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.

The Logic and Boundaries of Scepticism

In response to a couple of my recent postings, William Connolley has made what I consider to be some pretty absurd statements. The lack of logic and the imprecision of language he uses have elements in common with the more mainstream believers in “climate science”. I consider some of these below, along with other statements.

__________________________________________________________

Consider the statement

You’ve failed to realise that using the label “skeptic” doesn’t actually make you a skeptic.

Equally it does not mean that a person is wrong in using the label. Given the word has multiple broad definitions, demonstrating that another is not a genuinely a sceptic1 is extremely difficult.

___________________________________________________________

I am sceptical of the statement “the majority of twentieth century warming was caused by the increase in GHGs“. In a respected dictionary I find two definitions that both apply to my scepticism

  1. a person who questions the validity or authenticity of something purporting to be factual.
  2. a person who maintains a doubting attitude, as toward values, plans, statements, or the character of others.

If someone states a different definition of “sceptic“, (which they may have made up) it does not mean I am not sceptical. It just means that they are playing with words.

If someone says the statement is proven by scientific evidence they are wrong. Scientific statements are never proven, just failed to be falsified by the evidence.

If someone says that there is overwhelming scientific evidence in support of the above statement then they are wrong. There is insufficient data to demonstrate beyond reasonable doubt at the moment. There may never be the evidence, as much of stored heat energy of the climate is in the oceans. Most of the twentieth century data necessary to establish the hypothesis is missing.

If someone says of the statement I am not sceptical, but instead denying the scientific consensus they would be wrong. Firstly, the consensus IPCC does exclude the possibility that a minority of the warming was increased by greenhouse gases. Check out the 2013 AR5 WG1 SPM to verify. Secondly, even if they did make the claim, given that the IPCC has in the past made knowledge claims that it no longer holds to (e.g. radiative forcing components and the hockey stick), I am justified in being sceptical of their abilities to get it right this time.

Further, if someone says of the statement I am not sceptical due denying the scientific consensus, they are using an evaluation criteria that I reject. They are free to believe it is a valid criteria, but I believe it is equivalent to “hearsay” evidence in law.

My rejection of the claim that the statement “twentieth century warming was human caused” as being essentially true does not make me unsceptical of the weaker first statement. Nor is it sufficient to claim that I reject the IPCC Consensus, as they make even weaker statements.

___________________________________________________________

If someone were to demonstrate beyond reasonable doubt that a statement were true, then I would cease being sceptical. Personally I would accept the scientific evidence at a much lower level than that in support of the statements “smoking causes lung cancer”2 or “HIV causes AIDS”3.

___________________________________________________________

On reflection the last statement is not quite correct when applied to climatology. In the past I would have accepted a scientific statement based on expert opinion, or reasonable scientific evidence. But on many levels the climate community have breached the trust which any reasonable member of the public might bestow on an expert. They have failed to draw upon the accumulated wisdom of other areas, such as philosophy of science, decision theory, diplomacy or public choice economics. They have rejected things I value, such as comparing and contrasting different viewpoints, recognizing one’s own bias and listening to others. They have embraced principles I dislike, such as marginalizing opponents, and censoring of opposing opinions. But most of all many will never countenance the possibility of their own fallibility.

___________________________________________________________

If someone does not reject out of hand statements of Murray Salby (who rejects the notion that the rise in CO2 levels is human caused), it does not automatically mean they accept wholeheartedly what he says. Neither does posting articles on their blog mean they believe in what Salby says. There are a number of alternative reasons. For instance, they could feel that his sacking was not justified. Or they could mean the website owner is a pluralist in science, who believes that you should not reject new ideas out of hand. Or they could believe in academic freedom. Or they could be trying to act as a forum for different ideas. Instead, using that, or similar arguments shows an inability to consider other possibilities, or to countenance that those you oppose may have valid alternative positions. It is a normal human failing to deny the humanity of others, and one that I believe we should strive to counter. Further, those with high intelligence, coupled with dogmatic beliefs, are often those most guilty of casting those with opposing beliefs as being incapable of understanding their viewpoint.

