From Dr. Roy Spencer’s Global Warming Blog
by Roy W. Spencer, Ph. D.
In Part I I showed the Landsat satellite-based measurements of urbanization around the Global Historical Climate Network (GHCN) land temperature-monitoring stations. Virtually all of the GHCN stations have experienced growth in the coverage of human settlement “built-up” (BU) structures.
As an example of this growth, here is the 40-year change in BU values (which range from 0 to 100%) at 1 km spatial resolution over the Southeast United States.
How has this change in urbanization been expressed at the GHCN stations distributed around the world? Fig. 2 shows how urbanization has increased on average across 19,885 GHCN stations from 20N to 82.5N latitude, at various spatial averaging resolutions of the data.
NONE of the 19,885 GHCN stations experienced negative growth, which is not that surprising since that would require a removal of human settlement structures over time. In all of the analysis that follows, I will be using the 21×21 km averages of BU centered on the GHCN station locations.
So, what effect does urbanization measured in this manner have on GHCN temperatures? And, especially, on temperature trends used for monitoring global warming?
While we all know that urban areas are warmer than rural areas, especially at night and during the summer, does an increase in urbanization lead to spurious warming at the GHCN stations that experienced growth (which is the majority of them)?
And, even if it did, does the homogenization procedure NOAA uses to correct for spurious temperature effects remove (even partially) urban heat island (UHI) effects on reported temperature trends?
John Christy and I have been examining these questions by comparing the GHCN temperature dataset (both unadjusted and adjusted [homogenized] versions) to these Landsat-based measurements of human settlement structures, which I will just call “urbanization”.
Here’s what I’m finding so far.
The Strongest UHI Warming with Urbanization Growth Occurs at Nearly-Rural Stations
As Oke (1973) and others have demonstrated, the urban heat island effect is strongly nonlinear, with (for example) a 2% increase in urbanization at rural sites producing much more warming than a 2% increase at an urban site. This means that a climate monitoring dataset using mostly-rural stations is not immune from spurious warming from creeping urbanization, unless there has been absolutely zero growth.
For example, Fig. 3 shows the sensitivity of GHCN (absolute) temperatures to increasing urbanization in various classes of urbanization, based upon well over 1 million station pairs separated by less than 150 km.
By far the greatest sensitivity to a change in urbanization in Fig. 3 is in the 0-2% (nearly rural) category. We also see in Fig. 3 that the homogenization procedure used by NOAA reduces this effect by only 9% averaged across all seasons, and by even less (2.1%) in the summer season.
If we integrate the sensitivities in Fig. 3 from 0 to 100% urbanization, we get the total UHI effect on temperature (Fig. 4).
The temperature data used here is the average of the daily maximum and minimum temperatures ([Tmax+Tmin]/2), and since almost all of the urban heat island effect is in Tmin, the temperature scale in Fig. 4 would be nearly doubled for the Tmin UHI effect.
The black curve in Fig. 4 is a square-root relationship, which seems to match the data reasonable well for most of the GHCN stations (which are generally less than 30% urbanized). But this is not nearly as non-linear as the 4th root relationship Oke (1973) calculated for some eastern Canadian stations, using population data as a measure of urbanization.
But what I have shown so far is based upon spatial information (the difference between closely-spaced stations). It does not tell us whether, or by how much, spurious warming exists in the GHCN temperature trends. To examine this question, next I looked at how the NOAA homogenization procedure changed station trends as a function of how fast the station environment has become more urbanized.
NOAA’s homogenization produces a change in most of the station temperature trends. If I compute the average homogenization-induced change in trends in various categories of station growth in urbanization, we should see a negative trend adjustment associated with positive urbanization growth, right?
But just the opposite happens.
First let’s examine what happens at stations with no growth in urbanization. In Fig. 5 we see that the 881 stations with no trend in urbanization during 1975-2014 have an average 0.011 C/decade warmer trend in the adjusted (homogenized) data than in the unadjusted data. This, by itself, is entirely possible since there are time-of-observation (“Tobs”) adjustments made to the data, adjustments for station moves, instrumentation types, etc.
So, let’s assume that value at zero growth in Fig. 5 represents what we should expect for the NON-urbanization related adjustments to GHCN trends. As we move to the right from zero urbanization growth in Fig. 5, stations with increasing growth in urbanization should have downward adjustments in their temperature trends, but instead we see, for all classes of growth in urbanization, UPWARD adjustments instead!
