Home/Blog
It took the better part of two years to satisfy the reviewers, but finally our paper Urban Heat Island Effects in U.S. Summer Surface Temperature Data, 1895–2023 has been published in the AMS Journal of Applied Meteorology and Climatology.
To quickly summarize, we used the average temperature differences between nearby GHCN stations and related those to population density (PD) differences between stations. Why population density? Well, PD datasets are global, and one of the PD datasets goes back to the early 1800s, so we can compute how the UHI effect has changed over time. The effect of PD on UHI temperature is strongly nonlinear, so we had to account for that, too. (The strongest rate of warming occurs when population just starts to increase beyond wilderness conditions, and it mostly stabilizes at very high population densities; This has been known since Oke’s original 1973 study).
We then created a dataset of UHI warming versus time at the gridpoint level by calibrating population density increases in terms of temperature increase.
The bottom line was that 65% of the U.S. linear warming trend between 1895 and 2023 was due to increasing population density at the suburban and urban stations; 8% of the warming was due to urbanization at rural stations. Most of that UHI effect warming occurred before 1970.
But this does not necessarily translate into NOAA’s official temperature record being corrupted at these levels. Read on…
What Does This Mean for Urbanization Effects in the Official U.S. Temperature Record?
That’s a good question, and I don’t have a good answer.
One of the reviewers, who seemed to know a lot about the homogenization technique used by NOAA, said the homogenized data could not be used for our study because the UHI-trends are mostly removed from those data. (Homogenization looks at year-to-year [time domain] temperature changes at neighboring stations, not the spatial temperature differences [space domain] like we do). So, we were forced to use the raw (not homogenized) U.S. summertime GHCN daily average ([Tmax+Tmin]/2) data for the study. One of the surprising things that reviewer claimed was that homogenization warms the past at currently urbanized stations to make their less-urbanized early history just as warm as today.
So, I emphasize: In our study, it was the raw (unadjusted) data which had a substantial UHI warming influence. This isn’t surprising.
But that reviewer of the paper said most of the spurious UHI warming effect has been removed by the homogenization process, which constitutes the official temperature record as reported by NOAA. I am not convinced of this, and at least one recent paper claims that homogenization does not actually correct the urban trends to look like rural trends, but instead it does “urban blending” of the data. As a result, which trends are “preferred” by that statistical procedure are based upon a sort of “statistical voting” process (my terminology here, which might not be accurate).
So, it remains to be seen just how much spurious UHI effect there is in the official, homogenized land-based temperature trends. The jury is still out on that.
Of course, if sufficient rural stations can be found to do land-based temperature monitoring, I still like Anthony Watts’ approach of simply not using suburban and urban sites for long-term trends. Nevertheless, most people live in urbanized areas, so it’s still important to quantify just how much of those “record hot” temperatures we hear about in cities are simply due to urbanization effects. I think our approach gets us a step closer to answering that question.
Is Population Density the Best Way to Do This?
We used PD data because there are now global datasets, and at least one of them extends centuries into the past. But, since we use population density in our study, we cannot account for additional UHI effects due to increased prosperity even when population has stabilized.
For example, even if population density no longer increases over time in some urban areas, there have likely been increases in air conditioning use, with more stores and more parking lots, as wealth has increased since, say, the 1970s. We have started using a Landsat-based dataset of “impervious surfaces” to try to get at part of this issue, but those data only go back to the mid-1970s. But it will be a start.
Dr. Spencer,
Congratulations and thanks for your contribution to an accurate US temperature record.
Congrats from Germoney too, excellent technical contribution based on solid stats, but at the end of section 3 we read:
” These results can be compared to the Hausfather et al. (2013) analysis of the homogenized temperature data during 1895-2010
which found that “urbanization accounts for 14–21% of the rise in unadjusted minimum temperatures since 1895 and 6–9% since 1960”. ”
*
Frogs would say, with a touch of irony:
” La montagne Spencer/Christy/Braswell a accouché d’une souris Hausfahter. ”
And ultimately, it confirms once again that Watts’ surfacestations.org and the 2011 paper by Fall et al. are a blind-alley that tells us nothing really useful.
