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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257 Comments
Power Engineer
April 4, 2013 5:16 pm

UHI exists even at non urban towns such as ours which has only 4500 people….hardly urban. In the last 50 years traffic has grown 10 fold. Trees are largely gone. Lawns have been paved to provide parking for all the houses that have been converted to doctors’ and lawyers’ offices. If a temp station had been located here, it would show warming due to urbanization even though the surrounding air hadn ‘t warmed at all.
I agree that there is an “urban” definiton problem.

April 4, 2013 5:16 pm

” 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.”
actually, we look within 0.1degrees of the site ( roughly 11km at the equator) to see if there are ANY urban pixels within that radius. if there are any urban pixels within that radius the site gets classified as not rural

Pamela Gray
April 4, 2013 5:20 pm

Calibration checks are a must. Did you do that? Did you take a good random sample of your rural sites and check them from the ground, close up and personal? Did you take a history of those random sites to check for changes in their settings? This is standard practice. Calibrate. EVERY TIME YOU TAKE DATA!

April 4, 2013 5:26 pm

Gary Pearse says:
April 4, 2013 at 4:08 pm
Certainly, I trust Steven Mosher’s diligence in searching for the truth and believe he was surprised to find no significant affect.
###########################
yes you can imagine the scene when Zeke and I walk in with our improved screen developed for our poster
1. I dont use the modis 1km rule I use modis source data.
2. I use ISA as an ADDITIONAL screen because the modis dataset was a feb dataset
and there was some evidence of snow covering urban areas showing as rural.
3. I throw in Nightlights to handle those locations, like in australia where you have a mining town
that is small but has a big nightlights foot print.
4. I look 11km around each site, not 0.1 degrees which is a different distance by lat
So, ya, we walk in with our tighter screen and say, this will find it!!!!
DONK. surprised is a good word. Especially since you will find me being critical of of BEST over on CA, when I knew it all… haha. wrong.

u.k.(us)
April 4, 2013 5:26 pm

They made me yell “clear” before starting my trainer. I’ve seen doves, make moves to avoid my prop, that would make a fighter pilot cry.

April 4, 2013 5:34 pm

Nick Stokes says:
April 4, 2013 at 4:17 pm
Willis,
I see you spotted a plane at Honiara International Airport. But it doesn’t seem very crowded.
I know you can fly from there to Nadi and Port Moresby. And there is a weekly Virgin flight to Brisbane. But is that traffic going to seriously affect the temperature record?
================================================================
I think that is less about “how much impact” as the simple fact that there is some impact.
Correct data collecting should be the first priority, otherwise, too much guess work and assumptions.

April 4, 2013 5:37 pm

If you wish to get a lot of information on how UHI shifts over time, look to the new urbanists. They have lots of maps on how cities have sprawled out. Take a look at Buffalo, NY for a great example. They already have multi-decadal maps of sprawl. You just have to overlay the thermometers and the expected UHI effect over time as the sprawl took place.

Reg Nelson
April 4, 2013 5:38 pm

Steven Mosher says:
April 4, 2013 at 3:43 pm
@Geoff Sherrington
“Now THATS more like it. a man who has done some work.
A few questions and I will have a look at it.
1. What methodology did you use for classifying pristine?”
——————
Hang on a tick, Steven. You just said in an earlier post that after exhaustive work you determined that station selection\exclusion criteria didn’t change the trend.
Now, you turn around a few minutes later and ask Geoff what his selection criteria was. Seriously?
You already answered your own question, The answer is: “It doesn’t matter”.
————
“I will note this. almost every freaky site in the world comes from down under. outside the US it is the only place with long term cooling stations.”
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.

April 4, 2013 5:45 pm

If a handful of proxy locations is good enough to determine the earth’s temperature for proxy studies, you’d think a hand picked (no peeking at calculations first!) set of actual temperature recording weather stations with long records that were properly rural (not according to some sort of algorithm based on pixels) ought to be able to get a reasonable value and settle this once and for all.

Puppet_Master_Blaster_Master
April 4, 2013 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.

Bill H
April 4, 2013 6:12 pm

The Urban Heat Island is real. Anyone driving through a city can watch their car temp thermometer increase and decrease as the population density changes. It is not a problem however until those temperatures (which are inflated due to changes in land use) are then averaged into the global mean temperature. The land changes cause divergence from reality and show a warming trend, when in reality there is none.
BEST missed the band wagon on this one. They did all kinds of work and missed the forest through the trees. NOAA and GISS are silent on the CRN sitings and their temp records. That silence is affirmation to me that things are not as dire as the CAGW crowd wants everyone to believe.
In my humble opinion the determining of what constitutes “built up” and Rural is an exercise in futility and proves nothing..
Anthony’s approach of individual stations dealt with individually is the only proper course. Once that individual stations bias is determined only then can a correction be placed to bring it back into reality.
Localized Heat Island (anomaly) is much like weather. Weather, long term, is Climate and LHI long term is UHI.. It is simply a bias due to changes locally.

