Articles | Volume 15, issue 2
https://doi.org/10.5194/esd-15-215-2024
© 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/esd-15-215-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Mar 2024
Research article |
| 15 Mar 2024
| 15 Mar 2024
Sea-ice thermodynamics can determine waterbelt scenarios for Snowball Earth
Johannes Hörner
CORRESPONDING AUTHOR
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Aiko Voigt
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
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Cited articles
Abbot, D. S., Eisenman, I., and Pierrehumbert, R. T.: The Importance of Ice Vertical Resolution for Snowball Climate and Deglaciation, J. Climate, 23, 6100–6109, https://doi.org/10.1175/2010JCLI3693.1, 2010. a, b
Abbot, D. S., Voigt, A., Branson, M., Pierrehumbert, R. T., Pollard, D., Hir, G. L., and Koll, D. D. B.: Clouds and Snowball Earth deglaciation, Geophys. Res. Lett., 39, L20711, https://doi.org/10.1029/2012GL052861, 2012. a
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999. a
Brunetti, M., Kasparian, J., and Vérard, C.: Co-existing climate attractors in a coupled aquaplanet, Clim. Dynam., 53, 6293–6308, https://doi.org/10.1007/s00382-019-04926-7, 2019. a, b
Dadic, R., Mullen, P. C., Schneebeli, M., Brandt, R. E., and Warren, S. G.: Effects of bubbles, cracks, and volcanic tephra on the spectral albedo of bare ice near the Transantarctic Mountains: Implications for sea glaciers on Snowball Earth, J. Geophys. Res.-Earth Surf., 118, 1658–1676, https://doi.org/10.1002/jgrf.20098, 2013. a, b
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen, T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt, H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and Stevens, B.: ICON-A, the Atmosphere Component of the ICON Earth System Model: I. Model Description, J. Adv. Model. Earth Sy., 10, 1613–1637, https://doi.org/10.1029/2017MS001242, 2018. a, b
Gough, D. O.: Solar Interior Structure and Luminosity Variations, Sol. Phys., 74, 21–34, https://doi.org/10.1007/978-94-010-9633-1_4, 1981. a
Hoffman, P. and Schrag, D.: The snowball Earth hypothesis: testing the limits of global change, Terra Nova, 14, 129–155, https://doi.org/10.1046/J.1365-3121.2002.00408.X, 2002. a
Hoffman, P. F., Abbot, D. S., Ashkenazy, Y., Benn, D. I., Brocks, J. J., Cohen, P. A., Cox, G. M., Creveling, J. R., Donnadieu, Y., Erwin, D. H., Fairchild, I. J., Ferreira, D., Goodman, J. C., Halverson, G. P., Jansen, M. F., Hir, G. L., Love, G. D., Macdonald, F. A., Maloof, A. C., Partin, C. A., Ramstein, G., Rose, B. E. J., Rose, C. V., Sadler, P. M., Tziperman, E., Voigt, A., and Warren, S. G.: Snowball Earth climate dynamics and Cryogenian geology-geobiology, Sci. Adv., 3, e1600983, https://doi.org/10.1126/sciadv.1600983, 2017. a
Hörner, J. and Voigt, A.: Sea-ice thermodynamics can determine waterbelt scenarios for Snowball Earth, University of Vienna [code and data set], https://doi.org/10.25365/phaidra.429, 2024. a
Hyde, W. T., Crowley, T. J., Baum, S. K., and Peltier, W. R.: Neoproterozoic “snowball Earth” simulations with a coupled climate/ice-sheet model, Nature, 405, 425–429, https://doi.org/10.1038/35013005, 2000. a
Kirschvink, J. L.: Late Proterozoic Low-Latitude Global Glaciation: the Snowball Earth, in: The Proterozoic Biosphere: A Multidisciplinary Study, edited by: Schopf, J. W. and Klein, C., pp. 51–52, Cambridge University Press, New York, ISBN 978-0-521-36615-1, 1992. a
Klemp, J. B., Dudhia, J., and Hassiotis, A. D.: An Upper Gravity-Wave Absorbing Layer for NWP Applications, Mon. Weather Rev., 136, 3987–4004, https://doi.org/10.1175/2008MWR2596.1, 2008. a
Liu, Y., Peltier, W. R., Yang, J., and Hu, Y.: Influence of Surface Topography on the Critical Carbon Dioxide Level Required for the Formation of a Modern Snowball Earth, J. Climate, 31, 8463–8479, https://doi.org/10.1175/JCLI-D-17-0821.1, 2018. a
Marotzke, J. and Botzet, M.: Present-day and ice-covered equilibrium states in a comprehensive climate model, Geophys. Res. Lett., 34, L16704, https://doi.org/10.1029/2006GL028880, 2007. a
