Memorandum submitted by Richard S Courtney
(CRU 01)
In a press release at
http://www.parliament.uk/parliamentary_committees/science_technology/s_t_cru_inquiry.cfm
your Select Committee "announces an inquiry
into the unauthorised publication of data, emails and documents
relating to the work of the Climatic Research Unit (CRU) at the
University of East Anglia (UEA)."
And that press release also says:
"The Committee has agreed to examine and
invite written submissions on three questions:
What are the implications of the disclosures
for the integrity of scientific research?
Are the terms of reference and scope
of the Independent Review announced on 3 December 2009 by
UEA adequate (see below)?
How independent are the other two international
data sets?"
I am writing this as a response to that invitation
because - in the context of your questions - the most important
email among those hacked (?) from CRU may turn out to be one that
I wrote in 2003. I had forgotten it but Willis Essenbach found
it among the hacked (?) emails and circulated it. My submission
to you explains its meaning and significance.
The email demonstrates that six years ago the
self-titled 'Team' knew the estimates of average global temperature
(mean global temperature, MGT) were worthless and they acted to
prevent publication of proof of this.
The pertinent email can be seen at
http://www.eastangliaemails.com/emails.php?eid=384&filename=1069630979.txt
and I copy it as Appendix A for your convenience.
And, also for your convenience, I copy a draft
of the paper discussed in the email as Appendix B. I do not have
a completed version of it now (only a late draft that does not
include the references) but I could probably get a final version
from one of its 18 co-signatories. Additionally, I shall
post a hard copy of this document to you.
My tabulated paragraphs of submitted evidence
are as follows.
SUBMISSION
1. This submission concerns the importance
of an email (see Appendix A) from me that was among the files
hacked (?) from CRU. It demonstrates that in 2003 the self-titled
"Team" knew the estimates of average global temperature
(mean global temperature, MGT) were worthless, and they acted
to prevent publication of proof of this.
2. Climate change "attribution studies"
use computer models to assess possible causes of global climate
change. Known effects that cause climate change are input to a
computer model of the global climate system, and the resulting
output of the model is compared to observations of the real world.
Anthropogenic (ie man-made) global warming (AGW) is assumed to
be indicated by any rise in MGT that occurred in reality but is
not accounted by the known effects in the model. Clearly, any
error in determinations of changes to MGT provides incorrect attribution
of AGW.
3. The various determinations of the changes
to MGT differ and, therefore, there is no known accurate amount
of MGT change. But the erroneous MGT change was being input to
the models (garbage in, GI) so the amount of AGW attributed by
the studies was wrong (garbage out, GO) because "garbage
in" gives "garbage out" (GIGO). The attribution
studies that provide indications of AGW are GIGO.
4. I and others tried to publish a discussion
paper (see Appendix B) that attempted to explain the problems
with analyses of MGT. We compared the data and trends of the Jones
et al., GISS and GHCN data sets. These teams each provide
95% confidence limits for their results. However, the results
of the teams differ by more than double those limits in several
years, and the data sets provided by the teams have different
trends. Since all three data sets are compiled from the same available
source data (ie the measurements mostly made at weather stations
using thermometers), and purport to be the same metric (ie MGT
anomaly), this is surprising. Clearly, the methods of compilation
of MGT time series can generate spurious trends (where 'spurious'
means different from reality), and such spurious trends must exist
in all but at most one of the data sets.
5. So, we considered MGT according to two
interpretations of what it could be:
(i) MGT is a physical parameter thatat
least in principlecan be measured;
or
(ii) MGT is a "statistic"; ie an indicator
derived from physical measurements.
These two understandings derive from alternative
considerations of the nature of MGT.
6. If the MGT is assumed to be the mean
temperature of the volume of air near the Earth's surface over
a period of time, then MGT is a physical parameter indicated by
the thermometers (mostly) at weather stations that is calculated
using the method of mixtures (assuming unity volume, specific
heat, density etc). We determined that if MGT is considered as
a physical parameter that is measured, then the data sets of MGT
are functions of their construction. Attributing AGWor
anything elseto a change that is a function of the construction
of MGT is inadmissable.
