NASA’s GISS claims to have a statistical methodology to identify and remove urban biases. After dealving into the numbers, it looks more like they are not removing urban biases, but spreading their effect around multiple stations like peanut butter. My kids have a theory that I will not notice the fact they have not eaten their [fill in the blank] food if they spread it around the plant in a thin layer rather than leaving it in a single pile. This seems to be NASA’s theory on urban measurement biases. In addition, the GISS statistical methodology seems to be finding an unusual number of stations with a cooling bias, meaning that for some reason the instruments are actually less urbanized than say 50 years ago.
Steve McIntyre digs into some of these issues:
In my previous post, I calculated the total number of positive and negative NASA adjustments. Based on present information, I see no basis on which anything other than a very small proportion of negative urban adjustments can be assigned to anything other than “false local adjustments”. Perhaps there are a few incidents of vegetative cooling resulting in a true physically-based urban cooling event, but surely this would need to be proved by NASA, if that’s their position. Right now, as a first cut, let’s estimate that 95% of all negative urban adjustments in the ROW are not due to “true urban” effects i.e. about 1052 out of 1108 are due to “false local adjustments”….
If the purpose of NASA adjustments was to do station history homogenizations (a la USHCN), then this wouldn’t matter. But the purpose of the NASA adjustments was to adjust for the “true urban” effect”. On this basis, one can only conclude that the NASA adjustment method is likely to be completely ineffective in achieving its stated goal. As other readers have observed (and anticipated), it appears highly likely that, instead of accomplishing an adjustment for the “true urban effect”, in many, if not most cases, the NASA adjustment does little except coerce the results of one poorly documented station to results from other equally poorly documented stations, with negligible improvement to the quality of whatever “signal” may be in the data.
This does not imply that the NASA adjustment introduces trends into the data – it doesn’t. The criticism is more that any expectation of using this methodology to adjust for urban effect appears to be compromised by the overwhelming noise in station histories. Needless to say, the problems are exacerbated by what appears to be poor craftsmanship on NASA’s part – pervasive use of obsolete station versions, many of which have not been updated since 1989 or 1990(!), and use of population data that is obsolete (perhaps 1980 vintage) and known to be inaccurate.
This is the second part of this post, where Mcintyre first quantified the number of the "nreverse" urban bias adjustments:
negative urban adjustments are not an exotic situation. In the ROW, there are almost the same number of negative adjustments as positive adjustments. In the U.S., there are about 50% more positive adjustments as negative adjustments – again a noticeable difference to the ROW. Some commenters on my Peruvian post seemed to think that negative urban adjustments were an oddball and very anomalous situation. In fact, that’s not the case, negative adjustments are nearly as common as positive adjustments.