A couple of years ago, before I got involved in proxy studies, I was interested in the UHI question and wrote to Phil Jones to request the data used in Jones et al 1990, his study purporting to show the unimportance of urban warming. Jones said that it was on a diskette somewhere and too hard to find, Jones observing that the study had been superceded by other studies. (“Moved on” ?) Anyway, that was before I was wise to the ways of the Team and I didn’t pursue the matter.
However, Jones et al 1990 continues to be relied upon; it’s cited in recent literature and in AR4. So I thought that it would be interesting to re-visit the matter. I still don’t know what sites were used, but something turned up anyway.
Peterson 2003 reviewed literature on UHI and reported that, even in 2003, Jones et al 1990 was one of only two studies that used”homogeneous” data:
Only two large-scale studies were found that used homogeneous data. These are the time series analyses of Peterson et al. (1999) and the Russian and Chinese regions of the analyses presented in Jones et al. (1990). These analyses found no indication of significant urban influence on the temperature signal.
Elsewhere in the article, Peterson 2003 stated:
Jones et al. (1990) determined that the impact of urbanization on hemispheric temperature time series was, at most, 0.058 deg C century-1. This result was based on the work of Karl et al. (1988) for the United States and further analysis of three other regions: European parts of the Soviet Union, eastern Australia, and eastern China. In none of these three regions was there any indication of significant urban influence in either of the two gridded time series relative to the rural series’ (Jones et al. 1990). The homogeneity assessments varied with regions. The data for one region were assessed for artifacts due to factors such as site moves or changing methods used to calculate monthly mean temperatures.’ Another region used data from stations with few, if any, changes in instrumentation, location or observation times.’ The homogeneity of data used in the third region was not discussed. Their results showed that the urbanization influence is, at most, an order of magnitude less than the warming seen on a century scale.’
IPCC 4AR cited Jones et al 1990 twice as follows:
Urbanization impacts on global and hemispheric temperature trends (Karl et al., 1988; Jones et al., 1990; Easterling et al., 1997; Peterson, 2003; Parker, 2004, 2006) have been found to be small.
and:
Many local studies have demonstrated that the microclimate within cities is on average warmer, with smaller DTR than if the city were not there. However, the key issue from a climate change standpoint is whether urban-affected temperature records have significantly biased large-scale temporal trends. Studies that have looked at hemispheric and global scales conclude that any urban-related trend is an order of magnitude smaller than decadal and longer timescale trends evident in the series (e.g., Jones et al., 1990), a result that could partly be attributed to the omission from the gridded dataset of a small number of sites (<1%) with clear urban-related warming trends.
So Jones et al 1990 is still relied on in the literature. Jones said that they selected 38 non-urban sites in western Russia (the sites are not identified) and that in the 1930-1987 period, there was a cooling of about 0.2 deg C in the rural stations (RUSSR), as compared with a lesser cooling of ~0.1 deg C in the Jones network (JUSSR):
For the western part of the Soviet Union we selected a network of 38 stations from sites in non-urbanized areas with long records (Figure 1a). The sites include isolated meteorological stations, lighthouses, villages and other small settlements. The largest populated sites are nine towns with populations of the order of 10,000 people. All nine towns are located at least 80 km away from major cities. All the site records were assessed for artifacts due to factors such as site moves or changing methods used to calculate monthly mean temperatures. At 12 sites the observing station was moved slightly. Comparisons with neighboring sites were made before and after each change and ,w here necessary, corrections were made to ensure homogeneity of the rural station record, No corrections were deemed necessary for the remaining 26 stations where no station moves were reported.Over the 1930-87 period, a cooling of about ~0.2 deg C is observed in RUSSR. This cooling is about 0.1 deg C smaller in JUSSR but there are no statistically significant differences between the two series.
Jones also says that 60 station records were used to construct the gridpoint series, of which 25 stations in operation by 1901 and 32 were operating in 1987 and that there were 4 stations common to the gridpoint and rural time series. In the caption to Figure 2, he also says of the 38-site rural network that 20 were contributing in 1901; that 7 began recording after 1930 and that the number of missing values from 1930-1987 was 8% of the total. In addition to ~0.09 cooling trend in the Jones network from 1930 to 1987, he also reported (Table 1) that there was a 0.31 deg C upward trend from 1901-1987.
Their Figure 2a shows a plot of the temperature anomaly from their rural network (the comparandum Jones network plot is not shown). For comparison to their Figure 2a (shown a bit lower down), I made up a network of 41 HadCRUT3 gridpoints (this is a 5×5 network, while Jones used a 5×10 network). The network is shown here and can be compared to Jones et al 1990 Figure 1a.
Figure 1. HAdCRUT3 Grid selected to approximate 22 gridpoint-grid in Jones et al Nature 1990 Figure 1a.
Next here are plots showing first the rural network from Jones et al 1990 and then the annual average of the 41 gridpoints identified above for 1901-1988, showing the 1930-1987 trendline. The appearance of the two plots is similar. Jones et al say that their figure is for 1901-1987, but the closing uptick occurs in 1988 in HadCRUT3 rather than 1987. Maybe Jones actually plotted to 1988 – who knows?
The HadCRUT3 linear trend is 0.24 deg C over the period 1930-1987, as compared to a reported -0.09 deg C cooling in the Jones et al 1990 gridded series (and -0.21 deg C in the rural series). So there is 0.45 deg C difference in the 1930-1987 period between HadCRUT3 and the rural network used to show that the difference is less than 0.05 deg C per century. Since the Team is involved, one also has to pay attention to little discrepancies like 1987 versus 1988 endpoints – why would they calculate the trend on 1987 rather than 1988 if 1988 is illustrated in the plot? With the addition of 1988, the linear trend increases to 0.30 deg C over the period, increasing the discrepancy to 0.51 deg C over 1931-1987 between the rural network and HadCRUT3 – as compared to the reported 0.09 deg C in Jones et al 1990.
