What was the Global Average Temperture predicted for 2008 by GCM’s and what was the Error that was given in the year 1996?

I just want to compare the values and see by how much they are off.
I am just a high school student from austria participating in a physic contest, where we alo have to estímate errors and usually do this in the manner descibed above. I know this doesn’t account for “the models getting better” but rather gives you a measure of credability. I would expect that the models published in 1996 should at least be somewhat accurate when compared to the temperture observable today. It would be also interesting to know what the models from 1996 predict for the year 2020.

I do have a basic knowledge of statistics and think, that I could follow your arguments at least partially. As I am also a Skeptic I can’t relate to statements from the IPCC like “very accurately” or “showing good skill in predicting” something. However you also have to be a Skeptic regarding Skepicism and 100° does look a bit too much to me. I say this, knowing that “looks to much” is also something I would be skeptic about.
Therfore I think, that the values i requested would help me make my judgment on that topic much easier.
Thank you in advance for your reply,
Angel Usunov

They are not truly models but are instead simulations and poor ones at that.

The idea of taking a bunch of faulty computer simulations, tuning them to presumed levels of forcing, averaging their results, then declaring that more accurate than each model is hilarious. Not only hilarious but circular.

It would be, and is more honest to just plot the the straigh line assumption already built in to your model. Of course if you have a bunch of models that each generate random fluctuations but have an underlying baseline trend that was assumed at the beginning when you average them it will show the trend.

Why bother doing that? If I write a program that generates the plot y = mx + b – ((rand(time) % 100) + 50) it will generate a squiggly line each time I run it. Yet if I take thousands of runs and generate the average I will get the line y = mx + b. That should be obvious.

It is also obvious to any computer programmer worth his salt that you can take that random value that is subtracted and store it in a variable to be added back later. Thus simulating heat being sequestered in the oceans being later released. Again, do it right and your underlying assumption of y = mx + b, will be hidden by random fluctuations.

It will look “weathery”, and you can even tune the amount you subtract and lag off so that it fits whatever slope you like.

Thus you can have an underlying assumption of y = mx + b but can generate averaged slopes of n, as in y = nx + b, where m >> n. For any m and n you choose.

You can then claim that the temperture change we are experiencing n is much much less than what the actual forcing is. This allows one to scream the sky is falling, plus claim that the slope n, was “predicted” by your model. So your model has been “tested by empirical evidence”.

What a parlor trick.

Now take that parlor trick and use some bad statistics on it to claim that it’s “statistically significant” and you’ve got a full fledged magic show.

Problem is when they catch your mathematical mistakes. Then you need a coverup.

I’d actually like to wind this up now, as I have finals, and I can’t go on arguing all through summer. I feel I’ve advanced my argument as far as is worthwhile for now, and hopefully you feel the same way. We didn’t quite come to an agreement, but that’s to be expected.

So it was a pleasure to talk. Thanks for your time, explanations, clarifications, and corrections. And thanks for being polite throughout, despite my arguably less-polite tone in the original post. I tip my hat to you.

You wrote, “What it predicts is that the GCMs will go ±100 C in 100 years.”

No, it doesn’t. It estimates uncertainty in a prediction, not the prediction. We’ve been over this a few times, now, and reiterating your view using different words won’t make it right.

The cloud error is an error in energy, not in temperature. But the error in energy reflects the systematic uncertainty in cloudiness as it is calculated using GCMs. The growing uncertainty limits means that there is less and less information about cloudiness at greater and greater distances from the baseline climate (To). One ends up with a plausible-seeming future climate, but one that has no real information content.

Look at Collins’ 2002 paper (ref. 28) for an example of this sort of outcome. Collins discusses the initial value problem, rather than theory-bias, but the outcome is analogous. The projected climate was bounded but had zero fidelity with respect to the target climate. And that, using a perfect climate model.

To get an accurate output error (as opposed to a projection uncertainty), which is what you’re asking to see, someone would have to calculate an actual climate prediction factoring in the effect of the systematic cloudiness error during every time-step of the calculation. The compare the results with the target.

I’d like to see that, too. Good luck getting anyone to do it.