___________________________________________________________

Claims that doctorates in climatology (or related subjects) confer special skills or abilities, that non-scientists do not possess is just bluster. The subject has no established track record of understanding the climate system, but has plenty of excuses for failure. It ignores many distinctions learnt in other empirically-based subjects. But most of all, the subject demands belief in a particular scientific hypothesis. Any criticism of that hypothesis, or contradictory evidence, undermines their core beliefs. Thus being too close to the subject may be a positive disability in engagement. To counter this more traditional sciences have promoted belief in the scientific method rather than belief in the scientific hypothesis. Those areas with strong ideological beliefs, such as economics and politics, have in free societies recognized the values of pluralism.

Kevin Marshall

  1. In Britain, “skeptic” is spelt “sceptic”. So I use that spelling, except when quoting others.
  2. I discussed the evidence from Cancer Research UK here following an article at “The Conversation“.
  3. AVERT, an HIV and AIDS Charity based in the UK, gives a long and through article on the case for “HIV causes AIDS“. In terms of communication of their case, there is a lot that the climate community could learn from.

Two Comments on Antarctic Ice Accumulation

Jo Nova blogs on a study that claims the Antarctic continent is accumulating ice mass at a rapid rate. I have made two comments. One is opposing someone who claims that Antarctica is actually losing ice. The other is that the claimed rate of ice accumulation does not make sense against known data on sea levels.

Manicbeancounter

April 17, 2013 at 6:27 am · Reply

John Brooks says

I’m also interested that the mass of antarctic land ice follows solar irradiance. This makes perfect sense. However I can’t see why the effective of an increase in the greenhouse effect wouldn’t have exactly the same result.

Maybe you should look at the period covered by the graph John. There is an 800 year correlation of mass of antarctic land ice with solar irradiance, with the biggest movements in both prior to 1800. Insofar as the greenhouse effect is significant, it is nearly all after 1945.

And for some reason, I’ve got the idea in my head that antarctic land ice is decreasing.

Sure enough from the Carbon Brief link, this quote

Measurements from the Gravity Recovery and Climate Experiment (GRACE) satellite since 2002 have shown that the mass of the Antarctic ice sheet is decreasing at an average rate of 100 cubic kilometres every year – the size of a small UK city.

(emphasis mine)
The size of a city is usually measured in area, not volume. The ancient City of York, for instance, has an area of 272 square kilometres (105 square miles) and a population of 125,000. Or maybe they mean the volume of the buildings in a city? A famous building in New York is the Empire State Building. Not only is it quite tall it also has quite a large volume. Around 1,040,000 cubic metres or 0.001 cubic kilometres in fact. So does the Carbon Brief claim that a small UK city have a volume of buildings equivalent to 100,000 Empire state buildings? Or each average person in a small UK city occupies a building volume greater than Buckingham Palace?
Alternatively, does John Brooks quote a source that does not have a clue about basic maths?

Manicbeancounter

April 17, 2013 at 8:01 am · Reply

I think this paper does not stack up. I worked as a management accountant in industry for 25 years. One thing I learnt early on when estimating or forecasting was to sense-check the estimates. No matter how good your assumptions are, when estimating or extrapolating well beyond the data trend (where there is potential for error), the best check on the data is by reconciling with other data.
From the above

“The SMB of the grounded AIS is approximately 2100 Gt yr−1, with a large interannual variability. Those changes can be as large as 300 Gt yr−1 and represent approximately 6% of the 1989–2009 average (Van den Broeke et al., 2011).”

