Failed Arctic Sea Ice predictions illustrates Degenerating Climatology

The Telegraph yesterday carried an interesting article. Telegraph Experts said Arctic sea ice would melt entirely by September 2016 – they were wrong

Dire predictions that the Arctic would be devoid of sea ice by September this year have proven to be unfounded after latest satellite images showed there is far more now than in 2012.
Scientists such as Prof Peter Wadhams, of Cambridge University, and Prof Wieslaw Maslowski, of the Naval Postgraduate School in Moderey, California, have regularly forecast the loss of ice by 2016, which has been widely reported by the BBC and other media outlets.

In June, Michel at Trustyetverify blog traced a number of these false predictions. Michel summarized

(H)e also predicted until now:
• 2008 (falsified)
• 2 years from 2011 → 2013 (falsified)
• 2015 (falsified)
• 2016 (still to come, but will require a steep drop)
• 2017 (still to come)
• 2020 (still to come)
• 10 to 20 years from 2009 → 2029 (still to come)
• 20 to 30 years from 2010 → 2040 (still to come).

The 2016 prediction is now false. Paul Homewood has been looking at Professor Wadhams’ failed prophesies in a series of posts as well.

The Telegraph goes on to quote from three, more moderate, sources. One of them is :-

Andrew Shepherd, professor of earth observation at University College London, said there was now “overwhelming consensus” that the Arctic would be free of ice in the next few decades, but warned earlier predictions were based on poor extrapolation.
“A decade or so ago, climate models often failed to reproduce the decline in Arctic sea ice extent revealed by satellite observations,” he said.
“One upshot of this was that outlier predictions based on extrapolation alone were able to receive wide publicity.
“But climate models have improved considerably since then and they now do a much better job of simulating historical events.
This means we have greater confidence in their predictive skill, and the overwhelming consensus within the scientific community is that the Arctic Ocean will be effectively free of sea ice in a couple of decades should the present rate of decline continue.

(emphasis mine)

Professor Shepard is saying that the shorter-term (from a few months to a few years) highly dire predictions have turned out to be false, but improved techniques in modelling enable much more sound predictions over 25-50 years to be made. That would require a development on two dimensions – scale and time. Detecting a samll human-caused change over decades needs far greater skill in differentiating from natural variations on a year-by-year time scale from a dramatic shift. Yet it would appear that at the end of the last century there was a natural upturn following from an unusually cold period in the 1950s to the 1970s, as documented by HH Lamb. This resulted in an extension in the sea ice. Detection of the human influence problem is even worse if the natural reduction in sea ice has worked concurrently with that human influence. However, instead of offering us demonstrated increased technical competency in modelling (as opposed to more elaborate models), Professor Shepard offers us the consensus of belief that the more moderate predictions are reliable.
This is a clear example of degenerating climatology that I outlined in last year. In particular, I proposed that rather than progressive climate science – increasing scientific evidence and more rigorous procedures for tighter hypotheses about clear catastrophic anthropogenic global warming – we have degenerating climatology, which is ever weaker and vaguer evidence for some global warming.

If Professor Wadhams had consistently predicted the lack of summer sea ice for a set time period, then it would be strong confirmation of a potentially catastrophic problem. Climatology would have scored a major success. Even if instead of ice-free summers by now, there had been evidence of clear acceleration in the decline in sea ice extent, then it could have been viewed as some progression. But instead we should accept a consensus of belief that will only be confirmed or refuted decades ahead. The interpretation of success or failure. will then, no doubt, be given to the same consensus who were responsible for the vague predictions in the first place.

Kevin Marshall

Guardian Images of Global Warming Part 2 – A Starved Dead Polar Bear

In the Part 2 of my look at Ashley Cooper’s photographs of global warming published in The Guardian on June 3rd I concentrate on the single image of a dead, emaciated, polar bear.
The caption reads

A male polar bear that starved to death as a consequence of climate change. Polar bears need sea ice to hunt their main prey, seals. Western fjords of Svalbard which normally freeze in winter, remained ice free all season during the winter of 2012/13, one of the worst on record for sea ice around the island archipelago. This bear headed hundreds of miles north, looking for suitable sea ice to hunt on before it finally collapsed and died.

