Posted on 04/11/2012 9:11:25 AM PDT by Ernest_at_the_Beach
Clouds cover an enormous 65% of the planet and are responsible for about half of the sunlight that is reflected back out to space.[i] The effects of clouds are so strong that most of the differences between IPCC-favoured-models comes from the assumptions the models make about clouds. Cloud feedbacks are the “largest source of uncertainty”.[ii] Numerous studies show models project wildly different results for clouds, and yet few could correctly simulate clouds as recorded by satellites.[iii] One researcher described our understanding of cloud parameters as being “still in a fairly primitive state.” [iv]
Sunlight that travels 150 million kilometers can be blocked a mere 1km away from the Earth’s surface and reflected back to space. The situation is complicated though, because clouds also slow the outgoing radiation — which has a warming effect. In general lower clouds are thicker and have a large cooling effect, while higher clouds are thinner and tend to trap more heat than they reflect (i.e. net warming). Observations show the cooling effect of clouds dominates the warming effect. (Allen 2011[v]) which means that, in general, more clouds means more cooling.
(Excerpt) Read more at joannenova.com.au ...
fyi
More clouds means a lot more cooling during the day, but warming at night.
Clouds Have Made Fools of Climate Modelers
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By Ken Gregory June 2009
Climate models used by the International Panel on Climate Change (IPCC) assume that clouds provide a large positive feedback, greatly amplifying the small warming effect of increasing CO2 content in air. Clouds have made fools of climate modelers. A detailed analysis of cloud behavior from satellite data by Dr. Roy Spencer of the University of Alabama in Huntsville shows that clouds actually provide a strong negative feedback, the opposite of that assumed by the climate modelers. The modelers confused cause and effect, thereby getting the feedback in the wrong direction.
Climate sensitivity to increasing CO2 concentrations is usually expressed as the equilibrium change in temperature resulting from a doubling of the CO2 content in air. By itself, CO2 has only a small effect on global temperatures if nothing else changed. Doubling the CO2 content is calculated to cause only a 0.48 C (Miskolczi 2004) to 1.1 C (IPCC 2007) temperature increase, if water vapour, clouds and albedo did not change. This is the no‐feedback case. Climate models multiply the IPCC no‐feedback change by 3 or more times by using positive feedbacks. The second largest feedback used in climate models (after water vapour) is an assumed positive feedback from clouds. If a small CO2 induced temperature rise caused a reduction in low clouds, this would be a positive feedback as fewer clouds would let more sunlight through to warm the surface, resulting in a further temperature rise.
The modelers only do crude analysis of feedback from satellite data, by correlating temperature variation with the Earth’s radiation balance, which is the difference between the absorbed incoming solar radiation and the outgoing longwave radiation. They also observe that low clouds tend to decrease with warming and assumed that the warming caused the low clouds to decrease. There are generally fewer clouds in summer than winter, allowing more sunlight through to warm the Earth. But cloud changes also cause temperatures to change. When a cloud moves to block the Sun, temperatures fall. The amount of clouds can change in response to a general circulation change. So cloud changes are sometimes a cause of temperature change, and sometimes an effect of temperature change. The false assumption that all cloud changes are the effect of temperature changes led modelers to vastly over estimate the feedback from clouds.
Dr. Roy Spencer has developed a method to separate cause and effect of cloud variability. His technique is to plot yearly and quarterly average temperature and net flux readings from satellite data on a graph. These averages are plotted every day allowing the time evolution to be visualized. He found that the plots have two types of patterns – a set of linear striations with a common slope, and superimposed slower random spiral patterns.
Iong live clouds!
Lalalalalalalalalalalalala
Clearly the models continue to be shown inadequate in simulating atmospheric conditions. Perhaps responsible people will eventually come up with a new generation of models that can better calculate atmospheric conditions. Not exactly an easy task to accomplish.
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