The cloud error estimated in my article, in terms of energy, is about 1.5 times the total forcing of all the excess CO2 plus water vapor enhancement, accumulated since year 1900. As this error increases linearly (as theory-bias), while forcing increases only with the log of CO2 concentration, the uncertainly limits expand faster than the projected temperature. You may find that intuitively incredible, but really incredulity is not a valid criticism.

To take your own example, let’s say a bunch of models show that 2+2=5±0.1, where the error bar indicates the variance between models. Obviously, there is some systematic error on the order of 1. But then you say the systematic error is 10? If so, how did our answer even get close? You say that this is because this systematic error is only in an intermediate result of the model, not the output as such. Well, then what’s the uncertainty of the output as such? It had better be on the order of 1.

As for your above quote, of course your analysis doesn’t predict the climate will go ±100 C in 100 years. What it predicts is that the GCMs will go ±100 C in 100 years. Even if the different GCMs had perfectly correlated errors, we would expect to see the 100 C error by comparing the mean GCM results to the “reasonable” range of 0 to 10 C. That is, unless the 100 C error is not the kind of error I originally thought it was. By those error bars, did you really mean that GCMs had a 32% chance of giving results that were more than 100 C away from the “reasonable” range?

The article is misleading, because you use your calculated error as if it were straightforward systematic error in the output. How can you produce numbers like 0.44±15° C unless the numbers 0.44 and 15 are both talking about the same thing: the final result of the GCMs? So I assumed that’s what it was. But in order to calculate the error in the final result, you would have had to worry about things like feedback loops, uncorrelated/compensating errors, and some other things I brought up.

Under Figure 3, you’ll find this: “In terms of the actual behavior of Earth climate, this uncertainty does not mean the GCMs are predicting that the climate may possibly be 100 degrees warmer or cooler by 2100. It means that the limits of resolution of the GCMs—their pixel size—is huge compared to what they are trying to project.”

How is the article misleading?

You wrote, “When I see error bars of 100 C, I usually assume that means that 68% of the time, the results are within 100 C, and 32% of the time, they’re not within 100 C.”

But what I wrote about in the article concerned growing uncertainty from an intermediate result that is re-input in every step of a multi-step calculation, not about GCM outputs as such. The GCMs are made to propagate their mean result, not the limits of their uncertainties. In fact, those who publish GCM results virtually never show a true physical uncertainty. They only show the numerical variation in the mean projection from each of multiple runs. Those statistical error bars have no direct physical meaning. The IPCC reports are rife with such pseudo uncertainty limits.

Concerning your intuitions, most of science doesn’t make intuitive sense. It’s not intuitively sensible that Earth is a rotating spheroid travelling around the sun, instead of flat and stationary. It’s not intuitively sensible that we large complex beings evolved from microscopic bacteria, or that life self-organized from chemicals and energy. After repeated expsure, it’s easy to take all of that, and more, for granted and no longer contemplate the wild non-intuitiveness of most of the science we know.

If the large uncertainty bars are ridiculous — and in some sense they are — then maybe what should bother you is how anyone can claim that GCMs produce reliable climate forecasts, or that an anthropo-CO2 signature on climate has been detected.

Finally, thank-you for your interest. You should know that yours have been among the more substantive comments I’ve received since publication of the article.

The (+/-)100 C uncertainty has nothing to do with climate as such. It is not a prediction that some future climate may be 100 C hotter or cooler than our own…

Really? If so, your article is very misleading. When I see error bars of 100 C, I usually assume that means that 68% of the time, the results are within 100 C, and 32% of the time, they’re not within 100 C. If that’s not what you meant, how can we say, “The error is much greater than the predicted temperature change, therefore we can draw no conclusions”? We can’t even compare the error with the predicted temperature change (not straightforwardly, anyways), since they’re two different things.

As it is, I focused this whole time on the ridiculously large error bars, because they intuitively don’t make sense. First, I thought you were practicing innumeracy, and then, you were ignoring important details such as negative feedback loops. If you had just propagated the errors normally, and found an actual error of say, 10 C, I would have had no objections aside from a few niggling doubts about things that I can’t quite place. And for the record, the rest of my objections are merely niggling doubts–I am not capable of serious criticism/validation of your work.

