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.
Gene Selkov says: April 4, 2013 at 2:02 pm
ralfellis: maybe the values of V1 and V2 for your aircraft are based on the worst-case scenario at the worst airport where the machine is certified to land?
Surely your V1 depends on altitude, quite a bit. According to this chart, at 40C, you’re already at 7500 feet above the ground, compared to standard atmosphere:
=========================================
V1, V2 and Vr change with every take off, and a new calculation has to be made every time. The main criteria are runway length, runway altitude, runway state (wet?), temperature, atmospheric pressure, weight of aircraft and power setting. We tend to reduce the power to the minimum required, to save the engines.
However, the thing to note is that the temperature and altitude effect the engine thrust and runway requirements much more than they do the airspeed. The airspeed detector (a pitot) naturally compensates for much of the difference that altitude and temperature makes. Thinner air means lower airspeed detected, so you have to go faster, in terms of groundspeed, to make up the difference anyway. So the take-off airspeed changes very little with temperature alone – just a couple of knots up for the V1 and a couple of knots down for the Vr.
But the greater groundspeed (not airspeed) caused by high altitude or high temperatures does mean that longer take off runs are required. You will find that high airports often have very long runways. And if you don’t have a long runway, you have to shed some weight.
.
.
u.k.(us) says: April 4, 2013 at 2:02 pm
I fear I’m in way over my head, but pilots need to know the temperature, because it affects the length of the take-off run, which limits the length of runway left to stop in case of an engine failure.
===================================
True. As above, temperature and altitude effect engine thrust and runway requirements. Most engines are flat rated to 30 degrees c (capable of producing max power up to 30 degrees). Above that, the engine maxes out on internal temperature (gas temperature), not maximum power, and so thrust reduces.
But Mosher was taking about temperature effecting the take-off airspeed, which is incorrect.
.
I live in a rural area in the UK. I am conducting a small experiment on temperature using a pair of identical digital thermometers relaying to a central instrument. One is 1m from the back wall of our house and the second 20m away in ”clear air”. Every day there is a discrepancy between readings of up to 3C. The sensor near the house giving the highest readings. Not unsuspected.
I think Steven Mosher is holding his own here. Not impressed by Willis renaming the UHI to avoid a challenge!
I think Steven Mosher is holding his own here. Not impressed by Willis’s renaming the UHI to avoid a challenge.
On the face of it, suggesting UHI hasn’t impacted the recorded global warming trend seems illogical. Seems to me the challenge is to identify the trend (it has to exist, right?) and quantifying it. However, my starting logic of the ‘UHI has to exist’ and my demanding evidence of its existence while refuting contrary evidence as illogical, seems to me the parallel of the AGW believer’s dogmatic belief in the mystical power of anthropogenic CO2. I mean that power has to exist, right?
UHI is a storm in a teacup if you ask me. Real debate is on climate sensitivity & the role of feedbacks. The starting challenge would be to prove that the theory of a sensitive climate that responds to forcings is real..
Willis Eschenbach
‘annot tell well sited surface stations from poorly sited stations.’
Nothing can do this but physically checking the stations, the reasons this is not done is becasue of time and money , not becasue it would not improve the validity of the data , which it would, and so lead to improved respect for the data .
The underlining problem is before AGW theory , people accepted that to some degree weather information would always be bit hit and miss and that forecasts could be wrong so poor sitting was not such a issue . But once this data became all important in a ‘political sense’ to back up the ‘settled ‘ science claims which in turn where used to make great demands for change and spending . Issues such has sitting ,which in the past was consider unfortunate but acceptable, came under review and their failings become much more important.
Mosher
Can you please answer the following question to establish for certain what you are trying to say.
Do you believe that urban areas are hotter on average than rural areas in the same local, yes or no?
I am going to assume that your answer is yes, as that is the absolute, undeniable truth established by direct observation millions of time a day.
Now the trend in measured temperature between these areas, should all other things being equal follow the generic cooling/warming of that locale and there should be little if any differenc
I think that is what you are triyng to say and I agree.
