Berkeley Earth, Very Rural and Not

Jet exhaust as climate forcing

Guest Post by Willis Eschenbach

The good folks over at the Berkeley Earth Surface Temperature project have published their paper about urban heat islands. It’s called “Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications”, by Wickham et al, hereinafter W2013.

They find no urban heat island effect, saying at the close of the Abstract:

Time series of the Earth’s average land temperature are estimated using the Berkeley Earth methodology applied to the full dataset and the rural subset; the difference of these is consistent with no urban heating effect over the period 1950 to 2010, with a slope of -0.10 ± 0.24/100yr (95% confidence).

Having read the paper, I can’t say I’m surprised that they find no difference.

The W2013 authors have used the MODIS 500 metre dataset to identify urban areas. It’s a dataset that uses the MODIS satellite data to classify the planet into land-cover classes, including urban. The W2013 study uses the MODIS data to divide all the world’s temperature records into “very rural” and “not very rural”. Then they show there is little difference in the trends between the two groups.

GEhenderson field 2003 grid 100%Figure 1. Actual MODIS grid overlaid on Henderson Field, Guadalcanal, Solomon Islands. Picture is from 2003, prior to recent development. All gridcells would be classified by MODIS as not built-up, since less than 50% of the area of any given gridcell is built-up area. The ocean is at the upper right. Click to embiggen.

Let me talk a bit about the W2013 study and the MODIS map, and demonstrate by example the reason why I didn’t expect their “very rural” split to have much discriminatory power regarding temperature trends. As an example, let me use one of my favorite airports, Henderson Field on Guadalcanal.

Here is the description from the W2013 study of how they identified the “very rural” stations.

The MOD500 map is available as a raster image, providing a binary classification (urban or not urban) for a global grid with pixels of size 15 arc-seconds.

Rather than compare urban sites to non-urban, thereby explicitly estimating UHI effects, we split sites into very-rural and not very-rural. We defined a site as “very-rural” if the MOD500 map showed no urban regions within one tenth of a degree in latitude or longitude of the site. This choice should minimize errors that occur from MODIS classifications in fringe areas. We expect these very-rural sites to be reasonably free from urban heating effects.

This led me to investigate the MODIS “MOD500” map. The MODIS dataset is described in A new map of global urban extent from MODIS satellite data. Basically, it uses multi-spectrum radiation to discriminate between types of land cover, and to identify the signatures of various built-up areas..

Of immediate interest to this discussion is their definition of “urban areas”:

a. Urban areas

In both datasets, urban areas (coded class 13) are defined based on physical attributes: urban areas are places that are dominated by the built environment. The ‘built environment’ includes all non-vegetative, human-constructed elements, such as buildings, roads, runways, etc. (i.e. a mix of human-made surfaces and materials), and ‘dominated’ implies coverage greater than or equal to 50 percent of a given landscape unit (here, the pixel). Pixels that are predominantly vegetated (e.g. a park) are not considered urban, even though in terms of land use, they may function as urban space. Although ‘impervious surface’ is often used to characterize urban areas within the remote sensing literature, we prefer the more direct term ‘built environment’ because of uncertainty and scaling issues surrounding the impervious surface concept. Finally, we also define a minimum mapping unit: urban areas are contiguous patches of built-up land greater than 1 km^2. SOURCE

Now, each “pixel” that they mention above is one of the gridcells shown in Figure 1 above. Those are the actual gridcells used in the MODIS map. If more than half of a gridcell is “built environment” (houses, roads, runways, etc.) then the gridcell is counted as “built-up land”. However, there’s a final hurdle. You need to have five adjacent gridcells of built-up land to have those gridcells classed as part of an urban area in the MOD500 map.

This is kind of an odd definition. It means that any small hamlet is rural, since it won’t cover a square kilometre (about 250 acres). You have to have five contiguous built-up gridcells, which totals just over 1 km^2, for those gridcells to be classified as urban in the MOD500 map.

Figure 2 below shows how much of the land shown in Figure 1 could be built up, covered with houses and roads and parking lots, without anything being called “urban”.

GEhenderson field 2003 no contig 60%Figure 2. As in Figure 1, overlaid with theoretical possible buildup without any areas being classified as “urban” in the MODIS dataset. Gray areas are 100% built-up, white triangles show 40% built-up. A total of about 70% of the land is built-up. None of it is classified urban because nowhere are there are five contiguous built-up gridcells.

