This gives credence to Dr. Roy Spencer’s population adjusted ISH surface temperature data for the USA
population density.
Impact of population density on the surface temperature and micro-climate
of Delhi
Javed Mallick1 and Atiqur Rahman2,* 1Department of Civil Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia 2Remote Sensing and GIS Division, Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia University, New Delhi 110 025, India
Abstract:
Increasing urban surface temperature due to change of natural surfaces is one of the growing environmental problems in urban areas, especially in cities like Delhi. The present work is an attempt to assess the urban surface temperature in Delhi using remote sensing and GIS techniques. ASTER datasets of thermal bands were used to assess the land surface temperature (LST) using temperature emissivity separation technique. Ward-wise population density was calculated from the Census of India 2001 data to correlate the population density with LST. The study shows that surface temperature changes with the increase in the impervious surface area, which is related to the increase in the population density.
From the discussion:
Figure 4 shows the correlation between surface temperature and population density. A strong positive correlation between the two can be seen. The value of logarithmic regression (R2) is 0.748. The logarithmic regression equation between surface temperature and population density is Y = 1.059ln × Pop-density + 22.40. It means that with the increase in population density, surface temperature also increases. Furthermore, it is possible to predict night-time surface temperature on the basis of known population density.
….
Oke used the empirical method to represent the relationship between urban–rural temperatures as concentration on population. It is not easy to separate many contributors to the problem. However, one of the most noticeable and one that has proven to have an
extremely strong correlation with the UHI phenomenon or urban surface temperature is the population density of major cities32. The present study demonstrates a close
relationship between the population density, built-up area and surface temperature. The statistical analysis of nighttime surface temperature with population density indicates
that population growth tends to contribute to the urban surface temperature rise or UHI intensity and also to the micro-climate of Delhi.
In earlier times and even now, there are very few meteorological stations to record the surface temperature, and they may not be the true representative for the whole city. In such situations, LST derived from thermal satellite data are useful to study the variation of surface temperature over the entire city area that is an important parameter for micro-climate of urban areas. This study clearly shows spatial variation of LST over entire Delhi.
By superposing the population density map of Delhi over the surface temperature map, it can be clearly seen that high population density is one of the main contributing
factors for the high surface temperature, UHI intensity and also micro-climate of Delhi. To assess and address the issue of micro-climate and also to mitigate the impact of UHI on the city population, the outcome of such types of studies may be useful.
h/t to Dr. Willie Soon
paper here: MallickRahman12-June-ImpactofPopulationonTsfc+MicroClimate-NewDelhi
hey greenies, you better start planting some trees in your cities now instead of whining about rising CO2 levels.
Duh!
Unfortunately the relationship between LST and SAT is not straightforward.
So this and other studies have shown a good relationship between percent of impervious surface (ISA) and LST ( land surface temperature ), but the relationship between LST and SAT ( surface air temperature ) is not that clear, primarily because of horizontal advection.
Simply, you can have land that is adjacent to high percentage ISA and High population density that suffers the effect of upwind high LST. Think of a thermometer over grass that is downwind from a patch of concrete that has High LST ( at night when it gives up its retained heat ).
The relationship between Impervious surface percentage and population density is also confounded. It is regionally biased by building practices. This is one reason why Oke found different curves for the US, Europe, Asia and South America.
One curve doesnt fit all. and In some locals the curve is logistic.
This is easily seen by selecting areas that have High ISA, High LST, High SAT and no population. How do you get High ISA? High LST? High SAT and no population?
simple. Industrial sites. Population density figures are collected for residential population and not ambient population. So, if you have an airport or a refinery you will see High ISA, High LST, High SAT and no population, because census figures capture where people live not where they work. The effect of ambient population can be seen in Tokyo where the weekend UHI is less than the weekday UHI.
All very interesting. The point being there is no simplistic single factor adjustment you can apply to data to correct for UHI.
The other problem is that LST is not collected at the same times as Tmax and Tmin. Its utterly dependent on the over pass time. And its dependent on having the right synoptic conditions.
basically you can only collect the data when the conditions are correlated with the effect being its worst. Cloudless, windless, rain free days.
