Planet Earth Al Gore Explains ‘Snowmageddon’: Fox News Story

I was quoted by Gene Koprowski in his Fox News story “Planet Earth Al Gore Explains ‘Snowmageddon’“. (This accounted for the several hundred Google and other search engine redirects to this site from people searching for “dr william m briggs”.)

Apparently, our boy Gore was telling all who would listen that global warming is so evil, so unrepentantly vile, that anything that has gone wrong in the world did so because of climate change. To paraphrase David Stove, Gore didn’t quite say that wooden legs were caused by global warming, but I don’t think he’d like to hear it denied.

Koprowski (also picked up here):

But not surprisingly, some climate-change skeptics are a bit hot under the collar over Gore’s “scientific” explanation.

“Gore’s statement actually indicates a deeper problem — lack of precise predictions,” said Dr. William M. Briggs, a statistician and climate scientist. His research shows that there are no increased weather problems because of global warming, Briggs told FoxNews.com.

“He’s saying that anything bad that happens must be because global warming caused it. Activists like Gore are great at identifying events after the fact as being caused by global warming, but terrible at predicting them beforehand,” Briggs said.

My research points to world-wide tropical cyclones (hurricanes and typhoons). There is no evidence that these storms have increased in number, intensity or strength, or longevity. In fact, there might have been, over the past decade, a slight decrease in these attributes. But I think that this is well known.

The other argument I make is the better one. It takes no effort to point to untoward events after the fact and say, Jean Dixon-like, “See! More evidence that my theory is right!” If it is true that global warming will cause the Northern Hemisphere to experience cooler temperatures, then say so in advance. Don’t bustle to the cameras after things go wrong if you did not, or could not, say that they would in advance.

Vague predictions like “There will be snowstorms and rumors of snowstorms” do not count and are not evidence that the end is near. Take heed that no man deceive you. It is, after all, perfectly possible to forecast that there will be, say, “15% more snowfall in the 2010-2011 Northern Hemisphere winter”, or that “There will be at least three more Pacific ocean typhoons in 2011 than there were in 2010″, and so forth.

What is absurd is to point to a typhoon/cyclone/hurricane/snow storm after it has occurred and say that, “I could have predicted that if I wanted to. I chose not to because, among other reasons, I was busy. But that storm certainly indicates that my theory of climate change is true.”

Of course, it might be true that this storm was caused by mechanisms consistent with anthropogenic climate change theory; however, since every winter has its share of snowstorms, and that this winter is not unusual compared with history, this latest storm is also consistent with the theory that the climate is insignificantly affected by mankind. The same goes for weather events of other kinds.

It goes for non-events, too. Ever notice how talk of climate change always devolves to the apocalyptic? Floods! Droughts! Floods and droughts simultaneously! Windstorms! Deadly hurricanes! Heat waves! Democrats voting republican! One horror after another. This despite all historical and paleoclimatic evidence that warmer times were better, at least in terms biological.

Why won’t global warming be responsible for a “dramatic” increase in pleasant sunny afternoons? How come we won’t see an “unprecedented” number of warm, laconic evenings? Why won’t there be an “inconvenient” rise in bountiful harvests?

One reason folks like we (me and the regular crew here) are suspicious of global warming public scientists, activists, and miscellaneous proponents is because of their constant sourpuss attitude, their constant predictions of doom, their propensity to focus solely on the negative. They might even be right about all that, but when they tack on suggestions of how the rest of us should live our lives—which usually means surrendering freedom or money or both to government—we feel the winds blowing, all right. We also start feeling for our wallets.

New York City Democrats Remove Yet Another Right: No Smoking In Parks

The party that ever has “Rights!” on its lips, the party with the mania about diversity, the party that is most anxious that religious fundamentalists will take over and impose their puritanical wills on the rest of us, the party whose members remind us constantly of the dangers of the government meddling in our personal lives has, in a fit holy self righteousness, taken away yet another right, decreased diversity, imposed its puritanical will on the rest of us, and has used the law to meddle in our personal lives once more.

