Articles | Volume 17, issue 2
https://doi.org/10.5194/esd-17-303-2026
© Author(s) 2026. 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-17-303-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Mar 2026
Research article |
| 17 Mar 2026
| 17 Mar 2026
Snowball Earth transitions from Last Glacial Maximum conditions provide an independent upper limit on Earth's climate sensitivity
Department of Geological Sciences, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Navjit Sagoo
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Department of Meteorology, Stockholm University, Stockholm, Sweden
Johannes Hörner
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Thorsten Mauritsen
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Department of Meteorology, Stockholm University, Stockholm, Sweden
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Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
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Cited articles
Braun, C., Hörner, J., Voigt, A., and Pinto, J. G.: Ice-free tropical waterbelt for Snowball Earth events questioned by uncertain clouds, Nat. Geosci., 15, 489–493, https://doi.org/10.1038/s41561-022-00950-1, 2022. a
Budyko, M. I.: The effect of solar radiation variations on the climate of the Earth, Tellus, 21, 611–619, 1969. a
Burls, N. and Sagoo, N.: Increasingly sophisticated climate models need the out-of-sample tests paleoclimates provide, J. Adv. Model. Earth Sy., 14, e2022MS003389, https://doi.org/10.1029/2022MS003389, 2022. a
Caballero, R. and Huber, M.: State-dependent climate sensitivity in past warm climates and its implications for future climate projections, P. Natl. Acad. Sci. USA, 110, 14162–14167, 2013. a
Colman, R. and McAvaney, B.: A study of general circulation model climate feedbacks determined from perturbed sea surface temperature experiments, J. Geophys. Res.-Atmos., 102, 19383–19402, https://doi.org/10.1029/97JD00206, 1997. a
Colman, R. and McAvaney, B.: Climate feedbacks under a very broad range of forcing, Geophys. Res. Lett., 36, https://doi.org/10.1029/2008GL036268, 2009. a
Eisenman, I. and Armour, K. C.: The radiative feedback continuum from Snowball Earth to an ice-free hothouse, Nat. Commun., 15, 6582, https://doi.org/10.1038/s41467-024-50406-w, 2024. a, b
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's energy budget, climate feedbacks, and climate sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896.009, 2021. a, b, c, d, e
Good, P., Andrews, T., Chadwick, R., Dufresne, J.-L., Gregory, J. M., Lowe, J. A., Schaller, N., and Shiogama, H.: nonlinMIP contribution to CMIP6: model intercomparison project for non-linear mechanisms: physical basis, experimental design and analysis principles (v1.0), Geosci. Model Dev., 9, 4019–4028, https://doi.org/10.5194/gmd-9-4019-2016, 2016. a
Gregory, J. M., Ingram, W., Palmer, M., Jones, G., Stott, P., Thorpe, R., Lowe, J., Johns, T., and Williams, K.: A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, https://doi.org/10.1029/2003GL018747, 2004. a, b, c, d
Grose, M. R., Gregory, J., Colman, R., and Andrews, T.: What climate sensitivity index is most useful for projections?, Geophys. Res. Lett., 45, 1559–1566, https://doi.org/10.1002/2017GL075742, 2018. a
Hargreaves, J. C., Annan, J. D., Yoshimori, M., and Abe-Ouchi, A.: Can the Last Glacial Maximum constrain climate sensitivity?, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL053872, 2012. a, b, c
Haywood, A. M., Tindall, J. C., Dowsett, H. J., Dolan, A. M., Foley, K. M., Hunter, S. J., Hill, D. J., Chan, W.-L., Abe-Ouchi, A., Stepanek, C., Lohmann, G., Chandan, D., Peltier, W. R., Tan, N., Contoux, C., Ramstein, G., Li, X., Zhang, Z., Guo, C., Nisancioglu, K. H., Zhang, Q., Li, Q., Kamae, Y., Chandler, M. A., Sohl, L. E., Otto-Bliesner, B. L., Feng, R., Brady, E. C., von der Heydt, A. S., Baatsen, M. L. J., and Lunt, D. J.: The Pliocene Model Intercomparison Project Phase 2: large-scale climate features and climate sensitivity, Clim. Past, 16, 2095–2123, https://doi.org/10.5194/cp-16-2095-2020, 2020. 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., Le Hir, G., 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, Science Advances, 3, e1600983, https://doi.org/10.1126/sciadv.1600983, 2017. a
