Background
The recent post at WUWT covered a new analysis by Goddard & Tett (hereafter GT) showed how UHI has biased measurements in the UK. The paper concludes:
For an urban fraction of 1.0, the daily minimum 2‐m temperature was estimated to increase by 1.90 ± 0.88 K while the daily maximum temperature was not significantly affected by urbanisation. This result was then applied to the whole United Kingdom with a maximum T min urban heat island intensity (UHII) of about 1.7K in London and with many UK cities having T min UHIIs above one degree.
This paper finds through the method of observation minus reanalysis that urbanisation has significantly increased the daily minimum 2‐m temperature in the United Kingdom by up to 1.70 K.
The paper represents a trend in UHI studies toward using urban area or urban fraction to define areas as urban and to parameterize the effect: to express UHI as a function of urban area: This is in contrast to the early studies, for example, Oke (73) that tended to use population to parameterize UHI
Since Oke there has been considerable progress in understanding the complex phenomena of UHI and the science has moved beyond the simple approach of looking at population as a parameter that uniquely determines UHI. If everyone leaves a city, it will still have UHI.
Recently, at WUWT the following claim was made
This is actually not the case. This is a tiny fraction of the types of studies done.
Studies of over 400 large cites
Studies of the relationship between the shape and size of 5000 cities and UHI
And there are a growing number of papers (here, here, here ,here, ) that detail urban cool parks that may explain why UHI is so difficult to find the global record. Sites located in cities are not necessarily warmer than those in rural setting.
One of the most important advances has come in the area of quantifying the definitions of urban and rural. Oke and Stewart have transformed the field with their concept of the LCZ or local climate zone. Anyone who took pictures of temperature stations for Anthony’s surface station program will enjoy watching the entire video below and especially the parts after 23 minutes where microsite bias is discussed.
And now with the power of satellite imagery researchers can quantifiably categorize various type of urban/rural areas. This can be done automatically or manually: http://www.wudapt.org/lcz/ Stewart was motivated to do this categorization in part because a large number of urban/rural studies never objectively defined the difference between urban and rural and because they assumed that “urban” was a discrete category rather than a continuum.
GT Findings
GT found that the UHI effect in the UK was limited to biasing Tmin upwards, a result consistent with other findings. Wang (2017) looked at 750+ stations in China and also found a bias in Tmin of up to 1.7C at 100% urban cover. A figure that matches the result of GT.
Trends in urban fraction around meteorological station were used to quantify the relationship between urban growth and local urban warming rate in temperature records in China. Urban warming rates were estimated by comparing observed temperature trends with those derived from ERA-Interim reanalysis data. With urban expansion surrounding observing stations, daily minimum temperatures were enhanced, and daily maximum temperatures were slightly reduced. On average, a change in urban fraction from 0% to 100% induces additional warming in daily minimum temperature of +1.7 +- 0.3°C; daily maximum temperature changes due to urbanization are -0.4 +-0.2°C. Based on this, the regional area-weighted average trend of urban-related warming in daily minimum (mean) temperature in eastern China was estimated to be +0.042 +- 0.007 (+0.017 +- 0.003)°C decade1 , representing about 9% (4%) of overall warming trend and reducing the diurnal temperature range by 0.05°C decade . No significant relationship was found between background temperature anomalies and the strength of urban warming.
To many readers the maximum bias figure of 1.7C in Tmin at 100% urbanity may seem low, especially when you consider the figure at the top from Oke which shows a UHI of up to 8C. The difference lies in the methodology. Much of the early work done on UHI focuses on UHI max for any given day. They select conditions that show the largest values of UHI that can occur. Oke’s chart, for example, represents the maximum value of UHI observed on a given day. For example, he would select summer days with no clouds, and no wind and measure the max difference between a rural point of reference and a city point of reference. In the studies that show high UHI values they typically do not calculate the effect of UHI on monthly Tavg over the course of many years, as GT and Wang did. Since cloud free wind free days do not occur 365 days a year for years on end, the overall bias of UHI is thus lower for monthly records, annual records, and climate records. In one study the number of ideal days in a year for seeing a difference between urban and rural was 7 days of the year. A 40 year study of London nocturnal UHI, found that the average UHI was ~1.8C, and only 10% of the days experienced UHI over 4C. In short, Average monthly UHI is less than the maximum daily UHI observed at optimum conditions for UHI formation.
The current best estimate by the IPCC is that no more than 10% of the century trend for Tavg is due to UHI and LULC. If we take the century trend in land temperatures to be 1.7C per century, for example, then the 10% maximum bias would be .17C on Tavg. The IPCC does not make an independent estimate for Tmin or Tmax, only Tavg, because the major analysis products only use Tavg.
In summary, it is indisputable that UHI and LULC are real influences on raw temperature measurements. At question is the extent to which they remain in the global products (as residual biases in broader regionally representative change estimates). Based primarily on the range of urban minus rural adjusted data set comparisons and the degree of agreement of these products with a broad range of reanalysis products, it is unlikely that any uncorrected urban heat-island effects and LULC change effects have raised the estimated centennial globally averaged LSAT trends by more than 10% of the reported trend (high confidence, based on robust evidence and high agreement). This is an average value; in some regions with rapid development, UHI and LULC change impacts on regional trends may be substantially larger.
GT approach
Both GT and Wang look at the urban fraction over a 10km buffer surrounding the station. This is probably at the radius limits of the LCZ. There is no “typical” range for LCZ analysis, but in general analysts consider the zones 1 to 10km in size. In LCZ analysis the fraction of imperious surface is one of the quantifiable features that determine the LCZ type. In general, urban fraction divides LCZ thusly:
A) Areas with less than 10% impervious surface are “unbuilt”
B) Areas with 10-20% impervious surface are sparsely built
C) Areas with 20+ % built are what we would typically call urban
There are some notable exceptions to this, in particular some heavy industry areas may have small urban fractions less than 10%. From field testing we know that different LCZ zones have different temperatures. See table 2 here for a study of LCZ in Berlin over the course of a year.
