[Rocky]

It has always been a mystery to me why so many otherwise intelligent people are willing to put their complete trust in computer models of earth's climate. Anyone who has dealt with computer models knows the dangers and limitations of such models, even on systems which are far simpler than the climate.

[Jim Noble]

A Simple Truth; Computer Climate Models Cannot Work

Depends on what you mean by "work".

Al Gore is a billionaire. The EPA is much more powerful than it was 20 years ago. Australia almost crippled their economy. Fuel and heating costs are through the roof.

The models are working just fine. You just don't understand what they are intended to do.

[Delta Dawn]

Have your attention please...For the duration of the Obola outbreak here in America, we are going to need the climate change kids to sit in the corner over there as the adults deal with an actual crisis...Once Obola has been dealt with, then we will deal with you kiddies in the corner...

[Rocky]

One of the commenters at the original article posted a link to a very good simple illustration of how these models fail:

http://thepointman.wordpress.com/2011/01/21/the-seductiveness-of-models/

[Cincinatus]

Over-reliance on the results of computer models has infected all of science, not just climate science. Partly this is driven by the intellectual snobbishness of some physical scientists. They have traditionally looked down on the "natural sciences" (e.g., Earth and biological sciences) because those fields deal with complex and chaotic natural systems that in many cases defy mathematical analysis. Lord Kelvin is their spiritual God: "If you cannot express something as a series of numbers, you know nothing about it." Thus, the big push over the last 100 years to make the natural sciences more analytical and less descriptive.

There's nothing wrong with making models per se, but a model that fails to conform with verified observations is not worth anything. Moreover, a model that has to be constantly "tweaked" to give approximately correct answers is likewise useless as it has no predictive power.

Unfortunately, many scientists have been raised to become "button-pushers" who "turn the crank and out comes the (publishable) answer." Its relation to reality is only of incidental interest.

[E. Pluribus Unum]

Chaos theory originated with a weather scientist and a weather modeling program. He had to pause it to use the computer for something else. When he restarted it he entered variable values that were rounded-off from the variable values when he started. The program results immediately began to diverge from what they had always been before.

Bottom line is you have to have variables for every single atom in the atmosphere to an infinite level of precision or you aren't going to be able to get the model to reproduce itself, much less global climate.

[ThunderSleeps]

There are 4 fundamental problems that make the models useless for predicting conditions:

1. Data. The historical data is simply not available. The data that is available, is not sufficiently accurate to make these fine, nuanced predictions. Much of the data has to be scaled and adjusted by factors to account for such things as changing land use patterns around the weather station. These scaling factors and adjustments are bigger than the "trends" discovered in the data. Think about that for a minute.
2. Unknown interactions. As good as the scientists are, and as much as is now known about how the climate works, there is still a lot we simply do not know. That means the models are built upon an admittedly incomplete understanding of how climate works. Look at it this way, if everything about climate science were known, then what are all those climate scientists "researching" anyways? ;-)
3. Iterations. The models work by predicting what conditions will be like just a little bit in the future (a time step) based on the state of the model at the "current" time. The smaller the timestep, the more accurate the predicted result. Moving any significant delta into the future can require millions of iterations. Inaccuracies build up. Rounding errors build up. Sensitivity to initial conditions builds up. Turns out these kinds of models are only valid for small ranges of time/iterations. After that their connection to reality is tenuous at best.
4. Mathematical size of the problem. Going back to #2 somewhat, we don't know all the interactions of elements of the climate, flora and fauna, geology, the sun, etc. There are literally millions of interacting elements. The mathematics becomes a combinatorial explosion. We literally do not have enough computer memory nor processing power on the entire planet to solve this kind of problem (even if we understood it and could express it) before the sun goes nova.

Well, we can address #2. We are increasing our understanding of how the climate works. Will we ever completely understand it? I doubt it. But I'll give the scientists the benefit of the doubt, let's say #2 is solvable.

Number #3 might even be workable with good programming techniques. Hey, I'm a software (and modeling) guy, I can see ways to address this, but they are going to make the models even bigger, even slower. Let's give me and my kind equal benefit of the doubt and say we're clever guys/gals, we can figure this out.

The problem, well, problems are #1 and #4. We just aren't ever going to get better data. I guess if we wait around a hundred years or so we'll have a hundred years of pretty good, pretty accurate data. So let's say #1 can be addressed with patience. Of course, the AGW types don't want that. These hucksters want your money and they want it NOW!

#4 is the real problem child. You just can't get around the mathematics. You can eliminate variables that do not appear to contribute, but this introduces subtle errors that become significant during those millions of iterations (see #3). There is no free lunch, no shortcut.

The upshot is, the models are completely worthless for what many are trying to use them for - a means to "prove" their pet theory and excuse for a money/power grab.