Mosher: “microsite bias matters more than UHI, especially in the first kilometer”

Urban Stations in GHCN V4.
The urban heat island effect further raises summer temperatures in cities. CREDIT NASA
Guest post by Steven Mosher

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

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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

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.”

This is actually not the case. This is a tiny fraction of the types of studies done.

There are global maps of UHI

Maps of individual states

Studies of over 400 large cites

Studies of the relationship between the shape and size of 5000 cities and UHI

A study of hamburg

Urban cool and hot zones

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

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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).

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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.

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Editor
May 3, 2019 2:03 pm

Well thought out, well laid out, well developed, well explained.

Fascinating. Thanks.

w.

Steven Mosher
Reply to  Willis Eschenbach
May 3, 2019 6:32 pm

some of the quotes didnt get formated correctly

(Have passed on your problem to the Administrators) SUNMOD

Steven Mosher
Reply to  Steven Mosher
May 4, 2019 1:09 am

perfect thanks!

Reply to  Willis Eschenbach
May 3, 2019 6:53 pm

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.

Steven Mosher
Reply to  Bryan - oz4caster
May 3, 2019 9:34 pm

“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.

Javier
Reply to  Steven Mosher
May 4, 2019 2:04 am

Thats why we comapre with the AIRS satellite.

AIRS is the instrument (Atmospheric InfraRed Sounder), Aqua is the satellite. You need to read harder.

Steven Mosher
Reply to  Javier
May 4, 2019 6:27 am
Javier
Reply to  Javier
May 4, 2019 11:37 am

quibble much

Said the king of quibbling

Steven Mosher
Reply to  Javier
May 5, 2019 2:38 am

Now youre quibbling about who is the King,

ferd berple
Reply to  Willis Eschenbach
May 3, 2019 10:14 pm

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?

Steven Mosher
Reply to  ferd berple
May 4, 2019 1:16 am

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.”

Steven Mosher
Reply to  Willis Eschenbach
May 3, 2019 10:56 pm

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

Farmer Ch E retired
Reply to  Steven Mosher
May 4, 2019 5:36 am

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.)

Steven Mosher
Reply to  Farmer Ch E retired
May 4, 2019 6:36 am

“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.

Farmer Ch E retired
Reply to  Steven Mosher
May 4, 2019 7:24 am

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.

Steven Mosher
Reply to  Steven Mosher
May 5, 2019 2:37 am

“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…

Farmer Ch E retired
Reply to  Steven Mosher
May 5, 2019 3:59 am

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.

Steven Mosher
Reply to  Steven Mosher
May 8, 2019 3:12 am

Yes Farmer

I will try to find the Grommond study that looked at some of this.

Nylo
Reply to  Steven Mosher
May 5, 2019 9:06 pm

Steven,

a tiny numer of sites are in areas with 100% coverage
a large number have 0-10% coverage.

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.

Ged
Reply to  Nylo
May 6, 2019 6:30 am

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.

Steven Mosher
Reply to  Ged
May 8, 2019 3:23 am

“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.

Steven Mosher
Reply to  Nylo
May 8, 2019 3:18 am

“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

Tim Gorman
Reply to  Steven Mosher
May 8, 2019 5:08 am

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.

Reply to  Steven Mosher
May 9, 2019 12:22 am

What Tim said!!!

Ged
Reply to  Steven Mosher
May 6, 2019 6:26 am

It is a lot of good, hard work you did on this, so it always takes a minute to digest.

Spalding Craft
Reply to  Willis Eschenbach
May 4, 2019 5:19 am

Yes. Thanks for lucid article. Much of it was over my head but I think I understand the issue better.

Steven Mosher
Reply to  Spalding Craft
May 4, 2019 6:38 am

if you have any questions just ask.

Steven Mosher
Reply to  Willis Eschenbach
May 4, 2019 9:14 pm
Louis Hooffstetter
Reply to  Willis Eschenbach
May 5, 2019 6:46 am

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!

