There is an important new peer reviewed paper that further rasies questions on the robustness of using surface air temperature data to calculate the radiative imbalance of the cliimate system.
This peer reviewed paper is
McKitrick, R.R. and P.J. Michaels (2007), Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data, J. Geophys. Res., 112, D24S09, doi:10.1029/2007JD008465.
The abstract reads
“Local land surface modification and variations in data quality affect temperature trends in surface-measured data. Such effects are considered extraneous for the purpose of measuring climate change, and providers of climate data must develop adjustments to filter them out. If done correctly, temperature trends in climate data should be uncorrelated with socioeconomic variables that determine these extraneous factors. This hypothesis can be tested, which is the main aim of this paper. Using a new data base for all available land-based grid cells around the world we test the null hypothesis that the spatial pattern of temperature trends in a widely-used gridded climate data set is independent of socioeconomic determinants of surface processes and data inhomogeneities. The hypothesis is strongly rejected (P=7.1E-14), indicating that extraneous (nonclimatic) signals contaminate gridded climate data. The patterns of contamination are detectable in both rich and poor countries, and are relatively stronger in countries where real income is growing. We apply a battery of model specification tests to rule out spurious correlations and endogeneity bias. We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980-2002 global average temperature trend over land by about half.”
The conclusion includes the text
“These results are also consistent with previous findings showing that nonclimatic factors, such as those related to land use change and variations in data quality, likely add up to a net warming bias in climate data, suggesting an overstatement of the rate of global warming over land. They also provide support for attribution of some observed climate changes in recent decades to land surface modifications, rather than greenhouse gas emissions, a factor not typically evaluated in studies that attempt to attribute the causes of recent global warming.”
In a follow up, Ross McKitrick addressed the issue of spatial correlations, which could have reduced the significance of their results, in the article
The abstract of this contribution reads
“McKitrick and Michaels (2007) tested for independence between the spatial pattern of trends in surface climate data and the spatial pattern of socioeconomic indicators that serve as proxies for measurement inhomogeneities and anthropogenic surface processes. They found the relationship to be statistically significant, and in counterfactual simulation concluded that the extraneous signals explainabout half the post-1980 warming trend in surface data. This paper examines the robustness of these conclusions to treatment for possible spatial autocorrelation in the model residuals. Under a variety of weighting schemes, a robust LM test for no spatial autocorrelation is not rejected. Applying a correction for spatial autocorrelation anyway does not change the original conclusions.”
What is quite impressive of this study is that the data used is available for inspection (see).
This study provides further evidence on the use of inadequate data (in the multi-decadal trends of surface air temperature as reported in the 2007 CCSP Report – Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences) to accurately quantify the magnitude of global warming (or cooling). Two of our new papers, that have just been published in the past week, which further document the failure in the CCSP assessment process, will be reported on this week by Climate Science. This CCSP report was used in the completion of the 2007 IPCC Report.