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In 2007, the Intergovernmental Panel on Climate Change's Working Group One, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme, issued its Fourth Assessment Report. The Report included predictions of dramatic increases in average world temperatures over the next 92 years and serious harm resulting from the predicted temperature increases. Using forecasting principles as our guide we asked: Are these forecasts a good basis for developing public policy? Our answer is “no”. To provide forecasts of climate change that are useful for policy-making, one would need to forecast (1) global temperature, (2) the effects of any temperature changes, and (3) the effects of feasible alternative policies. Proper forecasts of all three are necessary for rational policy making. The IPCC WG1 Report was regarded as providing the most credible long-term forecasts of global average temperatures by 31 of the 51 scientists and others involved in forecasting climate change who responded to our survey. We found no references in the 1056-page Report to the primary sources of information on forecasting methods despite the fact these are conveniently available in books, articles, and websites. We audited the forecasting processes described in Chapter 8 of the IPCC's WG1 Report to assess the extent to which they complied with forecasting principles. We found enough information to make judgments on 89 out of a total of 140 forecasting principles. The forecasting procedures that were described violated 72 principles. Many of the violations were, by themselves, critical. The forecasts in the Report were not the outcome of scientific procedures. In effect, they were the opinions of scientists transformed by mathematics and obscured by complex writing. Research on forecasting has shown that experts' predictions are not useful in situations involving uncertainly and complexity. We have been unable to identify any scientific forecasts of global warming. Claims that the Earth will get warmer have no more credence than saying that it will get colder.
Global Warming: Forecasts by Scientists versus Scientific Forecasts*
Version 70 – 1 October, 2007
Kesten C. Green, Business and Economic Forecasting Unit, Monash University,
Victoria 3800, Australia.
Contact: PO Box 10800, Wellington 6143, New Zealand.; T +64 4 976 3245; F +64 4 976 3250
J. Scott Armstrong†, The Wharton School, University of Pennsylvania
747 Huntsman, Philadelphia, PA 19104, USA.
(This paper is a draft of an article that is forthcoming in Energy and Environment.)
In 2007, the Intergovernmental Panel on Climate Change’s Working Group One, a panel of
experts established by the World Meteorological Organization and the United Nations
Environment Programme, issued its Fourth Assessment Report. The Report included predictions
of dramatic increases in average world temperatures over the next 92 years and serious harm
resulting from the predicted temperature increases. Using forecasting principles as our guide we
asked: Are these forecasts a good basis for developing public policy? Our answer is “no”
To provide forecasts of climate change that are useful for policy-making, one would need
to forecast (1) global temperature, (2) the effects of any temperature changes, and (3) the effects
of feasible alternative policies. Proper forecasts of all three are necessary for rational policy
The IPCC WG1 Report was regarded as providing the most credible long-term forecasts
of global average temperatures by 31 of the 51 scientists and others involved in forecasting
climate change who responded to our survey. We found no references in the 1056-page Report to
the primary sources of information on forecasting methods despite the fact these are conveniently
available in books, articles, and websites. We audited the forecasting processes described in
Chapter 8 of the IPCC’s WG1 Report to assess the extent to which they complied with
forecasting principles. We found enough information to make judgments on 89 out of a total of
140 forecasting principles. The forecasting procedures that were described violated 72 principles.
Many of the violations were, by themselves, critical.
The forecasts in the Report were not the outcome of scientific procedures. In effect, they
were the opinions of scientists transformed by mathematics and obscured by complex writing.
Research on forecasting has shown that experts’ predictions are not useful. We have been unable
to identify any scientific forecasts of global warming. Claims that the Earth will get warmer have
no more credence than saying that it will get colder.
Keywords: accuracy, audit, climate change, evaluation, expert judgment, mathematical models,
public policy.
*Neither of the authors received funding for this paper.
† Information about J. Scott Armstrong can be found on Wikipedia.
“A trend is a trend,
But the question is, will it bend?
Will it alter its course
Through some unforeseen force
And come to a premature end?”
Alec Cairncross, 1969
Research on forecasting has been conducted since the 1930s. Empirical studies that compare
methods in order to determine which ones provide the most accurate forecasts in specified
situations are the most useful source of evidence. Findings, along with the evidence, were first
summarized in Armstrong (1978, 1985). In the mid-1990s, the Forecasting Principles Project was
established with the objective of summarizing all useful knowledge about forecasting. The
knowledge was codified as evidence-based principles, or condition-action statements, in order to
provide guidance on which methods to use when. The project led to the Principles of Forecasting
handbook (Armstrong 2001): the work of 40 internationally-known experts on forecasting
methods and 123 reviewers who were also leading experts on forecasting methods. The
summarizing process alone required a four-year effort.
The forecasting principles are easy to find: They are freely available on, a site sponsored by the International Institute of Forecasters. The
Forecasting Principles site has been at the top of the list of sites in internet searches for
“forecasting” for many years. A summary of the principles, currently numbering 140, is provided
as a checklist in the Forecasting Audit software available on the site. There is no other source that
provides evidence-based forecasting principles. The site is often updated in order to incorporate
new evidence on forecasting as it comes to hand. A recent review of new evidence on some of the
key principles was published in Armstrong (2006).
The strength of evidence is different for different principles, for example some principles are
based on common sense or received wisdom. Such principles are included when there is no
contrary evidence. Other principles have some empirical support, while 31 are strongly supported
by empirical evidence.
Many of the principles go beyond common sense, and some are counter-intuitive. As a result,
those who forecast in ignorance of the forecasting research literature are unlikely to produce
useful predictions. Here are some well-established principles that apply to long-term forecasts for
complex situations 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 in 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. 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.
Complex models also tend to fit random variations in historical data well, with the
consequence that they forecast poorly and lead to misleading conclusions about the
uncertainty of the outcome. Finally, when complex models are developed there are
many opportunities for errors and the complexity means 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 that were made between 1950 and 1980.
Given even modest uncertainty, prediction intervals are enormous. Prediction
intervals (ranges outside which outcomes are unlikely to fall) expand rapidly as
time horizons increase, for example, 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 are
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 were found to be more accurate than trend forecasts for
annual sales when there was substantial uncertainty in the trend lines (Schnaars and
Bavuso 1986). This principle also implies that forecasts should revert to long-term
trends when such trends have been firmly established, do not waver, and there are
no firm reasons to suggest that they will change. Finally, trends should be damped
toward no-change as the forecast horizon increases.
The Forecasting Problem
In determining the best policies to deal with the climate of the future, a policy maker first has to
select an appropriate statistic to use to represent the changing climate. By convention, the statistic
is the averaged global temperature as measured with thermometers at ground stations throughout
the world, though in practice this is a far from satisfactory metric (see, e.g., Essex et al., 2007).
It is then necessary to obtain forecasts and prediction intervals for each of the following:
1. Mean global temperature in the long-term (say 20 years or longer).
2. Effects of temperature changes on humans and other living things.
If accurate forecasts of mean global temperature can be obtained and the changes are
substantial, then it would be necessary to forecast the effects of the changes on the
health of living things and on the health and wealth of humans. The concerns about
changes in global mean temperature are based on the assumption that the earth is
currently at the optimal temperature and that variations over years (unlike variations
within days and years) are undesirable. For a proper assessment, costs and benefits
must be comprehensive. (For example, policy responses to Rachel Carson’s Silent
Spring should have been based in part on forecasts of the number of people who
might die from malaria if DDT use were reduced).
3. Costs and benefits of feasible alternative policy proposals.
If valid forecasts of the effects of the temperature changes on the health of living
things and on the health and wealth of humans can be obtained and the forecasts are
for substantial harmful effects, then it would be necessary to forecast the costs and
benefits of proposed alternative policies that could be successfully implemented.
A policy proposal should only be implemented if valid and reliable forecasts of the effects of
implementing the policy can be obtained and the forecasts show net benefits. Failure to obtain a
valid forecast in any of the three areas listed above would render forecasts for the other areas
meaningless. We focus primarily, but not exclusively, on the first of the three forecasting
problems: obtaining long-term forecasts of global temperature.
