William M. Briggs

Statistician to the Stars!

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The statistics of both climatology and meteorology.

An Ensemble Of Models Is Completely Meaningful, Statistically: Update 2

Robert Brown

The Answer to Senn will continue on Monday. Look for my Finger Lakes winery tour tasting notes Sunday!

Several readers asked me to comment on an ensemble climate forecasting post over at Anthony’s place, written by Robert G. Brown. Truthfully, quite truthfully, I’d rather not. I am sicker of climate statistics than I am about dice probabilities. But…

I agree with very little of Brown’s interpretation of statistics. The gentleman takes too literally the language of classical, frequentist statistics, and this leads him astray.

There is nothing wrong, statistically or practically, with using “ensemble” forecasts (averages or functions of forecasts as new forecasts). They are often in weather forecasts better than “plain” or lone-model predications. The theory on which they are based is sound (the atmosphere is sensitive to initial conditions), the statistics, while imperfect, are in the ballpark and not unreasonable.

Ignore technicalities and think of this. We have model A, written by a group at some Leviathan-funded university, model B, written by a different group at another ward of Leviathan, and so on with C, D, etc. through Z. Each of these is largely the same, but different in detail. They differ because there is no Consensus on what the best model should be. Each of these predicts temperature (for ease, suppose just one number). Whether any of these models faithfully represents the physics of the atmosphere is a different question and is addressed below (and not important here).

Let’s define the ensemble forecast as the average of A through Z. Since forecasts that give an idea of uncertainty are better than forecasts which don’t, our ensemble forecast will use the spread of these models as an idea of the uncertainty.

We can go further and say that our uncertainty in the future temperature will be quantified by (say) a normal distribution1, which needs a central and a spread parameter. We’ll let the ensemble mean equal the central parameter and let the standard deviation of the ensemble equal the spread parameter.

This is an operational definition of a forecast. It is sane and comprehensible. The central parameter is not an estimate: we say it equals the ensemble mean. Same with the spread parameter: it is we who say what it is.

There is no “true” value of these parameters, which is why there are no estimates. Strike that: in one sense—perfection—there is a true value of the spread parameter, which is 0, and a true value of the central parameter, which is whatever (exactly) the temperature will be. But since we do not know the temperature in advance, there is no point to talking about “true” values.

Since there aren’t any “true” values (except in that degenerate sense), there are no estimates. Thus we have no interest in “independent and identically distributed models”, or in “random” or “uncorrelated samples” or any of that gobbledygook. There is no “abuse”, “horrendous” or otherwise, in the creation of this (potentially useful) forecast.

Listen: I could forecast tomorrow’s high temperature (quantify my uncertainty in its value) at Central Park with a normal with parameters 15o C (central) and 8o C (spread) every day forever. Just as you could thump your chest and say, every day from now until the Trump of Doom, the maximum will be 17o C (which is equivalent to central 17o C and spread 0o C).

Okay, so we have three forecasts in contention: the ensemble/normal, my unvarying normal, and your rigid normal. Who’s is better?

I don’t know, and neither do you.

It’s likely yours stinks, given our knowledge of past high temperatures (they aren’t always 17o C). But this isn’t proof it stinks. We’d have to wait until actual temperatures came in to say so. My forecast is not likely much better. It acknowledges more uncertainty than yours, but it’s still inflexible.

The ensemble will probably be best. It might be, as is usually the case with ensemble forecasts, that it will evince a steady bias: say it’s on average hot by 2o C. And it might be that the spread of the ensemble is too narrow; that is, the forecast will not be calibrated (calibration has several dimensions, none of which I will discuss today; look up my pal Tilmann Gneiting’s paper on the subject).

Bias and too-narrow spread are common failings of ensemble forecasts, but these can be fixed in the sense that the ensembles themselves go into a process which attempts a correction based on past performance and which outputs (something like) another normal distribution with modified parameters. Don’t sniff at this: this kind of correction is applied all the time to weather forecasts (it’s called MOS).

