of the
Community Climate System Model (No. NCAR/TN-472+STR), Tech. rep.,
University Corporation for Atmospheric Research,
https://doi.org/10.5065/D6B27S71, 2007. a
Chelton, D. B., deSzoeke, R. A., Schlax, M. G., El Naggar, K., and Siwertz, N.:
Geographical Variability of the First Baroclinic Rossby Radius of
Deformation, J. Phys. Oceanogr., 28, 433–460,
https://doi.org/10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2, 1998. 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
Dukowicz, J. K. and Baumgardner, J. R.: Incremental Remapping as a
Transport/Advection Algorithm, J. Comput. Phys., 160, 318–335, https://doi.org/10.1006/jcph.2000.6465, 2000. a, b
Dupont, F., Higginson, S., Bourdallé-Badie, R., Lu, Y., Roy, F., Smith, G. C., Lemieux, J.-F., Garric, G., and Davidson, F.: A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans, Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, 2015. a
ETOPO1: Global Relief Model, National Centers for Environmental Information, https://doi.org/10.7289/V5C8276M, 2019. a
Girard, L., Weiss, J., Molines, J. M., Barnier, B., and Bouillon, S.:
Evaluation of high-resolution sea ice models on the basis of statistical and
scaling properties of Arctic sea ice drift and deformation, J.
Geophys. Res.-Oceans, 114, C08015,
https://doi.org/10.1029/2008JC005182, 2009. a
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.:
A new modeling framework for sea-ice mechanics based on elasto-brittle
rheology, Ann. Glaciol., 52, 123–132,
https://doi.org/10.3189/172756411795931499, 2011. a
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Modell., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007,
2013. 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, d
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, b, c
Hunke, E. C. and Lipscomb, W. H.: CICE: the Los Alamos Sea Ice Model
Documentation and Software User's Manual Version 4.0 LA-CC-06-012, Tech.
rep., Los Alamos National Laboratory, Los Alamos, NM 87545, USA, 2008. a
Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020. a, b
Jahn, A., Sterling, K., Holland, M. M., Kay, J. E., Maslanik, J. A., Bitz,
C. M., Bailey, D. A., Stroeve, J., Hunke, E. C., Lipscomb, W. H., and Pollak,
D. A.: Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean
Properties in the CCSM4, J. Climate, 25, 1431–1452,
https://doi.org/10.1175/JCLI-D-11-00201.1, 2012. a
Kimmritz, M., Danilov, S., and Losch, M.: On the convergence of the modified
elastic-viscous-plastic method for solving the sea ice momentum equation,
J. Comput. Phys., 296, 90–100,
https://doi.org/10.1016/j.jcp.2015.04.051,
2015. a, b
Kiss, A. E., Hogg, A. McC., Hannah, N., Boeira Dias, F., Brassington, G. B., Chamberlain, M. A., Chapman, C., Dobrohotoff, P., Domingues, C. M., Duran, E. R., England, M. H., Fiedler, R., Griffies, S. M., Heerdegen, A., Heil, P., Holmes, R. M., Klocker, A., Marsland, S. J., Morrison, A. K., Munroe, J., Nikurashin, M., Oke, P. R., Pilo, G. S., Richet, O., Savita, A., Spence, P., Stewart, K. D., Ward, M. L., Wu, F., and Zhang, X.: ACCESS-OM2 v1.0: a global ocean–sea ice model at three resolutions, Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, 2020. a, b
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling,
H., Rakowsky, N., Scholz, P., Sein, D., Wang, Q., and Jung, T.: Fast EVP
Solutions in a High-Resolution Sea Ice Model, J. Adv. Model.
Earth Syst., 11, 1269–1284, https://doi.org/10.1029/2018MS001485,
2019. a, b, c, d, e, f, g
Kwok, R., Hunke, E. C., Maslowski, W., Menemenlis, D., and Zhang, J.:
Variability of sea ice simulations assessed with RGPS kinematics, J. Geophys. Res., 113, C11012, https://doi.org/10.1029/2008JC004783, 2008. a, b, c
Lemieux, J.-F., Tremblay, B., Sedlác̆ek, J., Tupper, P., Thomas, S.,
Huard, D., and Auclair, J.-P.: Improving the numerical convergence of
viscous-plastic sea ice models with the Jacobian-free Newton-Krylov
method, J. Comput. Phys., 229, 2840–2852,
https://doi.org/10.1016/j.jcp.2009.12.011,
2010. a, b
Lemieux, J.-F., Knoll, D. A., Tremblay, B., Holland, D. M., 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, b, c
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., Lei, J., Dupont, F., Roy, F., Losch, M., Lique, C., and
Laliberté, F.: The Impact of Tides on Simulated Landfast Ice in a
Pan-Arctic Ice-Ocean Model, J. Geophys. Res.-Oceans, 123,
7747–7762, https://doi.org/10.1029/2018JC014080, 2018. a
Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L.,
Wang, Y., Peng, Y., Wang, L., Xue, W., Fu, H., Zhang, G. J., Wang, B., Li,
R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao,
W., Zhang, F., Xu, J., Zhao, X., Lu, C., Chen, Y., Luo, Y., Hu, Y., Tang, Q.,
Chen, D., Yang, G., and Gong, P.: Community Integrated Earth System
Model (CIESM): description and evaluation, J. Adv.