But is it necessary to use scientific forecasting methods? In other words, to use methods that
have been shown by empirical validation to be relevant to the types of problems involved with
climate forecasting? Or is it sufficient to have leading scientists examine the evidence and make
forecasts? We address this issue before moving on to our audits.
On the value of forecasts by experts
Many public policy decisions are based on forecasts by experts. Research on persuasion has
shown that people have substantial faith in the value of such forecasts. Faith increases when
experts agree with one another.
Our concern here is with what we refer to as unaided expert judgments. In such cases, experts
may have access to empirical studies and other information, but they use their knowledge to make
predictions without the aid of well-established forecasting principles. Thus, they could simply use
the information to come up with judgmental forecasts. Alternatively, they could translate their
beliefs into mathematical statements (or models) and use those to make forecasts.
Although they may seem convincing at the time, expert forecasts can make for humorous
reading in retrospect. Cerf and Navasky’s (1998) book contains 310 pages of examples, such as
Fermi Award-winning scientist John von Neumann’s 1956 prediction that “A few decades hence,
energy may be free”. Examples of expert climate forecasts that turned out to be completely wrong
are easy to find, such as UC Davis ecologist Kenneth Watt’s prediction in a speech at
Swarthmore College on Earth Day, April 22, 1970:
If present trends continue, the world will be about four degrees colder in 1990, but eleven
degrees colder in the year 2000. This is about twice what it would take to put us into an
ice age.
Are such examples merely a matter of selective perception? The second author’s review of
empirical research on this problem led him to develop the “Seer-sucker theory,” which can be
stated as “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 experts get substantial well-summarized
feedback about the accuracy of their forecasts and about the reasons why their forecasts were or
were not accurate, they can improve their forecasting. This situation applies for short-term (up to
five day) weather forecasts, but we are not aware of any such regime for long-term global climate
forecasting. Even if there were such a regime, the feedback would trickle in over many years
before it became useful for improving forecasting.
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.
Comparative empirical studies have routinely concluded that judgmental forecasting by
experts is the least accurate of the methods available to make forecasts. For example, Ascher
(1978, p. 200), in his analysis of long-term forecasts of electricity consumption found that was
the case.
Experts’ forecasts of climate changes have long been newsworthy and a cause of worry for
people. Anderson and Gainor (2006) found the following headlines in their search of the New
York Times:
Sept. 18, 1924 MacMillan Reports Signs of New Ice Age
March 27, 1933 America in Longest Warm Spell Since 1776
May 21, 1974 Scientists Ponder Why World’s Climate is Changing:
A Major Cooling Widely Considered to be Inevitable
Dec. 27, 2005 Past Hot Times Hold Few Reasons to Relax About New Warming
In each case, the forecasts behind the headlines were made with a high degree of confidence.
In the mid-1970s, there was a political debate raging about whether the global climate was
changing. The United States’ National Defense University (NDU) addressed this issue in their
book, Climate Change to the Year 2000 (NDU 1978). This study involved nine 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.
The methodology for climate forecasting used in the past few decades has shifted from
surveys of experts’ opinions to the use of computer models. Reid Bryson, the world’s most cited
climatologist, wrote in a 1993 article that a model is “nothing more than a formal statement of
how the modeler believes that the part of the world of his concern actually works” (p. 798-790).
Based on the explanations that we have seen, we concur. While advocates of complex climate
models claim that they are based on well established laws of physics”, there is clearly much
more to the models than the laws of physics otherwise they would all produce the same output,
which patently they do not, and there would be no need for confidence estimates for model
forecasts, which there most certainly are. Climate 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,
Keepin and Wynne (1984) wrote in the summary of their study of the International Institute for
Applied Systems Analysis’s “widely acclaimed” projections for global energy that, “Despite the
appearance of analytical rigor… [they] are highly unstable and based on informal guesswork.”
Things have changed little since the days of Malthus in the 1800s. Malthus forecast mass
starvation. He expressed his opinions mathematically. His mathematical model predicted that the
supply of food would increase arithmetically while the human population grew at a geometric
rate and went hungry.
International surveys of climate scientists from 27 countries, obtained by Brat and von Storch
in 1996 and 2003, were summarized by Bast and Taylor (2007). Many scientists were skeptical
about the predictive validity of climate models. Of more than 1,060 respondents, 35% agreed
with the statement, “Climate models can accurately predict future climates,” and 47% percent
disagreed. Members of the general public were also divided. An Ipsos Mori poll of 2,031 people
aged 16 and over found that 40% agreed that “climate change was too complex and uncertain for
scientists to make useful forecasts” while 38% disagreed (Eccleston 2007).
An examination of climate forecasting methods
We assessed the extent to which those who have made climate forecasts used evidence-based
forecasting procedures. We did this by conducting Google searches. We then conducted a
“forecasting audit” of the forecasting process behind the IPCC forecasts. The key aspects of a
forecasting audit that can be used to identify ways to improve the audited forecasting process are
examine all elements of the forecasting process,
use principles that are supported by evidence, or are self-evidently true and
unchallenged by evidence, against which to judge the forecasting process,
rate the forecasting process against each principle, preferably using more than one
independent rater,
disclose the audit.
To our knowledge, no one has ever published a paper that is based on a forecasting audit, as
defined here. We suggest that for forecasts involving important public policies, such audits
should be expected and perhaps even required. In addition, they should be fully disclosed with
respect to who did the audit, what biases might be involved, and what were the detailed findings
from the audit.
Reviews of climate forecasts
We could not find any comprehensive reviews of climate forecasting efforts. With the exception
of Stewart and Glantz (1985), the reviews did not refer to evidence-based findings. None of the
reviews provided explicit ratings of the processes and, again with the exception of Stewart and
Glantz, little attention was given to full disclosure of the reviewing process. Finally, some
reviews ignored the forecasting methods and focused on the accuracy of the forecasts.
Stewart and Glantz (1985) conducted an audit of the National Defense University (NDU
1978) forecasting process that we described above. They were critical of the report because it
lacked an awareness of proper forecasting methodology. Their audit was hampered because the
organizers of the study said that the raw data had been destroyed and a request to the Institute for
the Future about the sensitivity of the forecasts to the weights went unanswered. Judging from a
Google Scholar search, climate forecasters have paid little attention to this paper.
In a wide-ranging article on the broad topic of science and the environment, Bryson (1993)
was critical of the use of models for forecasting climate. He wrote:
…it has never been demonstrated that the GCMs [General Circulation Models] are
capable of prediction with any level of accuracy. When a modeler says that his model
shows that doubling the carbon dioxide in the atmosphere will raise the global average
temperature two to three degrees Centigrade, he really means that a simulation of the
present global temperature with current carbon dioxide levels yields a mean value two to
three degrees Centigrade lower than his model simulation with doubled carbon dioxide.
This implies, though it rarely appears in the news media, that the error in simulating the
present will be unchanged in simulating the future case with doubled carbon dioxide.
That has never been demonstrated—it is faith rather than science.” (pp. 790-791)
Balling (2005), Christy (2005), Frauenfeld (2005), and Posmentier and Soon (2005) each assess
different aspects of the use of climate models for forecasting and each comes to broadly the same
conclusion: The models do not represent the real world sufficiently well to be relied upon for
Carter, et al. (2006) examined the Stern Review (Stern 2007). They concluded that the authors
of the Review made predictions without reference to scientific validation and without proper peer
Pilkey and Pilkey-Jarvis (2007) examined long-term climate forecasts and concluded that
they were based only on the opinions of the scientists. The scientists’ opinions were expressed in
complex mathematical terms without evidence on the validity of chosen approach. The authors
provided the following quotation on their page 45 to summarize their assessment: “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).” While it is sensible to be explicit about beliefs and to
formulate these in a model, forecasters must also demonstrate that the relationships are valid.
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). They also forecast greater warming at higher
altitudes in the tropics 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,
when models predict global cooling, the forecasts are often rejected as “outliers” or “obviously
wrong” (e.g., Stainforth et al., 2005).
Roger Pielke Sr. (Colorado State Climatologist, until 2006) gave an assessment of climate
models in a 2007 interview (available via
You can always reconstruct after the fact what happened if you run enough model
simulations. The challenge is to run it on an independent dataset, say for the next five
years. But then they will say “the model is not good for five years because there is too
much noise in the system”. That’s avoiding the issue then. They say you have to wait 50
years, but then you can’t validate the model, so what good is it?
