Environmental educator Jane Shaw has an interesting article on the professional harrassment academics receive when they voice skepticism about the science behind climate chnage. Tenured professors who are skeptical have been ordered by university presidents to avoid publicly expressing their views. Even peer reviewed journals have given greater voice to the critics of the skeptics' research while preventing the authors from directly responding to the critcisms. One of the passages that caught my eye was the criticism MIT's Richard Lindzen when he began to question the models used to predict climate change.
Lindzen says that the science establishment gives priority to computer modeling of presumed climate forces and then tweaks the models, trying to make them conform to actual observations. Hand-in-hand with that tweaking is an effort to find and “correct” flaws in the empirical observations to make them conform to the simulations. Although science rightly seeks to make corrections, these are almost always in a single direction—toward conformity. That is outright data corruption.
Compounding this tendency is government funding, which furthers bureaucratic and political goals. Professional societies make lobbying their chief activity. The American Meteorological Society is represented by a former staffer for Al Gore. John Holdren, the new science advisor to the president, is a professor in Harvard’s government department, not a scientific department; his major job was with the Woods Hole Research Center, an environmental advocacy group that is often (and perhaps deliberately) confused with a scientific research center, Woods Hole Oceanographic Institution. He’s not a climate scientist.
The problems with these models and the "science" behind them are not unique to climate change models. Virtually all empirical modeling suffers from these kinds of biases. When was the last time an economic development impact study concluded that a convention center or sports stadium should not be built with public money? I can't think of a case. That's because these models focus on only on benefits. In fact, they are inherently unable to weight benefits and costs in a meaningful way. The models themselves can't distinguish between who provides the money, assuming that public and private sector spending is virtually identical in its economic impact.
In short, to adequately interpret these models, you can't just look at the outcomes. You have to examine the inputs and assumptions that drive their methodology. You have to get inside the black box. Unfortunately, few lay people have the training or interest to do that.