Local land surface modification and variations in data quality affect temperature
trends in surface-measured data. Such effects are considered extraneous for the purpose
of measuring climate change, and providers of climate data must develop adjustments
to filter them out. If done correctly, temperature trends in climate data should
be uncorrelated with socioeconomic variables that determine these extraneous factors.
This hypothesis can be tested, which is the main aim of this paper. Using a new database
for all available land-based grid cells around the world we test the null hypothesis
that the spatial pattern of temperature trends in a widely used gridded climate data
set is independent of socioeconomic determinants of surface processes and data inhomogeneities.
The hypothesis is strongly rejected (P = 7.1 1014), indicating that extraneous
(nonclimatic) signals contaminate gridded climate data. The patterns of contamination
are detectable in both rich and poor countries and are relatively stronger in countries
where real income is growing. We apply a battery of model specification tests to
rule out spurious correlations and endogeneity bias. We conclude that the data contamination
likely leads to an overstatement of actual trends over land. Using the regression
model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002
global average temperature trend over land by about half.
Peer Reviewed Paper concludes half of reported warming is due to data contamination
Small California counties show less warming than large counties
Most regions showed a stronger increase in minimum temperatures than with mean and
Areas of intensive urbanization showed the largest positive trends, while rural,
non- agricultural regions showed the least warming.
Strong correlations between temperatures and Pacific sea surface temperatures (SSTs)
particularly Pacific Decadal Oscillation (PDO) values, also account for temperature
variability throughout the state.
Southern California had the highest rates of warming, while the NE Interior Basins
division experienced cooling.
Large urban sites showed rates over twice those for the state, for the mean maximum
temperatures, and over 5 times the state's mean rate for the minimum temperatures.
The other key network in the seminal Jones et al 1990 on urbanization (relied upon
in AR4) is their Chinese network. The idea that China between 1954 and 1983 - the
age of Chairman Mao and the Great Leap Forward - could have achieved consistency
in temperature measurement that eluded the U.S. observing system (with changing times
of observation, instruments etc) is a conceit that seems absurd on its face. However
Peterson 2003 in a recent literature review held the Jones Chinese network as one
of only a few "homogeneous" networks. Jones et al 1990 described their QC procedures
Wei-Chyung Wang has been a respected researcher in global warming studies for
decades. I have formally alleged that he committed fraud in some of his research,
including research cited by the Fourth Assessment Report of the IPCC (2007) on
“urban heat islands” (a critical issue). Herein, the allegation is reviewed, and
of its implications are explicated.
. . .
Two conclusions are manifest. First, there is a serious lack of integrity in some
Wang’s research. Second, the insignificance of urbanization effects on temperature
measurements has not been established as reliably as the IPCC (2007) assumes.
Something else, more general, might also be argued: the failure of the scientific
community to require data to be made available constitutes a serious departure from
the transparency that is widely accepted to be a prerequisite for integrity in human
. . .
Regarding station movements over time, the papers of Jones et al. and Wang et al.
make the following statements.
The stations were selected on the basis of station history: we
chose those with few, if any, changes in instrumentation, location
or observation times. [Jones et al.]
They were chosen based on station histories: selected stations
have relatively few, if any, changes in instrumentation, location,
or observation times…. [Wang et al.]
. . .
The essential point here is that the quoted statements from Jones et al. and Wang
et al. cannot be true and could not be in error by accident. The statements are fabricated.
Chuine et al. had the data; so they must have known that their conclusions were unfounded.
In other words, there is prima facie evidence of scientific fraud.
To study the paper properly, I needed to have the authors' data. So I e-mailed Dr.
Chuine, asking for this. The authors, though, were very reluctant to let me have
the data. It took me eight months, tens of e-mails exchanged with the authors, and
two formal complaints to Nature, to get the data. (Some data was purchased from Météo
France.) It is obviously inappropriate that such a large effort was necessary.
Looking at the data made it manifest that there are serious problems with the work
of Chuine et al. In particular, the authors' estimate for the summer temperature
of 2003 was higher than the actual temperature by 2.4 °C (about 4.3 °F). This is
the primary reason that 2003 seemed, according to the authors, to be so tremendously
There is also another reason. The three warmest years on record, prior to 2003, were
1945, 1947, and 1952. (The instrumental record goes back to 1922, or even 1883 if
we accept some inaccuracies.) The estimate of Chuine et al. for the summer temperature
in each of those years was much lower than the actual temperature.
What is important here is not the truth or falsity of the assertion of Chuine et
al. about Burgundy temperatures. Rather, what is important is that a paper on what
is arguably the world's most important scientific topic (global warming) was published
in the world's most prestigious scientific journal with essentially no checking of
the work prior to publication.
Moreover—and crucially—this lack of checking is not the result of some fluke failures
in the publication process. Rather, it is common for researchers to submit papers
without supporting data, and it is frequent that peer reviewers do not have the requisite
mathematical or statistical skills needed to check the work (medical sciences largely
excepted). In other words, the publication of the work of Chuine et al. was due to
systemic problems in the scientific publication process.
The systemic nature of the problems indicates that there might be many other scientific
papers that, like the paper of Chuine et al., were inappropriately published. Indeed,
that is true and I could list numerous examples. The only thing really unusual about
the paper of Chuine et al. is that the main problem with it is understandable for
people without specialist scientific training.
Another Failure of Peer Review
One of the biggest cases of academic fraud in medical history
One of the largest known cases of academic fraud and misconduct made the news this
week when Anesthesiology News reported that a leading medical researcher was found
to have fabricated much, if not all, of the data in his research.
Scott S. Reuben, M.D., of Baystate Medical Center in Springfield, Massachusetts,
is said to have made up and falsified data in at least 21, and perhaps many more,
studies published at least since 1996, according to the results of a year-long investigation
by Baystate Medical Center. Jane Albert, a spokeswoman for Baystate, said that the
fraud was spotted after questions were raised about two studies for which Dr. Reuben
had not even received approval to conduct human research.
Recent Papers On The Uncertainty and Biases In the Long Term Assessment Of The Global
Average Surface Temperature Trend
The multi-decadal global surface temperature trend is used (inappropriately; e.g.
see) as the primary metric to diagnose the magnitude of global warming and cooling.
This post lists major unresolved issues with the use of this surface temperature
trend metric, along with examples of recent papers and weblog posts that build on
the set of problems identified in our paper
* The use of one average temperature trend, which neglects that long wave cooling
is proportional to the 4th power of temperature. This is a warm bias if the predominance
of temperature increases are at cold absolute temperatures and a cool bias if at
warm absolute temperatures [Section 2].