Spencer: Using hourly surface data to gauge UHI by population density

UHI is very variable depending on time of day, snow cover, wind speed, cloudiness, elevation (cold air sinks) etc. On a windy day, UHI may be undetectable. Along the Front Range, locations closer to the mountains (i.e. higher elevation) are usually warmer than lower elevations further from the mountains.
Perhaps a blind comparison of a large number of stations (like Dr. Spencer is doing) averages out all of micro-scale effects?

Spencer: “this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities.”
One would then reasonably expect to see much higher trend at rural stations than at urban ones, ok? However, the USA data show exactly the opposite – just 0.1 deg C of warming in 20th century, but 0.6-0.7 degrees C in the cities. How that can be, if the rural stations should have a larger warming bias?

Chris H (11:27:22) :
I wonder if some third world areas of high density population might have a lower heat production per unit area compared with first world. Would adjusting for per capita income help the analysis?
Hard working third world people irradiate more IR, while for sure first world climate modellers irradiate almost nothing, and their per capita income is many thousand times than real workers, though becoming each passing day of a more imaginary value.

“0.1 degrees C” was for RURAL stations in my previous comment….

So, does this involve making a large artificial correction to the data?

Last but not least…though really last: When we die we suddenly lose heat.

The layman reader might be interested (I am) in examples of various weather station locations with their population densities. Further, where does the typical (average?) station fit on the UHI graph? Has Dr. Spencer done a back of the envelope calculation to see what the warming bias has been in the official temperatures data sets, as suggested by this analysis?

Two questions on an important piece of work
(1) I guess the UHI effect has increased over time, so that a town of 10,000 in the year 2000 has more UHIE than a town (same density) of 10,000 in the year 1900. How do you propose to adjust for this?
(2) The maximum UHI you show is ~2ºC. How do you account for Anthony’s urban transect at Reno which showed a UHIE of ~6ºC IIRC?

Ivan, if a station is Rural, and still has 100 years of data, it hasn’t been doing much growing. In fact, when you get down to “small towns,” probably half, or more, are smaller than they were 50 years ago.

“Each data point represents a population bin average for the intersection of a higher population station with its lower-population station mate.”
This doesn’t make any sense. What is a “population bin”? How do stations “intersect”? I hesitate to venture a guess.
Also on graph 1, you mention warming per pop density increase. What time period? On the x-axis, is this the population density before or after the increase in population density?
Also, somewhere you need a rationale for why you’re trying the methodology. Even if you’re just looking for correlation, you need to give people a rational basis for why this reveals some causation. Otherwise, it’s correlation of pirates to global warming all over again.
Also, how are you assigning population density to a point? Some cities are larger than others but the population density where the station is located may not be representative. Have you ensured that the stations are located in the center of what would be expected to be a heat island?
Define “warm bias”. How is it calculated? What is zero warm bias?

Climategate Minute: Lord Monckton v. Al Gore: Doubling Down On Global Warming
http://www.pjtv.com/v/3185

Jakers — Why is this warming spurious? Again, these rural stations should not be considered urban? Is it really logical to accept that increasing density from zero to 20 people per sq. km will cause +0.77 deg. C of warming locally, without ths sq. km being inside a bubble?
It appears that what we have here is a graph showing sensitivity to temperature change, which if you inverted it and labeled the Y axis “sensitvity to temp change” it would make more sense.
This confirms what I would expect to see, that is, a rural station is prone to showing the actual temp increase as a big jump whereas the urban stations are biased to their already warmer microclimate by definition.
Lucy Skywalker — (2) The maximum UHI you show is ~2ºC. How do you account for Anthony’s urban transect at Reno which showed a UHIE of ~6ºC IIRC?
If viewed as change sensitivity then wouldn’t his graph be saying the same thing? (i.e. relative increase of apparent temp X will look different at point Y than point Z if Y is already biased upward.)

Interestingly this shows that there is an asymptotic limit being reached, which makes sense. There is only so much UHI that can be added.
But this also highlights another interesting angle. While I would claim rural stations with near steady UHI would be the place to find AGW warming, it seems if we look at centers of near steady population levels at the high end we would expect to see AGW outpacing the slowed increase of UHI.
In both graphs of density and UHI we see +0.2°C from 5000-7000. A lot of stations would seem to exist in these domains for many years or decades. Has anyone looked to see if there is more than a 0.2°C increase in these population centers over, say, the last 50 years?
NYC comes to mind!

Dr. Spencer, I am an admirer of your work and your work ethic. However, as a long term reviewer of technical studies, I am concerned about your Update results. The answer doesn’t look right to me. May I suggest that you apply the JFD rule that I promulgated in 1965, “If something doesn’t look right, it probably isn’t right”.
That is not to say that your results are incorrect but simply that you should take a closer look at the methodology and perhaps how the data are applied.

Dr. Spencer,
Excellent work.
i have a question on a secondary issue.
The per capita consumption of energy by the population is different in different countries. different in different cities, in the same country.
Would expect that UHI effect to be significantly different between two cities with very different energy consumptions?
Could be different by a factor of 10 or more.

Perhaps people tend to settle on spots that show a stronger than average propensity to develop UHI-effects. For instance, people could prefer valleys to hilltops, and at the same time, valleys could tend to show more pronounced UHI-effects, as compared to hill tops. Perhaps people prefer to settle at spots where the heat they are producing tends to stay around locally a little bit longer. Microclimats with such properties are perhaps selected for settlement. Because of such a selection effect, there could already be an important UHI-difference between spots with no population at all, and spots with only a small population.

