Jet exhaust as climate forcing
Guest Post by Willis Eschenbach
The good folks over at the Berkeley Earth Surface Temperature project have published their paper about urban heat islands. It’s called “Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications”, by Wickham et al, hereinafter W2013.
They find no urban heat island effect, saying at the close of the Abstract:
Time series of the Earth’s average land temperature are estimated using the Berkeley Earth methodology applied to the full dataset and the rural subset; the difference of these is consistent with no urban heating effect over the period 1950 to 2010, with a slope of -0.10 ± 0.24/100yr (95% confidence).
Having read the paper, I can’t say I’m surprised that they find no difference.
The W2013 authors have used the MODIS 500 metre dataset to identify urban areas. It’s a dataset that uses the MODIS satellite data to classify the planet into land-cover classes, including urban. The W2013 study uses the MODIS data to divide all the world’s temperature records into “very rural” and “not very rural”. Then they show there is little difference in the trends between the two groups.
Figure 1. Actual MODIS grid overlaid on Henderson Field, Guadalcanal, Solomon Islands. Picture is from 2003, prior to recent development. All gridcells would be classified by MODIS as not built-up, since less than 50% of the area of any given gridcell is built-up area. The ocean is at the upper right. Click to embiggen.
Let me talk a bit about the W2013 study and the MODIS map, and demonstrate by example the reason why I didn’t expect their “very rural” split to have much discriminatory power regarding temperature trends. As an example, let me use one of my favorite airports, Henderson Field on Guadalcanal.
Here is the description from the W2013 study of how they identified the “very rural” stations.
The MOD500 map is available as a raster image, providing a binary classification (urban or not urban) for a global grid with pixels of size 15 arc-seconds.
…
Rather than compare urban sites to non-urban, thereby explicitly estimating UHI effects, we split sites into very-rural and not very-rural. We defined a site as “very-rural” if the MOD500 map showed no urban regions within one tenth of a degree in latitude or longitude of the site. This choice should minimize errors that occur from MODIS classifications in fringe areas. We expect these very-rural sites to be reasonably free from urban heating effects.
This led me to investigate the MODIS “MOD500” map. The MODIS dataset is described in A new map of global urban extent from MODIS satellite data. Basically, it uses multi-spectrum radiation to discriminate between types of land cover, and to identify the signatures of various built-up areas..
Of immediate interest to this discussion is their definition of “urban areas”:
a. Urban areas
In both datasets, urban areas (coded class 13) are defined based on physical attributes: urban areas are places that are dominated by the built environment. The ‘built environment’ includes all non-vegetative, human-constructed elements, such as buildings, roads, runways, etc. (i.e. a mix of human-made surfaces and materials), and ‘dominated’ implies coverage greater than or equal to 50 percent of a given landscape unit (here, the pixel). Pixels that are predominantly vegetated (e.g. a park) are not considered urban, even though in terms of land use, they may function as urban space. Although ‘impervious surface’ is often used to characterize urban areas within the remote sensing literature, we prefer the more direct term ‘built environment’ because of uncertainty and scaling issues surrounding the impervious surface concept. Finally, we also define a minimum mapping unit: urban areas are contiguous patches of built-up land greater than 1 km^2. SOURCE
Now, each “pixel” that they mention above is one of the gridcells shown in Figure 1 above. Those are the actual gridcells used in the MODIS map. If more than half of a gridcell is “built environment” (houses, roads, runways, etc.) then the gridcell is counted as “built-up land”. However, there’s a final hurdle. You need to have five adjacent gridcells of built-up land to have those gridcells classed as part of an urban area in the MOD500 map.
This is kind of an odd definition. It means that any small hamlet is rural, since it won’t cover a square kilometre (about 250 acres). You have to have five contiguous built-up gridcells, which totals just over 1 km^2, for those gridcells to be classified as urban in the MOD500 map.
Figure 2 below shows how much of the land shown in Figure 1 could be built up, covered with houses and roads and parking lots, without anything being called “urban”.
Figure 2. As in Figure 1, overlaid with theoretical possible buildup without any areas being classified as “urban” in the MODIS dataset. Gray areas are 100% built-up, white triangles show 40% built-up. A total of about 70% of the land is built-up. None of it is classified urban because nowhere are there are five contiguous built-up gridcells.