Nice to have it somewhat quantified but not exactly a shocker.
Clearly we can halt AGW by eliminating Indians? (sarc- my best software is Indian, and I would not personally eliminate anyone)
I don’t know where the idea of the log(pop) model comes from. It’s often bandied about but I never saw a reason proposed for the log relationship. Just a guess?
I the case of this particular graph I would estimate that T-T0=k.pop^2 would provide a better fit.
In any case it’s good to see some serious, objective science is finally getting done and getting published. In the last 6 months or so I have noted some sanity returning to the field.
Okay, is this about Delhi or New Delhi? Those are not the same thing, though in close proximity. 9,810,000 population vs. 294,000, respectively.
“All very interesting. The point being there is no simplistic single factor adjustment you can apply to data to correct for UHI.”
Yet we can build a model to replicate the ever changing climate?
Nice. Very nice.
for folks who are interested in some of the problems associated with the differences between LST and SAT you can look at this
http://ftp.cavs.msstate.edu/publications/docs/2006/03/3986GIScience_pub_2006.pdf
P. Solar
“I don’t know where the idea of the log(pop) model comes from. It’s often bandied about but I never saw a reason proposed for the log relationship. Just a guess?”
The log relationship is probably the wrong functional form. The easiest way to see this is
to look at their underlying data. The lowest population density they look at is 300 people per sq km. If you plug in a population density of 2 people, you get a delta temperature of 12 C or so.
functionally you can expect the relationship to be logistic. tending to zero at one end and limited at the high end. the log fits are just done for ease of computation. Other studies use 1/4 power functions,
The first person to do a log(pop) was Oke, but he quickly found out that the function didnt work if you considered other parts of the world. His first study was focused on America and Europe.
Studies in Asia and South America came up with different curves. He added a component to the model that represented regional wind since wind can drive UHI to zero if it is above 7 meters per second or so. Its been recognized that part of the reason while population curves differ for different regions is that building practices are different. Its the building material and density of the surface changes that drive LST. Population is a consequence of that. Population does effect the waste heat portion of the energy balance equation. For the most part today researchers are more focused on explaining temperature in terms of the land surface: ISA, Vegetation and there are very few who actually look at population density. Basically to get a really dense population figure you need lots of concrete and tall buildings. So, ISA explains a great deal of the variance.
the true impact of the humans can only be gauged by looking at the same surface with and without people. So, the weekend UHI effect in tokyo is a good place to start to understand how much of UHI is due to the buildings and how much is due to the people in those buildings.
or how much is due to the road and how much is due to the car on that road dumping hot exhaust into the air. Most researchers dont look at population and the science tended to move away from population because the other factors — surface changes made by and for humans– explained more of the differences in LST.
Hi S. Mosher,
My memory of New Delhi during monsoon seasons is there were not consistently “cloudless” … or “rain free days”. I would like to understand how the site’s monsoon makes the study’s observations skewed.
Moshpup’s first post is quite reasonable. Unfortunately all the studies and temperature series he works on, and with, completely ignore what he just explained to us!!! They simply assume that it all averages out and their adjustments aren’t affected!! Of course, without detailed investigation of each station and its environment they are showing more religious belief than empiricism.
HAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHA
The correlation will be with electricity consumption.
It’s well established that surface temperatures in India are largely driven by aerosol levels (outside the monsoon season). Increased population results in increased burning of coal and biofuels (mostly for cooking), increased aerosol levels, and decreased (especially daytime) temperatures.
However, this relationship breaks down beyond a certain population density – it’s not feasible to have open fires in apartment blocks – and electricity and gas become the main cooking fuel, aerosols decrease and temperatures increase.
This is substantially the reverse of the situation in the developed world.
So, the weekend UHI effect in tokyo is a good place to start to understand how much of UHI is due to the buildings and how much is due to the people in those buildings.
or how much is due to the road and how much is due to the car on that road dumping hot exhaust into the air.