Dr. Spencer:
An interesting new approach to this seemingly-intractable problem.
I wonder if you could pull out a few “typical” station-pairs as examples — this would help me (& others) visualize what you are trying to do here.
@ JFD (12:39:48):
Second to his comment. Something doesn’t feel quite right here.
Thanks for presenting this preliminary work for outside comments. I hope your bosses give you more time & $$$ to pursue this project.
Best regards,
Peter D. Tillman
Consulting Geologist, Arizona and New Mexico (USA)

Steve Goddard (11:38:16) : “UHI is very variable depending on time of day, snow cover, wind speed, cloudiness, elevation (cold air sinks) etc. On a windy day, UHI may be undetectable.”
Conversely, on a windy day, might the UHI effect be further compounded, if radiative heat sources are upwind of the measuring station? And might that itself be a sizeable source of error, depending on the nature of the urban heat source (AC, dehumidifiers etc). I don’t know, I’m just asking :o)

Now this IS Science!
Is this the peer review of the future, in action?
What a contrast this is to the conniving secrecy of the team and the arrogance of the political / religious followers.
Love the approach – thinking out of the square in action.
Congratulations to Roy Spencer and all involved in seeking the truth.

UHI gauged against population density?
Is that ta useful “proxy”?
What about human land use?
Tarmac, concrete, agriculture use, deforesting?
As simple as albedo change for human landuse may cause large impact in rural areas with just a handful persons.

Dr Spencer
Thank you for sharing this work.
I think it would be really interesting to both follow up on suggestion above that there may be a variation in the UHI with development, and your suggestion that you could look at it over time. (They seem to me to be similar legs to the same issue).
It seems to me that the relationship is caused not by the number of people per se but by what those people do to their environment, and in particular the extent to which they modify it with heat retaining materials like concrete and asphalt, and put extra heat in through mechanization. These effects are far more likely in high income countries at lower population densities than in low income countries. They may also be far more likely now than in the past (which might reflect my nostalgic memory of grass verges and metal roads, and ice-creams to keep one cool on long summer afternoons, where now there are concrete footpaths, asphalt roads and air conditioning units!
My one concern is that going back to 1990 may not be far enough to capture the change — my nostalgic memories come from the late 1960s — so the cross country comparison may be the more instructive.

Dear Dr. Spencer,
as far as I remember, Ferd. Engelbeen did something similar with belgian/netherland/german longterm temps datas and UHI effect,
maybe eight to ten years ago. (sorry, the URL’s don’t work anymore)
Or was it Jan Janssens (solaemon)?
Conclusions were nearly same, steepest UHI effect/curve starts at/with lowest
populated area. This validates your approach.

Having earlier said that inverted, the graph looks like temp change sensitivity, another random “thought” (work with me here) occurs. Assuming more CO2 in an urban setting (cars, etc.) isn’t this also demonstrating how GHG’s work in a microclimate i.e. less upward swing per higher CO2 concentration? That could explain the curve.
As such this one graph looks to corroborate basic GHG theory, a lot of the IPCC stuff, i.e. everything important.

What is interesting to me is how the lines in the first two warming bias graphs bend down at about 500 pop per sq km, but after that, do not level off. I would have expected each additional person to have a marginal impact on warming bias, but that does not seem to be the case.

“Land Use/Cover Relationship for Thermal Urban Environment Studies Using Optical and Thermal Satellite Data”
From:
http://www.fig.net/pub/vietnam/papers/ts01g/ts01g_mallick_etal_3751.pdf
“satellite datasets are used to analyze the spatial structure of the thermal urban environment and the ‘hot’ surfaces within the urban settings were identified that is related to the urban surface characteristics and land use/land cover.”

I have a thermometer on my bicycle. Temperatures usually drop 3-4 degrees in the afternoon, pedaling from a wide paved road to a park two blocks away.
One road is enough to generate a significant UHI effect.

Where are the error bars?

Dr Spencer.
Firstly, thank you for undertaking this task. I hope all sides of the debate will contribute in a positive manner to ensure that your work ultimately achieves benchmark staus within climate science.
We are faced with a situation where total numbers of stations are declining, but I suspect that the proportion of airport stations is actually increasing (perhaps you could let us know out of interest the proportions in your sample set). I think it is important to attempt to account for them separately so that you can determine whether their effect is skewing your overall findings. On this basis I am in agreement with David (12:57:12).

Dr. Spencer,
How difficult woud it be to do the same analysis over time, as the population density of rural sites doesn’t increase uniformly? Areas that developed faster than others should show a warming bias compared to non-developing neighbors, then as those neighbors catch up their temperature should likewise catch up.
It should provide a confirmation of your delta T/pop density relation in time as well as across areas, further enhancing the robustness of the result.
P.S. Is saying “robust” kind of like saying “shrubbery”?

Great work!!
I suggest that the data be sorted by average temperature in a month. I believe that would show a strong effect. When the average temperature is between 60 and 70F, most residences will not be heating or cooling and I would expect the UHI to be less by a factor of 2. It would also create 12 times more data and give that much more resolution and confidence in the results.

scienceofdoom said: Even if we adjust for population maybe someone then does another study on rural temperature stations and finds that there is a vegetation growth effect due to temperature so we have to then correct the temperature in stations by a factor relating to temperature! and proximity of nearby vegetation..
Or even, a temperature effect due to vegetation.
New York City Temperature and Vegetation

If UHI is largely caused by man-made surface materials (concrete, black top, shingles, etc.), don’t we need to get an estimate of the surface area that is covered by those materials for the area surrounding the station? Also, what about average humidity levels?
Software plus decent satellite imagery would be a good start for generating this kind of metadata. Has anyone attempted to do this yet?

Personally, I do not like estimating temperature by guessing about it.
Trying to guess the effect from a certain amount of people, etc.
Some of the national parks have long histories of temp, why not start there?

It should be clarified what you mean by the pairs in which BOTH locations are at the lowest density category. It sounds as if there is no spread in population in these pairs, which would of course mean that you can infer no effect in terms of UHI. But I presume you mean that the bottom bin, 0-20, has been further divided (as is possible from the population data you’re using) and temperature differentials between pairs from different levels in this bin are extracted.
I believe your tentative language is wise; this is a nice looking piece of parlor science, but as you say before trying to make any bold assertions about it it would make sense to repeat the procedure with data from different years to demonstrate robustness. Still, with the size of the data set you can infer a pretty strong statistical correlation even as it stands. I agree with Anthony that this should be published with high priority, but I will agree with your implied intentions, that one should first establish robustness of the effect by varying your sample.
Also, I suspect that the population density statistics are as flawed, or nearly as flawed, as the temperature data sets, the only difference being that they are spoiled by fewer and less-well-paid cooks, and so of course are somewhat less spoiled.