The US National Snow and Ice Data Center (NSIDC) has monthly maps of sea ice extent. The Western Fjords were indeed ice free during the winter of 2012/13, even in March 2013 when the sea ice reaches a maximum. In March 2012 Western Fjords were also ice free, along with most of the North Coast was as well.  The maps are also available for March of 2011, 2010, 2009 and 2008. It is the earliest available year that seems to have the minimum extent. Screen shots of Svarlbard are shown below.

As the sea ice extent has been diminishing for years, maybe this had impacted on the polar bear population? This is not the case. A survey published late last year, showed that polar bear numbers has increased by 42% between 2004 and 2015 for Svarlbard and neighbouring archipelagos of Franz Josef Land and Novaya Zemlya.

Even more relevantly, studies have shown that the biggest threat to polar bear is not low sea ice levels but unusually thick spring sea ice. This affects the seal population, the main polar bear food source, at the time of year when the polar bears are rebuilding fat after the long winter.
Even if diminishing sea ice is a major cause of some starvation then it may have been a greater cause in the past. There was no satellite data prior to the late 1970s when the sea ice levels started diminishing. The best proxies are the average temperatures. Last year I looked at the two major temperature data sets for Svarlbard, both located on the West Coast where the dead polar bear was found. It would appear that there was a more dramatic rise in temperatures in Svarlbard in the period 1910-1925 than in period since the late 1970s. But in the earlier warming period polar bear numbers were likely decreasing, continuing into later cooling period. Recovery in numbers corresponds to the warming period. These changes have nothing to do with average temperatures or sea ice levels. It is because until recent decades polar bears were being hunted, a practice that has largely stopped.

The starvation of this pictured polar bear may have a more mundane cause. Polar bears are at the top of the food chain, relying on killing fast-moving seals for food. As a polar bear gets older it slows down, due to arthritis and muscles not working as well. As speed and agility are key factors in catching food, along with a bit of luck, starvation might be the most common cause of death in polar bears.

Kevin Marshall

Beliefs and Uncertainty: A Bayesian Primer

Ron Clutz’s introduction, based on a Scientific American article by John Horgan on January 4, 2016, starts to grapple with the issues involved.

The take home quote from Horgan is on the subject of false positives.

Here is my more general statement of that principle: The plausibility of your belief depends on the degree to which your belief–and only your belief–explains the evidence for it. The more alternative explanations there are for the evidence, the less plausible your belief is. That, to me, is the essence of Bayes’ theorem.

“Alternative explanations” can encompass many things. Your evidence might be erroneous, skewed by a malfunctioning instrument, faulty analysis, confirmation bias, even fraud. Your evidence might be sound but explicable by many beliefs, or hypotheses, other than yours.

In other words, there’s nothing magical about Bayes’ theorem. It boils down to the truism that your belief is only as valid as its evidence. If you have good evidence, Bayes’ theorem can yield good results. If your evidence is flimsy, Bayes’ theorem won’t be of much use. Garbage in, garbage out.
With respect to the question of whether global warming is human caused, there is basically a combination of three elements – (i) Human caused (ii) Naturally caused (iii) Random chaotic variation. There may be a number of sub-elements and an infinite number of combinations including some elements counteracting others, such as El Nino events counteracting underlying warming. Evaluation of new evidence is in the context of explanations being arrived at within a community of climatologists with strong shared beliefs that at least 100% of recent warming is due to human GHG emissions. It is that same community who also decide the measurement techniques for assessing the temperature data; the relevant time frames; and the categorization of the new data. With complex decisions the only clear decision criteria is conformity to the existing consensus conclusions. As a result, the original Bayesian estimates become virtually impervious to new perspectives or evidence that contradicts those original estimates.

