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.
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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.








The baseline needs to be established using only long term sites which are, stable in location and fit for purpose. My guess is they are few and far between.
Since they insist the end of the world is resting on this data we gotta go look.
And no fudging.
cn
Bill H says:
April 5, 2013 at 5:30 pm
plazaeme says:
April 5, 2013 at 1:13 am
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What about Palm Springs?
Long term LCI effect?
cn
Hmmm … I don’t seem to recall mentioning ANYTHING about correcting urban stations. At all.
I DID suggest, in trying to look at the UHI trend effect between rural and urban, that we uses several rural stations compared to the urban station. This does nothing to correct the urban station in any way. It simply helps moderate any site specific issues with the rural ones.
Chuck Nolan says:
April 5, 2013 at 7:34 pm
What about Palm Springs?
Long term LCI effect?
cn
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Long term LCI? Nope! Natural variation due to natural occurrence.. the next question (or should i say first is which Palm Springs Florida or Nevada or California or…)
Steve Mosher made the following 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.
Steve, that’s a safe bet that skirts the real issue. UHI’s clearly exist and have the potential to bias surface temperature trends. So does poor station siting, which is sometimes included in discussion of UHI. However, biases are only introduced into the trend when the amount of UHI or the quality of siting CHANGES appreciably over the period in question. Looking at stations TODAY tells us little about how much CHANGE occurred in the past and when it occurred. Did you have a good, private laugh when you posed this challenge? The DIFFERENCE in trend between “urban” and “rural” stations doesn’t tell us much about UHI biases unless you’ve got a group of urban stations whose UHI has increased and you’ve successfully excluded anthropogenic biases from the rural group. That’s unlikely with the limited existing information. The trend from a carefully selected group of rural stations is meaningful. BEST should simply report only the trend from the most rural stations they can find and stop suggesting that the difference between their rural and urban groups proves that UHI isn’t important.
I left some related ideas at your website.
“Say what? I said the UHI was wrongly named because the effect is not essentially urban. I said it should be called the LHI, the local heat island, because it was a LOCAL effect due to LOCAL issues.”
Anthropogenic Heat Island?
Is it such a problem, if WMO asks local staff to categorize their stations used for global data according to some rules? They can have a new categorization overnight.
Mosher says:
“2. Part of the problem is exactly what Willis suggested. The methodology cannot capture based on any definition of rural vs urban which weather stations have an artificial influence and which not. It has to be done station by station. But I think there are additional factors.”
Then you admit you did not do any kind of station calibration of your station selection methods with visual on the ground inspection so you could determine artificial (or natural vegatative) influence. Good to know.
By your own on the record doubts, the report is therefore invalid and should be removed from publication for lack of basic attention to standard research methodology.
Mosher: My hunch, with all these science beagles reading this thread, is that someone will do this quality calibration for you (if you release the station names used in your study) unless you and your co-authors get off the couch and do it yourselves ASAP (and describe your “random” selection method of course). Don’t want to? Request for station names from the researchers anyone? Do you really need someone to do a FOIA on you?
It is enough to have a look at their picture 1 to see clearly the effect of UHI on temperature and trends.
The Tokyo temperature would not have run the same way without the human population accumulating there.
Some decades ago the UHI was a recognized effect on trend and climatologists were naming a logarithmic dependency between the city population and the temperature.
What GISS does is to subtract 0.01 per century. This is laughable.
The cities grew, human population increased from 1 billion to 7 billions in the time when Berkeley make their temperature graph. And of course this effect is mostly visible in the higher latitudes where it is colder, there the difference – the resulted UHI is greater.
In their Fig 4 – trends for 70 years – one can very easily identify the urban agglomerations as red spots. Furthermore it is interesting to see that the warming is more in the North, where the UHI effect would be greater.
Looking back at Fig 1, one can see that Tokyo temperature stabilize at the end. Well, the cities are not growing at the pace they were growing in the first half of the century, so the delta UHI is not increasing as such.
And this is also the explanation for their negative trend after 1950 in cities. Europe, North America, Russia ceased to grow in population or grew in a moderate way.