Alternatively:
If the thermometers (mostly) at weather stations
are each considered to indicate the air temperature at each measurement
site and time, then MGT is a statistic that is computed as being
an average of the total number of thermometer indications. But
if MGT is considered to be a statistic then it can be computed
in several ways to provide a variety of results, each of different
use to climatologists. (In such a way, the MGT is similar in nature
to a Retail Price Index, which is a statistic that can be computed
in different ways to provide a variety of results, each of which
has proved useful to economists.) If MGT is considered to be a
statistic of this type, then MGT is a form of average. In which
case, the word "mean" in "mean global temperature"
is a misnomer, because although there are many types of average,
a set of measurements can only have one mean. Importantly, if
MGT is considered to be an indicative statistic then the differences
between the values and trends of the data sets from different
teams indicate that the teams are monitoring different climate
effects. But if the teams are each monitoring different climate
effects then each should provide a unique title for their data
set that is indicative of what is being monitored. Also, each
team should state explicitly what its data set of MGT purports
to be monitoring.
7. Thus, we determined thatwhichever
way MGT is consideredMGT is not an appropriate metric for
use in attribution studies.
8. However, the compilers of the MGT data
sets frequently alter their published data of past MGT (sometimes
they have altered the data in each of several successive months).
This is despite the fact that there is no obvious and/or published
reason for changing a datum of MGT for years that were decades
ago: the temperature measurements were obtained in those years
so the change can only be an effect of alterating the method(s)
of calculating MGT from the measurments. But the MGT data sets
often change. The MGT data always changed between submission of
the paper and completion of the peer review process. Thus, the
frequent changes to MGT data sets prevented publication of the
paper.
9. Whatever you call this method of preventing
publication of a paper, you cannot call it science.
But this method prevented publication of information
that proved the estimates of MGT and AGW are wrong and the amount
by which they are wrong cannot be known.
(a) I can prove that we submitted the paper for
publication.
(b) I can prove that Nature rejected it for a
silly reason:
"We publish original data and do not publish
comparisons of data sets"
(c) I can prove that whenever we submitted the
paper to a journal one or more of the Jones et al., GISS and GHCN
data sets changed so either the paper was rejected because it
assessed incorrect data or we had to withdraw the paper to correct
the data it assessed.
But I cannot prove who or what caused this.
10. It should also be noted that there is
no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and
unpublished) reasons although there is no obvious reason to change
a datum for MGT that is for decades in the past. It seems thatin
the absence of any possibility of calibrationthe compilers
of the data sets adjust their data in attempts to agree with each
other. Furthermore, they seem to adjust their recent data (ie
since 1979) to agree with the truly global measurements of MGT
obtained using measurements obtained using microwave sounding
units(MSU) mounted on orbital satelites since 1979. This adjustment
to agree with the MSU data may contribute to the fact that the
Jones et al., GISS and GHCN data sets each show no statistically
significant rise in MGT since 1995 (ie for the last 15 years).
However, the Jones et al., GISS and GHCN data sets keep
lowering their MGT values for temperatures decades ago.
11. Methods to correct these problems could
have been considered 6 years ago if publication of my paper
had not been blocked.
12. Additionally, I point out that the AGW
attribution studies are wrong in principle for two reasons.
Firstly, they are "argument from ignorance".
Such an argument is not new. For example, in
the Middle Ages experts said, "We don't know what causes
crops to fail: it must be witches: we must eliminate them."
Now, experts say, "We don't know what causes global climate
change: it must be emissions from human activity: we must eliminate
them." Of course, they phrase it differently saying they
can't match historical climate change with known climate mechanisms
unless an anthropogenic effect is included. But evidence for this
"anthropogenic effect" is no more than the evidence
for witches.
Secondly, they use an attribution study to 'prove'
what can only be disproved by attribution.
In an attribution study the system is assumed to
be behaving in response to suggested mechanism(s) that is modelled,
and the behaviour of the model is compared to the empirical data.
If the model cannot emulate the empirical data then there is reason
to suppose that the suggested mechanism is not the cause (or at
least not the sole cause) of the changes recorded in the empirical
data.
It is important to note that attribution studies
can only be used to reject hypothesis that a mechanism is a cause
for an observed effect. Ability to attribute a suggested cause
to an effect is not evidence that the suggested cause is the real
cause in part or in whole. (To understand this, consider the game
of Cludo. At the start of the game it is possible to attribute
the "murder" to all the suspects. As each piece of evidence
is obtained then one of the suspects can be rejected because he/she
can no longer be attributed with the murder).
But the CRU/IPCC attribution studies claim that
the ability to attribute AGW as a cause of climate change is evidence
that AGW caused the change (because they only consider one suspect
for the cause although there could be many suspects both known
and unknown).
Then, in addition to those two pieces of pure
pseudo-scienceas my paper demonstratedthe attribution
studies use estimates of climate changes that are know to be wrong!
