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Ice-free tropical waterbelt for Snowball Earth events questioned by uncertain clouds

Nature Geoscience volume 15, pages 489–493 (2022)Cite this article

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Abstract

Geological evidence of active tropical glaciers reaching sea level during the Neoproterozoic (1,000–541 Ma), suggesting a global ocean completely covered in ice, was the key observation in the development of the hard Snowball Earth hypothesis. These conditions are hard to reconcile with the survival of complex marine life through Snowball Earth glaciations, which led to alternative waterbelt scenarios where a large-scale refugium was present in the form of a narrow ice-free strip in the tropical ocean. Here we assess whether a waterbelt scenario maintained by snow-free dark sea ice at low latitudes is plausible using simulations from two climate models run with a variety of cloud treatments in combination with an energy-balance model. Our simulations show that waterbelt states are not a robust and naturally emerging feature of Neoproterozoic climate. Intense shortwave reflection by mixed-phase clouds, in addition to a low albedo of bare sea ice, is needed for geologically relevant waterbelt states. Given the large uncertainty in mixed-phase clouds and their interaction with radiation, our results strongly question the idea that waterbelt scenarios can explain the Neoproterozoic geology. Hence, Neoproterozoic life has probably faced the harsh conditions of a hard Snowball Earth.

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Fig. 1: Low-latitude clouds control the existence and absence of waterbelt states in the GCMs CAM and ICON.
Fig. 2: Differences in clouds and their SWCRE as obtained from the GCMs CAM and ICON as well as ICON WBF.
Fig. 3: Analysis of the waterbelt regime in a one-dimensional EBM.

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Data availability

The data and corresponding run scripts that support the findings of this study45 are available at https://doi.org/10.5445/IR/1000144276.

Code availability

The custom computer code used to generate the results45 is available at https://doi.org/10.5445/IR/1000144276.

References

  1. Kirschvink, J. L. in The Proterozoic Biosphere: A Multidisciplinary Study (eds Schopf, J. W. and Klein, C.) 51–52 (Cambridge Univ. Press, 1992).

  2. Hoffman, P. F. & Schrag, D. P. The Snowball Earth hypothesis: testing the limits of global change. Terra Nova 14, 129–155 (2002).

    Article  Google Scholar 

  3. Fairchild, I. J. & Kennedy, M. J. Neoproterozoic glaciation in the Earth system. J. Geol. Soc. 164, 895–921 (2007).

    Article  Google Scholar 

  4. Pierrehumbert, R., Abbot, D., Voigt, A. & Koll, D. Climate of the Neoproterozoic. Annu. Rev. Earth Planet. Sci. 39, 417–460 (2011).

    Article  Google Scholar 

  5. Hoffman, P. F. et al. Snowball Earth climate dynamics and Cryogenian geology–geobiology.Sci. Adv. 3, e1600983 (2017).

    Article  Google Scholar 

  6. Hoffman, P. F., Kaufman, A. J., Halverson, G. P. & Schrag, D. P. A Neoproterozoic Snowball Earth. Science 281, 1342–1346 (1998).

    Article  Google Scholar 

  7. Hoffman, P. F. Cryoconite pans on Snowball Earth: supraglacial oases for Cryogenian eukaryotes? Geobiology 14, 531–542 (2016).

    Article  Google Scholar 

  8. Brocks, J. J. et al. The rise of algae in Cryogenian oceans and the emergence of animals. Nature 548, 578–581 (2017).

    Article  Google Scholar 

  9. Dohrmann, M. & Wörheide, G. Dating early animal evolution using phylogenomic data. Sci. Rep. 7, 3599 (2017).

    Article  Google Scholar 

  10. Zumberge, J. A. et al. Demosponge steroid biomarker 26-methylstigmastane provides evidence for Neoproterozoic animals. Nat. Ecol. Evol. 2, 1709–1714 (2018).

    Article  Google Scholar 

  11. Abbot, D. S., Voigt, A. & Koll, D. The Jormungand global climate state and implications for Neoproterozoic glaciations. J. Geophys. Res. Atmos. 116, D18103 (2011).

    Article  Google Scholar 

  12. 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 (2015).

