By Steven Mosher,
AC Osborn made an interesting comment about airports that will give me an opportunity to do two things: Pay tribute to Willis for inspiring me and give you all a few more details about airports and GHCN v4 stations. Think of this as a brief but necessary sideline before returning to the investigation of how many stations in GHCNv4 are “ruralish” or “urbanish”. In his comments AC was most interested in how placement at airports would bias the records and my response was that he was talking about microsite and I would get to that eventually. Also a few other folks had some questions about microsite versus LCZ, so let’s start with a super simple diagram.
We can define microsite bias as any disturbance/encroachment at the site location which biases the measurement up or down within the “footprint” of the sensor. For a thermometer at 1.5meters, this range varies from a few meters in unstable conditions to hundreds of meters in stable conditions . In the recent NOAA study, they found bias up to 50 meters away from a disturbance. I’ve drawn this as the red circle, but in practice, depending on prevailing wind, it is an ellipse. The NOAA experiment (more on that in a future post) put sensors at 4m, 50m, and 124m from a building and found
The mean urban bias for these conditions quickly dropped from 0.84 °C at tower-A (4 m) to 0.55 and 0.01 °C at towers-B` and -C located 50 and 124 meters from the small-scale built environment. Despite a mean urban signal near 0.9 °C at tower-A, the mean urban biases were not statistically significant given the magnitude of the towers standard 2 deviations; 0.44, 0.40, 0.37, and 0.31 °C for tower-A, -B, -B’, and -C respectively.
While not statistically significant, however, they still recommend precaution and suggest that the first 100m of a site be free of encroachments. In field experiments of the effect of roads on air temperature measured at 1.5m, a bias of .1C was found as far as 10m away from roads. At airports this distance should probably be increased. At an airport where the runway is 50m+ wide, the effect the asphalt has on the air temperature is roughly 1.2C at the edge of the runway and diminishes to ~.1c by 150m away from the runway. (Kinoshita, N. (2014). An Evaluation Method of the Effect of Observation Environment on Air Temperature Measurement. Boundary-Layer Meteorology) Exercising even more caution, I’ve extended this out to 500m, although it should be noted that this could classify good sites as “bad” sites and reduce differences in a good/bad comparison. Obviously, this range can be tested by sensitivity analysis.
Outside the red circle I’ve depicted the “Local Climate Zone”. Per Oke/Stewart this region can extend for kilometers. In simple terms you can think of two kinds of biases: Those biases that arise from the immediate vicinity within the view of the sensor and have a direct impact of the sensor, and those that are outside the view of the sensor and act indirectly– say that tall set of buildings 800m away that disturb the natural airflow to the site. In the previous post, we were discussing the local scale; this is the scale at which we would term the bias “UHI.”
There is another source of bias, from far away areas, and I will cover that in another post. For now, we will use airports to understand the difference between these two scales. Let’s do that by merely picturing some extremes in our mind: An airport in Hong Kong, and an airport on a small island in the middle of the ocean. Both airports might have microsite bias, but the Hong Kong temperature would be influenced by the urban local climate zone with its artificial ground cover. The airport on the island is surrounded by nonurban ocean, with no UHI from the ocean. Simplistically, the total bias a site might be seen as a combination of a micro bias, local bias, and distant bias.
There are, logically, six conditions we can outline:
| Rural–natural | No Micro Bias | Warm Micro Bias | Cool Micro Bias |
| Urban–artificial | No Micro Bias | Warm Micro Bias | Cool Micro Bias |
It is important to remember that micro disturbances can bias in both directions, cooling by shading for example. And note that logically you could find a well sited site in an urban location. This was hypothesized by Peterson long ago:
“In a recent talk at the World Meteorological Organization, T. Oke (2001, personal communication) stated that there has been considerable advancement in the understanding of urban climatology in the last 15 years. He went on to say that urban heat islands should be considered on three different scales. First, there is the mesoscale of the whole city. The second is the local scale on the order of the size of a park. And the third scale is the microscale of the garden and buildings near the meteorological observing site. Of the three scales, the microscale and local-scale effects generally are larger than mesoscale effects….