13. None of this gives confidence that the
MGT data sets provide reliable quantification of change to global
temperature.
APPENDIX A
An EMAIL FROM RICHARD S COURTNEY DATED 23 NOV
2003 THAT WAS AMONG THOSE HACKED OR LEAKED FROM CRU
The copy of the email hacked or leaked from
CRU can be seen at
http://www.eastangliaemails.com/emails.php?eid=384&filename=1069630979.txt
and it is copied from there below. In the copy
below the words of others that are quoted by Courtney are reproduced
in a different font for ease of identification.
From:
To:
Subject: Re: Workshop: Reconciling Vertical
Temperature Trends
Date: Sun, 23 Nov 2003 18:42:59 EST
Dear All:
The excuses seem to be becoming desperate. Unjustified
assertion that I fail to understand "Myles" comments
and/or work on trying the detect/attribute climate change"
does not stop the attribution study being an error. The problem
is that I do understand what is being done, and I am willing to
say why it is GIGO.
Tim Allen said;
In a message dated 19/11/03 08:47:16 GMT
Standard Time, m.allen1@physics.ox.ac.uk writes:
"I would just like to add that those
of us working on climate change detection and attribution are
careful to mask model simulations in the same way that the observations
have been sampled, so these well-known dependencies of nominal
trends on the trend-estimation technique have no bearing on formal
detection and attribution results as quoted, for example, in the
IPCC TAR."
I rejected this saying:
"It cannot be known that the "masking"
does not generate additional spurious trends. Anyway, why assume
the errors in the data sets are geographical and not "?".
The masking is a "fix" applied to the model simulations
to adjust them to fit the surface data known to contain spurious
trends. This is simple GIGO."
Now, Tim Osborn says of my comment;
In a message dated 21/11/03 10:04:56 GMT
Standard Time,
"Richard's statement makes it clear, to
me at least, that he misunderstands Myles' comments and/or work
on trying the detect/attribute climate change.
As far as I understand it, the masking is applied
to the model to remove those locations/times when there are no
observations. This is quite different to removing those locations
which do not match, in some way, with the observationsthat
would clearly be the wrong thing to do. To mask those that have
no observations, however, is clearly the right thing to dowhat
is the point of attempting to detect a simulated signal of climate
change over some part of (eg) the Southern Ocean if there are
no observations there in which to detect the expected signal?
That would clearly be pointless."
Yes it would. And I fully understand Myles'
comments. Indeed, my comments clearly and unarguably relate to
Myles comments. But, as my response states, Myles' comments do
not alter the fact that the masked data and the unmasked data
contain demonstrated false trends. And the masking may introduce
other spurious trends. So, the conducted attribution study is
pointless because it is GIGO. Ad hominem insults don't change
that.
And nor does the use of peer review to block
my publication of the facts of these matters.
Richard
APPENDIX B
A draft version of the paper that was blocked
from publication and is the subject of discussion in an email
from Richard S Courtney dated 23 Nov 2003 that was among
those hacked or leaked from CRU
Repeated attempts to obtain peer reviewed publication
of various versions of this paper were blocked in the manner explained
in paragraphs 7 and 8 of the Submission to the Parliamentary
Science and Technology Committee from Richard S Courtney. This
draft has the same contents (except for the precise data: see
pragraphs 7 and 8 of the Submission) as each of the
final versions. But it is a pre-publication draft and does not
include its bibliography of references.
A CALL FOR
REVISION OF
MEAN GLOBAL
TEMPERATURE (MGT) DATA
SETS
1. Mean global temperature (MGT)
Mean global temperature (MGT) is the average
temperature of the air near the surface of the Earth derived from
measurements mostly made at weather stations using thermometers.
This short discussion paper calls for a revision to MGT procedures
and titles and for the results of that revision to be published.
One could imagine an instantaneous value for
MGT but there is no method to determine it. Therefore periodic,
individual measurements (mostly made at weather stations) are
used to determine an average MGT for periods of time such as days,
months or years. Determination of the annual MGT has particular
importance because historic data is utilized to compile time-series
of MGT since ~1880 and, thus, to gain an indication of the
change in MGT since then. This paper comments on the several reported
cumulative data sets for these annual values of MGT.
Three different research teams provide values
of MGT that are widely used (eg Jones et al., GISS, GHCN). They
present their results as "anomalies" from a set value
(usually the average MGT of a specified period of years eg 1961-90).
