Quantifying uncertainty in AGW

My friends, I need your help.

I have written a paper on quantifying the uncertainty of effects due to global warming, but the subject is too big for one person. Nevertheless, I have tried to—in one location—list all of the major areas of uncertainty, and I have attempted to quantify them as well. I would like your help in assessing my guesses. I am not at all certain that I have done an adequate or even a good job with this.

At this link is the HTML version of the paper I am giving in Spain (I used latex2html to encode this; it is not beautiful, but it is mostly functional).

At this link is the PDF version of the paper, which is far superior to the HTML. This paper, complete with typos, is about draft 0.8, so forgive the minor errors. Call me on the big ones, though.

I would like those interested to download the paper, read it, and help supply numbers for the uncertainty bounds found within. I would ask that you not do this facetiously or glibly, or that you not purposely underestimate the relevant probabilities. I want an open, honest, intellectual intelligent discussion of the kinds and ranges of uncertainties in the claims of effects due to global warming. For example, the words “Al Gore” should never appear in any comment. If you have no solid information to offer in a given area, please feel free to not comment on it.

The abstract for the paper is

A month does not go by without some new study appearing in a peer-reviewed journal which purports to demonstrate some ill effect that will be caused by global warming. The effects are conditional on global warming being true, which is itself not certain, and which must be categorized and bounded. Evidence for global warming is in two parts: observations and explanations of those observations, both of which must be faithful, accurate, and useful in predicting new observations. To be such, the observations have to be of the right kind, the locations and timing where and when they were taken should be ideal, and the measurement error should be negligible. The physics of our explanations, both of motion and e.g. heat, must be accurate, the algorithms used to solve and approximate the physics inside software must be good, chaos on the time scale of predictions must be unimportant, and there must be no experimenter effect. None of these categories is certain. As an exercise, bounds are estimated for their certainty and for the unconditional certainty in ill effects. Doing so shows that we are more certain than we should be.

My conclusions (which will make more sense, obviously, after you have read the paper) are

Attempting to quantify, to the level of precision given, the uncertainties in effects caused by global warming, particularly through the use of mathematical equations that imply a level of certainty which is not felt, can lead to charges that I have done nothing more than build an AGW version of the infamous Drake equation (Drake and Sobel 1992). I would not dispute that argument. I will claim that the estimates I arrived at are at least within an order of magnitude of the actual uncertainties. For example, the probability that AGW is true might not be 0.8, but it is certainly higher than 0.08.

The equations given, then, are not meant to be authoritative or complete. Their purpose is to concentrate attention of what exactly is being asked. It is too easy to conflate questions of what will happen if AGW is true with questions of is AGW true. And it is just as simple to confuse questions of the veracity and accuracy of observations and with the accuracy of the models or their components. People who work on a particular component are often aware of its boundaries and restrictions, and so are more willing to reduce the probability that this component is an adequate description of the physical world, but they are usually likely to assume that the areas on which they do not have daily familiarity are more certain than they are. Ideally, experts in each of the areas I have listed should supply a measure of uncertainty for that area alone. I would welcome a debate and discussion on this topic.

I also would not make the claim that I have accurately listed all the avenues where uncertainty arises (for example, I did not even touch on the uncertainty inherent in classical statistical models). But the ones I did list are relevant, though not necessarily of equal importance. We do have uncertainty in the observations we make and we do have uncertainty in the models of these observations. At the very least, we know empirically that we cannot predict the future perfectly. Further, the claims made about global warming’s effects are also uncertain. Taken together, then, it is indisputable that we are less certain that both global warming and its claimed effects are true than in either AGW or its effects alone.

Thanks everybody.


  1. Alas, I am not a statistician, but Climate Audit has many. I just checked and your blog is not yet listed over there. I suggest that if you want a really careful vetting of your paper by a lot of really critical intellects, you announce yourself in the latest unthreaded thread. I’m sure you’ll get lots of useful criticism.

