ABSTRACT: Despite the existence of a clear scientific consensus about global warming, opinion surveys find confusion among the American public, regarding both scientific issues and the strength of the scientific consensus. Evidence increasingly points to misinformation as a contributing factor. This situation is both a challenge and an opportunity for science educators, including geographers. The direct study of misinformation–termed agnotology (Proctor 2008)–can potentially sharpen student critical thinking skills, raise awareness of the processes of science such as peer review, and improve understanding of the basic science. This potential is illustrated with examples from a small, upper-division collegiate weather and climate class. Key Words: global warming, agnotology, misinformation, active learning

Need I say more? Oh, other than noting his good taste in reffing me :-?.

Well all right I will. They say (quoting only the headers):

Specific learning outcomes addressed by using agnotology are as follows:

1 Understanding the true nature of the scientific consensus on global warming
2 Understanding the processes of scientific inquiry
3 Strengthened critical thinking skills
4 Strengthened understanding of the basic science of weather and climate

I’m entirely happy with the last 3. I’m (obviously) also quite comfortable with the first point in general, but I think I’d rather they hadn’t included it in that list. 2 and 3 are general skills which anyone who is trying to make up their own minds needs; 4 is valuable in and of itself; 2-4 could be agreed on by all “sides”. 1 is a bit too specific to this particular debate, and too easily seen as advocacy (though the accompanying text, Assessing the veracity of agnogenesis claims about the scientific consensus requires looking at the documented evidence, and determining the value of the evidence presented (such as interviews or speeches compared with peer-reviewed research) could just about be considered neutral, if you were being understanding. At the least, I’d have demonted 1 to the end of the list as more of an end-result of applying 1-3.


* Agnotology as a Teaching Tool: Learning Climate Science by Studying Misinformation, Daniel Bedford, Journal of Geography, 109: 4, 159 — 165, DOI: 10.1080/00221341.2010.498121
* New Moon On Monday
* A nice story about trust, from Timmy
* Link to a screen capture of me on the front page :-)


  1. #1 John McManus

    While I knew about global warming in a vague way, Climategate propted me to work at it a bit.

    A blog I read that reported stuff from Asia, Africa, thje Balkans ( but has since descended into irrelavance) reported the emails and caught my eye. Googling around produced statements so incredulous I had to read the entire cantents of the hack. No context, no continuity, no information. It didn’t take long to figure that everything at places like WTFUWT, Bishops Swill, 2 Pielkies in a Baggie etc was a lie.

    The misinformation made it necessary to find the truth. Lots of reading, both peer reviewed literature and commentary.

    I like to think the misinformation made me think more critically. If not at least I now like stoats.

  2. #2 anon

    Part of the problem is that popular science writers, trying to get everyone to understand science, often simplify it to the baby-talk level so that the densest, least educated, most superstitious cretin can grasp it.

    That’s nice — until the cretin assumes that that’s all there is to it. Then he wonders why we’re paying all these fancy scientists so much to figure out things that are so obvious, and we get atrocities like Senator Proxmire’s Golden Fleece awards. Or he finds some weakness in the baby-talk translation, and assumes that that weakness also exists in the actual theory, and we get questions like “why are there still monkeys?” or “why is there so much snow if the climate is warming?”

    One of the key pieces of misinformation is the idea that science is simple enough for the majority of people to understand. If people could be convinced that their grade-school knowledge of science does not qualify them to argue with real scientists, we’d all be better off.

  3. #3 AJ

    The problem with Climate Science is that it is, by definition, science in slow motion. It’s fine to have models tuned to the past, but we have to wait decades to see if they can be falsified by future observations. In the meantime, however, those old projections will have been made irrelevant by new projections. We are always assessing a moving target that, in practice, can’t be falsified.

  4. #4 Steve Bloom

    I take the first one as meaning understanding what the claims of the consensus actually are (their “true nature”) as opposed to how they may have been misrepresented. As such, it does seem to be a reasonable starting point.

  5. #5 Chris O'Neill


    We are always assessing a moving target that, in practice, can’t be falsified.

    So if something is never falsified, then it’s unfalsifiable. I don’t think that’s the point of unfalsifiability. If the theory is correct then it will never be falsified. That’s not the same thing as unfalsifiable.