Gallo et al. (1996) examined of the effect of land use/ land cover on observed diurnal temperature range and the results support the notion that microscale influences of land use/land cover are stronger than mesoscale. A metadata survey provided land use information in three radii: 100 m, 1 km, and 10 km. The analysis found that the strongest effect of differences in land use/land cover was for the 100-m radius. While the land use/land cover effect ‘‘remains present even at 10,000 m….
Recent research by Spronken-Smith and Oke (1998) also concluded that there was a marked park cool island effect within the UHI. They report that under ideal conditions the park cool island can be greater than 5 C, though in midlatitude cities they are typically 1 –2C. In the cities studied, the nocturnal cooling in parks is often similar to that of rural areas. They reported that the thermal influence of parks on air temperatures appears to be restricted to a distance of about one park width….
Park cool islands are not the only potential mitigating factor for in situ urban temperature observations. Oceans and large lakes can have a significant influence on the temperature of nearby land stations whether the station is rural or urban. The stations used in this analysis that were within 2 km of the shore of a large body of water disproportionally tended to be urban (5.8% of urban were coastal versus 2.4% of rural).
Looking at airports will also help you cement the difference between the micro and the LCZ in your thinking. With that in mind we will turn to airports and look at various pictures to understand the difference between the micro and the local- the nearby city or the nearby ocean or field.
First a few details about airports. In my metadata I have airports classified as small, medium and large
First, the small: some are paved. Pixels (30m) detected as artificial surface are colored orange:
Some are dirt
Now large airports
We will get to medium, but first a few other airports by water, a 10km look, the blue dot is the station, red squares are 30meter urban cover
Zooming in
The medium airport I choose was one of Willis’ favorite airports, discussed in this post. Before we get to that visual, I encourage you all to read that post, because it put me on a 6 year journey. Willis is rather rare among those who question climate science. He does his own work, and he raises interesting testable questions. He doesn’t merely speculate; he looks and reads and does actual work. He raised two points I want to highlight:
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
I think we both agree that the micro, what he calls local, is important. However, the area outside of the immediate area cannot be discounted: Hong Kong airport next to a huge city is going to be influenced by that locale, whereas, a large airport ( see above) on an island next to the sea, is arguably not going to be biased as much.
The second point Willis made was about the problems with 500meter data. In particular the MODIS classification system which required multiple adjacent pixels before a pixel was classified as urban. At that time we did not have a world database at 30m; Today we can look at that station and calculate the artificial area using 30m data. The next 4 images show the site at various scales: 500m, 1000m, 5000m and lastly 10000m. At the microscale ( <500meters) it classified as greater than 10% artificial, at 1km greater than 10% artificial, and at 5km and 10km it was less than 10% artificial.
There were some concerns about the temperature at this station being used. However, there has never been enough data from this station to include in any global series, even Berkeley’s. Nevertheless, it lets us see the kind of improvements that can be made now that higher resolution data is available for the entire world. Also, even when airports are included in the data analysis, the bias can be reduced in some cases. Here a 2C bias is removed.
One last small airport to give you some kind of idea of that data that we can produce today.
AC Osborn also wanted to know just how many airports were in GHCN v4; and, I think it’s safe to say that many skeptics believe that the record is dominated by airport stations. Well, is it? We can count them and see. For this count I will use 1km as a distance cut off. There are couple ways to “determine” if a station is at an airport. The least accurate way is to look at the names of the stations. This misses a large number of airports. To answer the question I use GPS coordinates compiled for over 55000 airports world wide, including small airports, heliports, balloon ports, and seaplane ports. I then calculate the distance between all 27K stations and the 55K airports and select the closest airport. I then cross check with those stations in GHCN that have a “name” that indicates it is an airport.
For this we consider a 1km distance for being “at an airport”. While this is farther than the microsite boundary, the point of the exercise is to illustrate that not all the stations are at airports.
Using 1km as a cut off, I find there are 1,129 stations by small airports, 1830 by medium airports, and 267 by large airports. That’s from a total of ~27,000 stations.