A gigatonne of ice is equivalent to a cubic kilometre of water. If the land ice volume is increasing, the water must come from somewhere. Nearly all of that water needs to come from the oceans.
Now for some basic maths. A gigatonne is a billion tonnes. As water has a relative density of 1.0, a tonne of water (1,000 litres) is a cubic metre. Therefore a gigatonne of water is a cubic kilometre (1000^3 = 1,000,000,000 = one billion).
A further factor to consider is the area of the oceans. According to my Times Concise Atlas, the total area of the oceans and seas (excluding the enclosed waters like the Dead Sea and Lake Baykal) is 325,000,000km^2. A cubic kilometre of water added to an enclosed sea of one million square kilometres, would raise the sea level by just 1mm (1000mm x 1000m = 1,000,000mm in a kilometre). So 325km^3 = 325Gt-1 of new ice accumulation above sea level in Antarctica would reduce sea levels by 1mm, or 2100GT-1 by 6.5mm.
Some of the ice accumulation will be on ice shelves, so the impact of 2100GT-1 extra ice per annum extra ice might be to reduce sea levels by just 5mm per annum. Also sea levels might be rising by a little less than the 3.2mm a year that official figures claim, but there is no evidence that sea levels are falling. Further, any net ice melt elsewhere (mostly Greenland) is only adding 1mm to sea level rise. So the rest must be mostly due to thermal expansion of the oceans. I think that the evidence for the oceans heating is very weak and of insignificant amounts. Even Kevin Trenberth in his wildest flights of fantasy would not claim the missing heat (from the air surface temperatures) adds more than 1-2mm to sea level rise.
What this study does show is that by honestly looking at data in different ways, it is possible to reach widely different conclusions. It is only by fitting the data to predetermined conclusions (and suppressing anything outside the consensus) that consistency of results can be achieved.

My scepticism on global warming stems from a belief that scientific evidence is strengthened by being corroborated from independent sources. Honest and independent data analysis means that wildly different conclusions can be reached. Comparing and contrasting these independent sources leads me to believe that the public face of the global warming climate change consensus massively exaggerates the problem.

Kevin Marshall

The Role of Pivot Tables in Understanding Lewandowsky, Oberauer & Gignac 2012

Summary

Descriptive statistics, particularly in the form of pivot tables enable a bridging of the gap between the public pronouncements and the high level statistical analysis that can only be performed by specialists. In empirically-based scientific papers, data analysis by spread sheet enables the robust questions to be asked by the non-specialist and the expert reviewer alike. In relation to Lewandowsky et. al 2012, it highlights the gulf between the robust public claims and the actual opinion poll results on which it is based.

Introduction

In a blog post “Drilling into Noise” on 17 September, Stephan Lewandowsky (along with co-author Klaus Oberauer) makes an interesting comment

The science of statistics is all about differentiating signal from noise. This exercise is far from trivial: Although there is enough computing power in today’s laptops to churn out very sophisticated analyses, it is easily overlooked that data analysis is also a cognitive activity.

Numerical skills alone are often insufficient to understand a data set—indeed, number-crunching ability that’s unaccompanied by informed judgment can often do more harm than good.

This fact frequently becomes apparent in the climate arena, where the ability to use pivot tables in Excel or to do a simple linear regressions is often over-interpreted as deep statistical competence.

Now let me put this in context.

    The science of statistics is all about differentiating signal from noise. This exercise is far from trivial:

A more typical definition of statistics is

Statistics is the study of how to collect, organize, analyze, and interpret numerical information from data.

So Lewandowsky and Oberauer appear to seem to have a narrow and elitist interpretation.

“it is easily overlooked that data analysis is also a cognitive activity.”

Lewandowsky and Oberauer are cognitive scientists. They are merely claiming that this is within their area of competence.

Numerical skills alone are often insufficient to understand a data set—indeed, number-crunching ability that’s unaccompanied by informed judgment can often do more harm than good.

Agreed – but that implies that what follows should demonstrate something unique, they can only be gained by higher level or “scientific” analysis.

This fact frequently becomes apparent in the climate arena, where the ability to use pivot tables in Excel or to do a simple linear regressions is often over-interpreted as deep statistical competence.

