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Regression‐based "fingerprinting" methods in climate science employ total least squares (TLS) or orthogonal regression to remedy attenuation bias arising from measurement error due to reliance on climate model‐generated explanatory variables. Proving the consistency of multivariate TLS requires assuming noise variances are equal across all variables in the model. This assumption has been challenged empirically in the climate context but little is known about TLS biases when the assumption is violated. Monte Carlo analysis is used herein to examine coefficient biases when the noise variances are not equal. The analysis allows the explanatory variables to be negatively correlated which is typical in climate applications. Ordinary least squares (OLS) exhibits the expected attenuation bias which vanishes as the noise variances on the explanatory variables disappear. In some cases, TLS corrects attenuation bias but more typically imparts large and generally positive biases. OLS performs well when the true value of β=0$$ \beta =0 $$ whereas TLS performs quite poorly. This implies that TLS is not well suited for tests of the null. When β=1$$ \beta =1 $$ TLS tends to exhibit opposite biases to OLS. Diagnostic information specific to each data sample should be consulted before using TLS to avoid spurious inferences and replacing OLS attenuation bias with other, potentially larger biases. [ABSTRACT FROM AUTHOR]

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 DOI 10.3233/JEM-2010-0336

A sufficient condition for uniquely optimal emissions level in the presence of defensive activity is derived. An argument that rational people defend themselves against pollution externalities, at the very least through spatial separation from the source is presented.