In any case, that’s the history. Figure 3 shows the situation today:

GEhenderson field 2013 gridFigure 3. Actual MODIS grid overlaid on current view of Henderson Field. Picture is from January 2013. All gridcells would still be classified by MODIS as not built-up, since less than 50% of the area of any gridcell is built-up area. The gridcells shown in red are the nearest to being classified as built up, as they all have about 40% of their area covered with roads, houses, and runways.

Again, in the MOD500 map, none of these gridcells would be classified as urban. Even if those four gridcells were more than 50% built-up, still none of them would be classified as urban because there aren’t five of them and they aren’t contiguous.

However, the fact that the MOD500 map classifies all of these gridcells as being rural makes no difference—the Stevenson Screen housing the thermometers is still in a horrible location. Here’s why, borrowed from Anthony’s post on the subject. This shows a ground level view of the station taken from the terminal. Note the white box of the Stevenson Screen housing the thermometer and other instruments in the green area just left of center, below the smoke visible in the distance.

Here comes a plane! Weather station visible to the left of the plane’s tail, right of the white mast.

Coming into the terminal…

Hey, park it over here!

Uh, oh, look where the jet exhaust is pointed:

Hmmm, a new high temperature today?

Back to normal.

Now, that’s the problem with the location of the Henderson Field weather station. There are lots of good places to locate the Stevenson Screen in the local area … and it’s in a bad place. This situation is mirrored in many airports and weather stations in general around the planet.

The difficulty I have with the approach of using the MOD500 map to distinguish urban from rural is simply stated:

Many of the siting problems have nothing to do with proximity to an urban area.

Instead, many of them have everything to do with proximity to jet planes, or to air conditioner exhaust, or to the back of a single house in a big field, or to being located over a patch of gravel.

And sadly, even with a map averaged on a 500 metre grid, there’s no way to determine those things.

And that’s why I didn’t expect they would find any difference … because their division into categories has little to do with the actual freedom of the station from human influences on the temperature. Urban vs Rural is not the issue. The real dichotomy is Well Sited vs Poorly Sited.

It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. I’ve been agitating for a while to call it the LHI, for the “Local Heat Island”. It’s not essentially urban in nature. It doesn’t matter what’s causing the local heat island, whether it’s shelter from the wind as the trees grow up or proximity to a barbecue pit.

Nor does the local heat island have to be large. A thermometer sitting above a small patch of gravel will show a very different temperature response from one just a short distance away in a grassy field. The local heat island only needs to be big enough to contain the thermometer, one air conditioner exhaust is plenty, as is a jet exhaust …

The only approach that I see that has any hope of success is to painstakingly divide the stations, one by one, based on what is in their viewshed, and exactly how far are they from a variety of ground covers. The work done by Michel Leroy of METEOFrance in 2010 lays out one way to do this, as discussed here.

Because even with the outstanding resolution of the MODIS dataset, it still can’t tell us whether the siting of the weather station is up to snuff, or whether we’re just measuring the temperature of a jet engine at 50 metres … and that’s why I don’t find the results in the W2013 paper at all persuasive.

Best regards,

w.

PS—One curiosity. The published paper is a slightly polished version of their earlier pre-publication paper with the exact same title, available here. The curiosity is the re-ordering of the authors on the two title pages, viz:

W2013 author list

Internal politics?

I also note that my friend Steven Mosher is now listed as an author on the paper, my congratulations to him.

[UPDATE] Steven Mosher has pointed out that in the analysis, this particular station (Henderson Field) is classified as urban. I have no problem with that, as it is not a long ways from the capital and is likely classified urban by BEST (although likely not by MODIS) on that basis for their study.

Nor was the classification of that particular station my point, which I quote here from above:

And that’s why I didn’t expect they would find any difference … because their division into categories has little to do with the actual freedom of the station from human influences on the temperature. Urban vs Rural is not the issue. The real dichotomy is Well Sited vs Poorly Sited.

It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. I’ve been agitating for a while to call it the LHI, for the “Local Heat Island”. It’s not essentially urban in nature. It doesn’t matter what’s causing the local heat island, whether it’s shelter from the wind as the trees grow up or proximity to a barbecue pit.

Steven also adds the following comment:

My main concern is that people will think the article is about urban/rural (see the title … very rural, not) when the text is about “siting”.

