Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data
Ross McKitrick, Department of Economics, University of Guelph
Patrick J. Michaels, Cato Institute, Washington DC
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 data base 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=7.1E-14), 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.
Please cite this paper as
McKitrick, R.R. and P.J. Michaels (2007), Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data, J. Geophys. Res., 112, D24S09, doi:10.1029/2007JD008465.
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