Armed with this metric we can begin to classify temperature stations by the percentage of urban fraction in their local climate zone. In theory we don’t have to make a bright line distinction between rural and urban, but rather we have a metric for the relative urbanity of a site that goes from 0% impervious surface in the LCZ to 100%.
In Berkeley Earths study of UHI we broke some ground by being the first study to use satellite data for urban surface to classify the urban and the non urban. We used a MODIS data set with a 500m resolution. However, two things concerned me about that dataset: 1) the imagery was taken during northern hemisphere winter and could falsely classify snow covered urban as rural. 2) the true resolution was more like 1km as a pixel wasn’t defined as urban unless 2 adjacent 500m pixels were urban. 1kmsq is not a small area. To accommodate for this and to accommodate for location errors we looked at 10km radius around each site and a site was classified as Non rural if it had 1 urban pixel. Our results found no difference in trend between urban and non urban. Still, the 1 km sq resolution bothered me. We can now address that issue with higher resolution data.
Available satellite imagery has expanded since the publication of that paper and much more accurate data is now available. GT used 250m data, for example and “paywalled” data is available below 30meter resolution. For my study of GHCN version 4 metadata I considered two different sources:
A) ESA 300 meter data
B) 30 meter data made available here http://www.globallandcover.com/GLC30Download/index.aspx.
Each dataset has pro’s and cons. The 30 meter data is quite voluminous and comes in tiles complicating the process of determining urban fraction. The 300 meter data is easier to work with but doesn’t really work very well if you want to know what the surface is like within 100 meters of the station. It cannot work well for microsite analysis. Also, neither dataset is perfect. Every land classification system has errors: natural pixels (typically bare earth) that are classified as urban, and urban pixels that are misclassified as natural. It’s helpful, thus, to compare the 30meter data with the 300 meter data and to cross check both with other signs of urbanity such as population and night lights.
GHCN v4 will be the next land dataset published by NOAA for use in global average temperature studies. It is currently in beta and going through a validation and verification process. NASA GISS will adopt adjusted GHCN v4 as its primary data source for global land temperatures. And then they will apply their UHI correction which in practice does not reduce the trends in any substantial way. The number of stations in GHCN V4 has increased over V3 to more than 27,000 total stations. The dataset will come in two variants: Uncorrected by NOAA; and debiased by NOAA’s PHA algorithm.
To create enhanced metadata for this new set of stations the procedure is fairly straightforward. You take the latitude and longitude of the station and then locate it in the appropriate GIS dataset. For 30meter data which exists in UTM tiles, you have to re-project and stitch 2 tiles together to handle cases where a station may be located near to a tile border, or 4 tiles together when a station is located near a tile corner.
For every station we can create “buffers” or collections of all the land class within various radii. For this post I’ll report on the 10km radius to be consistent with GT and Wang who also look at 10km buffers.
One important note. The purpose of this is not to assess the specific site micro characteristics: surface properties within 0- 500 meters that are within the viewshed of the sensor. Rather I will look at the LCZ, the local area climate zone out to 10km and answer the question: just how urban are the temperature stations used by climate scientists who study the global average? Do we actual draw our samples from heavily urban areas as defined by Oke’s and Stewart’s LCZ classification system.
The map from GT is instructive here
Are the stations that will be used by NASA GISS in red zones or in blue zones? What fraction are in red? And what fraction are in blue areas? And what shade of blue?
Some other things to note. The land classification data is taken at 2015 for 300 meter data and 2010 for the 30 meter data. Underlying this analysis is the assumption that site areas are not “unbuilt” over time. I assume a station that shows 0% built area in 2010 did not have any built area before that time. One other subtlety that people miss is that stations that register as heavily built in 2015 may have been rural during their recording time. For example, you can have a station that reports temperatures for 1850 to 1885, and then stops reporting. The urban fraction data refers to the urban cover of that site at 2015 or 2010. If you simply classify this site as urban, it may not be accurate as you are interested in the temperature data that was collected in the 1850 to 1885 time period. If the station was rural during that period, and you classify it as urban because of its urban cover today, then you can confound urban/rural studies.
Using the same criteria as GT and Wang (2017) we can see that the vast majority of stations are located in LCZ’s that have less than 10% urban cover (blue line below).
The using 30 meter data results in slightly fewer stations in the 0-10% ranking. This is to be expected as 300 meter data is not small enough to detect roads or airport runways while 30 meter data can in most cases. Using the regression approach of GT and Wang, we can also make a first order estimate of the size of the Tmin bias in a global record constructed from stations with this magnitude of urban cover: ~.13C. This would translate into a ~.06C bias in Tavg, within the estimate made by the IPCC. Note this is a simplistic estimate that does not take the spatial distribution of the stations into account, and it could be higher, or lower, but not substantially.
One thing to note is that we are able to check how robust the procedure of looking at 10km buffers around the site is by using the same procedure with CRN stations which have been selected to minimize their urban exposure: over 95% of CRN stations have less than 10% urban cover within a 10km radius of the site.
The big picture takeaway is this. UHI studies like GT and Wang focus on UHI over long periods of time: years instead of days. When you just focus on UHI max during selected days at selected cities, you will get high values for max UHI. However, when you look at dozens to hundreds and thousands of stations over months and years, the bias figures for UHI drop substantially. It’s these figures that matter for UHI bias in the global land record. Further when you look at all the stations in the inventories rather than the worst cases, you see that the vast majority of stations are located in areas of low urban cover:0-10%
This brings me to my last two points. While the fraction of urban cover within a 10 km radius does give you comparability with GT, it misses two things. These two things could be more important and I think they deserve some more attention. Those issues are: UHI in small area towns and microsite bias. The potential UHI issue in the global record is not a large city issue. The charts above should tell you that. Areas with large dense urban cover do not dominate the inventories of stations. They just don’t. The more plausible cause of UHI in the global record would come from small areas of urban cover. It’s unfortunate that most people focus on the photos of large cities and the papers about large cities, when actually, the problem may be smaller cities, at least as the global record is concerned. My suggestion is to aim at the right target with your analysis and critiques.