Jeff Alberts
Reply to  Louis Hooffstetter
May 5, 2019 2:25 pm

“snarky/cryptic drive-by comments.”

Apparently you didn’t read farther down.

Steven Mosher
Reply to  Louis Hooffstetter
May 8, 2019 3:26 am

Thanks Louis .

My sense is that there are still some of the old readers around.

MS25
Reply to  Willis Eschenbach
May 7, 2019 1:53 pm

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.

RobR
Reply to  Willis Eschenbach
May 17, 2019 11:01 am

Just now read this and agree with Willis. Top-notch scholarship worthy of formal publication.

Tonyb
May 3, 2019 2:22 pm

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

Steven Mosher
Reply to  Tonyb
May 3, 2019 9:09 pm

there is a world outside of the collection of stations known as CET

Greg
Reply to  Steven Mosher
May 3, 2019 11:22 pm

Maybe that is why the last two sentences read:

The met office generally use data from the composite 1910 temperature records. These do not allow for uhi unlike CET.

tonyb
Editor
Reply to  Steven Mosher
May 4, 2019 12:21 am

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

Steven Mosher
Reply to  tonyb
May 4, 2019 1:23 am

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

tonyb
Editor
Reply to  Steven Mosher
May 4, 2019 7:55 am

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

Reply to  Steven Mosher
May 4, 2019 9:21 am

When Jones redid the UHI calculation after sufficient time had lapsed since Jones 1990, I believe he came up with a net .4C, IIRC.

tonyb
Editor
Reply to  Steven Mosher
May 4, 2019 11:23 am

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

tonyb
Editor
Reply to  Steven Mosher
May 4, 2019 11:29 am

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

Steven Mosher
Reply to  Steven Mosher
May 5, 2019 2:40 am

“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.

Jeff Alberts
Reply to  Steven Mosher
May 5, 2019 2:27 pm

“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.

Tim Gorman
Reply to  Steven Mosher
May 5, 2019 4:02 pm

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.

Steven Mosher
Reply to  Steven Mosher
May 8, 2019 3:10 am

“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.

Ged
May 3, 2019 2:23 pm

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.

Stephen Rasey
Reply to  Ged
May 3, 2019 2:45 pm

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.

Steven Mosher
Reply to  Stephen Rasey
May 3, 2019 6:43 pm

“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.

Stephen Rasey
Reply to  Steven Mosher
May 7, 2019 7:59 am

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.

Steven Mosher
Reply to  Stephen Rasey
May 3, 2019 6:50 pm

“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

Steven Mosher
Reply to  Stephen Rasey
May 4, 2019 1:42 am

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

Stephen Rasey
Reply to  Steven Mosher
May 7, 2019 8:07 am

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.

Mark Luhman
Reply to  Stephen Rasey
May 4, 2019 9:45 am

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.

Steven Mosher
Reply to  Mark Luhman
May 5, 2019 2:44 am

“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

Gator
Reply to  Ged
May 3, 2019 3:23 pm

It is crap. Once again the gang that can’t shoot straight has developed a data hiding machine.

Loydo
Reply to  Gator
May 3, 2019 5:48 pm

Agree, 1 km is way too small, the UHI affect is incredible and its spreading fast.
https://tinyurl.com/hfs8lhh

Steven Mosher
Reply to  Ged
May 3, 2019 7:11 pm

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

Nylo
Reply to  Steven Mosher
May 5, 2019 9:15 pm

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).

Editor
May 3, 2019 2:39 pm

Mr. Mosher,

Very good post!

Now I have to spend time to read it, because you made it worth reading it.

Thank You.

Steven Mosher
Reply to  Sunsettommy
May 3, 2019 7:37 pm

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.

DaveW
Reply to  Steven Mosher
May 4, 2019 7:22 pm

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.

Steven Mosher
Reply to  DaveW
May 5, 2019 2:52 am

“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

Jeff Alberts
Reply to  Steven Mosher
May 5, 2019 2:32 pm

“and to see what happens after adjustments and to run the adjustment code on different
groups.”

As soon as you group, you lose it.