Also, folks may get the idea that airports can never be CRN1 [a measure of station quality] … something which Fall 2011 doesn’t support, as many of CRN1 and CRN2 are at airports.

A clarification might help prevent future misunderstandings and gotchas.

My thanks to Steven for calling that classification to my attention, and also for his clarification.

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seanbrady

How many total thermometers in the data set? How about a crowd sourced division based on Google Earth into LHI or no-LHI? Then run the split through their same methodology to see if there is a difference?

Thanks Willis. As I point out here, http://wattsupwiththat.com/2012/07/29/press-release-2/ the resolution of the study we did is down to 10 meters. Leroy 2010 sees 100 meters as the limit to the effects he observes relating to siting. Like Marcott et al, this is a case of a low resolution sample missing the “spikes”.
Might be fun to see if we can link high temperature spikes at Henderson Field to flight arrival/departure times. The data must be there somewhere.

UHI exists. If the BEST team can’t find UHI, they should keep trying. And quit pretending UHI doesn’t exist.

Mike Bromley the Canucklehead in Switzerland

I can’t believe how chintzy science has gotten. Of everything else they trumpet about, the real world is not one of them.

JunkPsychology

Why would a knowledgeable reviewer recommend accepting a study that used this method?

Local Heat Island! I love it! willis’s eagle flies ever higher!

Resourceguy

If oceans cover 71 percent of the planet and we have more efficient temperature monitoring systems on the ocean and overhead in satellites, I’m more interested in the regional and hemispheric differences that present themselves between these major monitoring systems of land vs. ocean over time. Slicing and dicing land-based data sets does not cut it for this important issue.

HankHenry

WOW this is news! No such thing as UHI? Just about every weatherman in the country from time to time mentions UHI in their weather reports. But hey, it’s science so I guess it’s undeniable.

David Schofield

Just watched the BBC weather man; “overnight it will be 2 degrees in the towns and below freezing in the countryside”. Obviously didn’t get the memo.

John Tillman

But, as usual, it’s even worse. Hansen recognized UHIs, but his long secret algorithm to adjust for them always made them warmer.

Hey, park it over here!
That’s Hilarious!!!

No mention of Henderson Field is complete without the original history of this Japanese air base, captured by the Marines and scene of fierce fighting in a tropical jungle with 200 inches of rain per year. This 2500 square mile volcanic and raised coral reefs dot in the Pacific is covered with impenetrable jungles, hulks of war machines and ghosts of heroes. The land, sea and air battles included the last sinking of a war vessel by ramming, when a New Zealand destroyer rammed a Japanese submarine. This battle was described in “Guadalcanal” by Richard Frank. There were over 200,000 documented cases of Japanese cannibalism documented in WW II war crimes trials, which were ordered sealed for ‘national security’ reasons, per “Flyboys”.

Nick in Vancouver

Realistic? Not very realistic.

Michael Cohen

I think it would be helpful for casual readers to emphasize that the paper is reporting no effect on the global land surface temperature trend from urban heating, not that there is no urban heating.

Chris @NJSnowFan

Good read..
Pavement pavement pavement, Pavement does not emit water vapor like trees except after ir rains or during. Proven fact one full grown oak tree emits 7 tons of water vapor every day through it’s leaves. Water vapor cools the air and that is a proven fact. Pavement warm the air to dry and hot.
Urban heat islands are real.

Henry Bowman

A few years ago, this kid (with help from his father, I believe) showed reasonably well the difference between rural sites and urban sites in the U.S. I’m sure there are some problems with his methodology, but surely someone could do a good job, inasmuch as the data are out there:

JeffC

so a thermometer located in center of a patch of concrete in a 100 acre field would be considered rural … right …
did they even bother to run their model and then actually compare a “rural” area to what they could see with their eyes … I would imagine that almost every airport is considered “rural” …

dorsai123

[snip]

Gene Selkov

Anthony says:
> Might be fun to see if we can link high temperature spikes at Henderson Field to flight arrival/departure times. The data must be there somewhere.
I will be very interested to find that out, although I suspect a thermometer needs to be very sensitive and fast to pick up a gust of warm jet exhaust. I can more easily imagine runways having a substantial effect on thermometers located near them than the jet traffic on them. You can estimate the magnitude of the effect using this paper outlining the temperature contours of jet exhaust at different throttle positions:
http://www.boeing.com/commercial/airports/acaps/7471sec6.pdf
You need to combine that with the estimate of the “duty cycle” — how many times an hour is there a jet on the runway aiming its exhaust right at the weather station, and for how long.

