The Cassandra Effect and Insulting Climate Sceptics

There are two articles published today that are related. Bishop Hill posts about the “reverse Cassandra effect” and Jo Nova comments on Matt Ridley’s article in today’s Times on THE SCEPTICS ARE RIGHT. DON’T SCAPEGOAT THEM.

Bishop Hill refers to a Wired article on the late Julian Simon published some years ago:-

Simon always found it somewhat peculiar that neither the Science piece nor his public wager with Ehrlich nor anything else that he did, said, or wrote seemed to make much of a dent on the world at large. For some reason he could never comprehend, people were inclined to believe the very worst about anything and everything; they were immune to contrary evidence just as if they’d been medically vaccinated against the force of fact. Furthermore, there seemed to be a bizarre reverse-Cassandra effect operating in the universe: whereas the mythical Cassandra spoke the awful truth and was not believed, these days “experts” spoke awful falsehoods, and they were believed. Repeatedly being wrong actually seemed to be an advantage, conferring some sort of puzzling magic glow upon the speaker.

I believe that the Cassandra effect is still working. What is relevant is how you view awful. Take a classic example of the Cassandra effect. Ignaz Semmelweis found that doctors washing their hands between examining each patient reduced mortality rates. The implied “awful” truth that every experienced hospital doctor in 1840s Vienna had to accept was that, due to their ignorance, they had killed people when they were in the business of saving lives.

But for environmentalists the “scientific truth” that the human race is destroying the planet confirms their beliefs. Politicians whose mission is to make a real difference to the world – an honourable motive – can now take part in saving the planet from an evil menace. Maybe not as spectacularly as James Bond, or Flash Gordon, but they can still expect to receive plaudits and a place in history. Or at least a pat on the back from green activists in Bali, Copenhagen, Cancun….

For those who believe materialism is ultimately depraved; or humankind is inherently sinful; or capitalism will collapse through its inherent contradictions; or the rich got where they are through trampling over those like themselves; – all can latch onto the cause as well. For all these people the awful truth for the world is not so awful for them.

This is why the Cassandra effect is still very much with us. The awful truth is that politicians now find themselves in the same position of those doctors in 1840s Vienna. When they thought they were saving the world, they are in fact harming the futures of their constituents. As Matt Ridley points out in the Times today of climate change:-

Sceptics say it is not happening fast enough to threaten more harm than the wasteful and regressive measures intended to combat it. So far they have been right.

My next article will show that even the most extreme climate change believers can postulate a big enough harm from climate change than the wasteful and regressive measures intended to combat it.

Kevin Marshall

Observations on the Shollenberger Survey

In late 2012 there was a lot of adverse comment about the paper Lewandowsky, Oberauer & Gignac – NASA faked the moon landing:Therefore (Climate) Science is a Hoax: An Anatomy of the Motivated Rejection of Science (in press, Psychological Science). I did my own quick analysis using pivot tables, which was referred to elsewhere.

Last week, Brandon Shollenberger produced a shorter survey that, though tongue in cheek, aimed to replicate the findings of the Lewandowsky et al. He wrote

As you’re aware, Stephan Lewandowsky has written several papers claiming to have found certain traits amongst global warming skeptics. I believe his methodology is fundamentally flawed. I believe a flaw present in his methodology is also present in the work of many others.

To test my belief, I’m seeking participants for a short survey (13 questions). The questions are designed specifically to test a key aspect of Lewandowsky’s methodology. The results won’t be published in any scientific journal, but I’ll do a writeup on them once the survey is closed and share it online.

This was published at the blogs Wattsupwiththat, JoanneNova and BishopHill blogs. The poll is still available to view.

A few hours ago Jo Nova published Shollenberger’s initial findings, as “Warmists Are Never Wrong, Even When Supporting Genocide“. Using the same methodology that Lewandowsky et al (LOG12) “demonstrated” that those who reject the climate religion have a propensity to believe in cranky conspiracy theories, Shollenberger showed that believers in catastrophic global warming have a propensity to believe in genocide, paedophilia and human trafficking. Like for the LOG12, I have run the data through Excel pivot tables to reveal that Shollenberger was successful in undermining LOG12.

