weather divination is the only job i know of where you can be wrong virtually every day, and NOT get fired...
Posted on 07/08/2007 3:33:39 PM PDT by Matchett-PI
John A writes: After a brief search, I found the paper “ Global Warming: Forecasts by Scientists versus Scientific Forecasts”
This paper came to my attention via an article in the Sydney Morning Herald. It concerns a paper written by two experts on scientific forecasting where they perform an audit on Chapter 8 of WG1 in the latest IPCC report.
The authors, Armstrong and Green, begin with a bombshell:
In 2007, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme issued its updated, Fourth Assessment Report, forecasts. The Intergovernmental Panel on Climate Change’s Working Group One Report predicts dramatic and harmful increases in average world temperatures over the next 92 years. We asked, are these forecasts a good basis for developing public policy? Our answer is “no”.
So where is the problem? The problem, according to the authors, is that the IPCC and everyone else does not distinguish between forecasts of the opinions of experts and scientific forecasting (with emphasis):
Much research on forecasting has shown that experts’ predictions are not useful. Rather, policies should be based on forecasts from scientific forecasting methods. We assessed the extent to which long-term forecasts of global average temperatures have been derived using evidence-based forecasting methods. We asked scientists and others involved in forecasting climate change to tell us which scientific articles presented the most credible forecasts. Most of the responses we received (30 out of 51) listed the IPCC Report as the best source. Given that the Report was commissioned at an enormous cost in order to provide policy recommendations to governments, the response should be reassuring. It is not. The forecasts in the Report were not the outcome of scientific procedures. In effect, they present the opinions of scientists transformed by mathematics and obscured by complex writing. We found no references to the primary sources of information on forecasting despite the fact these are easily available in books, articles, and websites. We conducted an audit of Chapter 8 of the IPCC’s WG1 Report. We found enough information to make judgments on 89 out of the total of 140 principles. The forecasting procedures that were used violated 72 principles. Many of the violations were, by themselves, critical. We have been unable to identify any scientific forecasts to support global warming. Claims that the Earth will get warmer have no more credence than saying that it will get colder.
Armstrong and Green further point out that those principles of forecasting sometimes run counter to what most people, scientists included, expect. They also point to various failings of scientists who regard themselves as experts (with some emphasis added):
…here are some of the well-established generalizations for situations involving long-range forecasts of complex issues where the causal factors are subject to uncertainty (as with climate): • Unaided judgmental forecasts by experts have no value. This applies whether the opinions are expressed by words, spreadsheets, or mathematical models. It also applies regardless of how much scientific evidence is possessed by the experts. Among the reasons for this are: a) Complexity: People cannot assess complex relationships through unaided observations. b) Coincidence: People confuse correlation with causation. c) Feedback: People making judgmental predictions typically do not receive unambiguous feedback they can use to improve their forecasting. d) Bias: People have difficulty in obtaining or using evidence that contradicts their initial beliefs. This problem is especially serious for people who view themselves as experts. • Agreement among experts is weakly related to accuracy. This is especially true when the experts communicate with one another and when they work together to solve problems. (As is the case with the IPCC process). • Complex models (those involving nonlinearities and interactions) harm accuracy because their errors multiply. That is, they tend to magnify one another. Ascher (1978), refers to the Club of Rome’s 1972 forecasts where, unaware of the research on forecasting, the developers proudly proclaimed, “in our model about 100,000 relationships are stored in the computer.” (The first author [Amrstrong] was aghast not only at the poor methodology in that study, but also at how easy it was to mislead both politicians and the public.) Complex models are also less accurate because they tend to fit randomness, thereby also providing misleading conclusions about prediction intervals. Finally, there are more opportunities for errors to creep into complex models and the errors are difficult to find. Craig, Gadgil, and Koomey (2002) came to similar conclusions in their review of long-term energy forecasts for the US made between 1950 and 1980. • Given even modest uncertainty, prediction intervals are enormous. For example, prediction intervals expand rapidly as time horizons increase so that one is faced with enormous intervals even when trying to forecast a straightforward thing such as automobile sales for General Motors over the next five years. • When there is uncertainty in forecasting, forecasts should be conservative. Uncertainty arises when data contain measurement errors, when the series is unstable, when knowledge about the direction of relationships is uncertain, and when a forecast depends upon forecasts of related (causal) variables. For example, forecasts of no change have been found to be more accurate for annual sales forecasts than trend forecasts when there was substantial uncertainty in the trend lines (e.g., Schnaars & Bavuso 1986). This principle also implies that forecasters reverting to long-term trends when such trends have been firmly established, they do not waver, and there are no firm reasons to suggest that the trends will change. Finally, trends should be damped toward no change as the forecast horizon increases.