Reg Nelson

Muller would have saved himself a lot of wasted time and effort if would have just read this first:
http://wattsupwiththat.com/2013/01/20/noaa-establishes-a-fact-about-station-siting-nighttime-temperatures-are-indeed-higher-closer-to-the-laboratory/

MAK

Comparing very rural to very urban should not have any difference in the trend at all – assuming that both stations do not change their classification. If a site is already UHI-corrupted, comparing it’s trend to rural is stupid. You will not find anything.
The whole thing changes if a rural site gradually changes to urban site. This kind of change you don’t find using MOD500 dataset for site classifications.
Using MOD500 even to try to sort this out clearly tells us these scientist don’t know a thing what they are trying to accomplish. Or maybe they do – but then they really don’t want to find UHI effect.

OK, so the jet exhaust peaks out the temp.
Can’t we just fix that with smoothing?
/sarc

Curious George

The BEST methodology simply does not work. It is a commendable attempt at an objectivity, but unfortunately it fails.

Rud Istvan

Willis, excellent post. You actually point to a much bigger problem. Rather than define an issue (local heat island is excellent, even if there are more of them in urban areas) and then go actually get real data in terms of the definition, it seems more and more that what is done is to see what happens to be lying around, then repurpose it by massaging to get a paper published that is inherently shaky.
The correct way is what Anthony’s brilliant crowd sourced station siting database did. Actually go check the sites for the parameters needing to be measured. It is stunning that NOAA and gang still haven’t gotten out of their taxpayer funded offices to do the same. It must be easier to sit in the office drinking coffee and dreaming up more undocumented homogenizations to wish the problem away.
Let’s hope Anthony’s paper gets published soon.
Regards

Bob

Steve Mosher, I guess we shamed you into becoming an author. Congratulations/

Brad

Traditionally, the first author did most of the work, the last author had the funding or supervised the work, and the rest can be from most to least important, or simply alphabetical.

Ford Prefect

Edmonton Alberta – you know the most evil place on the planet – is a good place to compare “The Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications”, Wow you just can’t make a name like that up.
Edmonton has two airports one in the middle of the city and one out side the city. The overnight temp for both airports is given and one is usually cooler. If you were to bet which airport is cooler the one who picked the country would soon have all the money.

Duster

I have some concerns about “rural” vs. “wild” that Anthony can directly observe around Chico, especially to the west where the Sacramento Valley is heavily agricultural. There is little justification for thinking that merely because land is “rural” with a relatively scant human population, that there is little anthropic effect on temperatures. Agricultural land is just as extensively altered as urban land. While it won’t have the heat discharge from air conditioners, industry and transportation that a city does, most agricultural modifications increase insolation, and they do it on a far larger scale. Few if any sites in the USHCN are really in wild lands, possibly a few linked to fire look outs, otherwise they are historically biased to to places that were important to the Department of Commerce’s purposes. That is, places where people lived and worked.
The CRN in general seems to be better sited, but even there occasional sites like Whiskeytown in California are close to massive human alterations. The Whiskeytown CRN station is upslope from Whiskeytown reservoir, a large, artificial reservoir on Clear Creek. Being directly upslope from the lake means that warmer water at night should moderate cooling around the lake during the night.

The Village Idiot

This is exactly why we can’t trust the instrumental record. It’s just wrong. Tony said: “I don’t “deny” the instrumental record” . Just can’t see the difference between don’t trust and deny.
http://wattsupwiththat.com/2013/03/18/monday-mirthiness-watch-the-genesis-and-retraction-of-of-a-smear/
Although any record showing cooling gets the thumbs up 🙂

Bloke down the pub

Perhaps once the USCRN has got a couple more years under it’s belt, they’ll no longer be able to ignore the differences to the thermometers that are stuck up an airplanes tailpipe. That’s created a mental image that only Josh could do credit to.

joshv

I think both you and BEST are missing the point somewhat. How are your poorly sited stations introducing a trend? If it’s hotter near a runway, then of course that station will run hot, but it won’t show an artificial trend, on decadal time scales it will show an constant offset from a better site location nearby.
The UHI affect as I understand it is related to *changes* in urbanization, and I think this point is being lost on most everyone. Find locations were urbanization has been static, and compare those to ones where urbanization has changed. It doesn’t really matter if they are poorly sited or not – if the local conditions have remained static, that site should be a good control for a site that’s nearby where the conditions have not been static.