Figure 2. Top- clipped from Jones et al 1990 Figure 2a showing rural nework; bottom – calculated West Russian network annual average using HadCRUT3 gridcell.
Over the 1901-1987 period, the estimated linear trend in the HAdCRUT3 network is 0.44 deg C (1901-1988: 0.49 deg C), as compared with a reported linear trend of 0.38. This difference between versions is much less than the 1930-1987 difference. It’s hard to figure out exactly what’s going on here, as long as Jones refuses to identify the stations or release the data. Despite the many citations, it doesn’t appear that anyone, including the IPCC, has ever tried to directly verify these results. Does this study still stand for the proposition that UHI effects have been shown to be inconsequential? Well, the Coordinating Lead Author of this AR4 chapter was, um, Phil Jones. No one ever said that the Team needed a big locker room.
References:
Thomas C. Peterson, 2003. Assessment of Urban Versus Rural In Situ Surface Temperatures in the Contiguous United States: No Difference Found. Journal of Climate Volume 16, Issue 18 (September 2003) pp. 2941—2959 http://www.ncdc.noaa.gov/oa/wmo/ccl/rural-urban.pdf
Jones, P.D., et al., 1990: Assessment of urbanization effects in time series of surface air temperature over land. Nature, 347, 169—172.
Warwick Hughes, http://www.warwickhughes.com/papers/90lettnat.htm
Climate Audit 
99 Comments
Code for these graphics and statistics:
##USSR NETWORK IN JONES ET AL 1990
#for Jones et al Nature 1990 on urban warming
#MAKE USSR MASK
library(fields)
ussrtest<-cbind( c(t( array(rep( seq(47.5,67.5,5),10) ,dim=c(5,9)) )), rep( seq(32.5,72.5,5),5) )
i<- 37+(ussrtest[,2]-2.5)/5;j<- 19+(ussrtest[,1]-2.5)/5
temp<-rep(TRUE,nrow(ussrtest))
temp[42:45]<-FALSE
#PLOT USSR GRIDPOINTS
plot(xy.coords(ussrtest[,2],ussrtest[,1]),pch=".",type="p",xlim=c(25,82),ylim=c(40,77),ylab="")
world(xlim=c(30,90),ylim=c(30,75),add=TRUE)
points(xy.coords(ussrtest[temp,2],ussrtest[temp,1]),col="red",pch=19)
sum(temp)
##LOAD HADCRUT3 DATA SET
library(ncdf)
url<-"d:/climate/data/jones/hadcrut3" #previously downloaded
loc<-file.path(url,"HadCRUT3.nc" ) #
v<-open.ncdf(loc)
instr <- get.var.ncdf( v, v$var[[1]]) # 1850 2006
dim(instr)# [1] 72 36 1883
#this is organized in 72 longitudes from -177.5 to 177.5 and 36 latitudes from -87.5 to 87.5
K<-sum(temp)
test<- array(NA,dim=c(1883,K))
for (k in 1:K) {
test[,k]<-instr[i[temp][k],j[temp][k],]
}
dim(test)# 1883 41
cru<-apply(test,1,mean,na.rm=TRUE)
cru<- array(c(cru,NA),dim=c(12,1884/12) )
annual<-ts(apply(cru,2,mean,na.rm=TRUE) ,start=1850)
mean(annual[(1951:1980)-1849]) #[1] -0.05615611
mean(annual[(1961:1990)-1849]) #[1] -0.05989007
annual<-annual-mean(annual[(1951:1980)-1849])
#PLOT REPLICATED ANNUAL SERIES
par(mar=c(3,3,1,1))
plot(1901:1987,annual[(1901:1988)-1849],type="s",xlab="",ylab="",las=1)
year<-1930:1987
fm<-lm(annual[year-1849]~year);summary(fm)
lines(year,fm$fitted.values,col="red");abline(h=0,lty=2)
#CALCULATE LINEAR TRENDS OVER PERIODS
coef(fm)[2]*57 #0.2413054
year<-1930:1988;fm<-lm(annual[year-1849]~year);coef(fm)[2]*57 #0.30
year<-1901:1987;fm<-lm(annual[year-1849]~year);coef(fm)[2]*87 #0.44
year<-1901:1988;fm<-lm(annual[year-1849]~year);coef(fm)[2]*87 #0.49
Do we need to be careful? Wasn’t there also a problem with this data during the Soviet era because money from Moscow was based on cold temperatures and the numbers were inflated (deflated) to enhance the return?
The more I read, the more it seems that the new area of dubious science is far more fundimentatl than proxy temperatures,
it seems we are not even sure we know real, contemporary temperatures. Leaving aside what this means to calibrate the
past against an unknown present, how does this affect all sorts of assumptions about the degree and responses of climate
warming?
What do we know, and how do we know it is a fundimental question – oh well, perhaps it can be “adjusted” by the Hocky Team.
The meteorlogical station data was never meant to measure a global mean temperature. And through
much manipulation it apparently has become such. Methodologies appear ad hoc and data
is lost or missing or overwritten. What the heck is going on here?!
The assumptions being made about past, present and future temperature trends are
dependent upon this data.
I’ve always been skeptical of these data sets and so should all scientists. We are
taking the individual micro climate of each station and extrapolating the data
to cover many many square km’s of the earths surface based on statistical manipulation.
I’m a 20yr in the field meteorologist and just shrug my shoulders at all of the claims being
made about the data, the future etc…when in fact we cannot even rely on what we are
claiming to be solid evidence in the first place. I am not denying the trend of the ambiguous data sets
are in the positive over the last century or so, but the magnitude is unknown…
ok, how about .5C? Heck, I dunno…go ask Jones and Hansen…they seem to.
Frustrating.
I understand that a very significant fraction of the Soviet weather stations stopped recording data after the fall of the Soviet Union. There is potential for a bias between these two eras as well.
