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.
Sorry–siting
Global population has increased from 1650M in 1900 to 6,800M today … a factor of 4.
Any guess as to how much this increase has effected global av temps ?
If population density has increased at the same rate as population, this implies at least a 0.5 degree UHI.
Climategate Minute: Lord Monckton v. Al Gore: Doubling Down On Global Warming
http://www.pjtv.com/v/3185
This analysis says that just about any human activity (with population as the proxy) produces measurable temperature increases. Should this hold up under review, I’d like to see what a regional analysis shows. One thing to check is the elevation adjustment, though. It’s hard to believe one size (5.4 degr C/km) fits all stations. Nice work. Population at this resolution sure beats nightlights.
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.)
Hi,
Interesting piece of work. What are reasons for the the increase at low densities?
Perhaps an initial infrastructure-roads roads houses? fields? . Instead or in addition to using population density as a co-variable how about land use. I.e. tilled fields, pavement and structures.
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!
Questions for Dr Spencer:
Can you access or estimate historical population density data through out the last century / historical surface temp data base?
Is the raw / uncorrected data even available to do such work?
If yes for both, that would be a very interesting follow up paper
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.
Interesting start.
1) Some error bars would be nice.
2) What is the effect of economic development status. Ie is the affect weaker in less developed nations
3) Does the magnitude of the effect vary with temperature
4) Seasonal + diurnal effects?
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.
Nice bit of work, Dr. Spencer. It will come in handy when the day comes that NOAA/NCDC gets off their seats and starts looking for missing data instead of penciling it out.
For example, they have 90 months of data thrown out for 049490. I have only 9 months of missing data.
It won’t matter how well you adjust for UHI as long as they use a pencil like a meatcleaver and use filnet like there is no tomorrow.
If I understand UHI correctly, it’s not a question of heat sources but change of land surface and reduced wind cooling due to building structures.
I, too, would like a better explanation of the “station pairing”.
I really, really cannot understand what is reason for all of these mathematical speculations. Is not far easier to select ALL RURAL stations in the USA and compare thus obtained trend with the urban trend? Is that idea really so unimaginable and stupid?
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.
Airport sations could be separated from other stations, and analysed against (for example) km of runways. It would allow for the recognition of the qualitative difference with other stations (although in practise, the correlation between km of runways and population density is probably high, making this distinction unecessary, but we can’t know if we don’t check). It would be possible to see the impact of airport stations on the data. Another neet point to look at would be to analyse the stations using density AND the quality of the station as evaluated on Surfacestations.com
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.
@ur momisugly 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)
It looks like J Hansen has just hammered Australian main political party (Labor) to bits!
From The Australian Quote
”
Any second thoughts on emissions trading, Prime Minister? Rudd on ABC1’s Insiders on Sunday:
EMISSIONS trading has to be core, front and centre if you’re going to bring about large scale greenhouse gas emissions . . . that’s why we took that view, John Howard took that view, Peter Costello took that view, Malcolm Turnbull took that view. Thirty-five other Western countries around the world have taken that view.
Fran Kelly on Radio National Breakfast yesterday asks “the grandfather of climate change science” James Hansen, director of the NASA Goddard Climate Change Centre, about emissions trading:
“IT’S also favoured by people like former vice-president Al Gore, who has been described as a climate change warrior. It’s also favoured by policy makers here. We’ve been told the emissions trading scheme is the only viable and affordable mechanism to price carbon. They’re all wrong?
Hansen: Yes, it’s absolutely wrong. I’m very disappointed in Al about this. One of the big problems with cap and trade is there’s no way to make that global, as we found out with the Kyoto protocol. You just have everybody squabbling.”
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)
What % of the population within these UHI are smokers?
F. Cough
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.
Lucy Skywalker (12:12:04) : “How do you account for Anthony’s urban transect at Reno which showed a UHIE of ~6ºC IIRC?”
Recall that Dr. Spencer’s analysis averages out the diurnal effects. IIRC, Anthony’s transect was done some time just before midnight, when UHI effects tend to be larger than the average.
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.
Is it just me, or is this analysis problematic because it focuses on 2000, the year Al Gore was running for President and Gaia was having fainting spells?
Hey, someone had to point it out. 😉
I think the biggest jump in the graph occurs when a volunteer’s wife suggests they build a patio.
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.