Thus, it appears that NOAA’s homogenization procedure is spuriously warming station temperature trends (on average) when it should be cooling them. I don’t know how to conclude any different.
Why are the NOAA adjusments going in the wrong direction? I don’t know.
To say the least, I find these results… curious.
OK, so how big is this spurious warming effect on land temperature trends in the GHCN dataset?
Before you jump to the conclusion that GHCN temperature trends have too much spurious warming to be relied upon for monitoring global warming, what I have shown does not tell us by just how much the land-average temperature trends are biased upward. I will address that in Part III.
My very preliminary calculations so far (using the UHI curves in Fig. 4 applied to the 21×21 km urbanization growth curve in Fig. 2) suggest the UHI warming averaged over all stations is about 10-20% of the GHCN trends. Small, but not insignificant. But that could change as I dig deeper into the issue.
There are very good reasons why drug studies are done with a double blind procedure. If one has an expectation of what the results “should be”, there is a strong possibility of a bias in that direction, if one does not try to prevent subjective judgements.
A new pain killer that works better than a sugar pill in two Random Controlled Tests can, and probably will, get FDA approval even if it costs 100x more than aspirin. does not work as well as aspirin, has more adverse side effects than aspirin and has unknown long term adverse side effects.
That sums up the drug approval process,
Drug manufacturers spend $2 on marketing drugs for $1 spend on drug research and development.
Those conclusions are from the best article I ever wrote for my prior newsletter ECONOMIC LOGIC. in the 1977 to 2020 period. It was independently fact checked by two drug company insiders before publication.
“My very preliminary calculations so far (using the UHI curves in Fig. 4 applied to the 21×21 km urbanization growth curve in Fig. 2) suggest the UHI warming averaged over all stations is about 10-20% of the GHCN trends. Small, but not insignificant.”
Well, first to remember is that the global average is at least 2/3 ocean. So that brings it down to 3-7% of the trend. Smaller, and almost insignificant.
But also at least some of that is real, and should be reflected in the measurement. If the world is warming, the measurements should say so. Later you can argue whether UHI is a cause. The problem of UHI is not that it appears in the measurement, but that it may be over-represented in the sample.
GHCN stations are all land surface stations according to NOAA.
Yes. But the usually quoted global average is land and sea. There is no UHI at sea.
Roy is only addressing the GHCN stations and their homogenization. He made no claims about the globe. Furthermore, there are no adjustments necessary for either urbanization or elevation with sea surface temperatures. Land atmosphere temperatures should probably not be averaged with sea surface temperatures for the above reasons and the fact that the specific heat content of water is unique. You have presented a strawman argument.
“Roy is only addressing”
Yes. And I’m noting that what almost everyone else talks about, i terms of trend, is the land/sea average. And as a matter of simple arithmetic , land is only 1/3 of that, and the sea has no UHI.
You may not like land/sea, but it is what is universally talked about.
Spencer and Christy’s UAH shows land surfaces warming 50% faster than sea surfaces. Hard to believe that UHI is not some part of that difference, even if small. I assume the thermal inertia of the oceans is the main cause.
Why don’t we just wait for Dr. Spencer’s Part III, of which he says
“what I have shown does not tell us by just how much the land-average temperature trends are biased upward. I will address that in Part III”
By my simplistic reasoning, the UHI-homogenization bias on land-plus-sea average temperature trends should be 30 percent of the UHI-homogenisation bias on land-average temperature trends.
Then we’ll actually have numbers to bloviate about.
That is my reasoning too, as above. It brings Roy’s 10-20% of trend down to 3-6%.
Yes there is, it is what Tom Karl created – just before he retired.
(The human animal cannot lie, he knew he was doing a Bad Thing)
He did it by insisting that ocean temps were to be taken from the cooling-water intakes of large ships, instead of from the ARGO floats – which were showing no change in ocean water temperatures.
NB water temperatures, not ‘surface temps’ or what any old passing Sputniks could see and record.
Thus the ARGO floats deflated the entire Global Warming Balloon because the ‘missing heat’ was supposed to be in the water and they didn’t find it.
Large Ships don’t spend all their time, by any measure, millions of miles from civilation out on the ocean.
By definition the call at ports to collect and deliver their cargo and those ports are invariably on the coast in shallow water (no surprise) and also adjacent to large cities.
(Large cities are near always beside Large Waters as that confers a Nice Climate for the city dwellers. Water controls climate)
No matter, when the ships come near their destinations and are leaving, two things are important.