Robber
May 3, 2019 2:56 pm

Are there also differences in CO2 concentrations at urban versus rural measurement sites that are impacting night time temperatures?

Robert Austin
Reply to  Robber
May 3, 2019 6:24 pm

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.

May 3, 2019 2:57 pm

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)”

Evan Jones
Editor
Reply to  Leif Svalgaard
May 3, 2019 5:56 pm

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.

Steven Mosher
Reply to  Evan Jones
May 3, 2019 6:53 pm

I am guessing none of the readers clicked on or read the urban cool island links

davidmhoffer
Reply to  Steven Mosher
May 4, 2019 2:49 am

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.

Steven Mosher
Reply to  davidmhoffer
May 4, 2019 3:18 am

“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

Steven Mosher
Reply to  davidmhoffer
May 4, 2019 7:03 am

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

davidmhoffer
Reply to  davidmhoffer
May 4, 2019 11:45 am

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.

Steven Mosher
Reply to  davidmhoffer
May 5, 2019 2:56 am

“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.

Michael Jankowski
Reply to  davidmhoffer
May 5, 2019 11:41 am

“…Not cooler than they would have been with no city at all, just cooler than the sun backed fields around them…”

Bingo.

Jeff Alberts
Reply to  davidmhoffer
May 5, 2019 2:36 pm

“This approach is confrontational, makes no friends, and persuades no one.”

David! Shhhh! You’re cramping his style!

davidmhoffer
Reply to  davidmhoffer
May 5, 2019 10:16 pm

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.

davidmhoffer
Reply to  davidmhoffer
May 5, 2019 11:18 pm

DON’T support your assertion…

Never type while under the influence.

Michael Jankowski
Reply to  davidmhoffer
May 6, 2019 3:18 pm

“…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.

Steven Mosher
Reply to  Evan Jones
May 3, 2019 7:51 pm

yup Evan.

see all the work on urban cool islands.

readers wont but you will

Latitude
Reply to  Leif Svalgaard
May 3, 2019 6:41 pm

…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

Latitude
Reply to  Latitude
May 3, 2019 6:45 pm

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/

Steven Mosher
Reply to  Latitude
May 3, 2019 7:18 pm

Sorry latitude the denver numbers are Summer ONLY

Latitude
Reply to  Steven Mosher
May 4, 2019 6:27 am

LOL…so it only screws up the average in the summer

Steven Mosher
Reply to  Steven Mosher
May 4, 2019 7:06 am

“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

Latitude
Reply to  Steven Mosher
May 4, 2019 8:28 am

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?

Bryan A
Reply to  Steven Mosher
May 4, 2019 8:59 am

if fall is 0
if winter is 0
if spring is 0
if summer is 3C

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

Steven Mosher
Reply to  Steven Mosher
May 5, 2019 3:01 am

“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

Steven Mosher
Reply to  Steven Mosher
May 5, 2019 3:17 am

“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.

Jeff Alberts
Reply to  Steven Mosher
May 5, 2019 2:42 pm

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.

Steven Mosher
Reply to  Latitude
May 3, 2019 6:57 pm

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

Latitude
Reply to  Steven Mosher
May 4, 2019 9:07 am

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?

Latitude
Reply to  Latitude
May 4, 2019 9:10 am

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

Latitude
Reply to  Latitude
May 4, 2019 9:12 am

http://berkeleyearth.lbl.gov/stations/160294

..only a tiny small fraction….and can claim adjustments lower temps

Steven Mosher
Reply to  Latitude
May 5, 2019 3:43 am

“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..

Steven Mosher
Reply to  Latitude
May 3, 2019 8:05 pm

Psst

750 cities in china… 1.7C

you got Miami, oh and denver

748 to go.

Latitude
Reply to  Steven Mosher
May 4, 2019 9:30 am

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

Latitude
Reply to  Steven Mosher
May 4, 2019 9:59 am

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

Steven Mosher
Reply to  Leif Svalgaard
May 3, 2019 7:24 pm

“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

Steven Mosher
Reply to  Steven Mosher
May 3, 2019 8:34 pm

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.