Hörner, J., Voigt, A., and Braun, C.: Snowball Earth initiation and the thermodynamics of sea ice, J. Adv. Model. Earth Sy., e2021MS002734, https://doi.org/10.1029/2021MS002734, 2022. a
Huusko, L. L., Bender, F. A., Ekman, A. M., and Storelvmo, T.: Climate sensitivity indices and their relation with projected temperature change in CMIP6 models, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ac0748, 2021. a
Jonko, A. K., Shell, K. M., Sanderson, B. M., and Danabasoglu, G.: Climate feedbacks in CCSM3 under changing CO2 forcing. Part II: variation of climate feedbacks and sensitivity with forcing, J. Climate, 26, 2784–2795, 2013. a
Kageyama, M., Harrison, S. P., Kapsch, M.-L., Lofverstrom, M., Lora, J. M., Mikolajewicz, U., Sherriff-Tadano, S., Vadsaria, T., Abe-Ouchi, A., Bouttes, N., Chandan, D., Gregoire, L. J., Ivanovic, R. F., Izumi, K., LeGrande, A. N., Lhardy, F., Lohmann, G., Morozova, P. A., Ohgaito, R., Paul, A., Peltier, W. R., Poulsen, C. J., Quiquet, A., Roche, D. M., Shi, X., Tierney, J. E., Valdes, P. J., Volodin, E., and Zhu, J.: The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations, Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, 2021. a
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: generation CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199, https://doi.org/10.1002/grl.50256, 2013. a
Lunt, D. J., Bragg, F., Chan, W.-L., Hutchinson, D. K., Ladant, J.-B., Morozova, P., Niezgodzki, I., Steinig, S., Zhang, Z., Zhu, J., Abe-Ouchi, A., Anagnostou, E., de Boer, A. M., Coxall, H. K., Donnadieu, Y., Foster, G., Inglis, G. N., Knorr, G., Langebroek, P. M., Lear, C. H., Lohmann, G., Poulsen, C. J., Sepulchre, P., Tierney, J. E., Valdes, P. J., Volodin, E. M., Dunkley Jones, T., Hollis, C. J., Huber, M., and Otto-Bliesner, B. L.: DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data, Clim. Past, 17, 203–227, https://doi.org/10.5194/cp-17-203-2021, 2021. a
Marotzke, J. and Botzet, M.: Present-day and ice-covered equilibrium states in a comprehensive climate model, Geophys. Res. Lett., 34, https://doi.org/10.1029/2006GL028880, 2007. a, b
Mauritsen, T., Graversen, R. G., Klocke, D., Langen, P. L., Stevens, B., and Tomassini, L.: Climate feedback efficiency and synergy, Clim. Dynam., 41, 2539–2554, 2013. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1. 2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019. a
Meraner, K., Mauritsen, T., and Voigt, A.: Robust increase in equilibrium climate sensitivity under global warming, Geophys. Res. Lett., 40, 5944–5948, https://doi.org/10.1002/2013GL058118, 2013. a, b
Muglia, J. and Schmittner, A.: Glacial Atlantic overturning increased by wind stress in climate models, Geophys. Res. Lett., 42, 9862–9868, https://doi.org/10.1002/2015GL064583, 2015. a
Pierrehumbert, R., Abbot, D., Voigt, A., and Koll, D.: Climate of the Neoproterozoic, Annu. Rev. Earth Pl. Sc., 39, 417–460, 2011. a
Renoult, M.: Simulation outputs for the manuscript “Snowball Earth transitions from Last Glacial Maximum conditions provide an independent upper limit on Earth's climate sensitivity”, Zenodo [data set], https://doi.org/10.5281/zenodo.8117483, 2023. a
Renoult, M., Annan, J. D., Hargreaves, J. C., Sagoo, N., Flynn, C., Kapsch, M.-L., Li, Q., Lohmann, G., Mikolajewicz, U., Ohgaito, R., Shi, X., Zhang, Q., and Mauritsen, T.: A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP, Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, 2020. a