The second issue is the issue of microsite. Wang 2017 wrote
Changes associated with urbanization may impose influences on surface-level temperature observation stations both at the mesoscale (0.1–10 km) and the microscale (0.001–0.1 km). For a specific observing station, small local environmental changes may overwhelm any background urban warming signal at the mesoscale. Due to the lack of a high-quality data set of urban fraction at the microscale, we can hardly quantify the microscale urban influence on the observed temperatures.
In other words, the metadata that matters most is the metadata of the first kilometer. A good site in an urban setting can be better than a bad site in a rural setting. My bet is this: if you expect to find bias in the record, you should be looking at that first kilometer. Microsite is more important than UHI.
Mosher
You present a (pseudocolored?) map from GT and ask what percentage of pixels are red or blue. Readily available commercial image processing software will provide those answers to within on pixel precision.
Yup it would.
Two points:
1. If one were to measure and chart real-time temperatures that are influenced by UHI/microsite-impacts AND those that are purely ‘rural’ here in Sacramento (which I have neither seen done nor have done in a formal way myself), my guess is that we would see a common divergence consistently over 2.5 degrees C (and often higher). Whether that is for Tmin and/or Tmax, I don’t know. I simply report what I observe driving in and out of town at all hours; on the drive back from the airport with the windows open the shifts are indeed striking.
2. We can hand-wring, argue, re-calculate/calibrate, ridicule, accuse, and model all that we want…but what do we experience when we step outside, into our atmosphere? A climate catastrophe? A trend toward catastrophe? I see abundant green, record food yields, mostly ‘typical’ temperatures, the perennial set of season-specific weather phenomena (and occasional ‘records’), and in those coastal areas not subsiding, a manage-able rise in sea level…and people arguing incessantly over what is in terms of measurable impact, pure BS. For any person or organization to take the actual state of the Earth (yes, we have some serious localized environmental challenges we badly need to address–I’m not denying that)–and the ever-growing prosperity of humanity–and say that things are hopeless and disastrous for the next generation is, IMHO, raw child abuse. Perhaps it is our wealth that affords us the free time to be so guilt-ridden/self-loathing.
..a direct result of a prosperous society….the time to pontificate
“1. If one were to measure and chart real-time temperatures that are influenced by UHI/microsite-impacts AND those that are purely ‘rural’ here in Sacramento (which I have neither seen done nor have done in a formal way myself), my guess is that we would see a common divergence consistently over 2.5 degrees C (and often higher). Whether that is for Tmin and/or Tmax, I don’t know. I simply report what I observe driving in and out of town at all hours; on the drive back from the airport with the windows open the shifts are indeed striking.”
I’m guessing you did not click on the links I provided which would show you
a map of california and the estimated UHI per census tract.
In any case, I havent reviewed that california data, but people are trying to provide estimates
of local areas.
Regardless, we have what we have. A study of 750 cities in China over a long period
and 34 sites in the UK.
Here is a hint.
at 100% urban coverage the AVERAGE UHI was 1.7C in Tmin.
That means
A) some areas will be less than 1.7
B) some will be more
If I told you the average Trump voter made 43K a year would you respond that you made 150K?
Mr. Mosher–First, I have to commend (and thank) you for taking the time to address/respond to basically every person’s comments. That’s generous in the extreme and quite impressive.
Second: It’s a great set of links you included, many (nearly all) of which I’d not seen before. I have zero intention of arguing with any of your assertions. You make some interesting points (esp. about potential double-standards/hypocrisy from the skeptic camp–food for thought). So, let’s take my first point off the table.
My point about going outside and looking around is simply this: An army of Chicken Little’s has been created and is lamenting the perceived loss of perfect climate, and stirring up a frenzy of angst around impending climatic doom. Children in school are being told the end of the world is a few years out. We debate ad nauseam what should be measured, how it should be measured, and what it tells us about the future. Where does that leave us? Yes, this is all very intellectually interesting, and the mental (and mathematical) gymnastics are certainly entertaining. But at the end of the day, is the Earth slowly turning into a barren wasteland? I’d say no…so let’s get outside and enjoy it (but maybe not before I check out more of the links you included– 😉 ) Thank you again–and enjoy your weekend!
Thanks.!
enjoy your time outside!
“…but what do we experience when we step outside, into our atmosphere? A climate catastrophe? A trend toward catastrophe?”
I see that as a problem for our ‘catastrophic friends’. Every day they assail us with the facts about the climate crisis or lately the climate emergency and every day the weather remains much the same as it always has been. Warm, cold, wet or dry. We want disaster but none arrives.
Where’s the beef or that infamous ‘day after tomorrow’ we’ve been lectured about?
“I see that as a problem for our ‘catastrophic friends’. ”
Hmm. I dont believe in catastrophe.
Mr. Mosher: You don’t “believe” in catastrophe. I don’t either, but some of the really prickly science-types here will jump all over “believe”. Can you say you “know” to some degree of certainty that CO2 warming is not going to be catastrophic? Is that the catastrophe in which you don’t believe?
You get annoyed with those here who don’t read but spout skeptical talking points, and I’ll concede some comments here are not helpful to the skeptics who do read. You are concerned about the warming, do you have any thoughts on how unhelpful the catastrophists are to those concerned about the warming? I put it to you that the catastrophists impair your work far more than any knee-jerk skeptics like me. Good luck with them.
First off… why would you pick London as a representative for the significance of the UHI effect?
I think we can all see how the relative sunniness of a city might change the significance of the UHI for that city. That is, sunnier cities might reasonably exhibit more UHI effect than cloudier cities. Just stands to reason.
That said, London is a particularly bad example for determining that UHI isn’t as significant as widely believed. London is, on average, much less sunny than most cities. In the list of cities by sunniness on Wikipedia (https://en.wikipedia.org/wiki/List_of_cities_by_sunshine_duration), out of 51 European cities, only 9 are less sunny. To put it into perspective, London is significantly less sunny than Seattle (1633 hours vs. 2170) and Vancouver (1633 hours vs. 1938), and on and on. Why on earth would London be a good representative of how strong the UHI effect is in the global temperature data? It’s not.
But it’s a great city to focus on if you want to down-play the UHI effect.
“First off… why would you pick London as a representative for the significance of the UHI effect?”
err Nobody did that!