New York City Council Democrats have voted to ban smoking in city parks, beaches, pools, boardwalks, and, if it can be believed, marinas. Mayor Bloomberg, a man with much money and therefore with a near infinite belief in his infallibility, has said he will sign the new restrictions, and will do so with a smile on his face. One imagines it will resemble that of the Grinch’s.

Why did they take away the right to smoke in public? According to Speaker Christine C. Quinn, a Democrat, “The statistics don’t lie: second hand smoke kills. With this bill, all New Yorkers can now breathe easier and breathe cleaner air.” My dear lady, as a statistician I can tell you that if this is what the statistics are saying, then they are lying. A nasty habit, but one not unfamiliar to most statistics.

No council member mentioned that residents will still breath air polluted from tens of thousands of cars of which, even just one vehicle, in a single sight-seeing trip across our narrow isle, will pump out more “carcinogens” than an inveterate smoker can do in a week. Perhaps we should keep quiet about this, lest the government get ideas.

Another council member, Democrat Gale Brewer, said of her part in restricting her constituents’ behavior, that her vote “will help New Yorkers become healthier.” Ah, health. The modern be all and end all. This strange and recent worship of body is present perhaps because of the feminization of politics, or perhaps it is because of the increase of mothers and mamma’s boys (Bloomberg?) elected to office, or perhaps it is because of increasing secularization which teaches this is it!: miss your chance to be healthy now, and you miss out for all eternity.

You’re tired of seeing this, but it is my duty to remind you of the words of Mark Twain, a man surely wiser than Gale Brewer (D):

There are people who strictly deprive themselves of each and every eatable, drinkable and smokable which has in any way acquired a shady reputation. They pay this price for health. And health is all they get for it. How strange it is. It is like paying out your whole fortune for a cow that has gone dry.

All of us are willing to trade health for other benefits. This must be so because people regularly strap themselves to two-ton SUVs and hurtle their persons down crowded highways that lead to cabins on lakes, whereupon are found boats which trail ropes that are gripped by the SUV drivers with wooden slats tied to their feet. All this pleasure is purchased at the price of the likelihood of injury, even death.

Life is for living, not for crouching behind doors in fear and paranoia that something might—just maybe!—damage our health. This is important to acknowledge because we must never forget that this country once before lost its mind and wrote into its very constitution an amendment forcing health upon those who didn’t want it. If it happened before, it can happen again.

Now, the City Council, in its wisdom, graced its new law with a loophole which allows “actors in theatrical performances” to smoke where they like. Thus, when you are stopped for indulging tell the parks officer that you are rehearsing a play by La Rochefoucauld, and quote to him—by all means, with flair and through a cloud of smoke—”Attention to health is life greatest hindrance.”

Incidentally, unlike those epidemiologists who have received cash and free trips to exotic locales to write their papers and present their results damning second-hand smoke, I have never received anything—no money, no free smokes, no consideration of any kind—from any tobacco company, nor, to my knowledge, from any company even tangentially connected to a tobacco company. I do not smoke cigarettes and never have. This new law limiting freedom will scarcely effect me.

Are Tree Rings Low Pass Temperature Filters?

I don’t know the answer to this question: I am not an expert in this area, and I haven’t the time or resources to track down the data to discover the answer. I even wonder if there exists controlled experimental data. It must be the case that the objections I make below are well known and have been considered by those who regularly use tree rings as proxies for temperature. I’m just hoping that some regular reader might know where to go on this matter.

Ulf Büntgen and a pal or two saw their “2500 Years of European Climate Variability and Human Susceptibility” published recently in Science (thanks to reader Sylvain Allard for bringing this to my attention). The abstract begins:

Climate variations have influenced the agricultural productivity, health risk and conflict level of preindustrial societies. Discrimination between environmental and anthropogenic impacts on past civilizations, however, remains difficult because of the paucity of high-resolution palaeoclimatic evidence. Here we present tree ring-based reconstructions of Central European summer precipitation and temperature variability over the past 2500 years. Recent warming is unprecedented, but modern hydroclimatic variations may have at times been exceeded in magnitude and duration.

The admission is “paucity of high-resolution palaeoclimatic evidence”, meaning no direct measures of temperature and precipitation exists. Thus, the reliance—I emphasize the word—on tree-ring data as a stand-in for what was not measured.

