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

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

The Global Average Urban Heat Island Effect in 2000 Estimated from Station Temperatures and Population Density Data

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).

pop-density-2000

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.
pop-density-vs-rate-of-ISH-station-warming

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.]
ISH-station-warming-vs-pop-density

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.
ISH-station-warming-vs-pop-density-with-lowest-bin-full

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.

ISH-station-warming-vs-pop-density-with-lowest-bin-full-0-to-200

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.

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248 thoughts on “Spencer: Using hourly surface data to gauge UHI by population density

  1. Nice. A couple of comments, FWIW:

    1. You don’t define “station pairs.” I could guess what they are, but explicitly defining them and how they are selected seems important to your paper.
    2. Is it possible to extend this work to “energy density?” Or does population density pick that up?

  2. Thanks Dr Spencer, a very useful piece of work that convincingly confirms the UHI effect and documents its magnitude.
    A couple of comments:
    Can you make the USAF water coverage data public, bearing in mind all the calls for transparency and data availability?
    I guess that population density is shorthand for heat energy release per unit area. 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?

  3. 1. More information must be provided on how the station pairing was accomplished. What, in addition to the 150 Km proximity, was considered in the pairing?
    2. The two-station pair average graph would be more informative if the horizontal axis were logarithmic. Or perhaps simply non-linear, with the bin values equally distributed on the axis.
    3. Is Dr. Spencer certain that he obtained raw data from the ISH? I am quite tired of learning, after the fact, that alleged raw data has been manipulated in some way. See Darwin Zero for an example.
    4. Somehow 1.6 deg. C does not correspond, in my memory, to the UHI corrections in the IPC report. The chronicled Alaska report, for example, showed a much greater UHI effect.
    5. The elevation corrections appear quite reasonable.
    A marvelous piece of work!

  4. 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?

  5. I would venture that the first 1 degree on the curve (up to population density of 100/km^2) is not sufficiently accurate to be useful. The heat sources are too thinly spread for their impact to be related to the weather station which might be in their back garden, or 2 fields away. Focus instead on how much can be deduced about the 1-2 degree range. Rural is a very difficult judgement to make, but how valuable is the ability to correct for a change in population density from 1000 to 7000?
    I’m not suggesting that the 1st degree of population impact is over-estimated or not important – just that it’s use is much harder to justify as ‘not exagerated’.

  6. 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?

  7. Another interesting analysis on the “non-existent” UHI problem.

    It would be interesting to see how the analysis changed with all the stations pairs at different altitudes removed.

    The correction factor is the “average” but in practice – depending on the terrain – air can be easily pushed up or down changing it from its “potential temperature”. Perhaps this all averages out or perhaps not.

    The Japan UHI paper showed the huge variation in UHI vs population density from 561 stations, with a strong time of day bias (night time in cities is where the big effect takes place). UHI is strongest at night, almost non-existent during the midday to mid-afternoon time frame.

    Although UHI studies have taken a life of its own, maybe we should just stop thinking that precise measurements of a light ephemeral substance 6ft off the ground in a few thousand random locations around the world have much chance of being an accurate indicator of what’s happening to climate.

    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..

    Which is Why Global Mean Surface Temperature Should be Relegated

  8. 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.

  9. Do you mean “hourly surface DATA”?

    REPLY: yes, that was an editing error. My original title had both “data” and “area” in it but was too wordy, so I scaled it back. Somehow I deleted data and left area, fixed now thanks. – Anthony

  10. I always find Dr Spencer’s work to be imaginative, instructive and valuable. However, I can’t help but think that the time for using global temperatures to calculate changes in global heat is past. Temperature is a proxy measure for heat energy, so why use a proxy when we can measure heat energy directly using satellites? Climate science has become bogged-down with theoretical global temperatures and anomalies, using temperature data from weather stations that were intended to monitor local weather conditions, not global climate, which the Earth does not have!

  11. A most interesting piece of work! I am no expert here, but would it be possible to argue that the UHI effect should be larger in colder places like Alaska!?

  12. Recommend using logarithmic or geometric horizontal axis –
    reiterating mathman’s recommendation above:

    “2. The two-station pair average graph would be more informative if the horizontal axis were logarithmic. Or perhaps simply non-linear, with the bin values equally distributed on the axis.”

  13. “This graph shows that the most rapid rate of warming with population increase is at the lowest population densities.”
    But if this is a discussion of UHI effects, I find it hard to categorize those low densities as “urban”.
    “Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities.”
    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?

  14. After all screening, a total of 10,307 unique station pairs were accepted for analysis from 2000.

    20,000?

    Regards Michael Oakes

  15. 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?

  16. After all screening, a total of 10,307 unique station pairs were accepted for analysis from 2000.

    Sorry, re-read it. meaning from year 2000, I was expecting to be told from how many stations.

    If possible please withdraw these posts.

    Regards Michael Oakes

  17. 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?

  18. It would seem to me that population density could be one factor, while another factor could be surrounding area. For example, if there is one square kilometer with 200 people living in it, and surrounding that square kilometer there is natural wilderness, that would seem different than if there is one square kilometer with 200 people living in it, and surrounding it is a thousand more square kilometers with 200 people per square kilometer. If a station is sited on the edge of a large metropolis, the station itself may be in a low population density square kilometer, yet could be effected by the neighboring metropolis. (Maybe you’ve already accounted for this?)

  19. 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.

  20. Just curious, but were there any regional variations in the relationship? The reason I ask, is that there was a discussion here recently where someone was talking about electrical consumption in NYC as a potential forcing. It was an interesting thought and one I hadn’t really given proper consideration to before. There is a drastic difference, for example, between world average (2 kW/capita) and those wasteful (joking!) Canadians (11 kW/capita) *

    There’s also the long term up trend in total electric consumption from ~5 TW in 1965 to 15 TW in 2005 *

    To get to the point: Is there was any signal from heat generation due to electrical consumption and usage patterns that could be ascertained, or if the UHI effect was effectively consistent from region to region as a direct relation to population?

  21. “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?

  22. ” 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.”

    What was done if there was more than a 20% difference? The pair was dropped?

    10,307 unique pairs = how many unique stations?
    How many of these from the US?
    How many US stations met the NASA citing criteria?

  23. 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.

  24. 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.

  25. 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.)

  26. 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.

  27. 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!

  28. 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

  29. 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.

  30. 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?

  31. 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.

  32. 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.

  33. 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”.

  34. 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?

  35. 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.

  36. 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

  37. 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)

  38. 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.”

  39. 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)

  40. 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.

  41. 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.

  42. 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.

  43. 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.

  44. 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.

  45. wow, lots of questions. I’ll respond to as many as possible early in the morning…most are simply the result of the brevity of my post.

    Keep in mind that these are the average UHI effects across ~10,000 stations. Obviously, the UHI effects at specific stations could be much larger.

    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).

  46. 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.

  47. Is it possible to do a logarithmic plot as well? Especially if there is some evidence that there is a logarithmic relationship.

    What I also would like to know is what the next step might be in this piece of research since population density isn’t entirely reflective of amount of human infrastructure intervention on the thermometer record (human land use effect).

  48. 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.

  49. @Ivan (11:39:22) :

    —> “” 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? “”

    No. Spencer is talking 2nd derivative and you’re talking first derivative. Spencer is saying that you may see greater change in temperature anomaly at sites where the population is increasing from near zero to 1000 per square km. You are talking about greater temperature anomalies in the same areas. Do you see the difference? Spencer is saying depending on site, you might expect a rural station to show much greater change in anomaly per with respect to population changes, and you are asking why we don’t see greater temperature anomaly with respect to population. That is a mismatched question because Spencer is simply talking about a different slope than you are asking about.

  50. 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.

  51. Good idea. Never thought of it that way before. This makes me wonder has anyone ever done an affluence and UHI effect analysis? As a region get more affluent more paved roads instead of dirt, larger homes, more suburbia type energy usage.

    I can see packing more and more people into a small area (ie. Mexico City) as affecting UHI but I can also see massive suburban sprawl (ie. Calgary) having similar effect on UHI for quite different reasons.

  52. “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.”

    Roy: This section really needs cleaning up. You calculated a running average for any pair of stations within 150 km of one another using four time points per day and omitted any time points where the two stations didn’t both have a reading. That’s the 1-year pair-wise average of temperature difference. Then, you assign each of the stations to a population bin. What, exactly, are you averaging the second time? Are you averaging the average? I don’t see how you are averaging them in bins or why the bins are necessary.

    What I don’t understand about this blog is that people are congratulating Roy on proving something, when I don’t think his article can even be approached due to lack of definitions & clearly explained method. What is up with that?

  53. 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.

  54. Dr. Spencer, your post contains some very good analysis! Bravo! Respectably, you almost carried that analysis its final end, yet not quite.

    The above graphs, of Station Warm Bias vs. Population Density, need to be carried to its y-intercept. At that y-intercept, you should find the temperature anomaly increase that earth has experienced from some unspecified cause(s) over that period and should converge over longer periods of time to near the SST anomaly over the same period. That is the final answer as to why published temperature anomaly values are all hugely exaggerated.