Categorizing the responses

For the LOG12 I split the respondents according to the average response to the four LOG12 “climate science” questions.


Similarly, with the Shollenberger survey, I have categorised the respondents according to response to the three questions on global warming. This time I weighted the responses in relation to belief in catastrophic anthropogenic global warming. First I changed the 1 to 5 response to a 0 to 4 response. The weightings were then 1 for Ques 1, 2 for Ques 2 and 4 for Ques 3. By dividing by the maximum score of 28, I obtained a “believer” percentage. Questions are below.

Also, I have looked at the percentage with the outlier scores, along with the average scores.


Preliminary observations

Some brief preliminary observations that stand out from the pivot tables. These are the green bordered summaries below and the responses to the individual questions at the foot.

  1. Compared with LOG12, Schollenberger gets three times the responses and takes a week rather than 18 months to publish the results.
  2. Schollenberger shows the result of only publishing a survey on only one side of the global warming divide, whilst trying to analyse the other side. The vast majority of responses are from people you are not targeting.
  3. The three times response, in a much shorter time frame indicates that sceptics are far more interested in the subject of global warming than the believers.
  4. Proportionately, more far sceptics seem to visit “believer” blogs than “believers” visit sceptic blogs. This should not be controversial. Sceptics look to understand the view they oppose, whilst “believers” look for confirmation. Climate change is no different from many other areas, including many of the softer sciences.
  5. Schollenberger, in his three questions on belief in global warming captures a broader possible range of beliefs in the climate science, than LOG12 does in four questions. In particular it is possible to distinguish between those who believe humans have caused most of the recent warming, but it is fairly trivial, and those who (like the MSM) believes we are all doomed unless we abandon out cars for bicycles and go to 2W lightbulbs everywhere. The LOG12 questions were designed to polarize views into “pro-science” and “deniers”. Schollenberger thus achieves very quickly what millions of dollars spent on opinion surveys conceals. The extreme alarmism that justifies policy is not held by the majority who believe that anthropogenic global warming is an issue.
  6. Both surveys were uncontrolled for “scam” responses. That is for those on one side to be able to mischievously post as an opponent, but with reprehensible views. The Schollenberger survey had more, and (to a lesser extent) a higher proportion of scam responses. Given the knowledge of LOG12, this is not surprising. But, given the proportions of non-scam responses, “believers” seem to have a greater propensity to scam “sceptics” than the opposite.
  7. Thus Schollenberger can demonstrate that Lewandowsky’s conclusions are as much based on scam responses as his survey.



The Survey Questions


Number of Responses to questions 4 to 13, in relation to CAGW score.


Kevin Marshall

Is there a latent problem with wind turbines?

In a posting “Accelerated Depreciation” Bishop hill says

This article at a blog called Billo The Wisp is important if true. Turbine gearbox failures apparently happen typically after 5-7 years rather than the 20 years that we are normally led to believe wind turbines last for. Moreover, their failure can be completely catastrophic, leading to the destruction of the whole turbine.

My comment is quite sceptical.

I do not think that the thrust of this post is correct – that there is a problem that gearboxes in that they will only last for 5-7 years, that has been around for 25 years and that it was so serious that the US government set up a special department to investigate in 2007. Despite all of this, there is still a largely hidden and hugely costly problem of which people are not aware. Having been in the engineering industry for a number of years I would consider the following if involved in the decision to set up a wind farm.

First, wind turbines are electro-mechanical devices. They need servicing and occasional overhauling. Ease of maintenance is important, including the replacement of major components. I would want a recommended maintenance program, along with projected parts costs, required maintenance equipment (e.g. a crane) and standard labour hours.

Second, I would want data on long-term historical performance, service and maintenance costs of each manufacturer’s equipment.

Third, if there was a large wind farm, I would include some spare parts, including major components that should last the life of the equipment. This may include have complete sets of spare parts that can be quickly swapped out – so major maintenance can be done in a workshop and not 200 metres in the air.

Fourth, I would cross-check this against industry journals. Wind turbine manufacture is a huge business with a number of manufacturers selling into a large number of countries. Issues are discussed, like in any industry.