So, back to those other doubts…

I actually don’t have too many objections left. I’d like to thank you for the information about GCMs. It’s quite interesting to me, especially since I knew nearly nothing about them before I wrote this article.

I don’t think the citation of a few cases of pruning is enough to render all GCMs invalid. Obviously, pruning can bias the results, but there are plenty of circumstances when it is a valid scientific practice. To determine whether that is the case here requires looking at the details, which (sigh) I can’t do without some expertise.

I never described the phase-space as “flat.” If you look at the post immediately above your last one, you’ll see I used the term, “multi-dimensional phase space” with many orthogonal axes. Such a space cannot be flat (2D).

Climate is not in equilibrium. It’s a quasi-stable state far from equilibrium, rather of a sort described by the non-equilibrium thermodynamics of Ilya Prigogine.

The (+/-)100 C uncertainty has nothing to do with climate as such. It is not a prediction that some future climate may be 100 C hotter or cooler than our own, and so worrying about feedback loops is beside the point. It has to do with the progressive, stepwise calculation (not climate) and the resulting accumulation of uncertainty in cloud forcing energy. That is, the accumulating uncertainty is not acting on climate but clouding one’s analytical view, showing that the mean climate that is calculated is really only one of many possible climates. The uncertainty becomes so large that no information remains about the distribution of clouds, about the magnitude and type of cloud forcing, and thus about the status of the projected future climate.

Your coin-toss example of uncertainty implies random error. Cloud error is not random. There’s no reason to think it will scale as sqrt(N). Compensating errors does not mean the computed system will correctly evolve dynamically, even if it evolves reasonably. I.e., there are many ‘reasonable-seeming’ predictive outcomes but only one right one.

In one of his papers, Carl Wunsch says that ocean models typically don’t converge to a unique solution. But when he asks modelers the meaning of a non-converged result, they brush him off because the results ‘look reasonable.’ This behavior is far from reassuring.

Your comment about adjusting out biases using parameters is prescient. Climate modelers must use a “hyperviscous” atmosphere in order to suppress unresolved turbulence. This non-physical aspect of GCMs is partly compensated by adjustments of the parameter schemes in order to produce reasonable results. But the parameter adjustments are rather ad hoc so as to get the desired normalization climate, and this means the parameters become unphysical, too.

What does it mean with respect to physical meaning to have a prediction from an unphysical model?

With respect to temperature predictions, you’re contrasting an estimate of inherent uncertainty with the actual error between observation and prediction. These need not necessarily be the same order of magnitude if the model is adjusted to give reasonable results, or if the ensemble of predicted temperatures is pruned of the ‘unreasonable’ outcomes.

That last is what the ClimatePrediction folks did in a Nature paper (v.433, p.403, 2005), predicting up to +11 C air temperature increase by 2100. The fact that they tendentiously pruned their results didn’t stop publication in Nature (as it should have done), and hasn’t stopped other intelligent people from crediting their “prediction,” anyway, though. I find this behavior far from reassuring, too.

Take a look here: http://tinyurl.com/2jxqgs at what Hendrik Tennekes wrote about modern climate modeling. Tennekes is a well-respected climate physicist.

Let me clarify my philosophical position a bit. I think climate physics is a magnificent endeavor. The climate physics I’ve seen in published work is beautiful and impressive. Working to model climate is extremely worthwhile, and I support the whole program unreservedly.

But in my opinion, climate physics has been hijacked by politics, and the proper dispassion of many climate scientists has been badly compromised. I don’t think this is good for physics, nor is it good for science. Science lives and dies on trust. When the science is consciously bent by scientists to serve even a sincere political end, the sine qua non of science is lost. I think this is being done.

GCMs are academic research tools that are properly the objects of study — how do they behave under varying conditions, how does one improve them in order to model climate, what do they reveal about our understanding of climate physics. But today they are being used as engineering models, as though they are fit to make physically reliable predictions. They are unable to do that, which is why the debate about them, having been infected by politics, has become so acrimonious. The science is lacking and so the debate has become ad hominem. I’m very worried that science is going to take a big hit because of this. These scientists have sacrificed their analytical trust on an altar of political feeling.