    The causes of this small increase could take many forms. AGW proponents are going to try to blame it totally on CO2. Those like myself are going to place a large component on the secular increase in TSI seen over the last three or so cycles and has been termed in some places and papers as The Modern Solar Grand Maximum, but this has yet to be proven. A certain amount definitely needs to be placed on global-wide nuclear electricity generation as it is an unnatural source of heat whose tiny fraction of total input is still continually increasing. A certain amount can be placed in the factional increase in various fossil fuels over the last few decades, the heat given off by combustion, not CO2 secondary effects. But all in all, we are talking about a magnitude smaller delta temperature anomaly over past decades, not the 1.7ºC or so seen when UHI sites are improperly included.

    It seems our beloved water is the major player in the reason CO2 is not causing the so called “greenhouse gas effect” as many have tried to conjunct. The effects of water evaporation and condensation and water vapor itself dwarf any CO2 component in a planet scale climate system. This Earth does seem to have some mechanism(s) that consistently keep Earth’s temperature in balance with the overall input to the Earth climate system. I could list the many candidate hypotheses here but I would leave some out, which wouldn’t be fair, so I will restrain.

    Please carry your paper to its very end at the y-intercept over longer periods. In your last post I commented a simple hypothetical system that would continually measure this anomaly after all daily solar fluxes were removed. That will force science to finally turn to answer the real base questions that have needed answered for so long.

  55. 1. In meteorological dispersion modeling population density is just as acceptable as land use category in rural/urban determinations by the EPA. However, land use categories may convey a more direct correlation with the climate forcing in question.

    2. Anticipating attacks on such a study, I would settle for a smaller sample size which has a higher degree of credibility. In this light, I would reduce the distance between stations by at least half, and set a criteria for elevation difference.

    3. You may want to section the correlations into nightime vs. daytime temperatures in addition to what you have already presented.

  56. 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).

  57. G.L. Alston (12:34:41) :

    I’ll have to agree with G.L. Alston (12:34:41) : … this doesn’t seem to measure UHI as much as it does sensitivity to temperature change per population density. Which in and of itself could be of great use when doing site adjustments.

  58. 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”?

  59. Sangaman & Robert of Ottawa:

    You guys beat me to it. If there are 10 people living in a km^, chances are good they are farmers. Bring in a couple hundred more, and chances are, they’re into farming also. So we get substantial changes in land use which do not occur with corresponding population changes in urban areas. Point is, and consistent with Spencer’s work: it’s not CO2, its other human behaviors.

  60. Thanks Dr Spencer, an interesting read. Confirms work by others.
    Robert of Ottawa: UHI appears to work more at night, when buildings, roads etc cool more slowly than grass and trees. As well, the structures act like hairs or feathers to keep the ground warm on a cold morning. So it affects minimum temperatures more than max, which we’ve seen here in Australia.

    I’m pleased to see recognition that even very small settlements will have UHI. Any change to natural environment will change temperature a little- just clearing a few trees and ploughing the ground, for example. Adding buildings and concrete makes it worse, and you don’t need many people to do that.

  61. So presumably the population assiciated with each of thse stations has also varied with time. Is this data known, and can it be factored into the historic temperature records; since if population is affecting today’s data, it presumably also affected historic data.

  62. 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.

  63. First of all, good work.

    I have a question. Why adjust temperatures to sea level? The relationship is often not linear. I live in Reno NV and ski at lake tahoe resorts. There are often inversion layers here which leaves Reno colder than the higher ski resorts. Why add another adjustment, which can add error, if temps are normalized locally?

  64. 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

  65. If I read this right you are saying that the greatest rate of increase in temperature (due to UHI) will be at sites where there is a development at a site with a existing low population density. In that case, if we are looking at changes of temperature over time, urban weather stations will be the most stable (i.e. have the least changes due to urban development) over time. Is this the correct conclusion.

  66. 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?

  67. Dr. Spenser said: (13:20:43)

    “Keep in mind that these are the average UHI effects across ~10,000 stations. Obviously, the UHI effects at specific stations could be much larger.”

    This hits on what I’m wondering – what you’ve described is a way to adjust for UHI, but not problems with proper station siting, if I’m understanding correctly.

    Further corrections are needed for the siting problems.

  68. 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?

  69. Nice work. The study seems to suggest that land-use change (at least initially) has a higher impact on temperature than actual energy use by cars, industry, heating, etc.

    Perhaps there is also some sort of tipping point (ugh!) in the UHI-effect, where further urbanization actually has a cooling effect, f.ex through shading effects from tall buildings, funneling effects on wind, etc?

    Just speculating..

  70. 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.

  71. 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.

  72. Dr. Spencer,
    Thank you for a fine effort. Several people have asked for more information about station pairing and some have asked about differences in socio-economic status of the regions of paired stations. If all stations pairs are within 150 km of each other, most are probably somewhat similar socio-economic regions. For example, the difference between US and Canadian stations would not be significant. However, the difference between US and Mexican stations could be. Might I suggest a change or clarification in the pairing so the stations are paired within the same country or at least within the same socio-economic range?

  73. 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.

  74. This looks really cool, but I do have a couple concerns about using this to extrapolate distant historical data (past 100 years or so).

    1) Buildings may have changed both in materials used and in their shape (brick, glass, wood, concrete, etc).

    2) Energy use has almost certainly changed over the past 100 years with different technologies and how widespread frequently they are used (computers, air conditioning, heating, lighting, etc).

    I could go into specifics but I’m sure you get the point. That being said, it seems like UHI may be a bigger factor in the global record than many scientists admit – something I’ve suspected for quite some time.

  75. 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.”

  76. Dr. Spencer,
    Thank you for the comment just above. I understand the main concern is not the sensible heat from people’s bodies, but how do we know this is not significant? When you have a bunch of people in a room, you have to turn the a/c on. Has some study looked into the question and determined the sensible heat from bodies is not significant? If so, I would love to read the study.

  77. 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.

  78. (larger amplitude of absorption of insolation in high versus low latitudes)

    should have read

    (larger amplitude of absorption of insolation by vertical surfaces such as building in high versus low latitudes)

  79. 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?

  80. Great work. Following is some brainstorming:
    The Urban Heat Island effect (UHI) includes:
    1) Change in albedo affecting the radiative balance (fields vs asphalt).
    2) Spatial density of energy use (heating from nearby air conditioners, heaters or jets)
    3) Cumulative heat by convection from upstream areas.

    2) Since you are using international data, the spatial density of energy use may show a correlation with per capita GDP for the local population.

    3) The cumulative heat from upstream convection may correlate with the spatial energy density 2), the radius of the population density, and the wind speed. The upstream population is likely to scale with the population density.

    Note the fractal correlations of population size. See: Fractal dimension and fractal growth of urbanized areasShen G.1, International Journal of Geographical Information Science, Volume 16, Number 5, 1 July 2002 , pp. 419-437(19)

  81. 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.

  82. Correlation is not causation.

    Can anyone name a mechanism that would cause less densely populated areas to warm more? Or is it simply that the less densely populated stations are located in the areas of the globe that are for other reasons undergoing more warming (e.g. the poles) and at the same time are less hospitable to human occupation (hence lower pop. densities).

    I’d like to see these numbers broken out, for instance, by latitude bands, or by urban proximity (e.g. low-pop-stations within X km of any dense-population-station vs. low-pop-stations beyond X km of any dense-population-station). What if the stations showing the most warming are in the deserts, tundra and ice caps, and that the larger warming is thereby a reflection of their natural climate rather than anything having to do directly with population? That is, the people aren’t the cause of the warming, but rather the cause of the warming is also the cause for the lack of people.

    I’d also like to see these numbers according to population growth rather than static density. The UHI argument as I understand it is that 30 years ago there was nothing there, and today their a parking lot and an air conditioner vent, and that makes readings now seem warmer than they really are. So what does current population density have to do with this? If population density has been >2000 ppl./sq. km. for fifty years, how does that imply spurious warming in that 50 year span?

    I’m confused… right now this looks like playing with numbers, without a clear mechanism being defined or studied behind the statistics. It needs a lot more work.

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

  84. Jeremy (13:33:33) :
    @Ivan (11:39:22) :

    —> “” 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? “”

    No. Spencer is talking 2nd derivative and you’re talking first derivative. Spencer is saying that you may see greater change in temperature anomaly at sites where the population is increasing from near zero to 1000 per square km. You are talking about greater temperature anomalies in the same areas. Do you see the difference? Spencer is saying depending on site, you might expect a rural station to show much greater change in anomaly per with respect to population changes, and you are asking why we don’t see greater temperature anomaly with respect to population. That is a mismatched question because Spencer is simply talking about a different slope than you are asking about.

    ======

    But have we seen a larger rate of temperature increase in rural stations?

    “Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities.”

  85. 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.

  86. David (12:57:12) : “Airport sations could be separated from other stations, and analysed against (for example) km of runways.”

    Or maybe number of flights daily.

  87. 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.

  88. 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? A quick check would be to compare the latent heat of a sq km of atmosphere close to the earths surface with the heat output from 20 people.

