I don't think so Larry. I'll bet simulating biochemical systems is far more complex because there are many variables of nearly equal importance. Global warming is a simple energy balance at its heart. The rate of atmospheric warming is equal to the heat input (primarily the sun, followed most likely by Earth's internally generated heat) - the heat lost to space. There is nothing theoretically tough about this equation. It's basic energy transport that most engineers and many scientific fields should be familiar with. The hardest part is determining all the variables effecting heat loss.

Modeling day to day variation in weather within climate models would obviously make it extremely complex, but I don't think that is required as explained earlier. In addition nonlinearity is as important to the difficulty level of numerical modeling as number of variables. Many nonlinear systems are effectively simulated. I doubt global temperature prediction is highly nonlinear, but I don't know.

I don't think it should be all that tough to determine which variables are the most important currently. What may be tough is untangling interactions and how the relative importance of various heat loss methods may change with CO2 concentration and temperature. This can change what variables are safe to neglect in simulation. Increasing computer power should help resolve this by lessening the need to neglect apparently minor variables.

Hurricane predictions are not dead on accurate but the do generally provide a useful alert. They are also subject to short term effects which are highly variable. Other than impacts and Volcanos, I don't think the same can be said for global warming.

You also seem to be buying the totally bogus line that innacuracy in the models means GW won't happen. If most of the models predict warming and the ranges in predictions cover the observed range in climate data then the chances are pretty good that global warming is a real issue, but our models still need refining. http://www.sciam.com/2001/1001issue/1001scicit1.html (view on smallest text size)

The important fact is not to determine the exact effect, but rather that there is likely to be an effect. The variability in predictions should not be used as an excuse for inaction to avoid even the most likely effects.

Regards,
Mark