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.
Well thought out, well laid out, well developed, well explained.
Fascinating. Thanks.
w.
some of the quotes didnt get formated correctly
(Have passed on your problem to the Administrators) SUNMOD
perfect thanks!
Willis, I agree. Good job.
Mosher says:
“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.”
I completely agree with this assessment. It is the changes over time that could have an impact on trend analyses, especially at individual sites. If enough sites are effected, it could possibly effect regional or global trend assessments.
“I completely agree with this assessment. It is the changes over time that could have an impact on trend analyses, especially at individual sites. If enough sites are effected, it could possibly effect regional or global trend assessments.”
Thats why we comapre with the AIRS satellite.
AIRS is the instrument (Atmospheric InfraRed Sounder), Aqua is the satellite. You need to read harder.
quibble much
http://rankexploits.com/musings/2014/berkeley-earth-airs/
Said the king of quibbling
Now youre quibbling about who is the King,
to express UHI as a function of urban area:
=========
UHI is a function of time. Urban area is not. In dimensional modelling this is called a grain mismatch. It is a common problem in data warehousing and big data. Basically it means you are comparing apples to oranges and expecting pears.
If you want to compare two items to see if they correlate, they must share a common dimension. What is it?
Sorry fred
I linked to this.
https://www.nature.com/articles/s41598-017-04242-2
I’m guess you didnt read it and just looked for things you thought might be worth quibbling.
You can understand the role time plays by considering this
https://www.sciencedirect.com/science/article/pii/S2212095512000120
later work by the same team
https://www.hindawi.com/journals/amete/2014/948306/
please note these are not climate scientists so the usual slanders will have to be reformulated
they are mechanical engineers
“In previous work from this laboratory, it has been found that the urban heat island intensity (UHI) can be scaled with the urban length scale and the wind speed, through the time-dependent energy balance. The heating of the urban surfaces during the daytime sets the initial temperature, and this overheating is dissipated during the night-time through mean convection motion over the urban surface. This may appear to be in contrast to the classical work by Oke (1973). However, in this work, we show that if the population density is used in converting the population data into urbanized area, then a good agreement with the current theory is found. An additional parameter is the “urban flow parameter,” which depends on the urban building characteristics and affects the horizontal convection of heat due to wind. This scaling can be used to estimate the UHI intensity in any cities and therefore predict the required energy consumption during summer months. In addition, all urbanized surfaces are expected to exhibit this scaling, so that increase in the surface temperature in large energy-consumption or energy-producing facilities (e.g., solar electric or thermal power plants) can be estimated.”
I think some people are grappling with the MAIN fact.
a tiny numer of sites are in areas with 100% coverage
a large number have 0-10% coverage.
Of course I did a regression to show the amount of warming that is explained
by urban coverage %
if folks cant accept the simple fact of how many stations are in dense urban areas, I dont expect
them to understand or accept a regression.
And when they see how many stations have 0% urban at 500m, 1km, 5km, 10km, and 20km
they will merely talk about their hometown exeperience.
I will add that one of the reasons I went to higher resolution data was your criticsm
way back in the day.. it took years, but the data is finally out there
Steve,
Has your UHI analysis ever looked for influences due to humidity? (Since water vapor is a major contributor to the heat content of air and it dictates to a large extent the minimum nighttime temperatures.)
“Steve,
Has your UHI analysis ever looked for influences due to humidity? (Since water vapor is a major contributor to the heat content of air and it dictates to a large extent the minimum nighttime temperatures.)”
No, but A while back I did try to look for irrigation effects.
Grimmond and Oke did some work showing SOME places where the cities were
sucking water from rural areas and the rural areas ended up drier and hotter
as a result. I will try to hunt up the paper.
the MAIN focus is this
in the past UHI studies have looked at these
Nighlights as a criteria for Urban ( hansen)
Population as a criteria for Urban ( many)
Vegatative cover as a criteria for Urban ( gallo)
what I am looking at is upgrading these methods to take recognition of advances
in several areas.
1. Advances in satellite data
2. Advances in QUANTIFYING different types of Local Climate Zones
3. Using “urban area” as the main, but not only criteria, for categorizing sites
along a rural to urban continuum.
While humidity is important in the variance of UHI I’m not sure how it would work
in a classification scheme.
Steve – Thank you –
“While humidity is important in the variance of UHI I’m not sure how it would work in a classification scheme.”
Humidity, like heat content, adds a dimension or two of complexity to the UHI temperature evaluation. Too much focus on temperature when other significant factors are at play in my opinion.
“Humidity, like heat content, adds a dimension or two of complexity to the UHI temperature evaluation. Too much focus on temperature when other significant factors are at play in my opinion.”
Note, the argument is NOT that it doesnt play a role,
the point is how you would use it to CLASSIFY a landscapes relative URBANITY
Obviously folks study the effect of relative humidity on UHI
Example: a desert and city may both be dry.
but a desert being dry doesnt make it urban.
A rainforest and city may both be wet…
Steve – Since your answer to my question “Has your UHI analysis ever looked for influences due to humidity?” is no, I have remaining questions about the contribution of humidity on nighttime minimum temperatures which would contribute to the UHI temperature increase.
All of the city water being supplied to households and businesses is not returned to the city wastewater treatment systems. A portion evaporates.
My back yard has a storm water runoff pond with aeration fountains. We boil water in the kitchen as do millions of others, we irrigate, and much of the surrounding area is paved or has rooftops, preventing precipitation from being absorbed directly by the soil. Also, when it rains on a parking lot, rooftop, or other man-made heat sink, it is intuitive that more of the water will evaporate. The increase in dew point will increase nighttime minimum temperatures, and thus contribute to the UHI. My question does not relate to a classification scheme for urbanity, but rather trying to understand the effect of man-induced atmospheric moisture within a city on UHI. This may be a topic for another post.
Yes Farmer
I will try to find the Grommond study that looked at some of this.
Steven,
While this sounds like a good thing, when you have an algorithm capable of adjusting up rural stations data so that it matches the enhanced warming of nearby urban stations, then only a few urban stations can have a large effect on the global data.
This seems like a really good point that is under appreciated or completely ignored. We have seen it in action in older analyses, and it does turn the “most sites are rural” argument into meaninglessness if algorithms “urbanize” them. There is reason the raw data so starkly contrasts a lot of the homogenized data. Would love to see a deeper analysis of this with this UHI analysis in mind.
“This seems like a really good point that is under appreciated or completely ignored. We have seen it in action in older analyses, and it does turn the “most sites are rural” argument into meaninglessness if algorithms “urbanize” them. There is reason the raw data so starkly contrasts a lot of the homogenized data. Would love to see a deeper analysis of this with this UHI analysis in mind”
Of course, except the algorithms dont do that.
Later I will explain how they work bit for NOW I wanted folks to understand
A) if you want to make a smart argument you better start by COUNTING the stations
correctly and trying to document what you mean ny rural and urban
B) Then you can start to look at the other questions.
But many folks here seem to want to believe that all 27000 stations are in big cities
They are not.
“While this sounds like a good thing, when you have an algorithm capable of adjusting up rural stations data so that it matches the enhanced warming of nearby urban stations, then only a few urban stations can have a large effect on the global data.”
That is TESTABLE.
The problem is the way the algorithm works is by a “vote” of sorts.
Imagine 10 stations.
For these 10 stations you create All possible pairs
1;2, 1:3, 1:4…… 2:3, 2;4
Like so.
THEN you create a difference series for each pair.
If 9 stations tell you #10 has a JUMP at june 1953, then you adjust #10.
When you adjust #10, the algorithm has a Option
A) Adjust the past
B) adjust the current
MOST people choose option A. but it doesnt make a difference
Steven: “When you adjust #10, the algorithm has a Option”
The algorithm should have at least one more option – to discard the station until the reason for the jump is identified.
There *will* be a reason the jump occurred. The reason for the jump *has* to be identified in order to make a good decision on what action to take.
What Tim said!!!
It is a lot of good, hard work you did on this, so it always takes a minute to digest.
Yes. Thanks for lucid article. Much of it was over my head but I think I understand the issue better.
if you have any questions just ask.
I guess folks forgot this
https://wattsupwiththat.com/2013/04/04/berkeley-earth-very-rural-and-not/
Good job Moshpit! This is such a refreshing change from your usual snarky/cryptic drive-by comments. Excellent article.
I was an early supporter of yours but turned into a vocal critic when I thought you had sold out to the dark side. It appears I may have misjudged you. I certainly hope so, but only time will tell. In the meantime keep up the good work!
“snarky/cryptic drive-by comments.”
Apparently you didn’t read farther down.
Thanks Louis .
My sense is that there are still some of the old readers around.
I think there is an error.
And it is big.
It is here:
“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.”
So what happens, when there is wind?
Air is still warmed and more heat generated in the UHI than elsewhere, but the heat is blown away! It will then increase temperatures slightly elsewhere.
The UHI effect has to be estimated under wind-free conditions.
Just now read this and agree with Willis. Top-notch scholarship worthy of formal publication.
The met office has used a deduction of 0.2 c on CET to cater for the UHI factor some of which has been applied since 1974. There’s has been a major study going on there for some 18 months to ascertain if they should change this amount.
The population has increased by some 25% since 1974 with massive urbanisation And the current uhi factor seems low. The met office generally use data from the composite 1910 temperature records. These do not allow for uhi unlike CET.
Tonyb
there is a world outside of the collection of stations known as CET
Maybe that is why the last two sentences read:
As you yourself have said, CET is a reasonable if not perfect proxy for Regional (if not NH or Global temperatures) You are in good co as everyone from the Met Office to Hubert Lamb to the Dutch Met office has said the same.
tonyb
The topic here is UHI.
I know that people like to focus on individual sites.
why?
Well, those looking for extrema also focus on the single case
Alarmist focuses on ice loss in 2007
Skeptic focuses on his trips into the city
Lets recall what was previously published here
“The present situation is one of large, continuing lack of research attention. There is not even a detailed description of how large the UHI effect is, using a representative set of city examples, let alone its uncertainty.”
That statemnt went unchallenged by the biggest site of skeptics on the planet.
So one thing I wanted to turn peoples attention to was studies of 5000 cities,
studies of 750 cities, studies of 34 sites in the UK.
The world outside singltons
because, the VALUE you find in CET is that it does, on occasion, represent the better picture of those thousands.
Look, you never argue that the thousands are good because they match CET
you argue the opposite.
Which implicity means… the many beat the few
My purpose in using CET is that many of the great and the good including YOU say it is a reasonable proxy for a much larger area, secondly that it is a good record that reaches far into the past so tells us much about the evolving temperature and also that dealing with one set of highly scrutinised data I am very familiar with is far easier than dealing with thousands of cities.
If one is as good (CET) than why argue that thousands are better?
tonyb
When Jones redid the UHI calculation after sufficient time had lapsed since Jones 1990, I believe he came up with a net .4C, IIRC.
Thomas
You are correct. I think that is a far more realistic figure. The Met office have been conducting a study for some two years on whether to alter the uhi factor.
nice new site by the way on the green deal
tonyb
Mosh
Two more things. Firstly I forgot to say I thought this was a good paper. We don’t see enough of these interesting studies from you, more please.
As regards micro siting we have a good example of that with Malvern, one of the sites used in CET. I am convinced that the ‘hump’ you can see in CET in the 1990’s does not reflect reality. The Met office retired Malvern as they felt that it ran too warm during exceptionally warm and sunny summers. Malvern has a particular shape to its valley.
I dare say that there are sites all over the place which have some flaw or other perhaps only during particular circumstances and not all the time.
tonyb
“If one is as good (CET) than why argue that thousands are better?
tonyb
Because one of the reasons you focus on CET and justify that is precisely because it is correlated with the many.
The structure of your argument rests on the many being the standard.
“I know that people like to focus on individual sites.”
Individual sites are all that matter. Averaging sites together is physically meaningless.
And when you look at individual sites, you don’t see a uniform rise in temps.
Jeff Alberts: “Individual sites are all that matter. Averaging sites together is physically meaningless.”
Absolutely! The central plains of CONUS and the southeast area of CONUS have been identified as global warming holes. These are *large* areas of land. Yet NOAA also identifies these areas as having very high concentrations of CO2! A global “average” hides these exceptions to the “CO2 causes global warming” theory. By focusing on the “global average” the climate models can therefore ignore these exceptions to the theory. How then can the physics of the models be correct?
There is an argumentative fallacy known as the “sweeping generalization”. That’ is what “global warming” based on a “global average” is.
“ndividual sites are all that matter. Averaging sites together is physically meaningless.
And when you look at individual sites, you don’t see a uniform rise in temps.”
the Only person I know who averages sites is Tony heller.
We certainly dont average sites.
Somehow I just can’t buy this analysis and its conclusion. Seems more like damage control than an honest look at the data–particularly the data over time as land use becomes progressively more urban, how these stations are weighted in the homogenized data, and how they affect infilling in the homogenized data. It would be very trivial to give them a disproportionate effect in the homogenized products, even by accident, and we know how outlying pints can so dramatically skew averages.
Can’t put my finger on it, but something just feels off about this.
Here is just one thing that is “off”
A) Areas with less than 10% impervious surface are “unbuilt”
10% !?! 4,000 sq ft of impermeable surface per acre would qualify as “unbuilt”!!!
How about 0.01%, that is 100 sq m of concrete or asphalt in a 1 sq km.
“Here is just one thing that is “off”
A) Areas with less than 10% impervious surface are “unbuilt”
10% !?! 4,000 sq ft of impermeable surface per acre would qualify
So A bit of clarification.
I used 10% at 10km to MATCH the approach take by Wang and GT
In my classification system I actually go the extra step of checking for urban fraction
at 500, 1km, 5km and 10km.
And I go further by check for blobs of urban area.
And check the distance between the site and urban blobs
The number of “urban” stations increases obviously
Still perhaps you are forgetting
100% urban area at 10km yeild a UHI of .85min TAVG
0% urban area yeild a UHI of 0
And UHI scales with the urban area.
100% urban area at 10km yeild a UHI of .85min TAVG
You state that as indisputable fact. It could be off by a factor of 5. Where are your sources and are there others you discount?
And UHI scales with the urban area.
Linearly, I presume. That’s An assumption.
“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.”
Be patient there is plenty more to show everyone
Stephan
“Here is just one thing that is “off”
A) Areas with less than 10% impervious surface are “unbuilt”
10% !?! 4,000 sq ft of impermeable surface per acre would qualify as “unbuilt”!!!
How about 0.01%, that is 100 sq m of concrete or asphalt in a 1 sq km.”
Of COURSE I was concerned about this. Is that 10% concentrated? or dispersed?
Note; You assume it’s concentrated.
I did not. I did not assume it was either.
The first thing I did was TEST the proceedure on good stations
“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 next thing I did was look to see if its concentrated. That will be in an upcoming post
For THIS post the main point is this.
two major studies looked at 100s of sites
They used urban fraction at 10km
I start by using their approach to answering the question
Are GHCN V4 sites in Highly urban areas, AS DEFINED by GT & Wang?
Answer: No.
Fact.
next we dig deeper
Note; You assume it’s concentrated.
Wrong, again Steve. I made no assumption about concentration. I used 4000 sq ft per acre as a density, using units that people could compare to the land use of their suburb. In my book, such a density is not unbuilt. Since the vast majority of stations fall in that classification, the classification is too course and suspect.
Next step is to quarter that histogram bar into 4 separate bins. Then see if the stats hold up.
The also don’t take in consideration the increase of surface are when a city is build if you are only looking at the ground for surface are you need to look up and around. There is and increase of mass in a city all which can hold heat and emit heat. Funny Indian knew this when that scratch grooves in their clay pots so they would heat up faster. Some how the primitives knew more that climate scientist.
“The also don’t take in consideration the increase of surface are when a city is build if you are only looking at the ground for surface are you need to look up and around. There is and increase of mass in a city all which can hold heat and emit heat. Funny Indian knew this when that scratch grooves in their clay pots so they would heat up faster. Some how the primitives knew more that climate scientist.”
if you checked the LCZ I linked to you would see that building HEIGHT is a factor
in classifying urban settings
Turns out if you know the right data you can also get building heights.
