Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Shutdown of Southern Ocean convection controls long-term greenhouse gas-induced warming

Abstract

The effective climate sensitivity estimates the equilibrium response of near-surface temperature to doubling atmospheric carbon dioxide concentration and is a widely used metric to characterize potential global warming. Earth system models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) exhibit considerable spread in effective climate sensitivity estimates. Cloud feedbacks are thought to be the cause of this, with marine boundary layer clouds over the Southern Ocean playing an important role. Here, we show that Southern Ocean deep convection is a major contributor to the CMIP6 intermodel spread in effective climate sensitivity. By comparing two Earth system models with very different sensitivities, we find that greater storage of heat at depth can delay the Southern Ocean surface warming and associated cloud response, thereby delaying global surface warming by centuries. The link between Southern Ocean convection and effective climate sensitivity is seen across 41 CMIP6 models, with low-sensitivity models exhibiting substantial deep ocean warming. Our results reveal the influence of Southern Ocean convection on potential long-term climate warming.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Relationships among key climate variables in CMIP5 and CMIP6.
Fig. 2: Radiative feedbacks and surface temperature warming in response to quadrupled atmospheric CO2 concentration.
Fig. 3: Cloud changes, SST warming and SW cloud feedbacks.
Fig. 4: Ocean temperature response to GHG forcing.
Fig. 5: SST response to GHG forcing and convection shutdown.

Data availability

All CMIP data used are available from the Earth System Grid Federation (ESGF) server: CMIP5 model output (https://esgf-node.llnl.gov/search/cmip5/) and CMIP6 model output (https://esgf-node.llnl.gov/search/cmip5/). The data from the hosing experiment, NorESM2-hosing, used to produce panels a and c in Fig. 5 are available for download from Uninett Sigma2 (https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00053)59. The NorESM2-LM and NorESM2-MM experiments piClim-p4K used for the feedback comparison with CESM2 are available for download from Uninett Sigma2 (https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.0005460 and https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.0005661). Source data are provided with this paper.

Code availability

All code used for the analysis and plotting can be obtained from https://github.com/adagj/ECS_SOconvection. The kernels used in the feedback analysis in Figs. 1–3 can be obtained from https://github.com/apendergrass/cam5-kernels. The CESM2 source code can be accessed at https://github.com/ESCOMP/CESM. The NorESM2 source code can be accessed from https://github.com/NorESMhub/NorESM.

References

  1. Murphy, J. M. Transient response of the Hadley Centre coupled ocean–atmosphere model to increasing carbon dioxide. Part iii: analysis of global-mean response using simple models. J. Clim. 8, 496–514 (1995).

    Google Scholar 

  2. Gregory, J. M. et al. A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett. 31, L03205 (2004).

    Google Scholar 

  3. Andrews, T., Gregory, J. M., Webb, M. J. & Taylor, K. E. Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Lett. 39, L09712 (2012).

    Google Scholar 

  4. Rugenstein, M. et al. Equilibrium climate sensitivity estimated by equilibrating climate models. Geophys. Res. Lett. 47, e2019GL083898 (2020).

    Article  Google Scholar 

  5. Andrews, T., Gregory, J. M. & Webb, M. J. The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Clim. 28, 1630–1648 (2015).

    Article  Google Scholar 

  6. Knutti, R., Rugenstein, M. A. & Hegerl, G. C. Beyond equilibrium climate sensitivity. Nat. Geosci. 10, 727–736 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Myers, T. A. & Norris, J. R. On the relationships between subtropical clouds and meteorology in observations and CMIP3 and CMIP5 models. J. Clim. 28, 2945–2967 (2015).

    Article  Google Scholar 

  9. Myers, T. A. & Norris, J. R. Reducing the uncertainty in subtropical cloud feedback. Geophys. Res. Let. 43, 2144–2148 (2016).