To assess the ability of the 30m data to detect airport runways and other artificial surfaces we can look at the stations that are within 500 meters of a large airport and ask? Does our 30m data show artificial surface?. There are 131 stations within 500m of an airport. We know that no sensor data/image classification system is perfect, but we can see that in the aggregate the 30m data performs well.
We can also ask how many large airports are embedded in Local climate Zones that have less than 10% artificial cover out to 10km. As expected large airports are in local areas that are also built up at levels above 10%. You don’t get large airports where there are no people.
Conversely, you get small airports embedded in local zones that are not heavily built out, a few cases of small airports embedded in Local Climate Zones that are heavily built out.
Summary
Here are the points that I would like to emphasize.
1. We can discuss or differentiate between at least 2 types/sources of bias: the close and immediate and those sources more distant
2. Bias at the short range (micro) can be more important than bias at the long range.
3. A good site can be embedded in a “bad” area or “good” area, similarly for a bad site.
4. 30m data is better than 500m data
5. Skeptics should not argue that all the sites or a majority are at airports. They are not.
6. There are different types of airports.
7. One way to tell if there is a bias is by comparing Airports with Non airports.
“It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. ”
I think the term was coined in the late 1940s or early ’50s by a Univ. of Chicago geographer looking at precipitation patterns downwind of Gary Indiana. {I might have this off some because I haven’t seen this paper since 1965.} Heat, dust, and aerosols from urban and industrial activity (US Steel founded the place in 1906.) seemed to cause increased precipitation to the southeast.
Neither global warming nor weather-station location were things of interest.
Others may know more of this than I recall.
Weather stations at Airport are based there is given local information related to flying conditions. They are not designed to tell you about a wider area, and never have been. Their use in this role came about because they were ‘better than nothing ‘and in the old days of weather and it was accepted that is was ‘unsettled science ‘ the problems this caused where just part of the game . But in the new world of ‘settled science ‘ all that happened is that the measurements are sold as ‘unquestionable ‘ despite the reality that the old problems remain.
In addition as airports have got bigger with lots more hard standing, if you worked in such environments you will be aware hot these can get and moved from probs to jets , and stand behind a jet taxiing and you will feel a ‘wave of hot air ‘ passing over you. These problems may have got worse from when the stations where first set up, often in WW11.
So we have poorly sited weather stations, not designed to be use in the way they are, whose ‘problems ‘ have grown for a number of reasons over the last fifty years.
A couple of comments: First, the idea of a cool biased site, such as in a park within a city which may be 2 degrees cooler than the surrounding area; it’s not a cool bias but an area where the warm bias is less pronounced.
Second, the idea that shade may affect temperature, That’s why Stevenson Screens were invented and why all reporting sites have the same conditions. There will be artificial shade, there will be an artificial wind screen and all thermometers will be at the same height above the local ground. All will be painted with specific whitewash. These were decided so that the same conditions would allow sites in diverse places to be made comparable.
“A couple of comments: First, the idea of a cool biased site, such as in a park within a city which may be 2 degrees cooler than the surrounding area; it’s not a cool bias but an area where the warm bias is less pronounced.”
The point is cool bias realtive to the local area it represents.
https://sci-hub.tw/10.1002/joc.4747
ABSTRACT: The urban heat island (UHI) phenomenon has been studied extensively, but there are relatively fewer reports
on the so-called urban cool island (UCI) phenomenon. We reveal here that the UCI phenomenon exists in Hong Kong during
the day and is associated with the UHI at night under all wind and cloud conditions.
“The urban heat island (UHI) phenomenon has been extensively studied in the last decades, but there are fewer
reports on the urban cool island (UCI) phenomenon, i.e.
where the air temperature of the surrounding rural area
is warmer than that of the urban area. In contrast to the
UHI, the UCI phenomenon always occurs during the day,
and with relatively weak intensity (Kim and Baik, 2005;
Johansson, 2006; Erell and Williamson, 2007; Memon
et al., 2009). The reported causes of this phenomenon vary
from city to city (Table 1). In fact, Luke Howard observed
both the phenomenon of the UHI at night and that of the
UCI during the day in London in 1833 (Landsberg, 1981).
However, most studies since then have focused on the UHI.
To some extent, urban air temperature reflects the impact
of the urbanization process on the local climate. Here, we
study the UCI phenomenon in a high-rise compact city.
