Interview With A Climatologist

Today, an exclusive interview with famed international climatologist Dr Arturo Calor. Dr Calor is leading the scientific fight against man-made global warming.

Thank you for being with us, Dr Calor. Let me ask you this: how do we know the Earth is imperiled, that it’s really growing ever warmer?

Simply because the data shows that the globe is getting hotter. I’ll anticipate your next question and tell you about the data. We have thermometers of various kinds at several locations around the planet. Most of these have been in place a few tens of year, others longer; some have been moved, some have seen cities grow up around them, others have seen cities die. Some locations are new, some old. The thermometers measure the temperature at various heights, but usually near the surface. There is some error in this process, and incompatibilities, but we can down weight these difficulties because the subject is so important.

Because the instrumental record is so brief, we also infer temperatures using proxies.


These are things like widths of tree rings, ratios of isotopes of certain chemicals in other living things and ice, and so forth. What we do is match the proxies with known temperatures measured by thermometer, and then build a statistical model which estimates a parameter. We report that parameter as the error-free estimate of the temperature.

Wouldn’t it be better to remove these parameters—“integrate them out,” in statistical parlance—and report on the uncertainty of the actual predicted temperature, which will always be much higher than the uncertainty of the parameter?

No. Because then we would be less certain than we now are. Plus, we are very busy people and that sort of thing takes a lot of time.

I see. Very compelling. What’s the next step?

We take all of the measurements and estimates, process them through a filter which attempts to remove the largest mistakes, and then we average what’s left over together. We call this average the Global Average Temperature. This average, we now know, has been increasing. It has gone up by a little over a half degree centigrade since the beginning of the industrial revolution.

Do we know the plus and minus of this estimate?

I don’t follow you.

Well, all these different locations and ways of measuring temperature and all the models that predict but do not directly measure temperature. Surely there is some uncertainty in that half degree estimate.

Ah, I see. It is the duty of science to seek certainty, to dispel the dark clouds of ignorance. It would be of no use to concentrate on uncertainty. Many professionals are working on this problem, therefore we are very sure of the results.

I see what you mean. Can you tell us about satellites?

Ah, there we have something. Satellites have been measuring temperatures for a few decades now, and these also show that temperatures are going up (well, sometimes going up).

But satellites don’t directly measure temperature. Isn’t it so that they measure radiation and through a physical-statistical algorithm estimate temperature?

This is natural, yes.

This implies that there is uncertainty in that estimate: another plus and minus. Do you account for that in your estimates?

These satellites are calibrated by very complicated computers, a very expensive process. We are confident in the data they produce.

Taken in all, we are as sure as we are about anything that the temperature has increased a few tenths of a degree and that most of this increase is due to the activity of mankind.

How do we know that?

We build very beautiful, extraordinarily complex computer models which prove this. Although they are difficult to fully comprehend, at base they are very simple.

We know that carbon dioxide captures heat in the lower reaches of the atmosphere. The more CO2 there is, the more heat captured. We also know that a doubling of pre-industrial levels of CO2 will only raise the temperature an insignificant amount. Yes?

So we build into these models a feedback mechanism that says as more CO2 is added, the temperature increases non-linearly. We then run these models and we find exactly what we expected to see: increasing CO2 leads to a positive feedback in temperature!

But aren’t you just seeing what you put into the model? It’s not quite an independent verification of the theory.

You forget that we also have evidence that these models have produced simulations that look, after some processing, like actual observations. That should be enough proof that our theory is correct.

Perhaps. But aren’t there literally hundreds of knobs and dials that you need to tweak to “tune” the models so that they first produce those simulations? Do you have independent evidence that these models predicted new data better than predictions based on the assumption that your theory is wrong?

Look here, young man. I hope you are not going to take the denialist position. If we don’t do something now, by the time we confirm everything, it may be too late.

You can’t argue with that. Thank you for talking to us, Dr Calor.

My pleasure. I hope this interview increases the chance that my grant is funded.


  1. YOu have a bad tag for a subscript on the CO2 near the end of thisthis post that is making it damn near unreadable.

  2. “Famed international climatologist”?

    Could you please provide a link to a list of his publications? I’m having problems finding his papers in databases.

  3. That Dr. Calor is obviously really intelligent. I’m working on modernizing phlogiston theory and bringing it up to date and wonder if he would be intrested in a consultant position? Since he believes heat can be captured, like a rat in a trap, I’m sure his expertise would be useful in explaining how phlogiston is retained in material objects.

  4. Heh. So close to what “climate science” people actually say.

    Prof. Jones, for example, in an interview conceded that the Medieval Warm (and the Little Ice Age) existed despite being omitted from the Global record by “climate science” – justified this omission by “yes, the Medieval Warm occured for the entire Northern Hemisphere, but we do not have sufficient data for the Southern Hemisphere [paraphrase starts here] and so are quite free to assume it was as much colder as the North was warmer.”