Boy, those nearly decadal cycles in temperature sure resemble Solar cycles…
So all you have to do to make data homogeneous is perform some adjustments? LOL.
I really have a hard time accepting that there are no significant UHI or land use change effects on temperature. It defies common sense, as well as many other studies.
As I have said before, I’m amazed that so many scientists and the IPCC blindly accept Jones’ analyses, especially when he refuses to provide data and methodology. They continue to make their science less and less credible.
It would seem that the UHI effect could be studied without relying on old records. Has anyone just taken measurements from a high density grid around some cities and looked at the variation as a function of distance from the city?
#7. There are lots of analyses claiming to show a UHI effect. Merely google urban heat island. It’s just that Peterson and Jones and Easterling and Karl deny that this proves anything and the IPCC accepts these studies.
The arguments from Jones, etc. are essentially statistical arguments; that’s one reason why I’d like to see the data. The amusing thing about this Russian data is that Jones’ results are not replicable using HadCRUT3 data. But hey, they’re the Team. If their results can’t be replicated, well, they’ve “moved on”.
#2 Yes, we should be careful about Soviet data from the more remote areas. But it seems better to use it and cite reservations than to adjust and henceforth use the new ‘facts’.
Which brings me to #3
I pretty much agree. It looks as if the data is simply not sufficient to resolve this GW quandry. Able people on each side cite arguments – some very strong – for uncertainty or bias in any data they find disagreeable.
This is why I think Steve M. pursues the best method – insist on openness so that all work can be examined and, if needed, examined again. That, at least, finds procedural weaknesses and outright mistakes.
IMO, measurements and work in the next five to ten years will produce more clarity than everything done to date.
Looking at the Jones rural plot, it sure looks to me like there is an upward trend in the data — it looks to be about .4 degrees over the period. Something isn’t right here.
Joe d’Aleo sent me a nice chart showing global station numbers
and estimated global temperatures — I believe from GHCN. In an
amazing coincidence, temperatures went up just when the station numbers
dropped precipitously (again, mostly because of USSR dropouts). The axes
aren’t labeled, but it’s easy to see which is which.
Here:
Gosh, can’t get the link tool to work. Tried to paste the image into
the message. I guess I’ll just have to point to the link:
#7, 8. How hard would it be to do a rough UHI analysis from available data? Say, by calculating mean temperature within latitude bands for the US including/excluding data from weather stations within 25km of major population centres. I’d be very curious to see the results.
Re #7,
I have recently acquired the hardware to actually measure UHI by using a vehicle and traversing some country towns in SW West Australila. It is one of those jobs that are scheduled once time is found to do it.
Right now I have the temperature logger logging static temps in one spot to get an idea of what the diurnal change is, (all data is down loaded onto the PC into the logger software).
I have a gut feeling that I need another temp logger as a static base station so that the diurnal drift can be independently logged over time while the survey logger goes out and does it UHI measurement. (standard geophysical technique in which the base station signal is subtracted from the roving one to remove diurnal data drift – and so far the drift is anything but linear, from visual inspection).
So the project is in design stage until the second logger is acquired. At the same time a specific GPS logger will locate the position of the temp probe.
I have 3 months in Perth to do this, among other things, and I’ll send Steve the raw data once it’s collected (as well as to Warwick and Brooks who thought up this little project).
Anyone else interested in getting the raw data should email me direct rather than here as I don’t have the time to find out where I posted something here since I forget where I might have posted it.
This is going to be a fun project I think.
Yes, most recently in Brohan et al. However, in that paper Jones is linked via Folland at al. 2001,
In Folland 2001 they just made Jones 0.05 C ‘more statistical’ by saying that it is one-sigma value. And claimed that this uncertainty is symmetrical.
How to refute a study without doing anything (Brohan):
How to avoid extra work (Folland):
And this should be easy when compared to bucket adjustments 😉
#15
The results would not be sufficient. The biggest problem with the concept of UHI is that it is called UHI. Incorrectly implies that local, non-GHG, man-made heat effects are restricted to urban areas. A half an acre of blacktop in the middle of nowhere, or six sections of irrigated nowhere, can have plenty of effect on the local microclimate around a weather station.
I have not looked at the Jones et al. study in detail, and the writing is sufficiently opaque that I’m not sure what they really did. In any case, it seems to me there is a fundamental problem with it. It seems to test the hypothesis that urban areas exhibit faster temperature growth than do rural areas. However, this is not the relevant question; in fact, it appears to be nothing more than a poorly disguised strawman.
The important question, IMHO, is whether the process of urbanization induces changes that would show up as trends in temperature records. Simply stated, trend slopes should be expected to reflect the rate of landuse change — the rate of development of rural land into increasingly dense cities — not the level of urbanization that has occurred in the past. On a static planet (which may bear some resemblance to Russia during this period), there would be no reason to expect trends in either the rural or urban areas.
Jones et al. seem to have used a categorical predictor variable for urban/rural, and then considered the trends for the two categories of landuse. This makes no sense at all. Based on physical arguments, an area classified as rural under this scheme would likely exhibit an upward temperature trend as a component of urbanization; a fully developed “urban” area would exhibit no trend.
A much predictor variable would be the continuous variable “degree of urbanization”.
There also seem to be other statistical problems with this study (peer review has its limits, 😉 ). The study should have employed paired data to reduce the variability (which apparently is what Karl’s study in the U.S. did). Similarly, AFAICT the use of grid-cell temperatures in the study does nothing more than add fog.
If I am in error on any of these points, I would appreciate hearing about it.
George T:
I haven’t seen a breakdown of the Soviet stations, but it seems likely to me, that the stations most likely to be dropped by the
Soviets when money was cut, were the most rural stations, which also happened to be those stations furthest to the north.
Considering census data as an independent measure for UHI, I found that it matters when comparing Brussels and De Bilt, however (unreported) station moves have a far bigger impact.
http://home.casema.nl/errenwijlens/co2/homogen.htm
(Aplogies, the url is a bit of a hodgepodge, I still need to clean it up.)
I would just be very careful using old Soviet data. While urban airports are easy to verify, rural
weather stations are suspect- especially if they were located near a military installation. Jae,
I was thinking that the same thing. As a matter of fact, I thought the graph somewhat resembled
the PDO.
Is there any basis to the notion implicit in studies like Jones et al. that a global UHI bias can be assumed to be homogeneous in space and time?
After all, in the IPCC TAR UHIs were corrected for by subtracting a single linear trend for global temperatures, and this seems based on studies like this.
It seems to me that it would be far more reasonable to correct individual stations (or classes thereof) in the dataset and recalculate the global average.
#19. I agreee entirely. The original formulations of UHI (by for example Oke) actually proposed a log(population) heuristic to deal with landscape changes in rural areas. A log(population) formula is obviously sensitive only to changes in population rather than population itself.
I’ll bet that Jones’ Chinese sites, for example, if and when they are ever identified, will prove to be Chinese towns and cities of different, none of which are “rural” in landscape terms. Like you, I am amazed at the inability of these guys to perform elementary statistical studies without botching it – and then the perpetuation of these flawed studies in the literature.
I’m not saying that there are no sensible observations in these studies. Population or log(population) is itself only a type of proxy for landscape changes. Peterson, for example, points out that urban areas can have cool “park” islands and that growth of trees in suburbs is a type of cooling effect and acknowledges that urban-type landscape changes can take place in rural sites. If Toronto is any guide, landscape changes around the Toronto airport have been much more extreme over the last 50 years than around the University of Toronto (if that’s where the old station was) so the move to the airport might actually enhance a UHI signal.
#23,
I don’t think that they assume that at all. Can you provide a citation?
Something which bothers me in the whole UHI discussion is that there doesn’t seem to be a clear distinction between the UHI effect on global temperature and the UHI effect on the measurement of global temperature. Cities and towns are still only a small part of the total surface area of the planet or even the land area of the planet. Therefore, once one knows how large the UHI bias of temperatures is, it can be spread out and will probably be pretty small, as in the Jones type claims. But this assumes we accurately know the the temperature of the rest of the planet, i.e. the “rural” portions. But as we’re discussing here, it’s difficult to know what the true rural temperature is because there are so many ways for a station, even in antarctica, for instance, to be contaminated with human environmental modification. Thus in that particular case, if there are diesel vehicles they might spread excess carbon particulates in the neighborhood which would change the albedo locally and thus the amount of heat absorbed or emitted. This almost surely would increase measured temperatures, even if the station were isolated from a base and only visited once a month or less. In less isolated situations the effect will be more and more prominent.
Now we can’t actually measure what part of the measured rural temperature change is from ghgs and what from other human activity without a gigantic effort. But we should be able to get an idea of what the baseline temperature should be and therefore what the UHI measurement bias is by comparing the measured surface station “rural” increase with the satellite surface increase. I suspect we’ll find it to be something like .2-.5 deg per century, not .05 or whatever Jones thinks.
Have read this site with great interest for some time now. I guess that makes me a “lurker”?!
With regard UHI, Hinkel et al (2003) (The urban heat island in winter at Barrow, Alaska. International Journal of Climatology 23: 1889-1905), produced a very informative study.
Well worth a read.
#5 – Concerning the Soviet data see Fig 3 in my paper with Pat Michaels, available here. The data for the Figure and the other code/data/etc for the paper are here.
The Figure shows the rate of missing data in those Soviet stations that continued to operate during the collapse of the former Soviet Union.
The graph George posted shows the entire archive count, including duplicates and partial records. The GHCN record pares that down to about a third of the original, but the drop in stations at 1990 is still evident (see the graph, as of 1997, here.
And the station loss is not spatially uniform, making it impossible to claim the sampling frame is continuous across the late-80s/early 90s. To see the effect of the 1990 event mapped out visually, go to the University of Delaware global temperature archive here, click Available Climate Data; log in; under Global Climate Data select Time Series 1950 to 1999; then select Station Locations (MPEG file for downloading). Then sit and watch the movie. The remarkable things are, first, how bad the spatial coverage is outside the US and Europe, and second, what happens at 1990.
And of course it’s after 1990 that all these record breaking jumps in the global temperature took place. The AR4, like the TAR before it, simply dismissed this issue without discussion, appealing primarily to Jones et al. 1990.
RE 19: TAC says:
Exactly. Moving temp stations to more “rural” airports would in many cases increase the rate of change of detection of urban development IMO, since the airports were prb’ly fairly close to rural at the start, but are the very spots where urban-effect increases would most likely occur locally. So remaining in an established city would have incurred less relative local urban growth than a growing airport! Of course, if a proper correction weren’t applied for the move (how is that determined?), even more inaccuracy would be introduced.
#26: Dave,
The problem in two areas. There are only something like 2500 weather stations covering the entire globe. Which has, if I remember
correctly, something like 200,000 sq. km. of surface ares. All of these stations are in close proximity to human habitation.
A few of these habitations are small towns or villages. Most are near medium to large towns. A handfull are near farms. But even
farms affect the local climate via foliage changes and irrigation.
Secondly, we do not have any satellite measurements of ground temperature. The satellites that I have read about, read air
temperature at an altitude of several miles up.
19: TAC,
The only way I can think of to do a correction on a move of that sort, would be to keep the first sensor in place, and establish a
second sensor at the new location. Run both sensors for at least a year, preferably several years, so that you can get a track of
the difference between the two locations over a wide variety of weather conditions. Then you throw away the first sensor.
If this isn’t how it is done, then I’m sure someone will correct me. Hopefully not violently.
Re 30 MarkW:
Surface Area of the earth is about 200,000,000 square miles. Which makes it worse.
RE: #18 – Yes, exactly! That is why I keep telling people to stop using the term “UHI!”