Reg Nelson
April 4, 2013 6:35 pm

Steven Mosher says:
April 4, 2013 at 3:43 pm
@Geoff Sherrington
“Now THATS more like it. a man who has done some work.
A few questions and I will have a look at it.
__________
And what if Geoff’s reply was, “Why should I show you my work? You’re only going to try and find something wrong with it.”
Would you be happy with that response, Steven?
What if Geoff told you he he did a survey and 97% (of some of those) surveyed believed his conclusions to be correct? Would that convince you?
What if you found out that your taxpayer money payed for Geoff’s work and that he was trying to hide that fact from you? How would you feel about that? Would you trust Geoff?

April 4, 2013 6:38 pm

Well Willis, lets have a look at the data. You are concerned that this site will be Rural classified by our proceedure. Correct?
And to determine that you read a modis paper, but never actually wrote to the PI to get the actual data? Correct? or not?
Willis There are these sites I find in our records
Are they the ones you are concerned about?
128572 GUADALCANAL AAF -9.4333 160.0500 10.100 -9.9999 -9.9999 -9.999 Solomon Islands ZZ NA NA NA 80601 0 0 4
43 128573 HONIARA/NTF/AWS -9.4330 159.9670 0.000 -9.9999 -9.9999 -9.999 Solomon Islands NA NA NA NA 0 0 1
44 128575 HONIARA, BRITISH SOLOMONS -9.4170 159.9690 56.200 -9.9999 -9.9999 -9.999 Solomon Islands NA 91517 NA NA 0 1
45 128576 HONIARA/HENDERSON -9.4170 160.0667 9.000
Let me tell you how I found them
I will suggest you look at
http://www.climateaudit.info/data/station/berkeley/
Start here
http://www.climateaudit.info/data/station/berkeley/details.tab
download it. Its an R object
load(details)
Then pick up this
http://www.climateaudit.info/data/station/berkeley/other_unique_ids.txt
Thats a file of all the NON RURAL stations
Got that.
Then you match the ids in ‘details” with the station numbers in
“other_unique_ids.txt”
that will give you our “not rural stations”
When after you do that just search for stations between -9S and -10 S
Well, that result is shown above.
In short, the place you were concerned about was classified as not very rural.
Bottomline: I promise that people WILL find sites that are misclassified. One of the things that Zeke and I did was a sensitivity on the classification schemes. basically they are not very sensitive to errors and you WILL have classification errors. However, this particular site was classified as not very rural. Now please nobody claim that its rural.

April 4, 2013 6:41 pm

Reg Nelson says:
April 4, 2013 at 5:38 pm
Steven Mosher says:
April 4, 2013 at 3:43 pm
@Geoff Sherrington
“Now THATS more like it. a man who has done some work.
A few questions and I will have a look at it.
1. What methodology did you use for classifying pristine?”
——————
Hang on a tick, Steven. You just said in an earlier post that after exhaustive work you determined that station selection\exclusion criteria didn’t change the trend.
Now, you turn around a few minutes later and ask Geoff what his selection criteria was. Seriously?
You already answered your own question, The answer is: “It doesn’t matter”.
#####################################################
Perhaps, you misunderstood. I am interesting in what criteria he used for two reasons
1. so I can check his work
2. because I could be wrong and if he has a criteria I havent tested, a feature I havent controlled for, then I want to know so I can do a better job.
I’ve look at a TON of stuff. Not everything.. so I am asking him to tell me something to move the science forward. simples

April 4, 2013 6:44 pm

Bill H,
The CRN record really isn’t long enough to tell us anything at this point about the accuracy of the existing HCN network. It is worth pointing out that the trend of both is pretty much identical over the period of overlapping coverage, however: http://rankexploits.com/musings/wp-content/uploads/2013/01/Screen-Shot-2013-01-16-at-10.37.51-AM.png
While we still need more time to draw firm conclusions, it certainly doesn’t provide evidence of bias in HCN stations asofyet.

Bill H
April 4, 2013 6:45 pm

As a interesting exercise to see what biases for a group of stations exists, I would take and set up 6 stations around the current one at equidistant points and well sited. Then note the differences over say a year or two in temps. Stations within 750-1,000 feet of the original and use strict quality control without changing the original stations routines. Use Anthony’s surface-station criteria for a 1st class station.
It would be interesting to see how they all match up and how much bias the station has given wind directions and travel over roads runways etc. You could pick several airports for this experiment or any college campus where the original station is poorly sited yet there are good areas for well sited data collection points to be set up..
Just a though on how to identify bias and use a existing criteria, which is in use, to evaluate station siting and quantify the bias.