Of course, this isn’t the behavior that a lot of us have seen from the IPCC. A lot of the criticism levied at the IPCC was that the forecasts were too conservative, rather than the reverse.
Armstrong and Green don’t exactly endorse the notion of “scientific consensus” since its is clear to them that such things when they happen in close groups of people working in the same general field, tend to reinforce the bias rather than remove it. I seem to remember Edward Wegman saying much the same thing about group reinforcement.
What of forecasting by experts? Well it turns out that this appears to be no more a guide to the future than asking your mates down the pub:
The first author’s [Armstrong’s] review of empirical research on this problem led to the “Seer-sucker theory,” stating that, “No matter how much evidence exists that seers do not exist, seers will find suckers” (Armstrong 1980). The amount of expertise does not matter beyond a basic minimum level. There are exceptions to the Seer-sucker Theory: When forecasters get substantial amounts of well-summarized feedback about the accuracy of their forecasts and about the reasons why the forecasts were or were not accurate, they can improve their forecasts. This situation applies for short-term (e.g., up to five days) weather forecasts, but it does not apply to long-term climate forecasts. Research since 1980 has added support to the Seer-sucker Theory. In particular, Tetlock (2005) recruited 284 people whose professions included, “commenting or offering advice on political and economic trends.” He asked them to forecast the probability that various situations would or would not occur, picking areas (geographic and substantive) within and outside their areas of expertise. By 2003, he had accumulated over 82,000 forecasts. The experts barely if at all outperformed non-experts and neither group did well against simple rules.
This method of forecasting by expert opinion was very popular in the 1970s in climate science:
In the mid-1970s, there was a political debate raging about whether the global climate was changing. The United States’ National Defense University addressed this issue in their book, Climate Change to the Year 2000 (NDU 1978). This study involved 9 man-years of effort by Department of Defense and other agencies, aided by experts who received honoraria, and a contract of nearly $400,000 (in 2007 dollars). The heart of the study was a survey of experts. It provided them with a chart of “annual mean temperature, 0-800 N. latitude,” that showed temperature rising from 1870 to early 1940 then dropping sharply up to 1970. The conclusion, based primarily on 19 replies weighted by the study directors, was that while a slight increase in temperature might occur, uncertainty was so high that “the next twenty years will be similar to that of the past” and the effects of any change would be negligible. Clearly, this was a forecast by scientists, not a scientific forecast. However, it proved to be quite influential. The report was discussed in The Global 2000 Report to the President (Carter) and at the World Climate Conference in Geneva in 1979.
Such was the state of the art back then, but now with the advent of personal computers, canvassing experts to report their impressions of data has been transformed through the use of computer models. But are they any better at forecasting?
The methodology used in the past few decades has shifted from surveys of experts’ opinions to the use of computer models. However, based on the explanations that we have seen, such models are, in effect, mathematical ways for the experts to express their opinions. To our knowledge, there is no empirical evidence to suggest that presenting opinions in mathematical terms rather than in words will contribute to forecast accuracy. For example, and Keepin and Wynne (1984) wrote in the summary of their study of the IIASA’s “widely acclaimed” projections for global energy that, “Despite the appearance of analytical rigour… [they] are highly unstable and based on informal guesswork”.