As long as you keep yourself to way back when like the year 2000 you gonna get good enough population density statistics, but the more resent they are the more sketchy they are. Even just going through the EU data you have statistics from different time periods mixed in and presented as updated until you read the fine print. For cities like London, New York, and Tokyo, the year to year difference in pretty minute, but for cities in developing countries especially China you can have up towards 1 million people added per year for ten years straight and if the data is from 2003, or 2005, or what ever but presented as the most updated data 2010 you’ll have a difference in density in some cities that number upwards eight million people in the same municipality. The statistics from Africa is even worse where the most, fairly old, but accurate statistics comes from African Union and UN and the refugee camps, and the cities tend to grow a lot when most of the warring is over, but theirs not exactly a priority of keeping track by how much. In South America, or Mexico and below, poverty pretty much screws things up in the statistical department, just look at Mexico City or Rio de Janeiro, and they are supposed to be quite stable in their population growth.
So take care in choosing your years, and make sure to only choose years where there’s an actual official number for, lest you gonna fuel the fanatics, considering how they’re lashing out at people for spelling mistakes and all.

I expect that there will also be UHI variances as the temperature changes through the range where heat pumps and swamp coolers are effective. Heat pumps lose effectiveness at points that vary with the efficiency of the unit as the temperature goes down, and I don’t know the current range, but believe they will start being switched off between 20F and 10F as the temperature goes down. Thus the UHI from nearby heat pumps would accelerate as the temperature increases over about 75F as more A/C units switch on, and have an odd shaped curve as the temperature decreases through the heating zone until they turn off at around 10F – 20F on the way down. There would still be building heat loses below the cutoff point, but these would be much less than the compressor output.
Heat pump effect would be limited to areas that can afford them.

Sorry Roy, agree with JDN. The description of what you have done is largely incomprehensible.
To JDN. From what I can gather, the ‘increase in population’ is between station pairs, not a period of time. (other than that clarification I’m lost)
A couple of pages, Roy, on what the following actually means would help a lot:
“The following graph shows the average rate of warming with population density increase (vertical axis), as a function of the average populations of the station pairs. Each data point represents a population bin average for the intersection of a higher population station with its lower-population station mate.”

In your last post Dr Spencer I suggested something of the same thing. Stop trying to avoid UHI and instead quantify it. I suggested concentric rings of weather stations from city centre to outskirts that would allow the UHI signal to be quanitifed and separated from the “natural signal”.
I think your station paring is a similar approach but I think it is too thin because it doesn’t give you enough data on the UHI profile. I would think that UHI profile would vary with prevailing wind, direction (north vs south of the major centre) latitude (larger amplitude of absorption of insolation in high versus low latitudes) the major economic driver of the city ( a financial centre, a manufacturing centre and a transportation centre all grow up very differently). So I think the technique is valid, I just think that two station pairings are insufficient.

Dr. Spencer :
wayne (13:53:20) :
Seems I misread the exact meaning and implication of your Station Warm Bias vs. Population Density graph. The comment I made above addresses a very similar and related graph, Station Temperature Anomaly vs. Population Density which was not shown. Could you graph that also?

Ron Cram,
I don’t think body heat is much of an issue, since people are dispacing deer. 😉
It might create a noticable effect when you squeeze a couple thousand penguins together, though.
Anyway, for a good science-fair experiment, spreading a hundred or so remote sensing/WiFi thermometers across an area that included a mix of meadow, forest, pavement, and housing woud produce some interesting numbers.

I’ll bet GDP/area has tighter correlation with UHI than population density. Just a thought.

What I would like to see is a simple average of the real raw data from all truely defined rural stations that have continious data from 1943 to the present.
This would not be gridded or manipulated in any way.
It would not be a global average, but a representative sample (if 100% is a sample) of how temperature has changed over the last 65 year zig-zag climate cycle.
If that was too expensive, then a properly chosen statistical randomised sample would be quite accurate, as long as done by someone with appropriate statistical qualifications and a reasonably open mind.
If too impractcal, then perhaps a proper random sample from Anthony’s rural USA data base would sufice.
If we can establish what has happened to temperature in areas with minimal UHI change, then we will know what is happening to global temperature and why.
With due respect to Professor Spencer, this seems a much quicker, simpler and direct way of going.
I suspect that the reported temperature increase since 1880 is due to the combined effects of UHI and data manipulation. I may be wrong.
Someone with access to the data and sufficient knowledge should be able to bring this whole circus to a head in a few weeks.
We will then know if we are facing a climate disaster or a politically driven, economic one.

So let me see if I have this right, UHI produces a warming based on population density, check, makes sense — So then that ‘warming’ should be subtracted from the measured temperature to get the ‘real temperature’. T(real) = T – UHI(T) — UHI biases are always warmer than actual temperature, right?
So what happens when we get to the Chinese who continuously move their temperature stations away from airports and populations?
It’s beginning to seem like an impossible problem to solve with one set of relationships.