1/ Water in and around the port/harbour will be water that has recently drained off the watershed that the city is and the land that comprises the watershed of the large river that is always there.
That water will be warm, not least because the city heated it – how much is waste water from homes and factories not least?
Even before, as we’re always told, land is warmer than water so the fresh-water flow coming down the river will be warm also
2/ The port harbour will be ‘shallow water’ – less than 100 metres deep. So if/when the sun shines, not only is it heating the water as it does normally, the sun will ‘see’ the dark coloured mud on the seafloor – the solar heating effect will be massive
And that is what the ships will record as the arrive, wait for loading/unloading and as they leave.
Do the big ships spend what, 40 or 50% of their time actually doing that, sitting in warm water waiting – with their engines running and Tom Karl’s thermometers basking in the warmth
Thanks to Tom Karl, the UHI extends right out into the ocean.
Interesting resurrection of the Karl, et. al paper. Having other things to work on, I rarely spend much time digging into details of climate papers, but I recall spending a good bit of time with Karl et al a few years ago. As I recall, they 1) were extremely slipshod with uncertainties, 2) deliberately excluded the satellite data because it introduced cooling [my emphasis], and 3) arbitrarily chose to chose to adjust more precise data up to less precise data. And that is just for starters. The paper reinforced my opinion that climate “science” has a way to go to become an actual science, distinguished from the data analytic exercise it is now.
When the temperatures are recorded the time of day and position of the vessel are also recorded.
Also when ships are waiting at anchor the engine isn’t running.
Third, the temperature is taken at the main enging cooling intake so at least in theory is not influenced by the structure of the vessel.
Fourthly, it is very rare that sea water is so clear that the sunlight reaches down 100M, particularly in coastal waters.
But the main problem with cooling water temperature data is that when the ship is empty the intake may be 15′ below the surface while when the ship is full it may be 30′ or 40′ or 50′ below the surface and I don’t recall entering the depth of the intake anywhere in the temperature record book when I was on recording ships.
But Al “the climate blimp” Gore predicts 10% will become ocean weather statuions, due to climate change sea level rise, by 2030.
Or so I’ve heard.
And poor people all around the world get poorer while cumulatively TRILLIONS are spent to build solar and wind generation and electric cars that will do + or – 3-7% in changing the scary “global heating” trend.
The trillions that they are spending will most likely have a +/- 0.00% impact on the “global heating” trend.
The poor, including their utilities, are subsidized through, for example, Medicare/Medicaid/Obamacares. The middle class face redistributive change through capital depletion. The trickle down economic theory of single/central/monopolistic solutions has a minority benefit and profits through labor and and environmental arbitrage, and empathetic appeal in democratic/dictatorial regimes.
I don’t know where you got 3% to 7%, but I know it makes no sense. Manmade CO2 accounts for about 33% of the total CO2, not 3% to 7%.
There is no logical reason to believe Nut Zero, with over 7 billion people living in nations with no interest in Nut Zero, will ever stop the rise of the atmospheric CO2 level. And I think that is good news, because I love CO2 and so do my plants.
My guess is that Drake is thinking in ºF, and you are thinking in ºC. I am thinking in ºK and come up with 0.3%
He is talking about influence on total system . Land based measures are 1/3 of Global and GCHN is for US. hence his calculation that these UHI impacts are negligible against total area. That however is quite a stretch to limit the same UHI impact to only US. These are clearly happening everywhere on the land where we measure temps. so the impact with homogenized data sets is indeed 1/3 of the total global inputs. Meanwhile the infilling for oceans is even worse than land and very unreliable. For the record, I believe the evidence shows that there is a slight natural warming over the past 100 years. it is not unexpected, dangerous or anything out of normal variation. CO2 is a false flag.
Nick I can agree with what you said. But the land temperature matters too, because if the trend is really small on land (smaller than what the official measurements say) then trend in the oceans are likely even smaller. The buoys we have are some neat technology, but it has to be handled extremely correctly and accurately; we all know what Tom Karl and his crew did back in 2015. Plus this is good for people like me are just curious and would like to know what’s going on their natural playgrounds.
There a few problems I have with your reply Nick.
“Isn’t GHCN composed of just land based”
GHCN is just a collection of land-based stations. But no-one ever refers to an average of GHCN stations. Very rarely, mention is made of a global land average, which is an area weighted average (where rural stations are greatly up-weighted). But the almost universally quoted trend is the land/sea average anomaly, which is about 1/3 land and 2/3 ocean.
