The notion that climate is chaotic tends to be taken as a given, with little supporting argument.

No, complex dynamic systems with more than 3 independent variables are taken to be chaotic.

Clearly, if you turn down the sun, the temperature drops. Clearly, if you throw a bunch of SO2 into the stratosphere, the temperature drops. Clearly, if you turn the surface completely white, the temperature drops. And clearly, if you double the amount of an important GHG in the atmosphere, the temperature rises.

But these are single independent variables.  y=f(x),  Temp = f(SO2).    Yes, you can strip away single variable relationships, but they don't add up to "climate".

I see no problem with speaking in a meaningful way about future expectations.

I therefore challenge you:

Present to us, the best of the best "climate models" here on Grist.   But not models that explain the past, but models that predict future behavior.

I would like to see 50 and 100 year  projections, with 5 year intervals.   These models should show the relationship of anthrogenic CO2 gases to Global Mean Temperature.  

In fact, why not a year by year model?   There should be a definitive measure of how antrogenic CO2 is defined.

Then we can bet a beer or something on whether (pun intended) the model fits the results or not!

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I am not up on my chaos theory mathematics, but I don't think, as jabailo says above, that it has anything to do with a number of independent variables (Wikipedia says nothing on it: http://en.wikipedia.org/wiki/Chaotic_system).

I guess the best way would be to have counter-examples.  Although, a colony of ants can be represented by a chaotic system, we can still predict that they will make an ant hill.  We may be able to predict its size and its depth.  We may even be able to predict how fast it will grow, etc.

Andrew Eisenberg
The gateway project is wrong---http://www.livableregion.ca

What you are saying with y=f(x) is that we cannot create multiple regressions. Seems that one semester of statistics for me has just been completely erased by your post. Wow, you must be a real powerhouse in theoretical statistics. Models that 'explain the past' have to exist, that is how models are validated and if validated, then projections for the future can be made using these same models.

I think you're confused.  There's a result--a clever one, at that--in the theory of discrete-time dynamical systems that says that the existence of a cycle of period three implies the existence of a cycle of every other possible period, sometimes called the "Period Three Implies Chaos" theorem.

If the system is described by 3 or more independent variables satisfying coupled nonlinear differential equations, the possibilities naturally become even more diverse. Most surprisingly, however, a totally new kind of possibility arises for the equilibrium solution, that is neither a fixed point nor a limit cycle. This is called a  strange attractor . It is a sort of tangled structure in the space of the variables representing a region in which the system is not periodic, but at the same time is a region to which the system is confined for all time once it falls into the attractor. Moreover, the attractor has a  fractal  structure. The famous  Lorenz model  (which involves 3 variables) has a strange attractor with two "wings", shaped somewhat like a  butterfly,  with a fractal dimension of about 2.06 (for suitable values of the parameters of the model). The dynamical system can then exhibit chaos.

http://www.physics.iitm.ac.in/~suresh/shaastra/index.html ...

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Interestingly, the link embedded in this comment leads to a Wikipedia article on chaos theory that actually includes the following comment:

"Everyday examples of chaotic systems include weather and climate."

The idea that the climate, "is an inherently chaotic system, and as such its behavior can not be predicted." is important and needs to be addressed, but your article could clearly use some work.

I would try something along the lines of:

The scientific term 'chaotic' has a very specific meaning.  The behavior of chaotic systems are not unpredictable or random.  Chaotic systems develop in a deterministic way and are therefore, in principal, totally predictable.

In practice however, chaotic systems are very difficult to predict because their behavior is extremely sensitive to their initial conditions.  Make a slight error in measuring the initial conditions and observed behavior tends to depart very rapidly from predicted behavior.

The atmosphere is the classic example of a chaotic system, which is why predicting the weather more than a few days in advance is so frustratingly difficult.

However, if we were able to precisely measure the current state of the atmosphere and we fully understood all rules governing its development over time, we would be able to perfectly predict the weather.

As it happens we can only roughly measure the current state of the atmosphere and we don't fully understand all the rules that govern its operation.  However, this does not mean that weather is completely unpredictable.  Our approximations are good enough that we can make usably accurate predications over limited time horizons.

This leads to the obvious question, if weather predictions are next to worthless more than a couple of weeks out, how can we seriously make predict climate decades or even centuries in advance?

The answer is that 'climate' implies a much less specific prediction than 'weather'.  The less specific the prediction, the longer the time horizon over which we can predict with some degree of confidence.  

We can predict with reasonable accuracy the high temperature in Baltimore a few days in advance.  We can also make a pretty reasonable prediction of the average summer high temperature in Baltimore in February.  What can't predict in February, at least not any better than a random guess, is the high temperature on the 4th of July in Baltimore.

Average annual global temperature is about the least specific environmental measurement immaginable, so it is, unsurprisingly, something we can predict with reasonable confidence over very long timescales.

Having said that, I'm not particularly enamored with long range climate models.  They inherently include all kinds of unconfirmed assumptions about how the atmosphere-ocean-ice system operates and about future economic activity (which is not famously easy to predict).  It doesn't matter how much computing power you throw at the problem, a wild ass guess is still a wild ass guess.

The fact that the models can back predict historical environmental behavior proves absolutely nothing since that was exactly the data used to generate the models in the first place.  You can buy any number of software packages that back predict stock prices beautifully.  Unfortunately this does not make them effective predictors of future.

In my view we shouldn't be addressing global warming because the models are scary. We should address global warming because we don't know what the hell is going to happen, which is really scary. If you are driving blindfolded, taking your foot off the gas is a pretty good idea.

My suggestion is that you could start up a mutual fund similar to VICEX (find a catchy name) which is based on AGW being wrong is some way.  To be creditable though, you will need a climate scientist who thinks they can make money on such a venture, which is a very tall order.   For those ready to buck consensus, who know what they're doing, there's money to be made.

On the other hand, many hurricane projects are readily supported by private capital, and it seems likely that climate change research is getting there.

Wrong headed

The single best model approach is just wrong headed. First off, this is just too  important to rely on one model, and not have consistency checks. Hurricane prediction very successfully uses several models.  Over the years each model had various strengths, and these could be used in other models contributing to improvement.  When a model disagrees now, forecasters have a better sense of how and why, and can make adjustments.

Regardless, the author's assertion does not measure up to the observed behavior. Weather is chaotic, and where recurring patterns continue, the parameters observed in a time frame that appear stable are in what's known as a subcritical region. Those parameters that are observed in a time frame with wildly varying and unpredictable results tend to be within a supercritical region, which can be the result of variable perturbations of the system under observation.

Likewise, while anthropogenic carbon emissions will undoubtedly cause the world to warm up, the very nature of Earth's climate makes predicting the weather on any given day several years from now impossible.