1. ya got a guy who looked at 750 cities in china ( skeptic screams what about scaramento!)
2. ya got a guy who looked at 34 stations in the UK ( skeptic screams.. what about CET)
3. ya got a guy who looked at decades of London, cause he had the data.
What did they find?
UHI
When did they find it?
At night
duh
Dont like London?
here are 5000 cities in the citation I gave
https://www.nature.com/articles/s41598-017-04242-2
I think you miss the logic.
34 sites in UK— 1.7 max in Tmin
750 in china — 1.7 max in Tmin
London? 1.8
Data shows London kinda matches their analysis
Don’t know why your impressions trump data?
I question their data.
As I said up thread every Met Office/BBC weather forecast gives far higher than 1.7C for London compared to Urban areas, especially in the Winter nighttime and it has little to do with the amount of Sunshine.
Does their study provide the actual Raw readings and what sites they are from?
“I question their data.
As I said up thread every Met Office/BBC weather forecast gives far higher than 1.7C for London compared to Urban areas, especially in the Winter nighttime and it has little to do with the amount of Sunshine.
Does their study provide the actual Raw readings and what sites they are from?”
I question your questioning!
For the 5000 stations, the data is open go check
For the global map of UHI same thing. go check
You note
“As I said up thread every Met Office/BBC weather forecast gives far higher than 1.7C for London compared to Urban areas, especially in the Winter nighttime and it has little to do with the amount of Sunshine.”
I question their data.
you need to up your game AC
merely questioning aint science.
You question the Met Office?
I am not interested in 5000 stations that I cannot check for myself or see photographs of.
What Weather Stations did their study use for Inner London where the UHI is at it’s highest?
What weather stations did they compare it to in the rural settings?
“You question the Met Office?
I am not interested in 5000 stations that I cannot check for myself or see photographs of.
What Weather Stations did their study use for Inner London where the UHI is at it’s highest?
What weather stations did they compare it to in the rural settings?
of course I question them
For your other questions read the study.
The point is Simple.
the AVERAGE is one number.
the HIGHEST YOU CAN FIND is lower than the average.
todays math lesson
There is one thing that seems to be overlooked even by the more reasonable desktop analysers here.
Densely developed cities (and also forests) will also effect temperature because they push up the boundary layer. Tall structures in the path of natural airflows can actually increase localised velocities, but when the density at near ground level goes above a certain level, the airflow simply goes over the top. Long horizontal barriers perpendicular to airflow such as continuous building lines will attenuate airflow for a horizontal distance of seven times the height of the barrier. Where a second barrier occurs, airflow continues to ‘skim’ and does not return to the unimpeded pattern for the same seven-times-height distance.
Source: Su San Lee, PhD Thesis, Natural Ventilation and Medium Density House Forms in the
Tropics, 1998, Institute of Tropical Architecture, James Cook University.
Confirmed by my own on site measurements.
Yes, that university. UNESCO Professor of Architecture Dick Aynsley, the Director of the ITA moved on and the institute no longer exists.
“There is one thing that seems to be overlooked even by the more reasonable desktop analysers here.
Densely developed cities (and also forests) will also effect temperature because they push up the boundary layer. Tall structures in the path of natural airflows can actually increase localised velocities, but when the density at near ground level goes above a certain level, the airflow simply goes over the top. ”
Building height is in the LCZ definitions for precisely the reaso you mention
surface roughness as well.
I can estimate building height from the data I have, but its not that important to the SPECIFIC
point I am making here.
my specific point.
IF you think the stations are located in highly urban areas
You
Are
Wrong
” IF you think the stations are located in highly urban areas
You
Are
Wrong”
How can you possibly say that, have you personally visitied every single site?
Isn’t there a station in the middle of Sidney for instance?
http://joannenova.com.au/2017/01/sydney-observatory-where-warming-is-created-by-site-moves-buildings-freeways/
QUESTION: What’s the smallest number of molecules that can have a “temperature”?
How micro do we have to go to realize that a “temperature”, in general, probably does not exist as something that can be represented to tenths or hundredths of degrees. Rather, it seems to be a range of values, where tenths or hundredths have no meaning.
I would hazard a guess that almost all (apparent) modern ‘warming’ can be attributed to ‘adjustments’, population growth contributing to the UHI, and poor siting of stations.
“I would hazard a guess that almost all (apparent) modern ‘warming’ can be attributed to ‘adjustments’, population growth contributing to the UHI, and poor siting of stations.”
Hmm. not.
I do the UHI work with unadjusted data.
unadjusted rural sites show warming.
there was an LIA.
Plus, looking at satillite data from AIRS? matches the warming at the surface.
its getting warmer.
By their own admission NASA add 0.6C to 0.7C (Menne and Zeke H) to the warming Trend with their Adjustments, which are the Official Record.
Steven,
In Australia, this has been a hot button issue for some years.
Your conclusion about UHI in small area towns and,” the potential UHI issue in the global record is not a large city issue etc.” takes me to Dr. Jennifer Marohasy and her papers since Marohasy et al 2014.
In “ The Homogenisation of Rutherglen” published in “Climate Change : The Facts 2017” she points out that Rutherglen in northern Victoria where temperatures have been recorded since 1912 at the agricultural research station has seen its raw data temperature records indicating 0.3 C increase since inception homogenised to show a 1.6 C increase.
Rutherglen is part of the official Australian Climate Observations Reference Network- Surface Air Temperature ( ACORN SAT).The ACORN SAT catalogue clearly states there has been no documented site moves during the site’s history.(Bureau of Meteorology 2012).
She notes that homogenisation which she explains is used in the UK and US, in terms we all understand.
The adjustments at Rutherglen have cooled the earlier temperature records accentuating recent warming.
She shows how in 2 graphs.
The homogenisation is justified by BOM to account for “non-climatic variables”.
The Rutherglen material ultimately flows into the ACORN SAT values, and on to international records.
Most Australian and International researchers rely exclusively on this ACORN SAT record ( e.g. Coates et al 2014). Marohasy recommends more attention be paid to raw data.
Is Rutherglen what you would describe as “good”or bad site in a rural setting as distinct from a good site in an urban setting?
