Anyway, I look forward to reading future posts on the subject.

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.

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

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.

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.

Keep up the great work.

Muchas gracias! My spelling also obviously needs improvement.

Matt

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