…Weather is very difficult to predict; climate involves weather plus all these other
components of the climate system, ice, oceans, vegetation, soil etc. Why should we think
we can do better with climate prediction than with weather prediction? To me it’s
obvious, we can’t!
I often hear scientists say “weather is unpredictable, but climate you can predict
because it is the average weather”. How can they prove such a statement?
In his assessment of climate models, physicist Freeman Dyson (2007) wrote:
I have studied the climate models and I know what they can do. The models solve the
equations of fluid dynamics, and they do a very good job of describing the fluid motions
of the atmosphere and the oceans. They do a very poor job of describing the clouds, the
dust, the chemistry and the biology of fields and farms and forests. They do not begin to
describe the real world that we live in.
Bellamy and Barrett (2007) found serious deficiencies in the general circulation models described
in the IPCC’s Third Assessment Report. In particular, the models (1) produced very different
distributions of clouds and none was close the actual distribution of clouds, (2) parameters for
incoming radiation absorbed by the atmosphere and for that absorbed by the Earth’s surface
varied considerably, (3) did not accurately represent what is known about the effects of CO2 and
could not represent the possible positive and negative feedbacks about which there is great
uncertainty. The authors concluded:
The climate system is a highly complex system and, to date, no computer models are
sufficiently accurate for their predictions of future climate to be relied upon. (p. 72)
Trenberth (2007), a lead author of Chapter 3 in the IPCC WG1 report wrote in a blog
“… the science is not done because we do not have reliable or regional predictions of climate.”
Taylor (2007) compared seasonal forecasts by New Zealand’s National Institute of Water and
Atmospheric Research (NIWA) with outcomes for the period May 2002 to April 2007. He found
NIWA’s forecasts of average regional temperatures for the season ahead were 48% correct, which
was no more accurate than chance. That this is a general result was confirmed by New Zealand
climatologist 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, and so we don't expect to do terrifically well.”
Renwick is a co-author with Working Group I of the IPCC 4th Assessment Report, and also
serves on the World Meteorological Organization 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 (New Zealand Climate Science Coalition 2007).
Similarly, Vizard, Anderson, and Buckley (2005) found seasonal rainfall forecasts for
Australian townships were insufficiently accurate to be useful to intended consumers such as
farmers planning for feed requirements. The forecasts were released only 15 days ahead of each
three month period.
A survey to identify the most credible long-term forecasts of global temperature
We surveyed scientists involved in long-term climate forecasting and policy makers. Our primary
concern was to identify the most important forecasts and how those forecasts were made. In
particular, we wished to know if the most widely accepted forecasts of global average
temperature were based on the opinions of experts or were derived using scientific forecasting
methods. Given the findings of our review of reviews of climate forecasting and the conclusion
from our Google search that many scientists are unaware of evidence-based findings related to
forecasting methods, we expected that the forecasts would be based on the opinions of scientists.
We sent a questionnaire to experts who had expressed diverse opinions on global warming.
We generated lists of experts by identifying key people and asking them to identify others. (The
lists are provided in Appendix A.) Most (70%) of the 240 experts on our lists were IPCC
reviewers and authors.
Our questionnaire asked the experts to provide references for what they regarded as the most
credible source of long-term forecasts of mean global temperatures. We strove for simplicity to
minimize resistance to our request. Even busy people should have time to send a few references,
especially if they believe that it is important to evaluate the quality of the forecasts that may
influence major decisions. We asked:
“We want to know which forecasts people regard as the most credible and how
those forecasts were derived…
In your opinion, which scientific article is the source of the most
credible forecasts of global average temperatures over the rest of this
We received useful responses from 51 people, 42 of whom provided references to what they
regarded as credible sources of long-term forecasts of mean global temperatures. Interestingly,
eight respondents provided references in support of their claims that no credible forecasts exist.
Of the 42 expert respondents who were associated with global warming views, 30 referred us to
the IPCC’s report. A list of the papers that were suggested by respondents is provided at in the “Global Warming” section.
Based on the replies to our survey, it was clear that the IPCC’s Working Group 1 Report
contained the forecasts that are viewed as most credible by the bulk of the climate forecasting
community. These forecasts are contained in Chapter 10 of the Report and the models that are
used to forecast climate are assessed in Chapter 8, “Climate Models and Their Evaluation”
(Randall et al. 2007). Chapter 8 provided the most useful information on the forecasting process
used by the IPCC to derive forecasts of mean global temperatures, so we audited that chapter.
We also posted calls on email lists and on the site asking for help
from those who might have any knowledge about scientific climate forecasts. This yielded few
responses, only one of which provided relevant references.
Does the IPCC report provide climate forecasts?
Trenberth (2007) and others have claimed that the IPCC does not provide forecasts but rather
presents “scenarios” or “projections.” As best as we can tell, these terms are used by the IPCC
authors to indicate that they provide “conditional forecasts.” Presumably the IPCC authors hope
that readers, especially policy makers, will find at least one of their conditional forecast series
plausible and will act as if it will come true if no action is taken. As it happens, the word
“forecast” and its derivatives occurred 37 times, and “predict” and its derivatives occurred 90
times in the body of Chapter 8. Recall also that most of our respondents (29 of whom were IPCC
authors or reviewers) nominated the IPCC report as the most credible source of forecasts (not
“scenarios” or “projections”) of global average temperature. We conclude that the IPCC does
provide forecasts.
A forecasting audit for global warming
In order to audit the forecasting processes described in Chapter 8 of the IPCC’s report, we each
read it prior to any discussion. The chapter was, in our judgment, poorly written. The writing
showed little concern for the target readership. It provided extensive detail on items that are of
little interest in judging the merits of the forecasting process, provided references without
describing what readers might find, and imposed an incredible burden on readers by providing
788 references. In addition, the Chapter reads in places like a sales brochure. In the three-page
executive summary, the terms, “new” and “improved” and related derivatives appeared 17 times.
Most significantly, the chapter omitted key details on the assumptions and the forecasting process
that were used. If the authors used a formal structured procedure to assess the forecasting
processes, this was not evident.
We each made a formal, independent audit of IPCC Chapter 8 in May 2007. To do so, we
used the Forecasting Audit Software on the site, which is based on
material originally published in Armstrong (2001). To our knowledge, it is the only evidence-
based tool for evaluating forecasting procedures.
While Chapter 8 required many hours to read, it took us each about one hour, working
independently, to rate the forecasting approach described in the Chapter using the Audit software.
We have each been involved with developing the Forecasting Audit program, so other users
would likely require much more time.
Ratings are on a 5-point scale from -2 to +2. A rating of +2 indicates the forecasting
procedures were consistent with a principle, and a rating of -2 indicates failure to comply with a
principle. Sometimes some aspects of a procedure are consistent with a principle but others are
not. In such cases, the rater must judge where the balance lays. The Audit software also has
options to indicate that there is insufficient information to rate the procedures or that the principle
is not relevant to a particular forecasting problem.
Reliability is an issue with rating tasks. For that reason, it is desirable to use two or more
raters. We sent out general calls for experts to use the Forecasting Audit Software to conduct their
own audits and we also asked a few individuals to do so. At the time of writing, none have done
Our initial overall average ratings were similar at -1.37 and -1.35. We compared our ratings
for each principle and discussed inconsistencies. In some cases we averaged the ratings,
truncating toward zero. In other cases we decided that there was insufficient information or that
the information was too ambiguous to rate with confidence. Our final ratings are fully disclosed
in the Special Interest Group section of the site that is devoted to
Public Policy ( under Global Warming.
Of the 140 principles in the Forecasting Audit, we judged that 127 were relevant for auditing
the forecasting procedures described in Chapter 8. The Chapter provided insufficient information
to rate the forecasting procedures that were used against 38 of these 127 principles. For example,
we did not rate the Chapter against Principle 10.2: “Use all important variables.” At least in part,
our difficulty in auditing the Chapter was due to the fact that it was abstruse. It was sometimes
difficult to know whether the information we sought was present or not.