Then comes the key: “Recent warming is unprecedented”. Is it? How can we know? Well, by examining the reconstructions of temperature using tree rings: that is, by building statistical models of temperature as functions of tree-rings.

My question is this. Suppose, ceteris paribus, that temperature changes rapidly year on year. I leave “rapidly” undefined, as I do how the temperature changes (more in summer than in other seasons? equal change through all seasons? etc.; each possibly would presumably influence the way trees reacts to temperature). Can trees keep up with rapid temperature change?

My guess, based, it’s true, on vague biology, would be that the tree ring responds to this temperature change linearly when the year-on-year temperature change is slow, and it responds to the temperature change (say) logarithmically when the year-on-year temperature change is fast. That is, when temperature change is too quick, the tree can’t catch up and doesn’t respond as quickly to extremes. Tree rings would then, in effect, be a low pass filter on temperatures.

If that is true, then any reconstruction of temperature based on tree rings would always show less variability than would actual temperature measurements. The past would necessarily look calmer than the present, so to speak. Reconstructions would have more hockey sticks than at the Joe on a Friday night in January.

Now, if you knew how trees responded to rapid change, then you could of course incorporate this knowledge into your reconstruction models. But this removes these models from the land of simple regressions, and almost certainly, and unless the researcher is very careful, the results will be too certain in times of rapid change (the confidence or credible intervals should widen considerably when the regime switched from linear to logarithmic).

Then, too, we have the difference between estimates of the parameters of these models versus these models’ estimates of the actual observables (temperature). Use of the former—which is all you see in classical statistics—guarantees over-certainty.

Controlled experimental data would answer the question. Grow a strand of trees, paying attention to the ceteris paribus, then change temperature slowly, then rapidly and see what happens. Of course, since tree rings are laid down annually (I stand ready to be corrected here), this experiment will take some time.

You might then try to look to the wild where we have simultaneous (say) thermometer-based temperatures and tree rings, which must have been done. The problem here is observational bias. Chances are overwhelming that that ceteris paribus bit will not have been understood properly. I say this because it rarely is. This difficulty isn’t strong enough to bar these kinds of experiments, but it is sufficiently forceful such that we should always look at our results with some skepticism. Especially if our goal is to forecast temperatures changes of fractional degrees.

As I have said, all this is surely well known; thus this post is more a way for me to organize my thinking than any kind of review. I await enlightenment from you.

Will The Religious Out-Breed Us All?

Thanks to long-time reader and contributer Ari Schwartz for bringing this to our attention.

“It is widely agreed that religion has biological foundations—that belief in the supernatural, obedience to authority or susceptibility to ceremony and ritual depend on genetically based features of the human brain.” Thus does Robert Rowthorn begin his paper on “Religion, fertility and genes: a dual inheritance model” with a falsity. Thus we are not later surprised to learn that Rowthorn has “proved” that the religious—whatever they are, poor things—will out-breed the “normals”, to the extent that the genes of the enlightened folks will be watered down with, well, with holy water.

If what Rowthorn said was false, what is true is that some have said that religion has biological foundations. One reading makes this trivially true: we are biological creatures with brains that allow us to think up religious thoughts. But that’s not what Rowthorn has in mind. He says “religion promotes the evolution of genes that predispose people towards religious belief or behaviour.” Got it? Religion itself makes people religious. Sigh. This is what happens when people read Dawkins with minds far too open. Suddenly, any idea sounds good, no matter how illogical.

How’s it work?

For religion to influence genetic evolution it must convey some kind of selective advantage. Such an effect might come about through social bonding via ritual, formation of group identity through myth, honest signalling through participation in costly ceremonies and adherence to social norms through love or fear of God.

Religion—which must be sentient, like a meme; or something—also makes people fertile. That’s what scares the bejesus—and the Jesus—out of Rowthorn. “The more devout people are, the more children they are likely to have.” He’s particularly fearful of them Amish who have a “total fertility rate of 4.8″. Why, if that sort of rate keeps up, the world will be flooded with beautiful hand-made quilts, not to mention the glut of various sauces and jams that even now squirt like a fire hose out of Lancaster, PA.