    Article  Google Scholar 

  13. Hyde, W. T., Crowley, T. J., Baum, S. K. & Peltier, W. R. Neoproterozoic ‘Snowball Earth’ simulations with a coupled climate/ice-sheet model. Nature 405, 425–429 (2000).

    Article  Google Scholar 

  14. Rodehacke, C. B., Voigt, A., Ziemen, F. & Abbot, D. S. An open ocean region in Neoproterozoic glaciations would have to be narrow to allow equatorial ice sheets. Geophys. Res. Lett. 40, 5503–5507 (2013).

    Article  Google Scholar 

  15. Voigt, A. & Abbot, D. S. Sea-ice dynamics strongly promote Snowball Earth initiation and destabilize tropical sea-ice margins. Clim. Past 8, 2079–2092 (2012).

    Article  Google Scholar 

  16. Yang, J. & Peltier, W. The initiation of modern soft and hard Snowball Earth climates in CCSM4. Clim. Past 8, 907–918 (2012).

    Article  Google Scholar 

  17. Pollard, D., Kasting, J. F. & Zugger, M. E. Snowball Earth: asynchronous coupling of sea-glacier flow with a global climate model. J. Geophys. Res. Atmos. 122, 5157–5171 (2017).

    Article  Google Scholar 

  18. Collins, W. D. et al. Description of the NCAR Community Atmosphere Model (CAM 3.0) Technical Note NCAR/TN-464+ STR 226 (NCAR, 2004).

  19. Giorgetta, M. A. et al. ICON-A, the atmosphere component of the ICON Earth system model: I. model description. J. Adv. Model. Earth Syst. 10, 1613–1637 (2018).

    Article  Google Scholar 

  20. Boucher, O. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 571–657 (IPCC, Cambridge Univ. Press, 2013).

  21. McCoy, D. T., Tan, I., Hartmann, D. L., Zelinka, M. D. & Storelvmo, T. On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs. J. Adv. Model. Earth Syst. 8, 650–668 (2016).

    Article  Google Scholar 

  22. Vergara-Temprado, J. et al. Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles. Proc. Natl Acad. Sci. USA 115, 2687–2692 (2018).

    Article  Google Scholar 

  23. Abbot, D. S. & Halevy, I. Dust aerosol important for Snowball Earth deglaciation. J. Clim. 23, 4121–4132 (2010).

    Article  Google Scholar 

  24. Feulner, G., Hallmann, C. & Kienert, H. Snowball cooling after algal rise. Nat. Geosci. 8, 659–662 (2015).

    Article  Google Scholar 

  25. Stevens, B., Bony, S. & Webb, M. Clouds On–Off Klimate Intercomparison Experiment (COOKIE) Technical Report (Max Planck Society, 2012); http://hdl.handle.net/11858/00-001M-0000-0024-580A-3

  26. Storelvmo, T. & Tan, I. The Wegener–Bergeron–Findeisen process—iits discovery and vital importance for weather and climate. Meteorol. Zeitschrift 24, 455–461 (2015).

    Article  Google Scholar 

  27. Mauritsen, T. & Roeckner, E. Tuning the MPI-ESM1.2 global climate model to improve the match with instrumental record warming by lowering its climate sensitivity. J. Adv. Model. Earth Syst 12, e2019MS002037 (2020).

    Article  Google Scholar 

  28. Budyko, M. I. The effect of solar radiation variations on the climate of the Earth. Tellus 21, 611–619 (1969).

    Article  Google Scholar 

  29. Sellers, W. D. A global climatic model based on the energy balance of the Earth–atmosphere system. J. Appl. Meteorol. 8, 392–400 (1969).

    Article  Google Scholar 

  30. Sledd, A. & L'Ecuyer, T. How much do clouds mask the impacts of Arctic sea ice and snow cover variations? Different perspectives from observations and reanalyses. Atmosphere 10, 12 (2019).

    Article  Google Scholar 

  31. Dadic, R., Mullen, P. C., Schneebeli, M., Brandt, R. E. & 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 (2013).

    Article  Google Scholar 

  32. Abbot, D. S., Eisenman, I. & Pierrehumbert, R. T. The importance of ice vertical resolution for snowball climate and deglaciation. J. Clim. 23, 6100–6109 (2010).

    Article  Google Scholar 

  33. Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).