In any case, that’s the history. Figure 3 shows the situation today:
Figure 3. Actual MODIS grid overlaid on current view of Henderson Field. Picture is from January 2013. All gridcells would still be classified by MODIS as not built-up, since less than 50% of the area of any gridcell is built-up area. The gridcells shown in red are the nearest to being classified as built up, as they all have about 40% of their area covered with roads, houses, and runways.
Again, in the MOD500 map, none of these gridcells would be classified as urban. Even if those four gridcells were more than 50% built-up, still none of them would be classified as urban because there aren’t five of them and they aren’t contiguous.
However, the fact that the MOD500 map classifies all of these gridcells as being rural makes no difference—the Stevenson Screen housing the thermometers is still in a horrible location. Here’s why, borrowed from Anthony’s post on the subject. This shows a ground level view of the station taken from the terminal. Note the white box of the Stevenson Screen housing the thermometer and other instruments in the green area just left of center, below the smoke visible in the distance.
Here comes a plane! Weather station visible to the left of the plane’s tail, right of the white mast.
Coming into the terminal…
Hey, park it over here!
Uh, oh, look where the jet exhaust is pointed:
Hmmm, a new high temperature today?
Back to normal.
Now, that’s the problem with the location of the Henderson Field weather station. There are lots of good places to locate the Stevenson Screen in the local area … and it’s in a bad place. This situation is mirrored in many airports and weather stations in general around the planet.
The difficulty I have with the approach of using the MOD500 map to distinguish urban from rural is simply stated:
Many of the siting problems have nothing to do with proximity to an urban area.
Instead, many of them have everything to do with proximity to jet planes, or to air conditioner exhaust, or to the back of a single house in a big field, or to being located over a patch of gravel.
And sadly, even with a map averaged on a 500 metre grid, there’s no way to determine those things.
And that’s why I didn’t expect they would find any difference … because their division into categories has little to do with the actual freedom of the station from human influences on the temperature. Urban vs Rural is not the issue. The real dichotomy is Well Sited vs Poorly Sited.
It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. I’ve been agitating for a while to call it the LHI, for the “Local Heat Island”. It’s not essentially urban in nature. It doesn’t matter what’s causing the local heat island, whether it’s shelter from the wind as the trees grow up or proximity to a barbecue pit.
Nor does the local heat island have to be large. A thermometer sitting above a small patch of gravel will show a very different temperature response from one just a short distance away in a grassy field. The local heat island only needs to be big enough to contain the thermometer, one air conditioner exhaust is plenty, as is a jet exhaust …
The only approach that I see that has any hope of success is to painstakingly divide the stations, one by one, based on what is in their viewshed, and exactly how far are they from a variety of ground covers. The work done by Michel Leroy of METEOFrance in 2010 lays out one way to do this, as discussed here.
Because even with the outstanding resolution of the MODIS dataset, it still can’t tell us whether the siting of the weather station is up to snuff, or whether we’re just measuring the temperature of a jet engine at 50 metres … and that’s why I don’t find the results in the W2013 paper at all persuasive.
Best regards,
w.
PS—One curiosity. The published paper is a slightly polished version of their earlier pre-publication paper with the exact same title, available here. The curiosity is the re-ordering of the authors on the two title pages, viz:
Internal politics?
I also note that my friend Steven Mosher is now listed as an author on the paper, my congratulations to him.
[UPDATE] Steven Mosher has pointed out that in the analysis, this particular station (Henderson Field) is classified as urban. I have no problem with that, as it is not a long ways from the capital and is likely classified urban by BEST (although likely not by MODIS) on that basis for their study.
Nor was the classification of that particular station my point, which I quote here from above:
And that’s why I didn’t expect they would find any difference … because their division into categories has little to do with the actual freedom of the station from human influences on the temperature. Urban vs Rural is not the issue. The real dichotomy is Well Sited vs Poorly Sited.
It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. I’ve been agitating for a while to call it the LHI, for the “Local Heat Island”. It’s not essentially urban in nature. It doesn’t matter what’s causing the local heat island, whether it’s shelter from the wind as the trees grow up or proximity to a barbecue pit.
