Abstract

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m−2 K−1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.

Highlights

  • The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate

  • We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed

  • The following commonalities emerge: (1) sea-surface temperature (SST) is the most important cloud-controlling factor for climate change cloud feedbacks; (2) tropical low clouds are observed to decrease in extent or radiative impact with increasing SST, leading to the prediction of positive tropical low-cloud feedbacks to climate change; (3) the four studies that consider EIS agree that EIS contributes a negative feedback, it only partially offsets the positive feedback from SST; and (4) the three studies that consider additional factors beyond EIS and SST agree that these additional factors collectively make only a minor contribution to tropical low-cloud feedback

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Summary

Seeking Observational Constraints on Low-Cloud Feedbacks

How clouds respond to the climate warming is a major uncertainty in climate change science that hinders prediction of the temperature sensitivity to radiative perturbations (Boucher et al 2013). A second approach relies on observations of clouds to predict how they will respond to changes in the large-scale environment typical of climate warming This observational approach is the subject of this paper. We can predict how the low clouds will change with climate warming under the assumption that the sensitivities of clouds to their controlling factors are time-scale invariant This approach has been taken in five recent studies (Qu et al 2015b; Zhai et al 2015; Myers and Norris 2016; Brient and Schneider 2016; McCoy et al 2017, in chronological order; hereafter these studies will be named ‘‘Q15,’’ ‘‘Z15,’’ ‘‘M16,’’ ‘‘B16’’ and ‘‘M17,’’ respectively). We examine issues with this approach and how uncertainties in its predictions might be reduced

Cloud-Controlling Factors
Low-Cloud Feedbacks
Implications for Climate Sensitivity
Sources of Uncertainty
Fundamental Issues
Implementation Issues
Findings
Summary and Final Remarks
Full Text
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