However, the question I am interested in is….. ‘is there a trend for rural areas to become more urbanised, whilst retaining the definition of rural? Also is there a trend for urban areas to become even more ‘urban’ over time increasing the actual measured temperatures and introducing an artificial trend outwith the generic temperature trend?
Anthony’s study and anecdotal evidence seems to suggest that such a thing has happened. The only way to check this is to examine the actual site and current placing of the sensors and compare that to the past by whatever means is possible.
Without this information you and everyone else is guessing. You cannot however deny that if this extra ‘urbanisation’ of the measuring sites has been occuring, then an artificial bias has been introduced into the temperature record.
Alan
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.”
Try “rural” = no buildings or tarmac roads exceeing 3% of area.
“urban” = more than 50% covered by buildings and or tarmac.
Then go and explore, say, Brazilia, Heathrow, Milton Keynes, Mokhatam Hills, Bat Yam, Eilat, Dubai airport and hinterland etc etc etc.
All of these would fit the definition “rural” seventy years ago and several of these much more recently. They would all qualify as “urban” today.
But, of course, none of these had thermometers located in their grids all those years ago so there can be no true comparison.
But satellite data has been around for 30 years or more, perhaps Mr. Mosher could look at those grids that were definitely “rural” when the first data became available but are now “urban” accoridng to the latest data and then confirm that (1) there is no such thing as UHI and (2) that the temperature anomalies for urban areas correlate directly with those of rural areas. I suspect that the latter could be true but the former certainly is not and any interpretation of the data which suggests that it is true must be erroneous.
Alan Millar says:
April 5 2013 at 4:55 am
“Is there a trend for urban areas to become even more ‘urban’ over time …”
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. If they aren’t installing drain tile they are landscaping and sloping the ground to do the same thing. No one wants a part of their yard soggy a day after a rain. Same thing with golf courses and parks, if there is one project that’s a year round constant its surface water removal projects. So over time things dry out much quicker and temperatures rise faster but you can’t really see this urbanization from a satellite.
C’mon, Mosher. If urban areas are demonstrably warmer than surrounding rural areas, then there has to be a greater trend for that site than the rural sites, assuming the urban site was less urban/more similar to the rural sites back in ~1880 or whatever. Yes, there could be a few exceptions depending on the site-specifics, but I’m talking in general.
If you can’t find it, it means your measuring system/analysis techniques aren’t up to snuff.
Uh. No he is not holding his own. He has avoided my question. Did he or did he not calibrate his selection method with a random on-the-ground long-term quality check of stations classified as rural? Rural stations can be impaired over time by vegetative growth, or worse, poorly sited in terms of long-term lake, ocean, and atmospheric oscillation temperature trend effects that can be mistaken for AGW. I so no evidence of calibration here. None.
Below are changes of a few things that can affect surface temperature:
CO2 30% increase (of itself) (last 150 years)
The atmosphere has changed by 0.01% (in terms of CO2 content) (last 150 years)
Cities over 1 million increased by 40,000% (last 150 years)
Global ground water depletion ∼4,500 km3 (last 100 years) and rate of depletion increasing
Global grain production increase ∼40% (last 30 years)
Global vehicle ownership increase ~650% (last 50 years) (corresponding road building/surface change?)
I like this graph from Climate Audit, it`s raw I believe, could someone explain how if all the adjustments are done separately to each data set the temperature increase will be the same for both, ie no perceptible UHI signal between Rural and urban.
http://s446.photobucket.com/user/bobclive/media/peters27.gif.html?sort=6&o=71
knr says:
April 5, 2013 at 3:25 am
……………..
The underlining problem is before AGW theory , people accepted that to some degree weather information would always be bit hit and miss and that forecasts could be wrong so poor sitting was not such a issue .
———————————-
You’re right because 200 years ago people didn’t try to connect all temperatures worldwide.
They went through regional droughts, floods, hurricanes, tornadoes, blizzards etc so they knew every location throughout the world was somewhat different at any given moment.
Now our extremely straight forward scientists and leaders claim to know:
The entire world’s average temperature + or – 0.01 degrees.