    With a cp = 1.0 kJ/kg K, an air volume of 1 cu. km and a density of rho = 1.2 kg/m3 and a temperature rise of 0.8 degrees we need almost 1 Terajoule of energy! How fast does the atmosphere loose heat to space?

  89. ” Ivan (12:51:10) :

    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?”

    Keep asking the question Ivan. Maybe someone who has the raw data will do this. What is there to lose? Let’s us just be told the rural trend.

    I’m currently trusting no one. But the truth is still out there! Give us the data. Hypotheses can follow.

  90. 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.

  91. 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.”

  92. 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.

  93. 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.

  94. 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. :-)

  95. 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!

  96. 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.

  97. 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!

  98. 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.

  99. 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.

  100. 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?

  101. “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”?

  102. 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.

  103. 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!!!

  104. 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.

  105. 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’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.

  106. 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.

  107. 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.

  108. 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.

  109. 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?

  110. 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!”.

  111. 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.

  112. 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 !

  113. “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.

  114. son of mulder (15:27:59) :
    ” Ivan (12:51:10) :

    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?”

    Keep asking the question Ivan. Maybe someone who has the raw data will do this. What is there to lose? Let’s us just be told the rural trend.

    I’m currently trusting no one. But the truth is still out there! Give us the data. Hypotheses can follow.
    =====
    It would easy to have a an urban series and a rural series. But I’m not so keen on raw station data. If a station has crazy results, why include it? I suppose the stations that overstate temperature could offset those that understate, but I wouldn’t just assume it.

  115. 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.

  116. Is there a site, near one of the new generation of proposed nuclear power stations that is virgin? Could they not set up a set of temperature stations 1/2, 1, 2 and 4 miles in rings around the proposed site, take temperature measurements for the 4-6 years before construction begins and then look at the 3-10 years during and after construction?

  117. 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…

  118. more rural stuff

    snow fences. farmers may erect very long snow fences to retain snow on their fields. The common explanation is that this is to raise spring moisture levels in the soil but the real reason is to influence snow depth and start fights between Willis and Steve.

  119. My hunch is that with time, rural stations have deteriorated, maybe more so that urban stations. I would use a rating scale having to do with quality (such as the list provided by Anthony), not a population density measure, to determine spurious heat island and paired sensors. In fact Urban Heat Island is a misapplied label in my opinion. In essence, we are talking about spurious heat. This probably has less to do with Urban versus Rural, than with microclimate changes around the sensor wherever they happen to be. The new term should be defined as spurious heat island affect secondary to microclimate changes, not urban versus rural.

    Here is one reason why I think this is important. When you publish, you will be listing your stations. Your critics could find all kinds of problems with your stations that will bring doubt to your results. Unless you know the quality of your stations, I wouldn’t use them.

  120. 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.

  121. lws (17:11:44) :
    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 !

    ======
    I thought it was already a part of the cycle, but if I’m wrong droughts could be solved by people urinating more. I’ll drink to that !

  122. 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.

  123. transcript is up…this is the opening q&a for Phil, where Phil’s ‘most’ means ‘all’….

    UNCORRECTED TRANSCRIPT OF ORAL EVIDENCE
    SCIENCE AND TECHNOLOGY SUB-COMMITTEE
    THE DISCLOSURE OF CLIMATE DATA FROM THE CLIMATIC RESEARCH UNIT AT THE UNIVERSITY OF EAST ANGLIA
    Q78 Ian Stewart…Professor Jones, there has been some speculation that the primary data has been lost and manipulated. Are all the raw data used in your various analyses accessible and verifiable?
    Professor Jones: The simple answer is yes, most of the same basic data are available in the United States in something called the Global Historical Climatology Network.

    http://www.publications.parliament.uk/pa/cm200910/cmselect/cmsctech/uc387-i/uc38702.htm

    3 March: Quadrant Mag Australia: ABC gags Bob Carter
    by Michael Connor
    Quadrant Online previously reported that the ABC had invited Bob Carter to contribute to an online debate on The Drum following their publication of a series of five articles by Clive Hamilton.
    Left internet newsletters and blog sites were outraged that sceptics were to be allowed to comment on their ABC.
    Professor Carter submitted his article, on James Hansen and the Hansenism cult, and the ABC has rejected his article – which Quadrant Online is privileged to publish.
    James Hansen is visiting Australia. We can only guess at the pressures which have been exerted on the ABC to close down criticism of Hansen – and the cowardice which saw them conform. So much for Australia’s brave freedom fighters of the press.
    Read the essay the Left tried to ban, hear a voice the Left wants to silence:
    “Lysenkoism and James Hansen” by Bob Carter here…

    http://www.quadrant.org.au/blogs/doomed-planet/2010/03/abc-gags-bob-carter

  124. 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.

  125. geo (15:56:02) :
    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!
    ===========
    I had the same thought, but I haven’t checked Dr. Long’s article, so I’m not sure.

  126. 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. This does not imply that warming over time will be greatest in the rural areas, as some have speculated. If the rural areas have experienced growth, the warming will be much greater than the same amount of growth in an urban area, but the opposite is also true. Rural areas loosing population would, most likely, experience a cooling, provided the infrastructure around the site gradually returns to a more natural state.

    This comparison of site pairs does not determine the UHI for either location. That can only be determined when you look at population changes over the decades. Dr. Spencer is just trying to calculate realistic numbers to be applied to locations with population changes. If there is no population change, then no correction is needed to the raw data. Historically rural stations are often considered the best stations to use to determine climatic changes because population and land use changes are generally minimal or nonexistent.

    Is this the best way to do it? No. The best way would be to examine each site and determine all changes that have ever taken place and when. Then we would find that it is not just population affecting the readings but everything from tree growth, to paint, and surface changes and buildings and exhausts and numerous other heat sources. We would have to do it for every station and we would probably die of old age before we got half way through, but the end result would probably not be much different than Dr. Spencer’s down and dirty method.

    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. That study had ridiculously low values for UHI adjustments, but was used in determining ‘global warming’ for the last 20 years.

    The real benefit of the Spencer method over the Jones method is the amount of data in each. Even if Dr. Spencer has some questionable pairings in China, they will be unimportant when factored in with the other 9,800 pairings around the world. Sadly, the limited study done by Jones et al was not so immune.

    Thank you, Dr. Spencer.

  127. My very first lesson in graduate level statistics is to ask a statistician how many subjects I needed given this many variable. And that didn’t mean the number of variables I was studying, it meant the number of variables inherent in the subject pool.

    A simple example: If I wanted to study college behavior, I had to control for or actually study the effects of age, marital status, living choices (IE frat house, dorm, GDI, etc), etc. If I couldn’t control the variable, it had to be part of the study, which required an exponentially larger subject pool.

    Just a note: It has been more than a DECADE since I took my statistics class. So check with a more knowledgeable person. I could easily be wrong.

  128. 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.

  129. more rural stuff

    bird migrations. geese in particular will adjust their migration patters to avoid cities over a certain size. if this results in them choosing a different lake as a stop over point, you may get a big change in the lake. for example, lakes surrounded by pine trees are highly acidic, but goose guano neutralizes it every fall… until the birds go to a different lake. then the first lake dies off due to lackaguano disease. sometimes misdiagnosed as acid rain.

  130. Roy
    When applying such UHI per capita correlations over time, it may help to adjust for changes in energy use per capita over time. See my note above. e.g., See:

    Historically, countries rapidly increase per capita energy use during a industrialization phase, then settle into lower per capita energy use growth rate. e.g. See :
    Exponential growth, energetic Hubbert cycles, and the advancement of technology, Archives of Mining, Polish Academy of Sciences, Tad Patzek

    http://petroleum.berkeley.edu/papers/patzek/ArchivesofMiningPAS.pdf

    Note especially:

    “It is shown that the rates of oil production in the
    world and in the United States doubled 10 times, each increasing by a factor of ca. 1000, before reaching their respective peaks.”

    “Figure 7: Exponential rate of growth of world crude oil production was 6.6% per year between 1880 and 1970. Sources: lib.stat.cmu.edu/DASL/Datafiles/Oilproduction.html, US EIA.”

    “Figure 11: Between 1880 and 1940, the annual production rate of oil and, initially, associated lease condensate, in the US was increasing 9% per year!”

    “Figure 12: Between 1880 and 1960, the annual production rate of natural gas in the US was increasing 7.2% per year.”

    For data on per capita energy use, see:
    International Total Primary Energy Consumption and Energy Intensity
    US Energy Information Administration

    http://www.eia.doe.gov/emeu/international/energyconsumption.html

    e.g. for 2000 see:
    All Countries 1980-2006 for the International Energy Annual 2006

    http://www.eia.doe.gov/pub/international/iealf/tablee1.xls

  131. Pamela Gray,

    I bet that if you take a hundred climate change fanatics and off-topic presented them with two job offers, one in New York and one in L.A., the unimaginable difference in the two climates comes 33rd in their decision making, right behind closet space and nearby playground locations.

    If people were serious about their climates for actual reasons, as opposed to poseurs, they’d be afraid of moving from New York to New Jersey becaue they don’t think they could adapt to the weather there.

  132. 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.