Steven also adds the following comment:
My main concern is that people will think the article is about urban/rural (see the title … very rural, not) when the text is about “siting”.
Also, folks may get the idea that airports can never be CRN1 [a measure of station quality] … something which Fall 2011 doesn’t support, as many of CRN1 and CRN2 are at airports.
A clarification might help prevent future misunderstandings and gotchas.
My thanks to Steven for calling that classification to my attention, and also for his clarification.
BTW, occurs to me how this thread is, like the Nancy Green thread, also about the lack of resolution in data leading to improper/misleading conclusions. Seems to me the lack of resolution in terms of changes around stations is the issue here, and none of the analyses I’ve seen from BEST resolve this issue as the methods for classifying stations don’t have the resolution needed to detect the influence of man-mad structures on temperature stations. In essence, the BEST approach uses proxies for the changes in influence, when in fact the actual data should be available, albeit onerous to capture and analyze as it would have to be done manually for each site.
Paul says:
April 5, 2013 at 2:53 am
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.
Had nothing to do with “avoiding a challenge”, Paul, that’s all your imagination.
Regards,
w.
Seems to me we should know the answer to this question: “What is the threshold surface area of asphalt (or concrete or brick or…) that can be built within what distance of a temperature station without affecting the temperature measurement?”
Could be done experimentally, no?
Steve Mosher is holding his own in the obvious way. Everyone is saying studies should be done. He claims he has already done one, analyzed using many of the variables that everyone is suggesting, and that he got nothing. Fair enough. He is offering to repeat the studies using other people’s filters. Also fair enough. Until his work is scrutinized, I don’t see what else there is to say about it.
Well, miker613, Mr. Mosher has analyzed data, but not in a way that approaches the real issue: is there a trend difference between stations where no local man-made objects have been constructed versus those where they have, over the length of the temperature record.
What Mr. Mosher has done is a different analysis, one without the resolution needed to answer this question.
This thread has been very educational about lots of things, including the rationale for airport weather stations (thanks, pilots).
Anthony and Willis and commenters have done a great job over the last few years of educating readers about the ins and outs of measurement, which looks simple on the surface, but is in fact quite complicated. This reader has learned a lot, and hopefully others have, too.
WUWT is a free university, minus boring lecturers and having to sit in an uncomfortable seat frantically scribbling notes.
Well, he has made an offer: Present a filter, usable by machine (as opposed to Go there and check by hand), and he’ll try it. Sounds like he’s tried a number of them on his own.
Aircraft movements for Nadi, Fiji (NAN)
http://www.ats.com.fj/mms.aspx
Can’t find anything on HIR but haven’t really looked hard.
miker613 says:
April 5, 2013 at 12:02 pm
Well, he has made an offer: Present a filter, usable by machine (as opposed to Go there and check by hand), and he’ll try it. Sounds like he’s tried a number of them on his own.
——–
I have presented one. Usable by machine is a convenient, but not appropriate, condition. Doing what is accurate may not be convenient. If you have a database that lists construction by location by year please let us know. Lacking that, the work Anthony has done to document the environment around stations is the best I’ve seen to date. All done through direct observation, not by proxy.
Yes, you are very definitely right about that …… all of these debates over UHI, and over what kinds of trends the most popular temperature indices are actually displaying, are all mere Kibbuke Theater.
So what if Global Mean Temperature is generally rising faster than it did between 1940 and 1975? So what if there is a recent pause in the post 1975 upward trend? (So what if there isn’t actually any such pause?)
The historical evidence is that temperature trends can accelerate or decelerate very quickly over short time frames, both in the presence of an upwardly trending CO2 concentration, but also in the absence of an upwardly trending CO2 concentration.
As long as there remains no long-term declining trend in GMT which is statistically significant and which continues for 30 to 50 years, the climate science community will continue to attribute all upward trends in GMT to the effects of man-made GHGs. (“Nothing else explains it.”)
The only thing that matters is if those in positions of political power and influence pay any real attention to what the climate scientists are saying, or they don’t.