The Chicago Tribune has an article today with climate “questions and answers.” One of the questions is about the quality of the stations and biases. It references the surfacestation project and also notes a new study by NCDC that claims the poorly situated sites have a cooling bias. Here is the link to the full article. The relevant text is below.
http://www.chicagotribune.com/news/ct-met-0228-climate-science-questions-20100302,0,2670932.story
Chicago Tribune…
“Did the National Oceanic and Atmospheric Administration misplace weather stations and exaggerate warming?
Anthony Watts, a weather forecaster whose Web sites, Watts Up With That and surfacestations.org, have become focal points for climate skeptics, enlisted volunteers across the country to photograph weather stations. Citing NOAA’s own criteria, Watts concluded last year that hundreds of the stations were in poorly located sites, next to parking lots or heating vents or other areas that could inflate temperatures.
“The U.S. temperature record is unreliable,” Watts concluded. “And since the U.S. record is thought to be the best in the world, it follows that the global database is likely similarly compromised and unreliable.”
A new peer-reviewed study by scientists at the National Climatic Data Center, the federal office that tracks climate trends, agrees that problems with the locations of many weather stations are real.
But the temperature records from the poorly located stations cited by Watts actually have a slightly cool bias, not a warm one, according to the review, scheduled to be published in the Journal of Geophysical Research-Atmospheres.
“Fortunately, the sites with good exposure, though small in number, are reasonably well distributed across the country,” the researchers concluded, adding that the “good” or better stations cited by Watts show warming over time similar to NOAA’s overall data.
There also are multiple other surface and satellite measurements of global temperatures, all of which show a warming trend.
Watts says he is writing his own paper, which has yet to be accepted for publication by a scientific journal.”

JonesII (11:54:38) :

Last but not least…though really last: When we die we suddenly lose heat.

Well, that just shows how poorly our use of language correlates with the physical world. What we mean when we say ‘lose heat’ is ambiguous at best, and opposite to reality at worst.
We lose heat (energy) all the time. We lose it pretty much at the same rate as we generate it (being inefficient but robust energy producing chemical plants). If we do not, we tend to get ill or die.
What stops when we die is the energy creation (actually transfer of chemical to kinetic energy). In reality, once we die, we immediately start losing less energy.
From you friendly neighbourhood pedant.

Can somebody explain to us if Dr. Spencer’s work here supports or contradicts Dr. Long’s recent SPPI paper on rural UHI? Intuitively my first reaction is they can’t coexist in the same reality, but my inuits have been wrong before!

It seems to me that this is an attempt to boil a many-dimensioned surface down to a human-understandable 2D line. But why not create a neural network or similar representation of the multi-dimensional surface obtained from all of the available factors (altitude, population density over time, geographical location, electricity consumption over time, vegetation coverage, water etc. etc.)? I presume this is the sort of thing that goes on when the climate is “modelled” and is no doubt fraught with difficulties, but it would provide a framework for ‘plugging in’ new factors whenever troublesome blog commenters thought of them.

Re: ColorMeSceptical (Mar 3 15:23),
Nobody is saying that all the air around the thermometer is raised by 0.8 degrees. All Dr Spencer’s analysis shows is that the thermometer’s temperature readings are 0.8 degrees higher with a population density of 20 per sq. km. The only temperature that a thermometer really measures is its own.

Of some note to U.S. all.
Should things get to a point in the U.S. Senate and this evidence being mulled over herein on global warming is used to pass Cap and Trade,,, looks like 51 votes is all that will be needed just now,,,,, after all its all about the $.
Time has come to take a stand.

I went up in a glider once. We flew from farm to farm looking for objects like grain silos, and large metal roofs they create great thermals. Fresh plowed fields also create nice thermals rising at 200-300 ft per minute.
So I would say the all rural is not created equal. The heating effect is not permenant though. As soon as the crop starts growing it seems to change the updrafts considerably.

I’d call this one “thinking yourself into a puddle of mush”. Get enough variables in the game and the outcome is all in the eye of the beholder. Where’s Willis?

sphaerica (15:12:17) :
“Correlation is not causation.
Can anyone name a mechanism that would cause less densely populated areas to warm more?”
====
Because their populations may grow faster. A community of 500 people could easily become a community of 1,000 in a decade. Cities, particularly the largest ones, don’t grow so fast. Rapid increases are easy from a small base.

“ColorMeSceptical (15:23:14) :
I’ve been lurking here for a long time, often impressed by the quality of work from Dr Spencer et al. But this time I hear alarm bells ringing: Can a population of 20 per sq. km really raise the temperature by almost 0.8 degrees C? [….] ”
I have just done some preliminary daytime measurements with a datalogger and I have found that just passing through a road construction zone in the country is enough to raise the temp by nearly half a degree C, and that a small hamlet can have a similar effect.