How would BOM justify homogenisation of Rutherglen which it seeks to do? The problem has also become notorious with the Darwin records. (WUWT passim).
“Most Australian and International researchers rely exclusively on this ACORN SAT record ( e.g. Coates et al 2014). Marohasy recommends more attention be paid to raw data.
Is Rutherglen what you would describe as “good”or bad site in a rural setting as distinct from a good site in an urban setting?
How would BOM justify homogenisation of Rutherglen which it seeks to do? The problem has also become notorious with the Darwin records. (WUWT passim).”
some notes.
1. It’s a mistake to focus on individual sites to the EXCLUSION of other sites.
2. Looking at what the BOM did they applied multiple statistical approaches.
A statistical approach WILL ALWAYS have some values that stick out.
These approaches are validated by group statistics. ON AVERAGE they
remove bias. In particular cases they will miss the mark
Ruther
Lat: -36.1047
Lon 146.5094
Elevation: 175
DEM Elevation: 175
Distance from the Coast 219km
https://www.google.com/maps/place/36%C2%B006'16.9%22S+146%C2%B030'33.8%22E/@-36.1046957,146.5006453,2194m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-36.1047!4d146.5094
Population Density within 1km : 4.954285 people per sq km
Closest neighbor city is 16.34942km away, population: 5178
Closest airport is a medium sized field 18.37289km away
Mean Night lights, at 1km, 5km, and 10km: 0.05176377,
0.005601045,
0.09401573,
Urban area in sq km at 500meters, 1km, 5km, 10km:
0
0.0396
0.702
5.4711
Urban Area at 10km, using 300m data: 2.159435
Other land classes at 10km
199.902 sq km is vegatative
15.9644 sq km is trees
96.40336 sq km is cropland
The site itself ( within 300m) is classified as Cropland
This site has a LCZ with less than 10% urban. I would not expect to see any UHI
CAUSE, there is no signifant urban cover at LARGE scales
Microsite , the 500meter, figure above shows 0 urban surface. HOWEVER, I would reserve Judgement
on this as even with 30 meter data you can have missed pixels. There is a road close by
and the orientation of roads and airstrips can sometimes result in feature being smaller than
the sensor resolution. To put it simply, there are times when roads and airstrips can be ID’d
and times when they cant.
At this stage I am only interested in characterizing the MESO scale features.. Stuff outside the
first 500m or first 1km
What does the REGION look like, is the Local Zone built? if so how much?
Steven,
Thanks for the time and effort you have expended on Rutherglen and my query.
I appreciate your point about examining the REGION and the microsite considerations.
Thanks also for a most insightful paper.
In Santiago, Chile where I lived, the morning temps were a whole 10F higher near my fifteen story apartment than they were five miles away at work where houses were prevalent. This was for about four months of the year when the sun was most direct. The sun would heat the apartment buildings up and warm the whole neighborhood. You could feel blasts of warm air when you walked in the morning. The measured effect on a station would greatly depend on the response time of the thermometers ( fast electroniy vs traditional) as well as the whole complex structure of the environment. And Santiago is totally different than any urban areas in the US. How can you possibly come up with a set of general rules to cover UHI all over the earth? It’s a waste of time.
” And Santiago is totally different than any urban areas in the US. How can you possibly come up with a set of general rules to cover UHI all over the earth? It’s a waste of time.”
How?
Science!
1. You need a system that allows for a QUANTIFIABLE description of a site
2. The system: http://www.wudapt.org/lcz/
3. people get to work
like all science, work in progress
Santiago Chile:
Go Here
https://yceo.users.earthengine.app/view/uhimap
Enter your city. The data suggests different figures than you report
note this is SUHI… not apples to apples with UHI ( air temps)
All this “intellectual self gratification” merely to point out, the best measurements overall HAVE to be the Satellite measurements. HOWEVER, lacking any tracking of “moisture” content, so as to determine the “total enthalpy” of the system, makes all the temperature mechanizations, MOOT in terms of telling us ANYTHING about the “heat balance” of the atmosphere.
satellites dont measure temperature
Nothing measures temperature. Temperature is the Kinetic energy of particles that changes from particle to particle. We only measure proxies of temperature like the change in volume of alcohol or mercury. Satellites just measure a different proxy from temperature-related particles radiative emissions.
Err no. Satellites have to do more than that.
1. they use a radiative transfer model to change brightness into estimated T for Miles of
atmopshere.
2. They assume certain variables are constant, that are known not to be constant.
So in general, yes, one doesnt measure temperature directly, but the Difference
between connecting expanding liguid to temperature
and digital counts of a sensor to temperature are orders of magnitude different.
Is not measuring the brightness of something emitting energy is a better why to tell it temperature that inserting a probe in it since the probe itself will change the temperature. Measuring temperature accurately is a very difficult task, something lost on almost all people. The temperature data you think is telling you is so corrupted it is worthless for what you are trying to do. You cannot use a weather station to tell you what going on since if they give you a reading that is totally subjective to the environment they are in and you cannot control that environment well enough to know what going on, no amount of fudge factor is going to change that. If any other field, infilled, and adjusted data would lead to getting fired.
I thought you were an English major.
In both cases you get a reading that loosely relates to the actual temperature. Temperature is an intrinsic intensive property of matter. Conversion to an extrinsic extensive value leads you to an abstract value obtained through multiple assumptions.
The temperature of a house changes from room to room, and even from different parts of a room. A temperature value for the house is a fictional value. Imagine that for the entire planet surface. The value you get might be useful, but it is fictional.
This
“In summary, it is indisputable that UHI and LULC are real influences on raw temperature measurements. At question is the extent to which they remain in the global products (as residual biases in broader regionally representative change estimates). Based primarily on the range of urban minus rural adjusted data set comparisons and the degree of agreement of these products with a broad range of reanalysis products, it is unlikely that any uncorrected urban heat-island effects and LULC change effects have raised the estimated centennial globally averaged LSAT trends by more than 10% of the reported trend (high confidence, based on robust evidence and high agreement). This is an average value; in some regions with rapid development, UHI and LULC change impacts on regional trends may be substantially larger.”
should be in quotes
fixed…was not in quotes in original submission.