The cars are dumping aerosols into the air. And Tokyo is warmer at the weekend than during the week. Clearly ‘cars dumping heat’ isn’t a significant factor.
http://www.springerlink.com/content/u41h6p5367580731/
Why not just heavily instrument a small, carefully selected compact urban center and get real empirical data instead of this guessing? A few hundred weather stations and a data collection system can’t be all that expensive or difficult and you would have all the information you need. It would be a better use of money than dumping it into faster computers or studying mosquito habits.
That line shape is an enzyme rate or a binding constant
http://en.wikipedia.org/wiki/Michaelis_menten
gringojay says:
August 31, 2012 at 2:47 pm (Edit)
Hi S. Mosher,
My memory of New Delhi during monsoon seasons is there were not consistently “cloudless” … or “rain free days”. I would like to understand how the site’s monsoon makes the study’s observations skewed.
########################
Simple. To do this type of study you look through all the samples ( for example using REVERB) and you find a cloudless day. Every image is marked by percentage of cloudiness.
here is REVERB.
http://reverb.echo.nasa.gov/reverb/#utf8=%E2%9C%93&spatial_map=satellite&spatial_type=rectangle
here is http://ftp.. but you need to understand the various products and bands
ftp://e4ftl01.cr.usgs.gov/
To get a good estimate the LST you need cloud free days. They picked one day in october ( october 7 2001 ) october is pretty good to find cloudfree and rain free days.
So basically you are seeing the results from 1 day.
http://weatherspark.com/averages/33934/10/New-Delhi-India.
As for skewing the results. The problem is that the synoptic conditions you need for for clear visibility of the surface are also those conditions which tend to show the highest UHI. So usually folks refer to this as UHImax. That is the worst you can see. Still it’s important to look at LST.
I would imagine that if I showed folks the LST images for surface stations some of them might object to what they see. It’s pretty easy to pull down an image for any station and look at the LST around it. Like I said if I showed people one day of data that went against their notions, I am quite sure they would rightly complain about that.
Maybe I’ll write that up, there is a group working on making MODIS data products easier to load into R. Right now its a pain in the ass. Lots of hand work, but very interesting to have at hand.
I’d think the “log-like” shape results from horizontal vs vertical buildings. As people start to gather into villages, they spread out horizontally, so the number of buildings, streets and blocks would be linear with population.
Beyond a certain density you HAVE to go multi-story. No choice. As pop increases above that inflection point, the number of buildings, streets, and blocks increases much more slowly.
Steven Mosher says:
August 31, 2012 at 5:44 pm
“I would imagine that if I showed folks the LST images for surface stations some of them might object to what they see. ”
========
Lots of things I don’t like to see, what did you mean by “object”.
Phillip
“The cars are dumping aerosols into the air. And Tokyo is warmer at the weekend than during the week. ”
The study:
“On Saturdays and holidays (Sundays and national holidays), δT* was lower than on weekdays by 0.2–0.25°C at Tokyo,”
As I recall the anthropogenic heat flux in Tokyo was 400W and 1590 depending on the season.
Or Phillip you could go to the tokyo government site and download “Guidelines for Heat Island
Control Measures” In that city they have heat maps for every ward.
Or you could look at a similar study done in Korea and week the weekend effect
http://journals.ametsoc.org/doi/pdf/10.1175/JAM2226.1
This paper is good because it shows you the effects of windspeed and cloudiness, as I recall the weekend differences are covered toward the end of the paper, but read the whole thing, there is a huge pile of reading to do in this area. some things that might surprise you.
“kuhnkat says:
August 31, 2012 at 3:28 pm (Edit)
Moshpup’s first post is quite reasonable. Unfortunately all the studies and temperature series he works on, and with, completely ignore what he just explained to us!!! They simply assume that it all averages out and their adjustments aren’t affected!! Of course, without detailed investigation of each station and its environment they are showing more religious belief than empiricism.”
Sorry katman. All of the work I’ve personally done is with raw daily data. So, you must be mistaking me for somebody who works with CRU data, GHCNM data, or GISS data.