Well Roy, I didn’t anticipate exactly what you were trying to achieve from your previous posts. Well done for the ‘secrecy factor’, I concur that it’s better to keep the ‘rest of the field’ guessing when a hypothesis is in the embryonic stage!
Then again, why submit a hypothesis that supports a regime that can’t show climactic resolution without masses of ‘noise’? At the risk of a post edit I’ll include a post that I made on Tamino’s site to J (which was also edited):
J.
“If you think you’ve found an error in Tamino’s calculations, or a logical flaw in the conceptual framework for those calculations, you need to explain that. “I just feel that the data are insufficient” isn’t an explanation. Or at least it’s not an explanation that anyone else will find convincing.”
OK! One last try! I don’t see that it should be the responsibility of a mediocre engineer like myself to explain a definitive signal resolution to science types here. Especially when Tamino keeps telling me that “I don’t have a clue”.
I’ll start with an explanation of a ‘resolved signal’. To get some background on this I’ll begin with a single wire telecom digital data transmission (DTX). Single wire data transmission is commonly achieved in analogue (ATX) by use of a ‘carrier frequency’. The higher the carrier frequency, the greater the definition to the analogue signal (as can be seen on any oscilloscope).
Short explanation; a carrier signal is used for ATX because different wavelengths of an analogue signal travel at differing speed vectors through different media and the practise of modulating the analogue signal onto a carrier wave (amplitude modulation [AM]) maintains an identical transmission speed vector for all analogue wavelengths.
With DTX, if we can control the digital 0, or 1, signal by a co-ordinated time control mechanism, like within a computer, that would be OK. However, between computers (especially distant network, fax and Internet, etc) a DTX needs a carrier signal to maintain the signal integrity. In short, the digital 0, or1, has to be an AM DTX. Thus a ‘modem’ (mod [ulator] dem [odulator]) of some sort is needed to integrate, or interpret, the data within the carrier wave.
Now that we have a modem we can receive data from ‘everywhere’, but how fast?
A ‘received data transmission’ (DRX) has the same constraint as a ‘transmitted data transmission’ (DTX), except that some network and Internet ‘protocols’ use verification algorithms, etc that use up the DTX/DRX stream, but we’re more interested in signal integrity here.
The transmission rate of data is commonly understood as ‘the Baud rate’ and is stated as the ‘bits per second’ of digital data transfer. A ‘bit’ is simply a ‘bi [nary digi]t’, a ‘0’, or a ‘1’, but what determines the maximum rate of transfer for this? Its the carrier wavelength, or carrier frequency to add a time constant (‘frequency’ is the inverse of ‘length [distance] travelled/second’). I’ll admit that I’m happier with frequency units rather than wavelengths and Baude rate is a frequency measure anyhow. I’ll leave this up to you to verify, but the carrier wave needs to be a maximum of 1/3 the wavelength of the Baud rate, or at least three times the signal frequency (baud rate) to qualify as validly recognisable data!
Enough of point data validification. I’m confident that Tamino is correct in the respect of analysis, but four data readings in 24 hrs isn’t a full resolution of that station’s temperature data that was analysed. Were clouds evident, did it rain, etc? There isn’t enough station data to provide a feasible data-set of average station temps for an analysis in the first instance IMHO.
However, on the point of a region being verified from a few network nodes for whatever purpose,??? I’m truly sceptic. We need to discuss and elucidate this assumed proposition.
When I was younger I used to ride a motorcycle. In summer I would ride wherever I could in the UK. The thing that struck me was that in summer you could ride and feel the temperatures alter almost every 1/3 km of the journey. It was exhilarating, but during the autumn, winter and spring in the UK it was too cold to notice much difference (a ‘normal human condition’ to local temps, I guess). Now that I use a car I don’t feel this (don’t think I’m missing much). However, this says a lot about the points of data collection and the accuracy of a grid square to represent the regional temperature.
With all due respect J, we should believe the thermometers and ask why the disbelief in them for that region (assuming the thermometers are stationary, as in the suburban/urban/rural categorisation and accurate). I like raw data. Then I can decide for myself if there’s a weighting that needs to be made. If UHI expands, it’ll show in the raw data signal. Moreover, the appearance of UHI in the raw data would validate AGW. The main problem with this is that stations (network nodes) are so far apart that they can’t define grid box definitions, such as temperature, when they are so distant. There are spatial losses that can be assumed, but not known.
For example, Box 5 has 118 stations. Randomly select 59 of these stations and calculate the box temperature. It’ll be different to the result for 118 stations! Now take the 59 stations that were ‘not selected’ and calculate the box temperature. It’s different again, but opposite. However, average the results from the stations that were selected and the stations that were not selected and you’ll arrive at something closer to the 118 stations result. What’s more it’s possible to repeat this check with as many random samples as you like to arrive at a conclusion for error bands for the ‘sample rate’ (total number of stations). Warning! Before attempting any of this, ensure that raw station data is used! Otherwise an impaired verification of ‘nodal signal : grid box resolution’ may result.
When observing the convolutions of a point phenomenon of nature its important to observe the convolutions at adjacent points elsewhere as well. A station’s data (whatever baud rate) is only a point in a network that can report ~metres from its location. We need many more land stations to resolve the definition of land temperature for a large area IMHO. Don’t take my word for it though because ‘I don’t have a clue’.
Best regards, suricat.
End of post.
Surely any money introduced into a climate budget would be better spent on the number of surface sites available and the data that flows from them! Without this caveat climate science just isn’t a science, it’s only a conjecture (anecdotal).
Before you say it, yes! Satellite data has greater resolution than surface station data, but it lacks confidence and still doesn’t have a good resolution. However, surface station data currently relies on an unchanging quality that can’t be adequately defended due to lack of knowledge for the forcings to a network node (site) that doesn’t provide coverage for the ‘region’ greater than a few hundred metres, with regard to temperature anyhow.
Best of luck with your project, but I still have doubts for its outcome. Though, I hope my post here helps rather than hinders.
Best regards, suricat.

rural stuff
irrigation systems. they cover large areas and turn on intermittant
summer fallow. farmers may plow these under several times over course of summer.
fertilizer. depending on type may affect large areas
herbicides, same
erection of hog and poultry barns. these don’t look very large but they generate an enormous amount of effluent. how they dispose of it can affect a large area.
flax stubble. you would be shocked how many farmers burn their flax stubble
sun flowers. these are not harvested until after freeze up. completely different heat profile the next year when the farmer rotates crops and plants potatoes which are harvested before frost.
septic fields. most farms have one but in a lot of denser area there’s sewer lines going in. septic fields are pretty hot and switching to a sewer line eliminated the septic field and transfers the heat elsewhere
daughter factor. when I was young there was a farmer in the area who had 11 daughters. temperature was very warm at his farm, mostly after dark. discharge of light gauge shotgun loaded with salt seemed to cool it off.
might think of a coupe more…

So, GISS have been measuring increasing urbanisation, not global warming.

Side note: It amazes me how much of the world is not particularly liked as a place to live by most people, yet these very same place are where I WANT to live. Odd.

Nick Yates (17:48:07) :
So, GISS have been measuring increasing urbanisation, not global warming.
====
I guess UAH has too, although I’m not sure how.
So what? It’s anthropogenic global warming, anyway you look it at.

After a bit of thought: If you still want to compare population density, I would tighten the variables so that you are comparing apples to apples. Apples grown in the country versus apples grown in the city. In other words, limit, as much as you can, contaminating variables that can later be used to bring doubt to your subject pool.
First, I would choose only stations that originally met a pre-determine set of criteria for station citing. They all started out the same.
Second I would pair similarly current categorized urban and rural stations.
The idea is that you tighten the other variables so that you are assuring that you are only measuring similarly “categorized for quality” stations between rural and urban settings.
If you don’t control for variables, you have to list them all and then deal with the extremely high required number of subjects to minimize the variables. It is a standard in research design. The more variables you have, the more subjects you must use. And the number rises exponentially with each additional variable.

