Posted on behalf of an out-of-pocket Marlo Lewis.The first chapter of IPCC AR4 is an overview of the history of climate change science. The section on cloud modeling and climate sensitivity (1.5.2) contains surprising admissions of uncertainty (i.e., ignorance). A long paragraph on page 114 says, in part:

In spite of this undeniable progress [in cloud modeling], the amplitude and even the sign of cloud feedbacks was noted in the TAR as highly uncertain, and this uncertainty was cited as one of the key factors explaining the spread in model simulations of future climate for a given emission scenario. This cannot be regarded as a surprise: that the sensitivity of the Earth’s climate to changing atmospheric greenhouse gas concentrations must depend strongly on cloud feedbacks can be illustrated on the simplest theoretical grounds, using data that have been available for a long time. Satellite measurements have indeed provided meaningful estimates of Earth’s radiation budget since the early 1970s (Vonder Haar and Suomi, 1971). Clouds, which cover about 60% of the Earth’s surface, are responsible for up to two-thirds of the planetary albedo, which is about 30%. An albedo decrease of only 1%, bringing Earth’s albedo from 30% to 29%, would cause an increase in the black-body radiative equilibrium temperature of about 1°C, a highly significant value, roughly equivalent to the direct radiative effect of a doubling of atmospheric CO2 concentration.

I wonder if the converse also holds true. Would a 1% increase in albedo, due to expanding cloud cover, roughly cancel out the radiative effect of a doubling of atmospheric CO2 concentration?

The next paragraph indicates that 21st century warming could be high or low depending on uncertain cloud feedbacks:

The strong effect of cloud processes on model sensitivities to greenhouse gases was emphasized further through a now-classic set of General Circulation Model (GCM) experiments, carried out by Senior and Mitchell (1993). They produced global surface temperature changes (due to doubled atmospheric CO2 concentration) ranging from 1.9°C to 5.4°C, simply by altering the way that cloud radiative properties were treated in the model. It is somewhat unsettling that the results of a complex climate model can be so drastically altered by substituting one reasonable cloud parameterization for another, thereby approximately replicating the overall inter-model range of sensitivities.

Accordingly, says AR4, “The scientific community realized long ago that using adequate data to constrain models was the only way to solve this problem” (p. 116). “However,” AR4 acknowledges, “existing data have not yet brought about any reduction in the existing range of simulated cloud feedbacks” (p. 116, emphasis added). Far from being predictive, the most important thing the models reveal, it seems, is how little we know about clouds.