A few days ago I had a twitter discussion with Ronan Connolly. He and his father claim that 0.2°C per century of the temperature increase in the USA is due to urbanization and 0.1°C per century is due to micro-siting. That is quite a lot. Together it would be almost half of the temperature trend seen in the main global datasets.
One of the great things of America is that they have a climate reference network (USRCN). The observations are normally made by the meteorologists and contain non-climatic effects that are not relevant for the meteorologists, but they are to climatologists. Thus to track accurately what is happening to the climate, NOAA has set up a climate reference network that follows high climatological standards. The main thing for this post is that these stations are located in pristine locations, without any problems with urbanization and micro-siting.
We only have data from this network starting in 2005. That is only a decade of data, but if the problems with the normal data are as large as Connolly claims, I thought we might be able to see some differences between the reference network and the normal US historical Climate Network (USHCN). In the USHCN non-climatic effects have been removed as well as possible with the pairwise homogenization algorithm (PHA) of NOAA.
The figure of NOAA below (in Fahrenheit) shows that USHCN (normal) and USRCN (reference) track each other quite closely. If you look at the details, you can see that actually the USRCH is a little below USHCN in the beginning and a little above at the end. In other words, the temperatures of the reference network are warming faster than those of the normal network. The opposite of what the climate dissenters would expect.
Let's have a more detailed look at the difference between the two networks in the following graph. It shows that the warming in the reference network was 0.09°C stronger per decade. For comparison with the trend due to global warming, you could also say that it is 0.9°C stronger per century. That is just as much as the observed global warming trend.
That the trend in the normal data is an underestimate of the true warming is no surprise for me. The trend of the raw American data has a strong cooling bias. Removing of non-climatic effects (homogenization) increases the temperature trend since 1880 by 0.4°C. We also know that homogenization can make trend estimates more reliable, but cannot fully remove the bias. Thus it was likely that there was a remaining cooling bias.
The cooling bias could be due to a number of effects. An important cooling bias in the USA is the transition of conventional observations with a cotton region shelter to automatic weather stations (maximum-minimum temperature systems). This transition is almost completed and was more intense in the previous century. Other biases could be the relocation of city stations to airports. This mainly took place before and during the second world war. The increased in interest in climate change may have increased interest in urbanization and micro-siting, which may thus have improved over time due to relocations. (Does anyone know any articles on that? I only know one for Austria.) There is also a marked increase in irrigation of gardens and cropland the last century.
That the effect is this strong is something we should probably not take seriously (yet). We only have nine values and thus a large uncertainty. In addition, homogenization is less powerful near the edges of the data, you want to detect changes in the mean and should thus be able to compute a mean with sufficient accuracy. As a consequence, NOAA does not adjust the last 18 month of the data, while half of the trend is due to the last two values. Still an artificial warming of USHCN, as the climate dissenters claim, seems highly unlikely.
This cooling bias is an interesting finding. Even if we should not take the magnitude too seriously, it shows that we should study cooling biases in the climate observations with much more urgency. The past focus on detecting climate change has led to a focus on warming biases, especially urbanization. Now that that problem is cleared, we need to know the best estimate of the climatic changes and not just the minimum estimate.
Maybe even more importantly, it shows that we need climate reference networks in every country. Especially to study climatic changes in extreme weather in daily station data, data that is much harder to homogenize than the annual means. We are performing a unique experiment with our global climate system. Future scientists will never forgive us if we do not measure what is happening as accurately as we can.
[UPDATE. In case anyone wants to analyse the "dataset", here it is:
diff = [0.017 0.050 0.022 0.017 0.022 0.017 -0.006 -0.039 -0.033]; % Difference USHCN-USRCN in °C
year = 2005:2013;