From Dr. Roy Spencer’s Global Warming Blog
by Roy W. Spencer, Ph. D.
We are now getting close to finalizing our methodology for computing the urban heat island (UHI) effect as a function of population density, and will be submitting our first paper for publication in the next few weeks. I’ve settled on using the CONUS (Lower 48) U.S. region as a demonstration since that is where the most dense network of weather stations is. We are using NOAA’s V4 of the GHCN monthly dataset.
I’ve previously described the methodology, where I use many thousands of closely-spaced station pairs to compute how temperature between stations change with population density at 10×10 km resolution. This is done for 22 classes of 2-station average population density, and the resulting cumulative UHI curves are shown in Fig. 1.
It is interesting that the spatial (inter-station temperature difference) UHI effect is always stronger in the homogenized GHCN data than in the raw version of those data in Fig. 1. The very fact that there is a strong urban warming signal in the homogenized data necessitates that there must be a UHI impact on trends in those data. This is because the urban stations have grown substantially in the last 130 years. A recent paper by Katata et al. demonstrates that the homogenization technique used by NOAA does not actually correct urban station trends to look like rural station trends. It does breakpoint analysis which ends up adjusting some stations to look like their neighbors, whether urban or rural. To the extend that spurious warming from UHI is gradual through time, it “looks like” global warming and will not be removed through NOAA’s homogenization procedure. And since all classes of station (rural to urban) have undergone average population growth in the last 130 years, one cannot even assume that rural temperature trends are unaffected by UHI (see Fig. 2).
The regression estimates of change in temperature with population density (dT/dPD) used to construct the curves in Fig. 1 were used at each individual station in the U.S. and applied to the history of population density between 1895 and 2023. This produces a UHI estimate for each station over time. If I compute the area-average GHCN yearly summertime temperature anomalies and subtract out the UHI effect, I get a UHI-corrected estimate of how temperatures have changed without the UHI effect (Fig. 3).
The data in Fig. 3 are from my 1 deg latitude/longitude binning of station data, and then area-averaged. This method of area averaging for CONUS produces results extremely close to those produced at the NCDC “Climate at a Glance” website (correlation = 0.996), which uses a high resolution (5 km) grid averaged to the 344 U.S. climate divisions then averaged to the 48 states then area averaged to provide a CONUS estimate.
UHI Warming at Suburban/Urban Stations is Large
The UHI influence averaged across all stations is modest: 24% of the trend, 1895-2023. This is because the U.S. thermometer network used in Version 4 of GHCN is dominated by rural stations.
But for the average “suburban” (100-1,000 persons per sq. km) station, UHI is 52% of the calculated temperature trend, and 67% of the urban station trend (>1,000 persons per sq. km). This means warming has been exaggerated by at least a factor of 2 (100%).
This also means that media reports of record high temperatures in cities must be considered suspect, since essentially all those cities have grown substantially over the last 100+ years, and so has their urban heat island.
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This showed up in mainstream online news: but only for one day:
UHI. Western Sydney was 2C hotter , now up to 10C hotter than east.
“Western Sydney is one of the fastest growing—perhaps the fastest growing urban population anywhere in Australia. The mistakes [in urban design] being made in Western Sydney are being made in Melbourne, Brisbane, Perth. We’re not really thinking ‘How do we deal with the changing climate, particularly heat?’,” says Dr. Sebastian Pfautsch, Associate Professor of Urban Studies at Western Sydney University.
He describes the side-effects and challenges of overheated suburbs: from strains on public health, to economic instability, and low community cohesion.
https://www.climatecouncil.org.au/urban-heat-island-effect-western-sydney/
Up to 10°C between different urban areas due to micro climate differences.
https://www.westernsydney.edu.au/newscentre/news_centre/research_success_stories/first_western_sydney_microclimate_maps_reveal_extent_of_heat_variation_in_region
Among the measurement locations in each council, temperatures differed up to 6°C, and during days of extreme heat, more than 10°C. For example, peak air temperature in Sydney Olympic Park was 32°C while it was 45°C in Toongabbie during a heatwave at the end of 2018.
“The significant temperature variations recorded suburb-to-suburb and even street-to-street across the three local government areas demonstrate the need to assess and combat extreme heat at a local level,” said Dr Pfautsch.
And yet the CAGW crowd thinks they can identify global average temperature differences in the hundredths digit. They just ignore the statistical variance associated with the data and assume all temperature readings are 100% accurate with no measurement uncertainty.
““The significant temperature variations recorded suburb-to-suburb and even street-to-street across the three local government areas demonstrate the need to assess and combat extreme heat at a local level,” said Dr Pfautsch.”
There’s a practical idea. Let’s do things like this, instead of destroying our economies by trying to reduce CO2 emissions, which, apparently, looking at Dr Spencer’s new study, has little effect on the temperatures. Take out the UHI effect and CO2 is nowhere to be seen.