[Vision2020] Climatic Research Unit E-mail Hack Controversy: Are the CRU data “suspect”? An objective assessment.

[Ted Moffett]

http://www.realclimate.org/index.php/archives/2009/12/are-the-cru-data-suspect-an-objective-assessment/#more-2351
 Are the CRU data “suspect”? An objective assessment.
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— eric @ 15 December 2009

Kevin Wood, Joint Institute for the Study of the Atmosphere and Ocean,
University of Washington
Eric Steig, Department of Earth and Space Sciences, University of Washington

In the wake of the CRU e-mail
hack<http://www.realclimate.org/index.php/archives/2009/11/the-cru-hack-context/>,
the suggestion that scientists have been hiding the raw meteorological data
that underpin global temperature records has appeared in the media. For
example, *New York Times* science writer John Tierney
wrote<http://www.nytimes.com/2009/12/01/science/01tier.html?_r=1&scp=3&sq=john%20tierney&st=cse>,
“It is not unreasonable to give outsiders a look at the historical readings
and the adjustments made by experts… Trying to prevent skeptics from seeing
the raw data was always a questionable strategy, scientifically.”

The implication is that something secretive and possibly nefarious has been
afoot in the way data have been handled, and that the validity of key data
products (especially those produced by CRU) is suspect on these grounds.
This is simply not the case.

It may come as a surprise to some that the first compilation of world-wide
meteorological data was published by the Smithsonian Institution in 1927,
long before anthropogenic climate change emerged as an important issue
(Clayton et al., 1927). This volume is still widely available on the library
shelf as are updates that were issued periodically. This same data
collection provided the foundation for the *World Monthly Surface Station
Climatology, 1738-cont.* As has been the case for many years, any interested
party can access this from UCAR
(http://dss.ucar.edu/datasets/ds570<http://dss.ucar.edu/datasets/ds570.0>)
and other electronic data archives.

Now, it is well known that these data are not perfect. Most records are not
as complete as could be wished. Errors periodically creep in and have to be
identified and weeded out. But beyond the simple errors of the key-entry
type there are inevitably discontinuities or inhomogeneities introduced into
the records due to changes in observing practices, station environment, or
other non-meteorological factors. It is very unlikely there is any
historical record in existence unaffected by this issue.

Filtering inhomogeneities out of meteorological data is a complicated
procedure. Coherent surface air temperature (SAT) datasets like those
produced by CRU also require a procedure for combining different (but
relatively nearby) record fragments. However, the methods used to undertake
these unavoidable tasks are not secret: they have been described in an
extensive literature over many decades (e.g. Conrad, 1944; Jones and Moberg,
2003; Peterson et al., 1998, and references therein). Discontinuities may
nevertheless persist in data products, but when they are found they are
published (e.g. Thompson et al., 2008).

Furthermore, it is a fairly simple exercise to extract the grid-box
temperatures from a CRU dataset—CRUTEM3v for example—and compare it to raw
data from *World Monthly Surface Station Climatology*. CRU data are
available from http://www.cru.uea.ac.uk/cru/data/temperature. One should not
expect a perfect match due to the issues described above, but an exercise
like this does provide a simple way to evaluate the extent to which the CRU
data represent the underlying raw data. In particular, it would presumably
be of interest to know whether the trends in the CRU data are very different
than the trends in the raw data, since this could be taken as indication
that the methods used by CRU result in an overstatement of the evidence for
global warming.

As an example, we extracted a sample of raw land-surface station data and
corresponding CRU data. These were arbitrarily selected based on the
following criteria: the length of record should be ~100 years or longer, and
the standard reference period 1961–1990 (used to calculate SAT anomalies)
must contain no more than 4 missing values. We also selected stations spread
as widely as possible over the globe. We randomly chose 94 out of a possible
318 long records. Of these, 65 were sufficiently complete during the
reference period to include in the analysis. These were split into two
groups of 33 and 32 stations (Set A and Set B), which were then analyzed
separately.

Results are shown in the following figures. The key points: both Set A and
Set B indicate warming with trends that are statistically identical between
the CRU data and the raw data (>99% confidence); the histograms show that
CRU quality control has, as expected, narrowed the variance (both extreme
positive and negative values removed).
[image: CRUobject]
*Comparison of CRUTEM3v data with raw station data taken from World Monthly
Surface Station Climatology. On the left are the mean temperature anomalies
from each pair of randomly chosen times series. On the right are the
distribution of trends in those time series and their means and standard
errors. (The standard
error<http://en.wikipedia.org/wiki/Standard_error_(statistics)>provides
an estimate of how well the sampling of ~30 stations represents the
full global data set assuming a Gaussian distribution.) Note that not all
the trends are for identical time periods, since not all data sets are the
same length.*

Conclusion: There is no indication whatsoever of any problem with the CRU
data. An independent study (by a molecular biologist it Italy, as it
happens) came to the same
conclusion<http://www.gilestro.tk/2009/lots-of-smoke-hardly-any-gun-do-climatologists-falsify-data>using
a somewhat different analysis. None of this should come as any
surprise of course, since any serious errors would have been found and
published already.

It’s worth noting that the global average trend obtained by CRU for
1850-2005, as reported by the IPCC (
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter3.pdf),
0.470.54 degrees/century,* is actually a bit lower (though not by a
statistically significant amount) than we obtained on average with our
random sampling of stations.

*See table 3.2 in IPCC WG1 report.
------------------------------
References
Clayton, H. H., F. M. Exner, G. T. Walker, and C. G. Simpson (1927), World
weather records, collected from official sources, in Smithsonian
Miscellaneous Collections, edited, Smithsonian Institution, Washington, D.C.


Conrad, V. (1944), Methods in Climatology, 2nd ed., 228 pp., Harvard
University Press, Cambridge.

Jones, P. D., and A. Moberg (2003), Hemispheric and large-scale surface air
temperature variations: An extensive revision and an update to 2001, Journal
of Climate, 16, 206-223.

Peterson, T. C., et al. (1998), Homogeneity adjustments of in-situ
atmospheric climate data: a review, International Journal of Climatology,
18, 1493-1517.

Thompson, D. W. J., J. J. Kennedy, J. M. Wallace, and P. D. Jones (2008), A
large discontinuity in the mid-twentieth century in observed global-mean
surface temperature, Nature, 453(7195), 646-649.
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Vision2020 Post: Ted Moffett
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