    Article  Google Scholar 

  10. Brient, F. & Schneider, T. Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J. Clim. 29, 5821–5835 (2016).

    Article  Google Scholar 

  11. Bony, S. & Dufresne, J.-L. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett. 32, L20806 (2005).

    Article  Google Scholar 

  12. Vial, J., Dufresne, J.-L. & Bony, S. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim. Dyn. 41, 3339–3362 (2013).

    Article  Google Scholar 

  13. Webb, M. J., Lambert, F. H. & Gregory, J. M. Origins of differences in climate sensitivity, forcing and feedback in climate models. Clim. Dyn. 40, 677–707 (2013).

    Article  Google Scholar 

  14. Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C. & Eyring, V. Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? Earth Sys. Dyn. 11, 1233–1258 (2020).

    Article  Google Scholar 

  15. Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).

    Article  Google Scholar 

  16. Dong, Y. et al. Intermodel spread in the pattern effect and its contribution to climate sensitivity in CMIP5 and CMIP6 models. J. Clim. 33, 7755–7775 (2020).

    Article  Google Scholar 

  17. Gregory, J. M. Vertical heat transports in the ocean and their effect on time-dependent climate change. Clim. Dyn. 16, 501–515 (2000).

    Article  Google Scholar 

  18. Armour, K. C., Marshall, J., Scott, J. R., Donohoe, A. & Newsom, E. R. Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nat. Geosci. 9, 549–554 (2016).

    Article  Google Scholar 

  19. Newsom, E. R., Bitz, C. M., Bryan, F. O., AberNaturehey, R. & Gent, P. R. Southern Ocean deep circulation and heat uptake in a high-resolution climate model. J. Clim. 29, 2597–2619 (2016).

    Article  Google Scholar 

  20. He, J., Winton, M., Vecchi, G., Jia, L. & Rugenstein, M. Transient climate sensitivity depends on base climate ocean circulation. J. Clim. 30, 1493–1504 (2017).

    Article  Google Scholar 

  21. Danabasoglu, G. et al. The Community Earth System Model version 2 (CESM2). J. Adv. Model. 12, e2019MS001916 (2020).

    Google Scholar 

  22. Gettelman, A. et al. High climate sensitivity in the Community Earth System Model version 2 (CESM2). Geophys. Res. Lett. 46, 8329–8337 (2019).

    Article  Google Scholar 

  23. Seland, Ø. et al. Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 deck, historical, and scenario simulations. Geosci. Model Dev. 13, 6165–6200 (2020).

    Article  Google Scholar 

  24. Danabasoglu, G. et al. The CCSM4 ocean component. J. Clim. 25, 1361–1389 (2012).

    Article  Google Scholar 

  25. Hartmann, D. L., Ockert-Bell, M. E. & Michelsen, M. L. The effect of cloud type on Earth’s energy balance: global analysis. J. Clim. 5, 1281–1304 (1992).

    Article  Google Scholar 

  26. Tsushima, Y. et al. Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study. Clim. Dyn. 27, 113–126 (2006).

    Article  Google Scholar 

  27. Cheng, A., Xu, K.-M., Hu, Y. & Kato, S. Impact of a cloud thermodynamic phase parameterization based on calipso observations on climate simulation. J. Geophys. Res. 117, D09103 (2012).

    Google Scholar 

  28. McCoy, D. T., Hartmann, D. L. & Grosvenor, D. P. Observed Southern Ocean cloud properties and shortwave reflection. Part ii: phase changes and low cloud feedback. J. Clim. 27, 8858–8868 (2014).

    Article  Google Scholar 

  29. Tan, I., Storelvmo, T. & Zelinka, M. D. Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science 352, 224–227 (2016).

    Article  Google Scholar 

  30. Ceppi, P., Brient, F., Zelinka, M. D. & Hartmann, D. L. Cloud feedback mechanisms and their representation in global climate models. Wiley Interdiscip. Rev. Clim. Change 8, e465 (2017).