Peter Muller
Reply to  Steven Mosher
May 4, 2019 10:08 am

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.

Rud Istvan
May 3, 2019 3:12 pm

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.

Steven Mosher
Reply to  Rud Istvan
May 3, 2019 7:16 pm

“Perhaps inadvertently, your post also directly undermines the v2 homogenization algorithm used by GISS.”

GISS doesnt use the v2 homogenization algorithm

May 3, 2019 3:14 pm

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.

Clyde Spencer
Reply to  Michael Moon
May 3, 2019 5:02 pm

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.

Steven Mosher
Reply to  Clyde Spencer
May 3, 2019 9:08 pm

“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

Steven Mosher
Reply to  Michael Moon
May 3, 2019 6:47 pm

“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.

Mark Luhman
Reply to  Steven Mosher
May 4, 2019 9:52 am

“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.

Reply to  Michael Moon
May 4, 2019 9:46 am

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!

May 3, 2019 3:15 pm

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.

Steven Mosher
Reply to  nicholas tesdorf
May 3, 2019 7:47 pm

“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

Bob boder
Reply to  Steven Mosher
May 4, 2019 8:32 am

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.

Jeff Alberts
Reply to  Bob boder
May 5, 2019 2:48 pm

“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.

TW2019
May 3, 2019 3:16 pm

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?

Steven Mosher
Reply to  TW2019
May 3, 2019 6:59 pm

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

Reply to  Steven Mosher
May 3, 2019 8:51 pm

fixed

Steven Mosher
Reply to  Anthony Watts
May 4, 2019 1:25 am

thanks

Loydo
Reply to  Steven Mosher
May 3, 2019 8:57 pm

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.

Steven Mosher
Reply to  Loydo
May 4, 2019 1:45 am

Thats the IPCC.

most people miss what the science actually says

Newminster
Reply to  Steven Mosher
May 4, 2019 4:50 am

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!

Steven Mosher
Reply to  Steven Mosher
May 4, 2019 7:10 am

Your best weapon against the GND is the actual science.

Mark Cates
Reply to  Steven Mosher
May 9, 2019 12:50 pm

“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)

M Courtney
May 3, 2019 3:17 pm

Both GT and Wang look at the urban fraction over a 10km buffer surrounding the station.

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.

Steven Mosher
Reply to  M Courtney
May 3, 2019 7:39 pm

“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?

M Courtney
Reply to  Steven Mosher
May 3, 2019 11:21 pm

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”

crosspatch
May 3, 2019 3:21 pm

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.

Steven Mosher
Reply to  crosspatch
May 3, 2019 7:52 pm

The good thing is more older stations are being recovered

crosspatch
Reply to  Steven Mosher
May 4, 2019 10:27 am

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.

Gwan
Reply to  crosspatch
May 4, 2019 3:22 pm

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.

Steven Mosher
Reply to  crosspatch
May 5, 2019 4:11 am

“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.

Gator
May 3, 2019 3:21 pm

Wow! That was an impressive display of hand waving.

Latitude
Reply to  Gator
May 3, 2019 6:19 pm

………..LOL!

Steven Mosher
Reply to  Gator
May 3, 2019 7:48 pm

if you say so, it must be so

Gator
Reply to  Steven Mosher
May 3, 2019 8:07 pm

Another waive of the hand! How do they do it? Geniuses one and all…

Steven Mosher
Reply to  Gator
May 3, 2019 9:36 pm

extend arm,
wiggle

Gator
Reply to  Steven Mosher
May 4, 2019 1:22 am

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.

Steven Mosher
Reply to  Steven Mosher
May 4, 2019 3:22 am

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

Gator
Reply to  Steven Mosher
May 4, 2019 4:02 am

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?

Steven Mosher
Reply to  Steven Mosher
May 4, 2019 7:25 am

“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!

Gator
Reply to  Steven Mosher
May 4, 2019 7:50 am

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

Steven Mosher
Reply to  Steven Mosher
May 8, 2019 3:37 am

“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

Gator
Reply to  Steven Mosher
May 8, 2019 4:21 am

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!