“Usable by machine is a convenient, but not appropriate, condition. Doing what is accurate may not be convenient.” It’s always nice to have good data, but usually we have to go to war(k) with the data we’ve got. Are you claiming that there is no way to use all the metadata we have to detect the signal? That seems to be Mosher’s claim – but of course it pushes in the direction that there is no signal.
Steven Mosher says:
April 4, 2013 at 12:18 pm
[…] 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.[…]
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.
oops emphasis mine
miker613 says:
April 5, 2013 at 1:26 pm
“Usable by machine is a convenient, but not appropriate, condition. Doing what is accurate may not be convenient.” It’s always nice to have good data, but usually we have to go to war(k) with the data we’ve got. Are you claiming that there is no way to use all the metadata we have to detect the signal? That seems to be Mosher’s claim – but of course it pushes in the direction that there is no signal.
———–
What I am saying is I have not seen anyone show that the meta data, as used in the BEST process, relates to how one would expect to see temperature behave at stations where there were no man-mad structures before time T0 and man-made structures after T0. Have you seen this data?
Or to ask an even simpler question, what % do the stations classified in BEST as “urban” had a structure built within 1000 m during their tempertaure record? What % of “rural” stations? Are trends different for those where a structure was built versus those where no structure was built?
Or, more basically mike613, should one expect that the trend would be different at a station where no changes were made versus one where an asphalt pad was built next to the station?
I would certainly expect there to be a difference. If I did the work and found none I would remain skeptical and expand my analysis. If I did the work and found a difference I would remain skeptical and expand the analysis.
Another way to approach it is to look for stations with very high trends and very low trends , dig into the meta data, visually observe the stations, etc. searching for any relationship between siting and trend.
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?
Mosher/Willis … please correct me if I’m wrong, but, if I recall correctly – from what I;ve learned from past discussions on UHI, for some here an important point is missed in the discussion about UHI. That is that there are really two different issues involved with UHI.
One is the actual Urban effect on observed temperatures. We all know, and most have experienced, the fact that temps are higher as we move from rural into urban areas. This is clear proof there clearly IS an UHI effect on actual temperatures.
That we all know UHI is real as it affects real temperatures, makes it hard to reconcile with and understand the other context of the discussion – whether those UHI areas affect the area or global temperature trends.
Common sense says heck yes they do. If you drop an ice cube in a bowl of water, the temp of the water will decrease. If you drop a hot object in the same bowl of water, the water temp will increase. In that context I struggled with the question how could UHI not affect mean temperature trends?
The difference is when talking about temp trends, those all sciency type folks are talking about <anomalies and not actual temps.
Anomalies measure the temperature trend as a deviation from a standardized average base temperature period. The simple explanation is that actual temperature varies greatly due to many influences – elevation, population, siting issues, and the like.
Using the measured temp difference from each sites own standardized base period allows a comparison of the temp TREND – regardless of different site characteristics. Simplistically, using anomalies converts the apples, oranges, limes, pears, peaches, and lemons etc. of the disparate station’s data, into all “apples” … by comparing only the CHANGE in temps relative to a standard baseline average.
For purposes of mean temperature change, or trend, we want to know how much each stations temps changed over time, not what the actual temp was. Using anomalies creates a simple, standard method to compare differences (or similarities) between disparate sites.
To apply that to the UHI discussion here – while its true from a temperature standpoint that Urban areas DO exhibit warmer temps, from a temp change standpoint, an urban site and nearby rural site are very likely to show then same or very similar change in any given year. If you have a warm summer in an area it is highly likely to be similarly warm – a similar amount above average – for both a rural and a nearby urban site.
That is what Mosher, Zeke or others are talking about in these discussions – that the temp trend at an Urban site is usually little different than the trend for the same period at a nearby rural site.
It confused me for many years – then one day a light bulb went off and I understood the difference.
So it makes perfect sense that while there certainly IS an Urban Heat Island effect when it comes to instant temperatures, it also makes perfect sense that the CHANGE in temps during a fixed period between nearby urban and rural sites would be similar.