KEY point
stations ( 22K) are not in cities with tall buildings
It is crap. Once again the gang that can’t shoot straight has developed a data hiding machine.
Agree, 1 km is way too small, the UHI affect is incredible and its spreading fast.
https://tinyurl.com/hfs8lhh
It is pretty simple
1. GT, wang and others are classifying urban based on the percentage of urban cover in a 10km
buffer. That is what they did.
2. Using that approach they find a Bias in Tmin of around 1.7C. That is what they found
3. The rest of the field is also looking at the SIZE or scale of urban cover as a determining
factor in UHI; Bigger area = more UHI. smaller area = smaller UHI.
4. Oke and his student have moved away from using population as a metric, and use LCZ
5. Urban cover ( %) is a factor in their catagorization
Question?
Using the Criteria GT and Wang use “% urban cover at 10km”
Using the LCZ approach <10% is unbuilt
How many GHCNv4 stations are 100% urban cover at 10km?
How many GHCNv4 stations are 10% urban cover at 10km/
Simple question.
Factual answer.
Here is the bottom line
All of the really HIGH UHI number you see 1.7C, 3C, 10C….
they come from areas that are 100% urban area over LARGE buffers.
Guess what?
Tiny number of stations are there
Yes, but as I said above in a reply to another comment, you can have a small number and yet a big effect, if you are using the data of this station to adjust upwards the data of other nearby rural stations, by means of a bad algorithm that was originally designed to do the opposite (or so was said).
Mr. Mosher,
Very good post!
Now I have to spend time to read it, because you made it worth reading it.
Thank You.
You’re welcome
Let me summarise the argument.
1. GT used % of urban cover at 10km to quantitfy UHI, UHI scales with the %
2, Others confirm their results
3. They produce a map. see the text.
4. Stewart and OKE also use % urban cover to quantifiably categorize sites
They use a 10% cutoff for built versus unbuilt.
5 using these criteria I show that GHCNv4 sites (~22K out of 27K) are in unbuilt areas
pretty basic.
Folks think the sites are all in areas of heavily built urban areas
They are not.
Interesting article and linked lecture. Thanks.
What I understand from this is that about 1 in 5 sites are located in areas above your minimum urbanisation category and that you wonder if even sites in lowest urbanisation areas may have local site problems?
I think that if ~20% of sites are subjected to the UHI gradient, then that is a worry. Also, I wonder if the more heavily urbanised sites are used for infilling and adjusting nearby stations? If so, that would seem poor practice.
“Interesting article and linked lecture. Thanks.
What I understand from this is that about 1 in 5 sites are located in areas above your minimum urbanisation category and that you wonder if even sites in lowest urbanisation areas may have local site problems?
There are these LOGICAL possibilities
Urban Region: Good site
Urban Region: bad site (warm bias)
Urban Region: bad site (cool bias)
Rural Region: Good site
Rural Region: bad site (warm bias)
Rural Region: bad site (cool bias)
Most skeptics will not consider
“I think that if ~20% of sites are subjected to the UHI gradient, then that is a worry. ”
A) you can just not use those sites. answer doesnt change materially
B) using GT’s regression you would expect to see a small TOTAL bias in
line with IPCC estimates
“Also, I wonder if the more heavily urbanised sites are used for infilling and adjusting nearby stations? If so, that would seem poor practice.”
EXCELLENT point. The long range plan I have is to test the with and without urban
and to see what happens after adjustments and to run the adjustment code on different
groups.
You win the prize of reading my mind.
GREAT skeptic
“and to see what happens after adjustments and to run the adjustment code on different
groups.”
As soon as you group, you lose it.
Are there also differences in CO2 concentrations at urban versus rural measurement sites that are impacting night time temperatures?
No because CO2 absorption bands are saturated in the lower troposphere so minor variations in concentration have no effect. And besides, water vapour dominates CO2 in the lower troposphere except in the driest desert conditions.
In https://leif.org/research/Climate-Change-My-View.pdf on page 10, the UHI effect for Kyoto is estimated to be
“The recent rise of temperatures is attributed, primarily to the warming associated with the urbanization of the Kyoto area(estimated to be of the order of 3°C)”
Note well that bad microsite affects urban sites every bit as much as it affects rural sites. Look at the NYC Central Park site. It is very well sited and shows very little warming.
I am guessing none of the readers clicked on or read the urban cool island links
Mr. Mosher:
“I am guessing none of the readers clicked on or read the urban cool island links”
Well you’d guess wrong. I was going to comment at end of thread, but this seemed to be a better place to put it.
The first link goes to a paper that says UCI is pretty rare.
The second goes to a paper that atalks about cities in India surrounded by dry, baked fields that get much hotter than the cities themselves because the cities can “sweat” and so are cooler by comparison. Not cooler than they would have been with no city at all, just cooler than the sun backed fields around them.
The third and fourth papers talk mostly about using purpose designed micro sites to mitigate the effects of UHI. Not cancel them, mitigate them.
So without delving into all the math and stats, my first read of those papers is that UCI is a pimple on the UHI butt.
“So without delving into all the math and stats, my first read of those papers is that UCI is a pimple on the UHI butt.”
Well you cant conclude that.
you actually have to LOOK.
is UCI real?
YUP.
next?
Do any of the URBAN sites in GHCN exist at what could be described at an urban cool ialand?
That’s a QUESTION.
the answer to the question comes from looking at data.
Psst, seen Evan comment about Central park.
So, at this stage, I reseve judgement.
Why?
because data trumps my hunches
Some more stuff you wont delve into.
https://www.researchgate.net/publication/267693239_Urban_Cool_Island_in_Daytime_-Analysis_by_Using_Thermal_Image_and_Air_Temperature_Measurements-
https://www.researchgate.net/publication/303395066_The_urban_cool_island_phenomenon_in_a_high-rise_high-density_city_and_its_mechanisms
https://www.sciencedirect.com/science/article/pii/S2212095515300237
Surface Urban Cool Island Intensity (SUCII) of the city ranged from 3.5 to 4.6 °C.
https://www.mdpi.com/2073-445X/6/2/38/pdf
https://pdfs.semanticscholar.org/17fe/f326beb19a3b91ce313ae99532bd3ed0ee66.pdf
https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012005/pdf
https://journals.ametsoc.org/doi/full/10.1175/JAMC-D-11-0104.1
‘ Aqua MODIS detected the early-afternoon UHIskin (Fig. 3b), which had a maximum value (8-day average) of 12.4°C in August, and average values during the dry season are close to 0°C, with 31% of the time periods experiencing negative values that correspond to cool-island events. These results are shown for 2006 and were found to be representative of 2007–10 (figures not shown).”
http://www.ijesd.org/vol7/886-E0021.pdf
this is a study of Seoul where I live. The focus is on quantifying the effect of water on UCI, namely streams, ponds and lakes in the city. the effect is quite noticable
and most folks in the science are aware of it. Not you of course.
here is a restroration they did
https://inhabitat.com/how-the-cheonggyecheon-river-urban-design-restored-the-green-heart-of-seoul/
Pimple?
Pimple?
Here is your pimple
https://www.researchgate.net/publication/283523783_Thermal_impact_of_blue_infrastructure_Casestudy_Cheonggyecheon_Seoul_Korea
“After the Korean war (1950-1953) the Cheonggyecheon river was for more than 50 years covered with pavement and concrete overpass structures. The reconstruction of the expressway was carried out from 2002 to 2005. To estimate the thermal impact of the expressway into a water pathway remote sensing analysis (Landsat 7 ETM+) was undertaken. 20 Landsat-7 ETM+ images from 2000 till 2012 were used to compare the land surface temperature (LST) distribution during the time the expressway was there and through to the reconstruction and the establishment of the river stream. A built-up area of two km width surrounds the new water pathway and this was used as a reference area. The investigation could show that the establishment of the Cheonggyecheon stream forced a considerable thermal impact, i. e. an average decrease in the land surface temperature by seven degrees Celsius.”
https://www.researchgate.net/figure/Land-surface-temperature-decrease-after-the-reconstruction-of-the-Cheonggyecheon-gray_fig3_283523783
So rather than make a snap judgement david, I prefer to look and see.
you know, delve into the stuff
Mosher,
The studies you presented all suggest that UCI isn’t significant whether they are common in GHCN or not. I have to ask, did YOU read them?
You then proceed to tell me how stupid I am for not reading yet MORE literature. If you can’t confine your argument to the very evidence your presented, and instead have to introduce new evidence to support your position… well what value to also read this one? If I have an objection to it you’ll just call me stupid and introduce yet more information from yet more studies. This approach is confrontational, makes no friends, and persuades no one. If you’re going to link to evidence, you should at least have the decency to discuss that evidence.
I may as well have just replied Wrong! at we’d have accomplished as much.
“Mosher,
The studies you presented all suggest that UCI isn’t significant whether they are common in GHCN or not. I have to ask, did YOU read them?”
Wrong
The point is rather simple. The studies demonstrate that you cannot simply
ASSUME that an urban site is wamer than the rural areas.
You cant assume it. you have to show it.
In some cases whole cities are cooler than their rural surroundings.
In some cases there are cool zones.
So, you can’t assume. You have to actually look at the data.
“…Not cooler than they would have been with no city at all, just cooler than the sun backed fields around them…”
Bingo.
“This approach is confrontational, makes no friends, and persuades no one.”
David! Shhhh! You’re cramping his style!
Mosher;
“The point is rather simple. The studies demonstrate that you cannot simply
ASSUME that an urban site is wamer than the rural areas.”
At no time did I make such an assumption. I merely pointed out that your assumption that no one would read the links was wrong and that the four studies you linked to support our assertion.
Not that I actually give a d@mn. Even if the UHI contributes zero to the temperature record, the record does NOT support the hysteria in the media, nor the demands of the UN and other bodies to crush the global economy and cast billions into starvation and poverty. Would be nice to see you stand up once in a while in a public forum and speak truth to those people. All you need is your favorite word Mr. Mosher.
Wrong.
DON’T support your assertion…
Never type while under the influence.
“…this is a study of Seoul where I live. The focus is on quantifying the effect of water on UCI, namely streams, ponds and lakes in the city. the effect is quite noticable
and most folks in the science are aware of it. Not you of course…”
FFS it is as plain as a giant pimple on a nose, with conclusions such as, “The results show that larger water spaces are more useful in reducing urban heat…” and “…if stream area is increased by the presence of water space or green space, the effect of UCI could increase more…”
Jumping Jesus on a pogostick! Thank you for presenting this groundbreaking find! You could have just asked any junior or senior in civil engineering along with any number of people off of the street.
yup Evan.
see all the work on urban cool islands.
readers wont but you will
…all UHI studies come in around 3C
January 2018
S56 An Analysis of the Miami Urban Heat Island and its Potential Influence on Precipitation Distribution in South Florida
temperature analysis indicates an average UHI intensity of +2.53° C….
https://ams.confex.com/ams/98Annual/webprogram/Paper338437.html
Denver’s heat island is among the highest in the country, according to a 2014 Climate Central study, with an urban-rural temperature differential of 4.9° between the city’s center and outlying rural areas.
….that’s almost 25F hotter in the city
http://www.weathernationtv.com/news/denver-passes-ordinance-aimed-curbing-heat-island-effect/
Sorry latitude the denver numbers are Summer ONLY
LOL…so it only screws up the average in the summer
“LOL…so it only screws up the average in the summer”
No latitude.
if fall is 0
if winter is 0
if spring is 0
if summer is 3C
then you cant argue that annual averages 3C
Summer is the highest
You cherry picked the summer to say UHI is 3C
its not
Not even close.
750 cities in China say different
419 large cities say different’
5000 cities studied say different
But the key point is 22K of the stations are not in Denver, or Miami
or cities that large and dense
LOL….posting UHI is 5 degrees hotter than the real temperature… in the summer…is now cherry picking
So Mosh…exactly how much did Berkley adjust for UHI for Denver?..in the summer
Did they adjust their temperature down 5 degrees?
I’m not sure that is correct
If Fall, Winter and Spring were each 1,
The annual average would be their product 1+1+1+3=6 divided by their quantity 6/4=2
So in this case their product 0+0+0+3=3 divided by their quantity 3/4 = .75
“I’m not sure that is correct
If Fall, Winter and Spring were each 1,
The annual average would be their product 1+1+1+3=6 divided by their quantity 6/4=2
So in this case their product 0+0+0+3=3 divided by their quantity 3/4 = .75”
The point is Latitude is trying to compare the HIGHEST UHI recoded in a single city in a single season, with the AVERAGE UHI of 750 cities.
The average (all seasons) will be lower than the HIGHEST season, which latitude picked.
Futher, pointing out that one city has a higher average that a study of 750 cities
is kinda stupid and trivialy true.
Duh.. who knew that there were values both above and below a mean?
not latitude
“LOL….posting UHI is 5 degrees hotter than the real temperature… in the summer…is now cherry picking”
Err no, your Cherry pick was saying MOST UHI studies were 3C
And then when I am talking about ANNUAL values, to cite
the worst case, summer values for a single city
“So Mosh…exactly how much did Berkley adjust for UHI for Denver?..in the summer
Did they adjust their temperature down 5 degrees?”
Why would we when the stations WE USE don’t see a UHI of 5C in any season.
Longest station in denver started in 1880.
It has warmed .8 C in over 100 years.
the problem with your 5C claim, is that there MAY BE a place in denver that has 5C
UHI.
We aint measuring there.!
Same with Boulder. Shows about .8C since the 1880s.
Latitude: “Did they adjust their temperature down 5 degrees?”
Mosher: “Why would we when the stations WE USE don’t see a UHI of 5C in any season.”
Talk about dodging the question.
read harder
“This study furthers understanding of the Miami UHI and evaluates its influence on regional precipitation through analysis of surface station temperature data from the National Climatic Data Center (NCDC) and NOAA Cooperative Observer Network (COOP) in conjunction with South Florida Water Management District (SFWMD) 2 km x 2 km gridded precipitation data. The daily minimum temperature difference between designated urban and rural representative regions provides the working proxy for UHI intensity (defined as Tmin,urban – Tmin,rural). Preliminary temperature analysis indicates an average UHI intensity of +2.53° C with distinct seasonal variation and a daily minimum temperature maximum near the urban center. Daily UHI intensities are classified as strong (>2.78° C), average (2.28° C – 2.78° C), weak (0° C – 2.28 ° C) or negative (<0° C) (referred to as “urban cool islands”). Quantitative spatial precipitation analysis is then performed on daily and seasonal timescales and analyzed against derived UHI intensity."
Note this is UHI in TMIN ONLY
What part of this are you missing Mosh….
” an average UHI intensity of +2.53° C”
….did Berkley adjust Miami temps down 2 1/2 degrees….or not?
no they did not….
Miami
Raw monthly anomalies 1.23
After quality control 1.22
After breakpoint alignment 0.77
Regional expectation during same months 0.80 ± 0.15
National average during same months 0.92 ± 0.10
Global land average during same months 1.07 ± 0.04
http://berkeleyearth.lbl.gov/stations/160294
..only a tiny small fraction….and can claim adjustments lower temps
“The daily minimum temperature difference between designated urban and rural representative regions provides the working proxy for UHI intensity (defined as Tmin,urban – Tmin,rural).”
They Define UHI intensity as TMIN urban – tmin rural.
Like I said
And dude, the raw data for the airport is 1.23 C increase in over years
Dont know where they measured 2.33 UHI, when the city hasnt warmed that much
And YES, note that we adjust the airport DOWN.
In our record Miami and fort lauderdale are warming at the same mild pace compared to the nation
And yes in our record Miami is warming the same as
https://www.google.com/maps/place/Moore+Haven+Lock+%26+Dam/@26.8396971,-81.1223026,9691m/data=!3m1!1e3!4m5!3m4!1s0x88dbf43200deead9:0xfdc5dcf5dd624ea9!8m2!3d26.8400588!4d-81.087352
the problem is you believed the Miami UHI numbers..
Psst
750 cities in china… 1.7C
you got Miami, oh and denver
748 to go.
you are a hoot and a half…..
Did Berkley adjust Miami or Denver’s temps down 2 1/2 and 5 degrees or not?
