Spatial Autocorrelation and the Detection of Non-Climatic Signals in Climate Data Short title: Autocorrelation and socioeconomic influence on temperatures

Surface climate data undergo processing to remove non-climatic effects such as urbanization and measurement irregularities. Some studies have shown that the processing is inadequate, leaving a residual warm bias. This has been disputed on three grounds: spatial autocorrelation of the 2 temperature field undermines significance of test results; counterfactual experiments using model-generated data suggest earlier results were spurious; and different satellite covariates yield unstable results. Surprisingly, these claims have not been statistically tested. We combine the data sets of various teams with trend estimates from global climate models and test the competing hypotheses. We find that controls for non-climatic, socioeconomic influences are necessary for a well-specified model of surface trends, supporting the view that the climatic data are not adequately filtered. 1 Introduction 1.1 Background Empirical climatology relies on the assumption that surface temperature data over land have been adjusted to remove effects due to local non-climatic influences, such as population growth, urbanization, equipment changes, data quality problems (especially in developing countries), variations in local air pollution levels, etc. The assumption that the adjustments are sufficient underpins the standard interpretation of climatic data observed 1979-2002 trends in 440 surface grid cells on a vector of climatological variables (lower tropospheric temperature trends and fixed factors such as latitude, mean air pressure and coastal proximity) augmented with a vector of socioeconomic variables, including income and population growth, Gross Domestic Product (GDP) per square km, education levels, etc. If the data have been adjusted to remove all non-climatic influences then the spatial pattern of warming trends should not vary systematically with socioeconomic indicators. MM04 and MM07 both rejected, at very high significance levels, independence of the surface temperature field and the socioeconomic variables, thus concluding that the adjusted surface climatic data likely still contain residual influences of industrialization on local temperature records. They estimated that the non-climatic effects could account for between one-third and one-half of the post-1979 average warming trend over land in the temperature data. 4 Schmidt (2009, herein S09) defended the standard interpretation of surface climate data on four grounds. First, he noted that an overall warming trend is observed in numerous data sets. This is not under dispute, but since the surface data show a relatively large trend compared to data from satellites, the accuracy of the land-based data is the point at issue. Second, he argued that the surface temperature field exhibits spatial autocorrelation (SAC), which reduces the …