I generally agree that micro-site bias can be as important as UHI. For whatever it’s worth, I throw in yet another example of such potential bias:
http://www.desk-net.net/Tejon_looking_west.JPG
This weather station is located at the southern end of the San Joaquin Valley, very definitely in a rural area. There were complications in gaining access; I had to contact the Tejon Ranch Company headquarters. My understanding is that they had initially denied Anthony access. This may have been because they were in a knock-down fight with environmentalists at the time. At any rate, they seem to have mellowed by the time I got around to them, and their staff was friendly.
I mention this to make the point that micro-site analysis is not easy; there are no shortcuts to avoid in person inspection. Even when you gain access, you’re likely to go home wondering why you didn’t think to do this or that. In the case of Tejon, why didn’t I think to check which way that air conditioner fan was blowing? At least I had the presence of mind to ask how long the station had really been at that location. (The workers were emphatic that it had been there at least since 1972.)
But I would like to know how long the air conditioner was there. Did they leave it on all night in hot weather, or would office hour use possibly just affect the daily maximum? It didn’t look like a heat-pump that would be used in cold weather, but it wouldn’t have hurt to ask. And on and on.
Not to be negative, but if your purpose is to record ambient changes of fractions of a degree over decades of time, the USHCN stations are just not fit for purpose. (Fun to visit, though.)
“But I would like to know how long the air conditioner was there. Did they leave it on all night in hot weather, or would office hour use possibly just affect the daily maximum?”
Not many people get this about AC.
“Not to be negative, but if your purpose is to record ambient changes of fractions of a degree over decades of time, the USHCN stations are just not fit for purpose. (Fun to visit, though.)”
USHCN is not an official dataset any more. stopped in 2014
I dont use it. I dont know why heller and other think its important anymore.
However, the USHCN stations do match the gold standard of CRN after they have been adjusted.
…if your purpose…
Sorry, didn’t intend to imply that you personally were using it. Should have written, …if one’s purpose…
FTA…. “Using the same criteria as GT and Wang (2017) we can see that the vast majority of stations are located in LCZ’s that have less than 10% urban cover (blue line below).”
Yet the Surface Stations project documents that over 70% of the USHCN stations show significant siting errors that demonstrate heat island effects (human land use variations).
Am i misunderstanding the meaning of the “station count” variable?
“Yet the Surface Stations project documents that over 70% of the USHCN stations show significant siting errors that demonstrate heat island effects (human land use variations).
Am i misunderstanding the meaning of the “station count” variable?”
1. USHCN is not an official dataset since 2014.
2. Here I am Looking at MESO scale, not Micro scale.
UHI is at MESO scale 1km-10km
MICRO is at scales less than 1km, typically 500m within the viewshed of the sensor.
What I am showing is that at the MESO scale, at the LCZ scale, the vast major of sites
are in “unbuilt” areas. less than 10% built.
SO, if you want to find a problem
Focus on Anthony’s work.
In short, at the meso scale the vast majority of stations are in unbuilt areas.
at the micro scale?
unstudied except for Anthony’s work
It’s a pretty simple argument, trying to tell you guys the best field to plow
“What does “1. USHCN is not an official dataset since 2014.” mean Exactly, are none of those station now included in GHCN?
““What does “1. USHCN is not an official dataset since 2014.” mean Exactly, are none of those station now included in GHCN?”
1. USHCN used particular sources and processed them in a particualr way.
2. Some USHCN sites are actually 2 or 3 sites stitched together and given the same
identifier.
3. USHCN used a tw stage adjustment process: TOBS and then PHA.
4. USHCN also infilled missing data by extrapolating from other stations.
GHCN V4 does not stitch the stations together.
GHCN V4 does not use TOBS or infill.
So some of the METADATA will overlap ( station x in 1 is station y in the other)
But the time series data is different. data missing from USHCN has been added,
merged stations, separated..
basically if you use the files from USHCN datasets you dont know what you are doing.
If the purpose of this article is to demonstrate the final statements insicating the station siting is more important than urban development, i would have expected more data examining actual site conditions vs the variations in the urban fraction. Did i miss a site count CRN rating vs urban fraction characterisation in various studies?
“Trends in urban fraction around meteorological station were used to quantify the relationship between urban growth and local urban warming rate in temperature records in China. Urban warming rates were estimated by comparing observed temperature trends with those derived from ERA-Interim reanalysis data. With urban expansion surrounding observing stations, daily minimum temperatures were enhanced, and daily maximum temperatures were slightly reduced. On average, a change in urban fraction from 0% to 100% induces additional warming in daily minimum temperature of +1.7 +- 0.3°C; daily maximum temperature changes due to urbanization are -0.4 +-0.2°C. Based on this, the regional area-weighted average trend of urban-related warming in daily minimum (mean) temperature in eastern China was estimated to be +0.042 +- 0.007 (+0.017 +- 0.003)°C decade1 , representing about 9% (4%) of overall warming trend and reducing the diurnal temperature range by 0.05°C decade . No significant relationship was found between background temperature anomalies and the strength of urban warming.”
Should be in quotes
Steven Mosher,
You get a lot of “static” on this site.
Thank you for your persistence and for the interesting and well-written article.
Robert
It is pretty funny.
A WUWT post looking at 34 sites in the UK showed that UHI scales with % of urban cover.
They used a 10km radius
They showed that as urban cover goes from 0% to 100% UHI goes up.
It maxed out at 1.7C in Tmin
A study of 750 cities in china showed the same thing. used urban cover at 10km
found that more cover is more UHI. maxed out at 1.7C
Here is what I expected from skeptics
“Hey! my city has more!
“hey This city has more!
In short, they dont even address the argument. If I polled 750 Trump supporters and found no
white nationalists, the stupid response would be ” Hey this one guy over here is a Nazi!”
Good strawman is always indicator of great knowledge.
We sceptics prefer honesty. What we see everyday is more and more manipulated climate history.
What we don´t expect from you is your daily ad hominem disgustoids. What are you thinking to win with your teenager behaviour? If you are man, grow up.