The largest wind farms cost hundreds of millions. Businesses are not naïve. Even with large potential profits, there is always more money to be made through proper investment appraisal and protecting that investment through a thorough maintenance programme. If a major component of a wind turbine only lasted a third the length of time of the main structure, then replacing that component would become a part of the life-time costs. There would be huge incentives to minimize those costs through better design, such as ease of replacement of bearings. The only issue is that the real costs of wind turbines will never come down to a level where subsidies are no longer required.

NB a source of the reliability claims is this June 2010 article, which is now 3.5 years old.

Notes Labour’s Analysis of the Energy Market

Labour’s Green Paper on Energy has been found by Alex Cull (comment at Dec 2, 2013 at 1:03 PM) at the site “Your Britain“, in the Agenda 2015 section. Having read it, I can see why the Labour Party are not keen for the electorate to find the document. Some quick observations, that I believe are sufficient to show that Labour have not bottomed out the only, let alone the best, explanation of why retail prices have risen so fast in last few years. What this clearly shows is that Labour’s proposed policy freeze is not just misplaced; it is positively harmful to Britain having future low-cost and secure energy supplies.

Note 03/12/13: This post will be added to over the coming days.

Update 04/12/13: Note on declining investment in “clean energy”

Billions not Millions

The Executive Summary states

Lack of competition in the retail market has resulted in consumers paying £3.6m more than they need to each year.

Caption to Table 1 on page 7 states

Lack of competition in the retail market has resulted in consumers paying £3.6 billion more than they need to

Error in Calculation

The source of the £3.6bn is from Which?

The consumer group Which? found that 75 per cent of customers are on the most expensive tariffs offered by suppliers – their standard tariff – and are not getting the cheapest deal in the market. They estimate that since 2011, families across the country have paid £3.6 billion a year more than they need to as a result. That means that households are on average paying £136 each year because the retail market is not working in the way that a competitive market should. If this market was genuinely competitive, energy companies would face stronger incentives to drive their costs down and pass savings to consumers through lower prices and cheaper tariffs; but this is not happening.

That implies that

  1. In a perfectly competitive market, the single price would be the very cheapest rate available.
  2. As a consequence the big six energy companies are pocketing the difference.

So, there is a monopoly profit of greater than £3.6bn. Ofgem monitors the big six energy firms. The BBC reported on 25th November that

Overall, profits in generation and supply across the half-dozen firms fell from £3.9bn in 2011 to £3.7bn in 2012.

So the competitive market profit fell from £0.3bn to £0.1bn? I don’t think so. The price differential is due to competition working, not due to its’ failure. Like in many areas, if you shop around you can get a better deal than those who do not, as sellers will discount to win your business. If you do not shop around, you will get a bad deal. Look at insurance, hotel rooms, flights or even consumer goods. Reducing competition will cause profits will rise, and the savvy consumer will lose out. Regulate enough and even those who never haggle will not get a good deal.

Decline in those switching suppliers

…. a confusing system of 900 tariffs makes it hard for consumers to actively engage in this market. Since 2008, the number of people switching energy supplier has fallen by over 50 per cent, and switching levels are now at the lowest level on record. Low levels of switching means that the big energy companies have a ‘captured market’ which reduces the incentives to keep prices competitive.

Fig 1 shows a decline in number of people transferring between suppliers between year to year. This shows a decline from around … to …. Is this evidence of a decline?

All other things being equal, then it is evidence of declining competitiveness. But all other things are not equal. A supplier can take action to retain the business. There is passive action and non-passive action.

Passive action is when the customer tries to move away, or threatens to. They are can offered a better deal to retain the business.

Proactive action is to offer the customer a better deal. For instance, I moved supplier in 2012 on a 12 month contract. In July, just before the end of the deal, the supplier offered me their best deal. This I accepted, after a quick check.

A decline in transfers could therefore be due to suppliers taking action to retain custom. This saves on their costs, and consumer’s inconvenience, whilst keeping the market competitive. As the cost to energy companies is less, this can keep overall costs down.

A test of this is to look at the differential between the standard tariff and the competitive tariffs over time for each supplier. If that has widened over time in line with the decrease in those switching then the Labour Party are correct. If it has widened, I would be surprised given the increasing number and sophistication of the price comparison websites. It would be a failure both of government policy over many years and the market to respond to those incentives.