Miami > http://berkeleyearth.lbl.gov/stations/160294
Demver > http://berkeleyearth.lbl.gov/stations/172712
fine…this might take a while
Atlanta…..this UHI study by NASA for the EPA…says Atlanta can have a 15 degree UHI
…how much did Berkeley adjust for UHI?
http://berkeleyearth.lbl.gov/stations/28682
“The recent rise of temperatures is attributed, primarily to the warming associated with the urbanization of the Kyoto area(estimated to be of the order of 3°C)”
1. Which area within Kyoto?
2. What type of LCZ did they measure at in Kyoto?
3. is this daily intensty ( max) or Tmin, tmax, tavg?
In short what Oke and Stewart have shown is “the UHI of city X” is pretty meaningless
without the appropriate description of the exact measurement areas
because cities have LCZ that range from cool to very hot
One city in japan: Kyoto
750 cities in china:
“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.”
you only have 749 more cities in japan to study, Then you can see
what is the relationship between % of urban cover over a 10km radius and UHI
In short.
a study of 34 sites in UK
a study of 750 cities in China demonstrated
A) That UHI varies with urban cover over a 10km radius
b) Higher percentgae of cover means more UHI.
c) The maximum AVERAGE UHI was 1.7C in Tmin, for 100%
d) 0% cover 0% UHI.
Single cities do not change this analysis.
Question. How is Tavg for a site determined for the various datasets? Is it the sum of hourly T divided by 24 or Tmax plus Tmin divided by 2. It seems that if it is the later, then it won’t reveal if or by how long the period over which nighttime T is elevated. If (most likely?) the UHI extends the period of nighttime elevated T and the Tavg is determined by the first method, then Tavg should be increased as well. Does this make sense? Would the duration of elevated nighttime T most likely be “significant”? I ask the question only to clarify, not to challenge anyone’s serious work on this subject.
SM, an excellent post providing a new ‘kilometer rule’.
Perhaps inadvertently, your post also directly undermines the v2 homogenization algorithm used by GISS. It expressly violates your km rule. I showed directionally how and why in a guest post here some years ago (How good is GISS? 8/3/2015) using Anthony’s then available Surface Stations Project category 1 stations.
“Perhaps inadvertently, your post also directly undermines the v2 homogenization algorithm used by GISS.”
GISS doesnt use the v2 homogenization algorithm
USHCN has 1218 stations across the USA. UHI is most severe when the temperature sensor is within a few feet of a heat sink such as a road, parking lot, concrete or glass-and-steel building, or anything heavy and black. If there were photographs of the surroundings of each of the 1218 sensors it would be child’s play to sort out good ones from bad ones.
Reading and comprehending this post, on the other hand, is anything BUT child’s play. How about an executive summary in plain language with no abbreviations or initials? A kilometer is 3,280 feet. It is not plausible that a heat sink that far away could alter a temperature sensor’s reading.
Michael Moon
You said, “It is not plausible that a heat sink that far away could alter a temperature sensor’s reading.” Unless there was a wind blowing over the surface (parking lot) moving heated air downwind.
“Michael Moon
You said, “It is not plausible that a heat sink that far away could alter a temperature sensor’s reading.” Unless there was a wind blowing over the surface (parking lot) moving heated air downwind.”
The speed of the wind and the distance are important.
Advected UHI decreases exponentially
and you need winds that are neutral/stable
“USHCN has 1218 stations across the USA. UHI is most severe when the temperature sensor is within a few feet of a heat sink such as a road, parking lot, concrete or glass-and-steel building, or anything heavy and black. If there were photographs of the surroundings of each of the 1218 sensors it would be child’s play to sort out good ones from bad ones.”
USHCN is a different data set that nobody uses expect tony heller
For reference
WITHIN 500m the term used is MICROSITE
Outside 500m the term used is UHI
let see if I can explain.
“USHCN is a different data set that nobody uses expect tony heller” yep exactly since it give you answers you don’t want. Since there is a difference, until you understand why everything else you say is moot. If there is a difference and you just ignore it that not science it is junk science.
Once again, OK, 500 meters is less than a kilometer, actually half. I think your article is a red herring. 10 meters would be more like it. So no one uses USHCN except Tony Heller?
Deliberate obfuscation is not useful. You are trying to complicate a simple phenomenon. Local Climate Zones, indeed. The temperature sensors’ location either is, or is not, within a few feet of a large heat sink. If it is not its readings are probably accurate. If it is, readings will be influenced by an un-natural factor. A building or road a kilometer away, or even half a kilometer, is wildly unlikely to influence a temperature sensor.
You are still a fish farmer of red herrings. Obviate Obfuscation!
Another transparent rescue intervention by the Mosher looks much more like damage control for the Global Warming/Climate Change religion than a proper analysis of the UHI situation. Any new scientific research that reveals how much of the Climate Change religion is based on statistic manipulation and special pleading has to be immediately countered to keep the faithful believers within the fold. Stragglers must be immediately rounded up and re-indoctrinated.
“Another transparent rescue intervention by the Mosher looks much more like damage control for the Global Warming/Climate Change religion than a proper analysis of the UHI situation”
Huh?
1. paper posted here showed that UHI scales with % of urban cover.
2. They looked at urban cover over a 10km buffer.
3. More cover? higher UHI. less cover lower UHI
4. Question? How many stations are in areas of Hi cover versus low cover
5. Answer: 22K of 27K stations are in low cover: <10%
Observation: skeptics tend to focus on the exceptional cases. the high cover cases
and they talk as if they are the rule, rather than the exception.
data says most stations are in low cover.
Clue for skeptics. the REAL issue is Microsite, like anthony has been trying to tell you.
1. sites in cities that are well sited can be free of UHI
2. Sites in rural areas that are not well sited, can be worse than urban.
In general, focus on anthony's work, because the UHI argument isnt your best one
Steven
I appreciate your posting and actually contributing to the debate instead of snipping. You have give me a reason to have some respect for you again.
People read what Steven is saying, UHI is some what properly accounted for what may not be accounted for is small incursions near rural sites. This a good contribution, take the time and appreciate it.
“People read what Steven is saying, UHI is some what properly accounted for what may not be accounted for is small incursions near rural sites.”
Anthony has been talking, and showing examples, for years.
You say, “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. ”
Do you have a paper cite for that? The century trend covers a long period of steady urbanization, where you would expect to see UHI effects. Which century trend are you talking about? HadCrut? GISS? Land ocean or land only? Is this the usual retreat to arguing, well, it doesn’t matter because the ocean trend swamps all the issues with the land record?
Sorry the formating got screwed up
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.”
is from Ar5
fixed
thanks
This bears repeating:
“…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).
Hats off to you SM for making the effort.
Thats the IPCC.
most people miss what the science actually says
Steven, those of us who struggle with the science but aim to be honest do their best (and thanks for this post, which is helpful).
The people who “miss” the science because they also struggle with it and have other things to do are the politicians. Those who make damn’ sure the politicians “miss” the science are the ones who write the Summary for Policymakers.
The Climate debate is not a level playing field. We are well into “policy-based evidence making” as recent events in the UK have demonstrated all to well. Until we restore a degree of objectivity, posts such as yours — useful though it is — are irrelevant because nobody is listening any more. Except to the likes of Greta Thunberg!
Your best weapon against the GND is the actual science.
“Thats the IPCC. most people miss what the science actually says”
— Stephen Mosher (May 4, 2019)
I’m old enough to remember when we needed to listen to the IPCC and not the media because they summarized the science for us. Times change quick, keep up.
“the IPCC documents the consensus. the Press covers it poorly. The IPCC merely summarizes the science.”
— Steven Mosher (March 3, 2019)
10km.
Feeding that into a climate model will greatly distort the output of the model. Consider their resolution. How far are they spreading that?
The bias is tiny but the impact on measurement is huge.
If a climate model is based on those observations it would be terribly exaggerated.
The effete of UHI would still be catastrophic for climate the development of climate science.
“10km.
Feeding that into a climate model will greatly distort the output of the model. Consider their resolution. How far are they spreading that?”
since climate models have lower resolution I dont know how you can speculate.
“How far are they spreading that?””
spreading what?
Sorry, I was assuming that the models are based on observations. Thus making the point that a local distortion near the stations will mislead the models.
I also mistyped “effect” as “effete”
What happens when you begin, at the same time that urbanization is accelerating such as in China and India, begin to eliminate rural stations from the record so that two things are happening at the same time. How do those two things combined act to change things. We might see more than 10% of the increase due to UHI if the percentage of stations subject to UHI are an increasing percentage of the number of stations in the database.
The good thing is more older stations are being recovered
That is possibly a good thing but that wouldn’t be in the US. Where is this happening? In the US they have shifted to using the USCRN which has made the USHCRN obsolete. The CRN is a much better network and I would like to see the same design principles put into place in other areas of the world to get a more accurate view of climate. It would seem that Canada would be a good candidate for such a network but many countries in Africa and South America would likely have difficulty in maintaining one. A CRN style network across the US, Canada, China, and Russia would cover a very large portion of the land of the Northern Hemisphere and might give us some data we can take seriously. Same could be done across sparsely populated islands in both the Pacific and Atlantic.
In fact, my gut instinct would tell me that the combined data from scientific stations on basically uninhabited or very sparsely populated islands and other very remote locations would give us a better sample of overall global climate than land-based stations in populated countries as a change in global climate should be, well, global.
I have to agree with you Crosspatch .
Urbanization in the last 70 years must have helped increase the worlds temperature record and the worlds temperature .
Thousands of square meters of blacktop highways which absorb heat during the day and release it after sundown act like a heat pump and then all the concrete and brick buildings .
Just walk with bare feet on grass then onto grey concrete then onto blacktop OUCH then jump onto a white line , relief.
Don’t blame CO2 , blame urbanization for warming trends of the worlds temperature record.
“That is possibly a good thing but that wouldn’t be in the US. Where is this happening? In the US they have shifted to using the USCRN which has made the USHCRN obsolete. The CRN is a much better network and I would like to see the same design principles put into place in other areas of the world to get a more accurate view of climate.”
CRN matches the “bad” stations perfectly. which means they aint bad.
We are working on a global CRN
another network that guys will, in the end, doubt, because they can.
“In fact, my gut instinct would tell me that the combined data from scientific stations on basically uninhabited or very sparsely populated islands and other very remote locations would give us a better sample of overall global climate than land-based stations in populated countries as a change in global climate should be, well, global.”
I did that study. haha. in fact my islands dataset has been used by one other team.
Answer: you get the same answer. its warming.
Wow! That was an impressive display of hand waving.
………..LOL!
if you say so, it must be so
Another waive of the hand! How do they do it? Geniuses one and all…
extend arm,
wiggle
Yep, just as I expected. Another smug reply from the English major and marketeer who has climate all figured out. Note that every reply that Mosher makes regarding the measurements by his team is a claim of absolute knowledge. They know that our eyes are liars.
The dumbest guy in the room is always the one who believes he has all the answers. By all means Mosher, just keep waiving those hands, you have mesmerized many here.
Huh?
notice all my comments are about estimates and uncertainties and bias.
knowledge?
except for pure math and logic, we only have the ‘best available explanation”
you seem awefully certain about your views?
seems that way at least
notice all my comments are about estimates and uncertainties and bias
You are so self deluded.
Unless… you are willing to admit your “work” is simply your opinion, and based upon the opinions of others. Your “uncertainty” is well disguised, as you dismiss the opinions of those who do not buy into your hand waiving.
So, are you ready to admit that UHI could be far impactful more than your data torturing device reveals?
“So, are you ready to admit that UHI could be far impactful more than your data torturing device reveals?”
Sure it could be!
So here is what I did.
I read dozens and dozens of papers.
I found the ones from mechanical engineers ( not in climate studies ) to be the most helpful.
They suggested the following.
UHI scales with the size of an urban area.
What’s that mean?
A village 1km in size will have a lower UHI than a city of 1000sq km.
WHAT? outrageous ( sarc off)
UHI scales with size.
Then I checked other data, studies of hundreds, no thousands of cities !
What did I find?
I found that they all document that UHI scales with city size.
there are other factors of course, but that is what I found
I didnt stop there
I started my own study back in 2012
https://stevemosher.wordpress.com/2012/10/11/pilot-study-small-town-land-surface-temperature/
I wanted to focus on small cities.
Anyway, you are right
UHI COULD BE More impactful ! it could be!!
to start a test of that I decided to do something very simple.
UHI scales with the size of urban area. small area, small uhi.
Big area, big UHI.
So what is the simple first step.
COUNT THE STATIONS !
With 27000 stations how many will be in DENSE urban areas
— 100% urban cover over a 10km radius
How many will be in areas of low cover < 10% urban cover
Answer:
22K are in low cover
look at the chart.
Now, maybe areas with small urban area ( say 1 sq km) have HUGE UHI !
I have not seen that
Looked but no unicorn!
So once again, you have it all figured out. You studied low estimate papers so as to prop up the failed AGW hypothesis, and excluded all others. Find confirmation to support your bias. I get it. It’s what Bigfoot hunters do everyday.
Sure Bigfoot could be!
So here is what I did.
I read dozens and dozens of papers.
I found the ones from Bigfoot experts ( not in zoologiocal studies ) to be the most helpful.
They suggested the following.
Bigfoot is real.
What’s that mean?
A village idiot who sees something move in the dark is proof of Bigfoot.
WHAT? outrageous ( sarc off)
Bigfoot scales trees.
Then I checked other data, studies of hundreds, no thousands of Bigfoot sightings!
What did I find?
I found that they all document that Bigfoot everywhere.
there are other factors of course, but that is what I found
I didnt stop there
I started my own study back in 2012
https://stevemosher.wordpress.com/2012/10/11/pilot-study-Bigfoot-sighting-go-up-with alcohol intake/
I wanted to focus on rural Bigfoot.
Anyway, you are right
Bigfoot may no longer exist ! it could be!!
to start a test of that I decided to do something very simple.
Bigfoot sightings in small towns versus Bigfoot sightings in cities.
So what is the simple first step.
COUNT THE BIGFEET!
With 27000 sightings how many will be in DENSE urban areas
— 100% urban cover over a 10km radius
How many will be in areas of low cover < 10% urban cover
Answer:
22K are in low cover
look at the chart.
Now, maybe areas with small urban area ( say 1 sq km) have HUGE Bigfoot sightings!
I have not seen that
Looked but no unicorn, only Bigfoot!
Everything is proof of Bigfoot, just as everything is proof of man made global warming. Just ask the experts!
(Suggest you confine yourself to the topic at hand, and discuss that instead, leave out the snark) SUNMOD
“So once again, you have it all figured out. You studied low estimate papers so as to prop up the failed AGW hypothesis, and excluded all others. Find confirmation to support your bias. I get it. It’s what Bigfoot hunters do everyday.”
No Gator I read all the HIGH ESTIMATE papers
Thats when I learned why their estimates OF SINGLE CITIES ( seoul, hong kong, phoenix, ) are HIGH.
Thats when I learned that they picked to Optimum days for MAX UHI
Yes Mosher, we all know you get upset when UHI threatens your beliefs, and we all know why you wrote this paper. You are on a Bigfoot hunt, and because you cannot find one, you dress real data up in a big hairy rubber suit and pass it off as genuine. Why? Because Bigfoot does not exist in nature.
I had a discussion yesterday with a retired climatology professor who is a friend and neighbor, and we both had a great laugh over UCI’s. I guess that is the new ocean acidification! LOL
Keep on deluding Mosher!
Give it a rest, please, Gator. Ad hom is a bore.
What ad hom? I’m attacking Mosher’s piss poor attempt at marketing the same old debunked CAGWBS. The constant salesmanship of doom from the likes of Mosher is the true bore.
Mosher is a believer, which is fine people believe in all kinds of crazy crap, but he crosses a line when he attacks others for their beliefs. On top of that, all of Mosher’s comments have a common theme, and that is that all of the fraudulent adjustments to our data are fine and good. They are not. But Mosher wants to keep the alarm going, for whatever reason, but that reason is clearly not logical or ethical. As Lomborg has pointed, out we could save millions from starvation annually if we redirected precious resources away from climate alarmism.
If attacking pseudoscientists to save millions of innocent lives is considered ad hom, please sign me up and buy me the t-shirt. If giving civility to the likes of Mosher is considered sound science, call me a science denier. I don’t want to be part of any genocide.