The average temperature of all the earth’s seas at all depths top to bottom + or – 0.01 degrees
The average level of all the oceans in all the world to within + or – 0.1mm.
The average rainfall throughout the whole world within + or – 0.01mm.
Every puff of wind throughout everywhere on earth, instantaneously.
Plus they know all this information exactly for billions of years.
And they all swear life on earth will end and it’s all because of CO2.
They lie, cheat, steal, adjust and lose data and then refuse access.
After all this they call us deniers.
Me? I’m more like Thomas, I doubt most of it.
cn
Well my prejudices date from doing tier 1 automotive compliance work. Pretty much a no-no to try and validate a measurement point using external criteria. All ours were individually classified by empirically examining both the instrument and its method of measurement, oh the joys of Gauge R&R.. Change the instrument (even for a replacement with an identical model) re-run the validation, Change the method, re run the validation.
Want to establish the repeatability and reproducibility of these measurement points, go to each one and do the work. Is that a lot of work, yes, do your results have any value until its done , no.
Stephen has delivered some food for thought – and that is always a good thing. For example Arrhenius tells us that the radiative properties of CO2 in a test tube, a controlled closed environment make it a greenhouse gas. But Arrhenius tells us nothing about how CO2 behaves in a massive, chaotic open system like our atmosphere where it is mixed with massive amounts of constantly moving Nitrogen and oxygen, and even the trace gasses outnumber the poor CO2 molecules. Do we even know for example how much of that added energy is lost in thermal winds, or in the kinetic motion of raining out all that additional water?
Urban heat islands, could be like that – logic tells us that increased urbanisation around a site SHOULD result in a trend. Stephen says he has failed to find it – I don’t think he has said it doesn’t exist. Willis suggests that siting is the issue and not urbanisation, and logically I think that’s right. The issue really is about sites that have changed in any way – a thermometer placed in a location that has had a tree grow over is will have a trend just as real as one which has has an airconditioner exhaust installed next to it. Indeed at a coastal site, a simple structure , grove of trees impeding the afternoon sea breeze is likely to introduce a trend. In this case land use changes and desertification may be very significant positive trend producers which clearly this study doesn’t address
One further thing, Max-Min / 2 is a lousy average, the average temperature should include all measurements and be examined for temporal behaviour, if it turns out that all the 0.8 deg warming is occurring because 9AM temperature has increased 10 deg but every other time has cooled, that is quite a different diagnosis from a general increase in temp at all times of day. If only low daily temps are increasing and the hottr hours aren’t, which should in fact happen if humidity increases, what implication has that for the supposed Catastrophe – after all it can hardly be catastrophic if the mins are higher, but the maximums are less. Nor will it be a catastrophe if all the temperature rise occurs in winter, and none in summer. It also is not a catastrophe if all the warming occurs away from the population, other than needing a two brick high sea wall.
This is my point when I say climate science needs to answer the question of whether Arrhenius actually applies in a massive, chaotic, open system and not only that, they need to be able to state exactly WHEN and WHERE it will be hotter before any protestations of the need for “Action” can be made.
Stephen, other commenters have rightly pointed out that some of your statements do suggest a confirmation bias. Beware, I suggest you try to prove yourself wrong before you try to prove yourself right, and to do that you DO need to look at siting in detail.
Absolutely brilliant article, Willis. Why is it that everyone who supports CAGW shows no interest in the empirical? Why do none of them want to actually investigate the physical surroundings of weather stations? Why will none of them use Anthony’s surfacestations.org website? Apparently, they suffer a phobia for the empirical.
I have been a land surveyor in Kansas for over 37 years. “Back in the days” in the mid to late 70’s, we used steel tapes, called chains, to make our horizontal measurements. We learned in college text that these chains were calibrated at 70 deg F and that for every 15 degs over or below 70 deg the chains would shrink or stretch 0.01 feet and the long measurements were corrected. In Kansas the temps range from 0 to 110 degrees depending on the season. The difference didn’t amount to much for a small parcel of land but for rural land, like a section (1 mile sq), the correction can be substantial.
