Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data

Hopefully readers can comment on this recent paper published 8 Nov 2007 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D24S09, doi:10.1029/2007JD008465, 2007 by Ross R. McKitrick and Patrick J. Michaels.

I’m not qualified to assess their work. However, I’m interested in the obvious bias with which the results were received. Michaels comments on the deafening silence from big media:

What if a scientific paper appeared in a major journal saying that the planet has warmed twice as much as previously thought, that would be front-page news in every major paper around the planet. But what would happen if a paper was published demonstrating that the planet may have warmed up only half as much as previously thought?

Nothing. Earlier this month, Ross McKitrick from Canada’s University of Guelph and I published a manuscript in the Journal of Geophysical Research-Atmospheres saying precisely that.

Patrick J. Michaels is senior fellow in environmental studies at the Cato Institute and a member of the United Nations’ Intergovernmental Panel on Climate Change.

Scientists have known for years that temperature records can be contaminated by so-called “urban warming,” which results from the fact that long-term temperature histories tend to have originated at points of commerce. The bricks, buildings, and pavement of cities retain the heat of the day and impede the flow of ventilating winds.

For example, downtown Washington is warmer than nearby (and more rural) Dulles Airport. As government and services expand down the Dulles Access road, it, too, is beginning to warm compared to more rural sites to the west.

Adjusting data for this effect, or using only rural stations, the United Nations’ Intergovernmental Panel on Climate Change states with confidence that less than 10% of the observed warming in long-term climate histories is due to urbanization.

That’s a wonderful hypothesis, and Ross and I decided to test it.

We noted that other types of bias must still be affecting historical climate records. What about the quality of a national network and the competence of the observers? Other factors include movement or closing of weather stations and modification of local land surfaces, such as replacing a forest with a cornfield.

RTWT for a summary of what they found. And here’s the abstract, where nonsubscribers can purchase the PDF for $9.00:

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 database 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 = 7.1 × 10−14), 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.