So, are you human Spaulding Craft? Is being human more or less important than being polite to a salesman of doom who cares nothing for others? We will see…
” On top of that, all of Mosher’s comments have a common theme, and that is that all of the fraudulent adjustments to our data are fine and good. ”
No, The adjustments will NEVER be all fine and good
Adjustments Aim as REDUCING BIAS.. on average in objective tests
they REDUCE bias.
A) they are not perfect
B) in some cases they FAIL.
Here is the difference
I look at the average behavior, and test that. Works pretty good
Then I look at the messed up cases and try to improve the algorithm
You look for the worst case and declare the whole thing a fraud.
in any case please continue your baseless rants.
It helps me clarify my position for others
No, The adjustments will NEVER be all fine and good
Then stop acting as if they are Mosher. Be a real scientist, graph the real data, then include your opinion of what it you think it should be, include error bars, and admit that this is nothing more than your opinion.
Can you do that Mosher? Or will it be more of your self deluded rants of “Wrong!”?
This report is an attempt to quantify an observed problem, namely UHI influence on the temperature record. If any scientist had a choice of either macroscale or microscale they would choose microscale, as it is a part of the macroscale and you can mathmatically add things. If the microscale event is ten square meters of fresh black asphalt the microscale number is going up substantially. Remember when Anthony and a small army of volunteers looked at all reporting sites in the USA (as I remember)? That is the dataset I would want to utilize to start my study. Where is the small army of volunteers from the AGW advocates side?
“If the microscale event is ten square meters of fresh black asphalt the microscale number is going up substantially. ”
Not really.
It depends how close that is to the sensor. See the recent post on microsite
It is interesting that the Tmax was reduced in thier study.
It must depend on the Location, anyone that has visited Bath in the UK will know that UHI definitely affects the afternoon max temp in the summer.
The problem of UHI affecting a trend is not something that can be done retrospectively unless there are a lot of historic photographs of the site documenting the changes.
There is also the issue of prevailing wind changes either sharing a local heat source or removing it.
UHI at air ports is one area that needs studying.
A C Osborn, the point re wind is one i was going to raise.Those ideal still days where the UHI effect is greatest may not occur very often, but the heat has to go somewhere when the wind blows.It would be interesting to note prevailing wind direction where “rural” stations are located and then start looking upwind.
“A C Osborn, the point re wind is one i was going to raise.Those ideal still days where the UHI effect is greatest may not occur very often, but the heat has to go somewhere when the wind blows.It would be interesting to note prevailing wind direction where “rural” stations are located and then start looking upwind.”
This is known as UHA
Urban heat Advection
it should not surprise you that I have looked at this as well.
This post doesnt cover all the work.
Its aim is pretty simple
if you think GHCN stations are in areas of high urban cover 10-90%
you are wrong.
“UHI at air ports is one area that needs studying.”
UHI at airports would be…..
MICROSITE..
Call it what you want, but the expansion of Airports since WW2 has far outstripped the expansion of towns & cities, plus you have far more heat from Jets than you ever had from Prop driven aircraft.
What percentage of Stations used in GHCN are Airports?
“Call it what you want, but the expansion of Airports since WW2 has far outstripped the expansion of towns & cities, plus you have far more heat from Jets than you ever had from Prop driven aircraft.
What percentage of Stations used in GHCN are Airports?”
1. getting the terminology is important.
2, An airfield located outside a town could be in a rural Local climate zone
3. if the sensor is too close to the Buildings you could have a micro site issue
Distance matters…. Not just “being at an airport”
Do we have ANY field data on this
Gosh! we do
Just yesterday
‘A field experiment was performed in Oak Ridge, TN, with four instrumented towers placed over grass at increasing distances (4, 30, 50, 124, and 300 m) from a built-up area. Stations were aligned in such a way to simulate the impact of small-scale encroachment on temperature observations. As expected, temperature observations were warmest for the site closest to the built environment with an average temperature difference of 0.31 and 0.24 °C for aspirated and unaspirated sensors respectively. Mean aspirated temperature differences were greater during the evening (0.47 °C) than day (0.16 °C). This was particularly true for evenings following greater daytime solar insolation (20+ MJDay−1) with surface winds from the direction of the built environment where mean differences exceeded 0.80 °C. The impact of the built environment on air temperature diminished with distance with a warm bias only detectable out to tower-B’ located 50 meters away.”
50 meters.
So the REAL question is how many sites are within 50 meters of a built up area
ANY built area not just an airport
840 GHCN sites are within 100 meters of an airport
22 of those airports are closed
about 80 are large multi runway airports
about 560 are medium airports single runway
about 180 are small airports, dirt runway, no jets
1959 Sites are within 500 meters of an airport
3384 sites are within 1 km of an airport
17, 646 GHCN sites are More 5km from the closest airport
next.
Is Heathrow Airport in the GHCN?
If so it is not within 100m of the Airport it is IN the airport.
What is the distance for the affect of a Jet Wash on a Taxi Way to the Airstrip?
“Is Heathrow Airport in the GHCN?
If so it is not within 100m of the Airport it is IN the airport.
What is the distance for the affect of a Jet Wash on a Taxi Way to the Airstrip?”
there are 27000 stations.
you probably thought they are all at airports.
NOT.
Now you want to know about 1 station.
I will look it up even though you didnt say thanks.
CLUE. Nobody has SHOWN that jet wash is an issue.
There is a reason why it would not be.
If you want to look at the ground safty manuals for Aircraft you will find
some information about jet wash and relavant distances.
Heathrow?
yes it is in the data
At Berkeley we ADJUST it down 1C
http://berkeleyearth.lbl.gov/stations/160033
we adjust it to a value LESS THAN the global average.
go figure, we adjust it down.
I’m just a layman and here’s my very short version of Mosher’s post.
While UHI can be as high as 1.7 c for T min over very short periods the longer term for weeks, months, years or a century doesn’t add much UHIE to the record.
And T max is much lower and overall adds very little to the long term trend. Is this the basis of his argument or not.? Just asking.
The argument is this:
1. GT used % of urban cover at 10km to quantitfy UHI, UHI scales with the % of urban cover
2, Others confirm their results
3. They produce a map. see the text.
4. Stewart and OKE also use % urban cover to quantifiably categorize sites
They use a 10% cutoff for built versus unbuilt.
5 using these criteria I show that GHCNv4 sites (~22K out of 27K) are in unbuilt areas
“I’m just a layman and here’s my very short version of Mosher’s post.
While UHI can be as high as 1.7 c for T min over very short periods the longer term for weeks, months, years or a century doesn’t add much UHIE to the record.
And T max is much lower and overall adds very little to the long term trend. Is this the basis of his argument or not.? Just asking.”
Maybe I wasnt clear.
1. many studies will show UHIs with very high numbers, 3C, 5C, even 10C
2. These are typically UHI max, that is the maximum value you see on a given day
its the WORST UHI.
3. to get these max figures they look at days with no wind and clear skies
4. GT and Wang looked at many sites over LONG periods and they look at monthly averages.
not just 1 city on the worst days
5. the average UHI will be less than the max!
Not for places like London it is not, just watch any Weather forecast for the winter, night time temperatures are always much higher than the Local Rural areas. They make a point of saying so.
quote ” 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.”
At 0.06 C per decade the same as the estimate of Hausfather et al almost a decade ago.
Add the results from surfacestations.org (papers and presentations from Fall et al and Watts) showing the warming of poor sites (even after “homogenisation”) was 3 times higher than good quality sites.
No wonder most of us reject the GISS, NOAA, BEST and HadCRUT numbers!
“At 0.06 C per decade the same as the estimate of Hausfather et al almost a decade ago.”
yup. that work came out of a poster Zeke and I did at AGU
“Add the results from surfacestations.org (papers and presentations from Fall et al and Watts) showing the warming of poor sites (even after “homogenisation”) was 3 times higher than good quality sites.”
Err no.
Fall et all showed no bias in Tavg
The 2012 work was withdrawn, still in progress
But I can tell you that with 30 meter data I can automatically classify sites’
Unfortunately, along with a host of would-be “climate scientists,” IPCC has virtually no realistic comprehension of the keen difference between physically meaningful data and mere numbers produced by some misguided algorithm or another applied to highly corrupted measurements. 1.7C/century for the secular global trend of land-temperature is simply usupportable scientifically. And, as if GHCN v.3 wasn’t bad enough, Mosher now speaks of v.4 with tones of high anticipation. But there’s scarcely a hint of what it takes to vet century-long records to ensure that non-climatic factors do not dominate the apparent “trend.”
Anthony’s study showed that CONUS official temperature data is skewed very high, polluted by bad monitoring sites. The data keepers should adjust their products accordingly.
“Anthony’s study showed that CONUS official temperature data is skewed very high, polluted by bad monitoring sites. The data keepers should adjust their products accordingly.”
the 2012 work, was withdrawn and is still being worked on.
The point is the data that is being collected has many potential problems, and then it gets “normalized” for reasons that introduce new potential problems. Now I admit, this is fairly normal science, but then thinking one can predict trends accurate to 1/10th of a degree using this data is not just a stretch, it’s a fail.
Its the quality and accuracy of the data versus how it is used that is the real problem. Global Warming *might* have increased temperatures by 1.5 F over the last 100 years, then again it could be half of that. We cannot tell using the data we have, and no amount of guesswork using (usually invalid) statistical methods is going to fix it.
The best we can do is establish high quality sites and collect enough data to actually measure the trend instead of guessing and finagling the broken data. (broken for what they are attempting to use it for anyway, its fine for weather reporting).
Hurray!!!!! You have accurately stated what I and several others have preached for quite some time. Temperatures recorded to +/- 0.5 degrees simply can not be extrapolated to 1/10 of a degree no matter how many averages you use. It violates every rule of measurement theory. Significant digits do matter no matter how many mathematicians tell you different.
“Hurray!!!!! You have accurately stated what I and several others have preached for quite some time. Temperatures recorded to +/- 0.5 degrees simply can not be extrapolated to 1/10 of a degree no matter how many averages you use. It violates every rule of measurement theory. ”
Nope.
Sorry, my brother is correct. The theory of large numbers is based on doing many measurements on the same thing using the same measurement device. The theory simply does not apply to many measurements of different things using different measurement devices. In such a case the error band must be included with the average and that error band is the error band of the worst case measuring device. The “final average” is simply no more accurate than any individual measurement!
Hey Tim,
The theory of large numbers is based on doing many measurements on the same thing using the same measurement device. The theory simply does not apply to many measurements of different things using different measurement devices. In such a case the error band must be included with the average and that error band is the error band of the worst case measuring device.
That’s an interesting note – there is lot of confusion here around this subject. I cannot believe this cannot be resolved and tested empirically. Tim, maybe put your thoughts – along with real-life examples – in an article? Maybe WUWT admins deem such article as suitable for publishing here. That would be much better contribution as comments quickly vanish in the flood of more or less sensible ones.
Paramenter: A real life example. Design a splicing plate for 1000 steel girders. Do you drill the holes in the plate sized on the “average” length of the girders? Or do you drill the holes in the plate sized for +/- errors in the length of the girders?
hint: you can’t get away from errors in the length of an actual girder by averaging the lengths of a 1000 girder. So what is the “average” length actually telling you.
Sorry Tim.
we are not even averaging different measurements of different things.
we are using sample measurements to PREDICT the expected value at
places where we did not measure.
the digits of precision have nothing to do with the measurement.
Steven: “we are using sample measurements to PREDICT the expected value at
places where we did not measure.”
How do you predict *anything* if you don’t know the error band? If the predictions are based on anomalies that average less than the error band then you don’t know if the prediction means anything or not! A prediction of 0.1degF change is meaningless when the error band is 0.5degF! You truly don’t know if the change is actually positive or negative!
“the digits of precision have nothing to do with the measurement.”
Of course the digits of precision have something to do with the measurement. When your precision is mathematically created without regard to the rules of significant digits then your precision is a phantom.
Here is a simple question. Can you take two temperature readings from the same instrument at totally different times, average them, and then say you have a more accurate reading than either of the individual readings?
Sure, if you are a “climate scientist”.
Of course, saying it does not make it true.
“The best we can do is establish high quality sites and collect enough data to actually measure the trend instead of guessing and finagling the broken data. (broken for what they are attempting to use it for anyway, its fine for weather reporting).”
Make a prediction
1. Establish a series of stations with triple redundant sensors
2. Make sure they are sited well.
3. Measure for 10 years
Compare that to the “bad stations”
What will you see?
predict! will the bad stations match the good one?
It has already been done with the Reference set and it shows a difference, but then of course they do not need “adjusting”.
That’s what the Climate Reference Network is for. And it shows a slightly cooling trend for CONUS in the last 15 years or so, including a corrupted site in Kingston, RI., where a parking lot was built nearby.
Shouldn’t ALL temperatures for the US be “homogenized” to match the trend of CRN? All other methods must have unintended biases if they don’t produce similar results as from pristine raw data.
+1000
Sorry. But I seem to have missed the definition of microsite.
Microsite: Within the veiwshed of the sensor : two standards, 0-500m of the site, or 0-1000m
Meso: 1km – ~10km
Thanks Steven, I now have a clearer understanding of why it is so hard to get useful temperature data and trends.
Oh its easier than you think
the great thing is the surface record matches the most accurate satellite record compiled by AIRS.
It´s easy.
Just cool the climatehistory. And of course deny LIA, MWP, etc. And then you have very useful forged “data” to make useful trends.
Huh
I believe there is was an LIA
That means
1. I believe average temperature has a meaning
2. I believe a few hundred locations can estimate the temperature of the planet
3. I believe the thermometer record shows Warming.
it is warmer now, than it was then
This belief is based on 1 2 and 3.
I know skeptics who dont believe in global temps as a concept, yet believe the global
temp in the LIA was lower than today? huh
I know skeptics who believe in a LIA based on a few hundred locations, But they
Disbelieve a modern record that has thousands up thousands thermomemters
and they complain that its not enough. Weird, a few hundred proxies can establish cold
but thousands of thermometers cannot establish warmth.
I know skeptics who belive it has gotten warmer, but they disbelieve in the best evidence of that.
Weird.
So instead of focusing on the more uncertain science of attribution and senssitivity
Some weird skeptics insist the past was Cooler ( there was an LIA) But all the evidence
we have of it being warmer is fake, fraudulant, or the result of UHI.
Weird
Thank you for wasting ink, again.
I believe it has gotten warmer, I just can´t feel it. So it´s not very much. That is sensitivity.
Question was how hard it is to get useful temperature data and trends.
And yes, LIA was cooler. Just count the bodies. Hockey stick is joke.
Why 30´s-40´s warm is not anymore? Is it necessary wipe out very warm decades to get “useful data and trends”? When you modify data it´s not data anymore, it´s fraud.
We see everyday more and more distorted climate history. Do you really think it´s ok to manipulate past climate and believe nobody notes?
Weird.
“Some weird skeptics insist the past was Cooler ( there was an LIA) But all the evidence
we have of it being warmer is fake, fraudulant, or the result of UHI.”
And some weird english majors misrepresent people all the time, just to score points. The whole point is whether any warming is caused by industrial CO2, not whether “it” has gotten warmer. As I’ve said many times: Some places have warmed, some have cooled, some have stayed relatively static since we’ve started keeping records.
And no, averaging them all together doesn’t give you a global temperature, it just gives you an average number. If the preponderance of locations have warmed, then maybe we’ll have “global warming”, though it probably won’t really be global.
The other point is that climate chiropractors want us to believe they can determine the temperature of the Earth to 0.01 degree accuracy and perfect precision by adjusting data, past and present.
No one claims to know the exact temp of the LIA the way modern climate chiropractors claims to be able to discern the trend on GST over the past 140 years.
The rest of SMs comment is a compendium of red herrings and straw men and just plain nonsense.
“The whole point is whether any warming is caused by industrial CO2, not whether “it” has gotten warmer.
Yes that is the point. Now you have to explain why nearly every skeptic rejects the thermometer record, when you say they dont.
I appreciate the acknowledgement that earlier studies ‘assumed that “urban” was a discrete category rather than a continuum” The BEST study Wickham 2013, which you Mosher co-authord, treated ‘very rural” areas as discrete categories that have. not been affected by landscapes changes resulting in warming similar to urban effects.
