17 November 2014

Evidence that relationship between low-level cloud reflectivity and temperature in the current climate can predict the climate change response

Regression slopes of the natural logarithm of cloud optical thickness (τ) on cloud-top temperature (y-axis) as a function of cloud-top temperature (x-axis) from climate models (symbols) and satellite observations (dashed lines) The analysis is stratified by geographic regions (color) and over land (panel a) and ocean (panel b). Each climate model is represented by an individual dot, while the solid line represents the multi-model median value. Satellite observations are shown in dashed lines.
Scatter plot for cloud optical depth (τ) (panel a) and cloud water content (CWC) (panel b) of the actual feedback in a climate change simulation (in units of per Kelvin, y-axis) and that predicted from the relationship with temperature in the current climate (x-axis). Each symbol represents one of 13 climate models in one of 5 different latitude bands. The squared values of correlation coefficients are provided in the table.
Science

Predictions by climate models of the amount of warming that the planet resulting from an increase in greenhouse gases vary widely due to the different simulated responses of clouds to warming. Model cloud predictions are variable because clouds are among the least well-simulated components of climate models and also because their response depends on many cloud types (high vs. low) and aspects (amount, reflectivity, altitude). In this study, we dealt with one aspect of this complicated problem namely the cloud optical depth (which is proportional to cloud reflectivity) of low-level clouds. In analyzing 13 climate models, we determined if cloud optical depth-temperature relationships found in observations are replicated in climate models, and if climate model behavior found in control climate simulations provides information about the optical depth feedback in climate warming simulations forced by increasing carbon dioxide.

Approach

A positive relationship between cloud optical depth and cloud temperature exists in all models for low clouds with relatively cold temperatures at middle and high latitudes, whereas a negative relationship exists for warmer low clouds in the tropics and subtropics (Figure 1). This relationship is qualitatively similar to that in an earlier analysis of satellite observations (dashed lines), although modeled regression slopes tend to be too positive and their inter-model spread is large. In the models, the cold cloud response comes from increases in cloud water content with increasing temperature, while the warm cloud response comes from decreases in physical thickness with increasing cloud temperature.

The inter-model and inter-regional spread of low-cloud optical depth feedback in climate warming simulations is well predicted by the corresponding spread in the relationships between optical depth and temperature for the current climate. (Figure 2, upper panel). The good correlation suggests that how cloud optical depth varies with temperature is a relationship that has some degree of time-scale invariance. This characteristic also applies to how cloud water content varies with temperature (Figure 2, lower panel).

Impact

If time-scale invariance to this relationship exists, then observations of the relationship can be used to predict the response on climate change response that we do not yet know. Indeed, because models have a positive bias relative to observations in the optical depth-temperature relationship (compare solid to dashed lines in Figure 1), shortwave cloud feedbacks for climate changes may be more positive than climate models currently simulate. We conclude that the uncertainty in climate predictions could be reduced if climate models better simulated the observed relationship with temperature of cloud reflectivity and cloud water content.

Contact
Neil D Gordon
Publications
Gordon, ND, and SM Klein.  2014.  "Low-Cloud Optical Depth Feedback in Climate Models."  Journal of Geophysical Research - Atmospheres 119: 6052-6065, pp. 6052-6065.  https://doi.org/10.1002/2013JD021052.
Acknowledgments

This work was performed as part of a RGCM project entitled “Identifying Robust Cloud Feedbacks in Observations and Models” and under the auspices of the United States Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.