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Predicting Nothing

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From American Thinker, Anthropogenic Global Warming and the Scientific Method by Betsy Gorisch

excerpts:

Science is about ruling things out. Any good scientific hypothesis will make predictions about the natural world — ideally, it will predict at least one natural effect whose existence cannot be caused by anything other than the hypothesis being tested. Observations are then made to acquire evidence, and the evidence is evaluated against the hypothesis’s predictions. Evidence can either rule the hypothesis out or not; if the evidence differs from the hypothesis’s predicted effects, then the hypothesis is wrong and is considered to be ruled out, or falsified. That which has not been ruled out by evidence remains possible. If enough confirmatory evidence is accumulated, the hypothesis is elevated to the status of a theory. Scientific Method is, conceptually, no more complicated than that.

Karl Popper, the great philosopher of science, used a simple observational experiment to illustrate the scientific method’s requirement of falsifiability — the requirement that a hypothesis be stated in such a way as to allow its testing against evidence with a view towards ruling it out. He noted that most people had once assumed that all swans are white. This assumption was based on the observation, over time, of uncounted numbers of white swans — and each such observation was taken as evidence supporting the assumption. However, there came a time when a black swan was found in Australia, and its discovery served to disprove the assumption that all swans are white. In generalizing from this discovery, Popper understood that you would not test the hypothesis that all swans are white by undertaking a search for white swans — because no matter how many white swans you found, you would neither have proven, nor even properly tested, the hypothesis. Instead, you must mount an intensive search for a single non-white swan.  If you found even one of those, you would have ruled the hypothesis out. Alternatively, and without finding a non-white swan, it remained viable — but because there remained the possibility of a single undetected non-white swan, it could not be regarded as proven.

The AGW hypothesis that so many people claim accounts for what is essentially pretend global warming has never been treated this way. Initially, its proponents engaged in a search for supporting evidence: Elevated average annual temperatures, local glacial retreats, elevated-temperature indicators in proxy systems such as tree-ring records, measurable coincident increases in atmospheric CO2 concentration, and so on — a search for white swans. But these efforts ignored, and failed even to seek, either any alternative explanations or evidence that would have ruled the hypothesis out. AGW has failed the predictions test again and again; any true scientific hypothesis with so poor an evidence-based evaluation record would have been scrapped by now. Instead, its proponents elevated it to the status of a theory and, ignoring the fact that climate changes continually, renamed it “climate change.”

Models are essentially used as predictive tools, so they are only as good as the information upon which they are constructed. If there are any unknown components in the modeled system, then the model’s predictions will, almost by definition, be unreliable.  In the case of a system both as complex and incompletely understood as Earth’s atmosphere, the model’s construction will essentially be required to include untested, incomplete, and/or unproven function assumptions and data. In such a case, the problems and pitfalls of using these models to construct governing policies quickly become self-evident: People trying to rely on the models essentially cannot know what they are doing.  When, for example, their model does not predict their real-world observations, they tweak it until it does — which introduces errors-by-expectation into both output and the policies based upon it. These errors increase in magnitude, and therefore in effect, in a non-linear fashion directly proportional both to the size of the system and to the modeled outputs.

AGW’s predictions are not being reliably confirmed by observations. When stasis and/or cooling occur rather than warming — as has been the case over the last decade-and-a-half — atmospheric scientists fudge interpretations by saying that if it is cool, well, that is just weather; if it is warm, though, that is climate.  Alternatively, they claim AGW predicts the cooling — as, for example, with the recent polar-vortex outbreaks.  However, a theory that predicts everything predicts nothing — because a theory that predicts everything cannot be falsified through testing; nothing will serve to rule it out.

Read more: http://www.americanthinker.com/articles/2014/11/anthropogenic_global_warming_and_the_scientific_method.html#ixzz3K5Y6CxXN
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Noble Cause Corruption

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from Ed Driscoll in PJ Media, Earth in the Grubering

As the Watts Up With That blog notes:

Our critics sometimes dismiss skeptics as “conspiracy theorists” noting how unlikely it would be that thousands of  scientists would collude.   They miss the point.  We now know that Grubering takes place — we see it laid bare in the Obamacare campaign.  It was not strictly a “conspiracy”.  Rather it was an arrogant belief that lying was necessary to persuade a “stupid” public to adopt the policy preferences of the politicians and the academics in their employ.  Its Noble Cause Corruption, not conspiracy, that is at the root of this behavior.

Grubering also helps to define the relatively recent trend on the left not just to lie — that’s always been a component of the left — but to openly admit to lying as an unalloyed good to advance the Noble Cause.

 

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AGW Myths

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from Hot Air, What the mainstream media wont tell you about global warming by Jeff Dunetz

excerpt:

4) There is not ONE climate computer model that has accurately connected CO2 to climate change. In fact CO2 is at its highest levels in 13,000 years and the earth hasn’t warmed in almost 18 years. Approximately 12,750 years ago before big cars and coal plants CO2 levels were higher than today. And during some past ice ages levels were up to 20x today’s levels.

6) The climate models pushed by the global warming enthusiasts haven’t been right. Think about that one for a second. If you believe what people like Al Gore the polar ice caps should have melted by now (actually by last year), most coastal cities should be underwater and it should be a lot warmer by now. As my Mom always said, Man plans and God laughs. The Earth’s climate is a very complicated system and the scientists haven’t been able to account for all the components to create an accurate model.

