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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Justthinkin
April 4, 2013 11:37 am

“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.”
sunshinehours1 and others here. NOBODY is claiming that UHI do NOT exist. We just want you and others to show your homework as to how this is affecting the GLOBAL temperature and/or you new name,Climate Change. BTW…anybody with two active brain cells and not in it for fame/fortune do not claim there is no climate change,just what is causing it. Its been changing for 4 billion years,and will carry on doing so.

April 4, 2013 11:39 am

Mike Ballantine says:
April 4, 2013 at 11:24 am
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.
########################
thats a good point. i did that. guess what? no difference. some airports cool other warm.
remove them all and the global trend doesnt change.
Also, you can actually compare CRN stations 9 the one Anthony likes ) to nearby airports on an hour by hour basis.
guess what? yup. no difference.

April 4, 2013 11:42 am

davidmhoffer says:
April 4, 2013 at 11:29 am
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.
####################
Ah, so your opinion about UHI is not based on objective criteria. Cool.

April 4, 2013 11:47 am

Bob Kutz says:
April 4, 2013 at 11:19 am
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.
#############################
yes there is UHI effect. That has never been the question.
the question is does that effect BIAS the record.
The UHI effect is not homogenous in space or time. in fact, some rural sites are warmer than nearby urban sites ( differences in surface emmissitivity and albeo drive that )
Example: here are the land temperatures for different land classes. is urban the warmest?
NOPE! As Oke remarked long ago the urban – rural DIFFERENCE depends more on the rural than it does on the urban. So, sometimes the difference is big, sometimes its small, sometimes its NEGATIVE.
See temperatures by land class.
http://stevemosher.files.wordpress.com/2012/11/daylst-fig3.png

April 4, 2013 11:48 am

“the Stevenson Screen housing the thermometers is still in a horrible location.”
I was beaten to it by Mike Ballantine above, but the thermometers are in exactly the right place for their purpose and must be to provide the most accurate weather info for the pilots. In addition I don’t think the jet exhaust will have any affect as the wind sock in the background shows the wind blowing to the right while the jet coming onto the ramp is likely to be idling.
As Henderson Field doesn’t appear to be very busy it should be easy to see any jet exhaust affect as a spike in the temps at the times of the flights.
I like the idea of LHI as opposed to UHI as the latter implies that urban is the only place a temp can be above ‘normal’, but so called rural such as drained farmland can be warm as well. Ask any glider pilot.

kim
April 4, 2013 11:49 am

There are some people whose daughter’s papers I will no longer read.
=================

Don B
April 4, 2013 11:50 am

Willis, Roy Spencer did some work with UHI and population density. Whatever happened with that research?
http://www.drroyspencer.com/wp-content/uploads/ISH-UHI-warming-global-and-US-non-US.jpg

April 4, 2013 11:51 am

for the chart above if you thought land class 7 was urban you are wrong.
read the rest here and see how land class and temperature varies.
to understand WHY you have to understand albedo and emissitivity. Bottom line: urban aint the hottest. its warmer than most rural surroundings but you have to control for the type of rural aurrounding to understand why in some cases its high and in some cases its low and in some cases its negative. ON AVERAGE with the actual stations we have it turns out to be below the noise floor.
http://stevemosher.wordpress.com/2012/11/10/terrain-effects-on-suhi-estimates/

davidmhoffer
April 4, 2013 12:01 pm

Steven Mosher;
Ah, so your opinion about UHI is not based on objective criteria. Cool.
>>>>>>>>>>>>>>>>
No, my opinion is that your criteria is not objective. Not cool.

April 4, 2013 12:02 pm

Don B says:
April 4, 2013 at 11:50 am
Willis, Roy Spencer did some work with UHI and population density. Whatever happened with that research?
http://www.drroyspencer.com/wp-content/uploads/ISH-UHI-warming-global-and-US-non-US.jpg
####################
eh, his work was not replicatable. part of his issue is using an innaccurate population dataset. The dataset he uses actually clusters people together. A much better source is TIGER census data. That allows you to work with actual census tract counts rather than data that is gridded by an algorithm which is what Roy used.
Finally, population is a poor metric and Oke who first proposed it, later abandoned it because it was not dimensionally correct, so now we look at the impervious surface area.

April 4, 2013 12:03 pm

Mosher, if you are going to write papers about something that exists and then whine about your failure to find it and offer cookies to people so they try and do your job, you really are a big failure.
Why not find places with UHI and write a paper about the difference in temperatures?
Concluding “consistent with no urban heating effect” is just not believable.
Concluding “we know UHI exists but we couldn’t find it” is believable.

April 4, 2013 12:04 pm

‘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).”
That is one of the reasons why I tell people that they cannot use population as a metric.. or rather why certain datasets ( like those used by roy spencer0 are not good solutions.
there are two types of population datasets: ambient and residential.