It’s important to look at all sources and get your hands on everything. That gives you a leg up on people who just read about data.
Dennis Kuzara says:
August 31, 2012 at 5:08 pm (Edit)
Why not just heavily instrument a small, carefully selected compact urban center and get real empirical data instead of this guessing? A few hundred weather stations and a data collection system can’t be all that expensive or difficult and you would have all the information you need. It would be a better use of money than dumping it into faster computers or studying mosquito habits.
##################
that’s been done. The problem is you have a single sample and then you cant generalize.
Here is what is involved in instrumenting a city
http://pages.unibas.ch/geo/mcr/Projects/BUBBLE/
http://pages.unibas.ch/geo/mcr/Projects/BUBBLE/textpages/ov_frameset.en.htm
http://pages.unibas.ch/geo/mcr/Projects/BUBBLE/textpages/ov_frameset.en.htm
Kinda cool. You could compare
the rural sites
http://pages.unibas.ch/geo/mcr/dolueg/dbdoku/GEMP.en.html
with the urban sites.. and then look at how good the modelling was.
Even had a wind tunnel model of the city and tracer experiments.
Here Kuhnkat.
Some simple instructions
http://www.r-gis.net/?q=ModisDownload
Then once you have the data you have to do some nasty programming to pull out the data
for every site in the US ( for example ) takes a long time.
Steven Mosher: “All very interesting. The point being there is no simplistic single factor adjustment you can apply to data to correct for UHI.”
Certainly true, and a beautiful red herring on your part. The question is not whether or not there is a simple or complex relationship but whether there is a correlation. And, of course, you wisely note that there is such a correlation.
Am I to take you as making a legitimate broader argument that climate is too filled with confounding to make simple correctives not based on instrumentation? Or am I to take this blow hard obfuscation about what happens when the wind blows hard?
Concerning Tokyo, vehicles and the Weekend Effect, I made an incorrect assumption (i was in a hurry)
Central Tokyo is indeed significantly warmer on week days than on weekends, but the main cause seems to be waste heat from buildings and not motor vehicles. Central Tokyo has a very low ratio of cars to people – hardly anyone commutes to work by car.
In places where there are high ratios of cars to people, like the US Mid-west the Weekend Effect is reversed and weekends are warmer than weekdays, primarily due to warmer minimum temperatures ((Forster and Solomon, 2003).
This study from Toronto (high vehicle ratio) seems to indicate higher weekend ozone levels could be the cause. Its well known that in places with high numbers of motor vehicles have higher ozone levels at the weekend.
Weekday/weekend variations in tropospheric ozone concentrations were examined to determine whether ground-level greenhouse gases have a significant impact on local climate. The city of Toronto, Canada, was chosen due to a high volume of commuter traffic and frequent exposure to high ozone episodes. Due to day-of-the-week variations in commuter traffic, ozone concentrations were shown to vary significantly between weekdays and weekends. During high ozone episodes weekend air temperatures were significantly higher than those observed on weekdays. As no meteorological phenomenon is known to occur over a 7 day cycle the observed temperature variations were attributed to anthropogenic activity.
http://www.sciencedirect.com/science/article/pii/S135223100200184X
UHI in Delhi? A few weeks ago I posted a note in tips n notes about the below linked paper.
http://www.scirp.org/journal/PaperInformation.aspx?paperID=5405&JournalID=144
Urban Heat Island Effect over National Capital Region of India: A Study using the Temperature Trends. Manju Mohan, Anurag Kandya, Arunachalam Battiprolu
Full paper is open access at the link.
REPLY: Thanks, sometimes we miss things – Anthony
[snip your comments will not be allowed here under this handle, you aren’t Al Gore, you live in Norway. Who do you think you are fooling with this weak facade?]
This is an interesting study but the formula for the curve is simply empirical and therefore not likely to lead anywhere special.
The most notable thing to me is the fact that most of the UHI effect occurs at lowest densities. US data shows this result too.
Many of the comments here show insight, especially concerning aerosols. New Delhi must be one of the worst of the world’s smoggy cities. When I visited in October 2009 there was an article in the Times that assured the reader that Washington DC has a carbon footprint 13 times higher.