  133. I only bring up the variable contamination to prevent the “tree ring” problem, IE a subset of subjects causes most of the change.

  134. 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.

  135. Are you sure you are wise to post that here first? Aren’t some journals sniffy about wanting to present data that is original – i.e. has not laready been put into the public domain? By putting it here first aren’t you ruining the “scoop factor” and making it less likely that it will be picked up in a properly peer-reviewed journal?

  136. 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.

  137. I’m nOt in favor of smoke, or second hand smoke. I like going into Pubs and what not without it being there. This posting is to do with the ‘environment’ if you ride the subway. But this is really a comentary about Gore and his modus-operandi. HIs S.O.P. Regarding University of Tennessee’s granting of a Honorary Doctorate to Gore (Gore’s got cash. Perhaps he’ll give us an endowment) came this blog entry:

    ‘……………………….harleyrider1978 writes:
    Whats even crazier is al gore still grows tobacco! But hell,so do we.But we dont go around promoting smoking bans and lies about second hand smoke like al gore does………and global warming lies……….its turned out some of the same folks writing the global warming hype are also writing second hand smoke hype.its all psuedo-science and propaganda.

    According to independent Public and Health Policy Research group, Littlewood & Fennel of Austin, Tx, on the subject of secondhand smoke……..

    They did the figures for what it takes to meet all of OSHA’S minimum PEL’S on shs/ets…….Did it ever set the debate on fire.

    They concluded that:
    All this is in a small sealed room 9×20 and must occur in ONE HOUR.

    For Benzo[a]pyrene, 222,000 cigarettes
    “For Acetone, 118,000 cigarettes
    “Toluene would require 50,000 packs of simultaneously smoldering cigarettes.
    Acetaldehyde or Hydrazine, more than 14,000 smokers would need to light up.
    “For Hydroquinone, “only” 1250 cigarettes
    For arsenic 2 million 500,000 smokers at one time
    The same number of cigarettes required for the other so called chemicals in shs/ets will have the same outcomes.

    So,OSHA finally makes a statement on shs/ets :
    Field studies of environmental tobacco smoke indicate that under normal conditions, the components in tobacco smoke are diluted below existing Permissible Exposure Levels (PELS.) as referenced in the Air Contaminant Standard (29 CFR 1910.1000)…It would be very rare to find a workplace with so much smoking that any individual PEL would be exceeded.” -Letter From Greg Watchman, Acting Sec’y, OSHA, To Leroy J Pletten, PHD, July 8, 1997

    WHAT! DILUTED BELOW PERMISSABLE LEVELS……………’

    It’s the same story.

  138. …………..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.

  139. “Professor Jones: The simple answer is yes, most of the same basic data are available in the United States in something called the Global Historical Climatology Network.”

    Most, is not all. So that lies somewhere between 51% and 99%. He didn’t say all. That’s the simple answer. It’s not the answer one would expect from a leading scientist.
    The scientific answer would be precisely how much is not available in the GHCN, and what stations are withheld or partially withheld. And if he didn’t know on the spot, he’d get back to them with a precise answer.

  140. 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 :).

  141. Jim Clarke (18:23:56) :
    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. This does not imply that warming over time will be greatest in the rural areas, as some have speculated. If the rural areas have experienced growth, the warming will be much greater than the same amount of growth in an urban area, but the opposite is also true. Rural areas loosing population would, most likely, experience a cooling, provided the infrastructure around the site gradually returns to a more natural state.
    =======

    Why do you think Dr. Spencer isn’t implying that warming over time could be greatest in the rural areas?

    Isn’t that what he meant when he said –

    “Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities.”

  142. 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 ?

  143. Having started on a similar exercise in Australia, I gave up when I ran into obstacles that could not be comprehended.

    Some readers seem to misinterpret your first graph. Does it not say simply that when a big town is compared to a small town, the temperature difference is large? And that when big towns are compared to each other, there is a non-zero difference (because it is an absolute value) which is perhaps noise?

    The interest in the second graph is twofold. First, the turning point at about 1,000 people per sq km indicates (to use an earlier phrase) that UHI starts to max out when population gets large. Second, the main growth rate in UHI is counterintuitive – one might expect a sigmoidal curve because a few people should make little difference.

    Hence graph 3 is important, because it shows that there is possibly non-sign noise again. Small-small town comparisons of about 0.3 deg C this far apart might be expected.

    The reason why graphs 2 & 3 grow sharply at first surely amalgamates a lot of uncertainties, such as altitude corrections, instrument calibration, Anthony’s famous siting problems, etc.

    To Australia. I loooked at 15 sites at a range of latitudes, deliberately chosen to be rural. Unfortunately, since I started calculations in 1968, I had to use many once daily readings, which I blogged years ago as being a poor proxy for heat energy. The question was, do truly rural sites behave themselves? Answer, no. Those by the seaside showed essentially no change in Tmax or Tmin in 40 years. The inland sites showed increases up to 1 deg c in this period, with Tmax variously converging, diverging or running parallel to Tmin.

    I gave up because I could not reconcile the data from the inland sites (or the seaside ones, depending which are closest to correct). Some descriptive data:

    Aust code Name lat deg min sec long deg min sec Tmax av Tmin Av Rainfall mm/yr Elevation, m World code
    14508 Gove airport NT -12 16 22 136 49 30 E 30.8 22.4 1440 52 5342 50194150000
    3003 Broome airport West Aust -17 57 09 122 14 02 E 31.3 15.7 600 7 5352 50194203000
    2012 Halls Creek West Aust -18 13 54 127 40 07 E 33.6 20.0 560 422 5356 50194212000
    15135 Tennant Creek MO NT -19 38 27 134 11 03 E 31.9 19.8 452 375 5367 50194238000
    6011 Carnarvon airport West Aust -25 53 05 113 39 42 E 27.1 17.1 230 4 5392 50194300000
    5007 Learmonth airport West Aust -22 13 36 114 05 07 E 37.8 17.7 260 9 5393 50194302000
    36031 Longreach airport Qld -23 26 24 144 16 14 E 32.3 14.8 440 192 5423 50194346000
    7045 Meekatharra a/pWest Aust -26 36 38 118 32 44 E 28.9 15.9 240 520 5461 50194443000
    13017 Giles, West Australia -25 02 34 128 18 08 E 29.3 15.8 290 600 5470 50194461000
    44021 Charleville airport Qld -26 24 48 146 15 31 E 28.0 13.4 488 300 5485 50194510000
    9542 Esperance, West Australia -33 52 04 121 53 24 E 21.8 12.0 620 25 5572 50194638000
    18012 Ceduna airport, SthAustralia -32 07 26 133 42 05 E 23.4 10.4 300 15 5578 50194653000
    16001 Woomera airport Sth Aust -31 08 46 136 48 27 E 25.7 12.6 184 167 5583 50194659000
    48027 Cobar New South Wales -31 29 58 145 50 43 E 33.9 20.4 48 290 5629 50194711000
    11004 Forrest aerodrome West Aust -30 50 48 128 06 51 E 25.4 10.0 190 150 5854 50195646000

    Aust code Name Population
    ca. 2000 Setting History
    14508 Gove airport NT <1,000 On N coast, sea close on 3 sides Bauxite mine
    3003 Broome airport West Aust 11,500 On NW coast, sea close on 3 sides. Pearling, tourism
    2012 Halls Creek West Aust <1,000 Inland, sea in arc 400 km to N & W Aboriginal settlement
    15135 Tennant Creek MO NT 3,200 Inland, 500 km SW of Gulf of Carpentaria. Mining, gold, copper
    6011 Carnarvon airport West Aust 7,000 Coastal, <6 km from NS-trend of W coast Fishing port
    5007 Learmonth airport West Aust <1,000 Coastal, inside peninsula on W coast Defence
    36031 Longreach airport Qld 3,500 Inland, 550 km SW and W of arc of Coral Sea Early airport, pastoral
    7045 Meekatharra airport West Aust <1,000 Inland, 450 km from W coast/Indian Ocean Mining, gold
    13017 Giles, West Australia <100 Inland, 750 km from Southern ocean A spot on the road
    44021 Charleville airport Qld 3,500 Inland, 600 km SW and W of arc of Coral Sea. Pastoral centre
    9542 Esperance, West Australia 15,000 Coastal, on E-W stretch of Southern Ocean Whaling, tourism
    18012 Ceduna airport, SthAustralia 2,500 Coastal, on N-W trend coast of Southern Ocean Grain port
    16001 Woomera airport Sth Aust <1,000 Inland, 180 km NNW of nearest sea. Rocketry science
    48027 Cobar New South Wales 4,500 Inland, 600-800 km from ocean Mining, base metals
    11004 Forrest aerodrome West Aust <100 Inland, about 130 km N of Southern Ocean Strategic aerodrome

    The linear least squares fit (again reluctant to use) compares slopes as follows, deg C per year:

    STATION Tmax Tmin Tmax Tmin
    COAST COAST INLAND INLAND
    Broome airport -0.0002 0.0033
    Carnarvon airport 0.0158 -0.0001
    Ceduna AMO 0.0176 0.0089
    Charleville airport 0.029 0.0185
    Cobar MO 0.0369 0.0161
    Esperance 0.0072 0.0136
    Forrest air/p 0.018 0.0295
    Giles 0.0192 0.0238
    Gove airport 0.005 -0.0019
    Halls 0.0057 0.0111
    Learmonth airport 0.0125 -0.0048
    Longreach airport 0.026 0.0341
    Lord Howe Island 0.0121 0.011
    Macquarie Island -0.0032 0.0002
    Meekatharra air/p 0.0189 0.0073
    Tennant Creek MO 0.0144 0.0313
    Woomera airport 0.02933 0.014
    AVERAGE SLOPE 0.0084 0.0038 0.0219 0.0206

    Thus, not being able to reconcile the simplest of observations, I felt it imprudent to continue with analysis.

