A.) UHI offests are calculated from rural site readings.

B.) Although cities haven’t grown too much for a while, many rural sites have undergone a to-be-determined degree of “exurbanite creep”.

Question: Is this discounted? If the claim by some is that cities have not grown much since 1950, does this imply that there is no “exurbanite” adjustment? It would seem prudent to ask.

C.) Said creep may also tend to increase specific, “careless” site violations. (TBD by photo)

So, a potential for a double bias exists. Virtually all to the hot.

And since UHI adjustments are compared with possibly upward-biased rural data, it would seem quite possible that the UHI adjustment is too low (by how much I couldn’t say).

Site survey will help answer these questions.

It would be interesting to know what 100-year trends the well sited “virgin wilderness” stations reveal. (except for “tree creep”?) It’s not a much of a control group, but it’s better than nothing.

2.1 Land-based temperature data

Over land, temperature data come from fixed weather observing stations with thermometers housed in special instrument shelters. Records of temperature from many thousands of such stations exist. Chapter 2 outlines the difficulties in developing reliable surface temperature datasets. One concern is the variety of changes that may affect
temperature measurements at an individual station. For example, the thermometer or instrument shelter might change, the time of day when the thermometers are read might
change, or the station might move. These problems are addressed through a variety of procedures (see Peterson et al., 1998 for a review) that are generally quite successful at removing the effects of such changes at individual stations (emphasis – mine) (e.g., Vose et al., 2003 and Peterson, 2005) whether the changes are documented in the metadata or detected via statistical analysis using data from neighboring stations as well (Aguilar et al., 2003).

Subtle or widespread impacts that might be expected from urbanization or the growth of trees around observing sites might still contaminate a dataset. These problems are
addressed either actively in the data processing stage (emphasis – mine) (e.g., Hansen et al., 2001) or through dataset evaluation to ensure as much as possible that the data are not biased.

I haven’t read the entire 65 page report, but I’ve read enough to be curious as to your response, Anthony.

I recently linked to your site in a GW discussion on a conservative political blog, and one of the resident Lefties asked snidely if I had ever heard of “callibration”. I assume he meant that all the badly sited sensors you’ve discovered had most likely been recallibrated to accommodate changes in location or changes to the surroundings. I can’t recall seeing the issue addressed, but it sounds like a red herring to me. Why would you recallibrate a sensor to accommodate a nearby burn barrel as opposed to simply moving the burn barrel farther away? Any comments?

10.00 FEET (GROUND)