  6. #6 Neal J. King


    Climate science does not depend solely on the models: The models just make explicit the implications of many lines of scientific inquiry into cause & effect that have taken place over 100 years of study into the origins of the ice age and other climate variations; and into the physical mechanism of atmospheric phenomena (gas concentration variations, heat radiation, etc.).

    There is a great deal of science that has gone into the climate models, but most of it can be studied and verified (= tested and NOT found to be falsified) even without the models, because it is tied up very closely with physical principles that have been tested in so many other ways.

  7. #7 AJ

    Chris O’Neill:

    Thank-you for your semantic clarification of my semantics. My point is that, due to the slow motion nature of climate change, the projections are unfalsifiable in the short term and in the long term the old projections become irrelevant. For instance, how much debate do we hear regarding IPCC AR1 (1990)? This is a recipe for never ending debate, similar to the debate between Keynesians and Monetarists in economics, with the corresponding accusations of misinformation on both sides.

    Neal J. King:

    Models are a synthesis of the many disparate cogs in the climate machine. Individually these cogs can’t tell us very much about where the climate is heading. That is why the IPCC relies so heavily on the models in making their projections. Although the basic physics of the individual cogs is understood, there is a fair amount of uncertainty about their parameterization which, in the big chaotic picture, can lead to significant error propagations. There is also the matter of observational uncertainty, with respect to time resolution, spatial coverage, attribution, and error bars, which limits our ability to constrain parameter bounds.

  8. #8 chris

    AJ: re: “That is why the IPCC relies so heavily on the models in making their projections”

    Any projection is a model by definition. One can’t say anything about the future without recourse to a model.

    [I'm sympathetic to that -W]

    So I think your statement rather denigrates (IPCC) projections by unnecessarily insinuating a generalized deficiency in models. It really depends on what specifically you’re attempting to project in your projection!

    For example, given a “business-as-usual” emissions scenario leading to a [CO2] of 650 ppm in 2100 (say), we can estimate a global temperature range consistent with a climate sensitivity between 2 and 4.5 oC, and a time constant for the more rapid response times of the climate system (somewhere between 7 and 20 years say; ‘though I’m sure expert opinion would provide a better supported range than that).

    I expect that “model” would give a temperature range (at say 95% confidence) similar to that of the climate models that you seem to be damning by insuination of hopeless complexity. In other words although the climate and climate models might be “a synthesis of the many disparate cogs in the climate machine” (whatever that means!), in reality the particular parameter of interest (likely global temperature at some future time according to some emission scenario) doesn’t have to be finessed by some complex summation of all those “cogs”!. We don’t really need a “climate model” (a GCM) at all….though of course we do need a model since we’re talking about a yet-to-be-realized future…

  9. #9 AJ


    Leaving aside your post’s petty nit-pickiness (BTW… By “model” I meant a “Coupled atmosphere-ocean GCM” and not simply a “GCM” ;>), I would like to clarify a couple of points. I actually hold the belief that the models, in general, are deficient. I’m sorry you thought I was merely insinuating this. However, I wouldn’t describe my skepticism of the models as “damning by insuination of hopeless complexity”. Specifically I would merely urge caution in placing too much faith in the AR4 model projections due the the lack of a quality observational dataset of oceanic variables. That is, a quality ARGO network dataset was not available at the time that the projections were generated. My own comparisons of ARGO vs. the models do not give me a particular “warm and fuzzy” about their reliability. Granted there is a high likelihood that my amateur analysis is either flawed or immaterial. For AR5, however, I expect to see a more in-depth discussion of the models vs. observations. Perhaps at that time my opinion may change, even in the face of other uncertainties. How is this related to misinformation? It is my belief that there is a large percentage of the population who are under-informed about the uncertainties involved and accept projections on blind faith.

  10. #10 Geoff Wexler

    Re : #3

    I think your final sentence is false, especially if it applies to some key conclusions, such that the warming will continue. Your premise that things change too fast to test , would only apply for a rapidly varying boundary condition e.g. if the CO2 concentration were to go down as well as up during a decade.

    I am also unsure about the first sentence.

