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On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates


This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO2) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO2 radiative forcing. By considering tropospheric adjustments to CO2 as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO2 is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO2), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.

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  1. The GFDL model’s kernels are available at


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The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP7/2007–2013) under Grant agreement no 244067 for the EUCLIPSE project, and under GA 226520 for the COMBINE project. The work was also partially funded by the ANR ClimaConf project. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors would like to acknowledge Karen Shell and Brian Soden for making the NCAR and GFDL models’ radiative kernels freely available online at Finally, we thank Christelle Castet for her participation to this study, and we acknowledge the anonymous reviewers for their helpful comments and suggestions.

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Correspondence to Jessica Vial.

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

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  • Climate sensitivity
  • Feedback
  • Radiative forcing
  • Fast adjustment
  • Radiative kernel
  • CMIP5 climate model simulations
  • Climate change
  • Inter-model spread