If we assume ALL *observed* warming of the deep oceans and land since 1970 has been due to humans, we get an effective climate sensitivity to a doubling of atmospheric CO2 of around 1.9 deg. C. This is considerably lower than the official *theoretical* model-based IPCC range of 2.5 to 4.0 deg. C. Here’s the Phys.org news blurb from this morning.
Ah, the 1960s. Even in 1966, before global warming was a thing, The Lovin’ Spoonful was singing about (among other things) the unusual heat of the inner city.
In fact, the heat caused by urban environments was discussed way back in 1833 (190 years ago!) by Luke Howard (The Climate of London) who was the first to document the urban heat island (UHI) effect.
Today, virtually anyone who routinely travels between cities and rural areas has observed the localized warmth that cities produce.
It is important to emphasize that the UHI effect, along with “record warm” temperatures, would exist even if there was no “global warming”. This is because cities have grown substantially in the last 100+ years, replacing the native landscape with high heat capacity surfaces like buildings, pavement, and sources of waste heat. This leads to UHI warmth of up to 10 deg. F or more, mostly at night.
Yet, we are routinely told through media reports that the latest record warmth recorded in some of our cities shows how serious the global warming problem has become. For example, as shown in this graphic from the Miami Herald, the summer of 2023 experienced some record warmth in cities across the South.
Of course, conflating the urban heat island with global warming is a necessary component of such reporting, as the news report dutifully adds,
“Prominent scientific institutions around the globe including the National Oceanic and Atmospheric Administration agree that the warming is caused mainly by human-caused greenhouse gas emissions, NASA said.”
See how that works? A city has record warmth, so it must be due to global warming caused by burning fossil fuels. To be fair, not all the blame is always placed at the feet of Climate Change. For example, this 2014 article specifically discussed the role of the urban heat island in Phoenix weather.
Now, it is true that the southern U.S. had an unusually hot summer. Even our (UAH) satellite-based temperature product for the lower atmosphere showed this warmth in August:
In my last blog post, I showed our urbanization-adjusted average summer temperatures (based upon NOAA homogenized GHCN surface air temperatures) across all available stations in the Lower 48 states, and the result was that summer of 2023 was the 13th warmest (see Fig. 3 here) since records began (but with very few stations) in 1895.
But what role does climate change have in these records at selected cities? Most of what we hear through the media comes from urban reporting stations, or at least airports serving major urban areas.
The Summer of 2023: Phoenix versus Surrounding Stations
If the record hot summer in Phoenix is due to global warming, then it should show up at weather stations surrounding Phoenix, right? As part of our research project where we are quantifying the average urban heat island effect and its growth over time as a function of population density, I looked at the official NOAA GHCN monthly surface temperature data at Phoenix Sky Harbor Airport (red curve in the following graph) versus at all rural stations (0 to 100 persons per sq. km) within 10 to 100 km of Phoenix (blue curve). I also applied a small urbanization adjustment correction at the rural (or nearly-rural) stations based upon their individual histories of population growth.
The result? The summer of 2023 was only the 11th warmest summer on record.
So, we see that the urban heat island effect was the dominant cause of the summer of 2023 being a record warm year in Phoenix. The “vote” from surrounding rural and nearly-rural stations was that it was only the 11th warmest year. As a side note, the difference between the red and blue curves indicate a jump in Phoenix Sky Harbor temperatures of about 0.7 deg. F around 1988. This could be due to a weather station move, but I have not investigated it.
“But”, you might protest, “even the rural stations still show a strong warming trend”. Well, that is partly because I have used only “homogenized” temperature data, which NOAA has already adjusted to some extent leading to all nearby station temperature trends being more or less equal to one another. I’m still trying to determine if I can use the “raw” data to make such comparisons, since there are other data adjustments made in NOAA’s homogenization of the data that I’m not privy to.
Another thing to notice is that media reports will repeat NOAA’s claim that these new high temperature records are based upon data extending back to 1895. In general, this is not true. Most of these station records don’t go back nearly that far. For the Phoenix Sky Harbor location, the data started in 1933. A few of the other “record hot cities” start dates I’ve looked at so far are Miami, FL (started in 1948), Houston, TX (1931), and Mobile, AL (1948).
The bottom line is that there are unsupportable conclusions being drawn about the supposed role of climate change in record high temperatures being reported at some cities. Cities are hotter than their rural surroundings, and increasingly so, with or without climate change.
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.
The Version 6 global average lower tropospheric temperature (LT) anomaly for August 2023 was +0.69 deg. C departure from the 1991-2020 mean. This is a little above the July 2023 anomaly of +0.64 deg. C.
The linear warming trend since January, 1979 now stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).
Various regional LT departures from the 30-year (1991-2020) average for the last 20 months are:
| YEAR | MO | GLOBE | NHEM. | SHEM. | TROPIC | USA48 | ARCTIC | AUST |
| 2022 | Jan | +0.03 | +0.06 | -0.00 | -0.23 | -0.12 | +0.68 | +0.10 |
| 2022 | Feb | -0.00 | +0.01 | -0.01 | -0.24 | -0.04 | -0.30 | -0.50 |
| 2022 | Mar | +0.15 | +0.28 | +0.03 | -0.07 | +0.22 | +0.74 | +0.02 |
| 2022 | Apr | +0.27 | +0.35 | +0.18 | -0.04 | -0.25 | +0.45 | +0.61 |
| 2022 | May | +0.17 | +0.25 | +0.10 | +0.01 | +0.60 | +0.23 | +0.20 |
| 2022 | Jun | +0.06 | +0.08 | +0.05 | -0.36 | +0.46 | +0.33 | +0.11 |
| 2022 | Jul | +0.36 | +0.37 | +0.35 | +0.13 | +0.84 | +0.56 | +0.65 |
| 2022 | Aug | +0.28 | +0.32 | +0.24 | -0.03 | +0.60 | +0.50 | -0.00 |
| 2022 | Sep | +0.24 | +0.43 | +0.06 | +0.03 | +0.88 | +0.69 | -0.28 |
| 2022 | Oct | +0.32 | +0.43 | +0.21 | +0.04 | +0.16 | +0.93 | +0.04 |
| 2022 | Nov | +0.17 | +0.21 | +0.13 | -0.16 | -0.51 | +0.51 | -0.56 |
| 2022 | Dec | +0.05 | +0.13 | -0.03 | -0.35 | -0.21 | +0.80 | -0.38 |
| 2023 | Jan | -0.04 | +0.05 | -0.14 | -0.38 | +0.12 | -0.12 | -0.50 |
| 2023 | Feb | +0.08 | +0.17 | 0.00 | -0.11 | +0.68 | -0.24 | -0.12 |
| 2023 | Mar | +0.20 | +0.24 | +0.16 | -0.13 | -1.44 | +0.17 | +0.40 |
| 2023 | Apr | +0.18 | +0.11 | +0.25 | -0.03 | -0.38 | +0.53 | +0.21 |
| 2023 | May | +0.37 | +0.30 | +0.44 | +0.39 | +0.57 | +0.66 | -0.09 |
| 2023 | June | +0.38 | +0.47 | +0.29 | +0.55 | -0.35 | +0.45 | +0.06 |
| 2023 | July | +0.64 | +0.73 | +0.56 | +0.87 | +0.53 | +0.91 | +1.43 |
| 2023 | Aug | +0.69 | +0.88 | +0.51 | +0.86 | +0.94 | +1.54 | +1.25 |
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for August, 2023 and a more detailed analysis by John Christy, should be available within the next several days here.
Lower Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause:
http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt
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