Science Matters

Those who follow discussions regarding Global Warming and Climate Change have heard from time to time about the Bayes Theorem. And Bayes is quite topical in many aspects of modern society:

Bayesian statistics “are rippling through everything from physics to cancer research, ecology to psychology,” The New York Times reports. Physicists have proposed Bayesian interpretations of quantum mechanics and Bayesian defenses of string and multiverse theories. Philosophers assert that science as a whole can be viewed as a Bayesian process, and that Bayes can distinguish science from pseudoscience more precisely than falsification, the method popularized by Karl Popper.

Named after its inventor, the 18th-century Presbyterian minister Thomas Bayes, Bayes’ theorem is a method for calculating the validity of beliefs (hypotheses, claims, propositions) based on the best available evidence (observations, data, information). Here’s the most dumbed-down description: Initial belief plus new evidence = new and improved belief.   (A fuller and…

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James Ross Island warming of past 100 years not unusual

At Wattsupwiththat there is a post by Sebastian Lüning The Medieval Warm Period in Antarctica: How two one-data-point studies missed the target.

Lüning has the following quote and graphic from Mulvaney et al. 2012, published in Nature.

But the late Bob Carter frequently went on about the recent warming being nothing unusual. Using mainstream thinking, would you trust a single climate denialist against proper climate scientists?

There is a simple test. Will similar lines fit to data of the last two thousand years? It took me a few minutes to produce the following.

Bob Carter is right and nine leading experts, plus their peer reviewers are wrong. From the temperature reconstruction there were at least five times in the last 2000 years when there were similar or greater jumps in average temperature. There are also about seven temperature peaks similar to the most recent.

It is yet another example about how to look at the basic data rather than the statements of the experts. It is akin to a court preferring the actual evidence rather than hearsay.

Kevin Marshall

Insight into the mindset of FoE activists

Bishop Hill comments about how

the Charities Commissioners have taken a dim view of an FoE leaflet that claimed that silica – that’s sand to you or me – used in fracking fluid was a known carcinogen.

Up pops a FoE activist making all sorts of comments, including attacking the hosts book The Hockey Stick Illusion. Below is my comment

Phil Clarke’s comments on the hosts book are an insight into the Green Activists.
He says Jan 30, 2016 at 9:58 AM

So you’ve read HSI, then?
I have a reading backlog of far more worthwhile volumes, fiction and non-fiction. Does anybody dispute a single point in Tamino’s adept demolition?


Where did I slag off HSI? I simply trust Tamino; the point about innuendo certainly rings true, based on other writings.
So no, I won’t be shelling out for a copy of a hatchet job on a quarter-century old study. But I did read this, in detail

Tamino’s article was responded to twice by Steve McIntyre. The first looks at the use of non-standard statistical methods and Re-post of “Tamino and the Magic Flute” simply repeats the post of two years before. Tamino had ignored previous rebuttals. A simple illustration is the Gaspé series that Tamino defends. He misses out many issues with this key element in the reconstruction, including that a later sample from the area failed to show a hockey stick.
So Phil Clarke has attacked a book that he has not read, based on biased review by an author in line with his own prejudices. He ignores the counter-arguments, just as the biased review author does as well. Says a lot about the rubbish Cuadrilla are up against.

Kevin Marshall

John Cook undermining democracy through misinformation

It seems that John Cook was posting comments in 2011 under the pseudonym Lubos Motl. The year before physicist and blogger Luboš Motl had posted a rebuttal of Cook’s then 104 Global Warming & Climate Change Myths. When someone counters your beliefs point for point, then most people would naturally feel some anger. Taking the online identity of Motl is potentially more than identity theft. It can be viewed as an attempt to damage the reputation of someone you oppose.

However, there is a wider issue here. In 2011 John Cook co-authored with Stephan Lewandowsky The Debunking Handbook, that is still featured prominently on the This short tract starts with the following paragraphs:-

It’s self-evident that democratic societies should base their decisions on accurate information. On many issues, however, misinformation can become entrenched in parts of the community, particularly when vested interests are involved. Reducing the influence of misinformation is a difficult and complex challenge.