Spalding Craft
Reply to  Gator
May 4, 2019 3:45 pm

Give it a rest, please, Gator. Ad hom is a bore.

Gator
Reply to  Spalding Craft
May 5, 2019 7:04 am

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…

Steven Mosher
Reply to  Spalding Craft
May 8, 2019 3:46 am

” 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

Gator
Reply to  Steven Mosher
May 8, 2019 4:13 am

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!”?

Ron Long
May 3, 2019 3:25 pm

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?

Steven Mosher
Reply to  Ron Long
May 3, 2019 8:52 pm

“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

A C Osborn
May 3, 2019 3:36 pm

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.

bit chilly
Reply to  A C Osborn
May 3, 2019 5:45 pm

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.

Steven Mosher
Reply to  bit chilly
May 3, 2019 8:45 pm

“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.

Steven Mosher
Reply to  A C Osborn
May 3, 2019 7:50 pm

“UHI at air ports is one area that needs studying.”

UHI at airports would be…..

MICROSITE..

A C Osborn
Reply to  Steven Mosher
May 4, 2019 3:46 am

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?

Steven Mosher
Reply to  A C Osborn
May 4, 2019 8:10 am

“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.

A C Osborn
Reply to  Steven Mosher
May 4, 2019 9:33 am

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?

Steven Mosher
Reply to  Steven Mosher
May 5, 2019 3:52 am

“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.

Neville
May 3, 2019 3:40 pm

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.

Steven Mosher
Reply to  Neville
May 3, 2019 7:55 pm

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

Steven Mosher
Reply to  Neville
May 3, 2019 8:01 pm

“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!

A C Osborn
Reply to  Steven Mosher
May 4, 2019 3:42 am

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.

johndo
May 3, 2019 3:44 pm

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!

Steven Mosher
Reply to  johndo
May 3, 2019 8:50 pm

“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’

1sky1
May 3, 2019 3:46 pm

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.

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.”

Dave Fair
Reply to  1sky1
May 3, 2019 4:36 pm

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.

Steven Mosher
Reply to  Dave Fair
May 3, 2019 8:58 pm

“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.

Robert of Texas
May 3, 2019 3:52 pm

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).

Jim Gorman
Reply to  Robert of Texas
May 3, 2019 7:31 pm

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.

Steven Mosher
Reply to  Jim Gorman
May 3, 2019 11:07 pm

“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.

Tim Gorman
Reply to  Steven Mosher
May 4, 2019 4:59 am

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!

Paramenter
Reply to  Tim Gorman
May 5, 2019 9:06 am

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.

Tim Gorman
Reply to  Paramenter
May 5, 2019 9:25 am

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.

Steven Mosher
Reply to  Tim Gorman
May 8, 2019 3:29 am

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.

Tim Gorman
Reply to  Steven Mosher
May 8, 2019 5:16 am

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.

Jim Gorman
Reply to  Steven Mosher
May 4, 2019 5:13 am

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?

Nicholas McGinley
Reply to  Jim Gorman
May 7, 2019 2:50 pm

Sure, if you are a “climate scientist”.
Of course, saying it does not make it true.

Steven Mosher
Reply to  Robert of Texas
May 3, 2019 8:55 pm

“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?

A C Osborn
Reply to  Steven Mosher
May 4, 2019 3:54 am

It has already been done with the Reference set and it shows a difference, but then of course they do not need “adjusting”.

BobM
Reply to  Steven Mosher
May 4, 2019 6:54 am

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.

A C Osborn
Reply to  BobM
May 4, 2019 9:23 am

+1000

Coach Springer
May 3, 2019 3:56 pm

Sorry. But I seem to have missed the definition of microsite.

Steven Mosher
Reply to  Coach Springer
May 3, 2019 9:30 pm

Microsite: Within the veiwshed of the sensor : two standards, 0-500m of the site, or 0-1000m
Meso: 1km – ~10km

4 Eyes
May 3, 2019 4:03 pm

Thanks Steven, I now have a clearer understanding of why it is so hard to get useful temperature data and trends.

