As you know, my first interest and the bulk of the early articles for this blog dealt with the question of the urban heat island (UHI) influence on U.S. historical temperatures. Our paper is now available (pre-print version) on this topic, and Zeke (the lead author of the paper and the one who wrangled everyone together!) and Matthew Menne put together a good post on it over at realclimate.
Apart from the use of several different proxies for urbanization, and the thorough treatment of many UHI-related topics, I personally think an interesting aspect of this paper is how it delves into the potential issue of “urban bleeding” during homogenization. For those that have followed various discussions on the topic over the past few years, or have read this paper already, it is clear that the UHI signal appears much more strongly in the TOB data than in the F52 homogenized data. A while back I also had a post, using synthetic data, that showed how the F52 algorithm could potentially alias some of the heat from urban stations into rural stations, thereby removing the appearance of UHI without removing the UHI itself.
On the one hand, if you look at figure 9 in the paper, I think it confirms the concern that the homogenization process could potentially spread urban warming to rural stations, as seen in the urban only adjustments. On the other hand, I also think it shows that in the case of USHCN v2, this effect is pretty minor based on using only ISA < 10% for adjustments. Now, one might wonder about UHI spreading from stations with ISA < 10% (that is, whether these “rural” stations are not strictly “rural”, and are themselves are contaminated by the UHI). Thus, I thought it might be interesting to show another couple of figures here, which shows the difference in the “urban” vs. “rural” trends based on what cut-off in the ISA classification is used to define “rural”:
As you can see, the bulk of the UHI signal in TOB comes from those stations with ISA > 10%, such that the use of 10% seems a pretty solid cut-off for “rural”.
Nevertheless, for an additional demonstration, we can use only the most rural stations (< 1% ISA) from a dataset that has only been adjusted by other most rural stations (< 1% ISA). Here is that final result when compared against the gridded F52 all-adjusted, as well as GISS:
From a visual perspective, it seems fairly clear to me that there is not much difference, and the numerical results below seem to bear this out for the most part. The exception is one we discussed in the paper, where the USHCN v2 all may have some residual UHI in the early part of the record and require an additional adjustment (as the one used in GISS).
F52 all: 0.224 K/Decade
F52-ISA01-ruralAdj: 0.219 K/Decade
GISTemp: 0.208 K/Decade
F52 all: 0.072 K/Decade
F52-ISA01-ruralAdj: 0.054 K/Decade
GISTemp: 0.058 K / Decade
It is thus my opinion that the impact of UHI in the homogenized USHCNv2 is minor. This paper does not specifically speak about the UHI influence on a global scale, nor does it specifically consider micro-siting issues. However, my initial impressions regarding the homogenization lead me to believe that there is unlikely to be any strong micro-siting bias permeating throughout the USHCN dataset.
For those interested, Zeke has already linked to the code used for the paper here. The specific tests I ran for this post make use of that Java code and data, and the R script for graphing and batch files (which can easily be converted to shell scripts) are available from me here.