Our aim is not to explain all the possible causes of the
UCI phenomenon in different cities, but to understand why
the urban cool and heat island phenomena co-exist in such
cities.”
UCI is also termed GCI… green climate island
“As concluded by previous research, the impact factors of GCI include size, shape,
type and landscape pattern of the green space (Cao et al., 2010; Li et al., 2011; Li et
al., 2012; Oliveira et al., 2011). Jauregui (1990) finds that the influence radius on the
surrounding air temperature by Chapultepec Park in Mexico is 2 km and the
relationship between size and GCI range (GR) of green space is nonlinear. It is also
proved that there is a threshold of the green space area (Cao et al., 2010; Chang et al.,
2007; Lu et al., 2012; Mikami and Sekita, 2009). If the green space area exceeds the
threshold, the cooling effect will drop sharply. As to the shape, Chang et al. (2007)
research on GCI of the urban parks in Taipei and conclude that complex shape of the
green space leads to strong GCI. The research of Jonsson (2004) and Wong et al.
(2007) prove that the GCI of different types of green space differs obviously, trees
provide the highest GCI, while the shrubs and the grass provide the lowest. There are
also research indicate that the spatial arrangement and configuration of the green
space are significantly correlated with its GCI”
http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0187-62362003000300001
“Figure 3 clearly reveals the negative UHI, the daytime temperature being maximum along the rural-urban stretch and minimum in the city centre. The clearest difference is seen in the extreme points because during the day these areas are open spaces, exposed to the sun. The lowest temperatures were recorded in the urban area, where the buildings, which afford shade, reduce the incidence of direct solar radiation.”
###########################################
So to be clear.
1. The cool bias is a bias with respect to the LOCAL area.
2. A cool bias ( park in the city ) versus Rural zone far away, would be
RARE, but it can happen.
My Point at this stage is to lay out the LOGICAL possibilities and then DOCUMENT
how frequently these conditions happen.
I hope that is clear.
The BIG circle is a local area
the little circle is the site.
you want them to be similar.. not different.
LOGICALLY, there are different cases… that I outline
in reality we have LOOK and see which cases and how many.
So, do I think you find Many areas in cities where the temp is consistently cooler than rural?
Nope.
but LOGICALLY, it is possible.. so LOOK. dont judge without looking. Look. count.
Not liking this definition:
““A couple of comments: First, the idea of a cool biased site, such as in a park within a city which may be 2 degrees cooler than the surrounding area; it’s not a cool bias but an area where the warm bias is less pronounced.”
The point is cool bias realtive to the local area it represents.”
If the “local area it represents” is in the heart of a pronounced UHI, then that circle needs to be much bigger. Is it cooler than the actual temperature or cooler than the UHI temperature which we already know are different?
Simply stated, it is only a cool bias if it is cooler than the actual temperature, not if it is cooler than the warm biased UHI.
The author does his usual trick of dissembling. Or put crudely ‘bullshit baffles brains’.
A wonderful urban site at the top of the hill in Hampstead Heath. London. No ‘microsite problems’ at all.
Doesn’t change the fact that average London temperatures are at least 3C higher than those in the surrounding counties. I find it difficult to believe this is different in any other major metropolis.
“The author does his usual trick of dissembling. Or put crudely ‘bullshit baffles brains’.
A wonderful urban site at the top of the hill in Hampstead Heath. London. No ‘microsite problems’ at all.
Doesn’t change the fact that average London temperatures are at least 3C higher than those in the surrounding counties. I find it difficult to believe this is different in any other major metropolis.”
So you have surendered on the airport question?
With cities up to 20f hotter than the surrounding countryside and 27% of temp data in urban areas that will certainly give skewed data.
Nice overview. There’s this deal called value. This is an example of that.
Thanks Ragnaar
Whats the probablity that oother folks will acknowledge that counting is important?
Seems so bizarrre. On one hand you have guys beating the precision drum
at the same time they dont want to count the airports or even acknowledge that
a count may have surprised them.
Maybe U should have reported Fractional airports and they would have spoken up.
Counting!