Bindi, one of your problems is you don’t understand what orbital motion, without spin, looks like. If you understood that simple motion, you would see that Moon has no spin.
Do you have access to a tennis ball and string?
“And ultimately, it confirms once again that Watts’ surfacestations.org and the 2011 paper by Fall et al. are a blind-alley that tells us nothing really useful.”
Nonsense.
A reduced diurnal temperature range is particularly acute during cloudier nights. Urban areas already tend to cool more slowly than nearby rural locations, but under cloudy night or early morning conditions, the temperature difference between the two can reach up to +13C. I have confirmed this by comparing city temperatures with those recorded at their nearest CRN stations.
EDIT:
Nonsense.
The paper revealed a widespread and systematic suppression of diurnal temperature range across the national climate observatory network. That is significant and carries important implications for the accuracy of climate representation.
A reduced diurnal temperature range is particularly acute during cloudier nights. Urban areas already tend to cool more slowly than nearby rural locations, but under cloudy night or early morning conditions, the temperature difference between the two can reach up to +13C. I have confirmed this by comparing city temperatures with those recorded at their nearest CRN stations.
Revealed is doing too much work here, Walter.
All types of sites show the same mean temperature trend, so the significance you claim is not there.
I understand that Watts’ paper is not directly relevant to Spencer’s new work since Spencer is focused on TAVG. However, I wouldn’t go so far as to agree with Bindi’s claim that ‘Watts’ surfacestations.org and the 2011 paper by Fall et al. are a blind alley that tells us nothing really useful.’ That paper does have value, just for a different purpose.
It all depends on how one weighs negative results, Walter.
I myself like them, but if one rejects that they’re informative, than one must also reject that they indicate anything, including a blind alley.
You might like:
https://archive.ph/flRIT
” These results suggest that the DTR in the United States has not decreased due to global warming, and that analyses to the contrary were at least partly contaminated by station siting problems. ”
” Nonetheless, all the ongoing work to understand the consequences of a faster rise in minimum than maximum temperatures for ecosystems and human health might, just might, be misguided. ”
Yeah.
When you concentrate your look at bad stations and absolute temperatures, yu hardly could come to a different conclusion.
Thanks, Willard. That’s a great link.
“When you concentrate your look at bad stations and absolute temperatures, yu hardly could come to a different conclusion.”
This describes the vast majority of weather stations in the U.S.
Why is it so difficult to acknowledge that truly accurate climate data for the United States does not exist until the mid 2000s?
To my knowledge, China, Germany, and the UK face similar issues with their long term climate observation networks.
“The bottom line was that 65% of the U.S. linear warming trend between 1895 and 2023” RS
That is baloney whether published or not.
USCRN, with no UHI, since 2005, falsifies the theory.
It has more warming then nClimDiv that includes UHI
““The bottom line was that 65% of the U.S. linear warming trend between 1895 and 2023”
I’m not sure what that 65% of US warming means. The abstract says that 65% only applies to urban and suburban stations, and across all stations the figure is 22%.
Meanwhile we have WUWT and Heartland saying it’s 65% of all global warming.
Also, this article says that most of the UHI warming occured before 1970, which seems an important point, given that most of the CO2 warming has occured since then.
Why do people make this so complicated? CO2 evenly blankets the globe, in other words it is a constant per location. The quntum mechanics of the CO2 does no vary by location or over time, so once again, CO2 is a constant in any model. What has changed over time is the concentration over time, but the concentration shows a log decay with the backradiation. It is much like painting a window black. The first coat does a lot, additional coats does very litte. In other words, beyond a certain concentration CO2 does very little. It is much like taking asprin. The first pills reduce pain, but taking too much will not help the pain, and may make you sick.
In reality all the data you need is the data for temperatures over the oceans and Antarctica to remove the UHI effect. Simply limit the data to the stations that aren’t exposed to any UHI Effect? Adding additional corrupted data sets only decreases the explainatory power of the model and increases the error. I’ve done that and when you simply select the stations with a very low BI score, you basically get no warming. If you comnbine the temperatures over the oceans with the change in cloud cover, to see that what global warming we do see id due to fewer clouds over the oceans than CO2. CO2 has absolutely nothing to do with the warming of the globe. CO2 is a trace gas of 415 ppm that termalizes 15 micron LWIR which is associated with -80C temp in the IR spectrum.
Climate Science claims to be a science. I just outlined a very simply and common sense way to control for the UHI efect and there is no need to worry about the adjusted or unadjusted data. Simply use the raw data from a station that hasn’t been impacted by the UHI efect, it is that simple. Try the unadjusted data from Alice Springs Austraila using the older version of the GISS Data before they broke up the data file.
“CO2 has absolutely nothing to do with the warming of the globe”
CO2 is Life
99.9%+ of scientists since 1896 have been wrong and you are right? You are quite a comedian.
As far as your 99% of government scientists-Ike desribed them in his departing speech.
“Akin to, and largely responsible for the sweeping changes in our industrial-military posture, has been the technological revolution during recent decades.In this revolution, research has become central; it also becomes more formalized, complex, and costly. A steadily increasing share is conducted for, by, or at the direction of, the Federal government.Yet, in holding scientific research and discovery in respect, as we should, we must also be alert to the equal and opposite danger that public policy could itself become the captive of a scientific-technological elite.”
Dwight Eisenhower
‘99.9%+ of scientists since 1896 have been wrong and you are right?’
Yes.
Richard, the problem is in the understanding of the basic physics.
Every source that attempts to describe the CO2 nonsense spends 99.9% of the effort on explaining how sunlight converts to infrared and CO2 absorbs some of that infrared, blah-blah-blah. That much is valid. The invalid assumption is that the CO2 can then raise Earth’s average temperature. The basic physics they overlook (ignore?) is that 15μ photons from the atmosphere can not raise the temperature of a 288K surface. If that were possible then you could boil water with ice cubes!
“99.9%+ of scientists since 1896 have been wrong and you are right? You are quite a comedian.”
Once again, this is a science, not a popularity contents. Consensus means nothing in science. I provided evidence, please refute it. I can go to the GISS Website and find you countless stations that show no warming. The only warming you get is if you corrupt the measurments with the UHI effect. The fact that NASA publishes data with data known to be corrupted and they statistically “adjust” the data proves they either don’t know what they are doing, or they are covering up the facts. No one knows how these “adjustments” are made except the people making them. Why not make it trasparent and open source? Simple answer, they don’t want people seeing what they are doing. Once again, simply do your own research. Find stations with a low BI for UHI and you will find no warming. That is how a real scientist would perform a controlled experiment. You simply want to believe a lie.
Congratulations on getting it through the censorship of peer review.
As far as your 99% of government scientists-Ike desribed them in his departing speech.
“Akin to, and largely responsible for the sweeping changes in our industrial-military posture, has been the technological revolution during recent decades.In this revolution, research has become central; it also becomes more formalized, complex, and costly. A steadily increasing share is conducted for, by, or at the direction of, the Federal government.Yet, in holding scientific research and discovery in respect, as we should, we must also be alert to the equal and opposite danger that public policy could itself become the captive of a scientific-technological elite.”
Dwight Eisenhower
I use the estimate of 99.9% but in my 28 years of climate science reading I have only found one scientist who denied the greenhouse effect: Geographer Tim Ball of Cannada.
My reading is almost entirely “skeptic” scientists where there are a few who believe the effect of CO2 is very small.
Among all other scientists, 100% believe there is a greenhouse effect and manmade CO2 adds to it. That has been true since 1896. The consensus is based on evidence and has withstood a 129 year test of time
Among the believers are Dr.’s Spencer, Christy, Happer, Lindzen and Curry. All science PH.D.s. At least 99% of “skeptic” scientists. But you know better?
They disagree on how much warming CO2 causes but not the fact that CO2 impedes Earth’s ability to cool itself, usually described as “warming”.
The CO2 Does Nothing Science Denying Cult helps logical conservatives lose their ability to persuade others on the subject of climate before they even start speaking.
The Cult has a tendency to take over every comment thread o conservative websites.
They must believe the CO2 Does Something Consensus is a global conspiracy, since 1896, and not one scientist of the 99.9% consensus has ever decided to be honest about CO2. Not even on his or her deathbed.
A lot of opinions (beliefs) there, Richard. But beliefs ain’t science.
Now if you could actually define the GHE, that doesn’t violate the laws of physics, you would have something.
As I stated above, the problem is in the understanding of the basic physics.
Every source that attempts to describe the CO2 nonsense spends 99.9% of the effort on explaining how sunlight converts to infrared and CO2 absorbs some of that infrared, blah-blah-blah. That much is valid. The invalid assumption is that the CO2 can then raise Earth’s average temperature. The basic physics they overlook (ignore?) is that 15μ photons from the atmosphere can not raise the temperature of a 288K surface. If that were possible then you could boil water with ice cubes!
RG, Scroll down and read my reply to CO2isLife. Y
This is the only website you need to debunk CO2 causes warming.
http://www.john-daly.com/stations/stations.htm
CO2 evenly blankets the globe; in other words, CO2 concentration is a constant per location.
The quantum mechanics of the CO2 molecule doesn’t change per location, so the backradiation of CO2 evenly blankets the globe.
What has changed over time is the concentration of CO2 over time. The scientific challenge then becomes how to design a controlled experiment that isolates the impact of CO2 on temperatures. How do you tease out the effect of the Urban Heat Island, water vapor, and other exogenous factors?
In other words how do you design this experiment : Temperature = f(CO2) and not Temperature = f(CO2, Water Vapor, UHI, and other factors)
The solution is relatively simple. One simply needs to choose locations, mostly the dry hot and cold deserts, that are shielded from large swings in H2O and impacted by the UHI. When you do that, you see that the temperature doesn’t increase with an increase in CO2. The obvious reason for that is that CO2 shows a log decay in the backradiation with an increase in concentration, and the CO2 concentration and Backradiation function forms an asymptote, and at the current concentration, the slope is approaching 0.00 quickly.
In real science one needs to only find one example where the results don’t agree with the model to reject the null. The above link provides plenty of examples that defy the CO2 causes warming hypothesis.
Albert Einstein famously stated, “No amount of experimentation can ever prove me right; a single experiment can prove me wrong.”
From John Daly’s website, the chart of temperature plots at the Furnace Creek weathers station in Death Valley are fairly flat from 1922 to 2001. In 1922, the concentration of CO2 in dry air was 303 ppmv (0.59 g of CO2/cu. m.), and by 2001, it had increased to 371 ppmv (0.73 g of CO2/cu. m.), but there was no increase in the air temperature at this remote desert. The reason there was no increase in the air temperature at arid desert is quite simple: There is too little CO2 in the air to absorb out-going long wave IR radiation from the desert surface and there is low constant humidity.
The charts of temperature plots from over 200 weather stations from all over the world show no warming up to 2002 and falsify the proposal that CO2 causes global warming.
You are the first person to mention John Daly’s website at this blog. I go to a number of blogs every day and there is no mention of John Daly. Somehow we have inform all the people about John Daly’s website and in particular those in California, the UK, Germany and Australia. Got any ideas how we can do this?
It would really useful if we could post charts and figures as we can at WUWT.
How did you learn of John Daly’s website? There are over 200 charts of temperature plots from around the world that show no warming up to 2002. The chart of the plots of temperatures at the Furnace Creek weather station in Death Valley is my favorite and I post it often in comments at WUWT. I
If everyone learned of John Daly’s website, all this CO2 global warming nonsense would vanish overnight and the economies of the UK, California, Germany, and Australia would be saved for collapse.
