Constraints on Climate Sensitivity from Space-Based Measurements of Low-Cloud Reflection
Physical uncertainties in global-warming projections are dominated by uncertainties about how the fraction of incoming shortwave radiation that clouds reflect will change as greenhouse gas concentrations rise. Differences in the shortwave reflection by low clouds over tropical oceans alone account for more than half of the variance of the equilibrium climate sensitivity (ECS) among climate models, which ranges from 2.1 to 4.7 K. Space-based measurements now provide an opportunity to assess how well models reproduce temporal variations of this shortwave reflection on seasonal to interannual time scales. Here such space-based measurements are used to show that shortwave reflection by low clouds over tropical oceans decreases robustly when the underlying surface warms, for example, by −(0.96 ± 0.22)% K^(−1) (90% confidence level) for deseasonalized variations. Additionally, the temporal covariance of low-cloud reflection with temperature in historical simulations with current climate models correlates strongly (r = −0.67) with the models' ECS. Therefore, measurements of temporal low-cloud variations can be used to constrain ECS estimates based on climate models. An information-theoretic weighting of climate models by how well they reproduce the measured deseasonalized covariance of shortwave cloud reflection with temperature yields a most likely ECS estimate around 4.0 K; an ECS below 2.3 K becomes very unlikely (90% confidence).
© 2016 American Meteorological Society. Manuscript received 16 December 2015, in final form 11 May 2016. We thank Jennifer Kay for making available the combined CloudSat and CALIPSO data used in Fig. 1, Davide Panosetti for drafting Fig. 1, and Tobias Bischoff and Zhihong Tan for helpful discussions. 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) for producing and making available their model output. This research was supported by the Swiss National Science Foundation (Grant 200021-156109).
Published - Brient-Schneider-2016b.pdf
Supplemental Material - 10.1175_jcli-d-15-0897.s1.pdf