  11. #11 AJ

    Re: #10

    Fair enough, you have your opinion and I have mine. I guess it comes down to what metric is being used. Suppose we use, as some have suggested, the accumulation of heat in the oceans. It’s only been in the last ten years that we have had the ARGO network and the trends to date have been lower than AR4 model projections. That doesn’t mean that the models are wrong, it could be that we are just in the downward phase of some multidecadal oscillation. It could be that the heat is hiding in a blind spot such as under the Arctic ice or perhaps in the deep ocean. To test the models we will have to wait for a longer sampling period or for more spatially complete observations. By the time this data becomes available, there will be newer model projections and the AR4 projections will be irrelevant.

    Perhaps at some point in the future we will have enough high quality observational data to test the models adequately. To date, however, I don’t believe we have reached that point. Given the uncertainties, if one holds the belief that climate change will be benign or catastrophic, then it is my belief that it is based on faith and not hard science. So basically we have a religious debate, with each side accusing the other of misinformation.

  12. #12 chris

    You’re missing the point again AJ. Neil J King explained it and I did too in some detail. You’ve not bothered to address our posts and are repeating a sort of vague mantra of “model deficiency”. I could repeat the point Neil and I both made: if one is interested in fundamental responses of the earth climate system to enhanced greenhouse forcing one really doesn’t need a GCM. Of course one needs a model since (as I’m sure you’d agree) one cannot consider the future otherwise. But a model based on understanding of the physics of the greenhouse effect together with a large amount of empirical and paleo-data that gives us insight into the likely probability distribution of climate sensitivities relative to multidecadal timescales is sufficient to make future projections that are consistent with our understanding. Of course we’d expect our GCM’s also to be consistent with this (they use the same physics). But the fact that the climate system and GCM’s are complex (“a synthesis of the many disparate cogs”!) doesn’t mean that the broad responses aren’t predictable given some consideration of emission scenarios etc.

    And there seems to be abundant comparisons of model projection with empirical observation. The earth is warming under the influence of enhanced greenhouse forcing much a models predict with enhanced land, ocean and tropospheric warming, rising tropospheric water vapour, polar amplification of warming (with the Arctic leading the Antarctic as predicted) and stratospheric cooling. Oceans are accumulating heat, and the latitude-specific changes in precipitation trends seem to be occurring much as predicted. It’s instructive that the model prediction of enhanced tropospheric warming has held up during a 15 year period in which a rather incompetent analysis of satellite MSU was seemingly pointing in the other direction. So clearly our broad understanding of the physics of climate response to forcing can be considered robust up to now. The fact that the physics that informs us on these points can be usefully encapsulated within computational models doesn’t suddenly turn consideration of model projections into “blind faith” as you describe it – that seems an unnecessary insult to those that make an effort to understand this stuff.

    You made a more specific point on ocean heat content (OHC), but it seems an illogic one to me. The fact that there is considerable uncertainty in recent OHC measures needn’t of itself increase our uncertainty in the reliability of the models. After all the story of tropospheric temperature measurements should give us some confidence that our physics understanding means that we don’t have to go weak at the knees in the face of some so-far-unresolved problems with empirical measurements. New uncertainties at the growing ragged edges of research subjects (where scientists like to play!) don’t drive out higher level certainties! And if one considers the (upper 700 metres) accumulated heat predicted from GCM’s the data in Levitus (2009) [Geophys. Res. Lett. 36, L07608], for example, rather supports the expected OHC through around 2008 I believe. There has been an apparent slow down in accumulated heat in the upper ocean, with some evidence for accumulation in the deeper oceans. We know that the last several years have seen a couple of significant La Nina’s and an anomalously extended solar minimum, so we aren’t surprised if accumulated OHC has slowed somewhat. But these are stochastic (ENSO) and contingent (solar variability) events that are either unpredictable or only so as time averages. So we certainly don’t wring our hands about the deficiencies of models when we encounter divergence between model projections and empirical observations over short periods.

    That’s not to say that there aren’t uncertainties. But we do need to be a little more specific about what exactly we are either complaining about….or congratulating ourselves over!