A common misconception about myths is the notion that removing its influence is as simple as packing more information into people’s heads. This approach assumes that public misperceptions are due to a lack of knowledge and that the solution is more information – in science communication, it’s known as the “information deficit model”. But that model is wrong: people don’t process information as simply as a hard drive downloading data.

If Cook was indeed using the pseudonym Lubos Motl then he was knowingly putting out into the public arena misinformation in a malicious form. If he misrepresented Motl’s beliefs, then the public may not know who to trust. Targeted against one effective critic, it could trash their reputation. At a wider scale it could allow morally and scientifically inferior views to gain prominence over superior viewpoints. If the alarmist beliefs were superior it what be necessary to misrepresent alternative opinions. Open debate would soon reveal which side had the better views. But in debating and disputing, all sides would sharpen their arguments. What would quickly disappear is the reliance on opinion surveys and rewriting of dictionaries. Instead, proper academics would be distinguishing between quality, relevant evidence from dogmatic statements based on junk sociology and psychology. They would start defining the boundaries of expertise between the basic physics, computer modelling, results analysis, public policy-making, policy-implementation, economics, ethics and the philosophy of science. They may then start to draw on the understanding that has been achieved in these subject areas.

Kevin Marshall

ATTP on Lomborg’s Australian Funding

Blogger …and then there’s physics (ATTP) joins in the hullabaloo about Bjorn Lomberg’s Lomborg’s Consensus Centre is getting A$4m of funding to set up a branch at the University of Western Australia. He says

However, ignoring that Lomborg appears to have a rather tenuous grasp on the basics of climate science, my main issue with what he says is its simplicity. Take all the problems in the world, determine some kind of priority ordering, and then start at the top and work your way down – climate change, obviously, being well down the list. It’s as if Lomborg doesn’t realise that the world is a complex place and that many of the problems we face are related. We can’t necessarily solve something if we don’t also try to address many of the other issues at the same time. It’s this kind of simplistic linear thinking – and that some seem to take it seriously – that irritates me most.

The comment about climatology is just a lead in. ATTP is expressing a normative view about the interrelationship of problems, along with beliefs about the solution. What he is rejecting as simplistic is the method of identifying the interrelated issues separately, understanding the relative size of the problems along with the effectiveness and availability of possible solutions and then prioritizing them.

This errant notion is exacerbated when ATTP implies that Lomborg has received the funding. Lomborg heads up the Copenhagen Consensus Centre and it is they who have received the funding to set up a branch in Australia. This description is from their website

We work with some of the world’s top economists (including 7 Nobel Laureates) to research and publish the smartest solutions to global challenges. Through social, economic and environmental benefit-cost research, we show policymakers and philanthropists how to do the most good for each dollar spent.

It is about bringing together some of the best minds available to understand the problems of the world. It is then to persuade those who are able to do something about the issues. It is not Lomborg’s personal views that are present here, but people with different views and from different specialisms coming together to argue and debate. Anyone who has properly studied economics will soon learn that there are a whole range of different views, many of them plausible. Some glimpse that economic systems are highly interrelated in ways that cannot be remotely specified, leading to the conclusion that any attempt to create a computer model of an economic system will be a highly distorted simplification. At a more basic level they will have learnt that in the real world there are 200 separate countries, all with different priorities. In many there is a whole range of different voiced opinions about what the priorities should be at national, regional and local levels. To address all these interrelated issues together would require the modeller of be omniscient and omnipresent. To actually enact the modeller’s preferred policies over seven billion people would require a level of omnipotence that Stalin could only dream of.