Of the 89 forecasting principles that we were able to rate, the Chapter violated 72. Of these,
we agreed that there were clear violations of 60 principles. Principle 1.3 “Make sure forecasts are
independent of politics” is an example of a principle that is clearly violated by the IPCC process.
David Henderson, a former Head of Economics and Statistics at the OECD, gave a detailed
account of how political considerations influence all stages of the IPCC process (Henderson
2007). The clear violations we identified are listed in Table 1.
Table 1: Clear Violations
Setting Objectives
Describe decisions that might be affected by the
Prior to forecasting, agree on actions to take
assuming different possible forecasts.
Make sure forecasts are independent of politics.
Consider whether the events or series can be
Identifying Data Points
Avoid biased data sources.
Collecting Data
Use unbiased and systematic procedures to collect
Ensure that information is reliable and that
measurement error is low.
Ensure that the information is valid.
Selecting Methods
List all important selection criteria before selecting
Ask unbiased experts to rate potential methods.
Select simple methods unless empirical evidence
calls for a more complex approach.
Compare track records of various forecasting
Assess acceptability and understandability of
methods to users
Examine the value of alternative forecasting
Implementing Methods: General
Keep forecasting methods simple.
Be conservative in situations of high uncertainty or
Implementing Quantitative Methods
Tailor the forecasting model to the horizon.
Do not use "fit" to develop the model.
Implementing Methods: Quantitative Models with
Explanatory Variables
Apply the same principles to forecasts of explanatory
Shrink the forecasts of change if there is high
uncertainty for predictions of the explanatory
Integrating Judgmental and Quantitative Methods
Use structured procedures to integrate judgmental
and quantitative methods.
Use structured judgments as inputs of quantitative
Use prespecified domain knowledge in selecting,
weighing, and modifying quantitative models.
Combining Forecasts
Combine forecasts from approaches that differ.
Use trimmed means, medians, or modes.
Use tracked records to vary the weights on
component forecasts.
Evaluating Methods
Compare reasonable methods.
Tailor the analysis to the decision.
Describe the potential biases of the forecasters.
Assess the reliability and validity of the data.
Provide easy access to the data.
Provide full disclosure of methods.
Test assumptions for validity.
Test the client's understanding of the methods.
Use direct replications of evaluations to identify
Replicate forecast evaluations to assess their
Compare forecasts generated by different methods.
Examine all important criteria.
Specify criteria for evaluating methods prior to
analyzing data.
Assess face validity.
Use error measures that adjust for scale in the data.
Ensure error measures are valid.
Use error measures that are not sensitive to the
degree of difficulty in forecasting.
Avoid error measures that are highly sensitive to
Use out of sample (ex-ante) error measures.
(Revised) Tests of statistical significance should not
be used.
Do not use root mean square error (RMSE) to make
comparisons among forecasting methods.
Base comparisons of methods on large samples of
Conduct explicit cost-benefit analysis.
Assessing Uncertainty
Use objective procedures to estimate explicit
Develop prediction intervals by using empirical
estimates based on realistic representations of
forecasting situations.
When assessing PIs, list possible outcomes and
assess their likelihoods.
Obtain good feedback about forecast accuracy and
the reasons why errors occurred.
Combine prediction intervals from alternative
forecast methods.
Use safety factors to adjust for overconfidence in
Presenting Forecasts
Present forecasts and supporting data in a simple
and understandable form.
Provide complete, simple, and clear explanations of
Present prediction intervals.
Learning That Will Improve Forecasting Procedures
Establish a formal review process for forecasting
Establish a formal review process to ensure that
forecasts are used properly.
We also found 12 “apparent violations”. These principles, listed in Table 2, are ones for which
one or both of us had some concerns over the coding or where we did not agree that the
procedures clearly violated the principle.
Table 2: Apparent Violations
Setting Objectives
Obtain decision makers' agreement on methods.
Structuring the Problem
Identify possible outcomes prior to making forecast.
Decompose time series by level and trend.
Identifying Data Sources
Ensure the data match the forecasting situation.
Obtain information from similar (analogous) series or cases. Such information may help to estimate
Implementing Judgmental Methods
Obtain forecasts from heterogeneous experts.
Evaluating Methods
Design test situations to match the forecasting problem.
Describe conditions associated with the forecasting problem.
Use multiple measures of accuracy.
Assessing Uncertainty
Do not assess uncertainty in a traditional (unstructured) group meeting.
Incorporate the uncertainty associated with the prediction of the explanatory variables in the prediction
Presenting Forecasts
Describe your assumptions.
Finally, we lacked sufficient information to make ratings on many of the relevant principles.
These are listed in Table 3.
Table 3: Lack of Information
Structuring the Problem
Tailor the level of data aggregation (or segmentation) to the decisions.
Decompose the problem into parts.
Decompose time series by causal forces.
Structure problems to deal with important interactions among causal variables.
Structure problems that involve causal chains.
Identifying Data Sources
Use theory to guide the search for information on explanatory variables.
Collecting Data
Obtain all the important data.
Avoid collection of irrelevant data.
Preparing Data
Clean the data.
Use transformations as required by expectations.
Adjust intermittent series.
Adjust for unsystematic past events.
Adjust for systematic events.
Use graphical displays for data.
Implementing Methods: General
Adjust for events expected in the future.
Pool similar types of data.
Ensure consistency with forecasts of related series and related time periods.
Implementing Judgmental Methods
Ask experts to justify their forecasts in writing.
Obtain forecasts from enough respondents.
Obtain multiple forecasts of an event from each expert.
Implementing Quantitative Methods
Match the model to the underlying phenomena.
Weigh the most relevant data more heavily.
Update models frequently.
Implementing Methods: Quantitative Models with Explanatory Variables
Use all important variables.
Rely on theory and domain expertise when specifying directions of relationships.
Use theory and domain expertise to estimate or limit the magnitude of relationships.
Use different types of data to measure a relationship.
Forecast for alternative interventions.
Integrating Judgmental and Quantitative Methods
Limit subjective adjustments of quantitative forecasts.
Combining Forecasts
Use formal procedures to combine forecasts.
Start with equal weights.
Use domain knowledge to vary weights on component forecasts.
Evaluating Methods
Use objective tests of assumptions.
Avoid biased error measures.
Do not use R-square (either standard or adjusted) to compare forecasting models.
Assessing Uncertainty
Ensure consistency of the forecast horizon.
Ask for a judgmental likelihood that a forecast will fall within a pre-defined minimum-maximum interval.
Learning That Will Improve Forecasting Procedures
Seek feedback about forecasts.
Some of these principles might be surprising to those who have not seen the evidence—“Do not
use R-square (either standard or adjusted) to compare forecasting models.” Others are principles
that any scientific paper should be expected to address—“Use objective tests of assumptions.”
Many of these principles are important for climate forecasting, such as “Limit subjective
adjustments of quantitative forecasts.”
Some principles are so important that any forecasting process that does not adhere to them
cannot produce valid forecasts. We address four such principles, all of which are based on strong
empirical evidence. All four of these key principles were violated by the forecasting procedures
described in IPCC Chapter 8.
Consider whether the events or series can be forecasted (Principle 1.4)
This principle refers to whether a forecasting method can be used that would do better than a
naïve method. A common naïve method is to assume that things will not change.
Interestingly, naïve methods are often strong competitors with more sophisticated
alternatives. This is especially so when there is much uncertainty. To the extent that uncertainty is
high, forecasters should emphasize the naïve method. (This is illustrated by regression model
coefficients: when uncertainty increases, the coefficients tend towards zero.) Departures from the
naïve model tend to increase forecast error when uncertainty is high.
In our judgment, the uncertainty about global mean temperature is extremely high. We are
not alone. Dyson (2007), for example, wrote in reference to attempts to model climate that “The
real world is muddy and messy and full of things that we do not yet understand.” There is even
controversy among climate scientists over something as basic as the current trend. One
researcher, Carter (2007, p. 67) wrote:
…the slope and magnitude of temperature trends inferred from time-
series data depend upon the choice of data end points. Drawing trend
lines through highly variable, cyclic temperature data or proxy data is
therefore a dubious exercise. Accurate direct measurements of
tropospheric global average temperature have only been available since
1979, and they show no evidence for greenhouse warming. Surface
thermometer data, though flawed, also show temperature stasis since
Global climate is complex and scientific evidence on key relationships is weak or absent. For
example, does increased CO2 in the atmosphere cause high temperatures or do high temperatures
increase CO2? In opposition to the major causal role assumed for CO2 by the IPCC authors (Le
Treut et al. 2007), Soon (2007) presents evidence that the latter is the case and that CO2 variation
plays at most a minor role in climate change.