  13. #13 Daneel Olivaw

    I was tangentially aware of anthropogenic climate change as a science enthusiast and was caught off guard when I read, years ago, that it was a fraud. Then I started reading up on the science and was captivated by it. The depth of knowledge about our climate is impressive, IMHO. I would never have thought that we could pin point, far beyond any reasonable doubt, the excess CO2 to humans using isotopes or that we could detect any “fingerprint” that prove that the change is anthropogenic.
    Not only I learned a lot thanks to climate deniers but also decided to study Atmospheric Sciences at Uni. So there, contrarians did something good :)

  14. #14 AJ


    The reason I did not address the model issue is because it’s an obvious point. So much so that Captain Obvious himself would be very proud. My favorite simple model for projecting future global mean temperatures is simply to fit a second order polynomial to the temperature record. The reasoning being that we can also fit a similar curve to the co2 forcing. Taking the derivative we find that the rate of input into the heat pipeline is increasing linearly. In order for my projections to work then the rate of heat coming out of the pipeline would also have to be increasing linearly. Obviously this model is very simplistic and can be rightly criticized for ignoring several factors such as the slow response. I don’t believe my model will be given any consideration in AR5.

    I agree with many of the points you raise concerning agreement between the models and atmospheric observations. These are characteristics that, at this point, are mostly influenced by the fast response. I’m more interested in the slow response. To determine if the models are correct we have to look at the oceans. My comparisons of the ARGO vs. the models left me unimpressed. I intentionally left the details vague as I am an amateur and did not want to be accused of spreading misinformation;) Suffice to say that my skepticism of the models is actually based on a quantitative analysis and not simply a knee-jerk reaction.

    Your dismissing of an “incompetent analysis of satellite MSU”, however, brings up an issue related to agnotology. That is, willfully ignoring information that does not agree with your beliefs. I believe this condition is endemic to both sides of this debate and is worth further investigation.

    Lastly, you seem to be suffering from a condition that make analogies incomprehensible to you. Please read my offending sentence as “Models are a simulation of the many integrated processes in the climate system.”

  15. #15 chris

    O.K. AJ, not sure if we’re going to see eye-to-eye, and I’m still not certain what your specific problems with models other than that it seems your “comparisons of the ARGO vs. the models” left you “unimpressed”. It’s possible that there’s a problem with your “comparisons”, of course, ‘though you seem to be deliberately vague about this so there’s not much more we can say.

    I do want to comment on this though:

    ”Your dismissing of an “incompetent analysis of satellite MSU”, however, brings up an issue related to agnotology. That is, willfully ignoring information that does not agree with your beliefs.”

    But once again you’re misrepresenting the difference between belief and evidence. Your posts are littered with assertions that generalised confidence in model projections are the result of ”faith” or ”blind faith” or ”opinion” and that discussion of these issues amounts to ”a religious debate” and etc.

    But the confidence we might have in the projections of models comes not from ”blind faith”, but because the physics underlying the models has been shown to be so far robust, the uncertainties in the parameterizations notwithstanding. And one dismisses “incompetent analysis of satellite MSU” data, not due to “willfully ignoring information that doesn’t agree with” “beliefs”, but because the analysis of satellite MSU data was clearly, objectively and undeniably incorrect to the extent of incompetence (see bottom of post for the evidence that justifies this assertion). The empirical analyses were hopelessly wrong; the models were largely correct.

    AJ the fact that you consider assessment of model projections a matter of “belief”, “blind faith” and “opinion” rather than a matter of attention to the evidence, and straightforward recognition of flawed science to be “willfully ignoring information that does not agree with your beliefs” is going to be a massive hindrance to your understanding…
    Two scientists, Roy Spencer and John Christy at Uni Alabama spent 15 years getting the analysis of tropospheric temperatures from analysis of satellite microwave sounding units (MSU) hopelessly wrong and thus causing a certain amount of confusion. Already in 1991 it was pointed out [1] that their analyses were insufficient to distinguish the cooling they would soon try to sell from warming that would be consistent with surface measurements and models. It was repeatedly left to other to sort out the various messes in the analysis of MSU data: that the method of averaging different satellite records introduce a spurious cooling trend [2], that disregard of orbital decay introduced another spurious cooling trend [3]; that MSU-2 showed a spurious cooling trend due to spillover of stratospheric cooling into the tropospheric temperature signal [4], and later still that the diurnal correction applied by Christy and Spencer was of the wrong sign and gave yet another spurious cooling trend [5]. In the end these two had no choice but to accept that they’d messed up big time

    [1] B.L. Gary and S. J. Keihm (1991) Microwave Sounding Units and Global Warming Science 251, 316.
    [2] J. W. Hurrell & .K E. Trenberth (1997) Spurious trends in satellite MSU temperatures from merging different satellite record. Nature 386, 164 – 167.
    [3] F. J. Wentz and M. Schabel (1998) Effects of orbital decay on satellite-derived lower-tropospheric temperature trends. Nature 394, 661-664.
    [4] Q. Fu et al. (2004) Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends Nature 429, 55-58.
    [5] C. A. Mears and F. J. Wentz (2005) The Effect of Diurnal Correction on Satellite-Derived Lower Tropospheric Temperature, Science 1548-1551.