Steven Mosher
Reply to  4 Eyes
May 3, 2019 9:32 pm

Oh its easier than you think

the great thing is the surface record matches the most accurate satellite record compiled by AIRS.

F1nn
Reply to  4 Eyes
May 4, 2019 1:59 am

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.

Steven Mosher
Reply to  F1nn
May 4, 2019 3:30 am

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

F1nn
Reply to  Steven Mosher
May 4, 2019 5:03 am

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.

Jeff Alberts
Reply to  Steven Mosher
May 5, 2019 3:03 pm

“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.

Nicholas McGinley
Reply to  Jeff Alberts
May 7, 2019 2:56 pm

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.

Steven Mosher
Reply to  Jeff Alberts
May 8, 2019 3:49 am

“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.

May 3, 2019 4:03 pm

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

Jim Gorman
Reply to  Jim Steele
May 3, 2019 7:56 pm

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.

Steven Mosher
Reply to  Jim Gorman
May 3, 2019 10:00 pm

“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

Jim Gorman
Reply to  Steven Mosher
May 4, 2019 5:46 am

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.

Reply to  Steven Mosher
May 4, 2019 8:22 am

Mosher You missed my point again,

The real issue is determining how much rural areas have warmed due to landscape changes.

Gator
Reply to  Jim Steele
May 4, 2019 8:25 am

He is an expert at missing points, unless they support his beliefs.

Steven Mosher
Reply to  Jim Steele
May 3, 2019 9:43 pm

“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

Greg
Reply to  Steven Mosher
May 3, 2019 11:46 pm

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.

Steven Mosher
Reply to  Greg
May 4, 2019 1:52 am

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.

Steven Mosher
Reply to  Jim Steele
May 3, 2019 9:55 pm

“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.

Steve O
May 3, 2019 4:17 pm

The science of how to appropriately adjust the data seems to be in its infancy.

Steven Mosher
Reply to  Steve O
May 3, 2019 9:47 pm

actually not, its been done for hundreds of years.

JimW
Reply to  Steven Mosher
May 4, 2019 12:26 am

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?)

Steven Mosher
Reply to  JimW
May 4, 2019 1:54 am

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?

May 3, 2019 4:21 pm

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

Steven Mosher
Reply to  Michael
May 3, 2019 10:02 pm

“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.

Bob boder
Reply to  Steven Mosher
May 4, 2019 8:50 am

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.

Jeff Alberts
Reply to  Bob boder
May 5, 2019 3:07 pm

“and not a catastrophe whaling to happen”

Thar she blows!

Sorry, couldn’t resist.

Clyde Spencer
May 3, 2019 4:47 pm

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.

Steven Mosher
Reply to  Clyde Spencer
May 3, 2019 10:21 pm

“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

Clyde Spencer
May 3, 2019 4:58 pm

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.

Steven Mosher
Reply to  Clyde Spencer
May 3, 2019 8:07 pm

Yup it would.

Theyouk
May 3, 2019 5:31 pm

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.

Latitude
Reply to  Theyouk
May 3, 2019 6:27 pm

..a direct result of a prosperous society….the time to pontificate

Steven Mosher
Reply to  Theyouk
May 3, 2019 9:27 pm

“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?

Theyouk
Reply to  Steven Mosher
May 4, 2019 7:37 am

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!

Steven Mosher
Reply to  Theyouk
May 5, 2019 3:32 am

Thanks.!

enjoy your time outside!

Rick
Reply to  Theyouk
May 3, 2019 10:40 pm

“…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?

Steven Mosher
Reply to  Rick
May 4, 2019 1:56 am

“I see that as a problem for our ‘catastrophic friends’. ”

Hmm. I dont believe in catastrophe.

paul courtney
Reply to  Steven Mosher
May 4, 2019 8:23 am

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.

Anton Eagle
May 3, 2019 5:47 pm

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.