Now, are the original or adjusted ensemble forecasts any good? If so, then the models are probably getting the physics right. If not, then not. We have to check: do the validation and apply some proper score to them. Only that would tell us. We cannot, in any way, say they are wrong before we do the checking. They are certainly not wrong because they are ensemble forecasts. They could only be wrong if they fail to match reality. (The forecasts Roy S. had up a week or so ago didn’t look like they did too well, but I only glanced at his picture.)

Conclusion: ensemble forecasts are fine, even desirable since they acknowledge up front the uncertainty in the forecasts. Anything that gives a nod to chaos is a good thing.

Update Although it is true ensemble forecasting makes sense, I do NOT claim that they do well in practice for climate models. I also dispute the notion that we have to act before we are able to verify the models. That’s nuts. If that logic held, then we would have to act on any bizarre notion that took our fancy as long as we perceived it might be a big enough threat.

Come to think of it, that’s how politicians gain power.

Update I weep at the difficulty of explaining things. I’ve seen comments about this post on other sites. A few understand what I said, others—who I suspect want Brown to be right but aren’t bothering to be careful about the matter—did not. Don’t bother denying it. So many people say things like, “I don’t understand Brown, but I’m going to frame his post.” Good grief.

There are two separate matters here. Keep them that way.

ONE Do ensemble forecast make statistical sense? Yes. Yes, they do. Of course they do. There is nothing in the world wrong with them. It does NOT matter whether the object of the forecast is chaotic, complex, physical, emotional, anything. All that gibberish about “random samples of models” or whatever is meaningless. There will be no “b****-slapping” anybody. (And don’t forget ensembles were invented to acknowledge the chaotic nature of the atmosphere, as I said above.)

Forecasts are statements of uncertainty. Since we do not know the future state of the atmosphere, it is fine to say “I am uncertain about it.” We might even attach a number to this uncertainty. Why not? I saw somebody say something like “It’s wrong to say our uncertainty is 95% because the atmosphere is chaotic.” That’s as wrong as when a rabid progressive says, “There is no truth.”

TWO Are the ensemble models used in climate forecasts any good? They don’t seem to be; not for longer-range predictions (and don’t forget that ensembles can have just one member). Some climate model forecasts—those for a few months ahead—seem to have skill, i.e. they are good. Why deny the obvious? The multi-year ones look like they’re too hot.

If that’s so, that means when a fervent climatologists says, “The probability the global temperature will increase by 1 degree C over the next five years is 95%” he is making a statement which is too sure of itself. But that he can make such a statement—that it makes statistical sense to do so—is certain.

If you don’t believe this, you’re not thinking straight. After all, do you not believe yourself that the climatologist is too certain? If so, then you are equivalently making a statement of uncertainty about the future atmosphere. Even saying, “Nobody knows” is making a statement of uncertainty.

See the notes below this line and in my comments to others in the text.


1I pick the normal because of its ubiquity, not its appropriateness. Also, probability is not a real physical thing but a measure of uncertainty. Thus nothing—as in no thing—is “normally distributed”. Rather we quantify our uncertainty in the value of a thing with a normal. We say, “Given for the sake of argument that uncertainty in this thing is quantified by a normal, with this and that value of the central and spread parameter, the probability the thing equals X is 0.”

Little joke there. The probability of the thing equaling any—as in any—value is always and forevermore 0 for any normal. Normal distributions are weird.

The Consensus On Global Cooling

Look at that irreversible, plunging trend!

Look at that irreversible, plunging trend!

It is an interesting exercise to read press reports of the Consensus. The Consensus as was, not as is. The Consensus as of 1975, when the sky was literally going to fall, frozen into a giant blue cube and killing, oh, just about everything.

Reader Jim Fedako sent in the 28 April 1975 Newsweek article “Our Cooling World” by Peter Gwynne. The hyperbole then is the same as now: “serious political implications for just about every nation on earth,” “The drop in food production could begin quite soon,” “devastating outbreak of tornadoes”, “national boundaries make it impossible for starving peoples to migrate from their devastated fields,” and so forth.

Nothing but dead, dying, and soon-to-be suffering everywhere, with subtle lamentations for the (as-yet?) non-existent one-world government (“national boundaries…”). Given the similarity with news reports of today, it suggests activists have a limited palate of horrors and hobgoblins with which to terrorize, trotted out with depressing regularity. All that was missing were threats of sea-level rise. Why were there no reports then of an increase in beach property? Ocean water would have been sucked up in glaciers, see.