By the way, I am still home sick with a roaring sinus infection. So please pardon my drugged up typing and lack of attention to grammar.

I believe the stronger proportional effect at low densities is simply due to picking such a small grid resolution for population density. I would like to see the same analysis using the population in a 10KM grid. A lot of the low density stations are getting the heat blown in from the nearby city depending on the wind direction.
Also, a few facts:
An average person emits 60Watts. An average household consumes 1000Watts of electrical power. An average 1,000,000 person metropolitan area will consume about 1000 MegaWatts of electrical power and a similar amount of heat for transportation and direct heating. The power plants that supply the energy for that metropolitan area, typically 30-100 miles away, will emit a similar amount of energy as the whole metropolitan area.
So at 1000 people per square mile, the extra anthropogenic heat is several Watts/square meter depending on where the power is generated and how much industry is around. This explains most of the UHI without resorting to albedo changes and thermal mass changes.

Pamela Gray,
My kitty and bunnies send you hugs! Eat lots of chicken noodle soup, but avoid products made with bunny rabbits.
(They made me say that.)
For chronic sinus infections doctors are filling the sinuses with some kind of foam, which somewhat implies that you can snort “Great Stuff” to good effect, but I would avoid it lest your face explodes as the polyurethane foam expands.
My advice may explain why kids rely on Dr. Mom instead of Dr. Dad.

You noted that population density was not the only significant determinent using the example of airport siting. The increasing use of energy per capita may be one also, ie the UHI effect vs population for 1950 may be different from that of 2000 such that it may not be accurate to use the 2000 data to adjust land temperatures 50 to 100 years earlier.

…………..Oooops. Anthony. Sorry, I forgot. We are part of the tabacco-big Oil-
-GulfStream Aircraft Company-CLimate Skeptic-Massive House-Fleet of Escalades-earth endangering-Board of director types of a companies using underage children for labor-league of climate deniers!
I truly despair of this man Gore.

ABC 13 in Houston TX is reporting this is the sixth coldest winter on record in Houston (the coldest was 77-78 and warmest was 49-50) for the three months of Dec-Feb. The time period is late 1800s to present. No UHI included :).

The question is “Would the larger UHI increase per population increase cause more apparent warming overall for the country/world for the rural sites or not?”
Many people assume small towns grow to be bigger towns.
Is this a valid assumption ? I really don’t know. Some towns I have visited in west Texas seem to have abandoned buildings suggesting population loss.
A town which springs up around a new international airport like DFW will experience rapid UHI. [Which the record shows] That is pretty clear.
I checked and the adjusted data apparently didn’t adjust this effect out of the data.
A small military base close by showed no such warming.
A small town in western Nebraska may actually lose population as youngsters go to the big city. There may be a downward trend there may not.
Intuitively it seems that a stable rural site with little increase or decrease in population would represent the planet’s temperature fairly accurately. Who cares if the cities are getting warmer ?
Since we can exclude any additional warming in cities or even small towns when looking for CO2 influence why include them at all ?
Put another way: If we are looking for the effect of CO2 why include anything except primitive sites which remain primitive ?

This is very interesting. The closest official weather station to me is at Morrisville/Stowe Airport in Vermont. It is about 50 ft from Route 100 which is a major road. However, traffic on that road has significantly increased in the last 50 years or even 30. The population density is very low so it likely that the UHI had a big role in the temperature readings.

“It seems like many have misunderstood what Dr. Spencer is doing. All he is doing is comparing the temperature difference between two, relatively close locations with different populations and finding that the larger the population, the warmer the temperature. Furthermore, the largest differences, on average, occur when comparing ‘near zero’ population areas with those areas in the next higher population bins.”
That’s all fine and good. There is only one problem – what is the purpose of the entire exercise? Dr Long already has shown that warming at RURAL stations in the USA is no more than 0.1 deg C during 20th century. It seems reasonable to me to assume that this rural trend represents the real climatic trend. What is the exact purpose of speculating whether the artificial warming is greater when rural area becomes small town or when the small town becomes a large city. We don’t need neither small towns nor large cities, but only stations described by NOAA as “rural” (there are plenty of them in the USA with long record). Obviously, the settlements where rural stations compiled by Dr Long are placed, didn’t enter the phase of growing yet, so trend at these statiosn is 6 or 7 times lower than at urban ones.

Well, Dr. Spencer, you have had great critiques of your remarkable research here on WUWT. While I am not knowledgeable enough to contribute, one comment seems useful:
Jim Clarke (18:23:56) :
Dr. Spencer’s method appears reasonable and, equally importantly, doable. It gives us a number to adjust the temperature of any area with a changing population over time; a variable that is generally well known. Will it be perfect. No, but on average it will be valuable. It certainly appears more ‘robust’ than the now discredited Jones et al study that was done back around 1990.

Maybe someone else hit on this point already, but is population a good figure to use? If you put the measuring device in an area with no population, but in the middle of a heat island, the effect is still there. Likewise, even in densely populated areas, it is possible to have a well sited thermometer, is it not?

“Now why didn’t I think of that!” Not exactly a quote from Thomas Huxley, but the sentiment is the same.
Roy Spencer has struck gold with this one. Yes there are some points to be cleared up, but these are quibbles.

lws (19:27:41) :
Intuitively it seems that a stable rural site with little increase or decrease in population would represent the planet’s temperature fairly accurately.
Since we can exclude any additional warming in cities or even small towns when looking for CO2 influence why include them at all
I think the problem is the small sample size.
Take Texas, for example. The following sites, audited by surfacestations.org, are rated Class 1 or 2:
San Antonio – Urban – Significant moves 1940-42
Corpus Christy – Urban – On the Gulf of Mexico
Beeville – Rural – Significant move in 1922?
Catarina – “Ghost Town (pop. = 135) – sensor 24′ from house + large tree

Much of the global warming is in areas outside the U.S., areas that I think look pretty rural on the map:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=1&sat=4&sst=0&type=anoms&mean_gen=

But if you look at the USHCN data from Missouri and Kansas over the last 114 years, plotting against current population, you get a log relationship to population, which would confirm, in a different way, the relationships discussed in the post.