    Additionally, with the exception of Broome, the station is well away from the population, or the population is very small, so population is a non-effect.There was no syttematic difference between airport and non-airprt sites, though again only Broome would be expected to show an effect.

    Maybe there is an effect like this in your analtsis, Dr Spencer, that causes the counterintuitive response curve.

    The BOM require me to state that I have used their data. I acknowledge this, with thanks. In very occasional cases, I have in filled missing data, usually with an average of the daily numbers before and after, purely as an aid to calculating.

  144. Sorry, the last table did not reproduce. Here it is again:

    STATION Tmax Tmin Tmax Tmin
    COAST COAST INLAND INLAND
    Broome airport -0.0002 0.0033
    Carnarvon airport 0.0158 -0.0001
    Ceduna AMO 0.0176 0.0089
    Charleville airport 0.029 0.0185
    Cobar MO 0.0369 0.0161
    Esperance 0.0072 0.0136
    Forrest air/p 0.018 0.0295
    Giles 0.0192 0.0238
    Gove airport 0.005 -0.0019
    Halls 0.0057 0.0111
    Learmonth airport 0.0125 -0.0048
    Longreach airport 0.026 0.0341
    Lord Howe Island 0.0121 0.011
    Macquarie Island -0.0032 0.0002
    Meekatharra air/p 0.0189 0.0073
    Tennant Creek MO 0.0144 0.0313
    Woomera airport 0.02933 0.014
    AVERAGE SLOPE 0.0084 0.0038 0.0219 0.0206

  145. Sorry, the formatting has me foxed. The bottom line is an average of those above and reads from L to R: Tmax coastal, Tmin coastal, Tmax Inland, Tmin Inland. Slope in deg C per year. There are about equal numbers of coastal and inland sites, see first table.

  146. 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.

  147. Several folks have hit this point, but I think it needs to be placed in context.

    Proxies for UHI, whether luminosity or population density, only vaguely reflect local land use and none directly address micro-climate effects which will usually swamp all other influences, if present.

    Thus, you must find a way to tease out any micro-climate impacts before getting at the UHI contribution. In fact, I would not even call it UHI, but rather call it what it actually is, local land use influences. Take a look at Pielke Sr.’s work on the effect of land use in Florida and the agricultural valley in California to see this signal.

    In any case, until you can pull out the micro-climate signal, you don’t have a stable base for estimating the localized land-use contribution, whatever you might call it and whichever proxie you use.

  148. “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.

  149. Some conclusions that can be tested:
    1. The observed trend in temperature over the time of thermometer records is as likely due to changes in population density and in local environment of the station as in global temperature change.
    2. The siting of a temperature station is not nearly as important as the changes over time in the local environment
    3. The error in the thermometer record may exceed the warming we thought had occurred over the thermometer record.
    4. Humanity is likely responsible for the observed change in global temperatures as a result of deforestation, road and building construction, etc. A strong correlation to CO2 concentration appears to be unlikely.

  150. 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.

  151. Maybe some of my resources are helpful in your research: As I see you are mentioning statistical research: I have put one of the most comprehensive link lists for hundreds of thousands of statistical sources and indicators on my blog: Statistics Reference List. And what I find most fascinating is how data can be visualised nowadays with the graphical computing power of modern PCs, as in many of the dozens of examples in these Data Visualisation References. If you miss anything that I might be able to find for you or if you yourself want to share a resource, please leave a comment.

  152. 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?

  153. This is exactly the kind of detailed, documented scientific work that we need, to counter the increasingly thread-bare ”the science is settled” mantra.

  154. “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.

  155. Dr Spencer,

    Thank you for your post today.

    Your offer today for all of us to be part of the process of review for you paper gives real meaning to the idea of open science.

    I think of it as giving a commenter the opportunity to be like an owner of 1 share of reviewer stock in your paper. I hope thousands such reviewer stocks are issued in support of the process of reviewing your paper. It appears to be a great investment in the future, I expect the value of my 1 share to increase in value very quickly.

    Question: I see the merit of your approach of using only terrestrial sourced info from land surface station temps and population densities from SEDAC’s 1 km gridded global population density data. Have you vetted the SEDAC info? The very brief checking I did at the SEDAC site lead me to think that only part of the source of the data is census info. There appears to be some modeling involved. One such modeling portion is “The United National Environment Programme’s (UNEP) Accessibility Model”. There may be more modeling inherent in the data that I could not briefly detect. UN related modeling should be scrutinized/vetted carely.

    Thank you Anthony for your support of these reviews.

    John

  156. 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

  157. I found one major flaw. This paper forgot to blame mankind for something. Heck, it didn’t even blame America! You can’t get published without doing one or the other.

    I have an idea that might fix the paper though. End the report with, “… and if left unchecked, mankind with his monstrosities called ‘corporations’ will destroy the entire universe.” That should help get you published.

    Luck!

  158. Leif Svalgaard (16:48:24) :
    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

    Yes, that probably would do it. Also, a less densely populated but rapidly growing area could have a higher rate of temperature increase then a large city with a stable or declining population.

  159. Just a couple points to make:

    Some rural areas exhibit much larger warming than would be indicated by population density because they host regional shopping districts. As an example, look at the Lebanon (New Hampshire) Regional Airport station data. Lebanon is a community of about 13,000 people over about 40 square miles. 40 sqmi x 2.6sqkm/sqmi = 104 sqkm, resulting in under 130 people per sqkm and about a 0.5 warming. However, immediately around the airport are about a dozen malls which are used by people from surrounding communities such that the number of shoppers on any given day is easily several times the towns population. These shoppers are idling in traffic as well as participating in other high-emissions activities within a half mile radius of the airport.

    This would obviously result in far more warming locally, however, since these persons live in the region, they are simply concentrating their emissions as close as possible to the airport weather station and amplifying the UHI. The airport’s temp records are then used to homogenize more rural records (i.e. where the shoppers are coming from) and you wind up with even more amplification of temperature records even tho the more rural communities the shoppers come from SHOULD enjoy less UHI for the duration of time those shoppers are in Lebanon and not at home….

    If you look at the Lebanon airport records you’ll likely find the beginning of a significant warming beginning in the early 1970′s when the malls started up. The population of Lebanon itself has been pretty stable, plus or minus a few thousand, for over a century, with a slump in the mid 20th century after the mills closed and many farms in the region were abandoned and turned back to forest.

    Thus it would probably be helpful to adjust for vehicle traffic patterns, with daily warming in regional hubs and nightly warming in rural areas as people return home after work/shopping.

  160. Iws:
    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 ?…Put another way: If we are looking for the effect of CO2 why include anything except primitive sites which remain primitive ?

    ======

    AMEN!

  161. 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.

  162. there are problems here.
    how about a temp reading in central park New york city? pop density is high!
    sprawl might have a higher temp because of malls, parking lots etc.

    Anthony found one at an air port that move from one end (UHI city) to the other end (rual ) and changed 5 degrees ? that is just a mile or so.

    mods feel free to add the link to WUWT on that moving station.
    Tim L.

  163. Pamela Gray (18:25:49) : “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.”

    Pam, I’m sitting here LMAO. I’ve been reading your posts and thinking, golly, this sure doesn’t sound like Pam. I wonder if she’s drunk…

    Go to bed! And don’t reread your posts tomorrow.

  164. Dave F (20:12:58) : “…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?”

    Possibly, Dave, but it’s considered statistically unlikely.

  165. There is one underlying assumption in previous studies that needs to be
    questioned.

    I’ll take GISS as an example. Rural was determined by a population bin
    of 5K for the nearest city. IN the US Nighlights was used and nightlights was supposed to be a proxy for population density.

    The assumption is this. A site that is rural today ( say pop 4K) has always been
    rural all the way back to 1880. But if a population growth from 100 ( say in 1880) to 4K today imposes warming then the assumption of rural today = rural yesterday is clearly not adequate.

  166. 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.

  167. I don’t think this analysis covers all UHI increases related to population growth.

    many cities grow in overall population by increasing their size but not by changing the population per square kilometer that much.

    this increase in size should also increase the UHI, but I don’t see this effect represented here.

  168. 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.

  169. Ivan (19:46:46) :

    If this work stands to scrutiny, its nice to have a peer reviewed paper on Urban Heat Effect, Ivan.