This lack of understanding of economics and policy making is symptomatic of those who believe in climate science. They fail to realize that models are only an attempted abstraction of the real world. Academic economists have long recognized the abstract nature of the subject along with the presence of strong beliefs about the subject. As a result, in the last century many drew upon the rapidly developing philosophy of science to distinguish whether theories were imparting knowledge about the world or confirming beliefs. The most influential by some distance was Milton Friedman. In his seminal essay The Methodology of Positive Economics he suggested the way round this problem was to develop bold yet simple predictions from the theory that, despite being unlikely, are nevertheless come true. I would suggest that you do not need to be too dogmatic in the application. The bold predictions do not need to be right 100% of the time, but an entire research programme should be establishing a good track record over a sustained period. In climatology the bold predictions, that would show a large and increasing problem, have been almost uniformly wrong. For instance:-

  • The rate of melting of the polar ice caps has not accelerated.
  • The rate of sea level rise has not accelerated in the era of satellite measurements.
  • Arctic sea ice did not disappear in the summer of 2013.
  • Hurricanes did not get worse following Katrina. Instead there followed the quietest period on record.
  • Snow has not become a thing of the past in England, nor in Germany.

Other examples have been compiled by Pierre Gosselin at Notrickszone, as part of his list of climate scandals.

Maybe it is different in climatology. The standard response is that the reliability of the models is based on the strength of the consensus in support. This view is not proclaimed by ATTP. Instead from the name it would appear he believes the reliability can be obtained from the basic physics. I have not done any physics since high school and have forgotten most of what I learnt. So in discerning what is reality in that area I have to rely on the opinions of physicists themselves. One of the greatest physicists since Einstein was Richard Feynman. He said fifty years ago in a lecture on the Scientific Method

You cannot prove a vague theory wrong. If the guess that you make is poorly expressed and the method you have for computing the consequences is a little vague then ….. you see that the theory is good as it can’t be proved wrong. If the process of computing the consequences is indefinite, then with a little skill any experimental result can be made to look like an expected consequence.

Climate models, like economic models, will always be vague. This is not due to being poorly expressed (though they often are) but due to the nature of the subject. Short of rejecting climate models as utter nonsense, I would suggest the major way of evaluating whether they say something distinctive about the real world is on the predictive ability. But a consequence of theories always being vague in both economics and climate is you will not be able to use the models as a forecasting tool. As Freeman Dyson (who narrowly missed sharing a Nobel Prize with Feynman) recently said of climate models:-

These climate models are excellent tools for understanding climate, but that they are very bad tools for predicting climate. The reason is simple – that they are models which have very few of the factors that may be important, so you can vary one thing at a time ……. to see what happens – particularly carbon dioxide. But there are a whole lot of things that they leave out. ….. The real world is far more complicated than the models.

This implies that when ATTP is criticizing somebody else’s work with a simple model, or a third person’s work, he is likely criticizing them for looking at a highly complex issue in another way. Whether his way is better, worse or just different we have no way of knowing. All we can infer from his total rejection of ideas of experts in a field to which he lacks even a basic understanding, is that he has no basis of knowing either.

To be fair, I have not looked at the earlier part of ATTP’s article. For instance he says:-

If you want to read a defense of Lomborg, you could read Roger Pielke Jr’s. Roger’s article makes the perfectly reasonable suggestion that we shouldn’t demonise academics, but fails to acknowledge that Lomborg is not an academic by any standard definition…….

The place to look for a “standard definition” of a word is a dictionary. The noun definitions are


8. a student or teacher at a college or university.

9. a person who is academic in background, attitudes, methods, etc.:

He was by temperament an academic, concerned with books and the arts.

10. (initial capital letter) a person who supports or advocates the Platonic school of philosophy.

This is Bjorn Lomborg’s biography from the Copenhagen Consensus website:-

Dr. Bjorn Lomborg is Director of the Copenhagen Consensus Center and Adjunct Professor at University of Western Australia and Visiting Professor at Copenhagen Business School. He researches the smartest ways to help the world, for which he was named one of TIME magazine’s 100 most influential people in the world. His numerous books include The Skeptical Environmentalist, Cool It, How to Spend $75 Billion to Make the World a Better Place and The Nobel Laureates’ Guide to the Smartest Targets for the World 2016-2030.

Lomborg meets both definitions 8 & 9, which seem to be pretty standard. Like with John Cook and William Connolley defining the word sceptic, it would appear that ATTP rejects the authority of those who write the dictionary. Or more accurately does not even to bother to look. Like with rejecting the authority of those who understand economics it suggests ATTP uses the authority of his own dogmatic beliefs as the standard by which to evaluate others.