Remember the good old days? When national or royal academies of science would only publish articles of value and intrinsic worth? Papers which were insightful and had a reasonable chance to not only be true, but were untainted with mind-rattling gibberish? Maybe my glasses are rose colored, but surely a work like Rowthorn’s would never have passed the bar of the Royal Society even twenty years ago, a time when even the John Birch society would have rejected this man’s wild thesis.

Just for a start, Rowthorn has forgotten the Quakers, the thousands upon thousands of Catholic priests, nuns, and brothers, the growing population of Buddhist monks, Shinto priests, and myriad other holy men and women of various stripe whose main goal in life is not to pass on their religious genes. Even though we have no (or almost no) chromosomal material from any of these exceedingly religious people, yet we are able to replenish their stock year upon year. How can this be?

Nowhere does the economist Rowthorn—he is on the Faculty of Economics, Cambridge—acknowledge the idea that those Amish breeders are less well off than his presumably barren but richer colleagues. There is bountiful evidence that wealth is a bar to pregnancy, and not just personal wealth, but that of a community. The better off a region (or country) is, the fewer the kids the ladies of that region like to have. The love of money trumps the love of babies.

And how come the religious haven’t taken over by now, forcing their beliefs down our throats (the main fear of those discussing this paper on Slashdot)? Defections, says Rowthorn. Yes, even though the religious gene is pernicious, yet some people are able to overcome its influence. The people able to accomplish this miraculous feat—they become what they are not by sheer force of will, even though their wills were under the control of their genes—might be said to have been born again. They abandon their Earthly genes and adopt Enlightened memes which overpower their genes. Or something.

Perhaps it’s overwork or overexposure to economical equations that accounts for people like our Rowthorn. All those formulas have a way of inducing a sense of self that can be unhealthy. Maybe that’s why economists don’t have a lot of kids. I think I’ll model this.

The Similarities Between Psychics And Global Warming Activists

The statistical evidence series will continue after the weekend.

“I see the letter G.” The woman closed her eyes, cocked her head, and looked inwardly. She became grave, tense. “There’s…wait a second…it’s coming through…yes! I can just make out a body of water nearby.” She settled back, opened her eyes, a wide smile overcoming the frown. She waited for the applause that was sure to come.

The woman? A psychic telling a distraught family where the body of their daughter can be found. Or maybe an activist making guesses of where the next global warming calamity will occur. The two aren’t that far apart. Here’s why.

The New York Post reports that a “clairvoyant” was hired by the family of “Melissa Barthelemy, 24, of Buffalo”, who had gone missing. Police suspected foul play. The unnamed psychic was reported to have said she saw Melissa’s “body buried in a shallow grave overlooking a body of water.” Weirdly, the seer also predicted that there would be the letter “‘G’ in a sign nearby.”

Sadly, but also—let’s admit it—somewhat thrillingly, the police found Melissa where the psychic said she would be. And not just Melissa, a horrific mass grave in which “cops unearthed the skeletons of the victims, missing call girls, each wrapped in burlap bags on Long Island’s Gilgo Beach.” Long Island’s Gilgo Beach! There’s the body of water! There’s the G!

The evidence tells us the psychic was right. Therefore, the psychic is psychic; that is, this person (we don’t know whether it’s a man or a woman; I’ll assume it’s a woman) must have the powers she said she does. If you want to say it in a complicated way, the “body of water” and the “G” confirm the theory that paranormal powers exist.

And this is true: the evidence does indeed confirm the paranormal theory. If you’re a skeptic of psi powers, you might not like this conclusion, but that can’t be helped. When a theory predicts an event will happen, and if that event happens, that theory is confirmed to the degree the predicted events match the reality.

Are we done? As John Wayne would say, Not hardly. For that same body of water and “G” also confirm the theory that the psychic is just guessing, and for obvious reasons. Melissa was a Long Island resident, and Long Island is filled with rivers, creeks, lakes, ponds, cisterns, swimming pools; even a well known ocean is nearby. There is virtually no place that is not “near” a body of water. And the “G”? Well, anything with “Long Island” will qualify (can you locate the “G”?). Then there are gas stations, various “ings”, etc., etc. There was almost no way that the psychic could have been wrong. Her supporters will never suffer disappointment.