    Article  Google Scholar 

  31. Bjordal, J., Storelvmo, T., Alterskjær, K. & Carlsen, T. Equilibrium climate sensitivity above 5° C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020).

    Article  Google Scholar 

  32. Frölicher, T. L. et al. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Clim. 28, 862–886 (2015).

    Article  Google Scholar 

  33. Marshall, J. & Speer, K. Closure of the meridional overturning circulation through Southern Ocean upwelling. Nat. Geosci. 5, 171–180 (2012).

    Article  Google Scholar 

  34. Cessi, P. The global overturning circulation. Ann. Rev. Mar. Sci. 11, 249–270 (2019).

    Article  Google Scholar 

  35. Morrison, A. K., Griffies, S. M., Winton, M., Anderson, W. G. & Sarmiento, J. L. Mechanisms of Southern Ocean heat uptake and transport in a global eddying climate model. J. Clim. 29, 2059–2075 (2016).

    Article  Google Scholar 

  36. Kirkman, C. H. & Bitz, C. M. The effect of the sea ice freshwater flux on Southern Ocean temperatures in CCSM3: deep-ocean warming and delayed surface warming. J. Clim. 24, 2224–2237 (2011).

    Article  Google Scholar 

  37. De Lavergne, C., Palter, J. B., Galbraith, E. D., Bernardello, R. & Marinov, I. Cessation of deep convection in the open Southern Ocean under anthropogenic climate change. Nat. Clim. Change 4, 278–282 (2014).

    Article  Google Scholar 

  38. Bernardello, R., Marinov, I., Palter, J. B., Galbraith, E. D. & Sarmiento, J. L. Impact of Weddell Sea deep convection on natural and anthropogenic carbon in a climate model. Geophys. Res. Lett. 41, 7262–7269 (2014).

    Article  Google Scholar 

  39. Haumann, F. A., Gruber, N. & Münnich, M. Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate. AGU Adv. 1, e2019AV000132 (2020).

    Article  Google Scholar 

  40. Auger, M., Morrow, R., Kestenare, E., Sallée, J.-B. & Cowley, R. Southern Ocean in-situ temperature trends over 25 years emerge from interannual variability. Nat. Commun. 12, 514 (2021).

    Article  Google Scholar 

  41. Purich, A., England, M. H., Cai, W., Sullivan, A. & Durack, P. J. Impacts of broad-scale surface freshening of the Southern Ocean in a coupled climate model. J. Clim. 31, 2613–2632 (2018).

    Article  Google Scholar 

  42. Rye, C. D. et al. Antarctic glacial melt as a driver of recent Southern Ocean climate trends. Geophys. Res. Lett. 47, e2019GL086892 (2020).

    Article  Google Scholar 

  43. Heuzé, C. Antarctic Bottom Water and North Atlantic Deep Water in CMIP6 models. Ocean Sci. 17, 59–90 (2021).

    Article  Google Scholar 

  44. Zhang, L., Delworth, T. L., Cooke, W. & Yang, X. Natural variability of Southern Ocean convection as a driver of observed climate trends. Nat. Clim. Change 9, 59–65 (2019).

    Article  Google Scholar 

  45. Dufresne, J.-L. & Bony, S. An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J. Clim. 21, 5135 – 5144 (2008).

    Article  Google Scholar 

  46. Winton, M., Takahashi, K. & Held, I. M. Importance of ocean heat uptake efficacy to transient climate change. J. Clim. 23, 2333–2344 (2010).

    Article  Google Scholar 

  47. Geoffroy, O. et al. Transient climate response in a two-layer energy-balance model. Part i: analytical solution and parameter calibration using CMIP5 AOGCM experiments. J. Clim. 26, 1841–1857 (2013).

    Article  Google Scholar 

  48. Armour, K. C., Bitz, C. M. & Roe, G. H. Time-varying climate sensitivity from regional feedbacks. J. Clim. 26, 4518–4534 (2013).