Has anyone taken a look at this yet? Is it new? Reactions?
http://www1.ncdc.noaa.gov/pub/data/ushcn/v2/monthly/menne-etal2010.pdf
REPLY: Yes. See http://wattsupwiththat.com/2010/01/27/rumours-of-my-death-have-been-greatly-exaggerated/

A very nice piece of work. It would be useful to see some “applications” – estimates how much UHI particular surface weather stations have suffered from.

Is Dr Spencers study another proof of negative feedback?
The initial UHI from pristine land make a difference but if “adding more UHI” is the result not linear. That is negative feedback.

O just had a funny thought.
Dr. Mann likes to do reconstructions of past temperature based on tree rings.
Since we dont have population density information for all historical sites, I wonder if we could get him to reconstruct the historical population of various sites where we dont have information from those places that we do have information.

Roy Spencer (13:20:43) :
Also, as the post states, this effect is not due to the sensible heat generated by people’s bodies….it’s due to the sensible heat generated by things that people build, whether an active source (e.g. AC exhaust), or a passive source (pavement rather than grass).
The rapid UHI change in the low population factor may be due to something less high-tech, like land use changes e.g. vegetation clearance reducing cooling transpiration and exposing the earth to drying and greater heat gain during the day and slower heat release at night.

jorgekafkazar (22:02:43) :
Possibly, Dave, but it’s considered statistically unlikely.
Sounds like an assumption to me, that the station would be sited badly just because it is in an area with denser population. Perhaps Dr. Spencer and Anthony can mesh their work together here to show that population density correlates with improper siting?

I found the shape of the first two cumulative graphs interesting because of the big change in slope around 2,000 persons per km2. This level of population is far below the minimum urban threshold of roughly 5,000 per km2. (See graphs above that use 10,307 and 11,031 data points. )
Still, 2000 per km2 is a fairly high density for rural areas. Uninhabited areas have populations below 5 persons per km2. In 19th-century Ohio, a typical figure for rural population density was about 15 persons per km2, before the great migration of the US population to the cities. At a density of around 2,000 per km2, we are looking * at least * two kinds of populations: outer suburban (countries like the USA) and rural (areas like Java, Indonesia). The density could be explained by paddy rice cultivation in the tropics or suburban development anywhere in the world.
Once we get to Dr Spencer’s last graph with only 724 data points, we may be looking at a different phenomenon. The leftmost data point may indicate a temperature-related causative factor that makes the location undesirable for settlement. So what might be interpreted as a biased temperature reading caused by human settlement might better be interpreted with causation going in the opposite direction. The same unknown factor that makes the place unsuitable for settlement causes a real temperature difference observed. To illustrate: maybe the data point location is a volcano or a salt pan.
Dr Spenser’s study could be extended to relate temperatures obtained from satellite measurement to population of one-km2 cells with over 10,000 population. This would pick up cities like Chicago, London, Hamburg but probably miss many small cites. What would be of interest would be how high the dome of the urban heat island effect extends into the troposphere.
Dr Spencer’s model has the great merit that it is productive of a whole line of enquiry. I trust that he is as excited about the potential as many of the readers of this blog.

Mosher — Since we dont have population density information for all historical sites, I wonder if we could get him to reconstruct the historical population of various sites where we dont have information from those places that we do have information.
Searching for Atlantis? Why rely on archaeological data and boring fieldwork? Using new climatology-developed methods that obviate the need for such trifles, you too can locate Atlantis in the comfort of your living room or office.

Thanks for an interesting post, Roy, and for providing a better way of correcting for UHI effects which looks ‘do-able’.
Apologies if already mentioned, but could the effect be different in countries like India and China which are undergoing rapid industrialisation? Big changes to infrastructure and energy use with low population growth could bias the result.

One problem I can see is that there are changes to an area that do not depend on population. A small village with 5 houses surrounded by dense forrest in 1920 could change to a small village with 5 houses surrounded by corn fields in 2010. This would have a profound influence on the measured temperature.

Just a rough comparison here of estimates, so nothing solid, but globally, we have this:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=1&sat=4&sst=3&type=anoms&mean_gen=01&year1=2010&year2=2010&base1=1951&base2=1980&radius=1200&pol=reg
vs.
http://www.populationaction.org/Publications/Reports/Mapping_the_Future_of_World_Population/Medium_size_bitmap_image.jpg
I don’t have the software to overlay them and see how well they correlate, but Canada is an issue right off the bat.

A very nice piece of work. I regard UHI as one of the biggest issues in climate science. Even the Daily Telegraph (whose coverage not so long ago was completely biased in favour of AGW) recently printed a report about UHI. The headline was: “Global warming data skewed by heat from planes and buildings”. How times have changed….
This work confirms something that certainly makes sense: that UHI causes larger temperature changes for smaller growing populations and that the relationship is approximately logarithmic. Of course, the other side of the coin is that, as populations continue to grow, the effect starts to saturate. Once everything is covered with concrete there’s little opportunity for further UHI.
With this in mind, is it possible that the recent lack of warming (since around 1995) could be partly due to UHI reaching saturation in many parts of the world?
Chris

As just a bozo on the bus, I tend to agree that intuitively the best stations would be those in permanent wilderness areas. So another “amen” from me.
That said (maybe this is naive, so feel free to shoot it down), since in heavily urban areas (as long as they remain that way), there is least change in UHI over time, could they be more reliable than many rural areas that are merely assumed to have been static?
IOW, are the best stations sited in complete wilderness on the one hand, and in huge cities on the other?

