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.
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”.
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:
Figure 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:
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.








Finally, I was curious why you didn’t use your technique to investigate things like the Oke paper or the McKitrick paper which found evidence of UHI. I’d think you could use your method and the Oke or McKitrick datasets to determine whether they were wrong or not and if so why … seems like if you’re going to implicitly claim the previous studies are wrong, you should show it directly using their data.
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Ross’s paper is flawed because of gross data errors. Its his job to fix his errors. he is well aware of them.
Let me tell you how he calculated population growth.
Take the united states population in 1979: Now divide that population evenly into every 5 degree grid cell. yes Alaska and New York get the same density. DONK
Now take the population in 2000. divide it in every cell evenly. DONK.
basically he assume that population and population growth at a 5 degree resolution, uniformaly distributed is correct. DONK.
he also made the mistake of putting 56 million people in antartica and that many on St helena.
How? those are british stations so he just used UK population. DONK. if I did that you people would have me hung. I should check solomon islands for grins.
So, folks may give me grief for 500 meter data.. ross created 5 degree by 5 degree data.
DONK.
WRT to Oke? oke abandoned his idea of tying temperature to population, said it was wrong and limited. plus it only refers to UHI MAX not average UHI. Plus his formulation changes depending on where you go on the globe. Science has moved on. HOWEVER, population does matter for computing the anthro heat portion of the town energy balance ( TEB) see Sailors work.
I’ve also got some world wide numbers on anthro heat per sq km.
Paul Homewood says:
April 4, 2013 at 12:33 pm
I think one of the things Willis’s analysis shows is just how dangerous it is to rely too much on statistical wizardry and algorithms and not enough on proper local and practical knowlege.
GHCN’s Icelandic adjustments are a classical example of this. Who knows better the temperature history of Iceland? The experienced, knowledgable Iceland Met Office, or some computer programmer in GHCN?
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However, in this case we did in fact classify the station as urban. Trust me you will find mistakes, this was not one
Reg Nelson says:
April 4, 2013 at 10:08 am
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/
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written afterwards I in fact found this and pointed Anthony at it.
I will wait till its done
Indeed: http://whatcatastrophe.com/drupal/surveying_olga_2
Steven Mosher says: April 4, 2013 at 6:41 pm
Re : Criteria for Australian data posted above.
Hi Steven, great to see the official appointment. I know that you handle tons of data because you handled this some time ago, unless I tweeted it and forgot. You have to assign priorities to your time and it’s easy you could have missed the significance.
Criteria.
1. Local knowledge. I spent a career in mineral exploration and there are few places in Australia where I have not been.
2, Population. Varies a little, from a few seagulls per square mile to perhaps a dozen people at most in same sq mile.
3. Google Earth. Grab a site, take a look. Look for evidence of people. If you find any, see how far away the weather station is. Usually more than a mile.
4. Airports. If any. Not tarmac, just natural land with trees removed, some graded occasionally.Don’t think any take jets. At best, a light twin piston. Traffic probably a couple a week,
5. AWS or manual – some AWS because no people reside there.
6. Variety. Some by the seaside, some way inland. Some to the North, some to the South. Swing high, swing low. etc.
7. Progression. I started with the most unequivocal sites and kept adding until I thought there was a vary faint possibility that a site could feel UHI. I was VERY conservative.
Overall, do what Willis did. Have a look at Google Earth. Coordinates to 4 places after the decimal degree will usually pick up the view of the screen.
Caveats. I do not consider it statistically valid to fit a linear least squares regression as I have done. It is eye candy, but it helps. Second, one has to assume that quality control in remote places can be questionable. However, the range of trends over the decades shows patterns in which some reality is probably embedded.
If you think that Australia produces difficult results, try Antarctica. If you think Australia is hard, you have to widen your confidence bands.
Happy to answer any questions. Cheers Geoff.
Anthony Watts says:
April 4, 2013 at 9:19 am
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.
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1 the field is classified as urban
Also, I need to update you on my latest.
using your first set of classifications and various satilltite products I am able to classify stations according to the CRN classification. Basically you use a subset of your classifications ( 1-5 ) to train a classifier and then test the classifier on the held out data.