    Article  Google Scholar 

  49. Rose, B. E. & Rayborn, L. The effects of ocean heat uptake on transient climate sensitivity. Curr. Clim. Change Rep. 2, 190–201 (2016).

    Article  Google Scholar 

  50. Frey, W. R., Maroon, E. A., Pendergrass, A. G. & Kay, J. E. Do Southern Ocean cloud feedbacks matter for 21st century warming? Geophys. Res. Lett. 44, 12,447–12,456 (2017).

    Article  Google Scholar 

  51. Boé, J., Hall, A. & Qu, X. Deep ocean heat uptake as a major source of spread in transient climate change simulations. Geophys. Res. Lett. 36, L22701 (2009).

    Article  Google Scholar 

  52. Soden, B. J. et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008).

    Article  Google Scholar 

  53. Pendergrass, A. G., Conley, A. & Vitt, F. M. Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5. Earth Syst. Sci. Data 10, 317–324 (2018).

    Article  Google Scholar 

  54. Hurrell, J. W. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

    Article  Google Scholar 

  55. Reichler, T., Dameris, M. & Sausen, R. Determining the tropopause height from gridded data. Geophys. Res. Lett. 30, 2042 (2003).

    Article  Google Scholar 

  56. McDougall, T. J., Jackett, D. R., Wright, D. G. & Feistel, R. Accurate and computationally efficient algorithms for potential temperature and density of seawater. J. Atmos. Ocean. Technol. 20, 730–741 (2003).

    Article  Google Scholar 

  57. POP EOS https://pop-tools.readthedocs.io/en/latest/api.html#pop_tools.eos

  58. BLOM EOS https://github.com/NorESMhub/BLOM/blob/release-1.0/phy/eosfun.F

  59. Gjermundsen, A. Southern Ocean hosing experiment [Data set], Norstore, (2021).

  60. Olivié, D. piclim-p4k: +4k Pre-industrial Fixed sst Simulation for NorESM2-LM [Data set], Norstore, (2021).

  61. Olivié, D. piclim-p4k: +4k Pre-industrial Fixed sst Simulation for NorESM2-MM [Data set], Norstore, (2021).

Download references

Acknowledgements

A.G., D.O., Ø.S and M.S. received support from the European Framework Programme Horizon 2020 project CRESCENDO (Coordinated Research in Earth Systems and Climate: Experiments, Knowledge, Dissemination and Outreach, grant agreement no. 641816). All authors received support from the Norwegian Research Council funded projects INES (270061) and KeyClim (295046). High-performance computing and storage resources were provided by UNINETT Sigma2, the Norwegian infrastructure for computational science.

Author information

Authors and Affiliations

Authors

Contributions

All co-authors are part of the Norwegian Climate Centre (NCC) consortium that develops the NorESM model. D.O., Ø.S. and A.G. performed the NorESM2 model simulations and D.O. conducted and analysed the fixed SST simulations. A.G performed the [rest of the] model analysis, with M.B. and A.N. helping with the ocean analysis. A.G. and A.N. wrote the manuscript with help in writing and interpretation of the results from all the authors.

Corresponding author

Correspondence to Ada Gjermundsen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Levi Silvers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12, Tables 1–4 and model description.

Source data

Source Data Fig. 1

Numerical source data to generate Fig. 1.

Source Data Fig. 2

Numerical source data to generate Fig. 2.

Source Data Fig. 3

Numerical source data to generate Fig. 3.

Source Data Fig. 4

Numerical source data to generate Fig. 4.

Source Data Fig. 5

Numerical source data to generate Fig. 5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gjermundsen, A., Nummelin, A., Olivié, D. et al. Shutdown of Southern Ocean convection controls long-term greenhouse gas-induced warming. Nat. Geosci. 14, 724–731 (2021). https://doi.org/10.1038/s41561-021-00825-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-021-00825-x

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing