But these computer models go way beyond trying to compute the effect of minuscule increases of CO2 over time, they also vary thousands of other variable. Each of these variables slightly tweaked and run through the model over time ends up having either a positive or negative effect on warming over time. Variables can include something as simple as a single volcano eruption occurring on one date verses another date, or what if the oceans absorbs 1% less CO2 than what we think, or what if water vapor has a greater effect than what we think, etc. etc. All of these variables effect each other and one can enhance the effect of another.

Most of the computer models start their clock at a period in the near past where there is data to compare the results of the model. In other words if they start the model with a set of variables at 1920 and if 1998 comes out extremely cold (rather than its real world warmer than normal temperature) they discount the model as invalid and discard it. One would think this would be a desirable control. If the model can not predict the past how will it reliably predict the future? But one has to keep in mind that scientists admit they have a very poor understanding of many of the input variables and how they relate to each other. They also do not know if other influences that they haven't included may effect the climate. With thousands of variables, millions of variable combinations, and each model handling the differently over time it is very easy to keep testing the combinations until you find one that fits recent climate history. But that is no indication that it will be able to predict the future. So those that can seemingly predict the future by looking at how they've done over the past century may actually be no better at predicting the future than those that failed that test.

It is a modern version of the classic fable The Blind Men And The Elephant where each blind man touched a different part of the elephant and came to a different conclusion as to what it was they were touching. In our modern computerized world we simply don't know exactly what these models are showing us or how well we've fed them with information. We're still very much feeling around blindly looking for correlations with what we're seeing.

It should be noted that scientists admit to not understanding several important climate modifiers. Chief among them are the influence of weak and strong solar cycles. The historic record is clear that solar cycles influence the climate but the mere irradiance values do not seem to be able to cause these these climate swings. Other factors such as stronger radiative strength at certain wavelengths or increased magnetic influences may be at play but there are no good proxy measurements for these forces. The influence of water vapor, the most prominent greenhouse gas, is also poorly understood. Therefore these models start off crippled and many values fed in to the models are pure guesses.

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