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Spread in model climate sensitivity traced to atmospheric convective mixing

Abstract

Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.

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Figure 1: Multimodel-mean local stratification parameter s.
Figure 2: Basis for the index S of small-scale lower-tropospheric mixing and its relationship to the warming response.
Figure 3: The structure of monthly-mean tropospheric ascent reveals large-scale lower-tropospheric mixing in observations and models.
Figure 4: Estimated water vapour source MLT, large due to large-scale lower-tropospheric mixing and its response to warming.
Figure 5: Relation of lower-tropospheric mixing indices to ECS.

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Acknowledgements

This work was supported by the FP7-ENV-2009-1 European project EUCLIPSE (number 244067). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output, especially the participants contributing additional CFMIP2 experiments and diagnostics crucial to our study. The US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support for CMIP and led the development of software infrastructure in partnership with the Global Organisation for Earth System Science Portals. We also thank the National Center for Atmospheric Research and the Earth System Grid Federation for providing access to PCM output, the Australian National Computational Infrastructure, and the IPSL Prodiguer-Ciclad facility for providing a convenient archive of CMIP data. Finally, we thank B. Stevens, C. Bretherton and G. Schmidt for comments on early versions of the manuscript.

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Authors and Affiliations

Authors

Contributions

S.C.S. led the study and the writing of the paper, and did the calculations of LTMI and related diagnostics. S.B. computed cloud radiative effect and assisted in interpreting results and writing the paper. J.-L.D. computed ECS and assisted in interpreting results and writing the paper.

Corresponding author

Correspondence to Steven C. Sherwood.

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Extended data figures and tables

Extended Data Figure 1 Illustration of atmospheric overturning circulations.

Deep overturning strongly coupled to the hydrological cycle and atmospheric energy budget is shown by solid lines; lower-tropospheric mixing is shown by dashed lines. The MILC feedback results from the increasing relative role of lower-tropospheric mixing in exporting humidity from the boundary layer as the climate warms, thus depleting the layer of water vapour needed to sustain low cloud cover.

Extended Data Figure 2 Small-scale moisture source Msmall.

Vertical profile averaged over all tropical oceans, for two selected climate models (see legend) with very different warming responses, in present-day (solid) and +4 K (dashed) climates.

Extended Data Figure 3 Response of cloud fraction to warming.

Profile of average change in model cloud fractional cover at +4 K in the four atmosphere models with largest (magenta) and smallest (blue) estimated +4 K increases in planetary-boundary-layer drying, averaged from 30° S to 30° N (dashed) or 60° S to 60° N (solid). The drying estimate is obtained by adding the explicitly computed change in MLT, large to the change in Msm estimated from S via the relationship shown in Fig. 2a. The typical mean cloud fraction below 850 hPa is about 10% to 20%, and the changes shown are absolute changes in this fraction, so are of the order of 10% of the initial cloud cover.

Extended Data Figure 4 Response of large-scale lower-tropospheric mixing to warming.

Profiles of mean vertical velocity in regions of shallow ascent, in control and +4 K climates. The similarity of dashed and solid lines indicates that mass overturning associated with these regions is roughly the same in the warmer simulations, on average.

Extended Data Figure 5 Response of small-scale, low-level drying to warming.

Change in convective moisture source Msmall below 850 hPa upon a +4 K warming in eight atmosphere models and one CMIP3 coupled model; units are W m−2, with negative values indicating stronger drying near the surface. Zero contours are shown in white (a few off-scale regions also appear white). The models used for calculating Mlarge are the eight shown here plus two for which Msmall data were unavailable: CNRM-CM5 and FGOALS-g2.

Extended Data Table 1 List of CMIP5 coupled models used
Extended Data Table 2 List of CMIP3 coupled models used

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Sherwood, S., Bony, S. & Dufresne, JL. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014). https://doi.org/10.1038/nature12829

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