Studies estimating a UHI effect use some dubious definition of rural from which to estimate UHI via the difference between urban temperatures and rural temperatures.
The problem with such analyses is that rural areas can be warming at similar rates due to landscapes changes similar to warming in urban areas. UHI effects are strongly correlated with loss of vegetation. Loss of vegetational happens in rural regions. Studies show overgrazing of one field can raise temperatures 7F compared to normally grazed fields. Trees moderate warming via transpiration but rural areas can suffer a loss of trees causing temperatures to rise. Streams and standing water limits temperature rise, but rural areas often lose wetlands in order to cultivate the land for agriculture or grazing.
Any study that simply compares “built” areas to “less built” areas without accounting for the many landscape changes that cause warming will fail to properly dissect landscape warming from greenhouse warming. I have watched rural areas transformed from dirt roads to asphalt that will cause an increased warming trend, yet still be classified as “not built” and rural.
The only meaningful analyses require full analyses of the trend in changes in landscape microenvironment, whether rural or urban. Simplistically comparing arbitrary categories of rural vs urban will fail to separate the significant impact that landscape changes have on the global average temperature
I suspect many of these climate scientists dealing with a physical thing like temperature have never ever spent any time out in rural areas. And, I don’t mean driving through them. I mean spending hours, days, weeks, and years in the field making journal entries so they can have data to analyze in determining temperature profiles of different types of vegetation. That is beneath them. It is something us deplorables do, not them. How many do you think have spent a growing season monitoring what goes on with temperatures on the ground, above the ground, and above the tops of growing corn? Old small time farmers who wandered the fields with a hoe can tell you. They can also tell you what happens to temperature above a prairie hay field that is green and soaking up sunshine and CO2 versus what it does when the prairie grass goes brown and dormant.
We don’t have many “climate” scientists as far as I can tell. We have temperature forecaster mathematicians whose only purpose in life is to work on computers.
“I suspect many of these climate scientists dealing with a physical thing like temperature have never ever spent any time out in rural areas. And, I don’t mean driving through them. I mean spending hours, days, weeks, and years in the field making journal entries so they can have data to analyze in determining temperature profiles of different types of vegetation. That is beneath them. It is something us deplorables do, not them. ”
now this is funny. The most fun I had was processing some field data from Canada.
the climate scientist had placed a bunch of sensors in areas where we previously had no
data. And he put a few stations up at old locations that had stopped reporting.
The encounters with bears were not funny.
espcially the white ones
Were you ever out in the field doing this work? How many months and years did this scientist spend in the field evaluating personally what was being measured?
Part of what I am trying to get across to you is that the warmth of this old earth is more than just a temperature measurement. It also has to do with the humidity of the air at any given time and at any given place. If you’ve never walked over a plowed field, crawled through a barbed wire fence and went into an adjacent corn field to examine it then you won’t recognize the issue. If you’ve never had to delay harvesting a grain field because the humidity won’t let the grain dry properly while on the plant then you won’t recognize the issue. If you don’t spend months and years personally experiencing it first hand, then you don’t understand the whole issue.
You and many scientists deal with temperature readings as if they are the end all and be all. You want to use temperature as a proxy for the heat content of the atmosphere, average readings out, say the globe has a certain temperature, and therefore the heat content of the earth has increased also. That is ignoring the changes of water vapor and worse it assumes that humidity is constant over the whole earth. You want to help convince me that scientists really know what is happening? Tell me what the heat content of the earth is every time you reference the global temperature of the earth.
Mosher You missed my point again,
The real issue is determining how much rural areas have warmed due to landscape changes.
He is an expert at missing points, unless they support his beliefs.
“I appreciate the acknowledgement that earlier studies ‘assumed that “urban” was a discrete category rather than a continuum” The BEST study Wickham 2013, which you Mosher co-authord, treated ‘very rural” areas as discrete categories that have. not been affected by landscapes changes resulting in warming similar to urban effects.”
I don’t think you understand the full extent of the problem . having a criteria that is TOO strict
( classifies rural as urban) will mean you will have a harder time finding UHI.
Our criteria: 0% built pixel within 10km. if that is too STRICT, then ruralish sites get
put in the urban category
I think the point is that even once you have isolated the UHI effect, you have not gained an unbiased record, since even the non built , rural, zones are suffering land use warming.
UHI is on top of LU warming.
problem is we also have data for changes in land use.
answer?
Nothing to see.
This Why Anthony’s work is more important than you guys understand.
There three spitballs to throw at the wall
1) UHI ( which is also land use)
2) Land use ( for example, natural to irrigated agricultural)
3. Microsite: the effects in first 500 meters
1. Looked at UHI, ya dont find much <10% of the century trend
2. Looked at land use, same thing, you dont find much
That leaves your best argument, which is Anthony's argument
Now my suggestion is that ya'll should focus on your best spitball and see if it sticks
to the wall.
“Any study that simply compares “built” areas to “less built” areas without accounting for the many landscape changes that cause warming will fail to properly dissect landscape warming from greenhouse warming. I have watched rural areas transformed from dirt roads to asphalt that will cause an increased warming trend, yet still be classified as “not built” and rural.”
I have watched rural areas transformed from dirt roads to asphalt and seen cooling trends.
personal ancedotes dont really help advance the science.
In general; The temperature at a site is going to be a function of
1. Microsite details ( within 500mto 1km)
2. Meso scale details ( outside of 1km to around 10km)
If a road gets built next to your sensor in the woods(microscale) , then yes, you have a problem houston
if a road gets built in the woods 5km away from your sensor, not an issue
If a small town 1km sq, gets built 5km from your site, not an issue.
The science of how to appropriately adjust the data seems to be in its infancy.
actually not, its been done for hundreds of years.
Yes man has always fiddled the numbers to get them to support whatever story he wants to tell.
At last a comment I can support!
( you do realise that as the time goes by on your answers to questions raised , the more desperate to sound omnipotent you become?)
Skeptic Will happer claim to fame was figuring out how to adjust data!
Oh,
here is feynman on one of the greatest adjustments of all time.
https://www.youtube.com/watch?v=b9F8Wn4vf5Y
Do you wear glasses? What do they do to the raw data before it strikes your eyeball?
Because of the UHI effect can we ever consider measurements even near a city or town as being accurate.
Even if a weather stations is placed out in the country, if the wind is coming from the nearby City, that air will be warmer than the still air at the location of the weather station.
Perhaps a lot of stations throughout the countryside, and then to average out the results might work, but then that comes back to the cost of the people reading the results. Are remotely measured stations accurate enough. ?
Or perhaps cease to use ground based weather stations and just have both satellites and weather balloons instead.
But then no doubt the likes of the IPCC and its army of supporters would do their best to slant the figures their way.
MJE VK5ELL
“Because of the UHI effect can we ever consider measurements even near a city or town as being accurate.
Even if a weather stations is placed out in the country, if the wind is coming from the nearby City, that air will be warmer than the still air at the location of the weather station.”
the issue you discuss is called UHA
urban Heat Advection
The advection of UHI to the rural areas is a function of.
A) the Scale length of the urban area
b) the wind speed and type.
Imagine that, we actually study this.
I propose that UHI is an actual warming of the environment and should not be ignored as an actual influence on global temperatures, nothing to do with CO2, and not a catastrophe whaling to happen.
“and not a catastrophe whaling to happen”
Thar she blows!
Sorry, couldn’t resist.
Mosher
You said, “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.” Also, your urban boundaries may have an error of up to 150 meters, or an equivalent area of 22,500 m^2.
Using Landsat data, one is doing well to have accuracies of around 80% for most thematic classes, using a sensor designed for the application! When I was working for the City of Scottsdale, we consistently had issues of undeveloped areas of creosote classifying as asphalt because of the small leaves and consequent low NIR reflectance, yet large shadow component. Therefore, the average classification accuracy varied significantly depending on the proportion of the different spectral classes. Spatial resolution is a trade off because with very small pixels, the shadows become a distinct spectral class and unless contextual (object oriented) classifiers are used to post process, one doesn’t know what is hiding in the shadows.
“Mosher
You said, “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.” Also, your urban boundaries may have an error of up to 150 meters, or an equivalent area of 22,500 m^2.”
A) Yup.
B) That’s why I cross check with multiple data sources
C) That’s also why I create Visual Maps ( google map) of all the sites
D) Does NOT change the percentage of sites in relatively unbuilt areas
“Using Landsat data, one is doing well to have accuracies of around 80% for most thematic classes, using a sensor designed for the application! When I was working for the City of Scottsdale, we consistently had issues of undeveloped areas of creosote classifying as asphalt because of the small leaves and consequent low NIR reflectance, yet large shadow component. Therefore, the average classification accuracy varied significantly depending on the proportion of the different spectral classes. Spatial resolution is a trade off because with very small pixels, the shadows become a distinct spectral class and unless contextual (object oriented) classifiers are used to post process, one doesn’t know what is hiding in the shadows.”
the 30 meter data I used employed Object oriented classification.
but yes there is both a producer error and user error for different classes
HINT: 22K stations are in areas that have low ( <10% ) urban cover
A) producer error wont change this substantially
B) user error wont
C) the latest VIIRS nightlight data confirms this.
D) Visual review confirms this
F) Population density maps at 38m resolution using a diferent sensor to detect buildings
confirm this
So yes, you will get some pixels that are natural ( bare earth for example) that classify as urban
And you will get some urban pixels that classify as unbuilt.
Go figure! there is error.
but you can check for yourself. Here is what I did.
1. check the 300 versus the 30.
2. check the urban cover against the newest class of Nightlights Sensors
3. Check the urban cover against a population density dataset that uses an
entirely diferent approach to identifying buildings and allocating population to buildings
Over time Facebook will also be releasing some data on buildings and population.
5 meter resolution
In other words as time goes on and we get higher and higher resolution and a clearer picture of where these 27000 stations are.
folks will see this: the vast majority are not in Tokyo, Hong Kong, At some point some skeptic will actually look at the 27K sites and conclude..
hey! the vast majority of these sites are in "ruralish" areas, not concrete jungles!
That's the big point.
HOWEVER you measure it, the sites are not in concrete jungles
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.
What is the scientific basis for CO2 having minimal impact on daytime max temperatures?
In the daytime, there is more infrared radiation for CO2 to interact with. At night there is less infrared radiation for CO2 to interact with. It seems that CO2 should have a more pronounced effect in the daytime temperatures.
Since there is clearly asymmetric warming occurring in the nighttime temps, and since we know that nighttime temps are heavily impacted by non-CO2 effects like UHI, that suggests the Warmists are fixated on noise instead of a true signal.
The same could be said for lower and higher latitudes. There was supposed to be an equatorial hotspot, which again makes sense if CO2 is influencing the already high amounts of infrared radiation near the equator. Instead, the warming is happening in the arctic winter, when there isn’t much sunlight and isn’t much infrared radiation for CO2 to interact with. Maybe there’s a simple physical explanation, but to me these sorts of fundamental conflicts between theory and measurements should be lighting up every scientist’s BS detector.
KTM
“Maybe there’s a simple physical explanation, but to me these sorts of fundamental conflicts between the largest BS scam ever and measurements should be lighting up every scientist’s BS detector.”
Sorry. I fixed your last sentence to reflect reality. And you are right, it should. But money talks, always.
‘What is the scientific basis for CO2 having minimal impact on daytime max temperatures?”
You need to back to square 1. your question is ill posed
Are you disagreeing with my statement of the data, with assymetrical warming at night instead of day?
There are plenty of things that could cause assymetric warming at night, but CO2 isn’t one of them. If the theory doesn’t match the data, the theory needs to change.
Like I said, I’m asking for an explanation, i want to understand. If I’ve missed something simple please educate me. Buy if I haven’t missed anything and there is no rational basis for CO2 to cause this asymmetry, the clear interpretation is that the CO2 theory is fundamentally wrong.
KTM asks: “What is the scientific basis for CO2 having minimal impact on daytime max temperatures?”
Rising CO2 isn’t expected to have a “minimal impact for daytime max temperature”. For the planet as a whole over a long time, rising GHGs slow down the rate of radiative cooling to space (the GHE) creating a radiative imbalance that eventually is negated by warming. The situation is far more complicated for short periods of time at a particular location 2 meters above the surface.
Observations show that the amount the temperature falls on a clear night depends on how much water vapor (a GHG) is in the air. The emission of thermal infrared by GHGs in the atmosphere depends on the local temperature and the number of GHGs present. The fraction of thermal infrared photons emitted downward from any altitude (say 1 km above the surface) that reach the surface depends on how many GHGs lie between (1 km and the surface) that can absorb. More GHGs means BOTH more emission of thermal infrared photons and more absorption of thermal infrared photons, meaning they travel a shorter distance. These two large effects almost cancel.* However, when the average photon arriving at the surface comes from a shorter distance, it was emitted from an altitude where it was likely warmer. So more DLR is delivered from the atmosphere to the surface when the air is more humid.
*See https://en.wikipedia.org/wiki/Schwarzschild%27s_equation_for_radiative_transfer
A decrease in humidity, therefore, decreases the amount of DLR arriving at the surface. During winter and in deserts, the diurnal range (difference between Tmax and Tmin) is larger than in summer and in more humid locations. Since CO2 is a GHG, you might expect it to influence the diurnal range too. However, the changes in relative between deserts and more humid locations can be a factor of 2 and the difference in saturation vapor pressure (7%/K) between winter and summer would be about a factor of 2 if summer were 10 degC warmer. With rising CO2, we are also on our way to a 2-fold change. However, near the surface, there is much more water vapor (about 10,000 ppm) than CO2 (400 ppm). The radiative forcing from 7% more water vapor (1.5 W/m2) is almost half of the forcing from 100% more CO2. So the combined effects of rising CO2 (and the rising absolute humidity that accompanies it, water vapor feedback) are predicted to have a much smaller impact on diurnal range than the change from desert to a humid climate and from winter to summer. Rising anthropogenic GHGs are predicted to very modestly decrease the diurnal range and seasonal change, meaning there will be more warming at night and in the winter.
Local heat capacity has a dramatic influence on diurnal range. In the ocean, diurnal range is typically only about 1 degC. In cities, vertical structures add heat capacity (and surface area to absorb SWR) and lower the diurnal range, the main mechanism of UHI. In most of the troposphere, the diurnal range is low because there is little absorption of SWR by clear air during the day to raise the temperature. Best discusses the changes in diurnal range that their methodology has found, which doesn’t have a simple explanation. The average diurnal range at land stations is about 11 degC, and the changes in diurnal range amount to a few tenths of a degC, while global warming (in T_average) is more than 1 degC.
http://static.berkeleyearth.org/papers/Results-Paper-Berkeley-Earth.pdf
Above, I mostly discussed the effect of GHGs on DLR, but one really needs to consider energy transfer from all sources: SWR, LWR (OLR and DLR), latent heat and simple heat. The surface absorbs most SWR and can get really hot in the absence of any wind (blacktop or a beach in sunshine). Just getting that heat out of the surface and up to a thermometer in a station 2 meters above the ground has its complications and there can be large differences without wind. And the ground has a higher emissivity than air, so it cools faster at night than the atmosphere immediately above, creating a thermal inversion in the early morning hours in many locations. No one should claim rising GHGs aren’t the reason for rising temperature simply because the predicted effect on diurnal temperature range hasn’t been observed. Since climate models often make calculations every 15 minutes of run time, some have trouble reproducing the observed diurnal cycle.
Alarmists like to promote simple explanations for complicated climate phenomena such as diurnal range, so the public thinks climate science is “settled science”. Diurnal range is discussed in Section Section 2.4.1.2 of AR5 WG1 and summarized by:
Confidence is medium in reported decreases in observed global diurnal temperature range (DTR), noted as a key uncertainty in the AR4. Several recent analyses of the raw data on which many pre- vious analyses were based point to the potential for biases that differently affect maximum and minimum average temperatures. However, apparent changes in DTR are much smaller than reported changes in average temperatures and therefore it is virtually certain that maximum and minimum temperatures have increased since 1950. {2.4.1.2}
The ability of climate models to properly reproduce the observed diurnal cycle is discussed in Section 9.5.2.1 and shown in Figure 9.30. The Executive Summary doesn’t even mention the problems with modeling the diurnal cycle, so this is a major issue for many models
https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter09_FINAL.pdf
You spent a lot of time discussing the impact of greenhouse gases on nighttime temperatures. Are there more thermal infrared photons in the atmosphere during the day or during the night?