The reason that is important is that it will allow one to do the whole globe.
So. Start with your hand classification: Train the classifier ( using say C4.5 or C5 or SVM) and then test the classifier on the held out data.. That gives you an accuracy figure then you apply to the whole shebang
Interesting result. Impervious surface surrounding the site ( out to 500m) is statistically more important than the area immediately around the site.
hmmm some helpful references in here about the physics
http://www.instrumentalia.com.ar/pdf/Invernadero.pdf
http://sourcedb.cas.cn/sourcedb_igsnrr_cas/yw/lw/200911/P020091104357019526094.pdf
Steven Mosher says:
April 4, 2013 at 7:43 pm
Reg Nelson says:
April 4, 2013 at 10:08 am
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/
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written afterwards I in fact found this and pointed Anthony at it.
I will wait till its done.
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I totally agree. The proof is in the pudding. We should wait until these climate models have some predictive value before wasting anymore money on this rubbish. This will take some time, however, as the new trend is to say “may\could happen in 2050 or 2100”.
Fascinating that no one offers a check of a site. 5 meter grid with data logging temp sensors over say 100 meter square. Then compare the temperature represented by the test grid to the temperature sensor in the center of the grid (the temperature sensor being quality controlled). Of course I hear claims of accuracies to .001 but no knowledge if just a few meters away the is a .1 degree difference. Funny how the human body can “feel” those differences. A few meters one way or the other and a trend has been made.
Steven Mosher & others,
Here is another page giving coordinates of the weather stations from quite a few of the pristine stations I selected from Australia.
I culled from this list.
It is usually best to use the lats and longs because the weather station can be some distance away from where you lob on Google Earth if you simply type in the name.
http://www.geoffstuff.com/Pristine_157.xls
There is one site that actually shows a screen in a tourist photo. Point Hicks, near the lighthouse keeper’s residence 1857. Select the photo half way down the black fence to the east. This might be the one site most susceptible to showing a man-made influence, but then that has to be traded against the professionalism of the recorder. There is a tradition of excellence among lighthouse keepers (but also the opposite if they went strange through loneliness). Also, the dominant wind direction is from S-W to W, lessening any effects of people.
The data are from the Bureau of Meteorology, who have to be commended for a massive data compilation. Sometimes the Bureau does things that are hard to follow, but credit is due for these records. Thank you, BoM.
Steven Mosher says:
April 4, 2013 at 7:57 pm
“Also, I need to update you on my latest.
using your first set of classifications and various satilltite products I am able to classify stations according to the CRN classification. Basically you use a subset of your classifications ( 1-5 ) to train a classifier and then test the classifier on the held out data.”
——
Why don’t you just go out and observe what is actually happening in the real world? Conduct real, physical experiments and collect data. Climate Science isn’t cost intensive. How much does a decent, well-sited weather station network cost? Certainly less than what was blown on the Solyndra debacle.
Steven Mosher says:
April 4, 2013 at 7:57 pm
“hmmm some helpful references in here about the physics”
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Yep, well understood I’m sure.
Consensus, even?
Steven Mosher:
G I G O.
Mosher,
if you agree that UHIs exist… and we can clearly see land becoming more urban over time, how could anyone objective argue that a developing UHI would not introduce a bias over time. To argue that there is no bias either makes one an idiot or a political hack… take your pick.
The fact that you were not able to find the bias with lousy methods doesn’t prove anything.
In the olden days, it was 3 guys on top of a Saturn 5 rocket, nobody “mailed” it in.
Now, all we get is mail.
The BEST methodology doesn’t measure the amount of urbanization that has occurred at a location and that is the variable that matters. As Dr Spencer says,
The fact that the greatest warming RATE is observed at the lowest population densities is not a new finding. My comment that the greatest amount of spurious warming might therefore occur at the rural (rather than urban) sites, as a couple of people pointed out, presumes that rural sites tend to increase in population over the years.
In conclusion, the issue is whether increasing urbanization is influencing the surface temperature record, and this study tells us nothing useful in relation to that issue.