Also the UHI increase is logarithmic. The delta UHI is not as significant any more once the cities are already developed. The time of exponential population growth is gone, and with it also the exponential increase in measured temperatures.
As a personal estimation I would think that at least half of their temperature trend comes from UHI and LHI. But it does not matter so much for the future, as we see the trend for cities gets “cooler and cooler” as the cities will grow less and the effect is logarithmic.
Oh yes, soon they will try to adjust for negative UHI due to cities.
Gene Selkov says:
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
This for a B747-100. More typical for Honiara International Airport would be a B737 or A320 (as shown in the pictures). These are much smaller aircraft and lower to the ground.
Curt says:
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).
It’s also possible to get changes in the patterns of traffic, both aircraft and ground vehicles, without the infrastructure changing much at all. e.g. more small aircraft replacing fewer large ones. With exhaust from an E-170 being more likely to affect a thermometer near a runway or taxiway than that from an A380. Simply due to the former being nearer to the ground than the latter.
Tick tock tick tock tick tock tick tock tick tock tick…
When I did my research on the auditory brainstem response to high frequency tone bursts, we used brand new electrodes with known impendence (listed on the bubble pachage they came in) when place on abraided human skin. Guess what? Didn’t care what the package said about the impedence value. Thanks to sage advice from an experienced lab worker and mentor, I calibrated those electrodes EACH AND EVERY TIME I used them so that I could compare package values with each-time-I-used-them values. Mosher?
“package” Pam “package”…damed Sunday morning Irish Cream Coffee
Pamela Gray says:
April 6, 2013 at 9:05 am
Mosher: My hunch, with all these science beagles reading this thread, is that someone will do this quality calibration for you (if you release the station names used in your study) unless you and your co-authors get off the couch and do it yourselves ASAP (and describe your “random” selection method of course). Don’t want to? Request for station names from the researchers anyone? Do you really need someone to do a FOIA on you?
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The stations were released long ago on climate audit. before I joined the party. So have at them
Chuck Nolan says:
April 5, 2013 at 7:32 pm
The baseline needs to be established using only long term sites which are, stable in location and fit for purpose. My guess is they are few and far between.
Since they insist the end of the world is resting on this data we gotta go look.
And no fudging.
cn
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wrong. there are plenty of long term stations. See my posts
clipe
“So
1. the plume falls off rapidily.
2. if it were a common occurance it would show up in 1 minute data. it doesnt.
3. it doesnt show up comparing the best (CRN) to nearby airports.[…]
The “ground handling safety guidelines” are just that – guidelines. I always give idling aircraft a wider berth when downwind of the “plume”.
35 years on the ramp at YYZ teaches a person a lot about wind direction as it applies to heat plumes.
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having spent some time on the ramp and tarmack then you know that the picture in the post above is meaningless without wind informaton
Paul Homewood says:
April 4, 2013 at 12:29 pm
According to Richard Muller, himself,
“Urban areas are heavily overrepresented in the siting of temperature stations: less than 1% of the globe is urban but 27% of the Global Historical Climatology Network Monthly stations are located in cities with a population greater than 50000.”
http://notalotofpeopleknowthat.wordpress.com/2011/10/23/mullers-problem-with-uhi/
How many more would there be if they took the population down to, say, 5000? And excluded airports?
If BEST really want to exclude UHI, let them ignore all of these sites completely and give us figures based on only on sites that have been guaranteed as reliable by the local Met Offices. If such guarantees cannot be given, that country’s sites should be ignored completely.
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The population of the very rural, as we pointed out in the paper had a median of 3 people per sq km.
At some point I would expect people to understand that Anthony and Willis have diagnosed the real issue. not UHI but the siting. UHI is so varied, and rural as I have shown can be warmer than urban. Its all down to the siting
“So, even if Geoff’s finding are correct, they can still be dismissed because Australia is the “Bermuda Triangle” of climate? Is that what you are trying to imply? Seems like you’ve already come up with an excuse without even looking at Geoff’s work. Your confirmation bias is painfully obvious.
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Geoff sent me his work a long time ago. I believed I asked the same questions and we got no where. your assumptions are wrong
richard verney says:
April 5, 2013 at 1:20 am
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..
