Since few people who visit here will actually download and analyze data, I present some imagery of the new Urban Heat Island (UHI) dataset we have developed, at their full (~9×9 km or better) spatial resolution.

A Review: The Method

(Skip this section if you just want to see the pretty pictures, below).

To review, the dataset is based upon over 13 million station-pairs of monthly average air temperature measurements at closely-spaced GHCN stations between 1880 and 2023. It quantifies the average *spatial* relationship between 2-station differences in temperature and population density (basically, quantifying the common observation that urban locations are warmer than suburban, which are in turn warmer than rural). The quantitative relationships are then applied to a global population density dataset extending back through time.

The quantitative relationships between temperature and population are almost the same whether I use GHCN raw or adjusted (homogenized) data, with the homogenized data producing a somewhat stronger UHI signal. They are also roughly the same whether I used data from 1880-1920, or 1960-1980; for this global dataset, all years (1880 through 2023) are used together to derive the quantitative relationships.

I use six classes of station-pair average population density to construct the (nonlinear) relationship between population density and the UHI effect on air temperature. To make the UHI dataset, I apply these equations (derived separately in 7 latitude bands and 4 seasons) to global gridded population density data since 1800.

As I previously announced, our paper submitted for publication on the method showed that UHI warming in the U.S. since 1895 is 57% of the GHCN warming trend averaged over all suburban and urban stations. But because most of the U.S. GHCN stations that go into the CONUS area average are rural, the UHI warming trend area averaged across all GHCN stations is only 20% of that computed from GHCN data. Thus, there is evidence that GHCN warming trends for the U.S. as a whole have been inflated somewhat (20% or so) by the urban heat island effect, but by a much larger fraction at urban station locations. The UHI contamination of the area average trends could be larger than this, since we do not account for some regions possibly having increased levels of UHI contamination as prosperity increases (more buildings, pavement, vehicles, air conditioning, and other waste heat sources) increases but population remains the same.

Some Dataset Examples

Here are some examples of the UHI dataset for several regions, showing the estimated total UHI effect on air temperature in the years 1850 and 2023 (I have files every 10 years from 1800 to 1950, then yearly thereafter). By “total UHI effect” I mean how much warmer the locations are compared to wilderness (zero population density) conditions. I emphasize the warm season months, which is when the UHI effect is strongest.

Remember, these quantitative relationships hold for the *average* of all GHCN stations in 7 separate latitude bands. It is unknown how accurate they are at individual locations depicted in the following imagery.

First let’s start with a global image for April, 2023 that Danny Braswell put together for me using mapping software, for April of 2023 (click on the image for higher resolution… and if you dare, here is a super-duper-hi-res version):

And here are some regional images using my crude Excel “mapping” (no map outlines):

In my next post I will probably do some graphs of just how many people in the world live in various levels of elevated temperature just because the global population is increasingly urbanized. Over 50% of the population now lives in urban areas, and that fraction is supposed to approach 70% by 2045. This summer we have seen how the media reports on temperature records being broken for various cities and they usually conflate urban warmth with global warming even through such record-breaking warmth would increasingly occur even with no global warming.

Again, all of the ArcGIS format (ASCII grid) files are located here (public permissions now fixed).

As a follow-on to our paper submitted on a new method for calculating the multi-station average urban heat island (UHI) effect on air temperature, I’ve extended that initial U.S.-based study of summertime UHI effects to global land areas in all seasons and produced a global gridded dataset, currently covering the period 1800 to 2023 (every 10 years from 1800 to 1950, then yearly after 1950).

It is based upon over 13 million station-pair measurements of inter-station differences in GHCN station temperatures and population density over the period 1880-2023. I’ve computed the average UHI warming as a function of population density in seven latitude bands and four seasons in each latitude band. “Temperature” here is based upon the GHCN dataset monthly Tavg near-surface air temperature data (the average of daily Tmax and Tmin). I used the “adjusted” (homogenized, not “raw”) GHCN data because the UHI effect (curiously) is usually stronger in the adjusted data.

Since UHI effects on air temperature are mostly at night, the results I get using Tavg will overestimate the UHI effect on daily high temperatures and underestimate the effect on daily low temperatures.

This then allows me to apply the GHCN-vs-population density relationships to global historical grids of population density (which extend back many centuries) for every month and every year since as early as I choose. The monthly resolution is meant to capture the seasonal effects on UHI (typically stronger in summer than winter). Since the population density dataset time resolution is every ten years (if I start in, say, 1800) and then it is yearly starting in 1950, I have produced the UHI dataset with the same yearly time resolution.

