I believe this is a truly important piece of work. I hope Dr. Spencer will submit it to a journal. I’m grateful to Dr. Spencer for his email suggesting I post it here. Consider this early peer review. Beat it up, find any errors, and point out flaws, so that he can make it better. – Anthony
by Roy W. Spencer, Ph. D.
UPDATED (12:30 p.m. CST, March 3): Appended new discussion & plots showing importance of how low-population density stations are handled.
ABSTRACT
Global hourly surface temperature observations and 1 km resolution population density data for the year 2000 are used together to quantify the average urban heat island (UHI) effect. While the rate of warming with population increase is the greatest at the lowest population densities, some warming continues with population increases even for densely populated cities. Statistics like those presented here could be used to correct the surface temperature record for spurious warming caused by the UHI effect, providing better estimates of temperature trends.
METHOD
Using NOAA’s International Surface Hourly (ISH) weather data from around the world during 2000, I computed daily, monthly, and then 1-year average temperatures for each weather station. For a station to be used, a daily average temperature computation required the 4 synoptic temperature observations at 00, 06, 12, and 18 UTC; a monthly average required at least 20 good days per month; and a yearly average required all 12 months.
For each of those weather station locations I also stored the average population density from the 1 km gridded global population density data archived at the Socioeconomic Data and Applications Center (SEDAC).
All station pairs within 150 km of each other had their 1-year average difference in temperature related to their difference in population. Averaging of these station pairs’ results was done in 10 population bins each for Station1 and Station2, with bin boundaries at 0, 20, 50, 100, 200, 400, 800, 1600, 3200, 6400, and 50000 persons per sq. km.
Because some stations are located next to large water bodies, I used an old USAF 1/6 deg lat/lon percent water coverage dataset to ensure that there was no more than a 20% difference in the percent water coverage between the two stations in each match-up. (I believe this water coverage dataset is no longer publicly available).
Elevation effects were estimated by regressing station pair temperature differences against station elevation differences, which yielded a cooling rate of 5.4 deg. C per km increase in station elevation. Then, all station temperatures were adjusted to sea level (0 km elevation) with this relationship.
After all screening, a total of 10,307 unique station pairs were accepted for analysis from 2000.
RESULTS & DISCUSSION
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.
Using the data in the above graph, we can now compute average cumulative warming from a population density of zero, the results of which are shown in the next graph. [Note that this step would be unnecessary if every populated station location had a zero-population station nearby. In that case, it would be much easier to compute the average warming associated with a population density increase.]
This graph shows that the most rapid rate of warming with population increase is at the lowest population densities. The non-linear relationship is not a new discovery, as it has been noted by previous researchers who found an approximate logarithmic dependence of warming on population.
Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities. For instance, a population increase from 0 to 20 people per sq. km gives a warming of +0.22 deg C, but for a densely populated location having 1,000 people per sq. km, it takes an additional 1,500 people (to 2,500 people per sq. km) to get the same 0.22 deg. C warming. (Of course, if one can find stations whose environment has not changed at all, that would be the preferred situation.)
Since this analysis used only 1 year of data, other years could be examined to see how robust the above relationship is. Also, since there are gridded population data for 1990, 2000, and 2010 (estimated), one could examine whether there is any indication of the temperature-population relationship changing over time.
This is the type of information which I can envision being used to adjust station temperatures throughout the historical record, even as stations come, go, and move. As mentioned above, the elevation adjustment for individual stations can be done fairly easily, and the population adjustments could then be done without having to inter-calibrate stations.
Such adjustments help to maximize the number of stations used in temperature trend analysis, rather than simply throwing the data out. Note that the philosophy here is not to provide the best adjustments for each station individually, but to do adjustments for spurious effects which, when averaged over all stations, will remove the effect when averaged over all stations. This ensures simplicity and reproducibility of the analysis.
UPDATE:
The above results are quite sensitive to how the stations with very low population densities are handled. I’ve recomputed the above results by adding a single data point representing 724 more station pairs where BOTH stations are within the lowest population density category: 0 to 20 people per sq. km. This increases the signal of warming at low population densities, from the previously mentioned +0.22 deg C warming from zero to 20 people per sq. km, to +0.77 deg. C of warming.
This is over a factor of 3 more warming from 0 to 20 persons per sq. km with the additional data. This is important because most weather observation sites have relatively low population densities: in my dataset, I find that one-half of all stations have population densities below 100 persons per sq. km. The following plot zooms in on the lower left corner of the previous plot so you can better see the warming at the lowest population densities.