Roeckner, E., Mauritsen, T., Esch, M., and Brokopf, R.: Impact of melt ponds on Arctic sea ice in past and future climates as simulated by MPI-ESM, J. Adv. Model. Earth Sy., 4, https://doi.org/10.1029/2012MS000157, 2012. a
Schmidt, G. A., Annan, J. D., Bartlein, P. J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., Harrison, S. P., Kageyama, M., LeGrande, A. N., Konecky, B., Lovejoy, S., Mann, M. E., Masson-Delmotte, V., Risi, C., Thompson, D., Timmermann, A., Tremblay, L.-B., and Yiou, P.: Using palaeo-climate comparisons to constrain future projections in CMIP5, Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, 2014. a
Schrag, D. P., Berner, R. A., Hoffman, P. F., and Halverson, G. P.: On the initiation of a snowball Earth, Geochem. Geophy. Geosy., 3, 1–21, 2002. a
Sherriff-Tadano, S., Abe-Ouchi, A., Yoshimori, M., Oka, A., and Chan, W.-L.: Influence of glacial ice sheets on the Atlantic meridional overturning circulation through surface wind change, Clim. Dynam., 50, 2881–2903, https://doi.org/10.1007/s00382-017-3780-0, 2018. a
Sherwood, S., Webb, M., Annan, J., Armour, K., Forster, P., Hargreaves, J., Hegerl, G., Klein, S., Marvel, K., Rohling, E., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C., Foster, G., Hausfather, Z., von der Heydt, A., Knutti, R., Mauritsen, T., Norris, J., Proistosescu, C., Rugenstein, M., Schmidt, G., Tokarska, K., and Zelinka, M.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b, c
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, c, d
Voigt, A. and Marotzke, J.: The transition from the present-day climate to a modern Snowball Earth, Clim. Dynam., 35, 887–905, 2010. a
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, b, c
Wall, C. J., Kohyama, T., and Hartmann, D. L.: Low-cloud, boundary layer, and sea ice interactions over the Southern Ocean during winter, J. Climate, 30, 4857–4871, 2017. a
Wetherald, R. and Manabe, S.: Cloud feedback processes in a general circulation model, J. Atmos. Sci., 45, 1397–1416, https://doi.org/10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2, 1988. a
Yang, J., Peltier, W. R., and Hu, Y.: The initiation of modern soft and hard Snowball Earth climates in CCSM4, Clim. Past, 8, 907–918, https://doi.org/10.5194/cp-8-907-2012, 2012a. a
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, 2012b. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhu, J. and Poulsen, C. J.: Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity, Clim. Past, 17, 253–267, https://doi.org/10.5194/cp-17-253-2021, 2021a. a
Zhu, J. and Poulsen, C. J.: Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity, Clim. Past, 17, 253–267, https://doi.org/10.5194/cp-17-253-2021, 2021b. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., and DiNezio, P.: Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum, Geophys. Res. Lett., 48, e2020GL091220, https://doi.org/10.1029/2020GL091220, 2021. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T., Neale, R. B., Poulsen, C. J., Shaw, J. K., McGraw, Z. S., and Kay, J. E.: LGM paleoclimate constraints inform cloud parameterizations and equilibrium climate sensitivity in CESM2, J. Adv. Model. Earth Sy., 14, e2021MS002776, https://doi.org/10.1029/2021MS002776, 2022. a, b
Short summary
Geological evidence indicate persistent tropical sea-ice cover in the deep past, often called Snowball Earth. Using a climate model, we show here that clouds substantially cool down the tropics and facilitate the advance of sea-ice into lower latitudes. We identify a critical threshold temperature close to 0 °C from where cooling down the Earth is accelerated. This value can be used as a constraint on Earth's sensitivity to CO2, as recent cold paleoclimates never entered Snowball Earth.
Geological evidence indicate persistent tropical sea-ice cover in the deep past, often called...
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