My term is “Anthropogenic Energy Dissipation and Land Use Modifications.”
#32. MarkW likely meant that each of the 2500 stations represents the contribution from about 200,000 km^2? Of course, the stations aren’t uniformly distributed, and few are bobbing around in the sea. I’m quite sure that a 2500 pixel visible camera image of the Earth’s complete surface would be somewhat noisy. As T Ball hinted at, there are basic sampling statistics to worry about.
My term is Local Anthropogenic Temperature Tainting Effects.
Hot LATTEs.
🙂
RE: #35 – I like it …. it resonates with urban sophisticates …. great market segment targetting! 😉
One overlooked problem in urban-rural temperature trend comparisons is that both the urban areas and rural areas are growing in population. If both the urban location and the rural location grow by the same percentage in population (say 25%), then both would have equal growth in their UHIs. The net effect then would be that someone (say Peterson) would then erroneously claim there is no UHI effect in the temperature trends, when in fact both sites might have trends of equal magnitude. I think this is a common problem.
In the summer of 2005, I finished a study on the temperature evolution in Europe. I focused on differences between rural and urban stations.
My findings can be found on my website under The Earth: Climate Evolution in Europe (1881-2004).
Hope this study may be useful!
#31 I agree that you can calibrate for station moves. But it is difficult to adjust for the results.
As several have pointed out, stations relocated to airports might show a drop. But only for a while. Airports tend to grow and lay down massive concrete runways, build big parking structures, and attract automobile, truck, and plane traffic. Jet engines aren’t neutral about heat.
And at the prior station in the urban center any UHI effect probably built over decades as the city grew. Tall buildings, a decrease in sidewalk trees, airconditioning, and freeways of concrete almost define modern cities. So any UHI adjustment for more than a few years is a moving target.
I don’t see a solution. Comparative studies can figure out what the UHI effect was at some specific sites and some specific years. But not for every site or every year.
#35 Surely the term should be ALW, Anthropogenic Local Warming
Re above discussion: Doesn’t Warwick Hughes have it right with his presentation of infra-red images from satellites that clearly show pronounced UHI effects?
Follow links at http://www.warwickhughes.com/climate/index.htm
RE: #37 – In fact, the actual delta T in so called “rural” areas, especially since WW2, may be greater than it has been in so called “urban” areas.
RE: #41 – Hughes et al (aka “The Hard Rock Renegades of Oz” – LOL!) have done some really good work on this topic.
#38. Many CA readers will be interested in HAn Janssen’s website .
Jan, I couldn’t locate the post you linked to.
I happened to investigate the temperature data for the south-eastern part of this region a few years ago. I found that almost all the claimed warming in this region during the 20th century is due to the UHI. I wrote a paper about it. Naturally, I could not get it published, because it contradicts the claims of the “team” and other climate catastrophists.
#44: http://members.chello.be/j.janssens/Engaarde.html#Europe
very, very interesting!
#37
I would assume that if the sensor is already surrounded by urbanization and
if the immediate surroundings don’t change all that much, yet the cities areal
coverage increases, all that will do is expand the coverage of the heat isle itself,
not necessarily change much around a local sensor. But I don’t know at what point
this would occur during an ‘urbanization’ process over time, but one would think
there would be some sort of an equilibrium point as to how much more of a signal
you would get out of a longterm temp trend around an already ‘large’ city.
I fully understand the issue of a more rural site which grows into a more urban site
over time having a larger UHI signal than a site that had been located in a
relatively large urban setting from the get go. But once again, it comes down to the
most micro of micro climates.
I have done study in my own neighborhood (urban) and can show much cooler temps
(especially at night) if the sensor is placed in a large grass field or forested
area compared to the local urban center in my neighborhood. It can be several
degrees different on good de-coupling nights with clear skies. And these measurements
are made within just a few blocks of each other or even less. Quite amazing
really.
Using meteorlogical station data as a measure of GMT is very close to pure folly
in my humble, meteorological opinion.
within a few blocks to a less rural site
would have more signal than a urban site that has always been an urban site. It’s
sort of development would
Sorry for the garbage text at the bottom of my last post…
46: Yes, very very interesting!
Jan J & Steve M: Thanks for the link. Wow, I did not know there was so much variation in predictions for Solar Cycle 24.
37: tom,
You leave out the affect of winds in your analysis. If the wind is blowing at 10mph and if the sensor is in the center of the city. Then if the city is 40 miles across, the wind will be blowing over urban landscape for two hourse. But if the city is 80 miles across, then the wind will be blowing over urban landscape for 4 hours. Twice as much time for it to pick up heat.
These are UHI corrections by several authors on Uccle(Brussels):
Note that Jones doesn’t correct Brussels, the higfrequency residual is due to the fact that Jones uses jan-dec and GIS Dec-Nov annual averages.
#51
Right you are Mark about the wind. But I was just wondering if at some point an
equilibrium point would be met in a large city. Maybe the increase in temperature is logrithmic,
meaning it rises pretty sharply (relative term here) with initial urbanization
then the increase flattens some as the city gets larger. Maybe once the urbanization
is more than 80mi across, I dunno…but at the same time some cities just get more
dense and not necessarily more expansive. A lot comes into play no doubt but
my main point is that there probably is less of a net increase from growing but
already large city as compared to a rural grassland going urban over a similar
time period.
The wind issue is a valid point but if the airmass is homogeneous, yet being
well mixed from a steady wind the difference between city/rural on nights like
these are not that great from my experience, mainly refering to midwestern US cities.
Interesting is also that the Vienna (metropolitan) data and the Hohenpeissenberg (rural hilltop)data don’t show a difference since 1780, because central Vienna (where the Hohe Warte observatory is located) was already a metropole in 1780, so no significant change in landuse was occured in both locations.
http://home.casema.nl/errenwijlens/co2/europe.htm
Given that the UHI can affect the temperatures in cities by many degrees
e.g. http://news.bbc.co.uk/1/hi/in_depth/sci_tech/2002/leicester_2002/2253636.stm
where 8 degC is quoted for Manchester, compared to surrounding areas, I am intrigued
by the nature of the corrections that are made, given a suggested global warming to date of a few tenths of a degree.
