Two points: A) I never proposed a bet with you or anyone like you. The proposal was always restricted to people with certain characteristics.. B) The bet I proposed and that bet was very specific and none of your proposals matched.

You can read the characteristics highlighted here:

(Since the various bloggers seem to be all for demonstrating their confidence in various predictions by offering bets, maybe if the modelers are truly confident the central tendency over 30 years is 2C/century, they’ll take an even money bet. If the OLS trend over the first 30 years of this millenium is greater than 2C/century, they win. If it’s less than 2C/century, they lose. If 2C/century is really the central tendency, and 7 year trends far off the mark are just insignificant, commonly occurring blips, that even money bet ought to be attractive, right? )

The post did not indicate what is to be bet– as it happens, I’d bet chocolate chip cookies rather than money. But, possibly if an appropriate blogger came around promptly, (before new data arrives) I might have negotiated something.

But, you — an anonymous person with the initials “JM” popped in and immediately decided to over propose entirely different bets. Your first suggestion was:

Why don’t you recast it to represent the reality of your position. You win if the slope is less than 2 – 1.4 = 0.6C/century, they win otherwise. ie. you win if the IPCC is “falsified”, they win otherwise.

Note: I win if the slope is less than 2.0 C/century is not the same as I win if the slope is less than 0.6 C.century.

I turned this down. You proceeded to propose a scatter shot of bets worded differently from the one described in the post. Most seemed to have conditions pulled out of your… uhmm… fevered imagination. And, you continually announced things like “we are agreed them”, after posting your own proposal.

I’m not going to discuss this bet further with you because it’s pointless.

———-

Lucia

“I claimed Tamino said it was sufficiently long to perform a hypothesis test.”

Well of course it is – mathematically. The question is whether it is valid in physical terms, which it isn’t. I can do a hypothesis test on “every coin has two heads” and mathematically test it with a single toss. It doesn’t mean the result has any physical meaning. The error bars are too large to draw conclusions (which is Tamino’s point re. climate data over 7 years). You are technically correct, but the conclusion is meaningless in real world terms.

As I said Tamino was clearly making a rhetorical point, and your characterization of it is simply solipsistic, ie. a claim so weak that it cannot be realistically falsified.

‘I don’t ordinarily bet, but I might under specific circumstances that could result in my finding the bet a fun diversion ‘

But … this was a bet – against a 30 year dataset – that you proposed. I took you up on it, you squirmed and eventually wriggled off the hook, despite all my concessions and attempts to make it more pallitable to you.

Your bet, not mine. You refused it. Even after modified heavily in your favor. I think the conclusion that you lack confidence in you own analysis is obvious.

Thanks for the references re. 0.1C. I’ll follow up and get back to you.

Arthur (3411). Thanks for that, it’s very informative and encompasses something I hadn’t considered, please let me review that before I respond.

steven mosher

I found the following presentation useful for visualizing the insides of GCMs

It seems to be a numerical solution step by step fulfilling imposed boundary conditions. I am then guessing that the differences in the models consist of how they use the many parameters that control the equations on the boundaries to get a fit to some data. Like cooking? more of baking powder less of cinnamon etc.

As you say, it is a guess, unless one is willing to become an accredited climatologist.

A good link that shows temperature day by day from satellites .

One can see the seasonal variation and the dominance of the northern cycle to the global, and compare with previous years and averages to see the anomaly. June 2008 had climbed over June 2007 but is now leveling off to cross down again. It is like watching horses .

That said– the four things I actually discuss aren’t discussed quantitativley in the TAR. But, they do show that particular graph for three metrics — which is to their credit.

I mostly read the AR4, and WG1 of the IPCC decided to omit that level of quantitative information from the AR4. Or, if they show it, I don’t see it in chapter 8, where I would expect it. Instead, they do show figure 8.11 instead. (Chapter 8, page 619).

It’s intersting to compare figure 8.11 to the Taylor Diagram in the TAR. First– it has less information by design. The rms are compared, but not the correlation. Also, where the TAR uses distinct figures to inform readers which models are crummy amd which are better, the AR4 doesn’t distinguish which models are which in figure 8.11. (One might ask: Which is the wild hair model that has nearly 100% RMS error on precipitation? Is it the same one that’s not so hot on surface temperatures? Or a different one? )

In my opinion, the AR4 is very qualitiative in their text, and figures.

As for the Schmidt paper– I was discussing what the IPCC shows. Certaintly, there are individual papers out there that dicuss more– and on a variety of topics. But, as it happens, that paper isn’t really a discussion of the basis for the IPCC and it’s not even particularly quantitative. Mostly, there are colored figures we compare by eye. There are a few Taylor graphs (Figure 23) is for things modelers consider important. They are important– I don’t want to suggest they aren’t. But, Regardless of what climatologists discuss when comparing their various models to each other, the fact that one can find a paper on Model E is not the same as the IPCC compiling relevant information for readers to understand how the various model compare to each other or to metrics the IPCC is specifically discussing. The IPCC dicussions are mostly quantitiative. .

In any case, the Schmidt paper mostly does what I find not particularly useful as a consumer of information to guide what I believe about IPCC projections. (Which if fine– it’s not meant to be a paper for the general public.) That Schmidt Model E NASA paper is what it is: a journal article explaining model results for 1979. While it is laudable that NASA describes that in detail, it doesn’t tell us much about the specific results relied on by the IPCC. The Taylor diagrams aren’t even the things the IPCC included in the TAR Taylor diagrams.