I have not found pivot tables used before to analyse data in the climate arena. Nor have I seen simple linear regressions. The heavyweight statistical analysis from those who dispute the science has centred around one person – Steve McIntyre. In fact, to my knowledge, the first instance of when pivot tables were presented are primary analysis by sceptics was when I published my analysis

I would quite agree that pivot tables are not a replacement for deep statistical analysis. But it has role. My analysis using pivot tables, published on 1st September has a number of things which I identified independently which are not brought out in the original paper. These I present below. Then I will suggest how the reporting in the mainstream media might have been somewhat different if they had seen the pivot table summaries. Finally I will make some suggestions as to how the low level statistical analysis can contribute to relating to more “scientific” statistics.

Analysis using pivot tables

How Many Sceptics?

When I first glanced through the paper at the end of July, I wrote

It was an internet based survey, with links posted on 8 “pro-science” blogs. Five skeptic blogs were approached. As such, one would expect that “pro-science” responses would far outweigh “denialist” responses. I cannot find the split.

On obtaining the data, this was what first looked at. In the posting I looked at the 4 Climate Science questions, classifying into acceptors and rejectors (“denialist”) of the science.


Or summarising into 3 categories


Those who dogmatically rejected every question were outnumbered more than 10 to 1 by those who dogmatically accepted. Those who accept the science comprise three-quarters of the respondents. Most people would believe this to be material to a paper analysing those who reject the science.

NASA faked the moon landing|Therefore (Climate) Science is a Hoax

This is the beginning of the title of the paper. Pivot tables are great analysing this. The row labels are “Climate Science Belief”, the columns are CYMoon, and under “∑ values” enter the count of another column of values.

After a bit of formatting, and three more columns of simple formulas, I got this.


Just 10 out of 1145 respondents agree that NASA faked the moon landings. (I was not the first to publish this result. Anthony Watts piped me by a few hours.)

Strictly this is a claim against the “Climate Change” conspiracy theory CYClimChange and CYMoon. I did this table as well


Of the 64 who strongly accept that the climate change conspiracy theory, just 2 also strongly accept CYMOON. Even worse the title is the other way round. So the sample of those who believe NASA faked the moon landings is just 10. The sample size was just too small to make a prediction. Even worse, you could make the wrong result due to the next issue.

Identification of scam responses

One test was to look at the average agreement to each of 12 conspiracy theories that were independent of the climate area. So I rounded the average response to the nearest whole number for each respondent. And then did a pivot table.


I believe I was the first to identify publically the two that averaged 4 on the conspiracy theories and rejected the climate science. These are the two that Steve McIntyre has dubbed “Super-scammers”.

The biggest conclusion that I see is that the vast majority of respondents, no matter what their views on climate, don’t have much time for conspiracy theories. In fact, if you take out the two super-scammers, the most sceptical bunch are the group that dogmatically reject climate science.

This is confirmed if you take the average conspiracy score for each group.


Taking out the two super-scammers brings the average for the dogmatic rejectors from 1.63 to 1.49. With such small numbers, one or two outliers can have an impact on the data.

Measuring up against the public perception

There were two major newspaper articles that promoted the

The Guardian article on 27th July started

Are climate sceptics more likely to be conspiracy theorists?

New research finds that sceptics also tend to support conspiracy theories such as the moon landing being faked

Even a paper such as the Guardian, which prints all sorts of extremist dogma in denigrating sceptics, would have thought twice about publishing that comment if they had been presented with the tables.

The Telegraph article of 28th August included

“NASA faked the moon landing – Therefore (Climate) Science is a Hoax: An Anatomy of the Motivated Rejection of Science”, was based on a survey of more than 1000 visitors to blogs dedicated to discussion of climate change.

An astute reporter, on the basis of my pivot tables, could reasonably ask Professor Lewandowsky how it follows from just 10 respondents who support the idea that “NASA faked the moon landing” that you can make any sort of prediction about beliefs about climate. The questionnaires were placed on climate blogs, not conspiracy-based blogs, so surely any prediction should be framed the other way round?

It also included

The lead researcher, Professor Stephan Lewandowsky, from the University of Western Australia, said conspiracy theories are the “antithesis to scientific thinking” and those who believe them are more likely to reject the scientific consensus that humans contribute to climate change.