What gets me out of this series of articles is that the general point “meso scale effects are small but micro scale are important” is derived from microsite data. Are there any systems that provide singular meso scale readings(the greater than 1km range discussed)? How are those measurements compared in relation to aggregated microsite data?
Hi Gino.
The argument is a bit more complex than that.
Let me see if I can sketch it out
at the local scale ( what you call meso here) you have Potential UHI biases
that run from 0 to very high. Lets say rural region was 0C and Worst urban ever was 10C
Lets just say that for illustration.
at the local scale you also have biases that could run from RARE cases of cool bias, to cases
like the side of a runway… 1-2C bias
The question is?
A) what Portion of the Sites are in GOOD local zones ( toward the small local bias)
B) what Porrtion of the sites are in the BAD micro areas ( toward the large micro bias)
Anthony looks at B very closely
I look at A very closely
What I am finding is that the vast majority of sites are toward the better end of good local zone.
I am trying to dispell two Myths
Myth A) All the sites are in urban centers. They are not.
Myth B) all the sites are at airports. they are not.
If we want to get closer to the truth, one good step is to agree on some basic things. Like counting
Once people see that stations are not all in urban areas, and see that they are not all at airports..
Then we can maybe have a discussion about things more difficult than counting.
As it stands It looks like some folks refuse to acknowledge mere counting
Putting lipstick on a pig comes to mind.
http://joannenova.com.au/2019/05/albany-robbed-of-its-coldest-ever-april-day-bom-adjusts-temp-up-15-degrees-c/
This makes a 0.7 degree difference to the monthly average for a a single station but one used for calculating Australia’s average. The latter is from homogenised readings and heavily weighted to areas where temperature is infilled (guessed). I’m surprised how often the news reports Australia has the hottest season ever and the map shows most of the heat is large areas with no data within it or an extremely poor record. And this is not the first time BOM have been caught out. Then there is my own observation of very dubious minimum measurements.
In Australia, there is the switch to AWS that shows up as a step up in maximum temperatures due to short lived spikes being the maximum rather than averages over 10 minutes. Even with the old thermometers, maximums would be a brief blast of heat, much more likely with a patch of tarmac nearby. As mentioned above, I’m using my phone so I can’t read the paper but it seems to be looking at longer averages so it overlooks the effect of more common spikes in heat that pump up just the maximum reading rather than a longer average.
As for the minimums, my experience with frost on a vineyard is that very small differences make a huge difference to which patch gets damage. Even what a neighbour has down affected me. Moisture in the soil, grass growing, cultivated or compacted soil could make all the difference (no or very short grass with compacted soil provided the best protection). Such effects on minimum readings are less when its more humid and a warmer season.
Lastly, its not just UHI. We used to laugh at BOM temperatures in the early 80s. The town was a popular tourist destination that was starting to lose it popularity because it could get too hot and the official temperatures would seem strangely low. This was because they would water the lawn around it during the hottest part of the day.
So I take it you have given up defending the skeptical claims that the global record
consists only of temperatures taken at large airports?
Never my position in the first place. That strawman argument is completely out of the blue for me.
The complexity of measuring temperatures for a city can be illustrated by looking at a few examples across the metropolis of Melbourne, Australia reported by the Bureau of Meteorology for 3pm on May 7 and 8am on May 8, and mean min/max for April:
Olympic Park 18.6; 12.3 C; 12.0/22.0 C
Essendon Airport 18.1; 11.4 C 10.9/22.6 C
Tullamarine Airport 18.2; 11.3 C; 10.4/22.6 C
Scoresby 17.1; 10.8 C; 10.5/22.3 C
Moorabbin Airport 18.1; 12.2 C; 11.3/22.2 C
Laverton 19.7; 11.5 C; 10.6/22.4 C
The problem is robber
A) its complex but someone has to do it.
B) your first mistake was focusing on DAILY TEMPS
If you look at Monthly temps, and more specifically the average of min and max, the estimation
problem gets a lot easier
tel me the month, your latitide and your elevation, and your air temperature is fairly predictable
“tel me the month, your latitide and your elevation, and your air temperature is fairly predictable”
Well, Bordeaux, France and Toronto, Canada, seem to be quite close in latitude and elevation.