Clearly, any UHI adjustments to past thermometer data will depend upon how the UHI effect is quantified at these very low population densities.
Also, since I didn’t mention it earlier, I should clarify that population density is just an accessible index that is presumed to be related to how much the environment around the thermometer site has been modified over time, by replacing vegetation with manmade structures. Population density is not expected to always be a good index of this modification — for instance, population densities at large airports can be expected to be low, but the surrounding runway surfaces and airplane traffic can be expected to cause considerable spurious warming, much more than would be expected for their population density.
Anthony,
I would suggest that for Roy to get this paper published in a peer-reviewed journal he would need to make some comparisons with the previous work done on the topic including several equations (logarithmic as he alludes to) that have been published in the past. A good starting point would be the paper by:
Torok, S J, Morris, C J G, Skinne, C and Plummer, N, 2001. Urban heat island features of southeast Austalian towns, Australian Meteorological Magazine, 50:1-13.
This paper develops an equation for southeast Australia and also makes reference to past equations developed for North America and Europe.
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.”
BRILLIANT piece of work. Thank you Dr. Spencer. Research along these lines has, to my limited knowledge. been rare.
The source the the original water coverage data seems to be FNOC, the Fleet Naval Oceanographic Center, In Monterey, CA. I suspect the USAF added most of the data land locations. (OT, but I have called the FNOC Cmder many years ago when it was commanded by one Capt Fleet. His AUTOVON answer opening was long and hilarious. I believe it started something like “Captain Fleet speaking. Forecasts for the Fleet by Capt. Fleet of Fleet’s Fleet Naval Oceanographic Center…etc….”
The ISH data contains a number of Synoptic Stations (and METAR stations as well) but by definition synoptic reports are at 00ZPE6H( plus each 6 hours). But many of the current synoptics take obs each hour. For example, Jan Mayen Island, Norway, ICAO ENJA took 8,581 complete, hourly, FM-12, (Synoptic) observations in 2008. So they missed a few, but all in all this is a good record and is not all that unusual.
I haven’t investigated, but I suspect that some of these synoptics come from very low traffic airfields, and I suspect the extended reporting comes from automating the equipment.
JonesII (11:54:38) :
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.
George Turner (15:11:04) :
It might create a noticable effect when you squeeze a couple thousand penguins together, though.
That is the Penguin-Heat-Island or PHI. An effect proven and well documented by environmentalists in Antarctica, they just lack ??? to extend the effect to humans and cities. 🙂
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!
Thanks. A nice job.
I must admit I find the write-up hard to follow, so I can only make a few odd comments:
I’d expect the ordinate intercepts to necessarily be infinity and zero, respectively, for the first two graphs.
I think some logical steps have been omitted and should be re-inserted for clarity.
The use of anomaly change per population increase is non-intuitive and not very reader friendly, and has obviously led to confusion in the comments here.
“More warming” in rural stations seems to really mean more like “more percent increase in anomaly.” Which turns out to be almost zip, when expressed in °C. All of the interesting action is taking place at the right-hand end of the curves. Or is it?
Why are there connecting lines between points in the first graph?
As others have pointed out, obviously adding more years might produce interesting results, but I think addressing some of the comments above and then incorporating another clarifying graph and sample calculation and/or data table or two might be the first order of business. This is a good start and seems a rather novel approach. I hope it bears fruit.
I would not expect this relationship to be the same for all regions of the world, The pattern of densification in human settlements is not quite the same everywhere. Perhaps in some places, the USA, an initial change from “rural” to more dense brings a disproportionate change in paved surface, etc. etc.
It would be interesting to see this relationship derived separately for each settled continent, or some other division scheme of the Earth’s habitable surface.
Is there a reason for the 150km radius? Is that what others used? Not unreasonable, but just wondering.
I also wonder about the accuracy of the gridded data – is it uniform throughout? Why not try the experiment with the USA, starting with census block data, which we know is pretty good.
Interesting!
Dr Basil Beamish (15:36:42) :
I’m quite sure Roy doesn’t need to be told that any attempt at publication would have to take into account prior relevant literature. I see the presentation here as preliminary, and seeking feedback. I think venues like this can substitute for, or work similarly, as presentations at professional gatherings, but in a much more efficient, real time manner.