If there are more infrared photons in the atmosphere during the day, CO2 should have a more pronounced effect during the day than during the night. This should be especially true in summer and in hotter latitudes. The signal should be most apparent in hot and arid regions, although as you pointed out part of the amplification attributed to CO2 is due to hotter air being able to hold more humidity.
So i think there are two massive problems with the CO2 conjecture if we don’t see the predominant signal in arid daytime max temperatures, and an even bigger problem for the amplification conjecture if we don’t see it even moreso in the humid areas.
Where am i going wrong?
KTM: Please consider reading the Wikipedia article on the Schwarzschild equation for radiative transfer. (I wrote it, because there was no good resource that addressed common misconceptions that I struggled with for years. )
https://en.wikipedia.org/wiki/Schwarzschild%27s_equation_for_radiative_transfer
Emission of thermal infrared depends on temperature, absorption (to a first approximation). Therefore, there will alway be some temperature at which absorption and emission are in equilibrium with the local radiation intensity. That equilibrium is given by Planck’s law, aka Planck’s function B(lambda,T) that varies with wavelength and temperature. In other words, blackbody radiation is radiation in thermodynamic equilibrium with its environment of emitting/absorbing molecules. When integrated over all wavelengths, blackbody emission is W = eoT^4. GHGs are don’t behave like blackbodies; their emission is proportional to n*o*B(lambda,T), where n is the density of GHG molecules, o is their absorption cross-section (the sharp lines in the spectrum of a GHG mean o changes rapidly with wavelength) and B(lambda, T) is Planck’s function for emission of blackbody radiation.
The intensity of radiation traveling through any medium is being CHANGED by absorption and emission so as to approach equilibrium – blackbody intensity B(lambda,T). The RATE of change with distance traveled is proportional to the density of the GHGs and their absorption coefficient. At some wavelengths (in the “atmospheric window”), photons emitted by the surface escape directly to space unchanged because the absorption coefficients are effectively zero. For the strongest lines of CO2, 90% of the photons emitted by the surface are absorbed within the first meter – AND REPLACED BY ESSENTIAL THE SAME NUMBER OF PHOTONS EMITTED BY CO2 IN THE ATMOSPHERE. By the time that radiation has reached 1 km about the surface, the temperature is roughly 6.5 K lower and the intensity is about 5% lower because B(lambda,T) is lower. Since temperature usually drops with altitude where most absorption and emission occurs, upward traveling radiation emitted where it is warmer and B(lambda,T) is larger experiences more absorption than emission.
KTM asks: “Are there more thermal infrared photons in the atmosphere during the day or during the night?”
Absolutely.
KTM concludes: “If there are more infrared photons in the atmosphere during the day, CO2 should have a more pronounced EFFECT during the day than during the night. This should be especially true in summer and in hotter latitudes. The SIGNAL should be most apparent in hot and arid regions, although as you pointed out part of the amplification attributed to CO2 is due to hotter air being able to hold more humidity.” [My capitalization]
You not have specified what effect and signal you are talking about. Temperature change at is the result of all processes moving energy to and from a particular location: incoming and outgoing radiation both SWR and LWR, latent heat and sensible heat (conduction). The signal I was discussing above was the diurnal temperature range/change (DTR). Humidity has a large well-understood effect on the DTR, because it changes the altitude from which the average DLR photon reaching the surface is emitted. Clouds have a bigger effect on DLR.
KTM asks: “Where am I going wrong?”
You may be relying on intuition. There are a lot of non-intuitive aspects to radiation transfer in our atmosphere! Calculations using the Schwarzschild equation for radiative transfer can be done online using the MODTRAN program at:
http://climatemodels.uchicago.edu/modtran/
You can gradually improve your intuition by exploring simple situations. MODTRAN uses absorption coefficients measured in the lab and the temperature vs altitude profile shown on the right (which you can select using locality, which is tropical by default). You choose the density of each GHG, the locality and direction to look. The predictions of this program have been validated by many experiments run in the real atmosphere.
I suggest you start with 0 ppm for the density of all GHGs and “looking up” from the surface. The intensity of DLR is 0 when there are no GHGs in the atmosphere to emit thermal infrared photons. Now add 10 ppm of CO2. You should see a lot of energy arriving at 15 um/666 cm-1. At the peak, the amount of energy arriving at the surface from CO2 is about right for a blackbody at 300 K (tropical atmosphere), because the strong absorption of CO2. radiation traveling downward reaches equilibrium with the local surface temperature, producing radiation of blackbody intensity. Now double the CO2 concentration until you reach 300 ppm. The peak emission remains at blackbody intensity, but the CO2 band gets fatter; the weaker lines are contributing more. With 300 ppm, DLR from CO2 alone is 77.4 W/m2 and 600 ppm, 85.8 W/m2.
Now make CO2 0 ppm and water vapor scale 0.01. Water vapor varies with altitude and locality. Now gradually grow water vapor to 1.0 (the normal amount of water vapor at that location at all altitudes). Water vapor in the tropical atmosphere is delivering 358.9 W/m2 of DLR to the surface, substantially more than CO2. Now add 300 ppm CO2 to normal water vapor. DLR grows to only 366.1 W/m2, a measly 7.2 W/m2, because water vapor is already emitting the maximum intensity possible at many wavelengths, blackbody intensity. Now double CO2 to 600 ppm; 368.0 W/m2. Now up the water vapor scale to an impossible 10. At all wavelengths, the atmosphere is shining down with blackbody intensity appropriate for the surface temperature – the most energy that can be emitted by any object according to Planck’s Law.
Now change to “looking up” to “looking down from 70 km” to see how much energy the planet is emitting to space. Looking down, you see the photons emitted by the surface (which emits like a blackbody given the surface temperature) at transparent wavelengths and the photons reaching space emitted by GHGs in the atmosphere. Looking up is simpler to understand.
You can also change temperature by a few degree, while keeping either absolute humidity or relative humidity constant. You can look down from 1 km, to see how the blackbody radiation emitted by the surface and changed by absorption and emission by GHGs in the atmosphere traveling the first 1 km upward through the atmosphere. (Answer: little change because the temperature hasn’t changed much.
Starting with the simplest situations and working towards more complicated ones, you may gradually understand the non-intuitive combined effects of absorption and emission by many GHGs in an atmosphere whose temperature and density varies with altitude. (There is no GHE if temperature doesn’t vary with altitude.)
The strangest thing you will find is that net radiative cooling (OLR-DLR) doesn’t change much as the surface warms a few degC if relative humidity remains constant.
KTM
Something that you are overlooking is that the interactions between IR and CO2 are not broadband. The wavelength of the IR emissions is dependent on the surface temperature and the interaction is at specific absorption wavelengths. Those coincide almost perfectly at night. However, during the day, the peak emission shifts away from the maximum absorption.
Also, even if the IR adsorptions were equal in day and night, the daytime contribution is negligible compared to the direct heating from the sun.
The question most people are debating, is not how accurate the thermometer stations are, or where the thermometers are sited, important though these details are.
The question is whether the world is on a continuous warming trend outside the normal variation of climate change as evidenced by history.
If the answer is yes, the next question is what is causing that inexorable rise. Is it mankind’s activities or is it climate drivers beyond mankind’s influence?
Going back to the very detailed work on this article by Steve Mosher. It all looks very worthy and sound to me. The thing is. If the climate debate hinges on such detail, do we actually have a real world problem or just a complexity of data requiring manipulation and adjustment, to make it “credible” by those who gather the data?
I fully accept the old, “garbage in garbage out” maxim. Clearly data must be verified and validated as accurate before entering it into any system.
What concerns me is who controls the validation? The records show the computer models are all running hot.
Why is that?
Is it because the programs are badly constructed? If so why has it taken so long for nothing to be done to refine the computer programs? If the CO2 forcing ratio is driving incorrect outputs, change it to more accurately harmonise with real observed data?
I must declare an interest at this point. As a hobby beekeeper I quite like the prospect of a degree or two warmer, especially here in the UK.
“The question most people are debating, is not how accurate the thermometer stations are, or where the thermometers are sited, important though these details are.
The question is whether the world is on a continuous warming trend outside the normal variation of climate change as evidenced by history.”
Nope, Not the question
the question is:
How do you explain the observed warming?
Not, “is the observed warming unprecedented”
The reason this is so is because “Normal” only has a “conventional” meaning.
I beg to differ:
As Giavier said, the temperaure the last 150 years have varied between 288 and 288,8 K.
What is keeping it so stable?
Well Steve, I guess therein lies the big difference then, between those who advance the Man Made Global Warming story, or alarmists as they have come to be known, versus the climate realists who want to know if what is happening is anything unusual, i.e. should we be concerned?
As the generally accepted change in temperature globally is around the 1 deg C mark, over the past 170 years, and as there does not appear to be any data suggesting human inputs correlate with that temperature increase, we are left to ask, is the temperature change unusual, over historic time scales?
I would also suggest +/- 3 standard deviations is regarded as being within “normal” variation of a system.
Has the Earth’s temp moved outside that range in the past 170 years?
I don’t think so. Has it quietly increased over that time scale? yes it has, as far as our best efforts to measure it, can tell us.
Great stuff Mosh! I have been thinking this way for a long time, but lacked the skill and tenacity to do the right kind of analysis.
Steven Mosher May 4, 2019 at 1:30 am
…the effects of UHI are only seen below winds speeds of 7m/sec
—————————————-
Would there be a rising convection current produced by the thermal mass (at night) in a city? The rising warm current would have to suck cooler air in from the cooler non urban surroundings like a “sea breeze”. This effect would be quite localised.
Urban temps are a few degrees hotter than non urban during the day also, so ythis breeze would perhaps be present day and night>
“Would there be a rising convection current produced by the thermal mass (at night) in a city? The rising warm current would have to suck cooler air in from the cooler non urban surroundings like a “sea breeze”. This effect would be quite localised.”
I’ve read a few papers that suggest this.
particularly for coastal cities
The issue is pretty complex with lots of exceptions that lead to
“what about my city”
The main point here is the vast majority of sites are in areas with less than 10% urban cover
Next up, I will walk through the process of restricting it MORE…
Two main conclusions from UHI studies:
1. 10% of the warming measured is likely anthropogenic but not related to emissions and due to urbanization. 10% is very significant (<2% insignificant is a usual criterion). It means only 90% of the warming remains to be explained by natural and other anthropogenic factors. And nothing practical can be done about that 10%.
2. Although that 10% is pooled with the rest of the warming and distributed over the entire planet it is actually taking place at small very hot spots. If the planet is close to ~ +1 °C those hotspots have already been at +1, +1.5, +2, +2.5, and probably +3 °C. They are not only very liveable, but most people rather lives there.
Where is the climate crisis?
“1. 10% of the warming measured is likely anthropogenic but not related to emissions and due to urbanization. 10% is very significant (<2% insignificant is a usual criterion). It means only 90% of the warming remains to be explained by natural and other anthropogenic factors. And nothing practical can be done about that 10%."
err NO.
The UPPER BOUND on the bias is 10% of the century trend in LAND
if land has warmed by 1.2C, then the upper bound on bias is .12C
land is 30% of the globe.
math is left to the student for the total bias in the global record.
'°C those hotspots have already been at +1, +1.5, +2, +2.5, and probably +3 °C. They are not only very liveable, but most people rather lives there.
Where is the climate crisis?"
In the future.
and its a challenge not a crisis
“In the future.”
Oh really? Based on what? Models? Sheesh…
We are now living the coldest warm period of Holocene. Where is the challenge? Is the challenge in too warm past, which we must get rid off? You (climate “scientists”) have already tortured climate history from past century. Are you going to torture whole Holocene?
Good luck with that.
You are not talking to ignoramus here. We all know that global warming is not global
– It happens mainly in the northern hemisphere
– It happens mainly on land
– It happens mainly on winter
– It happens mainly on T(min) at nights
Whoaa, exactly like UHI effect. What a coincidence.
So you say without evidence. The fact is that global warming is a 300-year phenomenon and there is no evidence of the challenge you talk about. Quite the contrary, the challenge coming from the LIA has been alleviated by global warming.
“You are not talking to ignoramus here. We all know that global warming is not global
– It happens mainly in the northern hemisphere
– It happens mainly on land
– It happens mainly on winter
– It happens mainly on T(min) at nights”
The Arctic is not land … and it’s where most warming is taking place…..
Antarctica is a unique pole of cold because it is surrounded by ocean and cut-off from warmer mid-latitude air by the ACC and a strong polar vortex.
Not to mention it has an average height of 8000ft.
(might as well throw in the O3 hole – as that is an absence of a GHG in the Strat)
So, no wonder it is happening “mainly in the NH”.
Yes, winter sees most surface inversions form, where the GHE is maximised.
So, of course that is where most GW is taking place. ON LAND. IN WINTER. IN THE NH.
Stating the places where it (noticeably) happens most does not AGW.
~ 93% of the inbalance twixt incoming SW and outgoing LW is being sunk into the oceans.
Are they not global? Not count (because it’s hidden by virtue of mass and SH – the temp is divided by 1000x)?
It does not mean it’s not global because it happens most noticeably in the places on Earth where it has most effect.
The “D” word is powerful in you.
It is not sea surface either during half of the year, that is curiously the half of the year were warming is taking place.
http://ocean.dmi.dk/arctic/plus80n/anoplus80N_summer_winter_engelsk.png
So I guess that Arctic warming is not what you think it is. When there isn’t sunshine there isn’t radiative warming, and there isn’t albedo effect. All that is left is heat transported from lower latitudes and no amount of CO2 in the atmosphere can prevent that heat from escaping to space.
“Whoaa, exactly like UHI effect. What a coincidence.”…..isn’t it
If you refer to my comment.
There’s no UHI in the Arctic …. or do you want to argue there is?
That is very good question. 1°C does not crisis (or even catastrophy) make.
Steven,
Have you or somebody compared RAW values between GHCNM v1-v4?
I did this by calculating on unique lon/lat locations as read from inventory files (locations with two or more stations were skipped). I randomly selected January of 1980 where all sets has a good base of data.
GHCNMv1 had 3008 unique locations
GCHNMv2 had 2693 unique locations (here I excluded multiset locations)
GHCNMv1 and v2 had 1100 common locations with OK flagged data.
– 13% (143) had more than 0.5C change in RAW data
GHCNMv3 had 5428 unique locations
GHCNMv4 had 13253 unique locations
GHCNMv3 AND v4 had 4343 common locations with OK flagged data
– 9% (390) had more than 0.5C change in RAW data. We also have 157 re-flagged (-9999) locations.
All versions had 842 unique locations in common
– 36% (306) had more than 0.5 change in RAW data
I also compared v3 QCA and v4 QCF. There are 3919 common unique locations with OK flagged data
– 26% (1000) had the adjustment re-adjusted more than 0.5C. We also have 418 re-flagged (-9999) locations.
The source data does not look very stable…
“Steven,
Have you or somebody compared RAW values between GHCNM v1-v4?
I did this by calculating on unique lon/lat locations as read from inventory files (locations with two or more stations were skipped). I randomly selected January of 1980 where all sets has a good base of data.”
Hmm that would not be the best way of proceeding, since the versions can all have different source decks
For Example there is a change in the source data for V4.
lets see if I can explain.
take country X. in the past they could have submitted 2 seperate files to NOAA as unadjusted data
1. daily ( which is always raw)
2. Monthly unadjusted. who knows?
In GHCN v4 the change has been to source everything to daily IF you can.
This is the basic approach we use in berkeley.
the primary source is daily raw.
you only use monthly “raw” if there is no extant daily source
Why? Well even though monthly files say “raw” you cant be sure. For example. Some countries
may think 7 missing daily data points is fine, compute the average.
Others may think… 10 missing days is fine unless there are 5 days in a row.. ect ect.
When you go to daily data you can set a consistent rule.
So. always go back to the most primary source you can find. daily.
Use monthly data only if you have to.