As an example of what one can do with the data, here is a global plot of the difference in July UHI warming between 1800 and 2023, where I have averaged the 1/12 deg spatial resolution data to 1/2 deg resolution for ease of plotting in Excel (I do not have a GIS system):

If I take the 100 locations with the largest amount of UHI warming between 1800 and 2023 and average their UHI temperatures together, I get the following:

Note that by 1800 there was 0.15 deg. C of average warming across these 100 cities since some of them are very old and already had large population densities by 1800. Also, these 100 “locations” are after averaging 1/12 deg. to 1/2 degree resolution, so each location is an average of 36 original resolution gridpoints. My point is that these are *large* heavily-urbanized locations, and the temperature signals would be stronger if I had used the 100 greatest UHI locations at original resolution.

Again, to summarize, these UHI estimates are not based upon temperature information specific to the year in question, but upon population density information for that year. The temperature information, which is spatial (differences between nearby stations), comes from global GHCN station data between 1880 and 2023. I then apply the GHCN-derived spatial relationships between population density and air temperature during 1880-2023 to those population density estimates in any year. The monthly time resolution is to capture the average seasonal variation in the UHI effect in the GHCN data (typically stronger in summer than winter); the population data does not have monthly time resolution.

In most latitude bands and seasons, the relationship is strongly nonlinear, so the UHI effect does not scale linearly with population density. The UHI effect increases rather rapidly with population above wilderness conditions, then much more slowly in urban conditions.

It must be remembered that these gridpoint estimates are based upon the average statistical relationships derived across thousands of stations in latitude bands; it is unknown how accurate they are for specific cities and towns. I don’t know yet how finely I can regionalize these regression-based estimates of the UHI effect, it requires a large number (many thousands) of station pairs to get good statistical signals. I can do the U.S. separately since it has so many stations, but I did not do that here. For now, we will see how the seven latitude bands work.

I’m making the dataset publicly available since there is too much data for me to investigate by myself. One could, for example, examine the growth over time of the UHI effect in specific metro regions, such as Houston, and compare that to NOAA’s actual temperature measurements in Houston, to get an estimate of how much of the reported warming trend is due to the UHI effect. But you would have to download my data files (which are rather large, about 117 MB for a single month and year, a total of 125 GB of data for all years and months). The location of the files is:

https://www.nsstc.uah.edu/public/roy.spencer

You will be able to identify them by name.

The format is ASCII grid and is exactly the same as the HYDE version 3.3 population density files (available here) I used (ArcGIS format). Each file has six header records, then a grid of real numbers with dimension 4320 x 2160 (longitude x latitude, at 1/12 deg. resolution).

Time for Willis to get to work.

The Version 6 global average lower tropospheric temperature (LT) anomaly for October, 2023 was +0.93 deg. C departure from the 1991-2020 mean. This is slightly above the September, 2023 anomaly of +0.90 deg. C, and establishes a new monthly high temperature anomaly record since satellite temperature monitoring began in December, 1978.

The linear warming trend since January, 1979 still 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 22 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	-0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.15	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.01	+0.60	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.47	+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	+0.51	-0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.05	+0.16	+0.94	+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	+0.09	+0.17	0.00	-0.11	+0.68	-0.24	-0.11
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	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17
2023	Oct	+0.93	+1.02	+0.83	+1.00	+0.99	+0.92	+0.62

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for October, 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

Middle 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

After years of dabbling in this issue, John Christy and I have finally submitted a paper to Journal of Applied Meteorology and Climatology entitled, “Urban Heat Island Effects in U.S. Summer Surface Temperature Data, 1880-2015“.

I feel pretty good about what we’ve done using the GHCN data. We demonstrate that, not only do the homogenized (“adjusted”) dataset not correct for the effect of the urban heat island (UHI) on temperature trends, the adjusted data appear to have even stronger UHI signatures than in the raw (unadjusted) data. This is true of both trends at stations (where there are nearby rural and non-rural stations… you can’t blindly average all of the stations in the U.S.), and it’s true of the spatial differences between closely-space stations in the same months and years.

The bottom line is that an estimated 22% of the U.S. warming trend, 1895 to 2023, is due to localized UHI effects.

And the effect is much larger in urban locations. Out of 4 categories of urbanization based upon population density (0.1 to 10, 10-100, 100-1,000, and >1,000 persons per sq. km), the top 2 categories show the UHI temperature trend to be 57% of the reported homogenized GHCN temperature trend. So, as one might expect, a large part of urban (and even suburban) warming since 1895 is due to UHI effects. This impacts how we should be discussing recent “record hot” temperatures at cities. Some of those would likely not be records if UHI effects were taken into account.

Yet, those are the temperatures a majority of the population experiences. My point is, such increasing warmth cannot be wholly blamed on climate change.

One of the things I struggled with was how to deal with stations having sporadic records. I’ve always wondered if one could use year-over-year changes instead of the usual annual-cycle-an-anomaly calculations, and it turns out you can, and with extremely high accuracy. (John Christy says he did it many years ago for a sparse African temperature dataset). This greatly simplifies data processing, and you can use all stations that have at least 2 years of data.

Now to see if the peer review process deep-sixes the paper. I’m optimistic.

I’m not a statistician, and I am hoping someone out there can tell me where I’m wrong in the assertion represented by the above title. Or, if you know someone expert in statistics, please forward this post to them.