The average temperature in february? no chance.
External air temperature gauges on aircraft are not precision instruments. However:
-Gates are warmer than taxiing
-Taxiing readouts depend on who’s been around just before and at what power setting
-Most EU airport values are positively biased with respect to board instruments
-Lineup (waiting your turn) readouts can be nonsensical with those big fans ahead
-Airports are places where tens of MW of sheer power move the equivalent of Olympic pools of air in seconds
-A heavy take-off is not an innocent air motion event, repeat that every 3 minutes
-Airports have huge air condition / heating systems for buildings and parked aircraft
-A random number of ground equipment, trucks, carts, generators, operating at random locations
Using airport temperatures for policy making schemes is like yelling chow-time while swimming with a friendly herd of hungry piranhas.
“Using airport temperatures for policy making schemes is like yelling chow-time while swimming with a friendly herd of hungry piranhas.”
you should be happy to know these things
1. Nobody makes policy on this data.
2. Removing these measurements has no effect on the average
Question what is 131/27000?
Saab 2000 after RVSM
“The mean urban bias for these conditions quickly dropped from 0.84 °C at tower-A (4 m) to 0.55 and 0.01 °C at towers-B` and -C located 50 and 124 meters from the small-scale built environment. Despite a mean urban signal near 0.9 °C at tower-A, the mean urban biases were not statistically significant given the magnitude of the towers standard 2 deviations; 0.44, 0.40, 0.37, and 0.31 °C for tower-A, -B, -B’, and -C respectively.”
should be in quotes
“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”
SHOULD be in quotes
““In a recent talk at the World Meteorological Organization, T. Oke (2001, personal communication) stated that there has been considerable advancement in the understanding of urban climatology in the last 15 years. He went on to say that urban heat islands should be considered on three different scales. First, there is the mesoscale of the whole city. The second is the local scale on the order of the size of a park. And the third scale is the microscale of the garden and buildings near the meteorological observing site. Of the three scales, the microscale and local-scale effects generally are larger than mesoscale effects….
Gallo et al. (1996) examined of the effect of land use/ land cover on observed diurnal temperature range and the results support the notion that microscale influences of land use/land cover are stronger than mesoscale. A metadata survey provided land use information in three radii: 100 m, 1 km, and 10 km. The analysis found that the strongest effect of differences in land use/land cover was for the 100-m radius. While the land use/land cover effect ‘‘remains present even at 10,000 m….
Recent research by Spronken-Smith and Oke (1998) also concluded that there was a marked park cool island effect within the UHI. They report that under ideal conditions the park cool island can be greater than 5 C, though in midlatitude cities they are typically 1 –2C. In the cities studied, the nocturnal cooling in parks is often similar to that of rural areas. They reported that the thermal influence of parks on air temperatures appears to be restricted to a distance of about one park width….
Park cool islands are not the only potential mitigating factor for in situ urban temperature observations. Oceans and large lakes can have a significant influence on the temperature of nearby land stations whether the station is rural or urban. The stations used in this analysis that were within 2 km of the shore of a large body of water disproportionally tended to be urban (5.8% of urban were coastal versus 2.4% of rural).”
Should be in Quotes
To me the issue is how this is then applied to the wider models. How far does the station have influence when put into the model?
Stations are not put into GCMs.
Sometimes we have to take data and work with it as best as we can? Airports are probably reliable reporters. It takes work to maintain a weather station and keep it reporting. Wish we had enough people and resources to maintain and operate pristine weather stations all over the place but we don’t.
yes, when you work with historical data you have what you have and the best you can do
is try multiple approaches and see how your decisions change the answer
In the end people will find.
It’s getting warmer
There was an LIA
the important questions lie elsewhere
understand what I am saying… after 10 years of trying to find substantial errors in this data
I am telling my fellow conservatives to focus their firepower in other areas..
if you want to make an impact.
And most imprtantly, if you are going to make claims about the data.. ITS ALL AIRPORTS!!!
understand that you are just shooting yourself in the foot because less than 2% of the NOAA records
are from large airports.