Measurements of key variables such as local temperatures and a representative global
temperature are contentious and subject to revision in the case of modern measurements because
of inter alia the distribution of weather stations and possible artifacts such as the urban heat island
effect, and are often speculative in the case of ancient ones, such as those climate proxies derived
from tree ring and ice-core data (Carter 2007).
Finally, it is difficult to forecast the causal variables. Stott and Kettleborough (2002, p. 723)
Even with perfect knowledge of emissions, uncertainties in the
representation of atmospheric and oceanic processes by climate models
limit the accuracy of any estimate of the climate response. Natural
variability, generated both internally and from external forcings such as
changes in solar output and explosive volcanic eruptions, also
contributes to the uncertainty in climate forecasts.
The already high level of uncertainty rises rapidly as the forecast horizon increases.
While the authors of Chapter 8 claim that the forecasts of global mean temperature are well-
founded, their language is imprecise and relies heavily on such words as “generally,” “reasonable
well,” “widely,” and “relatively” [to what?]. The Chapter makes many explicit references to
uncertainty. For example, the phrases “. . . it is not yet possible to determine which estimates of
the climate change cloud feedbacks are the most reliable” and “Despite advances since the TAR,
substantial uncertainty remains in the magnitude of cryospheric feedbacks within AOGCMs”
appear on p. 593. In discussing the modeling of temperature, the authors wrote, “The extent to
which these systematic model errors affect a model’s response to external perturbations is
unknown, but may be significant” (p. 608), and, “The diurnal temperature range… is generally
too small in the models, in many regions by as much as 50%” (p. 609), and “It is not yet known
why models generally underestimate the diurnal temperature range.” The following words and
phrases appear at least once in the Chapter: unknown, uncertain, unclear, not clear, disagreement,
not fully understood, appears, not well observed, variability, variety, unresolved, not resolved,
and poorly understood.
Given the high uncertainty regarding climate, the appropriate naïve method for this situation
would be the “no-change” model. Prior evidence on forecasting methods suggests that attempts to
improve upon the naïve model might increase forecast error. To reverse this conclusion, one
would have to produce validated evidence in favor of alternative methods. Such evidence is not
provided in Chapter 8 of the IPCC report.
We are not suggesting that we know for sure that long-term forecasting of climate is
impossible, only that this has yet to be demonstrated. Methods consistent with forecasting
principles such as the naïve model with drift, rule-based forecasting, well-specified simple causal
models, and combined forecasts might prove useful. The methods are discussed in Armstrong
(2001). To our knowledge, their application to long-term climate forecasting has not been
examined to date.
Keep forecasting methods simple (Principle 7.1)
We gained the impression from the IPPC chapters and from related papers that climate
forecasters generally believe that complex models are necessary for forecasting climate and that
forecast accuracy will increase with model complexity. Complex methods involve such things as
the use of a large number of variables in forecasting models, complex interactions, and
relationships that employ nonlinear parameters. Complex forecasting methods are only accurate
when there is little uncertainty about relationships now and in the future, where the data are
subject to little error, and where the causal variables can be accurately forecast. These conditions
do not apply to climate forecasting. Thus, simple methods are recommended.
The use of complex models when uncertainty is high is at odds with the evidence from
forecasting research (e.g., Allen and Fildes 2001, Armstrong 1985, Duncan, Gorr and Szczypula
2001, Wittink and Bergestuen 2001). Models for forecasting variations in climate are not an
exception to this rule. Halide and Ridd (2007) compared predictions of El Niño-Southern
Oscillation events from a simple univariate model with those from other researchers’ complex
models. Some of the complex models were dynamic causal models incorporating laws of physics.
In other words, they were similar to those upon which the IPCC authors depended. Halide and
Ridd’s simple model was better than all eleven of the complex models in making predictions
about the next three months. All models performed poorly when forecasting further ahead.
The use of complex methods makes criticism difficult and prevents forecast users from
understanding how forecasts were derived. One effect of this exclusion of others from the
forecasting process is to reduce the chances of detecting errors.
Do not use fit to develop the model (Principle 9.3)
It was not clear to us to what extent the models described in Chapter 8 (or in Chapter 9 by Hegerl
et al. 2007) are either based on, or have been tested against, sound empirical data. However, some
statements were made about the ability of the models to fit historical data, after tweaking their
parameters. Extensive research has shown that the ability of models to fit historical data has little
relationship to forecast accuracy (See “Evaluating forecasting methods” in Armstrong 2001.) It is
well known that fit can be improved by making a model more complex. The typical consequence
of increasing complexity to improve fit, however, is to decrease the accuracy of forecasts.
Use out-of-sample (ex ante) error measures (Principle 13.26)
Chapter 8 did not provide evidence on the relative accuracy of ex ante long-term forecasts from
the models used to generate the IPCC’s forecasts of climate change. It would have been feasible
to assess the accuracy of alternative forecasting methods for medium- to long-term forecasts by
using “successive updating.” This involves withholding data on a number of years, then providing
forecasts for one-year ahead, then two-years ahead, and so on up to, say, 20 years. The actual
years could be disguised during these validation procedures. Furthermore, the years could be
reversed (without telling the forecasters) to assess back-casting accuracy. If, as is suggested by
forecasting principles, the models were unable to improve on the accuracy of forecasts from the
naïve method in such tests, there would be no reason to suppose that accuracy would improve for
longer forecasts. “Evaluating forecasting methods” in Armstrong 2001 provides evidence on this
Summary of audit findings
Our ratings of the processes used to generate the forecasts presented in the IPCC report are
provided on the Public Policy Forecasting Special Interest Group Page at These ratings have been posted since the time that our paper was
presented at the International Symposium on Forecasting in New York in late June 2007.
Prior to the publication of this paper, we invited other researchers, using messages to email
lists and web sites, to replicate our audit by providing their own ratings. In addition, we asked for
information about any relevant principles that have not been included in the Forecasting Audit. At
the time of writing, we have received neither alternative ratings nor evidence for additional
relevant principles.
The many violations provide further evidence that the IPCC authors were unaware of
evidence-based principles for forecasting. If they were aware of them, it would have been
incumbent on them to present evidence to justify their departures from the principles. They did
not do so. We conclude that because the forecasting processes examined in Chapter 8 overlook
scientific evidence on forecasting, the IPCC forecasts of climate change are not scientific.
We invite others to provide evidence-based audits of what they believe to be scientific
forecasts relevant to climate change. These can be posted on web sites to ensure that readers have
access to the audits. As with peer review, we will require all relevant information on the people
who conduct the audits prior to posting the audits.
Climate change forecasters and their clients should use the Forecasting Audit early and often.
Doing so would help to ensure that they are using appropriate forecasting procedures. Outside
evaluators should also be encouraged to conduct audits. The audit reports should be made
available to both the sponsors of the study and the public by posting on an open web site such as
Climate forecasters’ use of the scientific literature on forecasting methods
Bryson (1993) wrote that while it is obvious that when a statement is made about what climate
will result from a doubling CO2 it is a forecast, “I have not yet heard, at any of the many
environmental congresses and symposia that I have attended, a discussion of forecasting
methodology applicable to the environment” (p. 791).
We looked for evidence that climate modelers relied on scientific studies on the proper use of
forecasting methods. In one approach, in April and June 2007, we used the Advanced Search
function of Google Scholar to get a general sense of the extent to which climate forecasters refer
to scientific studies on forecasting. When we searched for “global warming” and “forecasting
principles,” we found no relevant sites. Nor did we find any relevant sites for
“” and “global warming.” Nor were there any relevant citations for the
relevant-sounding paper, “Forecasting for Environmental Decision-Making” (Armstrong 1999)
published in a book with a relevant title: Tools to Aid Environmental Decision Making. A search
for “global warming” and the best selling textbook on forecasting methods (Makridakis et al.