I don’t know what prior relevant literature Roy is familiar with, but if he’s not familiar with the paper you referenced, I’m sure he appreciates the notice.
O/T
But an interesting development.
The Government of Canada opened a new session of parliament today with the throne speech. The throne speech explains in broad terms the governments’ plans for the coming session.
Neither the word ‘climate’ nor the word ‘warming’ were in the speech.
Greg (15:13:45) :
I’ll bet GDP/area has tighter correlation with UHI than population density. Just a thought.
Sounds like something that could be explored in a multivariate context. Isn’t that what Michaels and McKitrick did?
“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,”
Does that mean they are jimmying the numbers up, to make the poorly located stations match?
Since this flys in the face of common sense, what are they doing to the numbers?
What are they comparing the numbers to, in order to decide there is a “cool bias”?
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.
sphaerica (15:12:17) :
“Correlation is not causation.
Can anyone name a mechanism that would cause less densely populated areas to warm more?”
I’ll try. Dr. Spencer is talking about population growth. Going from 0 people to 20 for instance. People in rural areas use pretty much the same modern conveniences as the people in urban areas, except more per capita. Automobiles for instance. Typically, in rural areas, using a nuclear family of 4 as an example, one automobile for each parent and usually one for each child as soon as the child is able to drive.(I haven’t even started on farm equipment.) Further, sqft of pavement per person would be significantly higher in the smaller towns. Those were just two examples off the top of my head. I hope that’s what you were asking.
The sensitivity demonstrated in the 2 graphs are a bit troubling to me but I’m very glad someone is starting to move in this direction. I find this approach a very logical step in understanding various biases(spurious heat) in our observation of our temps. More probably needs to be done in the lat/long areas and the socioeconomic conditions of other places.
Dr. Spencer, thank you!!!
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.
I’ll make an educated and kneejerk guess and stab at your question. I’ll bet that further investigation will find the night air temperatures increased more than the daytime air temperatures. The new buildings, asphalt roads, concrete roads, and other heat absorbing improvements absorb heat in the daytime and increase the average night temperatures by time shifting the releases into the night hours. Since there were only four synoptic observations in each daily period, there aren’t enough cool night datapoints in the daily dataset to lower the station average. Consequently, the arrival of any heat storage artifacts in the observation area results in time shifting the heat into only one night observation. Doing so would typically have a stronger effect on the daily average than 12 to 36 observations and datapoints per day having more of the cooler datapoints.
Still, four daily datapoints is far far better than only two daily datapoints with indeterminate event times used with the daily MIN-MAX-MEAN observations.
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.
ColorMeSceptical (15:23:14) :
Carry on your thought, but here is a hint. It is mostly in the albedo (reflection) difference that man makes, dark rooks, driveways, streets, dirt instead of tall grasses, etc.
If 1/4 of a sq.km has the albedo change from 0.30 to 0.10, the additional heat at 250 Wm-2 insolation would be 250 Wm-2 * 1/4 * 0.20 * 3600 s/hr * 24 hr * 1000000 m2/km2 = 1.08 x 10^12 additional joules received from the sun over that sq.km. (fast math, check it)
There is your Terajoule. The energy from home heating, cooling and their physical bodies of 20 people would be rather insignificant.
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.
sphaerica (15:12:17) :
Can anyone name a mechanism that would cause less densely populated areas to warm more?
Burning off of excess natural gas from oil wells. wildfires. http://incredimazing.com/page/Earth_at_Night-686
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?
Ivan (11:39:22) : 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 [for rural], but 0.6-0.7 degrees C in the cities. How that can be, if the rural stations should have a larger warming bias?
As Kum Dollison (12:14:36) speculated, rural may be becoming increasingly rural. You know, bored farmboys leaving for the big city. If one wanted to be a climate skeptic’s skeptic :>, you could argue that there has been a UHI cooling in the rural stations and AGW is “worse than we thought!”.
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.
Refreshing.
I have read so many “studies” with an agenda of proving something like “how much damage AGW will cause”. I am sure there are anti AGW papers with an agenda too.
I want to see science where the agenda is TRUTH ! What a novel idea !
Accurately adjusting for UHI is important and let the facts be what they are.
A great man once said
“You have a right to your own opinion you do not have a right to your own facts”
BTW : How about all of those lawn sprinklers in Phoenix. ? More people more sprinklers ? More car washes ?
For that matter how about western Nebraska agriculture sprinklers ?
Water vapor is a great greenhouse gas !