“If the UHI is not correctly being removed from the data, then it is wrong, period.
No. The purpose is to measure the temperature. Not to modify it because of putative causes.
The purpose of a global average temperature STATISTIC is climate change scaremongering.
Not one person lives in the global average statistic, which is not an actual measured temperature. We all live and work in local climates, with real measurements, not statistics.
There are some dingbats who claim everyone lives in the average temperature. That’s life — here are many dingbats — some even become President and VP. Only in America.
Then no TOB adjustments should be made?
TOB adjustments are to get the min/max average temperature right on a particular day. They correct a measurement issue.
Right?! Nobody read the thermometer at the “different” time on that day, so such guesswork is NOT a “correction.”
DATA is the thermometer readings. The fact that there are issues with the data merely confirms that the data was never fit for the purpose of measuring CLIMATE changes. After all the “adjustments” it’s even less fit for that purpose.
Nick’s argument is preemptive. It is:
You notice, laid out like this, the hidden assumption. It is that the ocean measurements are solid and not subject to query. That is the only way the argument works.
Its wrong, and its a diversion. The fact that land measurements may be overstated by the process of homogenization is important. The amount of overstatement is important.
Whether that is a large or small percentage of some other total is irrelevant. The important thing at this point for climate policy is to get the distortions out of the record, and this means dealing with them one at a time. Ocean warming is a completely separate subject.
One reason why land is important as a thing in itself is that land temps are commonly used by the media and policy makers to justify claims of catastrophic warming. We have all seen claims of unprecedented heat waves – in the Indian sub-continent, in the UK, in parts of the US.
Clean up the land record, and these will no longer appear unprecedented.
That is why this matters, and it has nothing to do with the ocean.
standard practice in climate is that when there’s uncertainty in a measurement, you just combine it with other measurements and the uncertainty vanishes into the averaging process 🙂
for example, say you have a ruler that measures only in feet
if there are 9 measurements of 10 feet and one measurement of nine feet, the object must be exactly 9.9 feet with an error of .1 feet
You nailed it! There are *NO* climate scientists or CAGW advocates that know how to properly handle uncertainty. Or, they do know and refuse to do so because it would expose the hoax!
Unfortunately, surface temperatures, at least in Australia, have been fabricated by the process of data homogenization which has been undertaken since 1993 by scientists within Australia’s Bureau of Meteorology. The latest round of homogenization (ACORN-SATv2.3) provides data up to December 2021.
Fake trends are created by:
· Selectively ignoring changes that happened, which affected temperature observations, and selectively adjusting changes that made no difference; and,
· Using data that are correlated with target site data (data to be homogenized), to both detect changepoints in target site data, and also make adjustments. Linear correlation of first-differences (Pearsons) with target-site data, is used to select up to 10 comparators whose data are used to make reference series. However, as comparators are not homogeneous, reference series embed similar inhomogeneities as the target site.
Simply stated, they combine up to 10 faulty datasets into a reference series, which is then used to both detect and adjust faults in data to be homogenized. What could possibly go wrong?
I invite you to take a peek at the latest series of reports at http://www.bomwatch.com.au detailing problems with ACORN-SAT. While I started with the Pilbara region of Western Australia, I’m moving-on to Halls Creek, a few Aeradio sites on the north-west coast where I have already completed most of the investigations, then back to Victoria and New South Wales. The attached picture shows sites that have been used to adjust data for Halls Creek. However, none of the data for those sites is homogeneous.
What has become clear is that as all major surface temperature monitoring groups use similar comparative methods, they all suffer from the same shortcomings and all arrive at basically the same trends.
Dr Bill Johnston
I consider Bill Johnston to be an expert on Australia temperature data collection.
… But he never mentions that the key to fixing Australia’s climate history is pasteurization.
The combined effect of adjustments, infilling, homogenization and pasteurization results in whatever numbers the BOM wants to report to the public. Because BOM are large and in charge, so the average Australia temperature is whatever they say it is, and don’t you forget it. To reduce temperature data collection costs in the future, BOM is thinking of pulling their monthly average temperature number out of a hat. They are currently debating what kind of hat to use. And more importantly, what color hat to use. Or so I have heard.
Actually the map is out of date. I’ll revise it after a good sleep.
I just realised that from Western Australia they went right across the Northern Territory into Queensland, to Camooweal and Boulia to find a matching site … such is what they do to fudge ACORN-SAT.
Someone should tell Nick Stokes.
