I have returned from Madrid, where the conference went moderately well. My part was acceptable, but I could have done a better job, which I’ll explain in a moment.
Iberia Airlines is reasonable, but the seats in steerage were even smaller than I thought. On the way there, I sat next to a lady whose head kept lolling over onto me as she slept. The trip back was better, because I was able to commandeer two sets. Plus, there were a large, boisterous group of young Portuguese men who apparently had never been to New York City before. They were in high spirits for most of the trip, which made the journey seem shorter. About an hour before landing they started to practice some English phrases which they thought would be useful for picking up American women: “Would you go out with me?”, “I like you”, and “You are a fucking sweetheart.”
My talk was simultaneously translated in Spanish, and I wish I would have been more coherent and that I would have talked slower. The translator told me afterwards that I talked “rather fast.” I know I left a lot of people wondering.
The audience was mostly scientists (of all kinds) and journalists. My subject was rather technical and new, and while I do think it is a useful approach, it is not the best talk to present to non-specialists. My biggest fault was my failure to recognize and speak about the evidence that others found convincing. I could have offered a more reasonable comparison if I had done so.
I’ll write about these topics in more depth later, but briefly: people weight heavily the fact that many different climate models are in agreement in closely simulating past observations. There are two main, and very simple problems with this evidence, which I could have, at the time, done a better job pointing out. For example, I could have asked this question: why are there any differences between climate models? The point being that eight climate models agreeing is not eight independent pieces of evidence. All of these models, for instance, use the same equations of motion. We should be surprised that there are any differences between them.
The second problem I did point out, but I do not think I was convincing. So far, climate models over-predict independent data: that is, they all forecast higher temperatures than are actually observed. This is for data that was not used to fit the models. This means, this can only mean, that the climate models are wrong. They might not be very wrong, but they are wrong just the same. So we should be asking: why are they wrong?
There was a press conference, conducted in Spanish. I can read Spanish much better than I can hear it, which is a fault I should work harder to correct, but it meant that I could not follow most of the comments or questions well. I was the critical representative, and a Professor Moreno was my foil. The most pertinent question to me was (something like) “Do I think it is time for new laws to be passed to combat global warming?” I said no. Professor Moreno vehemently disagreed, incorrectly using as an example the unfortunate heat wave in Spain that was responsible for a large number of deaths. Incorrect, because it is impossible to say that this particular heat wave was caused by humans (in the form of anthropogenic global warming). But the press there, like here (like everywhere), enjoyed the conflict between us, so this is what was reported.
Here, for the sake of vanity, are some links (in Spanish) for the news coverage. We were also on the Spanish national television news on the first night of the conference, but I didn’t see it because we were out. Some of these links may, of course, expire.
- ?Existe el cambio clim?tico?
- Estad?stico de EEUU rebaja la fiabilidad de las predicciones del IPCC contra la opini?n general
- Un estad?stico americano pone en duda la veracidad del cambio clim?tico
- Un experto americano duda de las consecuencias del cambio clim?tico
- Evidencias apabullantes
- Un debate sobre cambio clim?tico termina a gritos en Madrid
Madrid itself was wonderful, and my hosts Francisco Garc?a Novo y Antonio Cembrero were absolute gentlemen, and I met many lovely people. I was introduced to several excellent restaurants and cervesaria. The food was better than I can write about—I nearly wept at the Museo del Jamon. I felt thoroughly spoiled. Dr Novo introduced me to La Grita, a subtle sherry that is a perfect foil for olives. I managed to find some in the duty free shop, and I recommend that if you see some, snatch it up.
Come back over the next few days. By then, I hope to have written something on the agreement of climate models.
sorry: it is “cervecer?a” not “cervesaria” (plural “cervecer?as”)…
off topic: ?que tal las “tapas”???
great blog!!!!
My familiarty with statistical predictions is limited to
my experience handicapping horse races. When models overpredict dependent data, obviously the sources of data you’re combining aren’t independent.
When I considered a horse’s past speed rating as one factor, and combined it with jocky performance in an esoteric formula to predict the winner, my predicted win percentage always fell somewhat short of reality. I’m sure that part of the reason was
that jockey performance and horse performance are not independent factors- the better jockeys tend to get first pick of the better horses – A. McIntire
Jorge,
Muchas gracias! My spelling also obviously needs improvement.
Matt
Thank you sir for the update. My readers appreciate your insight and what really matters in the end is the content you were trying to communicate. It’s hard enough to communicate to someone even in your native language ideas and concepts that are foreign to them. You sir help bridge that gap.
Keep up the great work.
“Professor Moreno vehemently disagreed, incorrectly using as an example the unfortunate heat wave in Spain that was responsible for a large number of deaths.”
If Moreno and others of his persuasion actually had a policy that reduced the number of heat waves, then it follows the same policy will cause more cold waves. Cold causes more deaths than heat. So, in effect, Moreno is advocating a policy that causes more deaths. I don’t think he is being responsible.
