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... The regression model (1) can be rewritten in matrix notation as u+= XbT (2) where T is a 440x1 vector of temperature trends in each of 440 surface grid cells, X is a 440xk matrix of climatic and socioeconomic covariates, b is a kx1 vector of least-squares slope coefficients and u is a 440x1 residual vector. Spatial autocorrelation in the residual vector can be modeled using euu += Wλ (3) where λ is the autocorrelation coefficient, W is a symmetric nn × matrix of weights that measure the influence of each location on the other, and e is a vector of homoskedastic Gaussian disturbances, (Pisati [20]). The rows of W are standardized to sum to one. n equals 440 except in some regressions where grid cells are missing, as noted below. A test of 0: 0 =λH measures whether the error term in (1) is spatially independent. As argued in S09, it is likely the dependent variable is spatially autocorrelated. Anselin et al. [1] point out that if the alternative model allows for possible spatial dependence of T, i.e. e++= XbZTT φ (4), 12 where Z is a matrix of spatial weights for T which may not be identical to W, then conventional tests of 0=λ assuming an alternative model of the form e+= βXy will be b...

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...hich has not experienced any local air pollution, which can cause small but measurable local cooling or warming, and which has had the same equipment and recording methodology across time. Because there are few such sites in the world, long temperature records are typically taken from sites that fail one or more of these conditions. Hence the records must be modified in some way to reveal the climatic trend. The dangers of using raw temperature data for climate analysis has long been noted. In 1953, commenting on the growing use of weather records for measuring climate change, J. Mitchell Jr. [19] pointed to various sources of bias and cautioned: “The problem remains one of determining what part of a given temperature trend is climatically real and what part the result of observational difficulties and of artificial modification of the local environment.” This paper is concerned with the problem Mitchell, and many others since then, have discussed. We will examine the claims made by providers and users of climate data to the effect that the problems of non-climatic influences on climate data sets have been taken into account and rectified. We will show that this assumption has been tes...