I was a little too fast on the Send button… Yes, water vapor is indeed a very important variable since,
CLOUDS reflect… not water vapor. Water vapor *below* cloud-condensation-level (CCL) is visually *invisible*, so it is reasonable to assume that it would be absorbing more of the sun’s energy and reflecting less, which would act as a latent heat reservoir. However, moist air is less dense than dry air, so the moist air rises and when reaching CCL, will condense out as clouds. (Reminds me of an interesting story of my former Research Advisor, Dr. Telford, in a top secret meeting with hi-level military brass and other scientists and engineers from a TS passive instrumentation project, trying to find a solution to very difficult problem which had eluded the gathered ‘experts’). There was a group somewhere that was proposing to make small, man-made islands which would be floated in the oceans and all they would do is pump streams of water high into the air to try to increase atmospheric water vapor, and ultimately cloud cover to increase the planet’s albedo… I read about this in the last few years. LENR would certainly be able to supply the power to run the pumps and station-keeping of the islands. -Mark From: MarkI-ZeroPoint [mailto:zeropo...@charter.net] Sent: Sunday, August 05, 2012 3:22 PM To: vortex-l@eskimo.com Subject: RE: [Vo]:LENR Heat Vs. Coal Heat DaveR wrote: “I have noticed that nothing is generally discussed about the most important green house gas, water vapor. It is also known that the tops of clouds can reflect a lot of light back into space. Perhaps some serious study needs to be directed toward using cloud modification to reflect incoming light as an insurance policy.” Yes, water vapor is indeed a very important variable since, “They reflect about 20 to 25 percent of the incoming radiation our planet receives from the Sun, while absorbing only 3 percent of that radiation” http://www.reasons.org/articles/articles/climate-change-cool-clouds Below is a very recent paper in the journal, Atmospheric Research which discusses how some GCMs underestimate cloud cover, which would result in warmer temperatures since more of the sun’s energy is NOT being reflected into space, and is hitting the surface causing warming of oceans and land: Total cloud cover from satellite observations and climate models by: P. Probst, R. Rizzi, E. Tosi, V. Lucarini, T. Maestri Atmospheric Research, Vol. 107 (April 2012), pp. 161-170, doi:10.1016/j.atmosres.2012.01.005 Key: citeulike:10279862 Abstract Global and zonal monthly means of cloud cover fraction for total cloudiness (CF) from the ISCCP D2 dataset are compared to same quantities produced by the 20th century simulations of 21 climate models from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3). The comparison spans the time frame from January 1984 to December 1999 and the global and zonal averages of CF are studied. It is shown that the global mean of CF for the PCMDI-CMIP3 models, averaged over the whole period, exhibits a considerable variance and generally underestimates the ISCCP value. Large differences among models, and between models and observations, are found in the polar areas, where both models and satellite observations are less reliable, and especially near Antarctica. For this reason the zonal analysis is focused over the 60° S–60° N latitudinal belt, which includes the tropical area and mid-latitudes. The two hemispheres are analysed separately to show the variation of the amplitude of the seasonal cycle. Most models underestimate the yearly averaged values of CF over all the analysed areas, whilst they capture, in a qualitatively correct way, the magnitude and the sign of the seasonal cycle over the whole geographical domain, but overestimate the amplitude of the seasonal cycle in the tropical areas and at mid-latitudes, when taken separately. The interannual variability of the yearly averages is underestimated by all models in each area analysed, and also the interannual variability of the amplitude of the seasonal cycle is underestimated, but to a lesser extent. This work shows that the climate models have a heterogeneous behaviour in simulating the CF over different areas of the Globe, with a very wide span both with observed CF and among themselves. Some models agree quite well with the observations in one or more of the metrics employed in this analysis, but not a single model has a statistically significant agreement with the observational datasets on yearly averaged values of CF and on the amplitude of the seasonal cycle over all analysed areas. And below is a summary of why this is important in the debate over global climate models: ==================== A recent paper published in Atmospheric Research suggests that 20 of the 21 global climate models (GCMs) used by the United Nations Intergovernmental Panel on Climate Change (IPCC) may underestimate cloud cover percentages over the Earth’s surface. Such a discrepancy implies an overall warm temperature bias in the models. The paper authors operated these 21 GCMs for the years 1984 to 1999 and compared the model-estimated cloud cover to actual observations derived from the International Satellite Cloud Climatology Project (ISCCP). Largely ignoring polar clouds, which are less accurately measured, the authors found that all but one of the 21 GCMs underestimated annual cloud cover amounts between 1 to 19 percent. Although the models were qualitatively correct in terms of the cloud cover’s geographic distribution , the modeled cloud cover averaged 7 percent less than observations recorded during the 15-year sample period. Model performance was somewhat better in the tropics (30°N to 30°S), but exhibited more error in the mid-latitudes (30°N to 60°N and 30°S to 60°S). In addition, all of the GCMs underestimated the clouds’ seasonal variability. Src: http://www.reasons.org/articles/articles/climate-change-cool-clouds ==================== -Mark
