Affiliations: [a] Department of Economics, University of Guelph, Guelph
ON Canada N1G 2W1, Canada | [b] Nierenberg Foundation, 9494 La Jolla Farms Rd., La
Jolla, CA, USA
Correspondence:
[*]
Corresponding author: Ross McKitrick, Department
of Economics, University of Guelph, Guelph ON Canada N1G 2W1, Canada. Tel.: +1
519 824 4120, x52532; Fax: +1 519 763 8497; E-mail:
rmckitri@uoguelph.ca
Abstract: To generate a climate data set, temperature data collected at the Earth's surface
must be adjusted to remove non-climatic effects such as urbanization and
measurement discontinuities. Some studies have shown that the post-1980 spatial
pattern of temperature trends over land in prominent climate data sets is
strongly correlated with the spatial pattern of socioeconomic development,
implying that the adjustments are inadequate, leaving a residual warm bias. This
evidence has been disputed on three grounds: spatial autocorrelation of the
temperature field undermines significance of test results; counterfactual
experiments using model-generated data suggest such correlations have an
innocuous interpretation; and different satellite covariates yield unstable
results. Somewhat surprisingly, these claims have not been put into a coherent
framework for the purpose of statistical testing. We combine economic and
climatological data sets from various teams with trend estimates from global
climate models and we use spatial regressions to test the competing hypotheses.
Overall we find that the evidence for contamination of climatic data is robust
across numerous data sets, it is not undermined by controlling for spatial
autocorrelation, and the patterns are not explained by climate models.
Consequently we conclude that important data products used for the analysis of
climate change over global land surfaces may be contaminated with socioeconomic
patterns related to urbanization and other socioeconomic processes. Research
Supported by Social Sciences and Humanities Research Council of Canada Grant
Number 430002.
Keywords: Global warming, data quality, spatial autocorrelation, economic activity