All the best,
If the average temperature of Australia is not going up to match the models, it will be “fixed” by the BOM, otherwise known as the Bureau of Mediocracy.
I hope you don’t mind these lame jokes about Australia’s BOM — I don’t respect climate “authorities”
I ignore all global temperature averages before 1940
I only mention 1940 through 1975 because the huge arbitrary revisions after 1975 almost eliminated a lot of global cooling. That global cooling was the main reason a few scientists got so much attention predicting a coming global cooling crisis in the early 1970s — global cooling with CO2 rising was not what was expected.
So that cooling, which supported the wrong coming global cooling crisis predictions, had to be revised away. And it was.
I do consider the UAH data since 1979 to be worthwhile but do not think is has any value in predicting the future climate. Which will get warmer, unless it gets colder, as I hope for warmer.
And I even got my maps muddled-up!
The map, which shows the stations they used to homogenise Carnarvon is fine.
However, I re-did the map for Halls Creek, which is the next ACORN-SAT site we will publish on http://www.bomwatch.com, probably next week.
It shows that amongst others, they used ACORN-SAT sites at Boulia and Camooweal in Queensland to homogenise ACORN-SAT data for Halls Creek, which is in Western Australia.
The distance from Halls Creek to Boulia is 1375 km, and from halls Creek to Broome in 575 km.
As all the sites they use are affected by station moves and changes, its hard not to smile …
Using faulty data to correct faults in ACORN-SAT data has no statistical or scientific merit. Australia’s ACORN-SAT project should simply be abandoned.
Oh wait, the red dots are ACORN-SAT sites, the black dots are other stations they use to homogenise Tmax, and the grey dots are sites with more than 10-years of Tmax data.
All the best,
“To reduce temperature data collection costs in the future, BOM is thinking of pulling their monthly average temperature number out of a hat.”
That made me laugh! 🙂
Discussing land areas addresses where people live.
Few people experience living at sea.
For policy purposes, this seems important.
The discourse here and elsewhere is about global land/sea temperatures. There are good physical reasons for that. So whatever your idea, it is important to address what people are talking about.
In fact, as sometimes noted here, people do not experience the average temperature, whether at land or sea (especially an average anomaly). It is an indicator. If it goes up, then whatever people were experiencing, they are likely to find that it is warmer.
Perhaps the next analysis should be the Argo float data compared with the Karl “replacement” manure, and then we can sum up all the spurious “warming” inflating the supposed “trend.”
It matters because of the “everywhere is warming 2x faster than global average” meme. Because “everywhere” means where people live ie on land.
Perhaps the UHI Spencer & Christy are investigating will explain this discrepancy:
Wood for Trees: Interactive Graphs
It’s generally a bit warmer in built-up tar & cement urban areas than it is in meadows & forests rural areas.
Who would have thought?
But a center of a big city weather station should have similar UHI from year to year, such as in Central Park in NYC. A rural weather station can have much more UHI as a town grows around it.
I’m more curious as to what is our best estimate of relative heat/gain/duration total from urbanization in current modern civilization s atmosphere compared to preindustrial I remember seeing a video on the massive build up in China over the last 30 years- it was beyond anything else I have seen in the US ever.
I wrote a long article on UHI in 2019 for my climate blog that I can’t find a link to now.
My conclusion was that the NASA-GISS adjustment for global UHI was science fraud. Just enough of an adjustment to falsely claim they fully accounted for UHI, which did not surprise me at all.
I recall the total NASA-GISS global UHI adjustment added up to only -0.05 degrees C. in a century. Strangely, they added the UHI adjustment to historical data (warmed the past) rather than subtracting UHI from the current temperatures. Only government bureaucrats would think that way.
Why such a small? adjustment to the global average temperature statistic. One reason is 70% of the surface is oceans,
But I looked into the details: Somehow the NASA-GISS UHI adjustments included almost as many increases of warming adjustments for UHI as it did decrease of warming adjustments for UHI. They nearly offset each other.
You would expect mainly, or only, adjustments to decrease the temperature at land based weather stations to remove the effect of UHI.
Why so many increases?
I could not find any explanation.
Perhaps weather stations moved from cities to airports were claimed to have LESS UHI after the move?
Although that was hard for me to believe.
Airports are not proper siting for weather stations. The thermometers are surrounded by runways, hot jet exhausts and growing airline traffic. Also, there tends to be fast economic growth around airports. It was hard for me to believe that moving weather stations from urban environments to suburban airports reduced UHI but that was the only explanation I could imagine. NASA-GISS did not provide any explanation.