The implication to me, is that either these corrections are incredibly accurate governed by a well defined procedure, which seems unlikely from waht has been said above, or not to be trusted.
Re #25
Sorry Steve, no citation.
The IPCC TAR box 2.1 however, states:
“Extensive tests have shown that the urban heat island effects are no more than about 0.05°C up to 1990 in the global temperature records used in this chapter to depict climate change. Thus we have assumed an uncertainty of zero in global land-surface air temperature in 1900 due to urbanisation, linearly increasing to 0.06°C (two standard deviations 0.12°C) in 2000.” [I think page 52]
So basically I misstated my point. What I meant to say was that it seems that in the IPCC context one thinks that UHI bias which isn’t homogeneous in space nor time, can be corrected for on a global scale by assuming a linear “uncertainty trend”. This seems to suppose that UHI bias largely cancels out in the record which is something I find hard to believe.
I also am under the impression that trying to correct for something as diverse as the biasses caused by changes in station location, changes in the environment of stations, changes in land use and energy consumption etc. cannot simply be corrected for by statistical techniques. I’d say this requires detailed study of impact on individual stations.
Again, I might be projecting different issues onto Jones et al. If so, sorry for that.
The take home message is that the UHI value per se doesn’t matter, it is the change in land use that matters, for which population is a well documented metric (proxy).
re: #56
I’m going to try once more to make my earlier point as I don’t think it’s necessarily being “caught” by people here.
I might even agree with the IPCC quote above, but that’s not the danger of UHI in measuring surface temperatures. The danger is the bias over time in measuring temperatures. The cities, towns, little burgs and even development in rural areas result in increased temperatures on a small % of the earth’s surface. The creates a bias in the entire corpus of surface temperature measurements unless it’s detected and removed. The people who wrote the quote seem to be of the opinion that since the overall actual increase in the world’s surface temperature from UHI is small, therefore they can make a small correction and then ignore it. That is totally wrong and shows either a major lack of understanding of the issue or a willful attempt to hide the actual problem.
I add this last since several of the arguments against UHI such as the “night light” and “windy city” papers (someone who knows the papers I mean might want to provide links to them), try to argue that the actual amount of UHI in urban areas must also be small. This is silly since practically everyone who has observed actual weather knows that the areas surrounding cities is normally several degrees cooler most all the time. Until this is admitted and dealt with seriously, the team and their allies are not going to make much progress convincing knowledgable skeptics that UHI can be ignored or easily accounted for.
Sounds more like “extensive hand-wavings have shown…”
Dave (#58)
Thanks for making your point once more. Sorry to say that I think I don’t ‘catch’ it still. You say:
So every single site that is close to a factor of bias – may it be a city, a (set of) smaller area(s) of habitation, some development site, or a agricultural or forestry site that has been changing over time – is going to give biased records. These records, if included in larger sets, are going to bias the total resulting temperature change estimates. I can see that.
However then you write:
This seems to confuse me. So let me try and explain why I may be confused. The above sentence seems to be inconsistent with your statement that sites are being biased by nearby urban centers and land use changes, and therefore this biasses any larger dataset that includes data from these sites. After all if the total bias due to the biased site records is small, then you can use a small correction.
I suspect however that you mean to say that one should not simply correct the record with a single value based on the population(density) or similar proxies at a single time, but should be corrected with a correction as a function of time, based on whatever proxy or data also as a function of time.
I totally agree. In fact I think the best way to deal with land use change bias in records might be to model the appropriate factor – be it population or land cover – together with temperature or whatever one wants to correct, as a multivariate function. In other words, temperature trends may have a strong covariance with e.g. population(density) and this is what’s of interest, not whether the trend correlates with a change in population over a arbitrary period.
#56. Florens, I agree with your point about the need for detailed studies. I wish that these folks took more of an engineering approach to things, rather than resting on little Nature articles are little more than an extended abstract. If anyone is going to rely on the results, it would be helpful to have a report on each site.
In an internet age, with the broad interest in these topics, if one knew what sites were actually being used in these studies, I suspect that we’d start seeing some detailed parsing of the data like Hans Erren has done with De Bilt, Vienna and Uccle, so that we could see what it really said.
re #58:
Dave, the quote that you reference does not say that urban areas are 0.05C warmer than rural areas.
It is saying that at any particular sensor there has been a warming of 0.05C in the last 100 years due to development.
(Of course the phrase “any particular sensor” has no meaning, since what we are talking about is a statistical averaging of
all sensors over the globe. I’m just trying to keep the phrasing simple so that the point does not get lost in the verbiage.)
How this amount is determined is by comparing “rural” stations to “urban” stations over time. The belief is that rural stations
aren’t experiencing UHI, and urban ones are. Thus, the difference in the two warmings, can be attributed to UHI.
There are many, many problems with such an assumption. The first, and biggest, is the belief that “rural” stations aren’t
experiencing UHI. It’s fairly easy to show that they are. The next is the rather simple minded model. IE, all stations
near cities with less than a certain population are rural, and all stations near cities larger than the critical cut-off are
urban. A better model is to plot a graph of temperature increase vs. population.
On the other hand, all of this is conjecture, since the man behind the myth, Jones, refuses to tell anyone how he came up
with his magic number.
here is st petersburg russia
http://home.casema.nl/errenwijlens/co2/europe.htm
re: 44
Steve,
Speaking of Jan Janssen, I also found his comments on SPM4 interesting.
RE: #53 – Consider anywhere populated (urban or otherwise) in 1807. No electrical grid, most power is from horses, etc. Consider anywhere populated today. Over the past 200 years even places that have not incurred a change in population density have incurred an unbelievable change in energy density. In fact, consider even the change over the past 100 or even 70 years.