Differential between wholesale and retail prices

Figure 2 on page 11 is meant illustrate for the electricity and gas markets how the wholesale prices have stayed roughly the same, but the retail prices have widened. The graphic for the electricity market is shown below.

The explanation is as follows.

Wholesale energy prices have been relatively stable since the winter of 2011, rising by an average of 1 per cent a year. However, the large energy companies have increased energy prices by an average of 10.4 per cent a year over this period (Figure 3). This has led to a growing gap between wholesale and retail prices that cannot be explained by the growth in network costs or policy costs which account for 20 per cent and nine per cent of the bill respectively.

So the explanation is derived from the following logic

  1. Prices have risen by over 30% in the last 3 years.
  2. Wholesale prices form the biggest part of the cost to the consumer and have not moved very much.
  3. Other costs have grown, but now only account for 29% of the bill.
  4. By implication, the profits of the energy companies have increased at the expense of the consumer.

Let us first assume that the scales are comparable. The left hand scale is the wholesale cost in £/MWh. The right hand scale in the average annual retail cost per household. In 2010 the average household was paying about £430 for their electricity, compared with £550 in Jan-2013. The wholesale price component rose from around £280 to £310. So “other costs” rose by around £90. This is a huge increase in costs. With around 26 million households, this is around £2.4bn – well on the way to accounting for the £3.6bn claimed above. There is gas as well remember, so there could be an argument.

But what are the other costs?

These include

  1. Standing charges. The costs of operating the National Grid, and replacing meters in homes, along with subsidies for the poor.
  2. Renewables Obligations (RO) and Feed-in-tariffs (FIT). That is the subsidies that the owners of wind turbines and solar panels get over and above the wholesale price of electricity. For instance, operators of offshore wind turbines will get a similar amount in RO as from the market price.
  3. The small, but growing STOR scheme.
  4. The fixed costs of the retail operation. That is the staff to produce the bills, operate the call centres, along with the cost of a sales force to get you to switch.
  5. The net is the retail margin.

Let us assume that “network costs or policy costs” and policy costs doubled in three years as a proportion of the total electricity bill. That is from 14.5% to 29%. That would be £97 of the £90 increase in margin. This hypothetical example needs to be tested with actual data. However, the lack of the rise in profits is corroborated by OFGEM figures for the Big 6 Energy Companies, as I summarized out last week.

The margins on “supply” have not increased, and are still at the level of a discount supermarket. The margins on “generation” derive from selling at wholesale and the proceeds of the subsidies. Unless Labour are implying that the “Big 6″ are guilty of false reporting to OFGEM, the vast majority of the increase in differential between wholesale cost and selling price is accounted for by factors other than profits to the energy companies. Labour are implying the vast majority of the increase in differential between wholesale cost and selling price is accounted for by the profits to the energy companies, and therefore misleading the electorate.

Interpretation of clean energy investment figures

Figure 4 is the following chart

The fall in investment, at a time when it should be accelerating, is a result of the policy environment and protracted decision-making by Government. The Government has been widely blamed for failing to provide the policy certainty needed to de-risk investment.

There is an alternative way to interpret this data. Labour lost the general election in May 2010. What might be more significant is the passage of the Climate Change Act 2008. In the next year investment was nearly 3 times higher, then falling each year since. The Climate Change Act 2008 greatly enhanced the incentives for “clean energy” investment, hence the leap. There are only a finite number of opportunities, so the investment is reducing year-on-year. This being despite the biggest source of revenue coming from index-linked subsidies loaded onto electricity bills. Another reason is that many in the industry saw problems with the technology, that are only now coming to light. In particular the lifespan of the turbines might be shorter than previously thought. Further, the opposition to the wind turbines (where most of the investment is concentrated) is increasing, such as against the proposed Atlantic Array that would have blighted the Bristol Channel. Campaigners are also increasingly concerned about noise pollution.

Therefore, I propose that declining investment is not due to Government spin doctors failing to sweet-talk big business, but due to the reality of “clean energy” turning out to fall far short of the sales patter.

NB First time comments are moderated. The comments can be used as a point of contact.