Well, that tells most of the story of the virtues of natural gas and electricity for cooking and unleaded gasoline for vehicles: in downtown New Delhi at noon, it’s not easy to see more than a city block.
For Steve Mosher:
Distributions of many socio-economic variables like income and population density are not gaussian, ie normally distributed. The lognormal distribution gives a better fit. Generating a pseudo-variable using a multiplicative rather than an additive process will confirm this experimentally if you don’t want to do the math. I have found similar results in analysing satellite images: the intensities are usually not normally distributed, but log transformations are helpful for analysis.
Having said that, in the study shown, the rapid rise of temperature below 20,000 density seems to be the main item of interest. We might expect to get similar results applying the same method to much smaller cities. Except ASTER images are not easily found for cities of interest.
Even the BBC weatherman the other day said that it would be about 4 degs cooler in the country than in town overnight with a risk of some frost (in August).
And then it was reported
http://www.itv.com/news/update/2012-08-31/cold-august-night-on-record-for-parts-of-the-uk/
So the Delhi paper is unsurprising and a significant UHI contribution to 20th century warming would be not be unexpected.
How much is the UHI contribution to the 20th century global warming record?
son of mulder,
“How much is the UHI contribution to the 20th century global warming record?”
In my opinion, the best way to evaluate it is to consider proxies whose reliability is demonstrated for high frequency, eg tree ring densities.
The one thing I’ve noticed on these comments is that no one denies that UHI exists. How much seems to be the question. Maybe, before politicians and feather bedding climate scientists get their respective government to spend trilions of dollars on this scheme, some responsible citizens will take an Anthony Watts Data Recorder and drive through the HCN sites first recording the actual UHI. The price of a few Data Recorders vs Trillions of dollars? Sounds like a no brainer to me.
Anthony’s next project. Game on!!!
I do believe that if CO2 is a well mixed gas, I should see a similar temperature fingerprint for all sites where long term temperatures have been recorded. If I look at the temperature record for Los Angeles and compare it to the temperature record for Pavillion Wyoming; I don’t see the same fingerprint. The temperature difference between the two sites can be explained by UHI, not CO2. Ummmm, why am I Skeptical?
” phi says:
September 1, 2012 at 3:36 am
In my opinion, the best way to evaluate it is to consider proxies whose reliability is demonstrated for high frequency, eg tree ring densities.”
Silly old me, I’d be inclined to look at the global warming rate calculated from sites always rural and subtract it from the global warming rate for all sites.
Why tree rings etc?
It would be nice to have that graph with logarithmic scale on the population density axis, from this point of view it doesn’t look like logarithmic curve is the best match.
I must agree that it increases credibility of Dr. Spencer’s ISH-PDAT somewhat because it supports one of assumptions it is based on, particularly the one that exponential population growth (which may be considered standard) results in linear temperature trend change.
There’s still one thing I don’t like on ISH-PDAT left, though. And that’s the fact that resulting average trend after the adjustment is lower than average trend of unadjusted lowest-population sites. I haven’t yet got any explanation how that may happen if the math is done correctly.
For Steve Mosher:
Distributions of many socio-economic variables like income and population density are not gaussian, ie normally distributed. The lognormal distribution gives a better fit.
#############
Having worked with this stuff for the past 4 years I’m well aware that a log transformation looks inviting. Been there done that.
Having said that, in the study shown, the rapid rise of temperature below 20,000 density seems to be the main item of interest. We might expect to get similar results applying the same method to much smaller cities. Except ASTER images are not easily found for cities of interest.
###############
here is the rub.
If you look at population density for station in GHCN or BEST you will find over 50 % at much lower densities. In this example you have densities from 300 per sq km to 50000+.
I can pull out the actual distributions. The other problem is that the lowest density ( 0-20) is
very suspect population data since it is imputed and not actual counts.