“Science is about weeding out bad ideas,” he told The Daily Telegraph. “With conspiracy theories, you start out with a theory and stick to it no matter what the evidence. So it is not that surprising that conspiracy theorists would not accept scientific propositions … If the scientific evidence is overwhelming and you don’t like the conclusion, you have to find a way to reject those findings.”

An astute reporter, on the basis of my pivot tables, could reasonably ask why Professor Lewandowsky is still sticking to his hypothesis when such small numbers support the wacky conspiracy theories. They may then ask a supplementary question. Given that there were 15 questions on conspiracy theories (14 with results reported), and just 5 on free markets, was not the original purpose to establish the conspiracy theory hypothesis and the secondary one on political orientation?

Suggestions for the Role of Low Level Statistical Analysis

In summary, whilst would quite agreeing that spread sheet analysis using pivot tables are not a replacement for deep statistical analysis there are a number of ways where it can be a powerful aid.

Firstly, it is a quick way of getting a sense of what the data is suggesting. Pivot tables can enable a quick summary visually in lots of different ways. It may need additional classifications, such as my acceptors / rejectors. It also needs thought, and an almost a manic sense of trial and error.

Second, it can give a quick comparison to what is being derived from the higher level statistics or modelling. For scientists it is a way of reviewing the data, to make sure that they have the most important points, and have not gone up blind alleys. For non-scientists (and for those scientists reviewing the work of others) it is a way of quickly getting a sense of whether the conclusions are substantiated by the empirical evidence.

Thirdly, and most importantly, it is a means of communicating to the wider public. It provides a bridge between the mainstream media and the scientists. If climate scientists want to win the trust of the wider public, then they need to relate their work in more intelligible terms, capable of being cross-examined. Instead we have the high level models and a then lot of shouting about how wrong and biased are any criticisms. That leads to a lot of scientists, including Lewandowsky, who are totally incapable of perceiving that they could be wrong, or that there could be even modicum of truth in what the critics say. This denial is best summarized in the graphic displayed in the Lewandowsky and Oberauer posting of the “skeptics” view on recent warming trends. It is a total misrepresentation, used as a means of avoiding intelligent discussion.

 

Kevin Marshall

IPCC’s 1990 Temperature Projections – David Evans against Mike Buckley

The following comments by Mike Buckley (referenced here) are more revealing about the state of climate science than any errors on Evan’s part.


  1. Surface Temperatures v lower tropospheric temperatures.

    As a beancounter (accountant) I like to reconcile figures. That is to account for the discrepancies. Jo Nova, Anthony Watts and others have found numerous reasons for the discrepancies. The surface temperature records have many “adjustments” that brings reality into line with the models. Whatever excuses you can conjure up, as an accountant I would say that they fail to offer a “true and fair view”.

  2. Trend lines should not start at the origin.

    So you disagree with standard forecasting? That is you start with the current position.

  3. Trend lines should be curved.

    Agreed. This is for simplicity. See next point.

  4. Trend lines should be further apart.

    Are you saying that the climate models have a wider predictive band of 0.75 celsius over 25 years? If they were straight lines, over a century they cannot get within 3 degrees. If Dr Evans had not simplified, the range would have been much greater.

There is a way of more precisely comparing the models with the actuals. The critical variable is CO2 levels. Therefore we should re-run the models from 1990 with actual CO2 data. By then explaining the variances, we can better achieve better understanding and adjust the models for the future. But there is plenty of evidence that this needs to be done by people who are independent. It will not happen, as the actual rise in CO2 was similar to the highest projections of the time.

The philosopher of science Karl Popper is remembered for the falsification principle. A less stringent criteria is that progressive science confronts the anomalies and gets predictions ever closer to the data. Pseudo-science closes ranks, makes up excuses, and “adjusts” perceptions of reality to fit the theory. Progressive science is highly competitive and open, whilst pseudo-science becomes ever more dogmatic, intolerant and insular.

AGW – The Limits of the Science

Just posted to Wattsupwiththat.