Mosh,
Has your selection method, applied to Australia, overlapped with any of the ‘pristine’ sites I listed and if so, can you share the results? I selected them from a data sheet of several hundred Aust sites you sent me some time ago near the start of BEST. Ta Geoff.
Steve M says Powerengineers town of 4500 is urban based on Steve’s own qualification of 3 people/sq km being rural. As far as I can tell PE’s town might be 100.000 sq kms or it might be 1 sq km. Also our local airport used to be called a “rural” airport based on it’s connections, now it is an “International” airport based on its connections, same airport just a longer strip capable of handling larger aircraft in case of emergencies s.a. 911. Semantics can hide anything anyone would like to hide. This whole climate debate is sadly becoming a yelling match. Just tonight D. Suzuki blamed the pine beetle problem on AGW glossing over the natural facts that the pine tree population is 80 to 100 years old and is following a natural cycle documented by scientist and the local native populations as being thousands of years old.
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.
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“Urban” = any station where any man-made element has been added within a 1,000 yard radius of the station during the history of the tempertaure record.
“Rural” = any station where there has been no new man-made element added within a 1,000 yard radius of the station during the history of the temperature record.
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.
Passing over that this a false dichotomy. An objective measure of urbanization is the surface area of manmade structures.
Completely rural would have a score of zero, ie no manmade structures. An area where the surface area of manmade structures is several times that of the land area, would be fully urbanized.
* While horizontal and vertical surfaces have different effects on temperatures, the value of their effects on temperatures are probably similar enough for them to be combined into a single measure.
Steven Mosher says:
April 4, 2013 at 6:38 pm
Not in the slightest. I thought I was quite clear that the issue was not whether the site was classified as being rural or urban. Let me re-read the post … yeah, here’s what I said:
You seem to think that the point is airports, or rural versus urban. It’s none of those.
My issue is that the MODIS dataset, despite its outstanding resolution, cannot tell well sited surface stations from poorly sited stations.
w.
Philip Bradley says: April 5, 2013 at 12:03 am
I’d define rural as having a temperature trend that is essentially similar to the regional trend of other stations so defined.
But this does not get us far. I’d like to see a selection of BEST USA sites selected as non-UHI to see if they have a ‘background slope’ if I could call it that.
I’d look through the data myself except for some severe health problems here that are distracting.
So, let’s try to follow Willis rationale (which I like). Local Heat Island. Of course, there could also be Local Cooling Islands. But we can think, what would be more probable / abundant, cooling or heating islands, compared to areas not affected by human activity? I would say heating ones, and by far. And that makes the point for Willis idea on Local Heating Islands. The more human activity you have, the more heating (wherever). Because (I guess) there are far more human heating activities than human cooling activities. Unless I am wrong.
As a result of this work by Mr Mosher and his colleagues, perhaps they would communicate their findings to the UK met Office and BBC and ask them to stop the practice of telling us that “of course, the temperatures that we show are for towns, rural temperatures will be several degrees lower”.
Willis Eschenbach says:
April 5, 2013 at 12:26 am
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What dom the data sets say as to anomaly trends when only Class 1 sites ared used?
Ditto, when only Class 2 sites are used?
Ditto, when only Class 1 and 2 sites are used?
With powerful computers, it should be easy to output these data series (at least as faer as US data is concerned).
I would have thought that BEST would have looked at the classification of sites, and examined each catagory seperately and jointly to assess to what extent (if any) good siting makes a difference..
“Gene Selkov says:
April 4, 2013 at 2:02 pm
maybe the values of V1 and V2 for your aircraft are based on the worst-case scenario at the worst airport where the machine is certified to land?
Surely your Vy depends on altitude, quite a bit. According to this chart, at 40C, you’re already at 7500 feet above the ground, compared to standard atmosphere:”
Since you mention small airplanes: The indicated airspeed is measured with a pitot tube, which measures the stagnation pressure (the difference to static pressure, the dynamic pressure, depends on velocity). It is very convenient that all this is proportional to air density, so the pilot can use the same INDICATED airspeed everywhere.