Kevin Marshall

Freeman Dyson on Climate Models

One of the leading physicists on the planet, Freeman Dyson, has given a video interview to the Vancouver Sun. Whilst the paper emphasizes Dyson’s statements about the impact of more CO2 greening the Earth, there is something more fundamental that can be gleaned.

Referring to a friend who constructed the first climate models, Dyson says at about 10.45

These climate models are excellent tools for understanding climate, but that they are very bad tools for predicting climate. The reason is simple – that they are models which have very few of the factors that may be important, so you can vary one thing at a time ……. to see what happens – particularly carbon dioxide. But there are a whole lot of things that they leave out. ….. The real world is far more complicated than the models.

I believe that Climate Science has lost sight of what this understanding of what their climate models actually are literally attempts to understand the real world, but are not the real world at all. It reminds me of something another physicist spoke about fifty years ago. Richard Feynman, a contemporary that Dyson got to know well in the late 1940s and early 1950s said of theories:-

You cannot prove a vague theory wrong. If the guess that you make is poorly expressed and the method you have for computing the consequences is a little vague then ….. you see that the theory is good as it can’t be proved wrong. If the process of computing the consequences is indefinite, then with a little skill any experimental result can be made to look like an expected consequence.

Complex mathematical models suffer from this vagueness in abundance. When I see supporters of climate arguing the critics of the models are wrong by stating some simple model, and using selective data they are doing what lesser scientists and pseudo-scientists have been doing for decades. How do you confront this problem? Climate is hugely complex, so simple models will always fail on the predictive front. However, unlike Dyson I do not think that all is lost. The climate models have had a very bad track record due to climatologists not being able to relate their models to the real world. There are a number of ways they could do this. A good starting point is to learn from others. Climatologists could draw upon the insights from varied sources. With respect to the complexity of the subject matter, the lack of detailed, accurate data and the problems of prediction, climate science has much in common with economics. There are insights that can be drawn on prediction. One of the first empirical methodologists was the preeminent (or notorious) economist of the late twentieth century – Milton Friedman. Even without his monetarism and free-market economics, he would be known for his 1953 Essay “The Methodology of Positive Economics”. Whilst not agreeing with the entirety of the views expressed (there is no satisfactory methodology of economics) Friedman does lay emphasis on making simple, precise and bold predictions. It is the exact opposite of the Cook et al. survey which claims a 97% consensus on climate, implying that it relates to a massive and strong relationship between greenhouse gases and catastrophic global warming when in fact it relates to circumstantial evidence for a minimal belief in (or assumption of) the most trivial form of human-caused global warming. In relation to climate science, Friedman would say that it does not matter about consistency with the basic physics, nor how elegantly the physics is stated. It could be you believe that the cause of warming comes from the hot air produced by the political classes. What matters that you make bold predictions based on the models that despite being simple and improbable to the non-expert, nevertheless turn out to be true. However, where bold predictions have been made that appear to be improbable (such as worsening hurricanes after Katrina or the effective disappearance of Arctic Sea ice in late 2013) they have turned out to be false.

Climatologists could also draw upon another insight, held by Friedman, but first clearly stated by John Neville Keynes (father of John Maynard Keynes). That is on the need to clearly distinguish between the positive (what is) and the normative (what ought to be). But that distinction was alienate the funders and political hangers-on. It would also mean a clear split of the science and policy.

Hattips to Hilary Ostrov, Bishop Hill, and Watts up with that.


Kevin Marshall

Dixon and Jones confirm a result on the Stephan Lewandowsky Surveys

Congratulations to Ruth Dixon and Jonathan Jones on managing to get a commentary on the two Stephan Lewandowsky, Gilles Gignac & Klaus Oberauer surveys published in Psychological Science. Entitled “Conspiracist Ideation as a Predictor of Climate Science Rejection: An Alternative Analysis” it took two years to get published. Ruth Dixon gives a fuller description on her blog, My Garden Pond. It confirms something that I have stated independently, with the use of pivot tables instead of advanced statistical techniques. In April last year I compared the two surveys in a couple of posts – Conspiracist Ideation Falsified? (CIF) & Extreme Socialist-Environmentalist Ideation as Motivation for belief in “Climate Science” (ESEI).

