Models are illustrations. They illustrate our understanding of the phenomenon being modeled. The benefit of the model is that it allows extrapolation and exploration, i.e playing "what if?" games by modifying the parameters of the model. Randomness can be applied to represent observed ranges of inputs. Complex behavior can be seen which appears to approximate the real world allowing you to see dynamic outputs which tend to reinforce confidence in the model. Nevertheless, it is still only a model, and will only show you a reflection of what you already understand.
Contemporary climate models are woefully incomplete. They contain invalid and unproven assumptions, and in some cases even mistaken assumptions. They fail to account for vital macroscopic processes such as vertical wind profiles and cloud formation. If any assumption is incorrect, or any sufficiently significant process overlooked, then the models are incorrect.
Incorrect models cannot be validated, but can be calibrated to match a preexisting set of data. The problem with most climate models used to support Global Warming, is that the validation was never done. Calibration must be done before validation, and if calibration is even necessary it's because you are still empirically deriving a parameter(s) which wasn't adequately defined up front. You probably can't even tell which parameter was in error. Confidence is therefore unknown.
What this means is the worlds best climate models, no matter how much time they take to run, or how much confidence they claim, are only toys. The more they are run, the more they are tweaked to bring them in line with current conditions. It's a process of continuous calibration. This tweaking only serves to illustrate that the model was wrong and incomplete to begin with.
We know the models are incomplete from the get go, and we even know some of the reasons why. The logical thing to do is to put our resources toward research which can fill in the gaps, and increase our confidence in the models. One of the projects that could fill a giant gap in our understanding of the atmosphere is the Cloud project at CERN.
The Cloud project (Cosmics Leaving OUtdoor Droplets) is testing the hypothesis that a range of cosmic rays influence cloud formation. Cosmic rays originating in outer space interact in our atmosphere as condensation nuclei. This experiment will mark the first time a partical accelerator has been used for atmospheric research.
What you may notice when visiting the Cloud project page linked above, is that the proposal is now 10 years old. Unfortunately some early press reports quoted the lead researcher, Jasper Kirby, as saying cosmic rays "will probably be able to account for somewhere between a half and the whole of the increase in the Earth's temperature that we have seen in the last century."
This statement resulted in the loss of funding for the Cloud project, until a much maligned and humbled Jasper Kirby started to downplay the significance of the project.
Science is at the mercy of politics. The first data from the Cloud experiment should be available in 2010. So while we are blind, and making profound decisions based on the output of incomplete and erroneous models, there may come a day when we can see. Hindsight is always 20/20, but there are many who would rather keep us in the dark.
I will look forward to the results of this and many other projects which can help us to more fully understand the interactions of our atmosphere and all that influences it internally and externally.
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