I believe this is a truly important piece of work. I hope Dr. Spencer will submit it to a journal. I’m grateful to Dr. Spencer for his email suggesting I post it here. Consider this early peer review. Beat it up, find any errors, and point out flaws, so that he can make it better. – Anthony
by Roy W. Spencer, Ph. D.
UPDATED (12:30 p.m. CST, March 3): Appended new discussion & plots showing importance of how low-population density stations are handled.
ABSTRACT
Global hourly surface temperature observations and 1 km resolution population density data for the year 2000 are used together to quantify the average urban heat island (UHI) effect. While the rate of warming with population increase is the greatest at the lowest population densities, some warming continues with population increases even for densely populated cities. Statistics like those presented here could be used to correct the surface temperature record for spurious warming caused by the UHI effect, providing better estimates of temperature trends.
METHOD
Using NOAA’s International Surface Hourly (ISH) weather data from around the world during 2000, I computed daily, monthly, and then 1-year average temperatures for each weather station. For a station to be used, a daily average temperature computation required the 4 synoptic temperature observations at 00, 06, 12, and 18 UTC; a monthly average required at least 20 good days per month; and a yearly average required all 12 months.
For each of those weather station locations I also stored the average population density from the 1 km gridded global population density data archived at the Socioeconomic Data and Applications Center (SEDAC).
All station pairs within 150 km of each other had their 1-year average difference in temperature related to their difference in population. Averaging of these station pairs’ results was done in 10 population bins each for Station1 and Station2, with bin boundaries at 0, 20, 50, 100, 200, 400, 800, 1600, 3200, 6400, and 50000 persons per sq. km.
Because some stations are located next to large water bodies, I used an old USAF 1/6 deg lat/lon percent water coverage dataset to ensure that there was no more than a 20% difference in the percent water coverage between the two stations in each match-up. (I believe this water coverage dataset is no longer publicly available).
Elevation effects were estimated by regressing station pair temperature differences against station elevation differences, which yielded a cooling rate of 5.4 deg. C per km increase in station elevation. Then, all station temperatures were adjusted to sea level (0 km elevation) with this relationship.
After all screening, a total of 10,307 unique station pairs were accepted for analysis from 2000.
RESULTS & DISCUSSION
The following graph shows the average rate of warming with population density increase (vertical axis), as a function of the average populations of the station pairs. Each data point represents a population bin average for the intersection of a higher population station with its lower-population station mate.
Using the data in the above graph, we can now compute average cumulative warming from a population density of zero, the results of which are shown in the next graph. [Note that this step would be unnecessary if every populated station location had a zero-population station nearby. In that case, it would be much easier to compute the average warming associated with a population density increase.]
This graph shows that the most rapid rate of warming with population increase is at the lowest population densities. The non-linear relationship is not a new discovery, as it has been noted by previous researchers who found an approximate logarithmic dependence of warming on population.
Significantly, this means that monitoring long-term warming at more rural stations could have greater spurious warming than monitoring in the cities. For instance, a population increase from 0 to 20 people per sq. km gives a warming of +0.22 deg C, but for a densely populated location having 1,000 people per sq. km, it takes an additional 1,500 people (to 2,500 people per sq. km) to get the same 0.22 deg. C warming. (Of course, if one can find stations whose environment has not changed at all, that would be the preferred situation.)
Since this analysis used only 1 year of data, other years could be examined to see how robust the above relationship is. Also, since there are gridded population data for 1990, 2000, and 2010 (estimated), one could examine whether there is any indication of the temperature-population relationship changing over time.
This is the type of information which I can envision being used to adjust station temperatures throughout the historical record, even as stations come, go, and move. As mentioned above, the elevation adjustment for individual stations can be done fairly easily, and the population adjustments could then be done without having to inter-calibrate stations.
Such adjustments help to maximize the number of stations used in temperature trend analysis, rather than simply throwing the data out. Note that the philosophy here is not to provide the best adjustments for each station individually, but to do adjustments for spurious effects which, when averaged over all stations, will remove the effect when averaged over all stations. This ensures simplicity and reproducibility of the analysis.
UPDATE:
The above results are quite sensitive to how the stations with very low population densities are handled. I’ve recomputed the above results by adding a single data point representing 724 more station pairs where BOTH stations are within the lowest population density category: 0 to 20 people per sq. km. This increases the signal of warming at low population densities, from the previously mentioned +0.22 deg C warming from zero to 20 people per sq. km, to +0.77 deg. C of warming.
This is over a factor of 3 more warming from 0 to 20 persons per sq. km with the additional data. This is important because most weather observation sites have relatively low population densities: in my dataset, I find that one-half of all stations have population densities below 100 persons per sq. km. The following plot zooms in on the lower left corner of the previous plot so you can better see the warming at the lowest population densities.
Clearly, any UHI adjustments to past thermometer data will depend upon how the UHI effect is quantified at these very low population densities.
Also, since I didn’t mention it earlier, I should clarify that population density is just an accessible index that is presumed to be related to how much the environment around the thermometer site has been modified over time, by replacing vegetation with manmade structures. Population density is not expected to always be a good index of this modification — for instance, population densities at large airports can be expected to be low, but the surrounding runway surfaces and airplane traffic can be expected to cause considerable spurious warming, much more than would be expected for their population density.
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.
…………..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.
“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.
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 :).
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.
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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.”
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 ?
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.
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
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.
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.
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.
“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.
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.
Well, Dr. Spencer, you have had great critiques of your remarkable research here on WUWT. While I am not knowledgeable enough to contribute, one comment seems useful:
Jim Clarke (18:23:56) :
Dr. Spencer’s method appears reasonable and, equally importantly, doable. It gives us a number to adjust the temperature of any area with a changing population over time; a variable that is generally well known. Will it be perfect. No, but on average it will be valuable. It certainly appears more ‘robust’ than the now discredited Jones et al study that was done back around 1990.
Maybe 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.
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?
This is exactly the kind of detailed, documented scientific work that we need, to counter the increasingly thread-bare ”the science is settled” mantra.
“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.
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
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
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!
Much of the global warming is in areas outside the U.S., areas that I think look pretty rural on the map:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2010&month_last=1&sat=4&sst=0&type=anoms&mean_gen=
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.
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.
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 ?
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AMEN!