The major conclusion through their analysis of the survey

All the data really shows is that people who have no opinion about one fairly technical matter (conspiracy theories) also have no opinion about another fairly technical matter (climate change). Complex models mask this obvious (and trivial) finding.

In CIF my summary was

A recent paper, based on an internet survey of American people, claimed that “conspiracist ideation, is associated with the rejection of all scientific propositions tested“. Analysis of the data reveals something quite different. Strong opinions with regard to conspiracy theories, whether for or against, suggest strong support for strongly-supported scientific hypotheses, and strong, but divided, opinions on climate science.

In the concluding comments I said

The results of the internet survey confirm something about people in the United States that I and many others have suspected – they are a substantial minority who love their conspiracy theories. For me, it seemed quite a reasonable hypothesis that these conspiracy lovers should be both suspicious of science and have a propensity to reject climate science. Analysis of the survey results has over-turned those views. Instead I propose something more mundane – that people with strong opinions in one area are very likely to have strong opinions in others. (Italics added)

Dixon and Jones have a far superior means of getting to the results. My method is to input the data into a table, find groupings or classifications, then analyse the results via pivot tables or graphs. This mostly leads up blind alleys, but can develop further ideas. For every graph or table in my posts, there can be a number of others stashed on my hard drive. To call it “trial and error” misses out the understanding to be gained from analysis. Their method (through rejecting linear OLS) is loess local regression. They derive the following plot.

This compares with my pivot table for the same data.

The shows in the Grand Total row that the strongest Climate (band 5) comprise 12% of the total responses. For the smallest group of beliefs about conspiracy theories with just 60/5005 responses, 27% had the strongest beliefs in about climate. The biggest percentage figure is the group who averaged a middle “3” score on both climate and conspiracy theories. That is those with no opinion on either subject.

The more fundamental area that I found is that in the blog survey between strong beliefs in climate science and extreme left-environmentalist political views. It is a separate topic, and its inclusion by Dixon and Jones would have both left much less space for the above insight in 1,000 words, and been much more difficult to publish. The survey data is clear.

The blog survey (which was held on strongly alarmist blogs) shows that most of the responses were highly skewed to anti-free market views (that is lower response score) along with being strongly pro-climate.

The internet survey of the US population allowed 5 responses instead of 4. The fifth was a neutral. This shows a more normal distribution of political beliefs, with over half of the responses in the middle ground.

This shows what many sceptics have long suspected, but I resisted. Belief in “climate science” is driven by leftish world views. Stephan Lewandowsky can only see the link between the “climate denial” beliefs and free-market, because he views left-environmentalist perspectives and “climate science” as a priori truths. This is the reality that everything is to be measured. From this perspective climate science has not failed due to being falsified by the evidence, but because scientists have yet to find the evidence; the models need refining; and there is a motivated PR campaign to undermine these efforts.

Kevin Marshall






Feynman on Communist Science

I am currently engrossed in GENIUS: Richard Feynman and Modern Physics by James Gleick

In July 1962 Feynman went behind the Iron Curtain to attend a conference on gravitation in Warsaw. He was exasperated at the state of Soviet science. He wrote to his wife Gweneth:-

The “work” is always: (1) completely un-understandable, (2) vague and indefinite, (3) something correct that is obvious and self-evident, worked out by long and difficult analysis, and presented as an important discovery, or (4) a claim based on stupidity of the author that some obvious and correct fact, accepted and checked for years is, in fact, false (these are the worst: no argument will convince the idiot), (5) an attempt to do something, probably impossible, but certainly of no utility, which, it is finally revealed at the end, fails or (6) just plain wrong. There is a great deal of “activity in the field” these days, but this “activity” is mainly in showing that the previous “activity” of somebody else resulted in an error or in nothing useful or in something promising. (Page 353)

The failings of Government-backed science are nothing new.