Thus the statement that guys like Mosher, Zeke et al find no UHI effect when analyzing global mean temperature change – which uses anomalies – does make perfect sense. Since nearby rural and urban sites temps change form period to period relatively uniformly, it does make sense there is no UHI effect found on mean temp change.
Again – if I got any of that wrong, Mosher, Zeke etc. please correct.
As an aside, to complicate matters more, the base period is not always the same for different data sets. World Meteorological Organization policy suggests using the latest decade for the 30-year average. But even a quick Google search shows different data sets using differing base periods.
http://www.ncdc.noaa.gov/sotc/global/#introduction 1981-2010
http://www.ncdc.noaa.gov/ghcnm/maps.php 1961-1990 (1971-2000 for SST)
http://data.giss.nasa.gov/gistemp/ Table Data: 1951-1980 means
This does not cause problems per se, as long as you are comparing data within the same set, however it can become highly important if you were to try and compare dtat between two different data sets.
NCDC explanation on using anomalies:
Several folks above have touched on the the issue that is IMO relevant and should be addressed.
While in the short term an urban and nearby rural location are likely to experience very similar changes on an annual or similar basis, the overall discussion is about long term change. And I do not see how long term UHI effect is addressed in any of these studies.
Over the period of years relevant to the climate change discussion (say late 1800’s to present, or even 1920 to present where we have decent temp records) urban areas have experienced significant growth.
The premise that using anomalies allows a standard compare between two nearby urban and rural sites is correct ONLY to the extent those sites remain static – unchanged over the time period reviewed.
But we know that is NOT the case for most. Many sites that were rural 100 years ago are still similarly rural. However, virtually all urban sites have seen huge growth and expansion during that same 100 year period.
Because we DO know, from an actual temp standpoint, that UHI is definitively real, we know beyond doubt that the actual temperature has increased over this longer time period. That also means the deviation from the base average has also increased over this long time as urbanization has occurred.
As the rural sites have remained physically largely the same over the last 100 years, and the urban areas have grown dramatically – causing their actual temps to increase significantly – statistically there MUST be a resultant UHI influenced increase in mean temperature change, in the overall deviation from the standard average base temps, as a result of this accelerated urban divergence.
It could well be, with so much of the globe still rural (or oceans etc) that the signal, even despite massive urbanization the last 100 years, is too small to identify. Even that though seems highly unlikely with the amount and degree of urbanization over the last century.
Using a smaller area with significant urbanization – say continental US – there should be a clear UHI signal. It would seem running the trends analysis on the rural and UHI stations separately for the last 100 years or so, then comparing the temp change anomalies of the two, should show Urban temp anomalies increasing faster than rural during this longer term period.
That demonstrates with certainty in my opinion a UHI effect does exist. The question then is why it does not readily appear in the overall review. Either it is too small to be readily apparent – which seems unlikely – or there are other issues in play masking the accelerated UHI anomalies
Mosher, Zeke, Willis … etc. – please take a look and comment …
A. Scott says:
April 5, 2013 at 2:58 pm
Mosher/Willis …
———-
Your post is true if and only if there is no change in the vicinity of the temperature station over the history of the record. And it’s not about going from rural to urban, it’s about whether anyone put a heat source or sink nearby, for example a building, parking lot, AC exhaust, etc. at some time during the station history. How many stations do you think have been in the same environment for their entire history?
You could drill down even further to demonstrate there IS an UHI effect on mean temp trends by selecting any large city, and one, or better a handful of, nearby rural sites. Run the trend anomaly analysis on the rural sites, and separately on the urban city for the last 100 years and comapre them.
Mosher, Zeke, Willis or … ? Can one of you plot this for us.
One graph showing any large Urban city’s anomaly history for say last 100 years. Overlay the same anomaly history for a group of nearby representative rural sites.
And maybe a similar paired graph on continental US (or similar).. last same graph on a global mean basis.
A. Scott – I would wholly agree with you in terms of trends being a completely different matter than local temperature values – and I believe you have described the situation WRT anomalies very well. 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:
1) Use the metadata for the stations (NOAA and others) and reset the offsets when the site situation changes – consider the before/after periods to have different average offsets. This accounts for equipment and enclosure changes, although perhaps not nearby development.