I’m Using MODIS, there is plenty of data. Its just time consuming
“Am I to take you as making a legitimate broader argument that climate is too filled with confounding to make simple correctives not based on instrumentation? Or am I to take this blow hard obfuscation about what happens when the wind blows hard?”
No, just that the problem is very difficult if you think you can correct for UHI. Better to try to identify all the known causes and eliminate stations accordingly. So, I think GISS UHI correction is bunk and I think Spenser correction is bunk.
The next study by the greenies will be “Correlation Found Between City Population and Temperature – More people tend to live in the warmer cities” and use that to disprove the above.
son of mulder,
“Why tree rings etc?”
Because it is very difficult to ensure that rural stations have not been disturbed throughout the twentieth century. Trees are less accurate but they are usually conveniently located to avoid UHI.
If you admit that an inhabitant activity produce an UHI unit, the effect on thermometer will be dependent of the distance. The relationship is probably proportional to the inverse of the squared distance. If you are modeling a city by a circle of given density and the population increase by expanding the circle, you get something like duhi = K / r * dr. If you integrate, indeed you see this simple model connects UHI to the log of the population.
A consequence of this is that a very small urbanization close to thermometers has a big effect and therefore the use of rural stations is not enough to solve the problem.
Steven Mosher: “No, just that the problem is very difficult if you think you can correct for UHI. Better to try to identify all the known causes and eliminate stations accordingly. So, I think GISS UHI correction is bunk and I think Spenser correction is bunk.”
Good to hear. I don’t disagree with your position in general. But it is worthwhile to note that the same ISA issues you mention arise in a great number of rural stations as well. Such that while yours is an adequate and responsible solution it is also the case that it applies generally for a large number of general siting problems. Especially as the current crop of homogenization methods will exacerbate any extent issues arising from ISA considerations.
Steven Mosher says:
September 1, 2012 at 9:40 am
Steve, I tend to agree in that almost any UHI adjustment/correction/fudge factor will be bunk because each correction must be locality and station specific, if nothing else. Then you still have to compare closeby relatively comparable rural station data to see if there is some underlying correlation once the UHI is removed. All of this, on top of actually checking the various stations actual data/sitiing/site move issues, etc, etc.
In summary, it’s very hard to believe that anyone will do the job properly and thoroughly. Moreover, if they do, do we think the data will show the miniscule trend above and beyond any natural warming within the natural variability ‘temperature noise’? So, you do all that work and show what, exactly? – that we simply cannot use the available data for really serious analysis?
For my money, I reckon they should just keep it simple – if a rural (good quality) station shows say, x deg of warming then that should be the ‘benchmark’ and if a nearby city station shows y deg of warming at a significantly higher rate, then it should simply be ignored! To be fair, I think the whole surface temperature metric has lost its direct usefulness especially after all the chopping and changing, and can only ever be considered in a supporting role rather than as direct assured ‘evidence’?
Note the steepest rise is at very low pop. densities. UHI data corruption is everywhere!
“phi says:
September 1, 2012 at 11:47 am”
Too complicated, William of Ockham will be turning in his grave. And tree rings can’t be trusted as a proxy for anything other than fantasy. As I said in my earlier comment we have the weatherman in the UK pointing to a 4 deg C difference overnight between city & rural. So it would be good enough to define remote sites as those showing that sort of difference betwen themselves and nearby cities as rural.
So then you have your rural stations defined globally and just calculate average global warming for them over the last 100 years and subtract from the overall average global warming.
Somewhat on topic.
Warwick Hughes has a post up that mentions the ‘hotspot’ resulting from recent anomalously high temperatures at Giles, Western Australia.
Giles is noteworthy for being probably the most remote and pristine weather/climate measuring site in the developed world (excepting remote islands). There is no significant human settlement, agriculture or industry for several hundred Ks in all directions.
The other noteworthy thing about Giles, is the entire population (4) is composed of meteorologists.
