The USHCN Station History Adjustment procedure is credited to Tom Karl in Karl and Williams 1987. Karl has been very prominent in the IPCC movement. That’s not to say that the potential adjustment is wrong or unjustified, but that the adjustment needs to be scrutinized with the same care as one would scrutinize an adjustment by, say, Briffa, Jones or Mann.
Towards the end of Karl and Williams 1987, he says:
“Stations with nonclimatic progressive changes due to urbanization may lead to inappropriate adjustments at nearby stations… This latter problem is mitigated to some extent in the HCN since 70% of the stations have populations less than 10,000 in the 1980 census and 90% have populations less than 50,000.”
Here’s the full excerpt together with a discussion of IPCC AR4 consideration of the topic,
Obviously, as we look at more and more HCN stations, it becomes clear that, yes, many of these sites are in small towns, but, no, that doesn’t mean that they are unaffected by nonclimatic factors. Indeed, many of the microsite problems, including grotesque non-compliance with WMO quality standards, would not occur at an urban airport (e.g. you would probably not have a garbage incinerator discharging on a weather station adjacent to an airport runway.) However, it also becomes increasingly clear that Jones, Hansen (and Karl) appear to have exercised no due diligence whatever in ensuring that the USHCN sites met the assumptions of the Karl Adjustment.
At this point, one would have to say that the USHCN network contains a number of stations that do not meet the criteria necessary for the Karl adjustment procedure. At this point, the proportion in the total network is unknown, but based on the spot checks so far, would appear to be well over 50%. If 50% of the stations do not meet the standards necessary to start the Karl adjustment, then it becomes highly relevant to post-audit the entire system to assess the problems caused by recklessly applying an adjustment without any effort to ascertain whether it could be safely applied.
IPCC AR4 Consideration
IPCC AR4 has some comments on the Karl adjustment “off balance sheet”. They have several pages of text on issues that we’ve talked about here – bucket adjustments, urbanization adjustments. However this discussion of problems is not included in the main report but exported to Supplementary Material here. It is the only supplementary material. An appendix on smoothing, much of it taken from Mann’s article, is in the main document – go figure. BTW there are some interesting comments in this Appendix about Brohan et al 2006 that will make UC’s hair curl.
The second paragraph in the excerpt below describes the Karl adjustment method. They state that the accumulation of station history effects are likely to cancel out. This implies that one could reasonably use USHCN data (TOBS-adjusted version) without Karl’s station history adjustments (which seem highly speculative to me).
Note that they mention the microsite problem that we’ve been discussing through their citation of Davey and Pielke 2005 which raised this issue in respect to east Colorado. However they clearly do not grasp the nettle of systemic microsite problems potentially affecting a majority of sites in their network.
Long-term temperature data from individual climate stations almost always suffer from inhomogeneities, owing to non-climatic factors. These include sudden changes in station location, instruments, thermometer housing, observing time, or algorithms to calculate daily means; and gradual changes arising from instrumental drifts or from changes in the environment due to urban development or land use. Most abrupt changes tend to produce random effects on regional and global trends, and instrument drifts are corrected by routine thermometer calibration. However, changes in observation time (Vose et al., 2004) and urban development are likely to produce widespread systematic biases; for example, relocation may be to a cooler site out of town (Böhm et al., 2001). Urbanisation usually produces warming, although examples exist of cooling in arid areas where irrigation effects dominate.
When dates for discontinuities are known, a widely used approach is to compare the data for a target station with neighbouring sites, and the change in the temperature data due to the non-climatic change can be calculated and applied to the pre-move data to account for the change, if the discontinuity is statistically significant. However, often the change is not documented, and its date must be determined by statistical tests. The procedure moves through the time series checking the data before and after each value in the time series (Easterling and Peterson, 1995; Vincent, 1998; Menne and Williams, 2005): this works for monthly or longer means, but not daily values owing to greater noise at weather timescales. An extensive review is given by Aguilar et al. (2003).
The impact of random discontinuities on area-averaged values typically becomes smaller as the area or region becomes larger, and is negligible on hemispheric scales (Easterling et al., 1996). … Urbanisation impacts on global and hemispheric temperature trends (Karl et al., 1988; Jones et al., 1990; Easterling et al., 1997; Peterson, 2003; Parker, 2004, 2006) have been found to be small. Furthermore, once the landscape around a station becomes urbanized, long-term trends for that station are consistent with nearby rural stations (Böhm, 1998; Easterling et al., 2005, Peterson and Owen, 2005). However, individual stations may suffer marked biases and require treatment on a case-by-case basis (e.g., Davey and Pielke, 2005); the influence of urban development and other heterogeneities on temperature depends on local geography and climate so that adjustment algorithms developed for one region may not be applicable in other parts of the world (Hansen et al., 2001; Peterson, 2003).