So, the result is that even after finding that Schmid paper, a reader would still need to figure out which of the various configuraitons of Model E were used in the IPCC projections. Then the would need to hunt down the runs on which the IPCC relied and do all their own comparisons to the metrics the IPCC discusses. This is no criticism of the Schmidt paper– which is fine. But, with regard to the IPCC providing useful information, the IPCC would do well to compile comparisons, make more taylor diagrams and write more quantitative prose. The fact that the public is left with a research project to decide what the think is a problem for modelers and the IPCC with regard to communicating the variety of predictions from models.

Steven is describing what is, to a large extent, the engineering approach.

Interestingly enough, that’s how science is done too. I watched the Standard Model of subatomic particles being developed. There were lots of ideas, some of which were true. As the data came in they were winnowed out and in the end only one was left standing. Some of the competing ideas were very strongly defended and lasted a long time. But they ultimately couldn’t explain some new and crucial experiments and died, overnight.

Then, instead of vague prose telling us the models are “good”, with no quantification we could quickly read this information. Though predictive ability would still be unproven, at least we’d have a quantified evaluation of hindcast skill available to the public. Right now, all the public gets is the squishy assurances backed by claims to authority!

A simple Google search found this in less than 2 minutes. Skip to figure 23 on page 35 for the Taylor diagrams.

Schmidt, G.A., R. Ruedy, J.E. Hansen, I. Aleinov, N. Bell, M. Bauer, S. Bauer, B. Cairns, V. Canuto, Y. Cheng, A. Del Genio, G. Faluvegi, A.D. Friend, T.M. Hall, Y. Hu, M. Kelley, N.Y. Kiang, D. Koch, A.A. Lacis, J. Lerner, K.K. Lo, R.L. Miller, L. Nazarenko, V. Oinas, Ja. Perlwitz, Ju. Perlwitz, D. Rind, A. Romanou, G.L. Russell, Mki. Sato, D.T. Shindell, P.H. Stone, S. Sun, N. Tausnev, D. Thresher, and M.-S. Yao, 2006: Present day atmospheric simulations using GISS ModelE: Comparison to in-situ, satellite and reanalysis data. J. Climate, 19, 153-192, doi:10.1175/JCLI3612.1.

it’s never been exactly clear what is different between esemble runs. It seems clear that
its not a monto carlo on intial conditions or paramerterizations. But beyond that your guess
is as good as mine, ok maybe a bit better. I’m feeling generous.

Here is what I dont get. I dont get why people like JM demand a reference for a blog post,
but dont demand data for a scientific study.

So, JM. should Dr. Phil Jones release his source data? this is the data that determines GSMT.
and if a scientist doesnt release his data, what to make of that?

Here is what I suppose. I suppose that if we can ask bloggers to link to their source, if we can
ask authors to footnote, we can ask scientists working on a problem that thretens all of humanity
to post their source data and methodss. huh, JM? ya think.

This is a tenet of Lukewarmers. free the data. free the code.

My reasoning was this. The metrics must be connected to the harm. So, 3 metrics came to mind.

1. having GSMT Skill. ( versus a naive straigtline historically based forecast)
2. having Sea level Skill ( blah blah)
3. having precipiation Skill.

basically, we are woried about warmer temps, higher seas, and floods and droughts. So those
metrics should dominate the selection of the best models.

Now, I’m not sure how to weight these. I know looking at some IPCC stuff that the skill level of
models differs in these regards. But having 19 different models is just an engineering abomination and huge waste of intelligence, and undermines the credibility of the science rather than enhance it.

the 7% annual variation in incident radiation as the earth moves in its elliptical orbit would cause a 7C variation in average global temperature over the year – something which just doesn’t happen

This would be true if the response for solar variations was time-independent, or at least the full response was much shorter than 1 year. Some of the response is fast, but some of it is also quite slow – at least the 7-8 years we’ve been talking about on other threads here. So you would expect maybe half or less of the response within the annual cycle.

Second: it’s actually rather hard to find data on absolute global average temperature. What you will usually see is the temperature anomaly (GISS, Hadley, UAH, RSS, etc. all plot this) which is the difference between the measured temperature and a reference average for that month. You cannot derive the global average annual temperature cycle from these anomaly series because the main component has been removed by that differencing process, that’s what the “anomaly” means.

According to this page: http://www.spaceweather.com/glossary/aphelion.html
(note the quote from Roy Spencer!) the actual variation in global mean temperature between perihelion and aphelion for Earth is 2.3 degrees C, so not too different from the order of magnitude you would expect. The surprise is – it’s warmer at aphelion (when Earth is farther away). This could be partly a lagged response phase effect, but the explanation on that page makes sense too – aphelion happens during northern hemisphere summer, and the northern hemisphere has a much larger fraction of continental land area, so lower heat capacity, so more warming despite the lower insolation.

Anyway, the magnitude of the effect is pretty clearly consistent with what Lucia talked about, and the solar cycle amplitude of about 0.1 C is indeed referred to by IPCC.

Steve Mosher at 3399 is exactly correct in how the models should be treated, throw out the bad ones and improve the best one.

Steven is describing what is, to a large extent, the engineering approach.

The caveat is: approximate models get used. However, everyone admits the level of approximation– and generally in quantitative terms. Quantifying what we mean by “good agreement” is very important in engineering. So is comparing predictions to out of sample data. Both let you decide on safety factors, tolerances, figure out what can and can’t be done etc.

I sympathize that climate modelers don’t have much out of sample data to test against. But…. That’s no excuse for just relabling “in sample” data “out of sample”!