It was a Consensus, incidentally. “Meteorologists disagree about the cause and extent of the cooling trend…But they are almost unanimous in the view that the trend will reduce agricultural productivity for the rest of the century.” Note “almost unanimous”, which equaled ninety-seven or so percent of meteorologists—here curiously defined as people expert in weather and agriculture.

The distinctions between the old Consensus and the new one? The old Consensus was formed by meteorologists; now it’s climatologists. Though most are true believers, meteorologists are now among the prominent defectors from the current Consensus. Why? In 1975 climatology was only beginning to be a separate field, complete with their own grants (i.e. money from government), conferences in exotic locations, and journals in which to publish papers few would read.

Then, scientists were not agreed why the world was nearing a “tipping point”; that frightening term had not yet been invented, or it wasn’t in wide-spread use. They did however say that something had to be done, by which then as now meant government should increase in size and power. Makes sense: Consensus-holders depend on government for their salaries, and larger government means fatter and surer paychecks. For both, it didn’t and doesn’t matter what the government does, as long as they act in the name of the Consensus.

The older Consensus was only pretty sure that what was causing the planetary sickness was humanity. The new Consensus is morally certain of it. Both groups were convinced that whatever good happened to the planet was due to Nature, and that whatever bad happened was our fault. Scientific imagination has thus not advanced beyond paganism.

Members of the current Consensus say there is a dramatic distinction between them and the holders of the old Consensus: current scientists say that now—here and now—they know more than did the members of the old Consensus. This is true: they do know more.

But the certainty scientists in both Consensuses held in their prognosticative abilities is the same. Scientists know much more about (say) clouds now, but the folks of 1975 were convinced that what they knew was sufficient to forecast a trial by ice, just as scientists now insist it will be a gauntlet of fire.

Concentrating on the differences of knowledge is wrong, because it doesn’t answer the main question: Do they know enough? We had their word on it in 1975, just as we have sworn Congressional testimony today. Clearly, we cannot use ardency as a measure of truth. Neither is the apoplexy resulting from departures from the Consensus any guide. The very public exasperation against “deniers” is not convincing, and is not evidence, that the current forecasts are any better than the old.

A citizen is well justified to think: “Scientists were so sure before, and claim to be so again. But they were wrong before. Therefore it is rational to suppose they might be wrong again. Only a zealot would disagree. Plus, the dire threats of starvation and so forth are just the same then as now. So which is it? Is it a cooler world or a hotter one which spells death? And just what is the ideal, to-be-desired-for-all-time climate? Exactly now, please.”

Oh Good, We Have Consensus About Climate Change

Everybody who believes science should be conducted by vote, raise their hands.

See if you agree with me: agreeing with me isn’t proof that that which we agree about is true. Doesn’t mean that that which we agree about is wrong, neither, because this agreement is about something which is true. Rather, our consensus is not of much interest, except sociologically.

Consider other consensuses (consensi?). A century ago the intelligentsia thought it was a swell idea that people should have perfection forced upon them by making a Utopian omelette created by cracking a few tens of millions of skulls. The consensus among us civilians is that the intelligentsia was bat-guano crazy. The consensus among the intelligentsia now, on the other hand, hasn’t budged: government (meaning rule by themselves) knows best—about everything.

Medieval scientists agreed to a man that Ptolemy’s theory about the movement of celestial objects was true. They were rational to do so, because the thing worked. Well, mostly worked, or worked good enough for everyday purposes. Scientists now agree that a better theory has come along. This one works, too.

Modern scientists shook hands and were adamant the continents were fixed objects. Doctors laid their thumbs upon their noses when asked their opinion about hygiene (they were against it). And on and on.

The batting average for Consensus is like that of an aging player being sent down to the minors in July. We had such hopes for it early on, but it consistently failed under pressure, though we’re still willing to give it one more try.

The leftwards press delights in telling us there is a consensus among climate scientists. Why they should be so pleased that the sky is falling is a mystery, unless it’s another symptom of the bloodlust found in progressives. The Guardian—protecting British minds from the onslaught of reality since 1821—is giddy over the statistic that 97.1% (and not just 97.0%) of academic papers agree that “climate change is anthropogenic.”