Kevin Marshall

Financial costs of Fulcrum Power’s Green Diesel Plant

The BBC reports on a planning application submitted by Fulcrum Power to Plymouth Council to build a 20 MW diesel engine power station. This plant will operate backup for when renewables energy fails – mostly in the form of the wind failing to blow in the cold weather. Bishop Hill is, rightly, quite scathing because the diesel power is required to backup so-called green solutions. Josh weighs in with a cartoon


My posting is on the scandalous cost of this backup power station.

(Links are at the foot of the posting)

The BBC says

The application by Fulcrum Power is for a 20 megawatt (MW) Stor (Short Term Operating Reserve) power station on the former Toshiba plant at Ernesettle Lane, which company bosses said would cost “several million pounds”.

Its 52 generators will consume more than 1.1m litres of diesel a year, or about one tanker a week.

A litre of diesel with generate around 4kwh hours of electricity. (The normal measure is grams/kwh. A small diesel generator uses about 200 g/kwh and the RD of diesel is about 0.83 from memory). A 20 MW power station will therefore consume about 5,000 litres an hour of fuel. 1.1m litres will be consumed in just 220 hours, which means the plant is expected to operate for the equivalent of full power for just 2.5% of the hours in a year.

These companies will be paid a backup fee by the National Grid and then a rate per kwh generated. For this calculation I will look at just the cost per kwh. The fuel cost is easy. Diesel currently costs about £0.60 a litre, so that is £0.15 per kwh or 50% more than what I paid on my last electricity bill.

I tried to do some quick estimates and believe that the operating costs and cost of capital on “several million pounds” would be as much again. Being a little more curious, I did a search and found the “National Grid STOR Market Information Report No.19” on the National Grid’s Website. There is a bidding process every couple of months for Short Term Operating Reserve (STOR) capacity. Within the report is published the average winning and rejected bid rates. The most recent was season 8.6. As expected the bid is in two parts. First, a standby rate and second a (much higher) generating rate. There are bands, with the lower the standby rate, the higher the generating rate. I plugged the values into Excel and found that on all three rates Fulcrum Power could receive the equivalent of £0.65 Kwh. Gross Revenue would be around £2.86m. Deducting the cost of 1.1m litres for diesel leaves a contribution of £2.2m. There is probably a few hundred thousand of fixed costs, but payback on “several million pounds” looks to be pretty quick.


I have also done a check on other operating hours, shown below. The average in 2011-12 for STOR capacity was nearer 50 hours. At this level the revenue is much lower and more varied – from £1.66m to £2.12m. Dropping to just 5 hours per year still gives £1.34m to £2.04m.

Kevin Marshall

BBC Report

Fulcrum Planning Application

Bishop Hill blog report

Josh Cartoon

Cartoons by Josh

Fulcrum Power

National Grid STOR

National Grid STOR Market Information Report No.19


Tung and Zhou claim of constant decadal anthropogenic warming rates in last 100 years

Bishop Hill reports on

A new paper in PNAS entitled ‘Using data to attribute episodes of warming and cooling in instrumental records’ looks important. Ka-Kit Tung and Jiansong Zhou of the University of Washington report that anthropogenic global warming has been overcooked. A lot.

My comment was:-

My prediction is that this paper will turn out to have exaggerated the anthropogenic influence, rather than have under-estimated it.

The relevant quote:-

The underlying net anthropogenic warming rate in the industrial era is found to have been steady since 1910 at 0.07–0.08 °C/decade

Greenhouse gas emissions have not been increasing at a steady rate. The most important is CO2. A couple of years ago I tried to estimate from country data (filling in important gaps) how global CO2 emissions had increased. The increases per quarter century were

1900-1925 85%

1925-1950 60%

1950-1975 185%

1975-2000 45%

That meant global CO2 emissions increased more than 12 times (1100%) in 100 years. The conversion rate to retained CO2 seems to be roughly constant – 4Gt of carbon equivalent to increase CO2 levels by 1ppm. Furthermore, the C20th warming was nearly all in two phases. 1910-1945 and 1975-1998. Rather than temperature rise being related to CO2 emissions, it seems out of step. That would imply a combination of two things for the anthropogenic warming rate to be constant at 0.07–0.08 °C/decade. First is that CO2 has massively diminishing returns. Second is that CO2 emissions alone have a much smaller impact on the global average temperature changes (as reported in HADCRUT4), than this paper concludes.