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1. Anthony did this and found nothing.
2. we used his dataset and found nothing.
3. he now has a new dataset. Thats good because there are errors in his old one which are easily found using my methods. Perhaps I shall write that up
‘Despite the donking, all that you have described so far is that he did his analysis using country level populations … so what? Yes, it makes the analysis cruder, but it certainly doesn’t invalidate the analysis. Since the issue is population GROWTH and not the raw population, I fail to see the problem. As the authors point out, there are interesting results despite the broad brush approach:”
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now you sound like climate science Tm, saying that errors dont matter, when you havent tested whether they matter or not.
“Yes, the study should be redone at a more fine-grained level to give better answers, and no, that doesn’t make the current study useless or wrong.
I sure hope you have more issues with the McKitrick study than you’ve listed so far, because if that’s all you got, then it sure looks like you’re just invalidating the study based on meaningless objections, and not seriously analyzing their results. The McKitrick study may have serious problems, but if all it’s got is are the problems you’ve identified above, then there’s no serious problems at all.”
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Look, the reviewers asked us to comment on Mckittrick. My answer is simple. There are gross assumptions in mckitrick about his key variables. There are gross data errors. I have made him aware of these errors some time ago and pointed him at better data. To date he and others have said, ‘Fix my errors for me. prove my errors matter.” Sounds so much like Mann I want to puke.
So, I answered the reviewer. An analysis of the errors of Mckittrick are beyond the scope of this paper. I am under no obligation to prove his errors matter or dont matter. The errors prevent any meaningful comparison of the results, any meaningful comparison of the methods. Further, the whole regression is based on a non physical model and is dimensionally incorrect.
That said, there is merit to looking at changes over time. We do that by selecting stations have have not changed ( rate of change zero ) and comparing to stations that have changed.
k scott denison says:
April 5, 2013 at 10:22 am
Assuming that Mr. Mosher will return, I think them crux is this:
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Lets see. The main post seems to make a claim that this location would have been classified by us as very rural. Well, we classified it as Urban? Is there a correction to the main post or does this error not matter? we will see
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The issue isn’t urban/rural. The money question is is there a difference in trends between sites where no significant addition of man-made structures has occurred during the period of the temperature record versus stations where significant additions have occurred. I would suggest the metric for stratifying stations is something like a percentage change in the total man-made surface area within a radius of X meters of the station. Perhaps even stratifying into multiple classifications based on those where changes have been made within a narrow radius of Y versus 2Y, 3Y, etc.
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1. I can look at changes in man made surface between 1992 and 2006 or between 2001 and 2006.
2. understand that if these changes happen “at once” change point analysis will find them
OR the change is too small.
3, Gradual small changes are the challenge. Like trees growing.
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This type of metric would certainly be more accurate as it is well known, as Willis and Anthony have pointed out several times, that the most likely cause of inaccuracies is what is very local to the station, versus what is in the general vicinity. Have to look at what was literally next to the station at the beginning of the record and what has been built (looking first next to the station and then in concentric rings) around the station over time to see if that affects the trend.”
1. The sensor does not see what is very close to it unless you have windless condtions.
2, Within 500m is more relevant than within 30m
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Maybe this has been done and I’m simply unaware of the results. If I were to do the analysis I would start with a small set of stations where I know changes have and have not occurred. Do the analysis as a percent of man-made surface area within 100 meter circles by year, plot it all up, analyze and see what it tells me. Wish I had the data, time and skills to do so.
1 . the data exists.
2. it doesnt take much skill. I do it.
“If I remember right, there were ~30% of stations that showed cooling in the BEST data. What do these have in common? Are there differences in siting between these and those that show the strongest warming trends?”
See my analysis of cooling stations started on my blog.
Puppet_Master_Blaster_Master says:
April 4, 2013 at 6:05 pm
Obfuscation.
The people at BEST are really trying hard to ‘smoke screne’ the world. Guess they will soon start offering online casino betting plans, bitcoin investment options and ‘life’ insurance just like Al Capone’s ‘companies’ did.
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conspiratorial ideation