Microsite effects matter more than simple UHI because 22k of 27k sites are outside areas most affected by UHI
But then anything under 10% built is regarded as unbuilt.
If microsite effects are important then locations with less than 10% built are important. A small area of hard surface near or upwind of a rural or semi rural site can have a large effect.
Furthermore, 5k of sites in UHI areas is not insignificant since it amounts to nearly 20% of the total and the UHI effect on those sites can be large.
I think SM is unwise to minimise such factors.
“Furthermore, 5k of sites in UHI areas is not insignificant since it amounts to nearly 20% of the total and the UHI effect on those sites can be large.
I think SM is unwise to minimise such factors.”
what you think is not data.
A) a study of 34 sites in the UK and 750 sites in china suggest a maximum AVERAGE effect
of 1.7C to Tmin: this is .85 to Tavg.
B) If you use the linear regression of GT ( UHI versus %) and the regression of Wang (UHI versus %)
and apply this to GHCN You get .13C in Tmin
C) if you do a regression of % coverage versus temperature for all 27K sites..
you get a UHI effect of around .13C
D) IPCC estimated the UHI effect as < 10% of the century trend
By what mechanism could increased urban development reduce or not increase the daily maximum whilst increasing the daily minimum?
Sounds unlikely.
well data says otherwise.
your incredulity is not evidence.
Simple version.
In hourly studies of UHI it is typically shown that the rural sites warm faster than the urban sites.
The higher heat capacity of urban materials and shading, tend to be the explanations used
to explain this.
in some cases of course cities are COOLER than the rural areas.
hard to believe?
Yup, but data rulz right?
The majority of Towns & Cities do not get as cold as Rural sites, therefore they do not need to warm faster, they start off warmer.
When is a weather station site not a “microsite” ? It is always a microsite ! A microsite in a UHI location or a microsite in a rural location. So what !? UHI is a significant issue to any microsite located within a UHI environment.
Microsite refers to the scale.
Think of two scales
0-500 or 1000m is the micro scale
beyond 1000 m is the Local scale, or meso scale.
The bias at a site is going to be a combination of
A) the micro bias ( plus or NEGATIVE)
B) The bias at the local scale ( plus or negative)
Anthony looks at A.
I look at B
Looking at B ONLY, I conclude that at the LOCAL SCALE a large number of sites (22k of 27)
are NOT in heavily built areas
So, I suggest, that the UHI dog ( UHI is a LOCAL SCALE phenomena) wont hunt.
And I suggest that Anthonys work is more important.
namely ‘A’ trumps “B”
Local v micro is probably true as it goes but UHI clearly has an affect well beyond the local scale, particularly at coastal locations. The sea breeze where I grew up is a strong onshore created by the land sea heating differential it can extend to 60km and beyond out to sea depending on the angle of the coastline. It is known that the mountains of the Great Dividing range limits this circulation inland by various amounts on the East Coast of Australia while in Western Australia there are no topographical barriers and it is felt further inland and starts further out. Beyond the affect of synoptic winds, that the coastal urban sprawl has an effect on the strength duration and extent of this “local” circulation pattern is well documented.
In the almost 50 years since my father built his house in undeveloped bush, the surroundings have become an urban sprawl completely filling the 20km gap between it and the next town along the coast. How then, is it possible to ignore the effect of UHI at the larger scale of its surroundings, be they rural or sea surface temperatures?
To be very clear, its seem completely arbitrary to make the distinction between UHI and rural when clearly there is a demonstrated continuum in-between that might very well, turn out to be impossible separate out!
“…So, I suggest, that the UHI dog ( UHI is a LOCAL SCALE phenomena) wont hunt…”
Well you said earlier, “Looked at UHI, ya dont find much <10% of the century trend" (which sounds like a deference to the IPCC)…that it still pretty substantial.
You suggest the siting issues are larger. So UHI+microsite gets us to 20-25% or so at least. That is a huge admission.
Interesting, not disimilar to a post on here in 2012
https://wattsupwiththat.com/2013/04/16/revisiting-temperature-reconstructions-used-in-climate-change-modeling/
to express UHI as a function of urban area:
=========
This makes no sense to me. One measure is a function of time. The other is not. What is the axis you are using to correlate?
If you define UHI as delta temp/time then you should be comparing this to delta urban/time.
For exanple: Put a car on cruise control at 30 mph. Now put another car on cruise control at 60 mph. Both cars have the exact same acceleration (0) but very different distance travelled. So there is no correlation between acceleration (change in speed) and the distance travelled.
So why expect a correlation between change in temperature and city size.
One measure is a function of time, the other is not. So there is no common dimension on which they might correlate.
It looks like faulty math to me. About par for climate science.
“So why expect a correlation between change in temperature and city size.”
its not expected, its observed.
why deny what is observed.
Take a village 1km sq
measure the UHI ( delta from rural)
Grow that Village to 1000 sq km
measure the UHI
prediction?
My take on all this is:
-The Earth is 70% ocean.
– The Earth is thus a water planet.
– The GHE must affect the oceans temps to a depth of at least 300 meters if it is have a long term effect on land climate where humans and our civilizations reside.
– Ocean circulation and overturning must be accounted for. These are both multidecadal and multicentury processes.
– the whole Darwin BoM adjustment fraud of 100 year old station data highlights just how far the climate scammers are willing to go. Especially so when deep past land station data can be adjusted with little or no career/job consequence to the scammers.
Thus the whole UHI, microsite bias issues can be bypassed if we adequately monitor the oceans temps in toto to 2000 meters.
Argo is a huge step forward. But the data time is still too short for meaningful conclusions.
But now we must beware the Argo post hoc data adjustments too. Too much money is riding on CAGW for there not to be huge incentives for Argo adjustments to meet hypothesis.
Australia is important globally since it is a large land mass with very sparse data, so any data rigging goes a long way and there is not a dearth of contradictory evidence.
Aus is roughly equal to SH Africa and about 2/3 of SH S.America. ( Those other continents are hardly reliable either ).
IIRC UEA’s CRUFTem4 makes the rather curious step of calculating SH mean and NH mean then takes the average of those two to get a global mean temperature anomaly. Since most land is in the NH, this biases the global result in favour of SH data.