I trust UAH, since 1979, but not NASA-GISS’s version of the global average temperature statistic. Spencer and Christy appear to be honest volunteers with no financial incentive to over report global warming.
None of the global average temperature statistic accuracy questions are very important, unfortunately, because CAGW predictions are not based on any past climate trends on this planet.
Climate scaremonger only required only a rise of temperature and CO2 in the past. And never mind that didn’t happen in 1940 to 1975, or in 2015 through 2023. Those inconvenient to the CO2 is evil narrative data will be adjusted away. The 1940 to 1975 global cooling already has been adjusted away. The 1975 to 2023 flat trend will be adjusted away too.
Climate change and Nut Zero are all about leftist politics and power — not about real climate science and grid engineering.
Daily lists of 12 to 24 good climate science and energy articles I read that morning. The first two Spencer UHI articles don’t qualify because there is too much reading without a conclusion:
Honest Climate Science and Energy
One of the things not stated us that the uhi adjustments are skewed. Whilst the overall uhi adjustments when graphed allegedly look like a normal distribution, they have a trend with time.
They use this normal distribution trick to argue for using the Central limit theorem when calculating errors.
None of this arguing about minutia matters. The only smoking gun one needs to see is plotting the overall “adjustments” to the temperature record, against CO2 concentration. When you do this, it forms a straight line directly corresponding to CO2. So if the adjustments match the theory, the fraud is revealed.
Phrased another way, if you have to adjust the data to fit the hypothesis, then you are lying and not being in any way “scientific”. Any attempt to justify the “adjustments” is smoke and mirrors: the heart of the matter is the data does not match the theory, which means that CO2 as control knob theory is wrong.
Attached chart, from this link:
Very good point — Tony Heller has been following the US adjustments for a long time. — they are just what one would suspect — promoting the CO2 is evil narrative of the climate computer games and the 1979 Charney Report.
Heller drives the Climate Howler Global Whiners crazy, and they were not sane to begin with. I love that. He does not give Climate Howlers respect because they do not deserve respect.
His historical climate data news clippings are the best I’ve ever found. They got a lot more interesting a few years ago when every bad weather event became falsely announced as “unprecedented”.
Unfortunately, I posted a comment at Heller’s website saying that US acres burned by wildfires data pre-WW2 was not reliable.
I had done research and found out a portion of the large number of acres burned in the 1930s were done deliberately by the CCC in the southeastern US fire district for forest management, when they were not planting trees in the 1930s.
The huge amount of US acres burned in the 1930s was overstated for that reason. Heller disagreed, was wrong, did not even try refute the data I found, but did ban me from future comments on his website. A hot temper. Not every conservative objects to censorship. I’ve had several anti-Ukraine posts deleted here at WUWT.
I read Heller’s website every day anyway, and recently recommended this non-climate article on Ukraine even though the data may not be accurate — but no one else was reporting these data that I have been seeking for many months:
53% Ukrainian Casualties | Real Climate Science
the fact that homogenization “spreads around” the false UHI signal has been pointed out by others for more than a decade now, but never quite so rigorously afaik
typically the complaint is dismissed with red herrings like “you can’t measure temperature using raw averages” or now “land is smaller than sea”
lol can’t make this stuff up
the upshot is that despite official claims these temps are accurate to within .1 degrees (even when they are later changed by far more), the true error is much closer to 1 degree
and surface-based temperature readings have been obsolete since 1979
One small nit pick. It’s not true “error” but true “uncertainty”. All temperature measurements done in the field have systematic bias. You can’t just “cancel” systematic bias – but the climate scientists do.
uncertainty(total) = uncertainty(random) + uncertainty(systematic)
What climate scientists knows what each component is?
Some y ears ago I posted here an analysis of raw/homogenized for all the “1” (best) US stations surveyed by the WUWT Surface Station siting project. There were 14, of which 4 were urban and 3 of the four usable (the forth had a data quirk). It showed that homogenization did a reasonable job of removing UHI from the Urban stations, but at a cost of adding false UHI to all but one of the suburban/rural stations. Roy’s part two result on a much larger sample is similar.
Homogenization not only spreads UHI but it works to spread systematic bias in field measurement stations around to other stations.
My guess is that there is less than 1% of the temperature measurement stations today, even in the US, that has no systematic bias.
If there is excessive warming trend in USHCN, then why does USCRN have as much warming trend as USHCN has?