The "anomaly" is obtained by subtracting this average
temperature value from the determined MGT. Use of anomalies permits
direct comparison of the results between teams, because temperature
subtractions can be used to adjust the start points of the data
sets for comparison.
One important use of data sets of MGT anomalies
is in "attribution" studies of climate change. Attribution
studies model the effects that can alter climate, eg changes to
solar radiance, atmospheric injection of volcanic aerosols, etc.).
Differences between the model results and the observed changes
to MGT are usually attributed to anthropogenic climate change
(AGW). Any errors in the MGT data sets will clearly affect the
results of attribution studies which use those data sets.
There are significant variations between the
results of MGT calculated by the different teams that compile
them. The teams each provide 95% confidence limits for their results.
However, the results of the teams differ by more than double those
limits in several years, and the data sets provided by the teams
have different trends. Since all three data sets are compiled
from the same available source data (ie the measurements mostly
made at weather stations using thermometers), and purport to be
the same metric (ie MGT anomaly), this is surprising. Clearly,
the methods of compilation of MGT time series can generate spurious
trends (where "spurious" means different from reality),
and such spurious trends must exist in all but at most one of
the data sets.
The three MGT time series are shown in Figure
1.
Figure 1

In this figure, the trends (in °C/decade)
and the 2SD trend error are:
The Jones trend is significantly different from
the GISS trend (p<0.05), and the GHCN trend is very significantly
different from the GISS trend (p<0.01>).
The data sets in Figure 1 are derived from
the same available source data and, therefore, the differences
between the data sets in Figure 1 demonstrate either:
(a) that they are monitoring different climate
effects;
or
(b) that at least two of the data sets provide
wrong results (they differ in annual change by more than double
their stated 95% confidence limits in each of several years).
Each team claims to provide a true MGT, but
their results differ. Each uses a unique method to derive an indication
of some changes to the lower atmosphere, and these methods clearly
each provide a different indication of the changes. Therefore,
the minimum required amendment to MGT usage is for each team,
and others who refer to their data, to use a unique title for
the metric that they provide (eg "GISS Surface Temperature
Index" and "GHCN World Warmth Index").
In addition, and noting the importance of MGT
time series for bodies such as the IPCC, other changes to the
determination of these time series are warranted also, against
two different understandings of MGT. Either:
(i) MGT is a physical parameter thatat
least in principlecan be measured;
or
(ii) MGT is a "statistic"; ie an indicator
derived from physical measurements.
These two understandings derive from alternative
considerations of the nature of MGT:
(1) If the MGT is assumed to be the mean temperature
of the volume of air near the Earth's surface over a period of
time, then MGT is a physical parameter indicated by the thermometers
(mostly) at weather stations that is calculated using the method
of mixtures (assuming unity volume, specific heat, density etc).
Alternatively:
(2) If the thermometers (mostly) at weather stations
are each considered to indicate the air temperature at each measurement
site and time, then MGT is a statistic that is computed as being
an average of the total number of thermometer indications.
The following discussions consider MGT according
to each of these alternative understandings.
2. Consideration of MGT as a physical parameter
with a unique value for each year
The MGT data sets provided by the various teams
are often presented on the same graph (eg by IPCC) under the same
heading, and there has been no public objection to this by any
of these teams. This suggests that the teams agree MGT is a physical
parameter that indicates a unique value for the average temperature
of the air near the surface of the Earth for each year. But, the
data sets provide significantly different trends, and in each
of several pairs of years the annual change to MGT differs between
the data sets by more than double the calculated 95% confidence
limits of each data set. This paradox can be explained by application
of measurement theory.
When the measurement sites are considered as
being the measurement equipment, then the non-uniform distribution
of these sites is an imperfection in the measurement equipment.
Some measurement sites show warming trends and others cooling
trends and, therefore, the non-uniform distribution of measurement
sites may provide a preponderance of measurement sites in warming
(or cooling) regions. Also, large areas of the Earth's surface
contain no measurement sites, and temperatures for these areas
require interpolation.
Accordingly, the measurement procedure to obtain
the MGT for a year requires compensation for the imperfections
in the measurement equipment. A model of the imperfections is
needed to enable the compensation, and the teams who provide values
of MGT each use a different model for the imperfections (ie they
make different selections of which points to use, they provide
different weightings for eg effects over ocean and land, and so
on). So, though each team provides a compensation to correct for
the imperfection in the measurement equipment, each also uses
a different and unique compensation model.