While working on highway projects we attached thermometers on the chains to aide us in making the measurement corrections due to the long measurement.. We would find as much as a 8 degree difference between asphalt or concrete vs a grass or cultivated field. Another thing I remember is that when measuring over cultivated fields (usually wheat), depending on the season a cultivated field would show differences likely due to ground cover. In most of the growing season the wheat fields were cooler while they were green and golden as well as more humid. However after harvest they got warmer by as much as 5 deg F perhaps because the darker colors of exposed dirt that absorbed heat as opposed to reflecting it.
If a significant amount of land is cultivated, is this anthropologic influence significant or not?
Steve Mosher, if you’re listening, could you explain what you mean by “there’s no signal”? Do you mean (a) Urban areas are not actually any warmer on average than rural areas [that would seem very surprising], or (b) they are, but there aren’t enough of them to make any noticeable overall difference.
I’d also be interested in hearing your comment on something Steve McIntyre said long ago: It makes sense to use the satellite temperature data as the trusted baseline, and look for problems in the [far more complex] land-based temperature data if there is a discrepancy.
Excellent information, people accepted that to some degree weather information would always be bit hit and miss and that forecasts could be wrong so poor sitting was not such a issue .
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.
On the face of it, suggesting UHI hasn’t impacted the recorded global warming trend seems illogical. Seems to me the challenge is to identify the trend (it has to exist, right?) and quantifying it. However, my starting logic of the ‘UHI has to exist’ and my demanding evidence of its existence while refuting contrary evidence as illogical, seems to me the parallel of the AGW believer’s dogmatic belief in the mystical power of anthropogenic CO2. I mean that power has to exist, right?
——————
I don’t think Willis is just renaming UHI. I think he’s made it clear that he thinks the urban / rural distinction isn’t the significant one. He says pretty clearly that poor siting (Is that a word? Poor station site-positioning I guess) is the problem. Going on to say he’s refusing contrary evidence to support a dogmatic belief doesn’t appear to be justified in my view. Or am I misunderstanding something?
Whatever you refer UHI to, I travel to and from a city daily. In the travel I observe that the temperature in the vicinity of the local airport (w/weather station) and city is typically 5 to 9 F higher than the surrounding area that is pretty much unpopulated and well treed. And there is no change in altitude. The airport has no airline service to blame for extra heating. But the airport is of course devoid and altered from its natural surrounding. The question is: Did the same temperature differential occur before urbanization?
In reply to Michael Cohen @ur momisugly April 4, 2013 at 9:55 “I think it would be helpful for casual readers to emphasize that the paper is reporting no effect on the global land surface temperature trend from urban heating, not that there is no urban heating.”
I had that same impression BEST’s initial positon. However, this paper has these words: “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.” They are saying that rural areas are warming at the same rate as the total data set — which would imply either no urban heating or that the rural areas are so pervasive in the total data set that they swamp the urban heating effect from urban areas.
1. Since we can easily measure the existence of UHI/LHI with equipment as rudimentary as a car’s dashboard temperature gauge, the only logical conclusion is that it exists. The question is why it isn’t showing up in the temperature data trend.
2. Part of the problem is exactly what Willis suggested. The methodology cannot capture based on any definition of rural vs urban which weather stations have an artificial influence and which not. It has to be done station by station. But I think there are additional factors.
3. How would a UHI/LHI effect appear? It would NOT, imho, appear as a trend in the first place. It would appear as a one time step function at the time the artificial influence came into being. Build a building. One time step function. Build a parking lot 10 years later. One time step function. Relocate the building’s air conditioning exhaust 10 years after that. Three step functions spread out over 3 or 4 decades. Then mix them in with thousands upon thousands of other weather stations. Some don’t have step functions, others do, but we don’t know which ones are which and they certainly aren’t correlated in time. So we know the UHI/LHI exists, but it exists as thousands of step functions smeared together in the temperature record. No shock that there is not trend.