  2. Hi. Before I download the document, of which I will not likely be able to contribute much since I am not a scientist, I would like to note something about the abstract as written above. While the abstract mentions GW several times, it does not mention AGW at all. The conclusion is primarily about AGW until the last sentence.

    Noting the recent work by Anthony Watts to confirm a possible 66 year cycle, the concept of even GW may not be as secure as we are lead to believe. That is beyond the uncertainty with proving the subset of AGW.

    John M Reynolds

  3. What is the target audience? Is this paper for your blog or are you intending to send it to a scientific journal? If the latter, then (if I may be blunt) it needs some work, as the paper seems to be written in a rather relaxed ‘bloggy’ style in places. What do you mean by “the probability that AGW is true”? You say “significant at 2xCO2”, but then, sadly, “I leave the term `significant’ undefined for now”.

    Well to answer your question, one number that is completely wrong is Pr{chaos not important} = 0.9. A figure of 0.2 might be a better estimate.

    The irregular oscillations of the climate over many different time scales, combined with the failure of prediction (eg the recent leveling off) are strong signatures of chaos.

    Your reference Orrel (2005) is not the best – that paper is about weather not climate, and uses some very simple toy model equations which have no relation to weather or climate. A better choice would be Palmer (2000), cited by Orrel. That paper even has ‘uncertainty’ in the title so you should like it!

    Unfortunately a google search for “climate chaos” turns up mostly junk (as is the case for either of these words alone!) a good example being the blog
    where I have just posted a comment.

    Here are some that appear to be sensible-

  4. Paul,

    Thanks for your comments. I have done a poor job defining significant, no question about it. I tried to state it in terms of the IPCC forecast, which would certainly be significant. The problem is that I want to avoid circularity: AGW can’t just be significant if and only if it causes some undesirable effect.

    I am also trying not to be too specific on any one item because, obviously, you can write papers and papers on any of them. I was aiming more for a review-paper type thing.

    Also no question that it needs a lot of work. I agree with your Orrell comment, and included that book only because of the many references therein.

    Lastly, your guess that
    Pr{chaos not important} = 0.2
    is welcome.

    Thanks again.

  5. Matt:
    Do you have a due date? This weekend is for taxes – which like death are certain or as close to certain as makes no difference – so I will not be able to seriously look at your article until later next week.
    Still if all the comments are as candid and specific as Paul’s mine may be superfluous by that time.
    Good luck.

  6. The paper uses probabilities to describe the uncertainties that could arise in the three sequential stages in a statistical modeling: The first stage, measurement, determines what the variables are and how those variables are to be measured. The second stage, model building, determines what model is to be employed to fit the data. The third stage, inference and prediction, determines the interpretation and usage of the model.

    The conclusion made by B&P reminds me of an example I often use in my class: “kids who can play piano do better in school”. This statement should not imply that there are no other factors (or measurements) that could possibly affect a kid’s performance in school (stage 1). Nor does it imply that the model employed to make such conclusion (stage 2) is perfect. Moreover, one cannot conclude a causal relation (stage 3) between “being able to play piano” and “doing better in school”.

    And students ask ?Do your kids play piano?? Yes, they do? just in case there is indeed a causal relation.

  7. Yes, I would think a review by the people familiar with this area at Climate Audit would probably be just the thing you’re looking for.

  8. 1. Too informal for a published paper.

    2. I suggest doing a lit review, rather than relying too much on blog authors for probabiliites.

    3. I think it will be very tricky to do much useful wrt Prob AGW, but perhaps the attempt will have some use.

    4. I do think a review paper of ill (anjd good) effects which gives probabilites as well as source references has some usefulness.

    5. Seems awfully long-widned in making the asic point (which I have also noticed) of the prevalent confusion of ill effects depenadnt on AGW with AGW iteslf.

    6. It actually lacks concrete examples of the sin in comment 5.

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