Ross McKitrick, Department of Economics, University of Guelph
Patrick J. Michaels, Cato Institute, Washington DC
ABSTRACT:
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.
PREPRINT
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Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data
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.
- This is a pre-print of an article in Journal of Geophysical Research-Atmospheres . Further reproduction not permitted.
- I have written a non-technical summary of the background and findings of this paper
- Also see my National Post op-ed on the IPCC's handling of this issue.
- The programs are written in STATA
- The zipped archive is here
SPATIAL AUTOCORRELATION
- Critics have said the results can be explained away as an artifact due to spatial autocorrelation. I have written this paper in response. I submitted it to JGR. The editor said that it is, technically, a response to comments from critics, but none of our critics have submitted their comments for peer review, so they cannot proceed with the paper.
- Data and code for this paper are here.
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