Supplementary Information

This source of the emissions data is

Boden, T.A., G. Marland, and R.J. Andres. 2010. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2010

The CO2 levels are for Mauna Loa back to 1959, and estimated backwards from there to 1780.


The above chart shows by estimated CO2 emissions (expressed in units of 10Gt of carbon equivalents) shown as against the HADCRUT3 data set. This shows a slow rate of increase in CO2 emissions in the first half of the twentieth century, with falls in emissions during the Great Depression (1929-1933) and at the end of the Second World War (1945). From 1950 to 1973 there was a huge upsurge in emissions with the post-war economic boom, then stalls in 1973 (The OPEC oil embargo) and 1980-83 (global recession). After 2000 there was another surge in emissions, mostly due to rapid growth in China.

The temperature increases followed a different pattern. There were two periods of increasing temperatures in the twentieth century – From 1910-1945 and 1975-1998. The decadal changes graph below shows clearly the change in emissions. The temperature changes by decade exaggerate the falls in temperature in the Edwardian decade and the 1940s.


What is clearly illustrated is why I believe the anthropogenic influence on temperature was not similar in every decade from 1910, as Ka-Kit Tung and Jiansong Zhou claim.

Gergis 2012 Mark 2 – Hurdles to overcome

BishopHill reported yesterday on the withdrawn Gergis paper that

The authors are currently reviewing the data and methods. The revised paper will be re-submitted to the Journal of Climate by the end of July and it will be sent out for peer review again.

It is worth listing the long list of criticisms that have been made of the paper. There are a lot of hurdles to overcome before Gergis et al 2012 should qualify for the status of a scientific paper.

My own, quite basic, points are:-

  1. Too few proxies for such a large area. Just 27 for > 5% of the globe.
  2. Even then, 6 are well outside the area.
  3. Of these six, Gergis’s table makes it appear 3 are inside the area. My analysis is below.


  4. Despite huge area, there are significant clusters – with massive differences between proxies at the same or nearby sites.
  5. There are no proxies from the sub-continental land mass of Australia.
  6. Need to remove the Palmyra Proxy because (a) it has errant readings (b) fails the ‘t’ test (c) > 2000km outside of the area, in the Northern Hemisphere.
  7. Without Palmyra the medieval period becomes the warmest of the millennium. But with just two tree ring proxies, one at 42 O South and the other at 43 O S representing an range from 0 to 50O S, this is hardly reliable. See the sum of proxies by year. Palmyra is the coral proxy in the 12th, 14th and 15th centuries.


On top of this are Steve McIntyre’s (with assistance from JeanS and RomanM) more fundamental criticisms:-

  1. The filtering method of Gergis excluded the high quality Law Dome series, but included the lower quality Vostok data, and the Oroko tree ring proxy. McIntyre notes that Jones and Mann 2003 rejected Oroko, but included Law Dome on different criteria.
  2. Gergis screening correlations were incorrectly calculated. JeanS calculated properly. Only 6 out of 27 proxies passed. (NB none of the six proxies outside the area passed)


  3. The Gergis initially screened 62 proxies. Given that the screening included proxies that should not have included 21 proxies, but should it have included some of the 35 excluded proxies. We do not know, as Gergis has refused to reveal these excluded proxies.
  4. Screening creates a bias in the results in favour of the desired result if that correlation is with a short period of the data. RomanM states the issues succinctly here. My, more colloquial take, is that if the proxies (to some extent) randomly show C20th warming or not, then you will accept proxies with a C20th uptick. If proxies show previous fluctuations (to some extent) randomly and (to some extent) independently of the C20th uptick, then those previous fluctuations will be understated. There only has to be a minor amount of randomness to show bias given that a major conclusion was

    The average reconstructed temperature anomaly in Australasia during A.D. 1238-1267, the warmest 30-year pre-instrumental period, is 0.09°C (±0.19°C) below 1961-1990 levels.

UPDATE 03/08/12

The end of July submissions date seems to have slipped to the end of September.