I am astrophysics PhD student who runs numerical simulations studying galaxy dynamics. While I have absolutely no direct experience working on climate models, nor do I have any but the faintest idea of the inputs or physics that goes into them, I am quite familiar with the similar numerical modeling techniques that are used in astrophysics calculations. I would like to very briefly address your question of “why are there any differences between climate models?”
The systems I study are actually much simpler than any climate system. I model the mass of galaxies as a simple N-body, collisionless, self-gravitating fluid of point masses- which means that the only relevant physics involved are Newton’s laws that we all learned in high school. Yet different gravitational codes yield slightly different answers. The reason is that there is no analytic solution to the gravitational N-body problem – there are only numerical approximations.
So each code has to make different assumptions on how to best approximate the result. There is the additional constraint that the time it takes to actually perform the calculations on current computers has to be reasonably small (hopefully, less than the time it takes to earn a PhD!). In order to decrease the computational expense, more approximations are used. Obviously, different approximation methods will yield slightly different answers, but all should yield approximately the same result. Since we can’t measure our approximations against the “true” answer (such an answer does not exist), comparing different techniques to make sure they give similar results is the best method we have for checking our codes.
Keep in mind that the system I have described is an extraordinarily simple system. Climate models have many more layers of complexity – and thus approximations – to deal with. Again, there is no “true” answer against which to compare the results of simulations.
So, let me be clear about the very narrow point I am trying to make. You are certainly correct that results from different codes are not necessarily independent pieces of evidence. It is also worrying that the codes are not fitting independent data. Asking why the models disagree with the data and trying to improve them is of paramount importance. However, simply asserting that “the climate models are wrong” seems to me to be missing the point. It does matter how wrong the models are because all we have are approximations and there is no a priori method of determining how good those approximations are. So, the best we can do is make lots of different models using different assumptions and hope they give reasonably similar answers. “Reasonably similar” may not seem quantitatively satisfactory, but it’s the only way we have to do science.
Craig:
Your perspective is very helpful. Matt can respond for himself, but I think you slightly missed his point: Since ALL the models over-predict global temperatures and because of this consistent error the models are “wrong”. I also believe Matt is using wrong in the sense that there must be a recurring error in the models rather than the models are full of errors. I would use the term “incomplete” since it is slightly less perjorative.
The other difference is that the predictions of the models can be compared to actual observed measures – hence the over-prediction.
I am sure you are right that the models are based on a series of approximations both for theoretical and computational reasons. My surmise is that Matt would say, “yes, of course, but there are also approximations where we do not know but do not say we do not know.” This is another of Matt’s recurring themes.
Anyway, I am glad you have joined the discussion.
Craig,
Thanks very much for your explanations. They are very well put. The only thing I disagree with is that there is a true answer. We might not be able to measure the truth without error, but that does not mean it does not exist.
And of course by “wrong” I mean something like Bernie implied. If the models predict exactly what is subsequently observed, then the models are right, if they do not, they are wrong. They may still be wrong but “close enough”, but that is a separate argument. Right now, of course, they are wrong and consistently too warm.
I will write more about this in a separate post, and I may steal some of your comments.
Again, thanks.
Briggs
a little too late. I read over the pdf of the Spanish talk.
I think it is impossible to measure the degree of uncertainty in the measurements. There are cumulative uncertainties.
start first with simply measuring he temperatures
what is the accuracy of reading a thermometer? If I have 1000 thermometers, what is the range of temperatures recorded by the thermometers in the same room? what is the variability of thermometers produced by various manufacturers? what is the variability of housing designs, painting of the temperature boxes, placement of the boxes and what is the delta from observed temp to actual temp?
for digital recorders, what is the hysteresis of the measurement? what is the manufacturer’s determined precision and accuracy? how has this changed over time?
how does the statisitics of the measureing device itself vary with temperature? is a warm reading as accurate as a cold reading?
From my own experience with lab thermometers, boiled water often reads 100.5 or 99.6. freezing can be anywhere from 0 to 1. with digital themistors, in the 70’s these were highly slow and inaccurate within .6 degrees. With modern cooking thermometers, they take 3 minutes to come to equilibrium and are often wrong by 2-3 degrees.
The surface temperature record as we have it is not a validated measurement. No statistics on the recording devices as used world wide or the persons doing the observations have ever been made. It could not be used as a validated device or method for medical research for an FDA approvable drug study.
Fair points. I agree that the fact that the models are consistently predicting overly warm temperatures is very worrying and something I’d like to know more about. I guess I am just a little uncomfortable with making a binary choice between the models are “right” or “wrong” – it is much more complex and subtle than that.
Anyway, I look forward to reading future posts on the subject.
Roger Pielke Sr. has a very timely column on what the models really do. http://climatesci.org/2008/04/11/real-climates-agreement-that-the-ipcc-multi-decadal-projections-are-actually-sensitivity-model-runs/