Dr Roy, well done on getting past the gate keepers.
I see the UHI being a subset of humans influence. The others sources are farming, industrial sites, mining etc.
Farming will again have subgroups of influence. A single cow emits 1000+ watts of heat energy. Albedo and water vapour changes depending on crop type & farming practice.
Power stations will produce a lot of heat over a wide area. No power production is 100% efficient.
So it’s not just population density that corrupts the record.
Dr Roy, well done on getting past the gate keepers.
I see the UHI being a subset of humans influence. The others sources are farming, industrial sites, mining etc.
Farming will again have subgroups of influence. A single cow emits 1000+ watts of heat energy. Albedo and water vapour changes depending on crop type & farming practice.
Power stations will produce a lot of heat over a wide area. No power production is 100% efficient.
So it’s not just population density that corrupts the record.
Dr Roy, well done on getting past the gate keepers.
I see the UHI being a subset of humans influence. The others sources are farming, industrial sites, mining etc.
Farming will again have subgroups of influence. A single cow emits 1000+ watts of heat energy. Albedo and water vapour changes depending on crop type & farming practice.
Power stations will produce a lot of heat over a wide area. No power production is 100% efficient.
Heat pumps routinely exceeds 300%.
Willard doesn’t understand efficiency.
No surprise there….
When compared to using electricity directly.
Puffman doesn’t get another equation.
What else is new?
Urban temperature data describes air temperature outside. I wonder what fraction of the total volume of heated or cooled air in urban areas is inside structures and how much heat is added to or removed from the external urban environment by these heating and cooling systems. It is obvious this fraction of conditioned air is very different, much smaller, in rural and even suburban environments. This is among the many reasons to be skeptical of surface temperature records now or in the past.
Station well-siting, TMIN vs. TMAX (part 1)
*
I wrote above:
” And ultimately, it confirms once again that Watts’ surfacestations.org and the 2011 paper by Fall et al. are a blind-alley that tells us nothing really useful. ”
And the red krokodile answered:
” The paper revealed a widespread and systematic suppression of diurnal temperature range across the national climate observatory network. That is significant and carries important implications for the accuracy of climate representation. ”
*
Later on, even more of that from the red krokodile – who apparently doubts every day a bit more but without presenting any data confirming his doubts::
” This describes the vast majority of weather stations in the U.S.
Why is it so difficult to acknowledge that truly accurate climate data for the United States does not exist until the mid 2000s? ”
*
Aha. Have these supposedly well-sited historical USHCN stations now suddenly fallen out of favor?
*
Let us nonetheless take as source for such claims the USHCN station subset identified by Volunteers as ‘well-sited’ (source: NOAA, I lost the link and therefore can only present an upload of my download):
https://drive.google.com/file/d/1ipzDRdJppZDM6ii4qj9h1AKFrC3t0h94/view
and compare this USHCN station subset to all GHCN daily stations respectively located in the 1 degree vicinity of these USHCN stations, regardless of their siting quality:
https://drive.google.com/file/d/11sJjcVifKIecrYRr08jB93bjlbujJPDX/view
*
Note:
The goal here is to compare time series generated out of the data of the two stations sets, regardless what they really represent.
The 71 ‘well-sited’ stations are by no means representative of CONUS as a whole – simply because they are distributed across no more than 58 2.5-degree grid cells, i.e. only about one-third of the 171 cells occupied by the over 18,000 GHCN daily stations available in CONUS.
*
‘Climate representation’ means that we can’t rely on comparing single locations: we must compare their averages. Otherwise, we would speak about local meteorology.
*
To avoid comparison bias due to the small number of early-active GHCN daily stations located in the near the 71 USHCN sites, the comparison was limited to the period 1951–2011.
*
Unlike previous comparisons based on TAVG, the station sets are now compared with respect to TMIN and TMAX.