April 4, 2013 12:14 pm

How about a new classification for thermometers, thusly: “Out in the woods nowhere near any developed land/structures!” and, wait for it, “NOT out in the woods nowhere near any developed land/structures!” Would that clear it up? Or, if there ARE no thermometers “NOT out in the woods nowhere near any developed land/structures!” let’s put some out there and see which correlate to which, project should take a few months to produce relevant data at a 4- or 5-Sigma level. Anyone who wanted the correct answer to this question would have done this a long time ago. The rest of you are trying to prove something else, and what that might be I cannot ascertain.
May I have my grant now?

April 4, 2013 12:16 pm

yes there is UHI effect. That has never been the question.
the question is does that effect BIAS the record.

Is there any doubt that today different stations experience different amounts of UHI contamination? For clarity, I think we need to specify whether Local Heat Island (LHI) is or is not a subset of UHI. For the benefit of this study, LHI is not included in UHI. Agreed?
So the question then becomes, “Does UHI or LHI BIAS the record”.
LHI is real.
Different stations experience different LHI today.
To assume that LHI does not bias the readings means you must believe that a poorly sited LHI station today was poorly sited when it was set up. This strains credulity. Did people set up a station next to a parking lot or did the parking lot expand toward the station?

Robuk
April 4, 2013 12:17 pm

Steven Mosher says:
April 4, 2013 at 11:39 am
Mike Ballantine says:
April 4, 2013 at 11:24 am
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.
########################
thats a good point. i did that. guess what? no difference. some airports cool other warm.
remove them all and the global trend doesnt change.
Also, you can actually compare CRN stations 9 the one Anthony likes ) to nearby airports on an hour by hour basis.
guess what? yup. no difference.
——————————————
Total garbage,
Try comparing them since say 1945.

April 4, 2013 12:18 pm

“Thanks, Josh. Poorly sited stations do not necessarily introduce a trend. But it it quite common. For example, at Henderson Field they lengthened the runway in the 1980s to bring in the larger jets … which increased both the pavement within the viewshed and the number and size of the planes landing and blowing hot exhaust onto the thermometer. Since development is much more common than de-devlopment, most changes increase the surrounding temperature.
w.
########################
the temperature falloff for a jet plume is rather dramatic. I’ll refer you the ground handling safety guidelines published by airline manufacturers. Just request them or you can find a few of them on the web. Also, if the blowing of exhaust onto thermometers was a regular occurance it would be visible in the one minute data from airports. Its not. if it was persistent then you’d see it by comparing CRN data to nearby airports. Again, nothing.
This isnt to say that it cannot happen, only that the conditions are rare and not easily found in the actually data.
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.
All that said, airports, statistically speaking, make no difference whether they are included or excluded from the dataset. You can find worldwide airports on the internet. the FAA has taken down their resource because of terrorism. The open datset includes all airports and heliports.
volunteer built just like surface stations.

April 4, 2013 12:21 pm

Of course the media, and unfortunately the BESTers, still can’t lower themselves to the plebeian truths. All those highly trained individuals, quite embarrassing.
Thermometer Placement Oversight.
Where are the official plans to move and correct the stations that get built into a hot spot? Anybody know?
Why should politically aspiring scientists want stations moved, when they serve their agenda left just as they are?

Go Canucks Go!!
April 4, 2013 12:25 pm

Thanks Willis.
I ran 2 trend lines at TreesforWood. Both BEST and RSS were land only from 1980 until 2010. Best was running about .27 per decade and the satellite was running about .2 degrees. I don’t think the satellite data has UHI or LHI problems

Reg Nelson
April 4, 2013 12:26 pm

Wouldn’t it make more sense to take the poorly sited stations and set up nearby stations in better suited locations, then compare the results over a period of time? How difficult would that be?
The NOAA has already done this (to some extent) and found that stations located closer to their building have higher nighttime minimum temperatures than those located further away. That’s actual empirical evidence (and common sense). Anyone who claims that there is no UHI effect is therefore clearly a “denier”. LOL

AJ
April 4, 2013 12:28 pm

What’s the status of Watts et. al (2012?/2013?) ?

April 4, 2013 12:28 pm

‘Even more sadly, Steven, there’s not one word in the study about your magic airport test, or about your claimed study of the question, so once again it’s just another jolly Mosher anecdote … have you given up entirely on providing citations for your claims, or is this just a passing phase and your unscientific lack of citations will “self-correct” about when the rest of climate science self-corrects?”
the sensitivity tests are mentioned. Not in much detail, the answers didn’t change. In short,
When Zeke and I came on board we had just finished a massive sensitivity test. That basically allowed me to reclassify the stations using a screen that used, modis, isa, nightlights, airports, 10km screens, 25km screens. None of them gave different answers than the .1 degree screen.
so, the simple .1 degree screen gave an answer, sensitivity tests about that screen gave the same answer. Not really interesting.
At some point I have to figure out how to write up a paper that says i tried all this stuff and could not find a UHI signal.

Editor
April 4, 2013 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.