Satellite Remote Sensing of Land Use Change in Alabama of all places.
http://www.directionsmag.com/article.php?article_id=365
“The spatio-temporal information afforded by satellite data can be used to illustrate where changes have occurred and to quantify the rate of urban sprawl and concomitant loss of green space. These data provide the basis for understanding urban growth changes within an historical perspective. ”
Remote sensing by satellite of land use data is available and should be possible to correlate to historical and present temperature readings.
Both manual reading on ground and by satellites.
I suspect there is a strong correlation between land use and temperature that will explain most of AGW in CRU/GISS temp sets.

There’s an obvious criticism of the methodology here which you have already partially acknowledged, but which I think may potentially be badly skewing your low density UHI calculation. You mention that airports have a very low population density but may be subject to a lot of concrete and jet blast. This is true. More significantly, though, a lot has changed over time; 60 years ago, a local airfield may have been a military facility, and may have had a weather station, but could quite conceivably have had a grass strip and been serving piston engine fighters and bombers. 40 years ago, many “local airports” that would have had a weather station might have had a concrete runway and a perimeter track, but would mainly see landings from DC3s carrying the mail, rather than domestic jet passenger traffic. Today, the area of apron and taxiway, and the sheer number of jet aircraft movements, combined with the length of holds at thresholds, at the vast majority of the world’s airports, has increased vastly. The character of airports has changed out of all proportion. In addition, once an airport has developed in this way, the population density around it is unlikely to ever fall for so long as it continues to operate.
All of that is speculative, but the impact (or otherwise) of population growth and/or jet traffic on airport or airfield sites specifically could be analysed in isolation by looking at the temperature records from airfields that don’t have a population driven effect. Perhaps island airfields like Diego Garcia and Ascension Island. Ascension in particular might yield some interesting results, since it has a tiny population and very limited air traffic most of the time, but became the busiest airport in the world for a period of a few months during the Falklands Conflict in 1982.
However, I’m wondering what your figures for small population increase warming effects in low population density areas would be if you excluded all airports and airfields from consideration. Would that leave enough low population density sites to be meaningful?

Ivan 19:46:47 “That’s all fine and good. There is only one problem – what is the purpose of the entire exercise? Dr Long already has shown that warming at RURAL stations in the USA is no more than 0.1 deg C during 20th century. It seems reasonable to me to assume that this rural trend represents the real climatic trend. What is the exact purpose of speculating whether the artificial warming is greater when rural area becomes small town or when the small town becomes a large city.”
Yes, a valid comment if we never needed to know about human effects on planet Earth in all their varieties. If I read correctly, Dr. Spencer’s research is a valuable statistical method to do a first “down and dirty” (Jim Evans, if I remember correctly) assessment. Dr. Spencer can amend this research as he finds helpful critiques, but get it published. Then fine-tune it with follow-up contributions by “everyone”. Let’s find out about UHI and rural (wilderness, agricultural, and land-use) changes to our environment.
Temperature seems to be a key ingredient of our future given the changes Earth has gone through in the last 100,000, 20,000, 11,000, 1,000, 350 years. Anything we can do to keep Earth warm and livable should be part of our scientific-research future. Knowledge, real knowledge, comes first, (including what it takes to maintain “warmth” during the varieties of Earth’s orbits around the sun [corrected Milankovitch] and passage through arms of the Milky Way — tiny matters, of course).
Thank you, Dr. Spencer.

Peter Sørensen (01:31:21) wrote:
“One problem I can see is that there are changes to an area that do not depend on population. A small village with 5 houses surrounded by dense forrest in 1920 could change to a small village with 5 houses surrounded by corn fields in 2010. This would have a profound influence on the measured temperature.”
Comment from Hugh Roper: your point is true enough, but that situation as I understand it no longer comes under the heading of UHI effect (or ‘built environment’ effect as I prefer to call it). It is rather a matter of changing rural land use. Changing land use is already recognised as a climate forcing by IPCC, although they seem to limit their consideration of this forcing to very restricted conditions, such as the radiative effect of snow-covered arable fields as compared to the radiative effect of previous coniferous forest at the same location. This example would be a negative forcing as it would increase the local albedo in winter. IPCC does not give much consideration to other effects of changing rural land use. However the IPCC’s mission is to underline the supposed predominance of CO2 as the leading positive climate forcing, so it is only to be expected that they will play down the role of other potential positive forcings and emphasize negative ones.
A broader and more scientific (i.e. non-missionary) approach than that offered by the IPCC to the question of radiative and non-radiative climate forcings arising from land use changes is found at the blog of Roger A. Pielke Snr:
see http://pielkeclimatesci.wordpress.com
As far as I know the IPCC ignores the extensive published and peer-reviewed work of Pielke Snr and his co-workers in this important field of climate science.
Regards,
Hugh

Dr. Spencer:
Many thanks for triggering such an intriguing debate. I haven’t had time to read all the comments but clearly many have offered some interesting refinements. I recall looking at the trend data for Cambridge Bay a very small Canadian Arctic settlement and seeing a clear link between the number of dwellings and the temperature trend.
The amazing thing is you have shown that simple change in population density is strongly associated with temperature and therefore needs to be treated systematically in any assessment of global temperatures.
My guess is that Ross McKitrick will have a smile on his lips. I suspect that Roger Pielke Snr will also be very interested since I would assume that the primary thing that happens on average when a population density changes for 0 to 20 is that the new arrivals cut down the trees!!
Many thanks for such a stimulating approach to the problem. Perhaps it will be sufficient to get demographers, geographers and others to take a more systematic look.

Leif Svalgaard, Michael Jankowski,
I would hope that an anomalous reading from a nearby fire would be recognized and corrected (10C, 10C, 10C, 20C, 10C, 10C…). Even so, I doubt that could possibly contribute to a trend seen over 10,307 data points.
The argument about UHI areas already being warmed is probably better, and adds weight to the argument that actually temperature anomalies are higher, not lower, than reported as a result of UHI.
But, again… this study needs to be done addressing changes in population density, rather than simply static population density. That is what would be interesting. Over a ten year period, how does the difference in temperature anomaly compare to the change in population density.