*
In a first step, the comparison of the 71 well-sited USHCN stations with the 258 stations nearby will be based on absolute temperatures.
A comparison is then made on the basis of anomalies, as this eliminates the seasonal dependencies (or as Roy Spencer says: the ‘annual cycle’) in the time series.
Station well-siting, TMIN vs. TMAX (part 2)
*
1. Absolutes
TMAX
https://drive.google.com/file/d/1NCXucYzNi3IHgfVyqv1NDAPakXUOg-EW/view
TMIN
https://drive.google.com/file/d/11dytviUIvgQ4X5jjnuB0IWHe_ZpffuuP/view
Trends are useless here because as so often for absolute data out of few stations, the standard error is higher than the trend.
2. Anomalies (wrt the mean of 1981-2010)
TMAX
https://drive.google.com/file/d/1RzP181WalVgApyoYdJ9lR1jPwSDTr6NK/view
Trends /decade in °C for 1951-2011
258 GHCN daily: 0.06 +- 0.02
71 USHCN: 0.08 +- 0.03
TMIN
https://drive.google.com/file/d/1-IHF0hCUK8n-Qki14wwd0YCxQdBSeiy2/view
Trends /decade in °C for 1951-2011
258 GHCN daily: 0.13 +- 0.02
71 USHCN: 0.12 +- 0.02
Yeah: so it looks like when you remove the annual cycle…
Linear trends don’t matter much: polynomials explain more. But they give a first hint.
*
Let’s come back to the absolute data, to extract out of it TMAX/TMIN series for January and July, respectively, and compute the differences between 258 GHCN daily and 71 USHCN for TMAX and TMIN in these months:
https://drive.google.com/file/d/1c9s45kzAQc0_RB4rqX1A6tcgNFYh3ySs/view
Oh oh oh… What do you see here? The differences between TMIN and TMAX are way smaller than the differences between January and July.
*
I’m sure genius red krokodile soon will explain this to us.
Bindidon’s concerns about the small sample size of well-sited stations were addressed directly in the Fall, 2011 paper.
The authors focus specifically on the post 1979 period, when metadata quality is higher and enough CRN 1&2 stations exist for meaningful comparison.
Their findings show that for well-sited stations, there is no significant trend in diurnal temperature range since 1979, contrasting sharply with poorly sited stations that spuriously show a decreasing DTR trend.
It is also worth pointing out that Bindi continues to ignore station classification, which is essential to disentangle the effects of local siting from regional climatic factors.
*
In this comment:
https://www.drroyspencer.com/2025/03/hey-epa-why-not-regulate-water-vapor-emissions-while-you-are-at-it/#comment-1701103
— his earlier graphs comparing TMAX and TMIN trends between well-sited and randomly selected stations have now disappeared. I noted in my reply on March 21, 2025 at 10:17 PM that even though his setup differed from Fall et al., his results actually supported their findings.
Strangely, he keeps the graphs that argue siting quality is insignificant, like this one:
https://www.drroyspencer.com/2025/02/uah-v6-1-global-temperature-update-for-january-2025-0-46-deg-c/#comment-1698036
roy….”It took the better part of two years to satisfy the reviewers…”
***
For any of you alarmists claiming the review process is legit, here’s your evidence that it is not.
This is evidence of corruption in the review process. It is not the business of scientists to satisfy a review process, it is the business of scientists to research and write papers so they can be reviewed by their real peers in the scientific community.
When a journal editor farms out a paper to a reviewer, it is not the business of the reviewer to hold the paper up because he/she does not agree with it, and any reviewer doing so should be prosecuted. It is only the business of a reviewer to ensure the paper is produced in legitimate scientific context and not written by some hacker like Clint, Ark, or Norman. ☺ ☺ And especially not by Binny. ☺ ☺ ☺
Of course, a journal editor can refuse to publish a paper but that would be corruption as well, if his/her’s only reason was to prevent a skeptical view from being presented.
I just described the IPCC review process.
Such is the nature of science today that journals can present only views that agree with theirs.