All right, that was the 1980s. What about much more recently?
Carter, et al. (2006) examined the Stern Review (Stern 2007). They concluded that the Report authors made predictions without any reference to scientific forecasting.
I’m sure there’s lots more to be said about Stern’s methodology in other areas but we must press on
Pilkey and Pilkey-Jarvis (2007) concluded that the long-term climate forecasts that they examined were based only on the opinions of the scientists. The opinions were expressed in complex mathematical terms. There was no validation of the methodologies. They referred to the following quote as a summary on their page 45: “Today’s scientists have substituted mathematics for experiments, and they wander off through equation after equation and eventually build a structure which has no relation to reality. (Nikola Telsa, inventor and electrical engineer, 1934.)”
I assume the reference to Nikola Tesla isn’t meant to be complimentary.
Carter (2007) examined evidence on the predictive validity of the general circulation models (GCMs) used by the IPCC scientists. He found that while the models included some basic principles of physics, scientists had to make “educated guesses” about the values of many parameters because knowledge about the physical processes of the earth’s climate is incomplete. In practice, the GCMs failed to predict recent global average temperatures as accurately as simple curve-fitting approaches (Carter 2007, pp. 64 – 65) and also forecast greater warming at higher altitudes when the opposite has been the case (p. 64). Further, individual GCMs produce widely different forecasts from the same initial conditions and minor changes in parameters can result in forecasts of global cooling (Essex and McKitrick, 2002). Interestingly, modeling results that project global cooling are often rejected as “outliers” or “obviously wrong” (e.g., Stainforth et al., 2005)
Was Stainforth et al a reference to that ridiculous modelling exercise where they emphasized the top end 11C rise without mentioning all of the ones that fell into deep cooling? Yes it was. Obviously Stainforth knows which ones are outliers and therefore “obviously wrong” and which are not, because he’s an expert.
Taylor (2007) compared seasonal forecasts by New Zealand’s National Institute of Water and Atmospheric Research with outcomes for the period May 2002 to April 2007. He found NIWA’s forecasts of average regional temperatures for the season ahead were, at 48% correct, no more accurate than chance. That this is a general result was confirmed by New Zealand climatologist Dr Jim Renwick, who observed that NIWA’s low success rate was comparable to that of other forecasting groups worldwide. He added that “Climate prediction is hard, half of the variability in the climate system is not predictable, so we don’t expect to do terrifically well.” Dr Renwick is an author on Working Group I of the IPCC 4th Assessment Report, and also serves on the World Meteorological Organisation Commission for Climatology Expert Team on Seasonal Forecasting; His expert view is that current GCM climate models are unable to predict future climate any better than chance
Now clearly this is a serious problem with climate modelling on a regional level, but is it being reported that regional climate forecasts for even three months ahead do no better than flipping a coin?
Then there’s the Hurricane Forecasting Débacle of 2006:
…the US National Hurricane Center’s report on hurricane forecast accuracy noted, “No routinely-available early dynamical model had skill at 5 days” (Franklin 2007). This comment probably refers to forecasts for the paths of known, individual storms, but seasonal storm ensemble forecasts are clearly no more accurate. For example, the NHC’s forecast for the 2006 season was widely off the mark. On June 7, Vice Admiral Conrad C. Lautenbacher, Jr. of the National Oceanic and Atmospheric Administration gave the following testimony before the Committee on Appropriations Subcommittee on Commerce, Justice and Science of the United States Senate (Lautenbacher 2006, p. 3): “NOAA’s prediction for the 2006 Atlantic hurricane season is for 13-16 tropical storms, with eight to 10 becoming hurricanes, of which four to six could become major hurricanes. … We are predicting an 80 percent likelihood of an above average number of storms in the Atlantic Basin this season. This is the highest percentage we have ever issued.” By the beginning of December, Gresko (2006) was able to write “The mild 2006 Atlantic hurricane season draws to a close Thursday without a single hurricane striking the United States”.