Just what is this capital-C Consensus? My pal Gav Schmidt asked me to tell you (his words; “the update” is found on the page linked):

  1. The earth is getting warmer (0.6 +/- 0.2 oC in the past century; 0.1 0.17 oC/decade over the last 30 years (see update)) [ch 2]
  2. People are causing this [ch 12] (see update)
  3. If GHG emissions continue, the warming will continue and indeed accelerate [ch 9]
  4. (This will be a problem and we ought to do something about it)

The last one is in brackets because whilst many would agree, many others (who agree with 1-3) would not, at least without qualification. It’s probably not a part of the core consensus in the way 1-3 are. Most (all?) of us here on RealClimate are physical scientists — we can talk sensibly about past, present and future changes in climate, but potential impacts on ecosystems or human society are out of our field.

Since it hasn’t been hotting up recently at the rates quoted by Gav, “People are causing this” is ambiguous, and there is substantial uncertainty in the historical observations, the Consensus can only be of minor interest. Climate scientists have to agree on something and it is a good thing they’re trying to sort out how things work, but their forecasts haven’t been of sufficient precision to encourage the rest of us to pay too close attention. Not yet, anyway.

Gav rightly emphasizes the “we ought to do something about it” isn’t a major part of the Consensus, if it’s there at all. But the Guardian, representing the perpetually “outraged” crowd, think it is; indeed, they think the Consensus is nothing but that. One prominent fellow with a mind permanently and deeply scarred by youthful “experimentation” said the Consensus is “about activism” and “is about converting people.”

Politicians and journalists who couldn’t read a thermometer, even if you threatened to withhold their kickbacks and deny their bylines, are so eager to believe climate change is man-made because that makes it “a problem and we ought to do something about it.”

This explains the mysterious glee over every heatwave headline and the perverse delight politicians display when organizing hearings on the end of the world. They believe they are the “we.” Just as with the earlier consensus, they believe paradise is just around the corner if only, if only.


Thanks to Ye Olde Statistician and the many others who asked about this topic and provided references.

Coal-Fired Power Plants Fuel Suicide—Or Maybe Sanguinity

Smokestack Seppuku

Whatever you do, don’t look at that smokestack! Do, and next thing you know you’ll be drawing a knife across your throat. Suicide!

Or so says the press and John G. Spangler, M.D., M.P.H., a professor of family medicine at Wake Forest Baptist. The good doctor used a statistical model to “prove” that if you live in a North Carolina county which has a coal-fired power plant, the chance you will kill yourself—from disgust, despair, or moral desuetude we never learn—increases significantly over a county which has none.

Yes, and if you live in a county which has two such power plants, look out! Death lurks on every erg. That you should find yourself surrounded by three such institutions does not bear thinking. Yet we must and will.

Peer-Review Strikes Again

Spangler’s peer-reviewed findings appeared in the Journal of Mood Disorders with title “Association of Suicide Rates and Coal-Fired Electricity Plants by County in North Carolina.”

Bucking the trend in enlightened morals, Spangler starts his paper by claiming “Suicide is a tragedy”. He also admitted that environmental pollution is not “commonly thought of as relating to suicide.” (And for good reason, as we shall see.)

“It is hypothesized that suicide is related to having a coal-fired plant in a county, acting as a substitute measure of air pollution.” How do these ordinarily life-giving buildings (try living in North Carolina without air conditioning) encourage dark forces? Possibly by causing “abnormal cognition, neurological development or degeneration” and lowering “overall life satisfaction” you see.

Statistics To The Rescue

Here is what Spangler did. He gathered county-level suicide rates and various demographics, such as percent whites, median income and the like, and counted the number of coal-fired power plants. He also took genuine air-quality measurements of metals and other pollutants, which was wise. He then “regressed”, i.e. used an unnecessarily complicated statistical model, the suicide rate and the other variables together.

None of the variables except percent whites, median age, and number of coal-fired power plants were “significant.” Spangler claimed that for every increase of one plant the suicide rate increases by about 2 per 100,000. This led Spangler and the press to conclude, as summarized for instance in Scientific American, “that county suicide rates correlated very predictably with the number of coal-fired electricity plants within said county.”

