Excerpted from a World Climate Report blogpost about the following forthcoming journal article (emphasis added):
McKitrick, R. R., and P. J. Michaels, 2007. Quantifying the influence
of anthropogenic surface processes inhomogeneities on gridded global
climate data. Journal of Geophysical Research, 112, D24S09, doi:10.1029/2007JD008465.
~*~
There are countless potential contaminants to weather records, and
generally speaking, the United Nations Intergovernmental Panel on
Climate Change (IPCC) and many others make the assumption that these
contaminants are relatively inconsequential, well accounted for, and/or
their effects generally cancel out in determining long-term trends in
temperature. If true, there should be no significant relationship
spatially between socioeconomic variables and trends in temperature
over land areas. If significant relationships can be identified between
socioeconomic variables and temperature trends, then contaminants to
the temperature records would be confirmed.
McKitrick and
Michaels examined the gridded temperature dataset used by the IPCC and
many others – they then gathered for each grid cell information on
gross domestic product, literacy, months with missing data, growth in
human population, economic growth, and growth in coal consumption. To
make the analyses as rigorous as possible, they also added the
satellite-based lower-tropospheric temperature trend, sea level
pressure, a dryness index, length of coastlines, and latitude. They
used a very sophisticated set of calculations to identify any
socioeconomic signals in the temperature trend data, and to say the
least, the signals were loud and clear.
Almost all of the
socioeconomic variables were highly statistically significantly related
to the temperature trends. The authors note “Taken together, our
findings show that trends in gridded climate data are, in part, driven
by the varying socioeconomic characteristics of the regions of origin,
implying a residual contamination remains even after adjustment
algorithms have been applied. Users of gridded climate data products
need to interpret their results accordingly.” Furthermore, they state
“These results are also consistent with previous findings showing that
nonclimatic factors, such as those related to land use change and
variations in data quality, likely add up to a net warming bias in
climate data, suggesting an overstatement of the rate of global warming
over land.”
So what is the bottom line here? The frequency histogram below (Figure
2) shows the distribution of temperature trends for the gridded
dataset, the satellite-based lower-tropospheric data, and the data
adjusted to account for the socioeconomic variables. In commenting on
this figure, the authors state “our analysis does suggest that
nonclimatic effects are present in the gridded temperature data used by
the IPCC and that they likely add up to a net warming bias at the
global level that may explain as much as half the observed land-based
warming trend.”
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