Ross McKitrick|Patrick J. Michaels
Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data
University of Guelph|Cato Institute
local land surface modification variations data quality affect temperature trends surfacemeasured data effects considered extraneous purpose measuring climate change providers climate data must develop adjustments filter done correctly temperature trends climate data uncorrelated socioeconomic variables determine extraneous factors hypothesis tested main aim paper using new database available landbased grid cells around world test null hypothesis spatial pattern temperature trends widely used gridded climate data set independent socioeconomic determinants surface processes data inhomogeneities hypothesis strongly rejected p 71 10 14 indicating extraneous nonclimatic signals contaminate gridded climate data patterns contamination detectable rich poor countries relatively stronger countries real income growing apply battery model specification tests rule spurious correlations endogeneity bias conclude data contamination likely leads overstatement actual trends land using regression model filter extraneous nonclimatic effects reduces estimated 19802002 global average temperature trend land half