To say that we cannot make any predictions from models is inaccurate. However, a combination of the scarcity / inaccuracy of data and the highly complex nature of climate systems severely limit what we can be extrapolated. We are restricted to the most basic of “pattern predictions”. With respect to future temperature changes this is most probably restricted to the range of longer-term (30 plus years) trends. Prof. Bob Carter’s analysis is probably as far as we can go on the available data. That is we have a uniform, increasing, average temperature trend over the last 150 years, with 60 year cycles providing deviations around this trend. This trend is unexceptional when viewed from temperature data from ice-cores going back hundreds of thousands of years.

The attempt to cast every unusual weather event in terms of anthropogenic warming, and only selecting the data that fits the theories, not only risks policies that are inappropriate. It may lead us in failing to pick up the signals of potential trends for which the signal is weak, or where detection is from trends or patterns that do not fit theory. For example my house, along with hundreds of others in the area has been without water for over twelve hours now due to a burst water main, caused by the severe cold. A contributing factor to the delay in repair was the lack of resource available. Too much reliance on speculative forecasts of increasingly mild winters, and snow being a rare event has virtually eliminated contingency planning for extreme cold. Yet natural factors (e.g. La Nina, lack of sunspots) would have suggested otherwise.

The AGW science is not only costing us more for fuel. It is also putting us at greater risk of the consequences of extreme weather.

For Robert Carter’s views, see a video at http://video.google.com/videoplay?docid=-1326937617167558947#

Cure Worse Than The Problem? – Global Warming and California Prop 21 Compared

Warren Meyer seems to have got a little confused on his blog posting on Judith Curry’s ostracism by the alarmist community. He states

I find it just staggering that Judith Curry, whose hypotheses about man-made global warming probably overlap those of the hard core alarmists by 80-90%, can no longer be tolerated by alarmists.  Much as the Catholic Church radicalized Martin Luther when all he initially wanted to do was reform some practices (many of which the Church later reformed), the attacks on Curry seem to be having a similar effect.

The typical response by politicians, of course, is to try to get more money from taxpayers.  California has a ballot initiative this November proposing to raise vehicle licensing fees to all its citizens in order to fund state parks.  Unfortunately, this kind of funds earmarking by ballot initiative is already threatening to bankrupt California.  One problem with this approach is that it demolishes accountability — once an unelected state agency gets dedicated funds the legislature can’t touch, there is nothing that taxpayers can do if these funds are not spent wisely short of another time-consuming ballot initiative to revoke them.

In the case of state parks, the accountability problem is even worse, as the initiatives replace park user fees, which at least enforce accountability in that users can stop visiting if park services are not up to expectations, with a no-strings-attached bureaucratic windfall.  Compounding the problem, in many states the parks organizations report to rubber-stamp boards rather than the governor or any elected official, so taxpayers have absolutely no path to enforce accountability.

 

The quotation seems to belong to Meyer’s Coyote Blog. I would direct folks to the Reason Video on this issue.

http://www.coyoteblog.com/coyote_blog/2010/10/coyote-on-reason-tv-private-management-of-public-parks.html

However, maybe, there are just some words missing here, because the funding of state parks and global warming have some common policy issues.

Let me start with Global Warming. The case can be divided into two main areas. There is the science bit (the forecast warming and the catastrophic consequences) and the policy devised as a remedy (divided into the policy itself – the Legislation and actions – along with the expected / actual results). Or more simply Forecast, Consequences, Policy, and Outturn (FCPO).

In the UK, the Stern Review looked at this whole issue as a cost benefit analysis. By assuming fairly alarmist consequences (without the uncertainty) and assumes a relatively cheap (but highly effective) policy fix, gave benefits of the policy are 5 to 15 times the costs. It looks a clear-cut case for action, so anyone who denies this is either ignorant or has ulterior motives. But if you start assessing the uncertainty in the science, you will quickly find unverified hypotheses, implicit assumptions and measurement errors tending in the same direction. Furthermore the climate disruption consequential on this postulated warming is possibly more speculative still.