Steven Mosher
Reply to  Anton Eagle
May 3, 2019 10:33 pm

“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

Steven Mosher
Reply to  Anton Eagle
May 4, 2019 1:59 am

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?

A C Osborn
Reply to  Steven Mosher
May 4, 2019 4:16 am

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?

Steven Mosher
Reply to  A C Osborn
May 4, 2019 8:18 am

“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.

A C Osborn
Reply to  Steven Mosher
May 4, 2019 9:44 am

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?

Steven Mosher
Reply to  Steven Mosher
May 8, 2019 3:32 am

“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

May 3, 2019 5:58 pm

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.

Steven Mosher
Reply to  Martin Clark
May 3, 2019 10:42 pm

“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

A C Osborn
Reply to  Steven Mosher
May 4, 2019 4:32 am

” 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/

May 3, 2019 5:58 pm

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.

Jeff
May 3, 2019 5:59 pm

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.

Steven Mosher
Reply to  Jeff
May 3, 2019 11:23 pm

“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.

A C Osborn
Reply to  Steven Mosher
May 4, 2019 4:34 am

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.

Herbert
May 3, 2019 6:13 pm

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).

Steven Mosher
Reply to  Herbert
May 4, 2019 2:42 am

“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?

Herbert
Reply to  Steven Mosher
May 6, 2019 7:40 pm

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.

Mikey
May 3, 2019 6:28 pm

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.

Steven Mosher
Reply to  Mikey
May 3, 2019 7:14 pm

” 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

Steven Mosher
Reply to  Mikey
May 4, 2019 2:46 am

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)

Max Hugoson
May 3, 2019 6:46 pm

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.

Steven Mosher
Reply to  Max Hugoson
May 3, 2019 9:21 pm

satellites dont measure temperature

Javier
Reply to  Steven Mosher
May 4, 2019 2:25 am

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.

Steven Mosher
Reply to  Javier
May 4, 2019 3:08 am

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.

Mark Luhman
Reply to  Steven Mosher
May 4, 2019 10:05 am

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.

Javier
Reply to  Steven Mosher
May 4, 2019 11:47 am

orders of magnitude different.

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.

Steven Mosher
May 3, 2019 6:48 pm

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

Reply to  Steven Mosher
May 3, 2019 8:52 pm

fixed…was not in quotes in original submission.

Juan Slayton
May 3, 2019 6:53 pm

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.)

Steven Mosher
Reply to  Juan Slayton
May 5, 2019 3:57 am

“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.

Juan Slayton
Reply to  Steven Mosher
May 5, 2019 6:03 pm

…if your purpose…

Sorry, didn’t intend to imply that you personally were using it. Should have written, …if one’s purpose…

Gino
May 3, 2019 7:19 pm

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?

Steven Mosher
Reply to  Gino
May 3, 2019 8:41 pm

“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

A C Osborn
Reply to  Steven Mosher
May 4, 2019 4:50 am

“What does “1. USHCN is not an official dataset since 2014.” mean Exactly, are none of those station now included in GHCN?

Steven Mosher
Reply to  A C Osborn
May 5, 2019 4:03 am

““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.

Gino
May 3, 2019 7:27 pm

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?

Steven Mosher
May 3, 2019 8:03 pm

“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

robert_g
May 3, 2019 8:19 pm

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

Steven Mosher
Reply to  robert_g
May 3, 2019 9:06 pm

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!”

F1nn
Reply to  Steven Mosher
May 4, 2019 2:52 am

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.

May 3, 2019 9:01 pm

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.

Steven Mosher
Reply to  Stephen Wilde
May 3, 2019 11:05 pm

“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

May 3, 2019 9:07 pm

By what mechanism could increased urban development reduce or not increase the daily maximum whilst increasing the daily minimum?
Sounds unlikely.

Steven Mosher
Reply to  Stephen Wilde
May 3, 2019 10:27 pm

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?

A C Osborn
Reply to  Steven Mosher
May 4, 2019 4:54 am

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.

Streetcred
May 3, 2019 9:33 pm

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.