Model. Earth Syst., 12, e2019MS002036, https://doi.org/10.1029/2019MS002036,
2020. a
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Improving
ridging schemes for high-resolution sea ice models, J. Geophys.
Res.-Oceans, 112, C03S91, https://doi.org/10.1029/2005JC003355, 2007. a, b
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E.,
Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R.,
Hamilton, M., and Seidov, D.: NOAA Atlas NESDIS 73 World Ocean Atlas
2013, Volume 1: Temperature, 1315 East-West Highway Silver Spring, MD 20910-3282, USA, 2013. a
Madec, G. and Imbard, M.: A global ocean mesh to overcome the North Pole
singularity, Clim. Dynam., 12, 381–388, https://doi.org/10.1007/BF00211684, 1996. a, b
Murray, R. J.: Explicit Generation of Orthogonal Grids for Ocean Models,
J. Comput. Phys., 126, 251–273,
https://doi.org/10.1006/jcph.1996.0136,
1996. 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., 113, C03002, https://doi.org/10.1029/2007JC004143, 2008. a
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, b, c
Rigor, I. G., Wallace, J. M., and Colony, R. L.: Response of Sea Ice to the
Arctic Oscillation, J. Climate, 15, 2648–2663,
https://doi.org/10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2, 2002. a
Ringeisen, D., Losch, M., Tremblay, L. B., and Hutter, N.: Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies, The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, 2019. a, b
Rothrock, D. A.: The energetics of the plastic deformation of pack ice by
ridging, J. Geophys. Res., 80, 4514–4519,
https://doi.org/10.1029/JC080i033p04514, 1975. a
Scholz, P., Sidorenko, D., Gurses, O., Danilov, S., Koldunov, N., Wang, Q., Sein, D., Smolentseva, M., Rakowsky, N., and Jung, T.: Assessment of the Finite-volumE Sea ice-Ocean Model (FESOM2.0) – Part 1: Description of selected key model elements and comparison to its predecessor version, Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, 2019. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. 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
Tsamados, M., Feltham, D. L., and Wilchinsky, A. V.: Impact of a new
anisotropic rheology on simulations of Arctic sea ice, J. Geophys.
Res.-Oceans, 118, 91–107, https://doi.org/10.1029/2012JC007990, 2013.
a, b
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
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4, Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, 2018. a
Weiss, J. and Dansereau, V.: Linking scales in sea ice mechanics, Phil. Trans.
R. Soc. A, 375, 20150352, https://doi.org/10.1098/rsta.2015.0352, 2017. a
Xu, S., Song, M., Liu, J., Wang, B., Li, L., Huang, W., Liu, L., Xia, K., Xue,
W., Pu, Y., Dong, L., Shen, S., Hu, N., Liu, M., and Sun, W.: Simulation of
sea ice in FGOALS-g2: Climatology and late 20th century changes, Adv. Atmos. Sci., 30, 658–673, https://doi.org/10.1007/s00376-013-2158-4, 2013. a
Xu, S., Wang, B., and Liu, J.: On the use of Schwarz–Christoffel conformal mappings to the grid generation for global ocean models, Geosci. Model Dev., 8, 3471–3485, https://doi.org/10.5194/gmd-8-3471-2015, 2015. a
Xu, S., Ma, J., Zhou, L., Zhang, Y., Wang, B., and Liu, J.: Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM [Data set], Zenodo, https://doi.org/10.5281/zenodo.3842282, 2020. a
Zweng, M., Reagan, J., Antonov, J., Locarnini, R., Mishonov, A., Boyer, T.,
Garcia, H., Baranova, O., Johnson, D., Seidov, D., and Biddle, M.: NOAA
Atlas NESDIS 74 WORLD OCEAN ATLAS 2013, Volume 2: Salinity, 1315 East-West Highway Silver Spring, MD 20910-3282, USA, 2013. a
Short summary
A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45, 0.15, and 0.05°. Based on atmospherically forced sea ice experiments, the model simulates reasonable sea ice kinematics and scaling properties. Landfast ice thickness can also be systematically shifted due to non-convergent solutions to an
elastic–viscous–plastic (EVP) model. This work is a framework for multi-scale modeling of the ocean and sea ice with CESM.
A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version...