Unspecific predictions also plague the global warming forecasts of activists. I don’t mean all predictions of climate change, some of which are quite detailed. I have in mind those colorfully vivid sayings of doom given out by green organizations, typically in appeals for donations. These overly earnest folks say that if we don’t “do something”, bad things will occur. Near a body of water, usually.

Those who have “We have to save the planet!” ever on their lips are ready to interpret any untoward event as evidence that their worst fears are realized. Remember the Indonesian tsunami? That was near a body of water, and more than one activist was ready to blame it on mankind; some especially clever agitators were even able to point to global warming. This year’s cold and snow in the States? Global warming, too. Poverty in the third word? Climate change. A lot of racism is caused by the climate chaos, too. More prostitutes, pimps, and pirates? Reliance of fossil fuels.

People will always be creative enough to tie any environmentally bad thing—never, of course, good things—to the theory that mankind is responsible. Just as with psychics, whatever happens will be confirmation that their beliefs are true. They will not, so to speak, see that bodies of water are everywhere. This is why it is so difficult to convince the True Believer that his angst is misplaced.

For a more in-depth look at a psychic supposedly helping detectives solve a murder, see the Tabitha Horn case.

Model Selection and the Difficulty of Falsifying Probability Models: Part III

To clarify: to prove false means to demonstrate a valid argument with a certain conclusion. If a theory/model says an event is merely unlikely—make it as unlikely as you like, as long as it remains possible—then if that event happens, the theory/model is not falsified. To say, “We may as well consider it falsified: it is practically falsified” is like saying, “She is practically a virgin.” False means false; it has no relationship with unlikely.

A theorist or statistician has in hand a priori evidence which says model M1, M2, …, Mk are possible. Some of these, conditional on the theorist’s evidence, may be more likely than others, but each might be true. If these models have probability components, as most models do, and these probability components say that any event is possible, no matter how unlikely, then none of these models may ever be falsified. Of course, those models in the set that say certain events are impossible, and these events subsequently are observed to occur, then this subset of models can be falsified; the remaining models then become more likely to be true.

In Bayesian statistics, there is a natural mechanism to adjudge the so-called posterior probability of model’s truth: it is called “posterior” because the model’s truth is conditional on observation statements, that is, on what happens empirically (this needn’t be empirical evidence, of course; any evidence will do; recall that probability is like logic in that they study the relationships between statements). Each models’ a priori probability is modified to its a posteriori probability via a (conceptually) simple algorithm.

These a posteriori probabilities may be ordered, from high to low and the model with the highest a posteriori probability picked as “the best.” The only reason one would want to do this is if a judgment must be made which theory/model will be subject to further actions. The most common example is a criminal trial. Here, the theories or models are suspects in some crime. At the end, only one theory/model/suspect will face punishment; that is, at most one will. It may be that no theory/model/suspect is sufficiently probable for a decision to be made. But if the suspect is found guilty, it is not that the convicted theory/model/suspect is certainly guilty, for the other theories/models/suspects might also have done the deed, yet the probability that they did so, given the evidence, is adjudged low. This implies what we all know: that the convicted might be innocent (the probability he is so is one minus the probability he is guilty).

It is often the case (not just in criminal trials) that one model (given the evidence and a priori information) is overwhelmingly likely, and that the others are extraordinarily improbable. In these cases, we make few errors by acting upon the belief that the most probable model is true. Our visual system works this way (or so it has been written). For example, your brain assures you that that object you’re reaching for is a cup of coffee, and not, say, cola. Sipping from it provides evidence that this model was true. But as we all know, our vision is sometimes fooled. I once picked up from the carpet a “cookie” which turned out to be a wiggling cockroach (big one, too).

Now, since we began with a specified set of suspects (like my cookie), one path to over-certainty is to not have included the truly guilty in the list. Given the specific evidence of omittance, the probability the other suspects are guilty is exactly zero (these theories/models are falsified). But, in the trial itself, that specific evidence is not included, so that we may, just as we did with the green men, calculate probabilities of guilt of the (not-guilty) suspects. Keep in mind that all probability and logic is conditional on specific, explicit premises. The probability or certainty of a conclusion changes when the premises do.