  5. LOL!

    I speak from experience when I say you should probably add a satire tag. There are people who will run with this as gospel.

  6. Just a question out of curiosity. Do you expect anyone to engage in scholarly exchange with you by writing this post?

    Of course, this is your blog, and you can write anything you want for whatever reason.

  7. Why do you waste your time inventing ‘hombres de paja’ that are guaranteed to confirm your prior prejudices? Too scared to talk to actual climatologists?

    How about your assessment of the model fitting procedure in Lindzen and Choi (2011)…

  8. Great Friday Funny! but one statement is obviously wrong:

    “We call this average the Global Average Temperature.”

    I see it most often called simply the Global Temperature, just like e.g. my Office Temperature.

    I suppose they do this for simplicity, you know this way it’s readily comparable, in order for the Kyoto 2-degree thermostat and the taxes on my aged car to work smoothly.

  9. Briggs,

    Some time ago, before 2011, you did a blog in which you showed a warming trendline with the plus/minus uncertainties included…then…you drew a horizontal line thru the resulting graphical mess AND that line never breached the uncertainty band.

    This was a very elegant illustration of how, despite the seemingly obvious & compelling temp trends, the data was sufficiently noisy that the hypothesis of ‘no significant temp change had yet occurred’ could not be rejected.

    I could not locate that blog entry (too little time, etc.) — but it would nicely complement the statistical uncertainty referenced in your creative piece, above, and warrants a reference link (IMHO)!!!!

    READERS: For any of you wondering what a ‘hombres de paja’ is (“straw man”) the (G, maybe PG, rated) video (link below) illustrates this in less than 52 seconds wonderfully — The similarities to the composite climatologist, explaining models crafted to confirm prior predjudices, crafted to confirm prior predjudices (a satirical pun within a pun Gavin reinforced if [presumably] unwittingly) are readily apparent to the discerning observor:


  10. @Ken

    A simple empirical model can very elegantly illustrate how, despite the seemingly obvious uncertainty of the data, once you exclude the sources of noise, you’re left with a very clear picture of a linear component of the temperature trend, commonly called “global warming”:

    Take a look at Figure 1.

  11. @Gavin,
    Sure as soon as you startmaking models that work rather than analogies of climate scientists as CSI folks. Oh wait that might be correct — Climate Scientist Imbeciles (must be self -referential for you)

  12. Gavin, the last time I talked to you on this thread I asked about any sort of testability to your models–that was when you went with your tail between your legs. Climate models are crap and not worth a bucket of warm spit

  13. OK, say someone does want to put error bars on their graph. What “error” should they use?

    My question comes from frustration.

    Sometimes, “standard error” seems to mean standard deviation of the sample. Sometimes 2x stdev (as in P<0.05 infamy). Sometimes other things, it seems. In my own company, we use 3x stdev of the sample, with the sample containing at least 10,000 parts measured (not for error bars, but for general variation reporting, when it is inconvenient to just put in the graph of the distribution).

    I'm an engineer. I want to be able to reconstruct any report from the raw data. Or, at least, get a crude feel for the raw data from the report.

    So, if I encounter an error bar in the wild, what is the most likely thing it means?

  14. Dear Mr. Briggs,

    Gavin is reaching out to you, not me, not other readers, but you. Why? Think of it in a positive way. Perhaps he thinks highly of you? (Ha… I am tempted to say “Take his hand.”)

  15. From Grzegorz Staniak (G.S.), above, comes another reference to the paper at: pointing to figure 1 there.

    Figure 1 shows pretty much an absence of warming since 2000 (after the anomalous spike a year or two before) thru the end of the data, about 2009. That non-warming trend has continued to the present…and none of the major climate models predicted this.

    G.S. remarks: “A simple empirical model can very elegantly illustrate how, despite the seemingly obvious uncertainty of the data, once you exclude the sources of noise…”

    PROBLEM: the “noise” is truly difficult to exclude, with confidence, because many key factors involved are subtantially unknown & uncertain:

    The carbon cycle — fundamental to any model & understanding — remains mysterious to a significant degree. About half the carbon humanity produces is absorbed, regardless of the volume — why? Nobody is sure. Further, something on the order of 20 percent of the carbon generated then absorbed in nature goes to places unknown (that per the Director of NOAA during a symposium a few years ago).

    E.G.: That article addresses a spike in CO2 output that just as suddenly stopped, speculations aside nobody knows why. The article states: “…the rate of carbon-dioxide increase returned to the long-term average level of about 1.5 ppm per year in 2004, indicating that the temporary fluctuation was probably due to changes in the natural processes that remove CO2 from the atmosphere”” Thing is, then and now, NOBODY, has a firm grasp on what all the CO2 sinks are/where the CO2 that’s absorbed all goes. NOBODY. NOAA’s website has all kinds of references to measuring trends in various places…but a clear understanding of what’s happening where & why & where that CO2 is actually going remains, to a significant degree, elusive.