    If the Long paper was Peer Reviewed and stands to scrutiny too, and published in a paper, then it would be two papers, which together indicates what the warming has been in the US.

    Then you have Dr. Pinker’s paper that says something about how much Surface Solar irradiance has varied from 1983 to 2001.

    Its the revenge by proper Science, you know.

  170. 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.

  171. “sphaerica (15:12:17) :

    Correlation is not causation.

    Can anyone name a mechanism that would cause less densely populated areas to warm more?”

    Yeah…high-density areas are already “warmed” by UHI. Low-density areas are not. It’s harder to add more UHI to an area that already has higher levels of UHI.

  172. 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.

  173. Ivan – “That’s all fine and good. There is only one problem – what is the purpose of the entire exercise?”

    This is science. Look at problems/issues from different points of reference.
    Heck, there are still people to this day trying to find holes in E=MC2.

  174. 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?

  175. HIDE THE CODE HIDE THE CODE

    Heck, there are still people to this day trying to find holes in E=MC2.

    HIDE THE CODE HIDE THE CODE

  176. 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.

  177. As other people here have indicated, my guess is that low population density areas are very sensitive to microclimate issues (tarmac v. grass, nearby trees and buildings). Vegetation is cooled by transpiration but paved roads are basically night storage radiators. It’s now known that plants actively control their temperate because of the reaction rates of photosynthesis. Temperatures inside leaves can be tens of degrees cooler than the surrounding air.

    I would like to see a similar comparison done using energy use per km2 to see how the results compare with population densities. I don’t think there is a single right answer to UHI adjustments but Roy’s approach looks promising. Climategate means that GISS and CRU have lost their correlated duopoly on estimating temperature change and their approach will have to compete with others now.

  178. 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.

  179. 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.

  180. Yes GDP. Several posters have mentioned this as a potential correlate. A simple function such as Population Density x area GDP would create this weighting: High POP/High GDP > High POP/Low GDP and Low POP/High GDP > Low POP/Low GDP.

  181. 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.

  182. Wren (19:23:31) : You wrote:

    Why do you think Dr. Spencer isn’t implying that warming over time could be greatest in the rural areas?

    Isn’t that what he meant when he said –

    “Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities.”

    All cities were rural at one time or another. The UHI effect is greatest when rural stations transition to urban stations due to growth and development. Once that has taken place, additional growth and development adds an ever diminishing UHI effect. Spencer’s statement simply acknowledges that rural stations have a greater potential for additional, spurious warming from the UHI effect than cities do. That potential, however, is never realized if there is no development at the rural site. The potential in the cities has already been realized and must be calculated into the temperature record to discover any true climate change.

    Since we are looking for temperature trends over the last 100 years, the potential for spurious warming in the rural areas is not relevant. It is just potential. It may never actually be realized. The actual warming due to UHI is relevant and exists in the towns and cities, not in the persistent rural areas.

  183. Ivan (21:30:46) :

    Iws:
    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 ?…Put another way: If we are looking for the effect of CO2 why include anything except primitive sites which remain primitive ?

    ======

    AMEN!

    Double Amen.

    Also, I don’t care about the small “sample size.” I would rather have one truly good site per state than a thousand crappy sites.

  184. 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

  185. Thanks for posting this interesting proposal. I note you have found that the most significant effects are at stations with low population density, i.e. rural stations. I have 2 questions.
    1. Could it be that the term ‘UHI effect’, which was originally introduced in relation to larger cities, is no longer an appropriate term in this context? Do we now need a term such as ‘urbanisation effect’ or ‘built environment effect’?
    2. By always attempting to correct for ‘UHI effect’ (or ‘built environment effect’) is there not a risk of ignoring or playing down the significance of real local anthropogenic effects on climate? Working as I do in a park near the centre of a large city, I notice what seems to be a real year-round day and night temperature difference between the park and the surrounding built-up area. The park always seems cooler. This seems to me to be a real anthropogenic climate effect, which is part of global climate. Why does it have to be corrected away? Why not just accept it as it is?

  186. 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?

  187. Bad news. If its already been published (eg here – on-line), then a journal won’t publish it.

    Journals only publish what has not been published before.

  188. 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.

  189. Dr Spencer,

    Very interesting work.
    A couple of points caught my attention. Whilst a logarithmic function might fit the UHI up to a population density of 1500 to 2000 people/sq km, beyond that your data clearly shows a simple linear y=mx+c relationship. Moreover, the slope of this linear portion is very steep in the context of historical global warming. For example, from your graph, the UHI effect of 2000 people/sq km is, by eye, about 1.1 degrees C. At 7000 people/ sq km this rises to about 1.6 degrees C, a 0.5 degree difference. In the context of a 0.6 degree C twentieth century temperature rise, this is very significant. This is an important finding and it contradicts what Jones and others have to say on UHIs, namely, that there is little additional warming in older towns and cities, even though their populations will almost invariably have increased significantly over time.
    Another thought is that airport based sensors may skew the data comparison (satellite vs ground based thermometer) since a disproportionate number of land based temperatures are measured at airports since this data is needed by pilots. Even the largest airports have a zero population density, since nobody lives there. (I don’t know how SEDAC derive their population density data or how it is gridded but a large airport in a densely populated area, such as Heathrow has the potential to greatly reduce the mean population density of the grid in which it is contained)

    Andy

  190. 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?

  191. Thank you Dr Spencer for this interesting analysis. I would suggest some other ways to look at this.

    1) Is there a way to categorize land-use or type? For example, if the nearest 80% of the 11×11 km area adjacent to a site were forested for example, it may be possible to break the effects out. Interesting areas would be urban, agricultural, shoreline, mountainous, etc. There would be difficulties in the analysis and some overlap I suppose, but different pair comparisons might have something interesting pop out. Maybe each area could be classified in a useful way and a land-use parameter assigned and cross correlations developed.

    2) It would be interesting to see the same analysis applied to the 4 time of day observations before they are averaged.

    3) Can the pairs also be plotted in terms of latitude (average between the two points)? This may reveal something interesting.

    4) In the first graph, before the accum graphs, analyzing these separate groups may show other variables to attempt to control as you have with altitude. The variability tells me there are some factors not being controlled and possibly some weird data points with great influence. I’m confident you’re not using anything with the word “Yamal” in it, however.

    5) It would be interesting to do the same analysis on, say, 1950 or 1930 data.

    6) It would be interesting to see the effects on selected major urban areas (top 10), and then again on selected mid-scale cities (maybe one or two per region), then again for “best of the best” rural areas with good siting for a comparison of “accepted” temp trends versus temp trends adjusted for population growth per your analysis. How much of the accepted “signal” drops away in these areas? I suspect the analysis (since it is averaged over many pairs) might over-compensate the “best of best” stations.

    Great work, and thanks again for the opportunity to comment. I’d be happy to see what I can do with the data if you’re so inclined. I suspect some of the folks at the Air Vent might have a blast with this data as well.

  192. 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.

  193. 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

  194. I’m intrigued by the slope of the bias/population density curve at low populations too.

    I bet one big effect would be changes to the nighttime air inversion due to radiational cooling. Note this fits in nicely with the greatest UHI effects being at night.

    Radiational cooling inversions are typically very thin, only 10 meters or so. Their formation and dissipation are quite clear on my weather station plots. While I can spot them with just temperature data, temperature and wind makes it much easier. My station doesn’t include sky cover, that’s a vital component to readiational cooling.

    Adding some population and hence concrete structures to counteract radational cooling could result in big delays in forming the inversion.

    Further areas of study of overnight low temps that might yield interesting results include:

    Wind – if there is any wind at all there’s likely no inversion.

    Terrain – the inversion forms best in broad valleys. On hillsides radiational cooling manifests itself as a cool flow of air that flows down hillsides and mount stream valleys. My wife and I have take steps to counteract this on our mountainside property in New Hampshire. On plateaus an inversion can form, but it will try to drain off a bluff. While little like a plateau, the Concord New Hampshire airport is on a plain near the Merrimack River Valley, and they have dramatic radiational cooling. It doesn’t last long in the morning.

    Geology – The dry porous soil of the high deserts, e.g. around Flagstaff AZ allows dramatic cooling as the sun sets. OTOH, the high basalt walls in river valleys in eastern Oregon were not appreciated in the summer of 2003 as the basalt soaked up heat during the day and baked our camp sites at night.

    Unfortunately, most of the climate data we have is merely temperature and precipitation. However, much of what I suggest here can be studied with new observations, and very possibly with http://www.wunderground.com‘s network of private weather stations. ASOS sky data should be a useful proxy for sky cover for private stations in the area.

  195. 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.

  196. Dr. Spencer:
    One afterthought. I assume you are simply identifying a significant non-CO2 anthropogenic effect on the local temperature record? Population density is obviously a component of the aggregate UHI-effect but not the aggregate UHI effect itself.

  197. James Sexton, Wren,

    Dr. Spencer is working purely with population density, not change in population density. That is explicit throughout his post. Hence, this is pretty much an argument for the opposite of a UHI, or at best, has no impact on UHI as related to population growth. If anything, it suggests (as other studies have found) that temperature anomalies are hidden by, not exaggerated by, urban areas.

