Are Temperature Trends affected by Economic Activity?

In a recent paper, McKitrick and Michaels (2004, or “MM04″) argue that non-climatic factors such as economic activity may contaminate climate station data, and thus, may render invalid any estimates of surface temĀ­perature trends derived from these data. They propose that surface temperature trends may be linked to various local economic factors, such as national coal consumption, income per capita, GPD growth rate, literacy rates, and whether or not temperature stations were located within the former Soviet Union. If their conclusions were correct, this would hold implications for the reliability of the modern surface temperature record, an important piece of evidence indicating 20th century surface warming. However, numerous flaws with their analysis, some of them absolutely fundamental, render their conclusions invalid.

First of all, there are a number of issues that they did not address that logically must must be addressed for their conclusions to be tenable. MM04 failed to acknowledge other independent data supporting the instrumental thermometer-based land surface temperature observations, such as satellite-derived temperature trend estimates over land areas in the Northern Hemisphere (Intergovernmental Intergovernmental Panel on Climate Change, Third Assessment Report, Chapter 2, Box 2.1, p. 106) that cannot conceivably be subject to the non-climatic sources of bias considered by them. Furthermore, they fail to reconcile their hypothesis with the established large-scale warming evident from global sea surface temperature data that, again, cannot be influenced by the local, non-climatic factors they argue contaminate evidence for surface warming. By focusing on thermometer-based land observations only, and ignoring other evidence conflicting with their hypothesis, MM04 failed to address basic flaws in their arguments.

Perhaps even more troubling, it has been noted elsewhere that MM04 confused “degrees” and “radians” in their calculations of areal weighting factors, rendering all of their calculations incorrect, and their conclusions presumably entirely invalid.

The focus of this piece, however, is on yet another fundamental problem with their analysis as identified by Benestad (2004). Benestad (2004) repeated their analysis using a different statistical model (linear and generalised multiple regression model) and the same data set. Benestad (2004) first reproduced the basic results of MM04 (i.e., established similar coefficients for the various factors used by MM04) using the full data set. This established an appropriate baseline for further tests of the robustness of their statistical model. As described below, their statistical model failed these tests, dramatically.

For one thing, the statistical significance they cited for their results was vastly overstated. One of the most basic assumptions in statistical modeling is that the data used as predictors in the model are Independent and Identically Distributed (‘IID’). It is well-known, however, that temperatures from neighboring stations are not independent. Due to the large-scale structure of surface temperature variations, nearby measurements partly describe the same phenomenon. Any statistical analysis using such temperature data must account for the fact that the actual degrees of freedom in the data is far lower than the nominal number of stations (see e.g. Wilks, 1995). McKitrick and Michaels, however, failed to account for this issue in estimating the statistical significance of their results. Had they accounted for this “spatial correlation”, as Benestad (2004) points out, they would have found their results to be statistically insignificant.

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