That’s just in the first seven pages. On page 8 they begin their audit of scientific forecasting at the IPCC, and it goes downhill from there.
Full paper at http://www.forecastingprinciples.com/Public_Policy/WarmAudit31.pdf
Comments here: http://www.climateaudit.org/?p=1807#more-1807
Lawrence Solomon's "The Deniers" (a series of articles on the view of scientists who have been labelled "Global Warming Deniers"):
Other References:
weather divination is the only job i know of where you can be wrong virtually every day, and NOT get fired...
Junk Science: Live Earth’s Gross Groupies
http://www.foxnews.com/printer_friendly_story/0,3566,288195,00.html
Sunday , July 08, 2007
By Steven Milloy
Why is NBC airing Al Gore’s Live Earth concert this weekend? Why are Democrats, who claim to support the Fairness Doctrine, not objecting to this outright gift of unequal broadcast time to just one side (theirs) of a controversial political issue? Those are the terrific questions asked by FOX News’ John Gibson this week.
Here are some answers, John.
First, the parent company of NBC is the General Electric Company, a corporation that is aggressively lobbying for global warming regulation. GE belongs to a lobbying group called U.S. Climate Action Partnership (USCAP) – a group of regulation- and congressional pork-loving companies that have joined with radical environmentalists to push for mandatory reductions of greenhouse gas emissions and a so-called cap-and-trade system under which companies could buy and sell rights to emit greenhouse gases.
The corporate members of USCAP have different reasons for wanting global warming regulation. Oil company BP-America, for example, is owned by its British parent, BP.
European governments and companies expect that global warming regulation will give them a competitive advantage over U.S. businesses.
Global warming regulation, for example, would force the U.S. to use less coal and more natural gas in the production of electricity. This would increase natural gas prices, which would not only hamper U.S. growth but also profit the UK-based BP, which derives most of its revenues from gas sales.
Companies like Alcoa and Dupont expect that, in return for their support of global warming regulation, Congress will reward them by giving them free, but valuable emissions permits which the companies could then sell in the open market for huge profits. As USCAP says on its web site, its members want “rewards for early action.” But these rewards amount to nothing more than taxpayer-funded pork-barrel spending and global warming “earmarks.”
Electric utilities that belong to USCAP, like Duke Energy and PG&E Corp., expect to benefit from higher energy prices, which tend to be associated with higher profit margins. Additionally, these higher energy prices and profits — because they will be based in government-sanctioned environmental policies — will likely be politically protected (unlike the ratepayers who will foot the higher bills).
What’s GE’s particular interest? GE’s ostensible rationale is that it hopes to profit by selling high-priced global warming-related and alternative energy products, ranging from solar panels and wind turbines to compact fluorescent lightbulbs and nuclear power plant technologies.
But the problem with the profit-motive for GE, of course, is that the company is a highly-diversified conglomerate – its other business interests include medical technology, financial services, advanced materials, aircraft engines and security technology. GE is so diversified, in fact, that its overall business performance tends to track that of the general economy.
So if global warming regulation harms economic growth – as is near-universally expected – it likely will also harm GE’s business prospects, especially since the Ecomagination product line represents a very small part (about 7 percent) of GE’s total revenues.
So there must be more to GE’s lobbying for global warming regulation than profit. That additional motivation may be the self-promotion of GE’s CEO, Jeff Immelt.
Immelt inherited the CEO job from the legendary Jack Welsh. But while Welsh famously grew GE into a financial colossus, GE has been treading water under Immelt’s leadership. GE’s stock has mostly traded in a relatively narrow range since Immelt took over and has significantly underperformed the broader market.