April 4, 2013 6:52 pm

Pamela Gray says:
April 4, 2013 at 5:20 pm
Calibration checks are a must. Did you do that? Did you take a good random sample of your rural sites and check them from the ground, close up and personal? Did you take a history of those random sites to check for changes in their settings? This is standard practice. Calibrate. EVERY TIME YOU TAKE DATA!
###########################################
The modis data was in fact the most highly calibrated data we could find.
One issue with that calibration is that it was done on larger cites. So, I did my own calibration focused on tiny towns. I then doubled checked with other data sources ( several) other sensors
of course there will be “classification” errors some rural classified as urban, some urban as rural,
If you make the screen for rural too tight you actually push rural sites into the urban class.. which can cause you to see no difference. Most folks dont realize that.
What folks dont get that that UHI varies within a city. It caries by season, by latitude, by the rural surroundings. It doesnt happen on cloudy days or windy days.. when its all said and done
its a small average signal.. swamped by other things.. like siting perhaps.

April 4, 2013 6:55 pm

Power Engineer says:
April 4, 2013 at 5:16 pm
UHI exists even at non urban towns such as ours which has only 4500 people….hardly urban. In the last 50 years traffic has grown 10 fold. Trees are largely gone. Lawns have been paved to provide parking for all the houses that have been converted to doctors’ and lawyers’ offices. If a temp station had been located here, it would show warming due to urbanization even though the surrounding air hadn ‘t warmed at all.
I agree that there is an “urban” definiton problem.
#######################################
Our “Rural sites” had fewer than 3 people per sq km within a 5minute ( 10km) grid cell
majority had no people.
we would classify your town as urban.

April 4, 2013 6:59 pm

Doug Proctor says:
April 4, 2013 at 3:54 pm
The problem here is the same I see in industry these days: if you can’t dump and troll through datasets, then it ain’t either science or worth it. Manually looking at sites and making a decision is too “subjective” for science these days. Maybe because if you did this, you would have an “opinion”, but if it is just “data”, you are off the hook: hey! that is just what the program says!
###################################
well one of the reasons why we move to an objective basis is because for MANY years the UHI community has used airports as a rural comparator. Oke and his students have tried to put it on a more objective measureable basis. repeatability you know.
For Modis, the classification system does involve human input and refinement and calibration.

John another
April 4, 2013 7:09 pm

I was kinda hoping for an explanation for the difference between Albany and NYC.

Bill H
April 4, 2013 7:14 pm

Zeke Hausfather says:
April 4, 2013 at 6:44 pm
Bill H,
The CRN record really isn’t long enough to tell us anything at this point about the accuracy of the existing HCN network. It is worth pointing out that the trend of both is pretty much identical over the period of overlapping coverage, however: http://rankexploits.com/musings/wp-content/uploads/2013/01/Screen-Shot-2013-01-16-at-10.37.51-AM.png
While we still need more time to draw firm conclusions, it certainly doesn’t provide evidence of bias in HCN stations asofyet.
========================================
While not being long enough currently the trend is flat, as in less than -0.1 deg C over the time period. This is in line with many of the Class 1 stations Anthony and others have categorized showing a declining temp trend. Only high density areas are showing an increase in temp.
This leads me to believe that well sited stations are not showing the warming that high population density areas are. There are poor sited stations in both rural and high density areas. The key is to assess these one at a time and look at them given the growth changes vs Temp records. The problem is, there are not very many with close proximity, well sited stations with which to cross reference the temperatures and trends in an effort to see what the actual bias is.

Reg Nelson
April 4, 2013 7:16 pm

Steven Mosher says:
April 4, 2013 at 6:41 pm
Perhaps, you misunderstood. I am interesting in what criteria he used for two reasons
1. so I can check his work
2. because I could be wrong and if he has a criteria I havent tested, a feature I havent controlled for, then I want to know so I can do a better job.
_________
No, I completely understood. Why would you need to check his work? Why not accept it on faith? That’s how climate science works, isn’t it?
He showed the data in the spreadsheet. It’s all there. Have a look at it and try to replicate his work yourself — that’s how these things are done, right?
And you ignored the second part of my post that pointed out your confirmation bias.

Pamela Gray
April 4, 2013 7:18 pm

Steve, you know that your answer didn’t even touch what I asked. Did you or did you not choose a random sample of rural sensors and actually visually inspect them and the records kept for these stations? Yes or no. It’s called field research. Try it.

pottereaton
April 4, 2013 7:37 pm

Steven Mosher says:
April 4, 2013 at 3:43 pm
————————————-
I was hoping you would say that . . .

Pamela Gray
April 4, 2013 7:38 pm

People don’t warm stations, objects warm stations. And objects can be an overgrown tree or a BBQ next to the only house for miles around. As we have seen over and over again with photos, population density may easily have no bearing on microsite issues that create what is commonly called UHI. It is an unfortunate misnomer and leads one to think that UHI only relates to large towns and cities (IE urban). Without a random on the ground calibration of what your in the air choices were, the results could be meaningless.

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