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Microclimatic Inhomogeneities in Surface Air Temperature Records
Runnalls, K.E. and Oke, T.R. 2006. A technique to detect microclimatic inhomogeneities in historical records of screen-level air temperature. Journal of Climate 19: 959-978.

What was done
The authors developed a method to detect errors or biases in screen-level air temperature records at standard climate stations. The effects they treat are described by them as being "often quite subtle" and "due to alterations in the immediate environment of the station such as changes of vegetation, development (buildings, paving), irrigation, cropping, and even in the maintenance of the site and its instruments."

What was learned
After developing and applying the temperature-error-detection technique, Runnalls and Oke determined that the effects detected by their new methodology typically "escape detection by current generally accepted techniques," and they say that the existence of these effects is thus "a source of uncertainty in long-term temperature records," which confounding effects are described by them as being "in addition [our italics] to those presently recognized such as local and mesoscale urban development, deforestation, and irrigation."

What it means
Runnalls and Oke conclude that if the majority of the anomalies they discovered tend toward either net warming or net cooling, "even tenths of a degree in one direction take on real significance in the global climate change debate." Further to this point, they say that "intuition, experience, and review of classic microclimatic case studies suggests to us that the net impact of the most common changes (compaction due to trampling, increased paving, tree growth, removal or soiling of snow cover, construction of buildings and introduction of irrigation) lead to alteration of nocturnal controls on the surface heat balance (thermal admittance, sky view factor and roughness and shelter) in ways that reduce [our italics] nocturnal cooling and consequently increase [our italics] the minimum temperature."

These findings, in the words of Runnalls and Oke, ought to "raise skepticism about the criteria used to accept stations into some global datasets," as "even the most well-regarded sets accept stations based on evidence as loose as having no more than a few tens of thousands of people living nearby, or the lack of bright lights in the area, or pixels with low NDVI." Such criteria, as they continue, "fail to recognize the possibility that the immediate microscale environment of the screen is critical."

Reviewed 26 July 2006