    I think a lot of readers are making this mistake, and assuming that the post has to do with what they expect it to address, i.e. changes in population.

  198. 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.

  199. David L. Hagen (06:48:48) :
    What I find interesting is that Carrot Eater agrees that Zeke has reproduced Mene 2010.
    Zeke’s analysis clearly shows that the Data has been manipulated to raise the Overall Trend.
    Enough Said.

  200. sphaerica (07:32:21)
    Clearly Dr Spencer is not measuring the population dynamics, but he has shown that as population density increases so does the temperature albeit at a declining rate. I do not see how you can possibly argue that “this is pretty much an argument for the opposite of a UHI, or at best, has no impact on UHI as related to population growth. ” What you might argue is that UHI is harder to detect from simple population numbers as the population density gets higher. However, this stems from the fact that population density captures but one component of UHI.

  201. This study is an attempt to quantify UHI only. It does not take in to account any possible bias introduced by the very few stations currently used by GISS, or their methods of arriving at a climate trend as illustrated by current studies being done by E.M. Smith, A. Watts, and others.

    I like the idea, expressed in a few posts, of taking T-min and T-max readings at various locations currently used by GISS, and spreading out from there in equal distances with a calibrated recording instrument, at for instance, two mile intervals, striving for the same altitude, equal distances from large bodies of water etc, wherever possible, progressively working toward a true rural reading, and then achieving an UHI estimate for the actual stations used by the advocates currently in charge of NASA GISS.

  202. Re. sphaerica (07:32:30) :…”actually temperature anomalies are higher, not lower, than reported as a result of UHI.”

    USA rural only data show just 0.1 deg C of warming in 20th century, and so if what you say is correct, and the rural areas actually have more UHI induced warming bias, then it is cooler then we thought.

    The reality is neither warmer or cooler can be stated at this time, and the advocates at GISS decide which stations to drop and add every year, and how to transpose those measurements to nearby dropped stations, and therefore any analisis has to consider time factors relative to what GISS has done, an almost impossible task, to an opaque “science” never intended to measure a planetary heat mean.

  203. I hold that global average surface temperatures whether heat islands or otherwise are not going to get us any closer to answering the big question. Other than producing interminable arguments about measurements, validity, analysis, and statistical methods.

    Global average surface temperatures was an atempt of the current theory to find warming and relate it to CO2. It is now discredited. We must stop data mining.

  204. If an ever increasing number of stations used by GISS, move to an area where the anomaly is increasing, new and or expanded airports, then the transposed numbers to the dropped statons will show an increased trend.

  205. Also airports tend to be built in low flat areas surrounded by higher land, and sheltered from the wind compared to higher elevations. This kind of location, particularly in an urban setting, dramaticaly increases the UHI effect, especially at night.

  206. “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.”

    A matter of vertical surface area?
    In 1900, our big farm house for few people was deliberately built for maximum winter heat gain, surface area/ sun exposure, as were many others. The north side is flat, south side convoluted, west flat and shaded. No insulation. Adding a garage, a fence, limiting the breezes, made a large part of the yard a solar greenhouse in itself. (One would think solar panels would be bumpy, if this line of thought held water, though I guess they are on the insides.) I can’t calculate the effects of added shade though. I can usually (not this year, no sun) grow February strawberries on the south side while keeping the beer cold in the leftover snow piles on the north side.

    High density housing just gets denser per unit. Here people have room to add outbuildings and sprawling semi-rural subdivisions.

    The archives here always baffle me, but there was a fascinating old topic with loads of photos, showing the unexpectedly high infra red camera temperatures of buildings, far surpassing the rated” tolerances of roofing materials, for instance. This was not about heat loss, it pertained to the exposed surfaces of buildings, vertical, slanted, flat etc absorbing heat from the sun. Can’t find it.

  207. Re sphaerica (07:32:30) :

    sphaerica, you also need to consider that large areas of the world (The BRIC countries for instance) are rapidly growing, as compared to the US and europe, and therefore are showing far more warming, as the UHI effect would be in a stronger part of the curve; look up studies on UHI effect in China for instance, which show about 1 C per century effect to the trend as measured in China. This may be a large part of the reason the US data shows very little warming compared to the global average.

  208. Sir, your method of reporting and the statistical treatment of errors of measurements needs elaboration.

  209. Hugh Roper (05:21:04) :
    …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.

    Well said, and in case there is any misunderstanding by the other folks here the land use is seen as a negative forcing (~.2 W/m^2) by the IPCC*

    The explanation for this, as I understand it, is that replacing forests with crops (deforestation) increases albedo by roughly 10% (7-14% to 15-25%, ref*) – I am not sure on the details here, but I suspect that their storyline might be that UHI has already been adjusted out of the temperature records so there’s no need to include it in their forcings. It could also be that they included it in the land use assessment and the deforestation effect simply outweighs any UHI… so the Net land is still negative.

    Putting concrete, structures, etc aside for a moment… the delta for power consumption between 1965 and 2006 is 10.8 TW, with total consumption in 2006 of 15.8 TW. A significant chunk of that energy is heat loss – take the internal combustion engine. It is primarily a heat engine, it creates usable power almost as a side-product (18-20% average efficiency). This means that of the ~5 TW worth of Oil we use, 4 TW is directly lost to heat. Electric power generation (66% loss for coal and nuclear), transmission (7.2%) and usage also exhibit heat loss due to inefficiency on top of direct effects like Air Conditioning.

    I think that for a truly accurate assessment of UHI forcing, energy consumption patterns (which, FWIW, I do not believe can be accurately correlated with GDP – ref*) need to be factored in, and I believe that energy patterns could be used to develop a very accurate forcing assessment for this piece of UHI

    Note: land use, structures, roads, vegetation etc would still need to be addressed in addition to energy usage patterns… what I’m arguing here is that I think it should be addressed separately.

    Now, that being said, what I have been talking about is a fundamentally different idea and approach than Dr. Spencer is doing here. I asked him earlier if there was an discernible difference region to region with his assessment – I think there probably is under the covers – but all-in-all his approach is valid and certainly adequate if not exceeding where climate science is today: which in Economics terms is very macro, not very micro.

    That’s my $0.02 at least

  210. @Hugh Roper re: deforestation
    Replacing forests with crops most certainly does not increase albedo. I live in a bush lot on the edge of a farm field, I can observe heat shimmering off a ripened wheat field or freshly plowed black earth, I can walk from the shade of the trees into the noticeably warmer green corn field, agriculturization has as great effect on surface temperatures as UHI. Who is studying agriculture heat effect AHE ? Driving from my bush lot to my brothers country home in an open lot in summer I get 2+ degrees C warmer in the open rural lot.

  211. @ sphaerica (07:32:21) :

    “Dr. Spencer is working purely with population density, not change in population density. … I think a lot of readers are making this mistake, and assuming that the post has to do with what they expect it to address, i.e. changes in population.”

    Do you mean you assume that “a lot of readers” have ignored, like you apparently did, the clearly stated fact that Spencer’s figures are based on data from a single year? Please explain:

    1) what kind of “change in population density” during that single year you would like Spencer to include in his calculations, and

    2) why his results, especially the graph relating pop.density to warming bias, would not apply in ONE location at DIFFERENT times, even though they were calculated by comparing DIFFERENT locations at the SAME time.

    As I read the graph, a place whose pop.density was e.g. 1,000 when readings started, gradually increasing to 7,000 today, would have produced temperature readings running hot by 1.5 deg.C at the start, with this bias increasing to 2.2 deg.C today – i.e. a spurious warming trend of 2.2 – 1.5 = 0.7 deg.C . Am I misunderstanding this?

  212. Chris Wright (02:56:25) :

    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?
    Could not agree more! I have been analysing data for Sydney and a smaller city just 100km away, Newcastle. Up until the 1960′s Sydney minus Newcastle was negative. From then the difference grew positive but has plateaued in the last 10 years. Both show a warming that really starts in the 60′s when the skyline of Sydney was undergoing rapid change. Also I looked at rural stations Dubbo and Cessnock both within 500km of Sydney and with data going back to early 20th century. The rural stations show no warming and using the favourite snake oil ingredient of climate science; the linear trend, they show a slight cooling. It is my belief that yes, we have been warming but it is all UHI. Everything is true about warmest decade etc but is UHI not CO2.

  213. I was somewhat surprised to read in the Chicago Tribune that placing weather stations near incinerators, air conditioners and hot jet exhaust actually cools them.

    It seems to be more along the lines of hot is cold and cold is hot, a testament to the warmists’ unique ability to hold two contradictory thoughts in their heads at the same time.

    http://www.chicagotribune.com/news/ct-met-0228-climate-science-questions-20100302,0,2670932.story

    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.

  214. Sorry if I’m derailing here, but for grins I did some back of the envelope calculations regarding forcing from energy use:

    Urban Areas accounts for 1.5% of the earth’s land surface, in 2006 total power consumption was 1.58 TW, assuming an average use efficiency of 33% (meaning 66% loss due to heat), and assuming 50% of the usage is in said urban areas (50% of people live in urban areas as of 2008)… we are talking about a forcing in urban areas of 2.37 W/m^2. There would also be a forcing of 5.29 TW spread out among non-urban inhabited land, but unfortunately I could not find any way to quantify what sort of land mass that might be so no way to translate (just yet) to W/m^2.

