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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April 7, 2013 9:01 am

samsonsviews says:
April 5, 2013 at 3:03 am
I think Steven Mosher is holding his own here. Not impressed by Willis’s renaming the UHI to avoid a challenge.
##################################
haha neither am I, especially when the title infers the opposite.
And when the site he picked was actually classifed as poorly sited.

Pamela Gray
April 7, 2013 10:26 am

Let me be clear Mosher. You re-defined your selection method. So bring the list of stations your method designated extremely “rural” for the purposes of this present study. The list of all the stations is not what I am asking for and you know that. You obviously assume that the rural stations in the study are somehow devoid of spurious artifact. How did you make that determination without calibrating a randam sample. So let me ask AGAIN!!!! Did you randomely sample that rural list for station siting artifact? A simple yes or no would suffice. And I am sure as hell not going to do for you what you should have done and reported in your methodology section of the paper.

April 7, 2013 12:33 pm

Pamela Gray says:
April 7, 2013 at 10:26 am
Let me be clear Mosher. You re-defined your selection method. So bring the list of stations your method designated extremely “rural” for the purposes of this present study.
#############################
already posted on climate audit and refered to above.
The list of all the stations is not what I am asking for and you know that.
###################
seriously I thought that is what you are asking for so I supplied it.
“You obviously assume that the rural stations in the study are somehow devoid of spurious artifact. How did you make that determination without calibrating a randam sample. So let me ask AGAIN!!!! Did you randomely sample that rural list for station siting artifact? A simple yes or no would suffice. And I am sure as hell not going to do for you what you should have done and reported in your methodology section of the paper.”
I’m unclear what you mean by “spurious artifact”
When I went through the list on climate audit. I found artifacts. Others found artifacts.
http://climateaudit.org/2011/12/20/berkeley-very-rural-data/#comment-317667
http://climateaudit.org/2011/12/20/berkeley-very-rural-data/#comment-318011
I suspect somebody alerted Berkeley and I was invited to attend meetings.
The first artifact I found was due to the use of 0.1 degrees as a screen. This changes the distance of the screen as latitude changes. So, I figured out how many rural were mis classified.
Next artifact I found was a latitude based artifact. This owing to the time that Modis was constructed ( feb ) so I applied a filter for that.
Next artifact I was concerned about was the airport artifact ( see the squares willis draws )
airports will show up as rural because of the 500 meter and 1km rule. So I applied a filter for that.
Next artifact was the high density industrial artifact. Small areas with big industry but sun 1sq km. So I applied a filter for that
Next artifact was the 1km issue willis mentioned. So I went back to modis Source data.
So, I did not randomly sample the rural stations for artifacts. I exhaustively looked at them all.
I diagnosed characteristic flaws of urban categorization.
Using this improved filter I reclassified. It
http://climateaudit.org/2011/12/20/berkeley-very-rural-data/#comment-317667
I attended my first meeting at Berkeley. I explained how I had gone through the stations as classified and accounted for artifacts I had found. I gave them my “improved’ version.
Not randomly sample, exhaustively audited.
They ran the test with my stations….
No change in the results. So, I walked in ready to demolish the findings. I had publically called out mistakes I found ( long before willis trots out a BOGUS sample of mis classificaton) and I got involved to improve things. My improvement FAILED to find any difference. That was about 6 months of work I put into trying to build a classifier that would handle every situation and artifact could find by looking at thousands of sites over the past 5 years.
That said, my classifier is able to find errors in fall 2011 classification. do they matter?
dunno.

April 7, 2013 12:47 pm

Here is a trick test for folks still on this thread.
How would you classify these stations. would you call them CRN1?
[googlemaps https://maps.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=%0943.10917,%09-76.10333&aq=&sll=40.720364,-114.0274&sspn=0.021955,0.052314&t=h&ie=UTF8&z=14&ll=43.10917,-76.10333&output=embed&w=425&h=350%5D
[googlemaps https://maps.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=%0932.01667,%09-81.13333&aq=&sll=43.10917,-76.10333&sspn=0.010574,0.026157&t=h&ie=UTF8&z=14&ll=32.01667,-81.13333&output=embed&w=425&h=350%5D
[googlemaps https://maps.google.com/maps?f=q&source=s_q&hl=en&geocode=&q=40.72056,%09-114.03583&aq=&sll=32.01667,-81.13333&sspn=0.012281,0.026157&t=h&ie=UTF8&z=14&ll=40.72056,-114.03583&output=embed&w=425&h=350%5D
If you wonder where these stations come from. Well, here is a hint.
11 of 13 are airports.. CRN 1. but check out the 3 above.. hmm WUWT?
Lat Lon COOP. CRN Station
1 34.70417 -118.42750 42941 1 FAIRMONT
2 38.81780 -102.36080 51564 1 CHEYENNE WELLS
3 29.72583 -85.02056 80211 1 APALACHICOLA WSO AP
4 32.01667 -81.13333 97847 1 SAVANNAH WSO AP
5 46.42667 -105.88250 245690 1 MILES CITY FCWOS
6 43.10917 -76.10333 308383 1 SYRACUSE WSO AP
7 34.98944 -99.05250 344204 1 HOBART FAA AP
8 44.84280 -117.80860 350412 1 BAKER FAA AP
9 44.38139 -100.28556 396597 1 PIERRE FAA AP
10 27.76667 -97.45000 412015 1 CORPUS CHRISTI WSO AP
11 29.53330 -98.47000 417945 1 SAN ANTONIO WSFO
12 40.72056 -114.03583 429382 1 WENDOVER AWOS
13 41.31250 -105.67444 485415 1 LARAMIE AP

A. Scott
April 7, 2013 2:05 pm

KR says: April 5, 2013 at 3:52 pm
A. Scott – WRT the _changes_ in rural/urban nature, and analyzing or correcting for the effects of such changes on long term trends, there are several approaches:

KR – you note that identifiable changes in site situation and short term local heat island (LHI) changes are simply adjusted for. Which, if clearly identifiable as to cause (or which show up as a one time “step” change as opposed to a change over time) , makes sense.
What does not make sense is the long term trend changes being adjusted out similarly. The ongoing long term effects of urbanization and growth should be apparent in each urban temp record. We know beyond doubt from the data that urbanization causes increased temps.
At one point both the urban site, and the rural sites around it, were all rural. As the urban site was built out and population increased, its average temps increased. The surrounding rural sites did not show the urbanization effect. Both Urban and rural sites should show any overall warming or cooling similarly.
You cannot, and should not, adjust out the urbanization effect on the urban sites. It is an important part of the equation.
And the difference in trend between rural and urban sites should be readily apparent over the last 100 years or so as many urban sites grew dramatically.
That is why I suggested a simple test to start. Pick and urban site that we know experienced significant growth over the last 100 years. Then choose a group of adjacent rural sites that surround the urban site – the more available the better.
Plot the temp anomaly trend for the rural sites over last 100 years as one graph, and overlay that on the same plot of the urban site they surround. To make it more interesting, also plot the raw uncorrected, un-adjusted data for these same sites as well.
Now do the same for all rural sites vs all urban sites in the entire continental US – overlay the 100 year trends for each. And last, same thing for the world.
This will help visualize the data and difference in trends for us common folks. And provide a foundation for taking the discussion to the next step.

April 8, 2013 5:25 pm

Steve Mosher, its good you came and addressed the points people made, but you didn’t address the major flaw in this paper
UHI results from urbanization (horizontal and vertical) over time.
Whether a site is rural, or not, or how rural it is, at any particular time is irrelevant.

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