In regression analysis we use statistics to estimate the strength of the relationship between two variables, say X and Y.

Standard least-squares linear regression estimates the strength of the relationship (regression slope “m”) in the equation:

Y = mX + b, where b is the Y-intercept.

In the simplest case of Y = X, we can put in a set of normally distributed random numbers for X in Excel, and the relationship looks like this:

Now, in the real world, our measurements are typically noisy, with a variety of errors in measurement, or variations not due, directly or indirectly, to correlated behavior between X and Y. Importantly, standard least squares regression estimation assumes all of these errors are in Y, and not in X. This issue is seldom addressed by people doing regression analysis.

If we next add an error component to the Y variations, we get this:

In this case, a fairly accurate regression coefficient is obtained (1.003 vs. the true value of 1.000), and if you do many simulations with different noise seeds, you will find the diagnosed slope averages out to 1.000.

But, if there is also noise in the X variable, a low bias in the regression coefficient appears, and this is called “regression attenuation” or “regression dilution”:

This becomes a problem in practical applications because it means that the strength of a relationship diagnosed through regression will be underestimated to the extent that there are errors (or noise) in the X variable. This issue has been described (and “errors in variables” methods for treatment have been advanced) most widely in the medical literature, say in quantifying the relationship between human sodium levels and high blood pressure or heart disease. But the problem will exist in any field of research to the extent that the X measurements are noisy.

One can vary the relative amounts of noise in X and in Y to see just how much the regression slope is reduced. When this is done, the following relationship emerges, where the vertical axis is the regression attenuation coefficient (the ratio of the diagnosed slope to the true slope) and the horizontal axis is how much relative noise is in the X variations:

What you see here is that if you know how much of the X variations are due to noise/errors, then you know how much of a low bias you have in the diagnosed regression coefficient. For example, if noise in X is 20% the size of the signals in X, the underestimate of the regression coefficient is only 4%. But if the noise is the same size as the signal, then the regression slope is underestimated by about 50%.

Noise in Y Doesn’t Matter

But what the 3 different colored curves show is that for Y noise levels ranging from 1% of the Y signal, to 10 times the Y signal (a factor of 1,000 range in the Y noise), there is no effect on the regression slope (except to make its estimate more noisy when the Y noise is very large).

There is a commonly used technique for estimating the regression slope called Deming regression, and it assumes a known ratio between noise in Y versus noise in X. But I don’t see how the noise in Y has any impact on regression attenuation. All one needs is an estimate of the relative amount of noise in X, and then the regression attenuation follows the above curve(s).

Anyway, I hope someone can point out errors in what I have described, and why Deming regression should be used even though my analysis suggests regression attenuation has no dependence on errors in Y.

Why Am I Asking?

This impacts our analysis of the urban heat island (UHI) where we have hundreds of thousands of station pairs where we are relating their temperature difference to their difference in population density. At very low population densities, the correlation coefficients become very small (less than 0.1, so R2 less than 0.01), yet the regression coefficients are quite large, and — apparently — virtually unaffected by attenuation, because virtually all of the noise is in the temperature differences (Y) and not the population difference data (X).

With the approaching El Nino superimposed upon a long-term warming trend, many high temperature records were established in September, 2023.

(Now updated with the usual tabular values).

The Version 6 global average lower tropospheric temperature (LT) anomaly for September, 2023 was +0.90 deg. C departure from the 1991-2020 mean. This is above the August 2023 anomaly of +0.70 deg. C, and establishes a new monthly high temperature record since satellite temperature monitoring began in December, 1978.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

Regional High Temperature Records for September, 2023

From our global gridpoint dataset generated every month, there are 27 regional averages we routinely monitor. So many of these regions saw record high temperature anomaly values (departures from seasonal norms) in September, 2023 that it’s easier to just list all of the regions and show how September ranked out of the 538 month satellite record:

Globe: #1

Global land: #1

Global ocean: #1

N. Hemisphere: #2

N. Hemisphere land: #1

N. Hemisphere ocean: #4

S. Hemisphere: #1

S. Hemisphere land: #1

S. Hemisphere ocean: #1

Tropics: #6

Tropical land: #2

Tropical ocean: #8

N. Extratropics: #2

N. Extratropical land: #1

N. Extratropical ocean: #4

S. Extratropics: #1

S. Extratropical land: #1

S. Extratropical ocean: #1

Arctic: #11

Arctic land: 7th

Arctic ocean: 65th

Antarctic: 15th

Antarctic land: 26th

Antarctic ocean: 14th

USA48: 144th

USA49: 148th

Australia: 12th

Various regional LT departures from the 30-year (1991-2020) average for the last 21 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	-0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.15	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.01	+0.60	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.47	+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	+0.51	-0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.05	+0.16	+0.94	+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	+0.09	+0.17	0.00	-0.11	+0.68	-0.24	-0.11
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	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for September, 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

Middle 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

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