DayHay

Exactly what question were the authors trying to answer? Whether MODIS can detect UHI? Then they have proved it cannot. That is important
Perhaps if they started with “Does siting have any affect on homogenized temperature data trends?”
I guess none of the authors ride motorcycles. Take a ride at dusk and feet the real temperatures as you exit the city. Even as little as a 50 foot elevation change is very noticeable. But at least we know the world land temp to 0.00001 deg C……..ugh.

sadly willis, Zeke and I did a sensitivity test where we excluded airports for just this reason
So, no fruit cup for you

MAK says:
April 4, 2013 at 10:10 am
Comparing very rural to very urban should not have any difference in the trend at all – assuming that both stations do not change their classification. If a site is already UHI-corrupted, comparing it’s trend to rural is stupid. You will not find anything.
The whole thing changes if a rural site gradually changes to urban site. This kind of change you don’t find using MOD500 dataset for site classifications.
##########################
Thats why I checked the changes in population from 1900 to present.
the rural sites were virtually unchanged. the urban sites had large changes in population density.

George V

I saw an example of the JEHI (Jet Exhaust Heat Island) effect a couple of winters ago while on vacation in Traverse City Michigan. One morning I was checking the forecast temp for the day on the NOAA web site and checked the history file to see how cold it was the night before (because it seemed pretty durn cold). Sure ’nuff, it was cold, dropping to single digits around midnight from what recall. But the next hourly reading (some minutes after 1AM – can’t recall exactly) was 25 deg. F. The next reading was back in single digits. I suspect some bizjet or turboprop had taken off since there are no commercial flights at that time.

Curt

I believe that both “Urban Heat Island” and “Local Heat Island” (or “micro-ste”) effects must be taken into account, and separately. A station in New York’s Central Park might not have significant LHI effects, but would have large UHI effects. A rural site in a parking lot would have LHI but not UHI.
But if we’re analyzing trends, I agree that we must look primarily at changes in the environment around the station, and evaluate them carefully. For a long time now, many scientists have believed that the magnitude of the UHI effect is roughly a logarthmic function of population, so a rural site where local population increases from 1000 to 2000 would have the same increase as an urban site with an increase from 1,000,000 to 2,000,000.
Airport sites are particularly problematic, as the airports are often placed initially in rural areas where land is cheap and there are few neighbors to annoy. But there is an almost inevitable development of supporting infrastructure around the airport over the years — but since no one lives in this infrastructure, population estimates of UHI don’t catch this (though MODIS-type estimates could).

‘Now, each “pixel” that they mention above is one of the gridcells shown in Figure 1 above. Those are the actual gridcells used in the MODIS map. If more than half of a gridcell is “built environment” (houses, roads, runways, etc.) then the gridcell is counted as “built-up land”. However, there’s a final hurdle. You need to have five adjacent gridcells of built-up land to have those gridcells classed as part of an urban area in the MOD500 map.”
Actually not. 4 cells will do. If you like you can double check the accuracy of the urban classification by MODIS Urban by using the NLCD 30 meter impervious surface data.
you wont find anything. This issue was raised early on in web comments, so of course
I had to go get 30meter data to check.
it looks like this
http://stevemosher.files.wordpress.com/2012/10/nlcdisa2967178.png
http://stevemosher.wordpress.com/2012/10/08/sample-suhi/
http://stevemosher.wordpress.com/2012/10/02/city-size-and-suhi/
http://stevemosher.wordpress.com/2012/09/26/rastervis-to-the-rescue/
http://stevemosher.wordpress.com/2012/11/25/modis-r-package-tutorial/
or you can double check with the ORIGINAL SOURCE data for the modis urban dataset which allows you to use a 500 meter rule as opposed to the adjacent cell rule. The orignal datasets are done on an 8 day basis. huge amount of data. But you can actually go check the classification using: the 500K urban dataset ( with the 1km rule) or the 500 meter dataset without the 1km rule. bet you didnt check that. and then you can check the sensitivity of the classification at SUB 500 meter resolution using 30 meter data. its a lot of work, but go ahead.
I used some of that here
http://stevemosher.wordpress.com/2012/10/11/pilot-study-small-town-land-surface-temperature/
Oh, you can also check the data using 300 meter ESA data
http://geonetwork.grid.unep.ch/geonetwork/srv/en/metadata.show?uuid=geodata_%202055
If you want to know how to eliminate airports, see my metadata package.
Doesnt make a difference.