7) You are more likely to see the tooth fairy or a unicorn than a 97% consensus of scientists believing that there is man-made global warming. The number is a convenient fraud. Investigative journalists at Popular Technology reported the 97% Study falsely classifies scientists’ papers, according to the scientists that published them.  A more extensive examination of the Cook study reported that out of the nearly 12,000 scientific papers Cook’s team evaluated, only 65 endorsed Cook’s alarmist position. That is less than 0.97%. How did they come up with 97%? Well out of all the scientists who had a definite opinion, 97% agreed there was global warming and it was the fault of mankind. And how did the Cook folks determine which scientists believed what? They didn’t ask, they read papers written by these scientists and came up with their own opinion.

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Unwarranted Climate Predictions

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From The Weekly Standard, The Party of Reason, by Jeff Bergner

Excerpt:

Unrepeatable events like the evolution of the world’s species and the evolution of the world’s climate are inherently difficult to explain, and their future course is even harder to predict. Discernment of patterns over time does not constitute knowledge of future developments. The cyclical warming and cooling of the Earth over millennia is precisely not what is at stake; what is claimed is that man-made global warming is a new planetary phenomenon. In the absence of a hypothesis to account for the rate and direction of change, predictions of its future course are simple extrapolations from the past—that is, mere guesswork.

Even when there is such a hypothesis, predictions may be unwarranted. For example, evolutionary biology—which is held up by some climate change acolytes as the gold standard of settled science—teaches that species have adapted over time. With this theory in hand, evolutionary biology can infer the existence of certain intermediate life forms even in the absence of fossil evidence. If such fossils are found, their discovery supports the underlying theory.

But evolutionary biology does not predict the future course of evolution. Past experience suggests we should expect adaptation and natural selection to continue to operate. But evolutionary biology tells us nothing about the types, numbers, or characteristics of the species yet to come. If and when species evolve a certain way, all that can be said—after the fact—is that this must have come about through adaptation and natural selection. The ability to predict replicable events is one thing, the possibility of predicting the onetime evolution of the Earth, its species, and its climate quite another. In short, climate activists are asking far more of global warming models than is asked of evolutionary biology.

Today’s knowledge of global warming consists of longer and better records of temperatures observed around the world than ever before. This is historical knowledge. The careful recording of global temperatures over time is no different in principle from the recording of the U.S. unemployment rate or the rise and fall of kingdoms. From this kind of knowledge alone, nothing can be predicted about the future.

We also have models which purport to account for the rise of global temperatures, most of which focus heavily on carbon dioxide emissions as a “forcing” factor for global temperatures. The best, the Berkeley Earth Surface Temperature project, begins correlating temperatures and carbon dioxide levels in the mid-18th century, when global temperatures were beginning to rise. A persuasive model, however, would be able to map accurately earlier periods of rising and falling temperatures. More, it would contain within it an implicit hypothesis (about the climate sensitivity of the planet) that could generate a correct and potentially falsifiable prediction about the future. No model has done either. None predicted the relatively flat global temperatures of the past 17 years.

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Making Rulers Uncomfortable

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One of my favorite blog postings this year is The Left is Too Smart to Fail by Daniel Greenfield at Sultan Knish.  Science is for Stupid People is equally worthy and an excellent companion piece to the first article.

Excerpts:

Science, the magic of the secular age, is their church. But science isn’t anyone’s church. Science is much better at disproving things than at proving them. It’s a useful tool for skeptics, but a dangerous tool for rulers. Like art, science is inherently subversive and like art, when it’s restricted and controlled, it stops being interesting. 

But manufactured intelligence has the same relationship to intelligence as a painting of the ocean does to the real thing.

The real ocean is complicated and messy. So is real intelligence. Manufactured intelligence is the fashion model playing a genius in a movie. Real intelligence is an awkward man obsessing over a handful of ideas, some of them ridiculously wrong, but one of which will change the world.

Real intelligence isn’t marketable because it doesn’t make an elite feel good about its power.

Biblical fake prophets were often preferred to real prophets because they made rulers feel comfortable about the future. The modern technoprophet assures a secular elite that it can effectively control people and that it even has the obligation to do so. It tells them that “science” is on their side.The easy way to tell real religion from fake religion is that real religion doesn’t make you feel good. It doesn’t assure you that everything you’re doing is right and that you ought to keep on doing it.

The same holds true for science. Real science doesn’t make you feel smart. Fake science does.

No matter how smart you think you are, real science will make you feel stupid far more often than it will make you feel smart. Real science not only tells us how much more we don’t know than we know, a state of affairs that will continue for all of human history, but it tells us how fragile the knowledge that we have gained is, how prone we are to making childish mistakes and allowing our biases to think for us.

Science is a rigorous way of making fewer mistakes. It’s not very useful to people who already know everything. Science is for stupid people who know how much they don’t know.

A look back at the march of science doesn’t show an even line of progress led by smooth-talking popularizers who are never wrong. Instead the cabinets of science are full of oddballs, unqualified, jealous, obsessed and eccentric, whose pivotal discoveries sometimes came about by accident. Science, like so much of human accomplishment, often depended on lucky accidents to provide a result that could then be isolated and systematized into a useful understanding of the process.