Marshall, J., Adcroft, A., Campin, J.-M., Hill, C., and White, A.: Atmosphere–Ocean Modeling Exploiting Fluid Isomorphisms, Mon. Weather Rev., 132, 2882–2894, https://doi.org/10.1175/MWR2835.1, 2004. a
Pierrehumbert, R. T., Abbot, D. S., Voigt, A., and Koll, D.: Climate of the Neoproterozoic, Annu. Rev. Earth Pl. Sc., 39, 417–460, https://doi.org/10.1146/annurev-earth-040809-152447, 2011. a
Ragon, C., Lembo, V., Lucarini, V., Vérard, C., Kasparian, J., and Brunetti, M.: Robustness of Competing Climatic States, J. Climate, 35, 2769–2784, https://doi.org/10.1175/JCLI-D-21-0148.1, 2022. a, b
Ramme, L. and Marotzke, J.: Climate and ocean circulation in the aftermath of a Marinoan snowball Earth, Clim. Past, 18, 759–774, https://doi.org/10.5194/cp-18-759-2022, 2022. a, b
Rose, B. E. J.: Stable “Waterbelt” climates controlled by tropical ocean heat transport: A nonlinear coupled climate mechanism of relevance to Snowball Earth, J. Geophys. Res.-Atmos., 120, 1404–1423, https://doi.org/10.1002/2014JD022659, 2015. a, b, c, d
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, b
Semtner, A. J.: On modelling the seasonal thermodynamic cycle of sea ice in studies of climatic change, Clim. Change, 6, 27–37, https://doi.org/10.1007/BF00141666, 1984. a
Voigt, A. and Abbot, D. S.: Sea-ice dynamics strongly promote Snowball Earth initiation and destabilize tropical sea-ice margins, Clim. Past, 8, 2079–2092, https://doi.org/10.5194/cp-8-2079-2012, 2012. a, b
Voigt, A., Abbot, D. S., Pierrehumbert, R. T., and Marotzke, J.: Initiation of a Marinoan Snowball Earth in a state-of-the-art atmosphere-ocean general circulation model, Clim. Past, 7, 249–263, https://doi.org/10.5194/cp-7-249-2011, 2011. a
Warren, S. G., Brandt, R. E., Grenfell, T. C., and McKay, C. P.: Snowball Earth: Ice thickness on the tropical ocean, J. Geophys. Res.-Oceans, 107, 3167, https://doi.org/10.1029/2001JC001123, 2002. a
Webster, M. A. and Warren, S. G.: Regional Geoengineering Using Tiny Glass Bubbles Would Accelerate the Loss of Arctic Sea Ice, Earth's Future, 10, e2022EF002815, https://doi.org/10.1029/2022EF002815, 2022. a
Winton, M.: A Reformulated Three-Layer Sea Ice Model, J. Atmos. Ocean. Tech., 17, 525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2, 2000. a, b
Wolf, E. T., Shields, A. L., Kopparapu, R. K., Haqq-Misra, J., and Toon, O. B.: Constraints on Climate and Habitability for Earth-like Exoplanets Determined from a General Circulation Model, ApJ, 837, 107, https://doi.org/10.3847/1538-4357/aa5ffc, 2017. a
Yang, J., Peltier, W. R., and Hu, Y.: The Initiation of Modern “Soft Snowball” and “Hard Snowball” Climates in CCSM3. Part I: The Influences of Solar Luminosity, CO 2 Concentration, and the Sea Ice/Snow Albedo Parameterization, J. Climate, 25, 2711–2736, https://doi.org/10.1175/JCLI-D-11-00189.1, 2012a. a, b, c, d
Yang, J., Peltier, W. R., and Hu, Y.: The Initiation of Modern “Soft Snowball” and “Hard Snowball” Climates in CCSM3. Part II: Climate Dynamic Feedbacks, J. Climate, 25, 2737–2754, https://doi.org/10.1175/JCLI-D-11-00190.1, 2012b. a, b, c, d
Zhu, F. and Rose, B. E. J.: Multiple Equilibria in a Coupled Climate-Carbon Model, J. Climate, 36, 547–564, https://doi.org/10.1175/JCLI-D-21-0984.1, 2022. a
Short summary
Snowball Earth refers to a climate in the deep past of the Earth where the whole planet was covered in ice. Waterbelt states, where a narrow region of open water remains at the Equator, have been discussed as an alternative scenario, which might explain how life was able to survive these periods. Here, we demonstrate how waterbelt states are influenced by the thermodynamical sea-ice model used. The sea-ice model modulates snow on ice, ice albedo and ultimately the stability of waterbelt states.
Snowball Earth refers to a climate in the deep past of the Earth where the whole planet was...
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