sunshinehours1 says:
April 4, 2013 at 9:20 am
UHI exists. If the BEST team can’t find UHI, they should keep trying. And quit pretending UHI doesn’t exist.
#######################
Dude I’m still waiting for somebody to accept my challenge.
1. Define Urban or rural ex ante in a way that is objectively measureable.
2. i will divide stations into urban and rural per your definition.
3. i will compute the difference.
A cookie for anyone who can find the signal.
So, there is the challenge.

rogerknights

joshv says:
April 4, 2013 at 10:42 am
I think both you and BEST are missing the point somewhat. How are your poorly sited stations introducing a trend? If it’s hotter near a runway, then of course that station will run hot, but it won’t show an artificial trend, on decadal time scales it will show an constant offset from a better site location nearby.

Things that will increase the trend at an airport:
If larger planes are used that consume more fuel.
If jet planes are used, which blow their heat sideways.
If air traffic increases.
If the airstrip was originally dirt or grass.
If the airport was originally rural but the area around it becomes urbanized.

Nik Marshall-Blank

But we cater for this. We take the median for each day and discard all outliers greater than 25% of the median. This means in a single day out of 100 observations we have only 8 valid readings. Mmm… same times as the planes landed. Curious.

Bob Kutz

While I don’t disagree with your analysis, I do hold out that there is a UHI effect. There certainly is an LHI effect as well, but I see the UHI effect every single day.
My house is in town. I work in an industrial park outside of another town. The distance between is some 20 odd miles. The outdoor thermometer on my house agrees to my truck’s thermometer and the local cable provider’s ‘current temp.’ from their offices about 8 blocks from my house.
When I jump in the truck and head to work, it drops between 4 and 7 degrees F by the time I am out of town and near the local airport. Hot or cold; the temp drops when I go outside of town.
If I take the other way to work, it’ll drop another 4 to 6 when the road drops down to the river bottom for several miles. That is just local temperature variation. Being in the bottom of a hole certainly causes that.
The industrial park where I work is actually a WWII air corps training base. The runway is still in use as a local airport. This is different from the airport I pass on along the way.
The temperature here usually agrees between my truck, the ‘official’ temperature at the airport, our lab’s outdoor ambient temp. reading, and the temperature at the meteorology lab at the local community college, just across the industrial park from my office. Sometimes not. Sometimes our lab is effected by the rather large burner we run. Sometimes the airport thermometer is several degrees higher than the other two. My truck and the aeronautics/meteorology lab thermometers don’t disagree by more than a degree.
Now if I run into town during the day, another 6 miles to the south, the temperature is always 2 to 5 degrees warmer than out here at the industrial park. Always. And this town is down on the river. ‘In a hole’ so to speak. So the coolness of the indigenous riverbottom landscape is more than offset by the UHI effect of 50,000 people.
So I can point to two very definable ‘UHI’ here in southeastern Iowa where neither community is over 50,000 people.
I don’t know how this plays out in terms of data, but there is little doubt that the average temperature for these two, geographically dis-similar towns that are in relatively close proximity.
Then there is the large town about 50 miles to the northwest. The UHI effect there can be seen just by watching the local TV weather broadcast. The host occasionally points out that it can be close to 10 degrees below zero in most of the region before the thermometer at the downtown TV broadcast station dips below zero. Going into that town on a hot day proves the point every single time. It can be hot and humid out in the cornfields. But you get into the heart of that metro area and it is scorching. It can be more than 10 degrees difference between just 15 miles to the southeast vs. right down town. And it is worse at night. All that concrete holds the heat about as well as any heat sink you might intentionally devise.
As I said; I don’t know how this plays out in the climate data, but there is a UHI effect, distinctive from the LHI effect, which can be as local as the Stephens screen at an airport. The UHI effect covers most of the downtown and outlying areas in two small towns (say five or six square miles) and a fairly broad swath of territory in a large central Iowa city (probably approaching 100 square miles). Looking at google earth it is doubtful it is more than 10 sq. miles by the ‘50% in 5 squares’ method.