The flaw should already be obvious, and glaringly so, to those who know statistics. For those who don’t, stick around.

Even accepting the (hidden as yet) fallacy, there were some oddities about Spangler’s work that jumped out. He claimed that in North Carolina “sixteen [counties] had one plant; three had 2 plants (Gaston County, Halifax County, and Robeson County); and one had 3 plants (Person County)” This is 20 counties with 16 + 6 + 3 = 25 plants, which means 80 counties did not have any coal-fired power plants (NC has 100 counties).

Let’s Try This Ourselves

Spangler did not list the sixteen counties with just one plant. However, Sourcewatch a most progressive organization, has a list which appears complete, and from these we can infer the missing counties. See the tables below for details.

The suicide rates per country were also not in Spangler’s paper, but the CDC: 2003-2010 Final Data has them.

Here is a plot of number of coal-fired power plants by the the county-level suicide rate.

Smokestack suicides

Smokestack suicides

The median suicide rate for counties empty of coal-based electricity was 12.9, which was the identical rate in counties which had one plant. For those three—and only three—counties which had two plants, the median was 11.3. In the one county which had three plants, the rate was 10.6.

The green line is the “regression” of these two variables, which seems to indicate that increasing the number of plants decreases suicide rates, the exact opposite conclusion of Spangler’s. Seems that adding coal plants is good for you!

Statistics Are Scary For Good Reason

How can this be? Easy. For one, Spangler’s data could be slightly different because suicide rates change from year to year (my rates are aggregated from 2003-2010, and Spangler says his are from 2001-2005). But if that’s true, and because the number of coal plants in each county hadn’t changed, it means the data is too variable to draw any conclusions. It’s also suspicious Spangler doesn’t have a plot like this in his paper.

For another, regression does funny things to data, making lines which should go down, mysteriously go up. Especially when you toss an enormous number of variables at it hoping something will stick. And the more variables you throw, the more likely something will stick, even absurd things. Note that none of the actual environmental variables Spangler used showed up. These are the variables which could actually influence health, and yet all were unimportant.

The model itself is silly: there are only three counties with two plants, and one with three, yet Spangler (and I above) drew a regression line over this wee sample. But the mathematics doesn’t know this, so it will give a result. My green line is just as absurd as Spanger’s: there just isn’t enough data about increasing the number of plants to say anything cogent.

The Fallacy Revealed

And then there’s the fallacy hinted at above. It occurs when people infer individual-level conclusions from aggregate data. Something, or many various things, caused the differences in suicides between counties, but it does not follow that because a correlation was found in a statistical model that the variable identified had any causative effect.

If that were so, then moving to a county which had a higher proportion of whites or older folks would increase your suicide risk. That is obviously ridiculous, but if we follow the press reports and Spangler’s breathless intimations, that is the conclusion we would reach.

We should be especially suspicious here because no pollutants were noted, nor were any of the other demographic variables, like income and education. The county-level is just too crude a scale to be useful. The many journalists who picked up this story should have recognized this, as should have Spangler: a simple plot (like the one here) would have showed him his task was futile.


Tables of the data in Spangler’s paper, given in case my counts differ from his. The suicide rates for counties with no plants were taken from the CDC. Semora is an unincorporated town located partly in Caswell county and partly in Person county, which I assigned to Person so that it had 3 plants as indicated by Spangler.

Counties with one plant.
County (City) Rank Suicide rate
Haywood (Canton) #11 18.2
Rowan (Salisbury) #24 15.9
Brunswick (Southport) #38 14.2
Catawba (Terrell) #40 14.0
Rockingham (Eden) #39 14.0
New Hanover (Willmington) #45 13.1
Cleveland (Mooresboro) #47 13.0
Buncombe (Arden) #48 12.9
Wayne (Goldsboro) #51 12.8
Edgecombe (Battleboro) #52 12.3
Lenoir (Kinston) #58 11.5
Forsyth (Belews Creek) #64 11.1
Orange (Chapel Hill) #67 10.7
Chatham (Moncure) #77 9.8
Bladen (Elizabethtown) #82 9.0
Washington (Plymouth) #96 7.7
Counties with two plants.
County (Cities) Rank Suicide rate
Gaston (Mount Holly, Belmont) #30 15.3
Halifax (Weldon, Roanoke Rapids) #61 11.3
Robeson (Lumberton x 2) #66 11.0
County with three plants.
County (Cities) Rank Suicide rate
Person (Roxboro x 2, Semora) #69 10.6

The remaining 80 counties had suicide rates from 26.0 to 4.4.

