Globally, the troposphere should warm about 1.2 times more than the surface

Globally over 30 years, GISS temp increase is .0159 per year. This means RSS (or UAH) should show .019 increase but we see .0152 for RSS and .012 for uah.

[Response: Indeed. But bear in mind that the estimated trends have associated errors, so the difference between RSS and GISS doesn't really contradict the purported tropospheric warming rate.

All in all, I'd say the rate of tropospheric warming can't be constrained by observations sufficiently well to test the theoretical result of enhanced mid-troposphere warming.]

[Response: First of all, the "1.5 times" isn't for the globe as a whole, it's for the *tropics*, and the figure I've read is 1.4 (although it's still considerably bigger than 1).

It should also be noted that the uncertainties in the trends are quite large, especially when restricting to the tropics (because the scatter is larger for tropical data than for global).]

I’ve finally mamaged to track down a link to the ‘peer review’ of the UK Met Offices CPO9 25km gridded squares

Thank you for sharing it with us. I am not particularly happy with the results I have seen so far. For one thing they are projecting roughly 70 years into the future, but the sweet spot of projection is supposed to be in the neighborhood of 30-40 years into the future, where the the consequences of differences in emission scenarios are negligible.

But I suppose that part of what they are trying to do is show the difference between emission scenarios in order to motivate people to lower emissions. A bit at odds with preparing for the costs associated with climate change — assuming one has to choose between projections thirty years or seventy years into the future rather than performing both sets.

You are of course right that their reception of the new approach is luke warm. I see terms like “moderate confidence” and mention of the desire to present the results of a more traditional approach alongside the new, as well as the importance of clearly stating the assumptions behind the lew approach.

At the same time, they also state:

The focus on UK-scale climate change information should not obscure the fact that the skill of the global climate model is of over-whelming importance. Errors in it, such as the limited current ability to represent European blocking, cannot be compensated by any downscaling or statistical procedures, however complex, and will be reflected in uncertainties on all scales.

… where “downscaling”, “statistical procedures” are currently the less desirable alternatives we have to increasing the resolution of the global climate model. This would seem to suggest that they believe that this approach should continue to be pursued even if they are somewhat skeptical of the results so far.

I’ve finally mamaged to track down a link to the ‘peer review’ of the UK Met Offices CPO9 25km gridded squares

http://ukclimateprojections.defra.gov.uk/images/stories/Other_images/UKCP09_Review.pdf

But guess what? The review document is very brief, although if you read between the lines it is lukewarm on what the Met Office has done. It is also brief because the detailed points raised in the ‘Annexe’ to this report are not available online

In other words, be very careful not to draw conclusions that are only marginally supported (at best): “Hmm, it’s probably safe if I jump over there since I don’t see any rocks”.

Agreed — and I like the analogy, particularly since it stresses the possible consequences.

As for being careful, particularly in the context of different tests where the null hypothesis passes one but is rejected by the other, this what Tamino would mean by the results not being “robust.” Given my background in philosophy I might refer to it as a borderline problem, but likewise stress the need for more evidence.

Actually, where i first learned about this was in a college biology course for which I did basic experiments every week. For each experiment, I was expected to formulate a null hypothesis, collect data, do a statistical analysis o f the results and then discuss what I had found (basically, use the scientific method).

Unfortunately I have never taken a course in statistics or been formally exposed to it in school. What little I have learned has almost entirely been in an informal context, but I still try to be useful and learn where I can.

I remember at one point looking at how probability density calculus deals with mutually exclusive alternatives and then exploring the possibility of a continuous probability calculus where rather than mutually exclusive alternatives one is dealing with mutually independent alternatives — and discovered that I had more or less found a different way of talking about the Poisson distribution. But not particularly useful. Likewise I remember digging into quantum mechanics and discovering that the probability density operator could be viewed as continuous complex number truth values between arrays of statements, and that the mathematics behind quantum mechanics could largely be viewed as the translation of one set of statements and their truth values (e.g., description in terms of position space) into another set of statements and their truth values (e.g., description in terms of momentum space). A disjointed and scattered self-education.

Likewise I was digging into pseudo-Reimannian geometry and general relativity before I had actually taken a course in Euclidean geometry. Jumping ahead, but not with much follow-through. My interests are far-ranging, but my science background is actually quite limited. And while I taught myself calculus, it would take a while for me to get it back and without a great deal of work I doubt that I would ever be as good as I once was — which was good, but not good enough to actually teach a first year course in it.

george wrote:

It being my first exposure to statistics and all, at first, I was shocked. But then, after a few minutes, it naturally occurred to me that the “best” thing to do was select the test that allowed me to show what i wanted to show. So I did.

Just kidding. Actually, I asked my TA what to do because I was honestly quite puzzled by the whole affair. Naive fool that I was back then (some would say still am), I thought statistics were like an “8 ball” that always gave you the “correct” answer. (That was before I learned about lies, damned lies and the lying bastar…I mean statisticians who tell them. )

There have been times when Tamino has refered to it as an “art.” I suspect that at a fundamental level it is that way with all disciplines.

With regard to motivation, I believe that one’s primary, most fundamental motive should always be understanding. In this sense identification should always (at least logically) precede evaluation. And it is out of what one discovers regarding the world that one’s other motives should arise.