Don B

I have a car thermometer. Two or three times each week before sunrise I drive from town to the country and return within an hour. It is cooler away from town.
All of those authors, and none of them have a car thermometer.

Bob says:
April 4, 2013 at 10:16 am
Steve Mosher, I guess we shamed you into becoming an author. Congratulations/
#################
Not. I have never asked and will never ask to be a co author. i do the work I do and don’t care very much about were the credit is given. acknowledgments or byline? phhht. I do what i do.

Wickham et al:

We expect these very-rural sites to be reasonably free from urban heating effects.

It is not the absolute level of UHI effects that matter but the CHANGE in UHI effects, and as any economist will tell you, the law of diminishing returns really is something close to a law of nature: it is a very widespread phenomenon that the marginal effect of the first increments to any input will typically be larger than the marginal effect of further increments.
So the place where the weather station is located adds a bit of paving and some heating and air conditioning over the decades. The marginal effect on the very rural station is much greater than on the less rural and urban stations. The expectation going in could even be that very rural stations tend to experience greater UHI than less rural stations, yet Wickham treats the very rural stations as if they experience no UHI at all.
As an earlier commenter noted, it would be necessary to look at the transition of stations from more rural to less rural and compare to stations that changed less. What is the correlation between rate of transition to urban and the speed of warming over the 20th century?

Mike Ballantine

Data mining in corrupted data yields corrupted results. Willis’ example highlights a major problem with the instrumental record. Temperature measuring in the middle of an airport has only ONE valid and very important use. It gives the pilots information they need to set safe, efficient take off and landing speeds. Any other use is like using pliers to hammer nails.
IMHO, ALL airport sited temperature stations should be excluded from the climate data sets.

Nik Marshall-Blank

@Bob Kutz – Well there’s a whole new area to investigate now. Do these areas where the readings are taken correctly represent the temperatures are they are expected to cover?

davidmhoffer

Steven Mosher;
Dude I’m still waiting for somebody to accept my challenge.
1. Define Urban or rural ex ante in a way that is objectively measureable.
2. i will divide stations into urban and rural per your definition.
3. i will compute the difference.
A cookie for anyone who can find the signal.
So, there is the challenge.
>>>>>>>>>>>>>>>>>>
Sure. Define a methodology that doesn’t address the problem and in fact reinforces it instead and then bravely bet a cookie on it.

ralfellis

Same problem with the Central England Temperature series. This consists of three locations, averaged. But one of those locations is on Manchester airport, with is a major international airport with arivals every three minutes. And the Lat-Long I have puts the met station just across the grass from the engine run-up bays, in the middle of all the taxiways.

Robuk

Henry Bowman says:
April 4, 2013 at 9:56 am
A few years ago, this kid (with help from his father, I believe) showed reasonably well the difference between rural sites and urban sites in the U.S.
But that would mean venturing outside which could be dangerous, cars, cycles etc.

rogerknights says:
April 4, 2013 at 11:18 am
joshv says:
April 4, 2013 at 10:42 am
I think both you and BEST are missing the point somewhat. How are your poorly sited stations introducing a trend? If it’s hotter near a runway, then of course that station will run hot, but it won’t show an artificial trend, on decadal time scales it will show an constant offset from a better site location nearby.
Things that will increase the trend at an airport:
If larger planes are used that consume more fuel.
If jet planes are used, which blow their heat sideways.
If air traffic increases.
If the airstrip was originally dirt or grass.
If the airport was originally rural but the area around it becomes urbanized
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The one thing people miss is that airports in some areas actually are cooler and have cooling trends. why?
UHI is a function of these variables:
Incoming solar
outgoing LW
Storage capacity
Anthro heat.
Turbulance
For incoming solar airports are great because the building tend to be low and you dont get a radiative canyon ( see Oke for a description )
For outgoing LW they are great because of a sky view factor of 1. (see OKE)
Storage capacity: the is basically the heat that gets stored in concrete/asphalt etc.
This can impact Tmin. BUT you have to look at the wind speed. Airports are built with long fetches. See the fetch on willis’s airport. At 7m/s UHI disappears. if the surface roughness is low this happens at lower wind speeds
Anthro heat. yup the heat from the jets.
But this delta T has to occur at just the right time. It has to happen around Tmax.