So what is the probability that we have not included the proper theory/model/suspect? That question cannot be answered: at least, it cannot be answered except in relation to some evidence or premises. This applies to all situations, not just criminal trials. What might this external evidence look like? We’ll find out in Part IV.

Model Selection and the Difficulty of Falsifying Probability Models: Part II

I hope all understand that we are not just discussing statistics and probability models: what is true here is true for all theories/models (mathematics, physics, chemistry, climate, etc.). Read Part I.

Suppose for premises we begin with Peano’s axioms (which themselves are true given the a priori), from which we can deduce the idea of a successor to a number, which allows us to define what the “+” symbol means. Thus, we can eventually hypothesize that “2 + 2 = 4″, which is true given the above premises. But the hypothesis “2 + 2 = 5″ is false; that is, we have falsified that hypothesis given these premises. The word falsified means to prove to be false. There is no ambiguity in the word false: it means certainly not true.

Now suppose our premises leads to a theory/model which says that, for some system, any numerical value is possible, even though some of these values are more likely than another. This is the same as saying no value is impossible. Examples abound. Eventually, we see numerical values which we can compare with our theory. Since none of these values were impossible given the theory, no observation falsifies the theory.

The only way a theory or model can be falsified is if that theory/model says “These observations are impossible—not just unlikely, but impossible” and then we see any of these “impossible” observations. If a model merely said a set of observations were unlikely, and these unlikely observations obtained, then that model has not been falsified.

For example, many use models based on normal distributions, which are probability statements which say that any observation on the real line is possible. Thus, any normal-distribution model can never be falsified by any observation. Climate models generally fall into this bucket: most say that temperatures will rise, but none (that I know of) say that it is impossible that temperatures will fall. Thus, climate models cannot be falsified by any observation. This is not a weakness, but a necessary consequence of the models’ probabilistic apparatus.

Statisticians and workers in other fields often incorrectly say that they have falsified models, but they speak loosely and improperly and abuse the words true and false (examples are easy to provide: I won’t do so here). None of these people would say they have proved, for example, a mathematical theorem false—that is, that they have falsified it—unless they could display a chain of valid deductions. But somehow they often confuse unlikely with false when speaking of empirical theories. In statistics, it is easy enough to see that this happens because of the history of the field, and its frequent use of terms like “accepting” or “rejecting” a hypothesis, i.e. “acting like” a model has been proved true or falsified. However, that kind of language is often used in physics, too, where theories which have not been falsified are supplanted wholly by newer theories.

For a concrete example, take a linear regression model with its usual assumptions (normality, etc.). No regression model can be falsified under these premises. The statistician, using prior knowledge, decides on a list of theories/models, here in the shape of regressors, the right-hand-side predictive variables; these form our premises. Of course, the prior knowledge also specifies with probability 1 the truth of the regression model; i.e. it is assumed true, just as the irascible green men were. That same prior knowledge also decides the form of these models (whether the regressors “interact”, whether they should be squared, etc.). To emphasize, it is the statistician who supplies the premises which limits the potentially infinite number of theories/models to a finite list. In this way, even frequentist statisticians act as Bayesians.

Through various mechanisms, some ad hoc, some theoretical, statisticians will winnow the list of regressors, thus eliminating several theories/models, in effect saying of the rejected variables, “I have falsified these models.” This, after all, is what p-values and hypothesis testing are meant to do: give the illusion (“acting like”) that models have been falsified. This mistake is not confined to frequentism; Bayesian statisticians mimic the same actions using parameter posterior distributions instead of p-values; the effect, of course, is the same.

Now, it may be that the falsely falsified models are unlikely to be true, but again “unlikely” is not “false.” Recall that we can only work with stated premises, that all logic and probability are conditional (on stated premises). It could thus be that we have not supplied the premise or premises necessary to identify the true model, and that all the models under consideration are in fact false (with respect to the un-supplied premise). We thus have two paths to over-certainty: incorrect falsification, and inadequate specification. This is explored in Part III.