@StevenMosher. Thank you. Your previous posting on this issue was clear and informative, and this one has surpassed it. If all climate debate could be like this it would be a much better world.
I am very grateful to you because I am putting together a research proposal to, amongst other things, investigate microscale issues, not with respect to grand narratives, but just to measure the quality of
the data farmers get. I don’t know whether it will get funded, and I shan’t be the principal investigator,
but this material and the references will definitely improve the quality of the proposal.
Clear, considered, constructive, kudos!
We need to understand that weather stations at airports are there for entirely different reasons than contributing to the global temperature record.
They are there to serve the purpose of supporting flight operations.
Their temperature measurements are used to calculate the efficiency of airplanes.
When the atmosphere the plane is encountering is hot, the molecules of air are farther apart, and provide less lift to the airfoil of an aircraft’s wings.
Pilots have to carefully calculate their aircraft flight characteristics, and may have to reduce fuel load, or even restrict the number of passengers to lower weight to compensate for high air temps.
So, it s safer to err on the high side of true temperature to reduce the possibility of an overloaded aircraft. For this reason alone, high bias, airport temperatures are suspect, and no amount of “correction” will produce temperature data. Corrected temperatures are not data, and UHI and tarmac heating yield high biased temperatures.
Not sure how to resolve this, probably start putting error bars on all the charts of temperatures? Error bars so big that, while accurate, they will negate any predictive capacity for the data.
“Not sure how to resolve this, probably start putting error bars on all the charts of temperatures? Error bars so big that, while accurate, they will negate any predictive capacity for the data.”
A) dont assume, look.
B) check the data with and with airports.
C) if you want to test adjustments, you can try that as well
Since Large airports are less than 2% of the record, what do think you will find
Suppose the bias for a large airport is 5C every hour of the day, every day of the year. (its not)
now… calculate
The issue can be that smaller airport may be the only site used available with a much larger area, but it’s not in any way typical for that area because of the nature of an airport. The lack of coverage, particular historic sense, is the very reason for the need to use ‘proxies ‘in the first place. Add to that the need to ‘adjust’ past measurements due to problems in data collect, problems which remain largely unqualified. And you can see how the ‘quality ‘of what is known is no where near the settled science claims being made on it .
And then with get to ‘models ‘ which ‘work’ best with a very large barn door and lots of shots , due in part to the same reason that you cannot get a weather forecast for over 72 hours which offer much better than ‘in the summer it will be warmer than winter ‘.
The claims of ‘settled science ‘ are often made not because of past issues have be solved , but despite past problems being very much with us.
Just wondering, if one looks at the photos above, the manmade surfaces often look brighter than the natural surfaces in the vicinity.
Brighter surfaces should absorb less sunlight than darker ones.
Hence, the anthropogenic effect must not necessarily be warming..
With all of the apparent problems with the ground based measuring of temperature, is it possible in today’s hi-tack world to cease to use ground based measurements and to instead only use balloons and satellites ?
MJE VK5ELL
All this money spent on global warming and we don’t have a few radiometers in a geo orbit measuring the total returned energy to space? Isn’t that the direct value we are supposed to be worried about.
Question, are local biases to humidity also considered? Irrigation, sprinklers, changes in ground cover, etc.
One way to avoid freezing crops in autumn was to burn fires in the fields. The water vapor in the smoke was freezing and took out considerable amounts of latent heat. The smoke over cities in old days must have been a large warming source. I think more in old days than now. UHI in reverse?
What’s really important for detecting secular, as opposed to cyclical, climate change is not the proportion of “urbanish” vs. “ruralish” stations, but the validity of time-series over sufficiently long intervals to detect such change. Amidst much misdirection, there’s little revealed here concerning this key issue.
Steve – all I can say is you are an excellent climate science communicator.
Steve – It would be interesting to understand the influence of jet engine combustion on the relative humidity at airports – not just the heat sink effects of tarmac and structures. I’ve read that an Airbus A330 produces water vapor due to combustion of jet fuel at the rate of 7 tonnes per hour during cruse (producing con trails) and about 13 tonnes per hour on takeoff. One would expect the humidity (due point) to increase in and around airports, which in turn would increase nighttime minimum temperatures.