    Article  Google Scholar 

  34. Liang, X.-Z., Wang, W.-C. & Boyle, J. S. Atmospheric Ozone Climatology for Use in General Circulation Models (Lawrence Livermore National Laboratory, 1997).

  35. Klemp, J., Dudhia, J. & Hassiotis, A. An upper gravity-wave absorbing layer for NWP applications. Mon. Weather Rev. 136, 3987–4004 (2008).

    Article  Google Scholar 

  36. Zängl, G., Reinert, D., Rípodas, P. & Baldauf, M. The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: description of the non-hydrostatic dynamical core. Q. J. R. Meteorol. Soc. 141, 563–579 (2015).

    Article  Google Scholar 

  37. Semtner, A. J. A model for the thermodynamic growth of sea ice in numerical investigations of climate. J. Phys. Oceanogr. 6, 379–389 (1976).

    Article  Google Scholar 

  38. Fujino, K., Lewis, E. L. & Perkin, R. G. The freezing point of seawater at pressures up to 100 bars. J. Geophys. Res. 79, 1792–1797 (1974).

    Article  Google Scholar 

  39. Bitz, C. M. & Lipscomb, W. H. An energy-conserving thermodynamic model of sea ice. J. Geophys. Res. Oceans 104, 15669–15677 (1999).

    Article  Google Scholar 

  40. Yang, J., Peltier, W. R. & Hu, Y. The initiation of modern ‘soft snowball’ and ‘hard snowball’ climates in CCSM3. Part I: The influences of solar luminosity, CO2 concentration, and the sea ice/snow albedo parameterization. J. Clim. 25, 2711–2736 (2012).

    Article  Google Scholar 

  41. Winton, M. A reformulated three-layer sea ice model. J. Atmos. Ocean. Technol. 17, 525–531 (2000).

    Article  Google Scholar 

  42. Rasch, P. & Kristjánsson, J. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Clim. 11, 1587–1614 (1998).

    Article  Google Scholar 

  43. Lohmann, U. & Roeckner, E. Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model. Clim. Dyn. 12, 557–572 (1996).

    Article  Google Scholar 

  44. Donohoe, A. & Battisti, D. S. What determines meridional heat transport in climate models? J. Clim. 25, 3832–3850 (2012).

    Article  Google Scholar 

  45. Braun, C., Hörner, J., Voigt, A. & Pinto, J. G. Ice-free tropical waterbelt during Snowball Earth events questioned by uncertain clouds—dataset and analysis scripts. KITopen https://doi.org/10.5445/IR/1000144276 (2022).

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Acknowledgements

C.B. and A.V. received support from Deutsche Forschungsgemeinschaft (DFG) under grant agreement VO 1765/5-1. A.V. received partial support from the German Ministry of Education and Research (BMBF) and FONA: Research for Sustainable Development (www.fona.de) under grant agreement 01LK1509A. J.G.P. thanks the AXA Research Fund for support. We thank the German Climate Computing Centre (DKRZ, Hamburg) for computing and storage resources as part of project 1092 and T. Mauritsen for pointing us towards the WBF modification in ICON.

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Authors and Affiliations

  1. Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Christoph Braun & Joaquim G. Pinto

  2. Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria

    Johannes Hörner & Aiko Voigt

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Contributions

A.V. designed the study. C.B. and J.H. implemented modifications in CAM and ICON. C.B. performed the CAM simulations. J.H. and C.B. performed the ICON simulations. C.B. performed the analysis, created the figures and wrote the initial draft. C.B. and A.V. led the writing of the final draft. All authors contributed with discussions and edited the manuscript.

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Correspondence to Christoph Braun or Aiko Voigt.

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Nature Geoscience thanks Robin Wordsworth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: James Super and Tamara Goldin, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Temporal evolution of the global-mean ice edge latitude for the default GCM setups.

(a) CAM and (b) ICON. Global-mean ice edge latitude is calculated as the sine of global-mean ice-free surface fraction. Labels indicate the constant atmospheric CO2 concentration and the global-mean ice-edge latitude of the initialisation. Thin lines represent the 10-year rolling mean. Solid lines indicate simulations considered to exhibit (semi-)stable waterbelt states. Thick solid lines represent monthly-mean for 40-year periods that are analyzed as (semi-)stable waterbelt states.

Extended Data Fig. 2 Low clear-sky planetary albedo due to exposure of bare sea ice in the subtropical region in CAM and ICON.