    When CO2 is THE issue, significantly failing to understand where it actually goes tends to undermine one’s confidence in the resulting models — espcially when those models have a poor predictive track record. At least to those of us that pay attention to such things.

    Thus, the remark about getting rid of “uncertainties” carries the implicit assumptions that the “certainties” are reasonably well known. They are not — some very fundamental “need to know” factors remain significantly mysterious & speculative.

    To compensate for such uncertainties offsetting assumptions are built within the models — which carries inherent risk of self-reinforcing circular reasoning. And that is a recurring point of emphasis of this blog’s author.

  16. Ken:

    PROBLEM: the “noise” is truly difficult to exclude, with confidence, because many key factors involved are subtantially unknown & uncertain:

    Yes! I would say that declaring something other than actual instrument issues to be noise is really begging the question.

  17. @Ken

    the “noise” is truly difficult to exclude, with confidence, because many key factors involved are subtantially unknown & uncertain

    The factors taken into account in this particular paper have been researched for some time now, and while of course there are constraints on what can be said about them and with what confidence, they’re nowhere near as uncertain as you suggest. Solar irradiance is measured by satellites, as are volcanic aerosols. The influence of ENSO has been studied and quantified as well (see for starters). As you can see, the three factors plus “a linear warming trend of 0.14°C per decade” together explain 3/4 of GAT variance in the last three decades.

    To compensate for such uncertainties offsetting assumptions are built within the models — which carries inherent risk of self-reinforcing circular reasoning.

    Certainly not in this case. Lean & Rind use an empirical model here, and it has nothing to do with the missing sink: it doesn’t matter where the “missing” CO2 is absorbed, what counts is the radiative forcing of the “non-missing” surplus CO2 that stays in the atmosphere — and we have observations, not models, that tell us how much of it is there, and physics to tell us how much longwave radiation it sends back to the surface.


    Are you saying we cannot correct for solar irradiance influence even if we have satellite measurements of it, or for volcanic aerosols influence even if we see them raising the optical density of atmosphere? Why should it be so?

  18. AT Grzegorz Staniak: “….As you can see, the three factors plus “a linear warming trend of 0.14°C per decade” together explain 3/4 of GAT variance in the last three decades.”

    COMMENT: Maybe so…but then one reads about ‘name’ “climate scientists” like Trenberth & so-called “missing heat” (akin to Hansen’s remarks about unobserved heat “in the pipeline”), neverminding the peculiar terms (analogies?) used, these remarks & follow-up studies and follow-up papers & related public domain remarks point to a consistent pattern: every time the experts find an explanation things turn out different & they go scrambling back.

    The quote excertped above to a particular paper addresses one of many such heat explanations…that, in light of other reporting, just keeps reinforcing that the models are deficent. Here’s a recent report about a study that “found” more missing heat:

    Its like the little boy that cried wold too often, only these climatologists keep ‘crying’ “eureka, we’ve figured it out this time” (so-to-speak) and then keep having to go back and address very very very fundamental issues they didn’t even know about.

    PERHAPS the biggest thing that undermines the arguments of climatologists, for many of us, is their approach: upon any disagreement or countervailing finding they immediate assert it is wrong, that subsequent findings will repudiate it, they’ll quote prior studies made with skimpy data, and more than anywhere else resort to personal attacks, slander, ad-hominem, etc. Forbes has an assessement of the latter at:

    Thing is, such approaches are EXACTLY the same as those employed by Jeff Skilling & Andy Fastow as they endeavored to dupe the public, Wall Street and their own employees about the financial fakery upon which they were building Enron.

    Watch, or read a transcript of, “Enron: The Smartest Guys in the Room” — and skip to the parts where Skilling’s & Fastow’s tactics are described. Their statements can be, with only minor edits, be converted to commonplace remarks by climate scientists confronted by contradictory findings. Same for how CEO B. Ebbers maniuplated Worldcom to bankruptcy.

    The same tactics employed by Enron’s & Worldcom’s fraudsters are readily observed, repeatedly, from ‘name’ climate scientists. Certainly, ad hominem & related combative tactics of precisely the same sort repeatedly associated with large-scale fraudsters — approaches that routinely sidestep direct rebuttal of the findings on purely scientific terms — have no place in real science. Science thrives on findings, challenges, new discoveries and theories revamped as new findings are learned, not a cult-like domatic adherence to some belief orthodoxy that refuses to admit new information.

    There’s another corrollary anti-science pattern routinely observed among “climate scientists” — their reflexive endorsement of shoddy research that supports their orthodoxy when that research ought to be dismissed for its lack of rigor. Accepting the “right” answer for the wrong, or no good, reason(s) is not science, its blind faith. True science builds on merit & objectivity, not blind acceptance of whatever happens to come along that conforms to the doctrine du jour–that’s the realm of religion. But it too is commonly observed among the climate science industry.

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