Even if you accept this worst case scenario with all the damaging consequences as the near certain, there is still the fact that the policies proposed (like Kyoto & Copenhagen) would only be cost-effective if each individual government implemented them like a business plan – maximising revenues and minimising costs. In Britain (with a legislated 80% CO2 reduction by 2050), the levels of costs are often many times the cost boundaries set by Stern, and there are many other costs being incurred that are at best side-shows (targets on recycling, banning incandescent light bulbs, alcohol in gasoline). Yet no Government will implement the policy with the necessary ruthlessness and doggedness required to get the positive results.

Like when Steve McIntyre looked into Mann’s hockey stick, what looks superficially to be robust falls apart when you start looking at it the totality. If absolute minimum for policy is to be an justifiable expectation of improving the likely future state as a result of taking action, then there should be some top-level independent auditing. As no independent review has taken place – like a bank reviewing a business plan to justify a loan – any major gaps or faults in any area potentially undermines the whole case for taking action. But when if the minimum requires accelerating warming, strong positive feedbacks; along with cheap and effective policies and zero issues with policy implementation or cost overruns; then there is a serious problem. So it is only by making a number of extreme and untenable assumptions that the consensus policy be supported. The overall solution is likely to be worse than the actual problem.

How does this relate to California’s state parks? Unlike for global warming, the case for action is clear. The parks need new funding for maintenance. The proposed policy would be ineffective in this, as it is not just funding levels at issue, as part of the cost problem is ineffective management. The solution of separating revenue from paying customers (or the electorate) will exacerbate the management issue. Furthermore, free access to all will increase visitor pressure, potentially hampering conservation efforts, whilst having an element of unfairness towards the inner-city poor. The overall solution is likely to be worse than the actual problem.

Kent Wind Farm – A dead loss to society

The Kent wind farm subsidy is mostly a waste of money, even measured by UNIPCC’s case for taking drastic action on CO2.

First, two statements and a bit of data.

“… the Kent windfarm. £780m invested to chase £50 ROCs. Offshore is double bubble, so £100/MWh generated.” (Sep 25, 2010 at 1:41 AM | Atomic Hairdryer at BishopHill )

“An effective carbon-price signal could realise significant mitigation potential in all sectors. Modelling studies show global carbon prices rising to 20-80 US$/tCO2-eq by 2030 are consistent with stabilisation at around 550 ppm CO2-eq by 2100. For the same stabilisation level, induced technological change may lower these price ranges to 5-65 US$/tCO2-eq in 2030.” (P.18 UNIPCC Summary for Policymakers)

An alternative for a wind farm is a small power station consisting of diesel engines. The most modern diesel engines can produce less than 500kg of CO2 per MWh. (See note)

So the subsidy should be no more than the trading credit CO2 of 12.5-50 £/tCO2.

Based on these figures, it is possible to state that of the £100/MWh subsidy, at a very minimum £75 is a dead loss to society. At most it could at much as £95. This is before you undertake a present value calculation on the trading credits value in 2030, or start questioning the underlying economic assumptions. Further this is whilst accepting UNIPCC consensus position in its entirety.

For an alternative take, see Christopher Booker in the Telegraph

Note on CO2 output for a diesel power plant

A large container ship engine has around 470kg to 560kg of CO2 output per MW (emission comparison table on page 13), with around 58% engine efficiencies. (See a MAN Diesel & Turbo paper “How to influence CO2” – 5MB pdf). Power-plants can higher up to 90% more efficiencies by heat recovery processes, potentially cutting the CO2 out per MW to 350kg. However, this would need to be verified by actual measurements.

Note on carbon credits v Subsidies

A carbon credit aims at adding to the cost of producing CO2 directly, with the objective of encouraging the most cost-effective means of saving CO2. That is if cost saving is less than the cost of the credit, you purchase the credit. If it is greater, then you make the investment. For power plants it might be very effective for bringing forward investments in newer power plants. It would not be so effective in choosing between new power plants with massive differences in cost per unit of output.

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