1998) revealed two citations, neither related to the prediction of global mean temperatures.
Finally, there were no citations of research on causal models (e.g., Allen and Fildes 2001).
Using the titles of the papers, we independently examined the references in Chapter 8 of the
IPCC Report. The Chapter contained 788 references. Of these, none had any apparent relationship
to forecasting methodology. Our examination was not difficult as most papers had titles such as,
“Using stable water isotopes to evaluate basin-scale simulations of surface water budgets,” and,
“Oceanic isopycnal mixing by coordinate rotation.”
We also examined the 535 references in Chapter 9. Of these, 17 had titles that suggested the
article might be concerned at least in part with forecasting methods. When we inspected the 17
articles, we found that none of them referred to the scientific literature on forecasting methods.
Finally, we examined the 23 papers that we were referred to by our survey respondents.
These included Chapter 10 of the IPCC Report (Meehl et al. 2007). One respondent provided
references to eight papers all by the same author (Abdussamatov). We obtained copies of three of
those papers and abstracts of three others and found no evidence that the author had referred to
forecasting research. Nor did any of the remaining 15 papers include any references to research
on forecasting.
It is difficult to understand how scientific forecasting could be conducted without reference to
the research literature on how to make forecasts. One would expect to see empirical justification
for the forecasting methods that were used. We concluded that climate forecasts are informed by
the modelers’ experience and by their models—but that they are unaided by the application of
forecasting principles.
To provide forecasts of climate change that are useful for policy-making, one would need to
prepare forecasts of (1) temperature changes, (2) the effects of any temperature changes, and (3)
the effects of feasible proposed policy changes. To justify policy changes based on climate
change, policy makers need scientific forecasts for all three forecasting problems and they need
those forecasts to show net benefits flowing from proposed policies. If governments implement
policy changes without such justification, they are likely to cause harm to many people.
We have shown that failure occurs with the first forecasting problem: predicting temperature
over the long term. Specifically, we have been unable to find a scientific forecast to support the
currently widespread belief in “global warming.” Climate is complex and there is much
uncertainty about causal relationships and data. Prior research on forecasting suggests that in such
situations a naïve (no change) forecast would be superior to current predictions. Note that
recommending the naïve forecast does not mean that we believe that climate will not change. It
means that we are not convinced that current knowledge about climate is sufficient to make
useful long-term forecasts about climate. Policy proposals should be assessed on that basis.
Many policies have been proposed in association with claims of global warming. It is not our
purpose in this paper to comment on specific policy proposals, but it should be noted that policies
may be valid regardless of future climate changes. To assess this, it would be necessary to
directly forecast costs and benefits assuming that climate does not change or, even better, to
forecasts costs and benefits under a range of possible future climates.
Based on our literature searches, those forecasting long-term climate change have no apparent
knowledge of evidence-based forecasting methods, so we expect that similar conclusions would
apply to the other two necessary parts of the forecasting problem.
Public policy makers owe it to the people who would be affected by their policies to base
them on scientific forecasts. Advocates of policy changes have a similar obligation. We hope that
in the future, climate scientists with diverse views will embrace forecasting principles and will
collaborate with forecasting experts in order to provide policy makers with scientific forecasts of
We thank P. Geoffrey Allen, Robert Carter, Alfred Cuzán, Robert Fildes, Paul Goodwin, David
Henderson, Jos de Laat, Ross McKitrick, Kevin Trenberth, Timo van Druten, Willie Soon, and
Tom Yokum for helpful suggestions on various drafts of the paper. We are also grateful for the
suggestions of three anonymous reviewers. Our acknowledgement does not imply that all of the
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Appendix A: People to whom we sent our questionnaire (* indicates a relevant response)
IPCC Working Group 1
Myles Allen, Richard Alley, Ian Allison, Peter Ambenje, Vincenzo Artale, Paulo Artaxo, Alphonsus
Baede, Roger Barry, Terje Berntsen, Richard A. Betts, Nathaniel L. Bindoff, Roxana Bojariu, Sandrine
Bony, Kansri Boonpragob, Pascale Braconnot, Guy Brasseur, Keith Briffa, Aristita Busuioc, Jorge
Carrasco, Anny Cazenave, Anthony Chen*, Amnat Chidthaisong, Jens Hesselbjerg Christensen, Philippe
Ciais*, William Collins, Robert Colman*, Peter Cox, Ulrich Cubasch, Pedro Leite Da Silva Dias, Kenneth
L. Denman, Robert Dickinson, Yihui Ding, Jean-Claude Duplessy, David Easterling, David W. Fahey,
Thierry Fichefet*, Gregory Flato, Piers M. de F. Forster*, Pierre Friedlingstein, Congbin Fu, Yoshiyuki
Fuji, John Fyfe, Xuejie Gao, Amadou Thierno Gaye*, Nathan Gillett*, Filippo Giorgi, Jonathan Gregory*,
David Griggs, Sergey Gulev, Kimio Hanawa, Didier Hauglustaine, James Haywood, Gabriele Hegerl*,
Martin Heimann*, Christoph Heinze, Isaac Held*, Bruce Hewitson, Elisabeth Holland, Brian Hoskins,
Daniel Jacob, Bubu Pateh Jallow, Eystein Jansen*, Philip Jones, Richard Jones, Fortunat Joos, Jean Jouzel,
Tom Karl, David Karoly*, Georg Kaser, Vladimir Kattsov, Akio Kitoh, Albert Klein Tank, Reto Knutti,
Toshio Koike, Rupa Kumar Kolli, Won-Tae Kwon, Laurent Labeyrie, René Laprise, Corrine Le Quéré,
Hervé Le Treut, Judith Lean, Peter Lemke, Sydney Levitus, Ulrike Lohmann, David C. Lowe, Yong Luo,
Victor Magaña Rueda, Elisa Manzini, Jose Antonio Marengo, Maria Martelo, Valérie Masson-Delmotte,
Taroh Matsuno, Cecilie Mauritzen, Bryant Mcavaney, Linda Mearns, Gerald Meehl, Claudio Guillermo
Menendez, John Mitchell, Abdalah Mokssit, Mario Molina, Philip Mote*, James Murphy, Gunnar Myhre,
Teruyuki Nakajima, John Nganga, Neville Nicholls, Akira Noda, Yukihiro Nojiri, Laban Ogallo, Daniel
Olago, Bette Otto-Bliesner, Jonathan Overpeck*, Govind Ballabh Pant, David Parker, Wm. Richard Peltier,
Joyce Penner*, Thomas Peterson*, Andrew Pitman, Serge Planton, Michael Prather*, Ronald Prinn,
Graciela Raga, Fatemeh Rahimzadeh, Stefan Rahmstorf, Jouni Räisänen, Srikanthan (S.) Ramachandran,
Veerabhadran Ramanathan, Venkatachalam Ramaswamy, Rengaswamy Ramesh, David Randall*, Sarah
Raper, Dominique Raynaud, Jiawen Ren, James A. Renwick, David Rind, Annette Rinke, Matilde M.