So, in 2005, Immelt adopted image-building as his key to success – hence the re-branding of regular GE products into trendy “Ecomagination” products. The idea for GE’s Ecomagination marketing strategy came not from engineers who had new product ideas, but from PR consultants hired to burnish GE’s brand. Immelt then became a global warming regulation advocate, one of the first CEOs to do so.
Immelt has been feted by environmentalists and the media ever since, even being honored earlier this year by the World Resources Institute – a gloom-and-doom eco-advocacy organization that GE supports. At WRI’s 25th anniversary dinner in February, Live Earth concert organizer Al Gore personally presented Immelt with the WRI’s “Courage to Lead” award.
Also in attendance at the dinner was New York Times columnist Thomas Friedman who stated, “We are in a political season and the rules governing columnists at The New York Times is that we are not allowed to endorse presidential candidates. But I’m going to break that rule tonight if you promise not to tell anybody: I’d like to nominate Al Gore and Jeff Immelt as the geo-green candidates for 2008!”
Immelt hasn’t been good for GE’s stock price but he’s been quite adept at assuming the pose of a corporate eco-hero. So there is no need to wonder why a GE subsidiary (NBC) is so heavily promoting Live Earth.
As to Gibson’s second question relating to why the Democrats aren’t eager to apply the Fairness Doctrine that they’ve recently been promoting to NBC’s broadcast of the one-sided Live Earth global warming rally, the answer is pretty straightforward.
Democrats plan on making global warming a major campaign issue in 2008. The Live Earth concert represents a great way for them to market the issue to young people. Why would they want to spoil Gore’s global warming propaganda-fest by complaining about the lack of equal time for opposing viewpoints?
That said, I’m all for the Live Earth concerts. Between Al Gore and rock stars in private jets and limos, $300 tickets, the global warmers’ intolerance of dissenting views, and the concert’s wanton energy use, mass consumerism and trash generation, I can’t think of a better stage for displaying the over-the-top absurdity of Gore and his gross Green groupies.
Steven Milloy publishes JunkScience.com and CSRWatch.com. He is a junk science expert, an advocate of free enterprise and an adjunct scholar at the Competitive Enterprise Institute.
Nothing new under the sun...
“Bad officials are the ones elected by good citicens who do not vote.” George Nathan
“Get all the fools on your side and you can be elected to anything.” Frank Dane
“Democrats can’t get elected unless things get worse”
Jeane Kirkpatrick
“The sky is falling.” Chicken Little
Links do not work.
Is Gore the seer or the sucker, or can you be both at once?
My comment to algore—”stupid is as stupid does.” Take freshman Geology 101—there is a whold world out there that you are missing..all will become visible to you.
The links worked earlier. I just tried them and now they don’t. The server may be temporarily down. I’m sure it will be available again soon. I hope it wasn’t my post that caused too many hits on the web site. :)
Thanks!
((((ping))))
While the Climateaudit.org link is down, here’s the link to the actual paper under discussion:
http://www.forecastingprinciples.com/Public_Policy/WarmAudit31.pdf
Here’s the link to the Sidney Morning Herald article mentioned in McIntyre’s commentary:
http://www.smh.com.au/text/articles/2007/07/06/1183351452273.html
Is Gore the seer or the sucker, or can you be both at once?"
We know what he thinks we are.
ping
bttt
Click on POGW graphic for full GW rundown
New!!: Dr. John Ray's
GREENIE WATCH
Ping me if you find one I've missed.
“weather divination is the only job i know of where you can be wrong virtually every day, and NOT get fired...” ~ Chode
But they can always point their fingers at someone else if their “predictions” don’t pan out.
Where does the buck stop when the data for the computer models is being collected like this: http://www.norcalblogs.com/watts/weather_stations/
GIGO (garbage in - garbage out)
There it is in a nutshell. Complex climate models only predict what the modelers think they should. Garbage In, Garbage Out.
What attracts the modelers is the awesome personal power and importance they perceive, if they predict catastrophe. No one gets lots of attention for predicting rosy scenarios.
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