Climatological zonal-mean clear-sky planetary albedo determined from (a) CAM simulation at 10000 ppmv CO2 and (b) ICON simulation at 4063 ppmv CO2. Dashed contours indicate the water-equivalent snow thickness of 0.01 m, which marks the minimum thickness of a snow-layer to be considered in the determination of the surface albedo in ICON. For each simulation 40-year periods are analyzed. Further details are given in text S1.

Extended Data Fig. 3 CAM and ICON exhibit similar large-scale thermal structure across the seasonal cycle and the simulated CO2- range.

Monthly climatological zonal-mean cloud cover and air temperature indicated by isotherms determined from CAM at 2000 ppmv CO2 and 10000 ppmv CO2, and ICON at 4063 ppmv CO2. Each period of analysis spans 40 years after stabilizing in a stable waterbelt state.

Extended Data Fig. 4 CAM and ICON exhibit similar large-scale circulation in CAM and ICON across the seasonal cycle and the simulated CO2- range.

Monthly climatological zonal-mean cloud cover and mass stream function indicated by contours. Contour intervals are 5 ⋅ 1010 kgs−1. Positive contours are solid and negative contours are dashed. The zero contour is not shown. The mass stream function is calculated by \(\psi (p,\phi )=2\Pi {r}_{E}cos(\phi )/g\int\nolimits_{p}^{{p}_{s}}vdp\) with pressure p, surface pressure ps, latitude ϕ, radius of Earth rE = 6371 km, gravitational constant g = 9.81 ms−2, and zonal-mean meridional wind v. Same simulations and time periods as in Extended Data Figure 3 are used.

Extended Data Fig. 5 Temporal evolution of the global-mean ice edge latitude for the modified GCM setups.

(a) CAM pCOOKIE and (b) ICON WBF. See caption of Extended Data Figure 1 for further details.

Extended Data Fig. 6 Impact of pCOOKIE modification in CAM on clouds and cloud-radiative effects.

(a) Planetary albedo α, (b) total cloud cover, (c) shortwave CRE, and (e) longwave CRE for CAM standard and pCOOKIE. Shown are 40-year-means over all simulations after stabilizing in a stable waterbelt state. (d) 40-year-mean of zonal-mean cloud cover from a single simulation with CAM pCOOKIE after stabilizing in a waterbelt state at 5000 ppmv CO2 along with 273 K, 235 K, and 192 K isotherms.

Extended Data Fig. 7 Impact of WBF modification in ICON on cloud liquid and longwave cloud-radiative effect.

(a) and (b): Annual-mean zonal-mean specific cloud liquid from single simulations after stabilizing in a waterbelt state at 4063 ppmv CO2 (ICON) and 6000 ppmv CO2 (ICON WBF) along with 273 K, 235 K, and 192 K isotherms. (c) Annual-mean zonal-mean liquid water path LWP, and (d) longwave CRE averaged over all simulations with a (semi-)stable waterbelt state. The period of analysis spans 40 years after stabilizing in a (semi-)stable waterbelt state.

Extended Data Fig. 8 The geologically relevant domain of waterbelt states is impacted by the intensity of meridional heat transport.

Domains spanned by planetary albedo over icefree ocean αo and bare sea ice albedo αi,b for stable and accessible waterbelt impacted by the intensity of meridional heat transport. states (white) and stable but unaccessible waterbelt states (light gray). The orange circle marks albedo values for CAM pCOOKIE. The orange line marks the lower boundary of the stable and accessible domain and is calculated with the meridional heat transport parameter C = 1.6B corresponding to CAM pCOOKIE. The black dotted line marks the lower boundary of the stable and accessible domain if calculated with C = 1.5B corresponding to CAM (similar as in Fig. 3c). Weaker meridional heat transport stabilizes the waterbelt climate because it is characterized by strong meridional temperature gradients. The red dotted box indicates the range of plausible values for αo and αi,b.

Extended Data Table 1 Estimates for the longwave feedback parameter based on GCM simulations
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Braun, C., Hörner, J., Voigt, A. et al. Ice-free tropical waterbelt for Snowball Earth events questioned by uncertain clouds. Nat. Geosci. 15, 489–493 (2022). https://doi.org/10.1038/s41561-022-00950-1

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