The large differences between the results generated
by each team demonstrates that the compensation models usedby
all except at most one of the teamsmust contain large errors
that generate:
(A) spurious trends to MGT with time, and
(B) errors to MGT that are more than double the
calculated 95% confidence limits
But the fact that all the teams calculate their
errors demonstrates that each of the teams thinks that its particular
model is correct.
Use of an analogy may assist understanding of
the problem posed by use of an imperfect compensation model. Figure
2 shows that an area to be measured by an optical system
will have a distorted image if viewed at the wrong angle. Butif
the viewing angle is knownthen the true shape of the image
can be calculated so it can be measured. However, a distorted
image will still be measured if the compensation applies a wrong
correction for the angle. In terms of MGT, the non-uniformity
in results between the teams is analogous to different "wrong
correction angles". Perhaps most important, the magnitude
of the errors to MGT resulting from imperfect compensation models
cannot be known, because there is no independent calibration for
MGT.
Figure 2

MGT time series are often used to address the
question,
"Is the average temperature of the Earth's
surface increasing or decreasing, and at what rate?"
If MGT is considered to be a physical parameter
that is measured then these data sets cannot give a valid answer
to this question, because they contain errors of unknown magnitude
that are generated by the imperfect compensation models.
We know that the different compensation models
create significantly different results for MGT. Importantly, we
also know that the imperfect compensation models provide spurious
trends of unknown magnitude and sign in the MGT data sets. And
finally we know that the MSU data sets indicate strong warming
trends that do not occur in the available MSU satellite (eg Christyfull
reference needed) and weather balloon radiosonde (eg Angellfull
reference needed) data sets for lower atmosphere temperature.
A result that is a function of its construction
is a serious error. If MGT is considered as a physical parameter
that is measured, then the data sets of MGT are functions of their
construction. Attributing AGWor anything elseto
a change that is a function of the construction of MGT is inadmissable.
3. Consideration of MGT as a statistic with
a variety of possible useful values
The issues raised in Section 2 (above)
might be resolved by considering MGT as a statistic (as described
in Section 1 above) which does not have a unique value. According
to this consideration MGT is not measuredit is calculated
from measurementsand, therefore, it is not correct to use
measurement theory when considering MGT. Thereby, the arguments
advanced in Section 2 (above) become invalid because they
are based on measurement theory.
However, if MGT is considered to be a statistic
then it can be computed in several ways to provide a variety of
results, each of different use to climatologists. In such a way,
the MGT is similar in nature to a Retail Price Index, which is
a statistic that can be computed in different ways to provide
a variety of results, each of which has proved useful to economists.
If MGT is considered to be a statistic of this type, then MGT
is a form of average. In which case, the word "mean"
in "mean global temperature" is a misnomer, because
although there are many types of average, a set of measurements
can only have one mean.
Importantly, if MGT is considered to be an indicative
statistic then the differences between the values and trends of
the data sets from different teams indicate that the teams are
monitoring different climate effects. In this case, there is no
reason why the data sets should agree with each other, and the
95% confidence limits applied to the MGT data sets by their compilers
may be correct for each data set. Similarly, the different trends
indicated by the MGT data sets and the MSU and radiosonde data
sets could indicate that they are also monitoring different climate
effects.
To treat the MGT as an indicative statistic
has serious implications. The different teams each provide a data
set termed mean global temperature, MGT. But if the teams are
each monitoring different climate effects then each should provide
a unique title for their data set that is indicative of what is
being monitored. Also, each team should state explicitly what
its data set of MGT purports to be monitoring. The data sets of
MGT cannot address the question "Is the average temperature
of the Earth's surface increasing or decreasing, and at what rate?"
until the climate effects they are monitoring are explicitly stated
and understood. Finally, the application of any of these data
sets in attribution studies needs to be revised in the light of
knowledge of what each data set is monitoring.
4. Additional considerations
Clearly, the issues in Sections 2 and 3 (above)
need to be properly evaluated. However, all the above issues assume
the source data (mostly obtained from weather stations) used to
obtain MGT are correct. Though there are in fact serious reasons
to doubt the quality of much of this source data, their consideration
is outside the scope of this paper.
Another important consideration is that it is
not self-evident that MGT is the most useful or even a valid indicator
of global climate change. Doubts exist, because it can be argued
that a temperature reading is not sufficient to define the quantities
of heat stored in a volume of air. What needs to be resolved is
the difference between:
(a) the amount of energy the Earth has received
from the Sun; and
(b) the amount of heat retained in the atmosphere
by the greenhouse gases.