I laud your effort to try to track the differences between versions, but in the end it really does not
get you anywhere. And the reason is that the main source files, the primary sources, continue
to improve.
a station in v1, may have had the WWR as the source of its monthly data. not always raw.
in version 2 that same station may have been upgraded to a better source: the home country NWS Monthly
in version 3 the NWS may have added missing records it found in its archive.
in version 4, the NWS could have submitted daily data recently rescued.
So 4 different versions, 4 different sources, and the last source is arguably the best source
since it is constructed form daily data.
In short, you cant understand the differences without doing a full provance study.
Other wise what you will show is already known. When you find better sources between
version changes, you will get version to version differences.
Thanks Steven!
I honestly don’t think the local MET offices have a catalog of data to choose from for each location some 40 years ago. I do accept your explanation but you have to agree it is very surprising that the local MET offices withheld the daily info instead of sharing it. And now in v4 they can provide a better documented location. If it was 5 year old data yes, but almost 40!?
As an example, between v3 and v4 there were some 754 locations excluded. Could very well be because of the reasons you mention ie only monthly;- “but we now do have a close by locations with daily measurements”. Chocking!
Question is how high are the error bars from location/data selection vs UHI? And combined?
Steve Mosher, a very useful addition to the conversation. Thank you for posting here.
It seems clear that there is a warm bias to existing measurements that has not been generally accounted for, albeit a smallish one. So the warming predicted by the models is slightly more incorrect than we already know it is.
Any effort to reduce the exaggeration of the predicted average global temperature change is to be welcomed.
You are welcomed.
I look at it as follows
There was an LIA
its warmer now than in 1850.
The record shows about 1-1.2C of warming.
Some small fraction of it is due to UHI.
there are better arguments for skeptics to concentrate on.
instead, you have crazies who say average temperatures dont exist
you have nutjobs saying its a hoax, fraudulent records,
blah blah, blah,
on climate conservatives are speaking with a thousand different voices and not focusing on the best
arguments.
This oft-repeated trope is grossly inconsistent with what rigorously VETTED century-long records show for the DISCREPANCY of deviations of CONUS mean yearly temperatures:
http://s1188.photobucket.com/user/skygram/media/Publication1.jpg.html?o=0
Ca. 0.7C/century of UHI is scarcely a “small fraction” of the nearly trendless non-urban aggregate average.
OK I give up. What are the best arguments?
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?
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, it seems 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 to separate out!
Admins, admin, my post went to moderation and now appears multiple times. Please delete the botched post above, if possible.
Urban sites are severely compromised-
“At the same time meteorological services have difficulty in taking urban
observations that are not severely compromised. This is because most developed sites
make it impossible to conform to the standard guidelines for site selection and
instrument exposure given in the Guide to Meteorological Instruments and Methods of
Observation (WMO 1996) [hereinafter referred to as the Guide] due to obstruction of
airflow and radiation exchange by buildings and trees, unnatural surface cover and
waste heat and water vapour from human activities”
“Microscale – every surface and object has its own microclimate on it and in its
immediate vicinity. Surface and air temperatures may vary by several degrees in
very short distances, even millimetres, and airflow can be greatly perturbed by even
small objects”
“Mesoscale – a city influences weather and climate at the scale of the whole city,
typically tens of kilometres in extent. A single station is not able to represent this
scale”
https://www.wmo.int/pages/prog/www/IMOP/publications/IOM-81/IOM-81-UrbanMetObs.pdf
“Urban sites are severely compromised-”
actual we can test your hypothesis
and quantify the alarmist adverb
The WMO have spoken out on this subject many time and use the accurate verb carefully.
Someone should study temperature effects of irrigation. I would guess how quickly a surface dries has more to do UHI than thermal mass. My impression is that on a sunny day dry sidewalk equals hot and damp lawn equals cool.
Very good work from Steven Mosher, without the usual hyperbole seen on this site – no wonder it’s hard to swallow for so-called and self-called skeptics!
By the way, Steven, you must be an expert at Whac-a-mole, judging by your patient and constant replies….
Yes, it is totally awesome having an English major here who knows everything, even climate! He has an answer for everything, and anyone who questions him is permanently wrong.
Great stuff! LOL
(Then you have no dispute with his essay in detail then? Mocking the guest writer is a poor way use your time and waste my time having to watch you) SUNMOD
Interesting stuff.
One would wonder what the impact of infrastructure has on overall temp data (IHI?). All those highways and byways across the globe? Here in SWFL, you can frequently watch T-storms grow rapidly when they cross I75. Physics in action 😉
As a follower of BBC weather forecasts for over 50 years, initially because I worked outdoors and later out of habit I think that ln windless days UHI in UK cities is 4-5’C. This is all hear round and max and min temperatures. What ecfect the wind and cloud have I don’t know. It’s long been my belief that a blanket adjustment for UHI is almost as bad as none, and it’s doubtful that there is enough data available to make tbe calculations. All these papers are interesting and increase knowledge but in terms of getting to a solution it’s just p*ss**g into the wind as my dad used to say.
Thank you Steven for a monumental effort and an update on my education on this topic. I’m guilty of not reading the linked papers but read many of your responses to critiques and queries and am content that the work done was conscientious as I expected it would be from you (perhaps I’m wrong that there is an old Steven and a new Steven!).
As a jaded sceptic, I usually don’t let my guard down in the climate scrum, but accepting your findings has more clearly defined the real issues I am not happy about in the larger picture. Besides, despite criticisms of satellite temperatures by most of the mainstream, I know this data constrains departures in temperatures at the present end of the record (possibly the reason the mainstream doesn’t like this eye in the sky), but lets a free-for-all take place on the pre-1979 series.
What was done to flatten down the late 1930s -early 40s temperature highs to make the warming more concentrated on a hockey stick blade for the end of the century, I say without qualification is a felonious act. I was only a child in the 30s but the topic of the scortching continental drought in which records for temperature highs and heatwaves has never been surpassed was the topic of conversations for twenty years or more during gatherings of family and friends.
The swoon in temperatures during the subsequent 35 years of the “ice age cometh” fears I remember clearly. As a newspaper boy, my mother came with me on frigid winter evenings when I collected for the paper in the early 50s and I had degrees in engineering and science (geology) by the middle of this period and was current on the worry. The effect of the ‘flattening’ was to also erase this bitter cold third of a century.
The third thing is the cavalier thrusting down of the early part of the record on no good evidence. It can be no coincidence that all these interventions suited and fitted the alarmist theory. They couldnt have virtually all the warming of the post industrial rev having occurred by 1940! They couldn’t have a third if a century cooling during the era of accelerating CO2 emissions and warming to 1998 just to crawl vack out if the steep cooling period.
Finally, the algotithms that are still changing the entire series of temperatures constantly are totally unjustified. BESTs fix on all apparent shifts is too facile. The cooling itself which was real would have fallen victim to this procedure. I feel all these changes have robbed us from ever making real discoveries in climate for another century.
Microsite and bias is all I took from this massive post. It means the record is biased warm.
Nope. Doesn’t meet the criteria to measure the exacting anomalies we are told to take to the bank. Way too wide error bands which by the way we hardly ever get to see. Scrap the pig. Then you won’t need lipstick.
Why is it that climate temperature research gets this big wide door through “good enough” on uncontrolled and confounding variables while product research such as new medicines, new varieties of grass seed, or new types of cattle feed goes through the eye of a needle before they get to say good enough?
“…product research such as new medicines, new varieties of grass seed, or new types of cattle feed goes through the eye of a needle before they get to say good enough?”
‘One-Third Of New Drugs Had Safety Problems After FDA Approval’
https://www.npr.org/sections/health-shots/2017/05/09/527575055/one-third-of-new-drugs-had-safety-problems-after-fda-approval
That’s one big eye there!
That issue has to do with replication by an independent researcher. The research design requirements for new drugs are still way more stringent than climate research on temperature data.
Fascinating discussion! So how many angels can dance on the head of a pin?
I found this site when I found Anthony’s weather stations review. I had been asked to give my opinion to several policy makers on what really was going on with AGW. I had spent most of my professional life dealing with all sorts of data, data collection methods, accuracy, precision, etc and how they affected models we were attempting to use. (I am not a climatologist, though spent 30 years of my career discussing climate so my continued interest here.)
It appears to me that Mosher’s paper is little more than a defense of climate temperature trends and trying to at least mitigate the argument of UHI affects. All on equipment that when deployed never intended to be used to feed computer models or predict climate a decade, century or millennia in future. Weather stations were created to better predict weather not climate. Yet we don’t have to worry as much about siting of each station but just consider the instruments used over time and how they were maintained.
Some have CAGW starting with the Industrial Revolution, ca 1850. Does anyone imagine the same thermometers have been used since 1850? since 1930? Does anyone imagine that every time when a station’s thermometer was replaced due to damage or updated to a new style that the thermometers were standardized? My guess is the accuracy of thermometers are no more than plus or minus a degree prior to WWII and maybe half a degree since then. Yet it makes international news if a model or data system predicts an increase in the average temperature for the Earth of 0.1 degree C.
Mosher and others tend to pass over that for much of the Earth we have no weather stations, e.g., oceans, Sahara, Antarctica, etc. I spent several synopsis cruises collecting “bucket” temperatures while at the same time running hydrographic arrays. Temperature data from both entered some researcher’s data base.
If one plots urbanization against the Earth’s average temperature I will bet it is a pretty dang good correlation. In 1959 there were 3 million people in Florida; today over 20 million (double that with tourists). Land use went from farming, forest and wetlands to theme parks, hotels, lots of pavement, etc. In Central Florida alone we went from huge amounts of green spaces to huge amounts of buildings, concrete and asphalt. Shanghai in 1985 had a population of around 7 million today it is 34 million. Shanghai went from having a 1930s skyline in 1985 to a Tokyo skyline today.
Edwin, I ‘d actually like to see such a correlation! I bet it would be instructive.⁷
Gary
See Figure 4 at https://wattsupwiththat.com/2016/02/26/analysis-of-the-relationship-between-land-air-temperatures-and-atmospheric-carbon-dioxide-concentrations/
There is a sufficiently high correlation between world population and urbanization that I think it is safe to consider population to be a proxy for urbanization.
“Mosher and others tend to pass over that for much of the Earth we have no weather stations, e.g., oceans, Sahara, Antarctica, etc”
Yep, it’s pretty bad, mostly estimated in Africa, one fifth of the world’s land mass-
WMO- “Because the data with respect to in-situ surface
air temperature across Africa is sparse, a one year regional assessment for Africa could not
be based on any of the three standard global
surface air temperature data sets from NOAA NCDC, NASA-GISS or HadCRUT4. Instead, the
combination of the Global Historical Climatology
Network and the Climate Anomaly Monitoring
System (CAMS GHCN) by NOAA’s Earth System
Research Laboratory was used to estimate
surface air temperature patterns”
SM, I also find this post very interesting, and am just an interested reader trying not to get lost in the math. You encouraged a previous poster to ask questions, so here is mine. You gave the following reply to Latitude:
“if fall is 0
if winter is 0
if spring is 0
if summer is 3C
then you cant argue that annual averages 3C”
In such a scenario, how would the adjustment be done? Do you average the 3C over 4 seasons, warming 3 and cooling 1 resulting in something that doesn’t exist and has never existed, and then use it to adjust the temperature record for that station, or are the summer temperatures adjusted down the whole 3C first before the temperature record is used for whatever purpose? Or is something else done?
Mosh’:
Well all the “nothings to see” add up as do all the tweaks “corrections” and blatent fiddling. That is the name of the game. A tenth of a degree here, a tenth of a degree there; 10% here, 10% there. No chance is lost put a little thumb on the scales. Nothing too controversial individually and easily dismissed as “nothing to see” but there is whole industry of data vendors with a thumb on the scales.
Probably the only adjustment which does not warm the present or cool the past is HadSST 0.5 deg C post WWII drop. However, what they also do is remove most of the variability from most of the record: the early 2/3 of the data period. It is yet another attempt to make the data show acceleration at about the right time to fit the agenda.
Climategate clearly dismissed the quaint idea we pretty much all had about objective apolitical scientists working for the simple pleasure of discovery and science.
Steve Mosher says: “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.
[…..]
at the micro scale?
unstudied except for Anthony’s work….”
This is where the statisticians come into it.
Thousands of different instruments
Thousands of different conditions.
Yet minuscule error bars?
Errors propagate?
Who knew?
Who knew? Engineers designing steel girders and splice plates to connect them. Ignore the error propagation and you can wind up with a splice plate too short to span the distance between two girders on the short end of the error band. Doesn’t matter what the “average” length of the girder is. You have to design to the minimum and maximum values, i.e. the error band!
“Yet minuscule error bars?”
the erorr bars are actually tested out of sample.
take 43000 stations.
hold out 38000
build your average from 5000
construct the error bars.
Now test the 38000
Do they fall within the error bars?
yup
so not theory. Errors are propagated as a part of the process. then tested.
numbers
Steven: “the erorr bars are actually tested out of sample.”
This is a confusing statement.
You can’t eliminate error bars by comparing one average with another.
You can test to see if the error bars are correct, which is what your process seems to be doing. The problem is that the error span is never mentioned in any study. If the delta of the average is 1.5degC and the error span is +/- 0.5degC it makes a big difference in analyzing the delta!
Tim: You can divide the data use are using into two parts at random. Do the analysis on half of your data, including error-propagation. Then use the other half of the data to prove your error propagation is correct.
For example, you could divide 1000 pairs of independent and dependent variables (x,y pairs) randomly into two sets of 500. Perform a linear regression with 500 pairs, which affords slope and intercept along with confidence intervals for each. Now do a linear regression with the second 500 pairs. Are the new slope and intercept found inside the confidence interval calculated for the first regression?
Now randomly divide your data into two different sets of 500 pairs 100 times. Are about 95% of the slopes and intercepts calculated from the second half of your data found inside the 95% confidence intervals calculated with the first half of your data?
Ordinary least-squares regression (and derived confidence intervals) are based on certain assumptions about the noise in your data: random, same size for both small and large x, no error in independent variable. If your data doesn’t have these properties, incorrect confidence intervals might be exposed this way.
Wikipedia has articles on “resampling” methods for using part of your data to establish empirical confidence intervals for your analysis. I believe what Steve is describing is called cross-validation statistics in Wikipedia (aka rotation estimate or out-of-sample testing).
Frank: I understand everything you said. That’s why I said “You can test to see if the error bars are correct, which is what your process seems to be doing.”
The big problem comes in when you assume the (x,y) data points are perfectly accurate themselves. A confidence interval based on assumed accurate data points is *NOT* truly an error bar. It is basically a standard deviation of *assumed* accurate data points. Doing a linear regression on temperatures such as T +/-0.5 by only using T simply abandons the actual errors associated with the data.
The “y” values you are using have a measurement error associated with them. Prior to at least 1950 these errors were +/- 0.5 degrees. If you look at the possible temperature at any one station temp, you can’t determine between +0.5 and -0.5 therefore the probability is 1 for any value between +0.5 and -0.5.
Trying to use anomalies doesn’t change this although the mathematicians would like you to believe it does. Since you are subtracting a constant value from the temp reading, the original measurement error still applies. In fact, the percent error simply increases. For example, I subtract 15 degrees from 20 +/-0.5 degrees, I get 5 +/- 0.5 degrees. Why, because the max value is 20.5 – 15 = 5.5 and the min is 19.5 – 15 = 4.5 so the real value should be 5 +/-0.5 degrees.
The other problem is averaging numbers with measurement errors. Another example that illustrates this. Average 50 +/- 0.5 and 53 +/-0.5. The average can lie anywhere between 52 and 51. So you have 51.5 +/-0.5. You simply can’t say that averaging numbers with measurement errors reduces the error. Measurement errors do propagate throughout.
Mathematicians will tell you that the error is reduced by multiple measurements. What they fail to tell you is that this only applies when you measure the same thing multiple times with the same device. Then you can assume the measurement errors are random (Gaussian) and will tend toward a central value. Temperatures from different stations are never the same thing nor measured with the same device.
WMO want to install 5000 temp stations in Africa alone to get adequate Coverage.
