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But the ends justified the means....onward to Global Governance funding.

[Ernest_at_the_Beach]

It is a very technical paper...but jumping to the conclusions :

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4 Discussion and conclusions

Econometric tools are increasingly being used for climate data sets (see, e.g., Fomby and Vogelsang 2002, Mills 2010). We present two econometric methods for trend comparisons between data sets. Both add flexibility for multivariate comparisons and provide improved treatment of complex error structures. The multivariate testing method of Vogelsang and Franses (2005) yields the more robust estimator of the covariance matrix, but requires balanced data panels. Panel regression methods can accommodate comparisons of series of unequal lengths, but software limitations typically limit treatment of within- panel autocorrelation to the AR1 case. In our example the two methods yielded similar conclusions, indicating that the AR1 approximation in the panel model was likely not overly restrictive. In general, however, for the purpose of multivariate trend comparisons in climatology, we particularly recommend that the VF05 method enter the empirical toolkit.

In our example on temperatures in the tropical troposphere, on data ending in 1999 we find the trend differences between models and observations are only marginally significant, partially confirming the view of Santer et al. (2008) against Douglass et al. (2007). The observed temperature trends themselves are statistically insignificant. Over the 1979 to 2009 interval, in the LT layer, observed trends are jointly significant and three of four data sets have individually significant trends. In the MT layer two of four data sets have individually significant trends and the trends are jointly insignificant or marginal depending on the test used. Over the interval 1979 to 2009, model-projected temperature trends are two to four times larger than observed trends in both the lower and mid-troposphere and the differences are statistically significant at the 99% level.

Our methods assume trends are linear. We found no evidence for nonlinearity on the observed data, but some on modeled data in the MT. Also, the fact that the results are sensitive to the end date suggests that they might also be sensitive to the start date. Since the satellite data are unavailable prior to 1979 we cannot extend these series earlier. Interpretation of trend comparisons should therefore make reference to the time period analysed, which, ideally, should have some intrinsic interest. In this case the 1979-2009 interval is a 31-year span during which the upward trend in surface data strongly suggests a climate-scale warming process. As noted in the studies cited in the introduction, comparing models to observations in the tropical troposphere is an important aspect of testing explanations of the origins of surface warming.