A minimum of three parameters is required to
determine the amount of heat stored as a result of the greenhouse
gases, namely temperature, pressure and humidity. Global climate
change is the average of local climate changes, and significant
local climate changes may often occur without any change to the
local mean temperature. Indeed, the above minimum three parameters
are not sufficient to indicate the heat stored in some cases:
for example, a local temperature may remain constant when additional
heat added to a region melts ice in that region so heat is stored
as latent heat of melting. Furthermore, alterations to precipitation
or wind speeds may not be indicated by changes in MGT although
the amount of energy stored in the air mass can vary considerably.
(a) We conclude that the use of time-series of
temperature change alone has the potential to be a strongly misleading
indicator of global climate change.
5. Summary and Conclusions
We have discussed the validity of the ways in
which MGT is reconstructed and interpreted. However, we are also
concerned about the uses to which MGT time series are put in the
public discussion about possible human-caused global warming.
Whatever the interpretation placed on the reconstruction
of MGT over the last ~100 years, the following seminal points
remain true:
The three most-cited data sets (CRU, GISS, GHCN)
differ significantly in the temperature trends that they portray;
For the late 20th century warming period between
1972 and 2000, the trends are +0.61 C/century, + 0.48 C/century
and 0.76 C/century, respectively;
These rates of temperature change are
significantly higher than the rates for the lower atmosphere measured
by satellite MSUs (insert rate) and weather balloon radiosondes
(insert rate).
These discrepancies notwithstanding, all these
rates of change, even the highest, lie well within the variability
displayed by the long term (Holocene) record of temperature change
as captured in polar ice-cores and deep sea cores; and
The pattern of all three MGT estimates
between 1900 and 2005 signally fails to correlate with
the pattern of human production of CO2. In contrast, more than
60% of the variance in the MGT temperature signal correlates with
solar variability.
In the face of these facts, the degree to which
the debate on global warming is being influenced by the publicizing
of alarmist temperature scenariosbased on unverified, deterministic
computer modelsand by the encouragement of public hysteria
about atmospheric CO2, is of great concern to us. That concern
is deepened by the fact that senior governmental science advisors,
once-influential science journals and distinguished science academies
all currently continue to fuel such public alarmism.
SIGNATORIES
Courtney, R S
| Independent Consultant on Energy and Environment; an IPCC Peer Reviewer.
|
Aksberg, A H, | Author of Fearless Climate
|
Baltutis, J S | CDR, USN (RET) BA, Mathematics; MS, Operations Research (RET)
|
Ball, T | Environmental Consultant; former Professor of Climatology, U. of Winnipeg
|
Boehmer-Christiansen, S | Reader Dept. of Geography, U. of Hull, U.K.; editor, Energy & Environment
|
Böttiger, H | Independent publisher
|
Bijkerk, A | Independent Quaternary Palaeo-Climate Researcher; Environmental Consultant; Lieut.-Colonel of the Royal Netherlands Air Force
|
Carter, R M | Professor of Geology, Marine Geophysical Laboratory, James Cook University, Australia
|
Ellsaesser, H W | US Air Force (RET 1963); Lawrence Livermore National Laboratory (RET 1997)
|
Ferreyra, E | President of the Argentinean Foundation for a Scientific Ecology; Independent researcher in climatology
|
Hissink, L | Consulting Geologist; Editor of Australian Institute of Geosciences News
|
Hughes, W S | Geologist; since 1991has studied global temperature trend compilations; author of web pages http://www.warwickhughes.com/
|
Jelbring, H | PhD thesis "Wind Controlled Climate"; President Inventex Aqua AB, Sweden
|
McLean, J | Data analyst, independent climate researcher
|
Moura, R G | Electrical engineer and meteorologist
|
Thoenes, D | Professor (em) Chemical and Process Engineering, Eindhoven University of Technology (1979-1995)
|
Rorsch, A | Professor (em) Molecular Genetics Leiden University (1967-1997); member of the board of the Netherlands Organization of Applied Science TNO (1980-1995)
|
van der Lingen, G J | Geologist/paleoclimatologist, Climate Change Consultant Geoscience Research and Investigations New Zealand (GRAINZ
|
Winterhalter, B | Senior Marine Researcher (RET) Geological Survey of Finland
|
| |
Most of the signatories are members of Sceptical Climate
ScienceClimatescepticsthe global scientific discussion
group for climate scientists and other participants interested
in discussing pro and con "mainstream", neutral, alternative,
critical and sceptical views in climatology, but the signatories
represent only themselves.
Richard S Courtney
January 2010
|