Richard: The number of stations needed depends on how accurate you want to be. The US Climate Research Network (USCRN) has only about 135 stations in the continental US, but is intended to produce a superior record to many times more ordinary stations that use inferior equipment and poor siting. The paper defined the accuracy goals for the network and then determined that that goal could be met with 135 reasonably evenly space stations. That network has been running for more than a decade. There is little benefit to adding more stations because temperature anomalies are highly correlated over several hundred kilometers.
https://journals.ametsoc.org/doi/pdf/10.1175/1520-0442(2004)017%3C2961%3AAMTDSD%3E2.0.CO%3B2
Frank,
When the whole of Africa, one fifth of the world’s land mass, is estimated I am guessing you want a lot of stations.
The MET illustrate with their microclimate fact sheet why you would need them.
https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/n/9/fact_sheet_no._14.pdf
Even state of the art temp measuring equipment in a controlled environment have troubles-Pico technology ” Consider what you are trying to measure the temperature of. An example that seems simple at first is measuring room temperature to 1°C accuracy. The problem here is that room temperature is not one temperature but many.
Figure 1 shows sensors at three different heights record the temperatures in one of Pico Technology’s storerooms. The sensor readings differ by at least 1°C so clearly, no matter how accurate the individual sensors, we will never be able to measure room temperature to 1°C accuracy”
So things very close to a thermometer affect it much more than things a long way away.
Certainly seems plausible, but I don’t feel much wiser.
I dunno, I said it so some here have to disagree!
Wrong!
When the heavyweights start throwing the furniture around, it is best to wait for them to get tired before attempting reconciliation.
Steve
Thank you for the interesting post. You seem to have patience of Job on this one considering the number of replies to posts you have made. One thing that does puzzle me about this whole problem of contaminated temperature data is that if the contamination or bias from a given site is clear, then why on earth is it important to adjust or correct the data rather than simply discarding the site from the ensemble.
My main point is that surely the grossly obviously biased sites should be discarded from the record. Which leads to the next issue. I recall a post that I made on Real Climate a long time ago, and Gavin’s view was that the global trends could be adequately described by only 100 “pristine” sites. I am no statistician but that number seems reasonable if maybe a little light. Im sure that there is a statistical rule governing the minimum number. But the question still remains, why is it necessary to keep the biased sites at all? Adjustment after adjustment is still lipstick on a pig.
Regards
“Steve
“Thank you for the interesting post. You seem to have patience of Job on this one considering the number of replies to posts you have made. One thing that does puzzle me about this whole problem of contaminated temperature data is that if the contamination or bias from a given site is clear, then why on earth is it important to adjust or correct the data rather than simply discarding the site from the ensemble.”
I actually do BOTH.
A) reducing stations increases your spatial uncertainty
B) deciding that some stations are “bad” can lead to false catagorzation
C) we have evidence from CRN studies that the adjustments work
“My main point is that surely the grossly obviously biased sites should be discarded from the record. Which leads to the next issue. I recall a post that I made on Real Climate a long time ago, and Gavin’s view was that the global trends could be adequately described by only 100 “pristine” sites. I am no statistician but that number seems reasonable if maybe a little light. Im sure that there is a statistical rule governing the minimum number. But the question still remains, why is it necessary to keep the biased sites at all? Adjustment after adjustment is still lipstick on a pig.
Regards”
see above. yes with 100 or so perfect stations you can.
what if I told you the answer from these hundred matched the answer from the bad?
Hint: bias cancels
If the biases cancel then that is more likely chance or possibly the use of a large enuf sample. To be pure and robust surely it is better to discard those with known or suspected bias. It would certainly go a long way to removing a lot of suspicion regarding the validity of multiple adjustments.
“If the biases cancel then that is more likely chance or possibly the use of a large enuf sample. To be pure and robust surely it is better to discard those with known or suspected bias. It would certainly go a long way to removing a lot of suspicion regarding the validity of multiple adjustments.
Except you cant be SURE of suspected bias. So you test that.– to be more robust.
I get the idea of removing suspecion. however, when I have done these studies with
only the “best stations” folks will even question that.
is the data really raw! we want the paper copies!
etc ect ect.
no matter how you cut the data its warming.
“no matter how you cut the data its warming.- Steven Mosher”
That’s the equivalent of saying, no matter how you cut the data, the sea level trend is rising! It has been rising since records began but it is so insignificant that it’s not even detectable above noise and error, without great mental contortions and/or massive ideologically driven leaps of faith!
And there are countless examples – worldwide – of flat or cooling trends in the raw data that only show warming after adjustment. If you don’t think that is a problem, you are not really looking at the data!
Strange that- hmmm what did Phil Jones say about removing the blip- it sure was cut!
Why are all my comments going into moderation?
Admin, Admins, Anthony?
You might warn me first!
Let me know and I won’t waste your time or mine!
That would seem like an sensible administrative approach!
It is so cumbersome to comment here, since the “upgrade”. Now, you can’t edit typos, you can’t post images and the test page puts my comments into moderation – without there being an offending word – when that would seem the appropriate place to find out!!
I keep ending up with multiple posts or duplicated paragraphs that I didn’t intend to create.
No one can sensibly enter a conversation here, if every comment is disappeared without explanation, only to appear after an unspecified period and at a whim!!
WMO- “Homogenization of climate data series and spatial interpolation of climate data play a growing
role in the meteorology and climatology. The data series are usually affected by
inhomogeneities due to changes in the measurement conditions (relocations, instrumentation)
therefore a direct analysis of the raw data series can lead to wrong conclusions about climate
change”
What climate change- Prairie grass grew across the US until ploughed up , a drought resistant plant (illustrating the climate) no change there then though I believe precipitation has increased. Same old , same old drought resistant plants growing across Africa. Climate change would be the Sahara desert turning tropical again. There again the planet and deserts are greening.
Good to take a look back at this –
https://wattsupwiththat.com/2016/12/21/homogenization-of-temperature-data-by-the-bureau-of-meteorology/
Mr Mosher,
Thanks for an interesting article. Think you also for pointing out even more interesting piece by Goddard & Tett – I missed that one! As far as I understand you’re saying that ‘locally’ (country or region level) warming due to urbanisation can be quite substantial but if we look at matters ‘globally’ urbanisation signal dissolves and though still clearly detectable cannot constitute the main component of the warming trend for several reasons you have listed. That sounds reasonable to me. After all, warming trend, if any, most likely to be the result of several different factors. We add them together (adding few favourable assumptions if necessary), plus some natural variability, ignore some measurement uncertainty and – voilà! – we’ve got ‘unprecedented warming’.
Still, good to know that urbanisation plays significant role in such warming. And I wonder there are several other human-induced factors, as irrigation and deforestation that may play their roles too.
That canard is indeed what Mosher wants everyone to believe, hinting outlandishly that “biases cancel.” In fact, only unbiased random errors can cancel in the aggregate (spatial) average. The signal produced by urban growth–ubiquitous since the beginning of industrialization–is a systematic upward trend that varies enormously from station to station. The average of those station-specific trends certainly cannot “cancel,” but remains as an unspecified systematic error in the global average
The lame notion that UHI-corrupted records should not be discarded because of risk of “false catagorzation” merely provides cover for much data manipulation. The sad fact of the matter is that in many regions of the globe only data from urban stations are available. Thus there’s no way of meaningfully “testing,” let alone correcting, for the degree of bias. To argue that discarding such stations “increases your spatial uncertainty” is to acknowledge sheer disregard of systematic UHI bias. That’s how politically convenient data products are manufactured for sale.
That canard is indeed what Mosher wants everyone to believe, hinting outlandishly that “biases cancel.”
That’s another worrying tactics from alarmists – there may be several problems with uncertainty, historical records, coverage, urbanisation effects, gaps in records but luckily ‘globally’ that all will cancel out. Seems to be that this ‘explanation’ is treated as kind of universal acid that can digests any critical argument.
The sad fact of the matter is that in many regions of the globe only data from urban stations are available.
Is it? Steve argues that 22K stations out of 27K are classified as ‘non-urban’. Well, I’m sure definition of what is rural vs urban can be discussed. I’m sure there will be more debates around effects of UHI on ‘global’ averages.
A misleading statistic . What he doesn’t reveal is that only a small percentage of “non-urban” records outside the USA and Australia are long enough to provide any indication of SECULAR climate change. Century-long, nearly intact, non-urban records are rarely found in much of the globe.
“That canard is indeed what Mosher wants everyone to believe, hinting outlandishly that “biases cancel.”
Well, the IPCC says there is a residual bias of 10%
My aim is to see if its higher or lower
by
LOOKING
AT
THE
RAW
DATA
if you object to looking at the data, let everyone know
if you have a BETTER definition of “rural” go ahead, I will test that
But it has to be objective and measureable and defended by actual field tests
When the raw data is severely and systematically corrupted by numerous factors, simply looking at it without any scientifically adept means of vetting is, at best, an exercise in self-deception. Adherence to such shibboleths is not the mark of an objective scientist, but of a tendentious polemicist.
“complex phenomena of UHI”
It is not complex at all. If the temperature sensor is too close to a significant heat sink the temperature stays higher for several hours after T-max.
You are just trying to justify higher T-min’s measured in places where the sensor is poorly sited. And, you seem to have forgotten that a significant weather phenomenon exists everywhere virtually all the time, called, “Wind.”
An English major doing “Climate Science,” or something. Hard to tell exactly what you are doing, but, UHI is not complex at all.
“…It is not complex at all…”
It is to some!
‘It is not complex at all. If the temperature sensor is too close to a significant heat sink the temperature stays higher for several hours after T-max.”
heat sinks are one aspect of UHI, radiative canyons are other, reduced evapotranspiration in another.
Anthrogenic heat flux is another aspect. If it were merely all “heat sinks”, then the problem would
be easy. Surface roughness matters, aerosols contribute, albedo matters.
Here all the causes
In both cases you get a reading that loosely relates to the actual temperature.
Also a ghost town has a UHI
The UHI for Hohe Warte in the the center of Vienna Austria has not changed since the start of the observations in 1780.
Hey Tim,
A real life example. Design a splicing plate for 1000 steel girders. Do you drill the holes in the plate sized on the “average” length of the girders? Or do you drill the holes in the plate sized for +/- errors in the length of the girders?
Analogies, though useful to illustrate a point, cannot prove anything. In order to make the case clear we need to operate on atmospheric temperature records.
If you can’t apply the lesson of the girder analogy to the temperature record then you aren’t understanding what averages do.
Consider: The US central plains and southeast are recognized global warming holes where the temperature simply isn’t increasing as the “CO2 global warming theory” predicts. Yet NOAA shows these areas as having very high concentrations of CO2 in the atmosphere. Again, not what the “CO2 global warming theory” would predict.
Yet by focusing solely on the “global average” the climate models avoid having to address these exceptions to the theory. If the models don’t accurately predict reality then just how good are the base physics equations used in the models? Better yet, how can it be “global warming” if the entire globe isn’t warming the way the models predict?
Even worse, it misleads those formulating policies being formed to address “global warming”. Instead of focusing on what is happening in the central plains and the southeast as possible solutions to other areas we get all kind of outrageous policy suggestions such as 100% solar and wind electricity generation.
Think of this analogy – The Dow Jones stock price average. The DJIA is made of up of lots of individual readings of stock prices, just like temperature readings. So exactly what does the DJI average tell you? Can it tell you that the financial sector is growing while the energy sector is dropping? Don’t you need to know about the individual sectors in order to develop an investment strategy? Think of these sectors as geographical regions with temperature profiles. Can a global temperature average tell you that northwest Africa is warming while the US central plains is cooling? Don’t you need to know what is happening regionally in order to develop an action strategy? A solution that works in NW Africa may actually make things worse in the central US!
I wouldn’t have known about the central plains warming hole if I hadn’t been keeping 5 min data on a 24/7/365 basis since 2002. It was only when I went looking for confirmation of what I was seeing that I found out others had seen the same thing. This receives almost no attention in the media, even the academic media – everyone focuses on the “global temperature average” to the exclusion of all detail analysis by region.
I have a BSEE. I worked in the telephone utility business for 30 years. I learned quickly that average call volumes are meaningless when it comes to peak/valley call volumes. If you design solely based on the average call volume your equipment will be overloaded regularly during peak call volumes. Once again, averages tell you nothing about reality.
Hey Tim,
If you can’t apply the lesson of the girder analogy to the temperature record then you aren’t understanding what averages do.
I love analogies too. They can nicely round up an argumentation providing easy to understand illustrations. But analogies cannot supersede direct evidence.
Once again, averages tell you nothing about reality.
In some limited sense they can. Think about holidaymakers looking for a warm and sunny spot to recover after dark and cold winter. They look into averages (average monthly temperature and average number of sunshine hours) and can quickly figure out where to go.
Paramenter: “They look into averages (average monthly temperature and average number of sunshine hours) and can quickly figure out where to go.”
The average temperature in places like Phoenix appears quite reasonable. But it gets very hot during the day and very cold at night (i.e. a desert environment). It all averages out to a nice medium. But the average still doesn’t tell you much about reality.
If you really want to check out the environment at a location then use cooling-days and heating-days. go here: http://www.degreedays.net This measurement is not an average, it’s a measurement of how long each day the temperature is above/below a specified base temperature. You can graph a months worth of data and see what the environment is actually doing. It’s also instructive to download the past three years worth of data to look at. There are lots of stations around the world, including in the US central plains that show the number of cooling-days per month is going down. This matches what we are seeing in states like IA, KS, and NE where we are seeing fewer days every summer with temperatures at or above 100degF.
“Consider: The US central plains and southeast are recognized global warming holes where the temperature simply isn’t increasing as the “CO2 global warming theory” predicts. Yet NOAA shows these areas as having very high concentrations of CO2 in the atmosphere. Again, not what the “CO2 global warming theory” would predict.”
Theory does not predict uniform warming sorry.
Even Models of the theory ( theory and models are different things)
dont have REGIONAL skill.
You cant criticize a science you dont understand
Steven: “Theory does not predict uniform warming sorry.
Even Models of the theory ( theory and models are different things)
dont have REGIONAL skill.
You cant criticize a science you dont understand”
I’m sorry, this reply is basically nonsense. You obviously didn’t take any time out to think about what you are saying!
1. If you do not have uniform warming then why is it called “global” warming? it should be called “regional” warming.
2. The theory predicts warming where the CO2 concentrations are the highest. The fact that this isn’t happening in some areas calls the theory into question. It isn’t an issue of “uniform” warming at all.
3. If the models cannot reproduce regional effects then what good are they? They do nothing but hide reality. The reality is that we should be looking at regions that are not warming for possible solutions to use in regions that are warming. If the actual reality is hidden by the models then how are those solutions ever gong to be identified?
I understand the use of models quite well. I worked in long range planning for a major telephone company for a decade. Models of the future were what we used to develop investment strategies. If the models we used didn’t represent reality then we wasted the money of the ratepayers and the utility regulators made us pay.
Claiming that I don’t understand the science is an argumentative fallacy known as Poisoning the Well. In other words, attack the messenger if you can’t attack the message itself in the hope you can discredit the message.
“If everyone leaves a city, it will still have UHI.”
_______________________________________________
when the working city dwellers leave the city between Christmas and New Year to celebrate the holidays with the families outside the city
The city council has a hard time keeping the streets clear of snow and keeping the empty drainage pipes from bursting with icing.
The city dwellers do not know that: they are not there.
Germany was forced to acknowledge that it has to delay its phase-out of coal:
“Over the last decade, journalists have held up Germany’s renewables energy transition, the Energiewende, as an environmental model for the world.
“Many poor countries, once intent on building coal-fired power plants to bring electricity to their people, are discussing whether they might leapfrog the fossil age and build clean grids from the outset,” thanks to the Energiewende, wrote a New York Times reporter in 2014.
With Germany as inspiration, the United Nations and World Bank poured billions into renewables like wind, solar, and hydro in developing nations like Kenya.
But then, last year, Germany was forced to acknowledge that it had to delay its phase-out of coal, and would not meet its 2020 greenhouse gas reduction.”
https://www.forbes.com/sites/michaelshellenberger/2019/05/06/the-reason-renewables-cant-power-modern-civilization-is-because-they-were-never-meant-to/
Sailor has great papers.
those who dont like reading can watch