Rusticucci, Abdoulaye Sarr, Michael Schulz*, Jagadish Shukla, C. K. Shum, Robert H. Socolow*, Brian
Soden, Olga Solomina*, Richard Somerville*, Jayaraman Srinivasan, Thomas Stocker, Peter A. Stott*, Ron
Stouffer, Akimasa Sumi, Lynne D. Talley, Karl E. Taylor*, Kevin Trenberth*, Alakkat S. Unnikrishnan,
Rob Van Dorland, Ricardo Villalba, Ian G. Watterson*, Andrew Weaver*, Penny Whetton, Jurgen
Willebrand, Steven C. Wofsy, Richard A. Wood, David Wratt, Panmao Zhai, Tingjun Zhang, De'er Zhang,
Xiaoye Zhang, Zong-Ci Zhao, Francis Zwiers*
Union of Concerned Scientists
Brenda Ekwurzel, Peter Frumhoff, Amy Lynd Luers
Channel 4 “The Great Global Warming Swindle” documentary (2007)
Bert Bolin, Piers Corbyn*, Eigil Friis-Christensen, James Shitwaki, Frederick Singer, Carl Wunsch*
Wikipedia’s list of global warming “skeptics”
Khabibullo Ismailovich Abdusamatov*, Syun-Ichi Akasofu*, Sallie Baliunas, Tim Ball, Robert Balling*,
Fred Barnes, Joe Barton, Joe Bastardi, David Bellamy, Tom Bethell, Robert Bidinotto, Roy Blunt, Sonja
Boehmer, Andrew Bolt, John Brignell*, Nigel Calder, Ian Castles*, George Chilingarian, John Christy*,
Ian Clark, Philip Cooney, Robert Davis, David Deming*, David Douglass, Lester Hogan, Craig Idso, Keith
Idso, Sherwood Idso, Zbigniew Jaworowski, Wibjorn Karlen, William Kininmonth, Nigel Lawson,
Douglas Leahey, David Legates, Richard Lindzen*, Ross Mckitrick*, Patrick Michaels, Lubos Motl*, Kary
Mullis, Tad Murty, Tim Patterson, Benny Peiser*, Ian Plimer, Arthur Robinson, Frederick Seitz, Nir
Shaviv, Fred Smith, Willie Soon, Thomas Sowell, Roy Spencer, Philip Stott, Hendrik Tennekes, Jan
Veizer, Peter Walsh, Edward Wegman
Other sources
Daniel Abbasi, Augie Auer, Bert Bolin, Jonathan Boston, Daniel Botkin*, Reid Bryson, Robert Carter*,
Ralph Chapman, Al Gore, Kirtland C. Griffin*, David Henderson, Christopher Landsea*, Bjorn Lomborg,
Tim Osborn, Roger Pielke*, Henrik Saxe, Thomas Schelling*, Matthew Sobel, Nicholas Stern*, Brian
Valentine*, Carl Wunsch*, Antonio Zichichi.
... Detailkritik wird u. a. an der Methodologie der Prognoseverfahren geübt. Auf die exemplarische Analyse eines Kapitels im Vierten IPCC-Assessment Report (WG 1) Bezug nehmend wird beispielsweise moniert, dass mindestens 72 von 140 Prinzipien verletzt wurden, die bei der korrekten Erstellung von Vorhersagen Beachtung erheischen (Green/Armstrong 2007). ...
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Klimawandel und Klimapolitik sind seit mehr als einem Jahrzehnt wohletablierte Politikthemen in Deutschland, in vielen OECD-Staaten und in den internationalen Beziehungen. 1997 wurde auf einem Weltklimagipfel der Vereinten Nationen das sog. Kyoto-Protokoll vereinbart.
... I am a forecasting researcher who has published a peer-reviewed journal article describing my audit of the methods used by the Intergovernmental Panel on Climate Change (IPCC) to make forecasts about global temperatures over the 21 st Century (Green and Armstrong 2007). I do not claim to be an expert on climate, but I do claim that forecasts of dangerous manmade global warming are not scientific. ...
Technical Report
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Forecasts of global warming are not scientific and should not be used for policy making.
... 4 This is despite the fact that warranted skepticism is an essential element to the advancement of science and the execution of the scientific method. Even though the predictions from climate change science are imperfect and the research is presented with appropriate caveats by organizations such as the IPCC, many people aligned with liberal political leanings reflexively denounce and negatively label anyone with potentially legitimate concerns regarding scientific uncertainty (Armstrong, K. C. Green, & Soon, 2008; K. C. Green & Armstrong, 2007;; K. C. Green, Armstrong, & Soon, 2009;Hoffman, 2011;McCright & Dunlap, 2011). By denying that such uncertainty exists, the result is that the policy choice is presented as "riskless", such that the costs of implementing climate change policies, if man-made climate change is actually a false positive, are completely ignored (Leiserowitz, 2006). ...
Accountability is often presented as a panacea for behavioral ailments. This one-size-fits-all approach to a multi-dimensional construct ignores a key component of the effectiveness of accountability systems: situational context. Situational contexts such as highly stochastic environments (e.g., financial markets, world politics) and politically-charged domains (e.g., national security decision-making, domestic policy) form accountability boundary conditions, beyond which previous experimental effects may not generalize. In a series of studies, I explore the relatively under-explored frontiers of accountability effects, including those that apply to highly stochastic environments; politically-charged outcomes, where the tendency towards motivated reasoning dominates; and rapidly evolving states of information, where one’s ability to update one’s beliefs has serious implications for the quality of one’s judgments and decisions. In this series of studies, I find that accountability effects only appeared under certain conditions. In general, holding people accountable for their judgments did not improve performance on highly stochastic or politically-charged tasks—in fact, it sometimes made performance worse. However, certain types of accountability were able to boost performance in some contexts. These studies demonstrate the value of incorporating situational context into accountability experiments.
Technical Report
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Technical Report
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My submission relates particularly to the following clause in the Terms of Reference: Identify the central/benchmark projections which are being used as the motivation for international agreements to combat climate change; and consider the uncertainties and risks surrounding these projections.
Technical Report
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Statement Our research findings challenge the basic assumptions of the State Department's Fifth U.S. Climate Action Report (CAR 2010). The alarming forecasts of dangerous manmade global warming are not the product of proper scientific evidence-based forecasting methods. Furthermore, there have been no validation studies to support a belief that the forecasting procedures used were nevertheless appropriate for the situation. As a consequence, alarming forecasts of global warming are merely the opinions of some scientists and, for a situation as complicated and poorly understood as global climate, such opinions are unlikely to be as accurate as forecasts that global temperatures will remain much the same as they have been over recent years. Using proper forecasting procedures we predict that the global warming alarm will prove false and that government actions in response to the alarm will be shown to have been harmful.
Technical Report
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Scientific understanding about the Earth's climate is tentative at best. As a result of uncertainties over what causes climate to change and how and when, there are rival theories and arguments among scientists about how to interpret the evidence. Rather than join these arguments, we have examined the processes that have been used to analyze the available data in order to derive forecasts of climate over the 21 st Century. We have concluded that the forecasting process reported on by the Intergovernmental Panel on Climate Change (IPCC) lacks a scientific basis...