‘Australia is important globally since it is a large land mass with very sparse data, so any data rigging goes a long way and there is not a dearth of contradictory evidence.”
actually not.
with australia removed nothing changes.
‘IIRC UEA’s CRUFTem4 makes the rather curious step of calculating SH mean and NH mean then takes the average of those two to get a global mean temperature anomaly. Since most land is in the NH, this biases the global result in favour of SH data.”
Years ago I emulated the CRU method and tested this.
Makes no difference if you do it their way or if you do the whole globe
Hear, hear !
Steve: Thanks for taking the time to write a careful article for this website.
If I understood correctly, you didn’t deal with what appears to me to be the most important issue: CHANGING BIAS. Let’s suppose you had a network of stations evenly spaced in 0.1 km by 0.1 grids covering a hundred different urban areas, semi-urbal areas and rural areas, with complete micro-site information for each station. You process the data from half of these stations through some sort of regression or neural network so that you can predict the relationship/bias between any pair of stations given the weather that day (wind, cloudiness, precipitation, season, etc) based on their total site bias on all distance scales. You use the other half of the data to validate your method. When you are done, you can say that all “site bias” (local climate zones, UHI, microsite effects) have biased Tave in 2018 upward by X degC compared with a purely rural planet. So what? If this bias of X is constant with year, the warming trend will not be effected. And there is no way to back in time and and collect the data needed to calculate how much bias existed at various sites at some time in the past.
IMO, the ideal time in the past would be about 1970, since forcing has been rising at a relatively steady rate since then (about 0.4 W/m2/decade) and since about 75% of forcing has developed since then. (There would be little to be gained from going back further to a time when the data is less reliable.) If you could say the increasing site bias of all kinds added 0.3 degC to the warming between 1970 and today, that would have a big impact on our empirical estimate of climate sensitivity from EBMs. However, if we only know the bias today, we can’t say anything except perhaps what the maximum effect bias might have had if the planet had been totally rural in 1970. Of course, 70% of the surface temperature change input into EBMs comes from the ocean, so siting bias of all kinds probably won’t add 0.3 K to total GLOBAL warming.
Steve: Another subject I like to ask about is wind. It seems to me that even a poorly sited station (micro-site to UHI) is likely to give a more useful temperature reading on a windy day when the entire local boundary layer and surface are equilibrating, than on a calm day, when a small local heat capacity and local radiative cooling and/or heating create opportunities for bias.
Has anyone ever looked at pairwise station comparisons on windy vs calm days? Intuitively, one might expect more local agreement on windy days. It would be interesting to understand if more breakpoint are from data obtained during calm periods than during windy ones.
“Has anyone ever looked at pairwise station comparisons on windy vs calm days? Intuitively, one might expect more local agreement on windy days. It would be interesting to understand if more breakpoint are from data obtained during calm periods than during windy ones.”
Yes Parker has
famous paper discussed at Climate audit
Bottom line, depending on the station the effects of UHI are only seen below winds speeds of 7m/sec
aprox
Steve: I remembered Parker’s approach to UHI from CA, but didn’t remember his name. However, I’m thinking of wind as being more useful broadly. You and others have analyzed the data from thousands of stations with empirical breaks in the record averaging about once a decade (IIRC). Metadata explains few of these breaks and I’m not aware of a rational for why so many appear to exist with a high degree of statistical certainty. Fortunately, these breaks add only about 0.2 K (IIRC) to land surface warming.
Assume for the moment that on a windy day a station is sampling the temperature of a environment with a much higher local heat capacity than on a calm day. In that case, local site biases are likely to have a much smaller effect on temperature readings on windy days. For example, on a calm day, the ground can be more than 10 degC warmer than the air 2 meters above. So even the height of vegetation on the ground can impact the transfer of heat to the thermometer above. Almost every kind of site artifact I can imagine will cause less of a problem when the wind is blowing than when it is calm. Many empirical breakpoint might disappear if the temperature record were composed only of non-calm days. Imagine have global and regional temperature anomaly trends for days when the wind was 0-X m/s and X-Y m/s and Y-Z m/s, and the third record had almost no breakpoints to split or homogenize. Would we be better off? Could the third record be called the boundary layer temperature? Would it be more comparable to the SAT produced from the lowest grid cells in climate models or during re-analysis?
Unfortunately, monthly temperature (anomaly) wouldn’t be the basic unit of information, meaning analysis would need to start at step one, a big problem if one doesn’t have a clear idea of how that will make the analysis better.
“Bottom line, depending on the station the effects of UHI are only seen below winds speeds of 7m/sec aprox”
Unless of course, UHI contributed to the creation of the winds in the first place, thus obscuring the effect*
*i.e. Sea breeze circulation
A more probable explaination is that above 7 m/s the airflow is now in the turbulent regime. Once you cross over from laminar flows, you no longer have traceable streams. Eveything gets mixed together so any heat from the upstream area will have been dissipated in to the general thermal mass. The heat is still there its just spread around.
“Bottom line, depending on the station the effects of UHI are only seen below winds speeds of 7m/sec aprox”
Which means, only wind free days should be used to estimate UHI contribution to gobal warming. On windy days, the extra heat generated in the UHI is blown away and not measured in the proper place for attribution.
Obfuscation: obscuring of the intended meaning of communication by making the message difficult to understand, usually with confusing and ambiguous language. The obfuscation might be either unintentional or intentional (although intent usually is connoted), and is accomplished with circumlocution (talking around the subject), the use of jargon (technical language of a profession), and the use of an argot (ingroup language) of limited communicative value to outsiders.
Anthonys study has been verified by big letters, NOAA.
And n o w this. Very very conveniently chosen moment.
So Wattsup Mosher, switching camps, perhaps? Have you seen the light?
Err No.
I have been making the same argument for about 10 years.
Psst, you didnt read the NOAA paper
Huh. Been saying the same thing for 10 years.
Psst., you didnt read the NOAA study
Read the log, eh?
I ‘d read a lot more if they were not hidden from plebs. Samizdat science is not for me.