4. Not all step functions are going to be positive. Moving that building exhaust could produce a negative step function as easily as a positive one. My guess is that as urban influences gather, they are mostly positive, but at some point they most likely saturate. There’s only room for so many buildings, parking lots, air conditioners, etc, next to one weather station. As the effect becomes saturated, any additional changes are increasingly likely to be negative, and at some point I’m guessing a rough equilibrium would occur. Think of it like four buildings, one on each side of the weather station. Each one would have been a positive step function when it was built. But once you get to four, you’ve run out of sides, so you are maxed out. After a few decades, one building burns down. Negative step function, but it shows up right in the data where we are looking for a positive trend to prove UHI. I believe Watts et al 2012 may be demonstrating exactly this effect when you look at the poorest quality stations.
5. My recollection from BEST a while ago was that 1/3 of stations show a cooling trend in their data. OK, how do we know that it wouldn’t have been 2/3 without the influence of UHI/LHI? Or maybe none? I just don’t see how you can take a UHI effect that grows via a handful of events at each weather station, spread out over decades, averaged with thousands of other weather stations with similar events but at different points in time, and calculate by ANY means what effect UHI has (or doesn’t have) on the over all trend.
Steven Mosher says:
April 4, 2013 at 7:38 pm
Despite the donking, all that you have described so far is that he did his analysis using country level populations … so what? Yes, it makes the analysis cruder, but it certainly doesn’t invalidate the analysis. Since the issue is population GROWTH and not the raw population, I fail to see the problem. As the authors point out, there are interesting results despite the broad brush approach:
Your next issue is a couple of errors …
And you’re telling me that results for Antarctica somehow invalidate his underlying thesis? That’s a procedural issue, and one that he dealt with in the paper:
So that’s Antarctica, and no, I don’t care about the island of St. Helena. I’m sure it’s wrong, all results are wrong, … but by how much? I say again, the study is looking at population GROWTH, not population, so I doubt that the growth figures for the St. Helena population are that far off … but if they are, so what? Throw it out? How does that affect his results?
A coarse grained study is somehow without value? Say what? The HadCRUT results are on a 5X5 grid, and yet they’re used every day.
Steven, that is as shameless a job of study assassination as was done by the IPCC when they gave a bunch of trivial, bogus excuses (but no less trivial or bogus than yours) for throwing out Ross’s work in the TAR. All you’ve identified so far are meaningless issues that do not affect the strength or the significance of the results. Yes, it is a large-grained study, done on a country level … so what? It still gives significant information.
There are valid country-level studies done every day, Steven. And as the authors of this one point out, they found significant results at the country level, so you can’t just turn up your nose and claim that it’s not good enough. They find valid and significant results at the country level, so they must be doing something right. Your insistence that valid and significant results can be ignored simply because they are at a country level, DONK, just makes you look as stubborn as a … aw, never mind.
Yes, the study should be redone at a more fine-grained level to give better answers, and no, that doesn’t make the current study useless or wrong.
I sure hope you have more issues with the McKitrick study than you’ve listed so far, because if that’s all you got, then it sure looks like you’re just invalidating the study based on meaningless objections, and not seriously analyzing their results. The McKitrick study may have serious problems, but if all it’s got is are the problems you’ve identified above, then there’s no serious problems at all.
w.
Assuming that Mr. Mosher will return, I think them crux is this:
The issue isn’t urban/rural. The money question is is there a difference in trends between sites where no significant addition of man-made structures has occurred during the period of the temperature record versus stations where significant additions have occurred. I would suggest the metric for stratifying stations is something like a percentage change in the total man-made surface area within a radius of X meters of the station. Perhaps even stratifying into multiple classifications based on those where changes have been made within a narrow radius of Y versus 2Y, 3Y, etc.
This type of metric would certainly be more accurate as it is well known, as Willis and Anthony have pointed out several times, that the most likely cause of inaccuracies is what is very local to the station, versus what is in the general vicinity. Have to look at what was literally next to the station at the beginning of the record and what has been built (looking first next to the station and then in concentric rings) around the station over time to see if that affects the trend.
Maybe this has been done and I’m simply unaware of the results. If I were to do the analysis I would start with a small set of stations where I know changes have and have not occurred. Do the analysis as a percent of man-made surface area within 100 meter circles by year, plot it all up, analyze and see what it tells me. Wish I had the data, time and skills to do so.