I mention this to highlight even apparently pristine locations can have problems. Although I have no idea what the problem is at Giles.
http://www.warwickhughes.com/blog/?p=1728
Philip, here is the BoM page on Giles:
http://www.bom.gov.au/wa/giles/photos.shtml
Many photos. It would be a curious to see what could be learned by placing a few dataloggers at distance from the station at 2M heights at increasing distances from the station. While the Tmin effect seems so large compared to the facility itself, it is possible that the buildings and radar disrupt the boundary layer in nighttime prevailing wind by causing turbulent mixing, and thus a warming. Given there’s nothing else around for hundreds of kilometers except scrub bushes, this might be what is going on.
See McNider et al 2012 http://pielkeclimatesci.wordpress.com/2012/07/12/guest-post-by-richard-mcnider-on-the-new-jgr-atmosphere-article-response-and-sensitivity-of-the-nocturnal-boundary-layer-over-land-to-added-longwave-radiative-forcing-2012/
A check of wind directions against the Tmin anomaly might help confirm this.
Thanks Anthony..
Some things that caught my attention in the photographs.
A swimming pool
Trees in the station area are taller than the nearby bush, which says irrigation to me
A parking lot full of 4WDs, camper vans and trailers. In Australia we call them ‘grey nomads’, older people who spend most of their time travelling around and this would be one of only a few spots people can stop in that region. Generators are popular with them to run TVs, etc in the evening.
Unfortunately, Giles is a thousand miles from Perth, rather to far for a trip to check it out.
To add to my previous comment.
Swimming pools are very effective heat sinks, especially somewhere like Giles where skies are cloud free most of the time. They release their stored heat at night. I wonder when that swimming pool was installed?
One of the photographs shows green grass in front of the staff accommodation. Grass in this region is a sure sign of irrigation.
The significance of irrigation (which is always done at night here) is that night time irrigation reduces temperatures. The experience from the move of the Perth official site from a location next to an irrigated park to an un-irrigated location was by an average -1.5C.
Irrigation has become increasing restricted in Western Australia over the past decade or so, and increasing politicized. If irrigation has been reduced at Giles, it will have raised night time temperatures.
Steven Mosher
Owyagoinmate, orright?
You mention above that UHI is complicated and that one has to qualtify confounding variables to get to a pure example of UHI. Fully agreed. The topic of Giles, population 4, in the centre of Australia has just come up on Warwick Hughes’ web site. Have a look at the photos.
http://www.warwickhughes.com/blog/
Here is the start of some Giles analysis I did in 2010 or so. The start period was chosen to avoid complications with the change from recording in F to C, which I wrongly thought at the time was 1966. It was formally Sept 1972, as I found out later.
http://www.geoffstuff.com/Giles.xls
I cannot think of any influence that would be related to UHI at this site, though that depends on defining UHI properly. Part of it can be due to events a few meters away, like a new layer of asphalt on formerly grassy ground under the screen with the thermometry gear, while some of it might be from regional causes as is well known and illustrated by the Delhi example. Also, there is usually a general unproven assumption that instrument drift is negligible.
When I look at this response from Giles, all I can conclude is noise, noise and more noise, partly because of a lack of detailed meta data for the close proximity possible cause of UHI at Giles. You already have a number of Australian examples like this. Some have rising temperatures, some have falling temperatures. Some have Tmax and Tmin converging over time, others have them diverging, probably illustrating that a linear least squares fit is inappropriate for temperature/time series like these. I have no idea why the temperature has risen at Giles as shown.
So the question becomes, “What is the true inherent weather noise at pristine sites and how can it be incorporated into analysis at more complicated sites?”
Until we can answer that question confidently, we can but play with UHI.
So in other words, Warming is strictly due to the over concentration of people in one locale. Well, the solution is obvious, isn’t it? Live only in the suburbs (low density communities) or underground. Knock down the cities and plant crops in their stead. /snark/
The first effect of “”UHIE” is clearing of the land, at least where vegetation had already been present (true for most sites in habitable regions, but not deserts). Removing vegetation reduces local evaporative cooling. That’s even before any structures are added. Building structures adds IR-absorbing surfaces that also don’t evaporate water, increasing the effect.
Bottom line is “rural” needs to be defined as similar to conditions before any major man-made changes occurred.