A Common Fallacy In Global Warming Arguments

Our post today is provided by Terry Oldberg, M.S.E., M.S.E.E., P.E. Engineer-Scientist, Citizen of the U.S. That’s a lot of letters, Terry! Oldberg joined our Spot the Fallacy Contest, which had been laying fallow. He says he found multiple instances of equivocation in global warming arguments. What say you?

Summary and Introduction

No statistical population underlies the models by which climatologists project the amount, if any, of global warming from greenhouse gas emissions we’ll have to endure in the future. This absence of a statistical population has dire consequences. They include:

  • The inability of the models to provide policy makers with information about the outcomes from their policy decisions,
  • The insusceptibility of the models to being statistically validated and,
  • The inability of the government to control the climate through regulation of greenhouse gas emissions.

Rather than describe global warming climatology warts and all, the government obscures its unsavory features through repeated applications of a deceptive argument. Philosophers call this argument the equivocation fallacy.

The Equivocation Fallacy

The failure of global warming research is concealed by multiple instances of the equivocation fallacy (EF), an example of which is (Jumonville):

Major premise: A plane is a carpenter’s tool.
Minor premise: A Boeing 737 is a plane.
Conclusion: A Boeing 737 is a carpenter’s tool.

The mistake can be exposed by replacement of the first instance of “plane” by “carpenter’s plane” and by replacement of the second instance of “plane” by “airplane.”

Major premise: A carpenter’s plane is a carpenter’s tool.
Minor premise: A Boeing 737 is an airplane.
Conclusion: A Boeing 737 is a carpenter’s tool.

A term that has several meanings is said to be “polysemic.” The technique to expose the fallaciousness of any example is to disambiguate all of the terms in the language in which an argument is made.

Polysemic terms in climatology

Climatologists often use polysemic terms. Some of these terms are words. Others are word pairs. The two words of a word pair sound alike and while they have different meanings climatologists treat the two words as though they were synonyms in making arguments. Examples are (Oldberg):

  • model
  • scientific
  • project-predict
  • projection-prediction
  • validate-evaluate
  • validation-evaluation

An example

In “Is Climate Modeling Science?,” Real Climate’s Gavin Schmidt attacks an opponent’s claim that climate models are not scientific. His argument, though, draws an improper conclusion from an equivocation.

Were climate models of the past built under the scientific method of inquiry? Schmidt argues that: At first glance this seems like a strange question. Isn’t science precisely the quantification of observations into a theory or model and then using that to make predictions? Yes. And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements? Yes, again. So the fact that climate modeling was recently singled out as being somehow non-scientific seems absurd.

Dr. Schmidt’s argument appears to be:

Major premise: All scientific models are built by a process in which the predictions of these models are validated.

Minor premise: All climate models are built by a process in which the predictions of these models are validated.

Conclusion: All climate models are scientific models.

This argument contains the polysemic terms “model,” “scientific,” “prediction” and “validate.”

Disambiguating “model”

The word means: a) a kind of algorithm that makes a predictive inference and b) a kind of algorithm that makes no predictive inference. For reference to the kind of algorithm that makes no predictive inference, I’ll reserve the French word modèle. Models and modèles have remarkably different characteristics, as we’ll see.

Disambiguating “predict-project” and “prediction-projection”

To “predict” is to do something different than to “project” yet most global warming climatologists use the two terms synonymously (Green and Armstrong). The idea of a “prediction” is closely related to the idea of a “predictive inference.” This relationship follows because a “predictive inference” is a conditional prediction, like these:

Given that it is cloudy: the probability of rain in the next 24 hours is thirty percent.