Palmyra Atoll Coral Proxy in Gergis et al 2012

There is a lot of discussion on Bishop Hill (here and here) and Climate Audit of a new paper in Journal of Climate “Evidence of unusual late 20th century warming from an Australasian temperature reconstruction spanning the last millennium“, with lead author, Dr Joëlle Gergis. The reconstruction was based upon 27 climate proxies, one of which was a coral proxy from Palmyra Atoll.

There are two issues with this study.

Location

The study is a “temperature reconstruction for the combined land and oceanic region of Australasia (0°S-50°S, 110°E-180°E)“. The study lists Palmyra Atoll as being at 6° S, 162° E, so within the study area. Wikipedia has the location at 5°52′ N, 162°06′ W, or over 2100Km (1300 miles) outside the study area. On a similar basis, Rarotunga in the Cook Islands (for which there are two separate coral proxy studies), is listed as being at 21° S, 160° E. Again well within the study area. Wikipedia has the location at 21° 14′ 0″ S, 159° 47′ 0″ W, or about 2000Km (1250 miles) outside the study area. The error has occurred due to a table with columns headed “Lon (°E)”, and “Lat (°S). Along with the two ice core studies from Vostok Station, Antarctica (Over 3100km, 1900 miles south of 50° S) there are 5 of the 27 proxies that are significantly outside the region.

Temperature Reconstruction

Palmyra Atoll reconstruction is one of just three reconstructions that has any data before 1430. From the abstract, a conclusion was

The average reconstructed temperature anomaly in Australasia during A.D. 1238-1267, the warmest 30-year pre-instrumental period, is 0.09°C (±0.19°C) below 1961-1990 levels.

From the proxy matrix I have plotted the data.


This indicates a massive change in late twentieth century temperatures, with 1996 being the most extreme on record.

The other two data sets with pre-1430 data are tree ring proxies from Mount Read, Tasmania and Oroko, New Zealand. These I have plotted with a 30 year moving average, with the data point at the last year.


There is something not right with the Palmyra Atoll proxy. The late 20th century trend is far too extreme. In the next posting I will compare to some other coral data sets.

George Monbiot’s narrow definition of “charlatan”

Bishop Hill quotes George Monbiot

I define a charlatan as someone who won’t show you his records. This looks to me like a good [example]: http://t.co/5hDF57sI

Personally, I believe that for word definitions one should use a consensus of the leading experts in the field. My Shorter OED has the following definition that is more apt.

An empiric who pretends to wonderful knowledge or secrets.

Like John Cook’s definition of “skeptic“, Monbiot’s definition is narrower and partisan. Monbiot was referring to maverick weather forecaster Piers Corbyn. If someone has a “black box” that performs well under independent scrutiny, then they are charlatan under Monbiot’s definition, but not the OED’s. This could include the following.

  • A software manufacturer who does not reveal their computer code.
  • A pharmaceutical company that keeps secret the formulation of their wonder drug.
  • A soft drink manufacturer, who keeps their formulation secret. For instance Irn-Bru®.

The problem is that these examples have a common feature (that Piers Corbyn would claim to share to some extent). They have predictive effects that are replicated time and time again. A soft drink might just be the taste. Climate science cannot very well replicate the past, and predictions from climate models have failed to come about, even given their huge range of possible scenarios. This is an important point for any independent evaluation. The availability of the data or records matter not one iota. It is what these black boxes say about the real world that matters. I would claim that as empirical climate science becomes more sophisticated, no one person will be able to replicate a climate model. Publishing all the data and code, as Steve McIntyre would like, will make as much difference as publishing all the data and components of a mobile phone. Nobody will be able to replicate it. But it is possible to judge a scientific paper on what it says about the real world, either through predictions or independent statistical measures of data analysis.

A Climate Change / Global Warming Spectrum

In politics, most people’s views can be placed on a spectrum, when it comes to climate change / global warming there is no such perspectives. The views are often polarized, particularly by those who believe in a future climate catastrophe. This is an initial attempt at a grid aimed at clarifying the issues. Your constructive advice is sought on how this might be improved.

