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.
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Anthropogenic aerosol and cryosphere changes drive Earth’s strong but transient clear-sky hemispheric albedo asymmetry
Communications Earth & Environment Open Access 12 September 2022
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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.
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.
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).
Gregory, J. M. et al. A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett. 31, L03205 (2004).
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).
Rugenstein, M. et al. Equilibrium climate sensitivity estimated by equilibrating climate models. Geophys. Res. Lett. 47, e2019GL083898 (2020).
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).
Knutti, R., Rugenstein, M. A. & Hegerl, G. C. Beyond equilibrium climate sensitivity. Nat. Geosci. 10, 727–736 (2017).
Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).
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).
Myers, T. A. & Norris, J. R. Reducing the uncertainty in subtropical cloud feedback. Geophys. Res. Let. 43, 2144–2148 (2016).
Brient, F. & Schneider, T. Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J. Clim. 29, 5821–5835 (2016).
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).
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).
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).
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).
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).
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).
Gregory, J. M. Vertical heat transports in the ocean and their effect on time-dependent climate change. Clim. Dyn. 16, 501–515 (2000).
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).
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).
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).
Danabasoglu, G. et al. The Community Earth System Model version 2 (CESM2). J. Adv. Model. 12, e2019MS001916 (2020).
Gettelman, A. et al. High climate sensitivity in the Community Earth System Model version 2 (CESM2). Geophys. Res. Lett. 46, 8329–8337 (2019).
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).
Danabasoglu, G. et al. The CCSM4 ocean component. J. Clim. 25, 1361–1389 (2012).
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).
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).
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).
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).
Tan, I., Storelvmo, T. & Zelinka, M. D. Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science 352, 224–227 (2016).
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).
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).
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).
Marshall, J. & Speer, K. Closure of the meridional overturning circulation through Southern Ocean upwelling. Nat. Geosci. 5, 171–180 (2012).
Cessi, P. The global overturning circulation. Ann. Rev. Mar. Sci. 11, 249–270 (2019).
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).
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).
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).
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).
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).
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).
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).
Rye, C. D. et al. Antarctic glacial melt as a driver of recent Southern Ocean climate trends. Geophys. Res. Lett. 47, e2019GL086892 (2020).
Heuzé, C. Antarctic Bottom Water and North Atlantic Deep Water in CMIP6 models. Ocean Sci. 17, 59–90 (2021).
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).
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).
Winton, M., Takahashi, K. & Held, I. M. Importance of ocean heat uptake efficacy to transient climate change. J. Clim. 23, 2333–2344 (2010).
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).
Armour, K. C., Bitz, C. M. & Roe, G. H. Time-varying climate sensitivity from regional feedbacks. J. Clim. 26, 4518–4534 (2013).
Rose, B. E. & Rayborn, L. The effects of ocean heat uptake on transient climate sensitivity. Curr. Clim. Change Rep. 2, 190–201 (2016).
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).
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).
Soden, B. J. et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008).
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).
Hurrell, J. W. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).
Reichler, T., Dameris, M. & Sausen, R. Determining the tropopause height from gridded data. Geophys. Res. Lett. 30, 2042 (2003).
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).
Gjermundsen, A. Southern Ocean hosing experiment [Data set], Norstore, (2021).
Olivié, D. piclim-p4k: +4k Pre-industrial Fixed sst Simulation for NorESM2-LM [Data set], Norstore, (2021).
Olivié, D. piclim-p4k: +4k Pre-industrial Fixed sst Simulation for NorESM2-MM [Data set], Norstore, (2021).
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.
The authors declare no competing interests.
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.
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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
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