About the second point, the U.S. population has been growing (from 179 million in 1960 to 308 million in 2010 according to the US Census). So a particular station that was in the same location in 2014 as it was in 1964 could well have a more urban surrounding in 2014. In fact, one knows that surely on average this will be the case. Since more urban surroundings lead to higher temperatures, this must be a biasing factor. Does anybody have any idea how large this biasing factor is? Is there any literature on that issue?
"However, over the Northern Hemisphere land areas where urban heat islands are most apparent, both the trends of lower-tropospheric temperature and surface air temperature show no significant differences. In fact, the lower-tropospheric temperatures warm at a slightly greater rate over North America (about 0.28°C/decade using satellite data) than do the surface temperatures (0.27°C/decade), although again the difference is not statistically significant. "
Yes it's been talked about extensively even in the public for over a decade at least. In blogs and comment fields and columns the "but it's just the urban heat island" is a common myth that pops up all the time and has to be debunked constantly.
Some GISS temperature data for example excludes urban stations. Classification by night lights in satellite images. These rural stations also show similar trends.
I'm in danger of doing it also, but I think you are reacting defensively to a valid specific question because of your views on the larger climate reality. There is definitely an effect on individual stations as a result of changes to their surrounding environment. The question is whether the corrections applied to correct for it significantly affect the results of any given analysis.
The magnitude of the changes made are quite large compared to the effects being measured. They average to zero, but are bimodal centered around about +1F and -1F:
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/techreports/Technical%20Report%20NCDC%20No12-02-3.2.0-29Aug12.pdf
I think it's a brilliant visualization, but if we are to presume the corrections are necessary and correct, it would also be reasonable to question what conclusions can be drawn from an analysis of data that does not include such corrections. At the least, I think it would be interesting to see their analysis applied to the more rural CRN1 and CRN2 stations versus the majority of lower quality CRN3, CRN4, and CRN5 stations that make up the bulk of the readings.