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This thesis falls into the scientific areas of stochastic hydrology, hydrological modelling and hydroinformatics. It contributes with new practical solutions, new methodologies and large-scale results to predictive modelling of hydrological processes, specifically to solving two interrelated technical problems with emphasis on the latter. These problems are: (A) hydrological time series forecasting by exclusively using endogenous predictor variables (hereafter, referred to simply as “hydrological time series forecasting”); and (B) stochastic process-based modelling of hydrological systems via probabilistic post-processing (hereafter, referred to simply as “probabilistic hydrological post-processing”). For the investigation of these technical problems, the thesis forms and exploits a novel predictive modelling and benchmarking toolbox. This toolbox is consisted of: (i) approximately 6 000 hydrological time series (sourced from larger freely available datasets), (ii) over 45 ready-made automatic models and algorithms mostly originating from the four major families of stochastic, (machine learning) regression, (machine learning) quantile regression, and conceptual process-based models, (iii) seven flexible methodologies (which together with the ready-made automatic models and algorithms consist the basis of our modelling solutions), and (iv) approximately 30 predictive performance evaluation metrics. Novel model combinations coupled with different algorithmic argument choices result in numerous model variants, many of which could be perceived as new methods. All the utilized models (i.e., the ones already available in open software, as well as those automated and proposed in the context of the thesis) are flexible, computationally convenient and fast; thus, they are appropriate for large-sample (even global-scale) hydrological investigations. Such investigations are implied by the (mainly) algorithmic nature of the methodologies of the thesis. In spite of this nature, the thesis also provides innovative theoretical supplements to its practical and methodological contribution. Technical problem (A) is examined in four stages. During the first stage, a detailed framework for assessing forecasting techniques in hydrology is introduced. Complying with the principles of forecasting and contrary to the existing hydrological (and, more generally, geophysical) time series forecasting literature (in which forecasting performance is usually assessed within case studies), the introduced framework incorporates large-scale benchmarking. The latter relies on big hydrological datasets, large-scale time series simulation by using classical stationary stochastic models, many automatic forecasting models and algorithms (including benchmarks), and many forecast quality metrics. The new framework is exploited (by utilizing part of the predictive modelling and benchmarking toolbox of the thesis) to provide large-scale results and useful insights on the comparison of stochastic and machine learning forecasting methods for the case of hydrological time series forecasting at large temporal scales (e.g., the annual and monthly ones), with emphasis on annual river discharge processes. The related investigations focus on multi-step ahead forecasting. During the second stage of the investigation of technical problem (A), the work conducted during the previous stage is expanded by exploring the one-step ahead forecasting properties of its methods, when the latter are applied to non-seasonal geophysical time series. Emphasis is put on the examination of two real-world datasets, an annual temperature dataset and an annual precipitation dataset. These datasets are examined in both their original and standardized forms to reveal the most and least accurate methods for long-run one-step ahead forecasting applications, and to provide rough benchmarks for the one-year ahead predictability of temperature and precipitation. The third stage of the investigation of technical problem (A) includes both the examination-quantification of predictability of monthly temperature and monthly precipitation at global scale, and the comparison of a large number of (mostly stochastic) automatic time series forecasting methods for monthly geophysical time series. The related investigations focus on multi-step ahead forecasting by using the largest real-world data sample ever used so far in hydrology for assessing the performance of time series forecasting methods. With the fourth (and last) stage of the investigation of technical problem (A), the multiple-case study research strategy is introduced −in its large-scale version− as an innovative alternative to conducting single- or few-case studies in the field of geophysical time series forecasting. To explore three sub-problems associated with hydrological time series forecasting using machine learning algorithms, an extensive multiple-case study is conducted. This multiple-case study is composed by a sufficient number of single-case studies, which exploit monthly temperature and monthly precipitation time series observed in Greece. The explored sub-problems are lagged variable selection, hyperparameter handling, and comparison of machine learning and stochastic algorithms. Technical problem (B) is examined in three stages. During the first stage, a novel two-stage probabilistic hydrological post-processing methodology is developed by using a theoretically consistent probabilistic hydrological modelling blueprint as a starting point. The usefulness of this methodology is demonstrated by conducting toy model investigations. The same investigations also demonstrate how our understanding of the system to be modelled can guide us to achieve better predictive modelling when using the proposed methodology. During the second stage of the investigation of technical problem (B), the probabilistic hydrological modelling methodology proposed during the previous stage is validated. The validation is made by conducting a large-scale real-world experiment at monthly timescale. In this experiment, the increased robustness of the investigated methodology with respect to the combined (by this methodology) individual predictors and, by extension, to basic two-stage post-processing methodologies is demonstrated. The ability to “harness the wisdom of the crowd” is also empirically proven. Finally, during the third stage of the investigation of technical problem (B), the thesis introduces the largest range of probabilistic hydrological post-processing methods ever introduced in a single work, and additionally conducts at daily timescale the largest benchmark experiment ever conducted in the field. Additionally, it assesses several theoretical and qualitative aspects of the examined problem and the application of the proposed algorithms to answer the following research question: Why and how to combine process-based models and machine learning quantile regression algorithms for probabilistic hydrological modelling?
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ABSTRACT Organizations that use time series forecasting on a regular basis generally forecast many variables, such as demand for many products or services. Within the population of variables forecasted by an organization, we can expect that there will be groups of analogous time series that follow similar, time-based patterns. The co-variation of analogous time series is a largely untapped source of information that can improve forecast accuracy (and explainability). This paper takes the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. Bayesian pooling uses data from analogous time series as multiple observations per time period in a group-level model. It then combines estimated parameters of the group model with conventional time series model parameters, using “shrinkage” weights estimated empirically from the data. Major benefits of this approach are that it 1) minimizes the number,of parameters to be estimated (many other pooling approaches suffer from too many parameters to estimate), 2) builds on conventional time series models already familiar to forecasters, and 3) combines,time series and cross-sectional perspectives in flexible and effective ways. Provided are the necessary terms, concepts, and methods to understand Bayesian pooling
Principles of Forecasting: A Handbook for Researchers and Practitioners summarizes knowledge from experts and from empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. It applies to problems such as those in finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). The book is edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania. Contributions were written by 40 leading experts in forecasting, and the 30 chapters cover all types of forecasting methods. There are judgmental methods such as Delphi, role-playing, and intentions studies. Quantitative methods include econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of `if-then principles', and they summarize evidence on these principles. The project, developed over a four-year period, represents the first book to summarize all that is known about forecasting and to present it so that it can be used by researchers and practitioners. To ensure that the principles are correct, the authors reviewed one another's papers. In addition, external reviews were provided by more than 120 experts, some of whom reviewed many of the papers. The book includes the first comprehensive forecasting dictionary.
Conjoint analysis is a survey-based method managers often use to obtain consumer input to guide their new-product decisions. The commercial popularity of the method suggests that conjoint results improve the quality of those decisions. We discuss the basic elements of conjoint analysis, describe conditions under which the method should work well, and identify difficulties with forecasting marketplace behavior. We introduce one forecasting principle that establishes the forecast accuracy of new-product performance in the marketplace. However, practical complexities make it very difficult for researchers to obtain incontrovertible evidence about the external validity of conjoint results. Since published studies typically rely on holdout tasks to compare the predictive validities of alternative conjoint procedures, we describe the characteristics of such tasks, and discuss the linkages to conjoint data and marketplace choices. We then introduce five other principles that can guide conjoint studies to enhance forecast accuracy.
Review of J. Scott Armstrong's 1978 book on forecasting. Click on the DOI link above to read the review
Physical, mathematical, and observational grounds are employed to show that there is no physically meaningful global temperature for the Earth in the context of the issue of global warming. While it is always possible to construct statistics for any given set of local temperature data, an infinite range of such statistics is mathematically permissible if physical principles provide no explicit basis for choosing among them. Distinct and equally valid statistical rules can and do show opposite trends when applied to the results of computations from physical models and real data in the atmosphere. A given temperature field can be interpreted as both “warming” and “cooling” simultaneously, making the concept of warming in the context of the issue of global warming physically ill-posed.
A review of the recent refereed literature fails to confirm quantitatively that carbon dioxide (CO2) radiative forcing was the prime mover in the changes in tempera- ture, ice-sheet volume, and related climatic variables in the glacial and interglacial epi- sodes of the past 650,000 years, even under the "fast-response" framework where the convenient if artificial distinction between forcing and feedback is assumed. Atmospheric CO2 variations generally follow changes in temperature and other climatic variables rather than preceding them. Likewise, there is no confirmation of the often-posited significant supporting role of methane (CH4) forcing, which—despite its faster atmospheric response time—is simply too small, amounting to less than 0.2 W/m2 from a change of 400 ppb. We cannot quantitatively validate the numerous qualitative suggestions that the CO2 and CH4 forcings that occurred in response to Milankovic orbital cycles accounted for more than half of the amplitude of the changes in the glacial/interglacial cycles of global tempera- ture, sea level, and ice volume. Consequently, we infer that natural climatic variability— notably the persistence of insolation forcing at key seasons and geographical locations, taken with closely related thermal, hydrological, and cryospheric changes (such as the water vapor, cloud, and ice albedo feedbacks)— suffices in se to explain the proxy- derived, global and regional climatic and environmental phase-transitions in the paleocli- mate. If so, it may be appropriate to place anthropogenic greenhouse gas emissions in context by separating their medium-term climatic impacts from those of a host of natural forcings and feedbacks that may, as in paleoclimatological times, prove equally signifi- cant. (Key words: glacial-interglacial cycles; water vapor, cloud-and-ice insulator, and albedo feedback; Milankovic orbital insolation forcing; atmospheric CO2 and CH4 forc- ing.)
This paper demonstrates that the widely prophesied doubling of atmospheric carbon dioxide levels from natural, pre-industrial values will enhance the so-called 'greenhouse effect' but will amount to less than 1°C of global warming. It also points out that such a scenario is unlikely to arise given our limited reserves of fossil fuels - certainly not before the end of this century. Furthermore, the paper argues that general circulation models are as yet insufficiently accurate for civil engineers to rely on their predictions in any forward-planning decisions - the omission of solar wind effects being a potentially significant shortcoming. It concludes that the only certainty is that the world's fossil fuel resources are finite and should be used prudently and with proper respect to the environment.