When there are contentious or politicized issues, a spectrum of opinions emerge where there is free discussion of ideas. This is true in politics and the Christian religion. In both, there is not just a one-dimensional spectrum of ideas, but multi-dimensional perspectives. For instance, in politics it has been argued that the left-right spectrum should be split into economic and moral issues. The United States Libertarian Party has had a simple survey running since 1995. A more comprehensive (but still American-orientated) survey is the Political Spectrum Quiz.

Another idea comes from Greg Craven, who did a series of zany You-Tube videos on Climate Change, particularly such as The Most Terrifying Video You’ll Ever See” and “How it all ends“. He claimed that for the mass of non-scientists it was best to take a risk-based approach, grading the science on the credibility of those who made the claims. One objection with his analysis was it was based on polar extremes. That is either the worst climate catastrophe imaginable, or it is all a hoax. I proposed that there was a spectrum of possible outcomes, with the apocalyptic catastrophe at one extreme and the null outcome at the other. Basically there is a spectrum of views.

For this spectrum, the possible scenarios are from the null outcome on the left, rising to a huge climate catastrophe on the right.

Craven’s argument was to consider either 0 or 1000, whereas I claimed that the UNIPCC scenarios (representing the “consensus” of climate scientists), allowed for a fair range of outcomes. I have provided a log scale, as this puts clear distance between someone who believes in a low risk of catastrophe of extreme catastrophe to someone who says there is no risk at all. For instance, if someone believes that there is a 1% chance of the worst case, a 9% chance of loss of 100 and a 90% chance of a loss of 10, then their score would be 0.01*1000 + 0.09*100 + 0.90*10 = 28. In other words, for that person, especially if they are risk averse, there is still a very significant issue that should justify serious consideration of some type of global policy action.

But this measure of the prospective level of climate catastrophe needs to be based upon something. That something is scientific evidence, not people’s intuitions or gut feelings. If we imagine that the uncertainties can be measured as risks (as neoclassical economists do) then then the worst case scenario can only be attained if there is near certain, unambiguous scientific evidence in support of that prediction. If the evidence is weak statistically, gives highly variables results depending on methodology or data sets, or only tangential to the prediction, then a lower risk weighting lower than 1 will need to be ascribed. For an overall picture, we need to ascribe a weighting to the body of evidence. I propose a traffic light system. In outline green is for an overwhelming body of evidence, red is for no proper evidence whatsoever, and amber is for some weak evidence. Something along the following lines:-

Basically, an unambiguous case for impending global catastrophe must have a substantial body of strong scientific evidence to substantiate that case, with little or no contrary evidence. I will develop on another day the analogy with evidence presented to a criminal court by the prosecution. However, for the present, an analogy that is relevant is that this conclusion is only reached once the evidence fails to fall over under independent cross-examination.

This gives us a grid with the magnitude of the climate catastrophe on the X axis, and the scientific case on the Y axis. The grid, with my first opinion of where people various groups are placed, is given below. I know it is controversial – the whole aim is to get people to start thinking constructively about their views.

Alarmist Blogs (for instance Skeptical Science and Desmogblog) have an extreme black-and-white one world where they are always right, and anyone who disagrees is the polar opposite . “Deniers” is a bogeyman construct of their making.

If one reads the detail of UNIPCC AR4 report, the “Consensus” of climate scientists allow for some uncertainties, and for scenarios which are not so catastrophic.

The more Sceptical Scientists, such as Richard Lindzen, Roger Pielke Snr and Roy Spencer, view increasing greenhouse gases as a serious issue for study. However, they view the evidence as being both much weaker than the “consensus” and pointing to a much less alarming future.

The most popular Sceptic Blogs, such as Wattsupwiththat, Climate Audit and Bishop Hill I characterise as having a position of “The stronger the evidence, the weaker the relevance“. That is they allow for a considerable spread of views, but neither dismiss rise in CO2 as of no consequence, nor claim that the available evidence is strong.

Finally, the Climate Realists such as Joanne Nova and the British Climate Realists website. They occupy a similar position as the “deniers”, but from a much more substantial position. They can see little or no evidence of catastrophe, but huge amounts of exaggeration dressed up as science.

What are your opinions? What position do you think you lie on the grid? Is there an alternative (and more informative) way of characterizing the different positions?

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