Given that it is not cloudy: the probability of rain in the next 24 hours is ten percent.

A “prediction” is an unconditional predictive inference. For example, “The probability of rain in the next 24 hours is thirty percent.” Notice there is no condition.

A predictive inference is made by a model but not a modèle. On the other hand, a modèle is capable of making projections while a model is incapable of making them. The “projection” of global warming climatology is a mathematical function that maps the time to the projected global average surface air temperature.

Disambiguating “validate-evaluate” and “validation-evaluation”

As the long time IPCC expert reviewer Vincent Gray tells the story, many years ago he complained to IPCC management that its assessment reports were claiming its modèles were validated when these modèles were insusceptible to being validated. After tacitly admitting to Dr. Gray’s charge, the IPCC established a policy of changing the term “validate” to the similar sounding term “evaluate” and the term “validation” to the similar sounding term “evaluation.” Thereafter, many climatologists fell into the habit of treating the words in each word-pair as if they were synonyms. A consequence was for the two polysemic terms validate-evaluate and validation-evaluation to be created.

A model is said to be “validated” when the predicted relative frequencies of the outcomes of events are compared to the observed relative frequencies in a sample that is randomly drawn from the underlying statistical population, without a significant difference being found between them. As it has no underlying statistical population, a modèle is insusceptible to being validated. However, it is susceptible to being “evaluated.” In an evaluation, projected global average surface air temperatures are compared to observed global average surface air temperatures in a selected time series.

Disambiguating “scientific”

According to Wikipedia, “A scientific theory is a well-substantiated explanation of some aspect of the natural world, based on a body of knowledge that has been repeatedly confirmed through observation and experiment.” For a model, validation serves the purpose of confirming through observation and experiment. Does evaluation serve the same purpose for a modèle?

It does not. In an evaluation, projected temperatures are compared to observed temperatures but a judgment is not made in which claims made by a modèle are confirmed or denied. Thus, “scientific” cannot legitimately be used as a modifier of “modèle.” On the other hand, “scientific” can legitimately be used as a modifier of “model.”

Translating Gavin Schmidt’s argument

With the help of the disambiguated terminology developed immediately above, Dr.Schmidt’s argument can be translated into a form free from equivocation. His argument now reads:

Major premise: All scientific models are built by a process in which the predictions of these models are validated.

Minor premise: All climate modèles are built by a process in which the projections of these modèles are evaluated.

Conclusion: (none logically possible)

No conclusion is possible because Dr. Schmidt’s argument is not of the form of a syllogism. His original conclusion that “All climate models are scientific models” is a consequence from drawing an improper conclusion from an equivocation.

Contrasting a model and a modèle

This contrast is illustrated in this table:

model modèle
makes predictive inference makes no predictive inference
makes predictions makes no predictions
underlying statistical population no underlying statistical population
makes no projections makes projections
susceptible to validation insusceptible to validation
insusceptible to evaluation susceptible to evaluation
product of scientific method not product of scientific method
conveys information to user conveys no information to user
makes climate controllable does not make climate controllable

The last two lines of the above table deserve amplification. If there were any, predictions from a climate model would convey information to a policy maker about the outcomes from his or her policy decisions prior to these outcomes happening; the availability of this information might make the climate controllable. Currently, however, we have no climate models. We do have climate modèles but they make no predictions hence conveying no information to a policy maker. Thus, after decades of effort and the expenditure of several hundred billion U.S. dollars on global warming research, the climate remains uncontrollable. Nonetheless governments, including our federal government, persist in trying to control the climate.

The “models” of AR4

Every entity in AR4 which is referenced by the polysemic term “model” is an example of a modèle. If the language of the methodological arguments that are made in the Federal Advisory Committee Climate Assessment Report (FACCAR) were to be disambiguated, the authors of the FACCAR would be compelled to admit that the items in the above list are descriptive of the climate modèles that are currently being used in making policy on emissions of greenhouse gases by the federal government. If these admissions are not made, there will be continuing catastrophic waste of the capital of the people of the U.S. on: a) attempts at controlling the uncontrollable and b) foolishly framed, deceptively described global warming research. To make these admissions would require courage and integrity on the part of the Advisory Committee.

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