Some thoughts by Roger Pielke Sr. on the effects of land use change here.
re: #60
Let me try to explain my point statistically to better reach the audience here. Imagine a field of all possible temperature measurement stations. Make a random selection of N of these stations. Some number K of these stations will be in built-up areas and will have a UHI value of +U(k) which we’ll assume you can measure accurately The rest of the N we’ll denote as J. If you now make two averages, one with the raw measurement and one with adjusted values, and then divide by N you will end up with a value V for the average UHI over the entire globe (or whatever subset of it you’re dealing with).
Now, as far as I can see, The Jones strategy is to assume that V(0)= 0. Therefore they’re saying that they’ve measured UHI by comparing the relative increase in temperatures between pairs of rural and urban stations and they’ve come up with V(t) = .05 deg C per century. But as we’ve been discussing here, they’re actually only measuring U(k-j). Now it may or may not be true that U(j) = 0 for t=0 but we certainly don’t know that U(j,t) is 0. We presume there’s still a set of stations J’ which have U(j’) = 0 but it’s unlikely that these are among the initially selected J. By construction they’re relatively near growing cities. Therefore it’s likely that they’ve become k’s to a greater or lessor extent.
This is the contamination I’m concerned with. It may be that in the larger universe of J’s the unbiased ones may predominate and result in a relatively small total temperature increase from UHI, but since most of the “rural” stations in any real-world selection of N aren’t really that rural either initially or over time, we’re not going the get a high enough adjustment for UHI of individual stations and this will be extrapolated into seeing a high temperature trend where it should be a low temperature trend.
There’s a long-term temperature measurement done in the very center of Prague (Czech Rep.), in Klementinum near the river Vltava. The graph shows yearly averages (in blue)
red – 10-year smoothing
yellow – quadratic trend
Rather interesting – the recent temperatures resemble those in 1800’s, like there’s no change…
Graph
Analog vs Digital Rounding of Temperatures: I haven’t seen this mentioned anywhere else, but I’ve wondered whether anyone has looked into this. I have a hypothesis that there may be slight tendency for humans to round downward while reading an analog thermometer. This bias would be eliminated when the weather station upgraded to digital thermometers. A bias of a few tenths of a degree C is believable and would be significant.
RE: #69 – Especially if the observer is looking at an upward angle at an analog thermometer.
68: Looks like the Little Ice Age hit Prague.
For those interested in looking at real time temperature for a intermediate size
town look at:
http://testbed.fmi.fi/
The site provides real time temperature data for Helsinki (Finland). The whole
capital erea Helsinki, Espoo, Vantaa and Kauniainen together have a population
of roughly 1000 000.
Some background:
Land Area = 184 kmⰮ
Population= 565000, ca. 3000 persons/kmⰠ.
Energy consumption: 15 GWh (electric + area heating). Equivalent to ca. 8W/mⰠcounting
the land area. Traffic heat input is not included (needs to be estimated separately).
The energy production above is for winter time because at present it is ca. -15 C 🙂
going down to ca. -20 C during the night.
#65
Point well taken…I am a big believer that urban development causes a warm bias
over time, especially when going from truly rural to more urban such as we are
discussing here. I’m just trying to tease out other possible outcomes that may
argue against my hypothesis, shall we say ‘falsify’, ya know like good scientists
used to do (ahem…wink wink). Thanks for all of your viewpoints.
RE#64, good comment by Jansen about 0.006 deg C/decade being called “negligible” by the IPCC, yet even this value over the 20th century would be 10% of the warming. Tough to call 10% “negligible.”
I’m wondering about the immediate environment of the sensor. Obviously, the condition of the box that protects the sensor over time is of some importance. Also, any nearby structures (including tree trunks) may radiate to the sensor box. I happen to have all sorts of remote temperature sensors around my place. They all read within a degree F when together, yet around the yard (0.5 acre) they are easily as much as 3 degrees C in disagreement. It’s clear that pockets of different temperature air are moving around continuously, wind, or no wind. Temperatures near trees can depend on the micro-climate of the location — presumably caused by the tree breathing.
The same sort of thing can be noted while observing through a large telescope. Pockets of air, a metre or so in size, either drift across the pupil slowly, or, if it is windy, the air pockets are more disturbed. All this would seem to indicate that measuring simple temperatures is far from simple; each individual location would need long-term statistics using many sensors.
What is needed is a classic gage R&R or MSA. Try to get the so called “climate scientists” to take a Sigma approach, though … it’s like trying to give a cat a bath.
Re #76. Well, yes, except that we’re probably looking at some level of chaotic fluctuations. I could imagine the number of DOF would be large. Is it even possible (these days) to get representable measures from chaotic phenomena, and what would they mean? Could it be that the available temperatures can’t deliver meaningful “averages” no matter how much they are massaged?
RE: #77 – The simple work of trying to design the MSA would indeed reveal a number of inconvenient truths about near surface measurements.
I sent the following request to Phil Jones today:
Re 79,
Steve, did you cc NAS, Nature, or IPCC? Please let us know what kind of response you get.
I think it is very important to note that for testing for a UHI effect the way Jones did, all you have to do to prove “no effect” is to be sloppy in separating the urban and rural sites. If a portion of your rural sites are “in fact” becoming more urban over time, and some of your urban sites are “in fact” in stasis (equilibrium) level of urbanization, then you will not detect a difference in temperature rise between the two data sets. A fundamental aspect of statistics has always been to be able to clearly differentiate your effect from noise. Here, noise (due to station classification error) is sufficient to “prove” that there is no UHI, which the IPCC wants as their outcome (since this means less work for data analysis).
Re #81 – and I would expect IPCC to cherry pick the studies that suit their purpose.
