Over at Andrew Sullivan's book club, where they're discussing Lomborg's book, one of the letter writers wrote the following:

I was preempted by at least two persons in what seems to be the key point of skepticism about the current global warming discourse, the extreme hubris involved in trying to forecast the long-term temperature of the earth based on primitive computer models, when we can't even forecast local weather with any certainty...

I've heard Rush Limbaugh make this argument as well--how can we predict long-term climate when we can't even forecast tomorrow's showers?

While I'm a global warming skeptic myself, and it's a seductive argument, it's wrong. I believe that the reason that we can't make long-term predictions about climate is because of the chaotic, non-linear nature and complexity of the phenomenon, but the ability to make long-term predictions is actually unrelated to our ability to make short-term ones.

To understand why (or at least to see another example of the two being unrelated), consider another similar phenomenon--the stock market. Most financial advisors will tell you to put your money into stocks, because over the long haul, they're going to go up. And if you look at the sweep of finance history, such advice would have been borne out. But that doesn't mean that they can predict what the market will do next week, or even tomorrow (if they could, they wouldn't have to make a living providing advice...)

How can they make a long-term prediction when they can't make a short-term one? Because the long-term one is not based on the short term--it is not a series of predictions adding up to a long one. While a broad trend can have a reasonable probability assigned to it from fundamental underlying causes, the various ups and downs as it gets there are subject to different, unforeseeable forces. There is "noise" in the pattern of climate, or markets, and this noise is what we experience as unexpected weather, or daily fluctuations in stock prices. And while we can extrapolate current data to derive a predicted signal, we can never predict noise with any reliability, by definition.

Actually, if you are going to use stocks as an example to refute this argument, then I would contend that over the truly long haul, your argument is wrong.

Stocks have "always" gone up over a relatively short period of history. Revolution, catastrophe, or some inevitable change by an unlimited number of possible factors may quite possibly result in stocks making a total and unrecoverable loss. And if we are using this an example to refute an argument about the long term climate, then I think it's fair to measure the stock market over several hundred or thousand years, just as we are debating climate trends over millions of years.

Enjoy your blog,
- Ben

I essentially agree, but I think a more measured way to look at the problem can be made. Think about it this way: If the tools are examined, it is found that both the initial data points and the simulations themselves have gone unchanged for many years, and aren't calibrated to reality. Given that, the conclusions based on *interpretations* issued from these flawed tools are highly suspect. Most likely, the conclusions are wrong.

Another important point that gets swept under the rug: The conclusions from these *computer simulations* are the only thing driving many of the nutty laws and technological mandates that are coming down the pike. Imagine if such latitude was given to another field of science or technology? Take pharmacology as an example.

"Well, the simulations said that this new drug would end kidney disease. It didn't indicate anything about an increase in the chance of a stroke ten-fold. Sorry about that! We really thought the simulation was right..."

The proponents of the Global Warming hysteria claim proof and demand lifestyle and industrial changes based on curious tools and out-of-date information. They would call it "scientific responsibility". It is neither scientific nor responsible.

Maybe the correct criticism is: why should we believe their long term models when they have failed to predict the recent past correctly? (i.e., cooling, not warming, at the poles, cooling in the satellite data, etc.) This suggests bad models. You are, of course, correct. Tomorrow's weather is irrelevant.

Are you sure this piece was not
intended as an "Onion" submission, "Classical
Theory of Differential Equations Disproved by
Rand Simberg"?
Your blog is one of the finest, don't mess it
up with an anti-anti-parody of itself.

No. Climate models, while differential equations, are different equations--they are not extended weather models.

Weather is much higher resolution, over a much shorter period of time. There may come a time, perhaps with quantum computing, that the two can be merged, but that's not how it's being done now.

Not to get all mathy and technical, but I think your poster who thinks you don't understand DEs needs to get a bit more education in that department. Climate models use PDEs (partial differential equations) and non-linear ODEs (ordinary differential equations), both of which are generally only solvable by computer modeling of numerical approximations. I am pretty sure that neither you, me, or anyone else has come up with a better method, although not for lack of people trying! Seriously, though, you've hit the nail on the head. Climate models, like stock market models, are devilishly complex (in the mathematical sense), and exquisitely sensitive to initial conditions and parametric changes, making predictions very, very tough.
Also, to the poster pointing out the problems with past climate models, I would suggest reading Gleick's "Chaos" for a good primer on the invention of climatological models (by Lorentz) in the 1960's or so, and their development over the last 30-40 years. It also explains in layman's terms why climatology is not particularly related to meteorology.

The main problem that I have with all of these models is that they assume that the output of the Sun is unchanging. We have only been able to accurately monitor the Sun's output since SOHO was put up. The Sun's energy drives almost everything and if it is varying then that blows away all of the models.

I would also point out that 500 years ago when we had the Little Ice Age, the year the Thames stayed frozen for the entire year, Solar sunspot activity was at it's lowest. The Sun went a couple of years without any at all. The sunspot activity now is as high as it's ever been. I believe that there is a correlation.

I should also mention that our understanding of the effect of the Oceans, especially the deep currents, upon the climate is very poor.

Best Regards