    This does not include any effect for land use (concrete, roads, structures, etc) – we are just talking about waste heat from power consumption. I think it’s also important to note that there was 300% increase in power use from 1965-2005 (5 TW to 15 TW).

    This is a VERY rough/sophomoric attempt, does anyone know of anyone who has done real research from this perspective?

  215. That’s fascinating, but I’m wondering if you can really compare the “heat island” effect that a city has based simply on population. Would a city in a warm region have the same kind of impact on temperature readings as a cooler city?

    For example, would the same method for calculating the warming bias of, lets say, a station in Lima Peru be applicable to a station in St. Petersburg Russia? Or is it possible that the warming bias is completely different?

    Also, does a heavily populated area that is primarily residential and commercial have the same effect as an area that is of the same density but has more heavy industry?

    You’d really have to look at a huge number of stations to make sure that these kinds of issues were not clouding things.

  216. sphaerica (15:12:17) :

    “Correlation is not causation.

    Can anyone name a mechanism that would cause less densely populated areas to warm more?”

    Sure, I grew up in a farming community, we had cow pastures, corn fields and broad leaf tobacco fields.

    The tempuratures in the summer in the cow pastures was ‘warm’, in the corn fields ‘sweltering’ and in the broad leaf tobacco fields ‘unbearable’.

  217. I’ve been over the description of the procedure again and again. I don’t get it. The values on the x-axis of the graphs don’t match the values you claimed you used for bins. e.g., the last value on each of the graphs has an x-axis value of about 6900. Yet no combination of the bin sizes you gave can produce a value of 6900.

    I echo the sentiments of many others: you need to describe the procedure you used much better.

    And the relationship between the first and the second graph? Do you REALLY use the data from the first graph to produce the data from the second graph? And if so why? Why not just directly calculate the values on the second graph.

    I can see that each of the differences between bin produces a unique point on the graph; so it is conceivably possible to use the data from the first graph to produce the second graph, by virtue of the fact that any pair of bin values produces a unique value on the graph. My concern, though, is you have log-linear bins, peform a very complex operation from a statistical perspectifve on the data, and produce a log-linear output. Is it real? I can’t tell.

    If your point is that there’s no relationship between temperature difference, then just say so, and move on to the correct calculation.

    The correct calcuation, surely, is to calculate the warming-relative-to-zero calculation before binning the data.

  218. Oh gee. And how DO you perform the warming-relative-to-zero calculation?! IT would be odd to use a linear projection, and then produce a result that you shouldn’t have used a linear projection, becaue the correct relationship is log-linear.

    THere’s something terribly not right here. And you don’t give enough info to tell what it is.

  219. sphaerica (15:12:17)

    I guess another trend in the last 50 years, which hasn’t been discussed to my knowledge specific to climate, is a decreasing average number of people in a household (for the US at least – ref*), falling from 3.6 in 1940 to 2.57 in 2003 so for the same population we’re talking about a 30% increase in number of homes since the 40′s, but back on topic…

    There are 247 acres in a km^2, so by the time you have 635 people in a km^2 (going off US numbers, not accounting for streets) you should have at least a home on every acre. Typical suburban density is probably 4 times that, but I would imagine that with a relatively low density (at least 1 house per 2 acres) you’d have a significant footprint of roads – which could probably account for a good portion of the early increase.

  220. Can I suggest the next effort be a look at GDP (and/or other economic metrics) as proxies for UHI?

    If the proposed mechanism for UHI is urban development, above basic heating, then population growth in a third world shanty town on a city’s outskirts may not deserve equal weighting with a first world apartment blocks, car parks and factories etc…

    A larger economy, a higher GDP, should translate to higher levels of development and energy use, and perhaps, a better proxy for UHI.

    It may also be more accurate to look at particularly energy intensive and heat retaining economic activities: Manufacturing, housing/property development, electricity use, fuel use (coal, oil, gas, et al), etc…

    And considering asphalt seems to be a principle UHI culprit, what about the area of sealed roads to UHI? Or sales/use of asphalt to UHI?

    A large proportion (i.e ~2/3rds) of economic activity in first world economies are in the service industries, which would be less energy intensive, although may still serve as useful proxies for levels of property development affecting UHI.

    The main problem seems to be an accurate and long record of such economic data, and, perhaps more fatally, regional (as opposed to national or state) records of economic metrics, to compare to regional temperature records. But some of the metrics will be available for some regions, and it would be interesting to see if a correlation exists where a comparison can be made.

  221. wakeupmaggy (08:27:10) : writes
    “… there was a fascinating old topic with loads of photos, showing the unexpectedly high infra red camera temperatures of buildings, far surpassing the rated” tolerances of roofing materials, for instance. This was not about heat loss, it pertained to the exposed surfaces of buildings, vertical, slanted, flat etc absorbing heat from the sun. Can’t find it.”

    Hi wakeupmaggy!
    Try http://www.thermoguy.com/globalwarming-heatgain.html
    The content has changed a bit, but the advertiser may still have the old material. It is still instructive.
    Hugh

  222. I am a nit picker. I would really like to have a look see at the micro “climate” and macro “climate” around each of the chosen sites. After Anthony’s surface station survey I think this would be a very important second pass for the data now that the stations of interest have been identified.

    Is the “rural” stations sitting in a farmer’s pasture, bare ground cropland or next to someone’s barbque? Is the “rural” small town station sitting in a parking lot next to the apartment’s heat exchanger unit? Is the “city station” sited on the dairy farm sitting in the middle of the city of Rochester?

    These type of questions need to be answered to make the study robust and given Anthony’s corp of volunteers the answers could be found for most of the stations.

  223. So, to conclude we haven’t seen any explanation from dr Spencer whatsoever as to why is it more appropriate when analyzing the USA data to try to “correct” Jones or NCDC data-sets using various mathematical techniques, rather than to simply compare the rural and urban trends? There might be too little rural stations with the long record in other countries (as dr Spencer notes), but there are plenty of them in the USA.

    So, dr Spencer, what do you think about dr Long’s finding that rural warming in the USA 48 was only 0.1 deg C during 20the century and 3 times lower than UAH 48 trend 1979-2009? Are we going to have any answer to that?

  224. The argument invoked both by dr Spencer on his answers section on his blog, as well as by some commentators here, that rural stations have a larger potential warming bias is in my opinion a red herring, since we are talking about the rural stations that have never grown whatsoever, so did not have any warming bias at all. As dr Long had shown they had 6 times lower trend than urban ones.

    If method proposed by dr Spencer produces just a little bit lower temperature trend for the USA than Jones finds, (and 5 times higher than the rural trend) then it is highly questionable of what use that same method of correction could be if applied to other countries? Why would anyone believe it would produce any better results there?

  225. I know I have come a bit late to this discussion, but this analysis appears to have a serious flaw – basically a warm bias. Unless I have misunderstood, Dr Spencer uses station pairs to calculate warming per population density incease against the 2-station pair population density average. He then integrates the area under this curve to give the UHI (or station warm bias against population density – I will call this the ‘UHI plot’). If the UHI in reality has the logarithmic profile (or decreasing gradient at higher populations) expected then this process must lead to an overestimate of UHI.

    Here is the problem, estimates of the warming per population density increase at a 2-station average population of 1000 (say) could include station pairs (2000,0), (1500,500),(1100,900) etc. Reading off Dr Spencer’s UHI plot gives estimates of warming per population density increase of 0.55, 0.3, 0.2degC/1000 respectively. ie reducing with reducing population spread. All will be overestimates of the true graph gradient at that point if the logarithmic shape holds.

    If you don’t believe me, a simple test would be to take example station pairs from a ‘model’ with UHI-population relationship given by the Dr Spencer’s UHI plot. Run the analysis on these and see if you return to the same population density-temperature bias relationship. I would predict a bias towards overestimate of UHI. If the method cannot return the correct result from such a basic test then its usefulness must be in serious doubt.

  226. The should be some contolling for geography. Population density correlates with proximity to oceans and seas and away from elevation. Without controlling for geography in some way, the question is left whether you have found a correlation with population density or merely that both your variables are dependent on the same true independent variable, geography.

  227. Am probably missing something, but here goes: Let N(t) be population size in year t and [CO2]atm(t), atmospheric concentration of CO2, likewise in year t. Dr. Spencer’s analysis identifies log N(t) as a critical variable. Why not take the next step and perform stepwise multiple regression against log N(t), [CO2]atm(t)? That would tell us how much of the variance in temperature can be accounted for by log N, and, of what remains, how much is due to atmospheric carbon vs. the passage of time. Significant dependence of warming on the last could reflect several factors – among them: 1. increasing per capita heat generation resulting from greater power consumption / heat production (a likely possibility in the desert southwest as swamp coolers give way to air conditioners) 2. regional changes in land use, including greater extensiveness of individual UHIs. 3. long-term climatal changes, e.g., post-LIAA recovery, that are presently poorly understood.

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