In case anybody needs population data:
http://www.populstat.info/
Just a quick note to remind you all that the change from human read thermometers to remotely read electronic thermometers could introduce a bias as well – the screened box holding the thermometer needs to be opened for a person to read it, but can stay shut for a remotely read unit. Does anyone know if this would introduce a warming or cooling trend to the data – I suspect warming.
With no need to open the box on a regular basis, or even to go and have a look at it, it probably wouldn’t be cleaned very often either – inside or outside. These boxes are painted white, so a neglected box that gets dirty on the outside would show an increase. Increases could also occur in the minimum temperature recorded if the inside of the box was allowed to accumulate dust and dirt, thus insulating the sensor somewhat. Given enough neglect, the air flow through the box could be seriously compromised as well – another potential warming bias.
With less visits to the box, we could also see neglect of the surrounding environment as well, perhaps leading to heat traps.
Excuse my laziness and ignorance, but does anyone know if this has been studied, and if so, what the results show?
#84. Peterson etc are aware of this sort of issue and there are discussions of this type of issue in the literature.
Re: #28 where Ross McKitrick says:
I read the McKitrick and Michaels paper and, for what it is worth from a statistical and modeling novice, I thought the model construction was put together with some thought and creativity. Two points from the model results were striking to me:
1. There were no significant effects of population on temperature — which would indicate agreement with Jones.
2. Out of sample results indicated that the model could only explain 9% of the temperature changes.
I would be interested in hearing more about these two results and any further work that you have done with modeling along these lines.
Re:83
Gridded population data are available here:
http://sedac.ciesin.columbia.edu/gpw/
#86: In that paper we are concerned with within-sample inferences, so the key tests are the P-values in Table 4. In models where you want to forecast out of sample the out-of-sample test is crucial; that’s why Steve and I emphasized the importance that the hockey stick out-of-sample r2 was insignificant. That model was not being used to say something within the sample period, but comparing 1998 to 1400, ie 500 years out of sample. For our model the prediction test is one of a battery of tests to try and rule out spurious or fluke results. It would have been nice if the r2 was higher, but at least it was significant. In the follow-up paper (which is under review) we use a complete global grid and the same test r2 goes up quite a bit.
Population is insignificant, as you note. Either they have successfully removed the population effect or it is an inadequate way to measure the size of the contamination.
So,if IPCC a UN organisation would one fine day read Climate Audit
they would have rural outback temperature sites protected as part the
World Heritage…So let us all lobby for that and weⳬl have a more
accurate surface temperature record from 2012…or so
#86, 88…
my thought is that PEOPLE are not the primary cause of UHI. STRUCTURE, ie blacktop, buildings, perhaps electronic infrastructure are what cause UHI. in the absense of significant human population, my local weather station at the airport will register a higher temperature, secondary to the many acres of blacktop and concrete in the immediate area. population may end up being a matter of only secondary importance.
RE: #90 – I wonder what the impact is of, for example, the Mojave Airport (and other large aircraft mothball areas)? What is the impact of turning massive areas of desert caliche into well cultivated and well watered top soil with crops growing in it a goodly percentage of each year? Etc, etc, etc …
91…or more in my neck of the woods, plattsburgh, NY air force base (closed) in the adirondacks. or niagara falls, NY air force base, (not closed due to political wrangling, but not being used as much as its acreage would support). lots of blacktop, very low population, still a likely high UHI.
😉
“…The total temperature increase from 1850 – 1899 to 2001 – 2005 is 0.76 [0.57 to 0.95]°C. Urban heat island effects are real but local, and have a negligible influence (less than 0.006°C per decade over land and zero over the oceans) on these values.”
There you have it! Half the world’s population of urban-dwellers (and their engines) provide negligible influence on global warming.
Therefore, farmers and hermits are the culprits?
I’ve excerpted Russian station data from the most recent GHCN listing for Warwick Hughes who is making progress at possible identifications of the Jones stations. See his website. For reference, here’s the script for reading and excerpting the stations:
This extracts 1764 stations extending a list of 1359 stations from which Warwick has matched many plausible candidates.
Steve M – You fellows are much more adept at handling large volume of GISS data than I, but I have taken a look at a few UHI phenomenon in several areas:
San Antonio TX
Oklahoma City OK
Edmonton AB
These cities all have a number of “rural” stations at various point surrounding. They all show significant and consistently increasing UHI effect. I don’t think there is any doubt that the UHI effect is real and significant, as my observation is that the increase is 0.75C per century for San Antone and 1.1C per century for OK City.
In the case of Edmonton, I compared the Municiple Airpot, near city centre, to the International Airport, some 30 km to the south and adjacent to Leduc and Nisku, two areas with recent rapid oilfield industrial growth. The startling thing is that you can track Edmonton population growth by the increasing temp differential until the mid-70’s when the industrial complex adjacent to the Int Airport went through a growth spurt, along with the Leduc population. I guess we could use the differential as a population proxy!
I think any attempt to “correct” this data for UHI is frivolous and misleading, as the differences are greater the the apparent global warming of the past century.
I don’t have a website to post the graphs but could forward to you to post.
Regards
RD
roger you don’t need a website, you can use imageshack
http://imageshack.us/
RE: #96 – Indeed. Let me pick a region out of the air – the I-80 corridor – aka the following California counties – Solano, Yolo, Sacramento, Placer, and Nevada. Look at what has happened in these counties since the end of WW2. Can anyone in their right mind believe that there has not been a general increase in temperature in these places, owing strictly to the increase in human caused local energy flux combined with amazing and extensive albedo modifications in urban, suburban and rural portions of this corridor? Consider also the amount of conversion of rural land to other types during that same period.
Interesting study of UHI in Singapore. You can literally see Changi Airport and the docks (also large expanse of concrete) in the thermal image.
Click to access UHI%20_2004-001_%20rev.pdf
In ClimateGate e-mails, Phil Jones receives notice that UHI around St Petersburg is .8-1C, .2C per million.
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