2) Breakpoint analysis (BEST), numerically looking at changes/shifts in one station that are inconsistent with those around it, on the assumption that entire groups of nearby stations don’t change at once – again, break the station data into two at that point. That can account for equipment and enclosure changes, local influences, whether a shading tree has been cut down, a nearby plot paved, etc. Interestingly, this matches the metadata corrections quite well, and perhaps indicates that local developments are not a huge influence.
3) Mosher in his analysis took a _very_ stringent view of what a ‘rural’ station would be, with the reasonable expectation that areas that became urban remained urban. The “very rural” areas match the overall records, supporting the claim that UHI is not an issue with trends. And hence his requests for alternative methods of identifying rural areas (he having tried a number of them) as a crosscheck.
Local heat influences are a different matter entirely from UHI. If a local influence exists, it may shift the baseline average for that station either up or down. Only a consistent bias in how LHI changes would affect long term trends, given the number of stations. I would note, however, that (a) the BEST method should identify LHI changes, and (b) since the metadata and breakpoint methods give essentially the same results, it would seem that local influences do not have a significant effect on trends.
k. scott dennison … your issue – station siting – is relevant, but completely different and separate from the UHI discussion here.
It is also however, exactly why I suggested including a GROUP of nearby rural stations when comparing to an urban station – which would tend to filter and one station’s issues.
Here’s an idea: how about we look at individual station data and get it “right” before we rush to use surrounding stations to correct urban stations? How about we dig into the data before rushing to average and correct it? That is what is s compelling about the CET: it is a well understood record.
Have you ever seen a bank of tall trees all the same height and columnar in shape next to a farm complex? Why do farmers plant those trees like that? The use of such growing wind breaks can and do affect ground temperatures. So you see how vegetative growth and changes surrounding a rural station can affect its temperature trend over a long period of time. Rural stations situated near a large lake or reservoir can undergo similar changes in its environment. Rural stations located in the direct path of El Nino and La Nina long term oscillations can fluctuate wildly as opposed to one in the protective (and warm) surrounds of an urban site.
Calibrate Mosher, calibrate. Go back and do what you should have done before sending the article to print. Do a random study of the two extremes you have chosen to study but at ground level and with the historical changing environment in mind. Impinging trees can be every bit as good at changing ground temperatures over time as a growing asphalt meadow.
The greater question is this: With such a long list of co-authors, how come no one thought of this most basic quality control measure? Hmmmm?
plazaeme says:
April 5, 2013 at 1:13 am
So, let’s try to follow Willis rationale (which I like). Local Heat Island. Of course, there could also be Local Cooling Islands. But we can think, what would be more probable / abundant, cooling or heating islands, compared to areas not affected by human activity? I would say heating ones, and by far. And that makes the point for Willis idea on Local Heating Islands. The more human activity you have, the more heating (wherever). Because (I guess) there are far more human heating activities than human cooling activities. Unless I am wrong.
======================================================
Unfortunately you would be wrong.
Natural status of the surroundings (or as close to natural as one can get) is the the base line. I do not see many people running their conditioners to cool the outside. Watering the lawn or close area might cause a drop if done in the heat of the day but beyond that if the thermometer is properly covered the ambient air temp will be steady.
Station siting criteria are important because you need to limit external biases. This is something that escaped our scientific community along with its importance to policies which govern the populace. Using garbage data to drive agenda creates GARBAGE POLICIES and AGENDA.
Scott says:
April 5, 2013 at 5:59 am
Around here in my semi-urban area, I’ve noticed homeowners are constantly installing groundwater drain tiles to quickly remove surface water after a rain and route it to the storm drains.
There have been a number of large scale changes in agricultural practices over the last 70 years, one of which is field drainage in wetter areas like northern and western Europe.
The implicit assumption that local anthropogenic influences on temperatures are restricted to urban areas is entirely false, and because agricultural areas are much larger in extent than urban areas, changes in agricultural practices likely have a larger effect on global average temperatures than urbanization.
I have previously argue that temperatures measured at lighthouses would be the best uncontaminated global temperature change measure. As measurements only start when the lighthouse has been built, and agriculture and urbanization is rare in the vicinity of a lighthouse.