2016
DOI: 10.1175/jcli-d-15-0352.1
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Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

Abstract: This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant sou… Show more

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Cited by 119 publications
(146 citation statements)
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“…The same may be said about intermodel differences in OHU. This is a tantalizing proposition, as cloud feedbacks continue to be the largest source of spread in estimates of climate sensitivity [10,21,59]. If part of that spread is driven in systematic ways by patterns of sea surface heat fluxes, it may be more reducible and falsifiable than typically acknowledged.…”
Section: Resultsmentioning
confidence: 99%
“…The same may be said about intermodel differences in OHU. This is a tantalizing proposition, as cloud feedbacks continue to be the largest source of spread in estimates of climate sensitivity [10,21,59]. If part of that spread is driven in systematic ways by patterns of sea surface heat fluxes, it may be more reducible and falsifiable than typically acknowledged.…”
Section: Resultsmentioning
confidence: 99%
“…The climate feedback parameter is equal to the sum of the Planck response and feedbacks from water vapor, lapse, surface albedo and clouds. We use average climate model values for the forcing and non-cloud feedbacks as reported in Caldwell et al (2016) (Table 3). Further assuming a high-cloud altitude feedback ) of ?…”
Section: Implications For Climate Sensitivitymentioning
confidence: 99%
“…The differing model experiments means that the rapid cloud adjustments to CO 2 are included in some studies. In addition to these four estimates, we also consider the average cloud feedbacks in tropical subsidence regions calculated from the abrupt CO 2 quadrupling simulations analyzed in Caldwell et al (2016). These feedbacks primarily reflect the shortwave feedbacks from low clouds due to the absence of upper-level clouds.…”
Section: Large-eddy Simulation Cloud Feedbacksmentioning
confidence: 99%
“…Cloud feedbacks remain the main source of uncertainty in predictions of climate sensitivity (e.g., Dufresne and Bony, 2008;Vial et al, 2013;Webb et al, 2013;Caldwell et al, 2016). One reason for this uncertainty is that clouds simulated by climate models in the current climate exhibit large biases compared to observations (e.g., Zhang et al, 2005;Haynes et al, 2007;Chepfer et al, 2008;Williams and Webb, 2009;Marchand and Ackerman, 2010;Chepfer, 2012, 2013;Kay et al, 2012;Nam et al, 2012;Klein et al, 2013), leading to low confidence in the cloud feedbacks predicted by the models.…”
Section: Introductionmentioning
confidence: 99%