Likewise, in discourse one should begin with the assumption that this is what motivates others — and that their arguments are likewise an attempt at identification — until sufficient evidence accumulates to the contrary. Of course there are those who argue that one may never know what motivates another. But this is incoherent insofar as the employment of language itself tacitly assumes that we are capable of knowing what motivates one-another, that is what ideas they wish to communicate, what they wish to get across.

First, where I said, ” your say a ditto-head” in my second paragraph I should have said either “your typical ditto-head” or “say a ditto-head” and preferably the former rather than the latter. Second, at the end of that paragraph said, “he should know better” when I meant to say “he should still know better.”

My apologies.

How large does the uncertainty interval have to be before one is no longer “establishing” the “correct magnitude” of the “trend”?

… then begins his second to last post which I found well-thoughout, detailed, and well-said:

I think it is more fuzzy than that and in fact to answer the ‘how large’ question requires knowing the intended application of the result. For example if the CI included 0 that would be very different than if it didn’t – it would mean failure to establish (with that data) whether it was warming or cooling. That CI could be quite narrow and failure to exclude 0 would still be a problem.

Can’t really find a single point that I would disagree with here. But not like that should be the primary focus of a discussion in any case.

Incidentally, yes, her denialism is more subtle than for example that of Watts or your say a ditto-head, and this is part of what I mean by stating that she is more like Pielke than Watts. But part of the reason why I think of the two being ethically similar is that given what Pielke would presumably know as the result of his greater degree of understanding, he should know better.

I would presume that the reason why he is more subtle and less provocative, play-acting the role of a man-in-the-middle whose objectivity consists of being able to see the truth in both sides (when truth is almost entirely on one side, not the other) is simply a matter of his “more delicate” sensibilities. Nevertheless, both profit from being in the spotlight, although I believe that the primary incentive that each pursues has more to do with the ego than with any financial considerations.

As for who will be responsible for more devastation, I would say that is anyone’s guess. And I suspect it always will be.

“Danger! Sharp rocks below the water surface. You may want to think twice before you commit to jumping off”

In other words, be very careful not to draw conclusions that are only marginally supported (at best): “Hmm, it’s probably safe if I jump over there since I don’t see any rocks”.

Actually, where i first learned about this was in a college biology course for which I did basic experiments every week. For each experiment, I was expected to formulate a null hypothesis, collect data, do a statistical analysis o f the results and then discuss what I had found (basically, use the scientific method).

I will never forget the first time I used statistics because I did an analysis using two different tests (at the suggestion of the TA) and one test rejected the null hypothesis while the other did not.

It being my first exposure to statistics and all, at first, I was shocked. But then, after a few minutes, it naturally occurred to me that the “best” thing to do was select the test that allowed me to show what i wanted to show. So I did.

Just kidding. Actually, I asked my TA what to do because I was honestly quite puzzled by the whole affair. Naive fool that I was back then (some would say still am), I thought statistics were like an “8 ball” that always gave you the “correct” answer. (That was before I learned about lies, damned lies and the lying bastar…I mean statisticians who tell them. )

So, anyway, I asked my TA and he said “select the test that allows you to show what you ant to show. ”

Just kidding.

What he actually said was “Watch what you conclude , especially for this experiment, since I’m the one grading it!”

The real problem was that I did not have enough data. I was pretty much at the bare minimum o f what was needed for either statistical test. The fact that I got different answers was likely a direct result of that. That’s what I said in my discussion … and my TA gave me an F.

(I think it was actually a B)

Though it was probably one of the best courses I had in college (taught by homing pigeon navigation expert William Keeton), I must admit that (30 years later) I have forgotten most of what I learned.

But I will never forget the statistics lesson.

The lesson applied to short term temperature trends is obvious, to me at least:

Use extreme caution when drawing conclusions/making claims about the temperature trend over the past decade or so — even about “what the trend is probably not” — especially when using different assumptions (about noise, for example) and/or different data sets and/or slightly different time periods give you a different result.

By “gimmick,” I mean a trick that she uses repeatedly over time. This isn’t quite the same as being a one-trick pony in that the latter would suggest that it is her only big gimmick. In any case, she is dishonest, but more along the lines of a Pielke than a Watts. But ethically I put these two at roughly the same level so in that sense there isn’t much of a difference.

Forgot to comment on this part. This must be a more recent claim as I hadn’t seen this version – I only saw this stuff when it first appeared and stopped following it shortly afterwards. At that time I don’t recall any result that would have made cooling appear more likely than warming. This does sound like a completely nonsensical claim – it is also interesting that with additional data the estimate gets crazier!

It might be interesting to do an animation of the month by month estimates from there as they must have swung all over the place – and presumably will now start swinging the other direction.

Well, you aren’t the only one who failed to notice things. Much farther down in the piece that I had been referencing she makes some qualifications, backtracks, and in essence says she doesn’t really mean it and makes somewhat less outrageous claims. But as far as I can tell, this is her gimmick.

Frank O’Dwyer wrote in the previous post (that I have now just seen):

Actually some of the detailed content of the posts comes very close to that. (Or at least they did when she started, I stopped reading it early on in 2008 or so) But caveats like that hardly matter under screaming headlines whose purpose seems to be little more than to put IPCC and ‘false’/’falsified’ in the same sentence.

Exactly!

Anyway, thank you for the discussion and pointing out where I had missed some things. I will check out your previous post a little more carefully and see whether I have anything intelligent to say, but I wanted to get this out sooner rather than later.

PS Is she a one-trick pony?