  16. #16 Alan Bryant

    I have never understood why it is necessary to communicate/convey/train anyone to view something
    the way I view something.
    It seems ridiculous if you ask me.
    Either someone sees it your way or they don’t.
    Most of the time, intelligent folks reach a conclusion
    based on research and debate.
    That was how I was raised.
    Take climatology for instance.
    30 years ago it was global cooling.

    [You're wrong: and innumerable other such pages. If you're still capable of believing as you don't, it is because you haven't tried to be better informed -W]

    Now its global warming.
    Some scientist touts his theory and a few years later, the science just comes up wrong.
    How will CAGW play out in history books 50 years from now?
    Who will be branded as fools and who’s the hero’s?
    Hard to say if you ask me.
    What I find intriguing is this notion that there is no need for debate, yet posters are confronted with having to deal with skeptics that disagree with the theory of man-made global warming.
    If the science was settled, then why do professors from MIT disagree with CAGW?
    I don’t understand any of this.

    [Yes, I think that is true. You need to become better informed, that is the solution to your failure of understanding -W]

    Lets get this debate out of the way and be done with it.

  17. #17 Geoff Wexler

    Conflict between observations and theory {(e.g.) the now obsolete one about the lack of warming of the troposphere.}.

    Sometimes the trivial needs to be restated.
    The assumption that observations should always trump theory is wrong. It neglects the fact that ‘theory’ is often the summary of a lot of earlier observations, which have been repeated many times, whereas the observations themselves rely on some new ‘theoretical’ work involving calculations and assumptions, which have not been repeated in an analogous way. It would be an interesting project to collect examples of this. Each case has to be judged on its merits.

    [Memories are short: -W

  18. #18 Richard Simons

    Alan: The 1970s thoughts of global cooling were not particularly wrong. At the time, there were increasing levels of aerosols in the atmosphere, reducing global temperatures. There was also a paper saying that, if all else stayed the same, in 15,000 (or possibly 120,000) years Earth would enter another ice age. These were picked up by a news magazine, the assumptions and the time scale were ignored and it turned into ‘New Ice Age Imminent!’

    Since then, pollution controls have reduced the amount of aerosols and the rising concentration of CO2 is swamping any effect due to the Milankovitch cycles.

    why do professors from MIT disagree with CAGW?

    What exactly do you mean by CAGW? The term is only used by people who would like to deny that climate change is taking place but are unable to do so because the evidence is now so overwhelming so they now resort to claiming that it will have little effect on human society.

    I don’t understand any of this.

    At heart, it’s quite straightforward. Energy reaches Earth’s surface from the sun, passing through the atmosphere. Some of the energy leaving Earth gets absorbed, mainly by water vapour and CO2 acting rather like a sleeping bag. The amount of CO2 in the atmosphere has increased substantially due to human activity, reducing the energy leaving Earth and thus increasing its temperature. This has all been well established for at least the past 50 years.

    Denialists (not skeptics as they never question those who support their views) are rather vague on what they believe. When quizzed, most either avoid answering or express the hope that there is some massive (as yet unknown) negative feedback mechanism.

  19. #19 deconvoluter

    These were picked up by a news magazine

    in the U.S., and by Nigel Calder in the UK, who has built a half a career out of amplifying such ideas.{ This is W’s territory, but I see that NC has just repeated this forecast… no mention of aerosols mind you}.

  20. #20 Marco

    Alan Bryant, I can find professors at reputable institutes which do not support evolution, but rather “intelligent design” (Philip Johnson, a retired Berkeley professor) or even creationism (Roy Spencer comes to mind).

    No surprise finding an ideological hack at MIT, then.