Are we using bad data?

So, you think scientists don't post and contribute to blogs?

 

In the main they don’t. But I am happy if in this instance you want to bust a gut and prove that the person posting jacks latest rubbish is an actual scientist - not just someone with an out of date qualification in meteorology from the time when the study of meteorology was little better than a hillbilly weather indicator

 

And you base that on what?

.I think you'd find many, many, scientists DO blog expansively.

 

Okay - prove it!
https://www.researchgate.net/figure...-The-percentage-figures-in-the_fig3_303842300

Lols!

 

Seems like a pretty good number to me. Assuming they're current and accurate.

 

Credentialism is among the stupidest arguments.
The science is here: Corrupted Climate Stations: The Official U.S. Surface Temperature Record Remains Fatally Flawed

 

That's the difference between a skeptic and a true believer: the true believer -- like you -- thinks the choice is just of whom to believe, whereas the skeptic thinks the choice is whether to believe or think.

 

The answer to the OP question is: Yes.
Further Investigations on Errors in Weather Station Data Evaluations
Guest Blogger
In conclusion, all three analysis approaches had similar results that point towards substantially less global warming within the last 140 years than previously thought. . . .

 

Ya' think?

https://www.theguardian.com/science...ers-push-research-credibility-to-crisis-point

 

Don't take the UK temperature record at face value.
EXCLUSIVE: A Third of U.K. Met Office Temperature Stations May Be Wrong by Up to 5°C, FOI Reveals
CHRIS MORRISON

Nearly one in three (29.2%) U.K. Met Office temperature measuring stations have an internationally-defined margin of error of up to 5°C . . . .

 

The UK Met Office has some explaining to do.
Met Office Must Account for the ‘Junk’ Temperature Data Propping up Net Zero Insanity

BY CHRIS MORRISON

Pressure is likely to grow in the coming days for the U.K. Met Office to make a full public statement about the state of its nationwide temperature measuring stations. . . .

 

More fraud has been exposed.
TOO HOT TO HANDLE: NIWA’s misleading temperature records
Ian Wishart

“Claimed record after claimed record, there’s evidence that NIWA’s cherry picked data is being used (and coloured in dark red for the most extreme bits) to bolster the narrative that extreme…

A spot audit of NIWA’s 2023 flagship Annual Climate Summary report and some of its predecessors over the past five years has uncovered multiple major errors that cumulatively exaggerate the impact of climate change and drive public fear.

Based on a detailed analysis of historic newspaper reports containing temperature, rainfall and other weather data and comparing them to NIWA’s published climate summaries, the audit has found

• Claimed temperature records that were never real records
• Claimed rainfall records that were never real records
• NIWA published false information on New Zealand’s biggest historic cyclone, creating a false narrative that Cyclone Gabrielle was bigger and driven by climate change
• Cherrypicked data is being used to exaggerate the apparent frequency of climate records being broken
• The practice of reporting records by month is giving NIWA twelve times more opportunities to claim records in any given location, creating media and public confusion and a sense of relentless overwhelming climate change
• NIWA is unaware of New Zealand’s highest temperatures
• NIWA’s claim that only three days in our history have experienced temperatures higher than 40C is wrong: the real figure is nearly triple that
• That there’s little evidence of climate change increasing extreme maximum temperatures in New Zealand, absolute records from a century ago are still intact. . . .

 

Cooking the books on sea level rise:
The Greatest Scientific Fraud Of All Time -- Part XXXII (Sea Level Rise Edition)
March 18, 2024/ Francis Menton

• 
  The Greatest Scientific Fraud Of All Time is the fraud by which government functionaries alter data collected and previously reported in official data bases in order to support a narrative of impending catastrophic global warming. No other scientific fraud in world history comes close to this one in scope or significance.
  
  
• The prior 31 posts in this series have all concerned alteration of one particular sort of data, namely temperature records.
  
  
• But why should we really care that the earth’s atmosphere is getting a little warmer? The UN has supposedly set some kind of Maginot Line at a 1.5 deg C temperature increase from 20th century levels — an amount so small that you can barely feel it when it occurs each day. The 1.5 deg mark is just not that all that scary. So the bureaucrats need a Plan B to scare the bejeezus out of the people. Plan B is sea level rise.
  
  
• So don’t be surprised to learn that the sea level data, produced by NASA, have recently been altered — and of course, in a way to enhance the global warming scare narrative.
  

READ MORE

 

The Urban Heat Island Effect rides again.
Urban Legends of Climate Change: Palm Springs, California
November 5th, 2024
This is the first of what will likely be a series of posts regarding urban heat island (UHI) effects in daily record high temperatures. My previous UHI work has been using the GHCN monthly average station data of “Tavg” (the average of the daily maximum [Tmax] and minimum [Tmin] temperatures). So, I’m moving from Tavg to Tmax (since record high temperatures are of so much interest), and daily rather than monthly values (although I will also sometimes include monthly results to provide context).

This post is mostly a teaser. Toward the end I will describe a new dimension to our UHI work I’m just starting.

The 2024 Poster Child for U.S. Warming: Palm Springs, CA

I was guided by a Google search on U.S. record high temperatures for 2024, and it seems Palm Springs, California was the place to start.

With a name like “Palm Springs” this place sounds like a wonderful spot to lounge under palm trees and enjoy the cool, refreshing spring water that surrounds you. Instead, the location is largely a desert, with the original downtown spring spitting out 26 gallons a minute of hot water. The “palms” do exist… they are “desert palms”, naturally growing in clusters where groundwater from mountain snowmelt seeps up through fissures connected to the San Andreas fault.

Like all U.S. metropolitan areas, the population growth at Palm Springs in the last 100+ years has been rapid. Even in the last 50 years the population has nearly doubled. Natural desert surfaces have been replaced with pavement and rooftops, which reach higher temperatures than the original desert soil, and the “impervious” nature of artificial surfaces (little air content) means the heat is conducted downward, leading to long-term storage of excess heat energy and, on average, higher temperatures. More on “impervious” surfaces later….

The Palm Springs Airport Weather Observation Site

The following Google Earth image shows the current location of the official ASOS (Automated Surface Observing System) site at the Palm Springs Airport, which recorded an all-time record high temperature of 124 deg. F on July 5 of this year.


What is somewhat amusing is that ASOS meteorological instrument siting guidance favors natural surfaces for placement, but since most of these weather stations are at airports (and since they primarily support aviation weather needs, not climate monitoring needs), the “natural” location is usually right next to runways, aircraft, and paved roads.

The next Google Earth image is zoomed out to show the greater Palm Springs area, with the ASOS site in the center (click on the image to zoom, then click to zoom more).


Record July Temperatures and Urbanization

It only makes sense that people want to know the temperature where they live, and most of the U.S. population resides in urban or suburban locations. Yet, the temperatures they experience are, probably without exception, higher than before people moved there and started building roads, buildings, and airports.

But what is misleading for those following the global warming narrative is that record high temperatures reported at these locations almost always mention climate change as a cause, yet they have no way of knowing how much urbanization has contributed to those record high temperatures. (Remember, even without global warming, high temperature records will continue to be broken as urbanization increases).

As mentioned above, on July 5, 2024 Palm Springs broke its all-time high temperature record, reaching 124 deg. F. There are 26 other daily GHCN stations within 40 miles of Palm Springs, all with varying levels of urbanization, but even more importantly, at very different elevations. If we plot the high temperatures reported for July 5 at those stations as a function of station elevation, we see that Palm Springs is an “outlier”, 5 degrees warmer than would be expected based upon its elevation-corrected expected temperature (the dashed regression line):


Now, keep in mind that many (if not most) of those 26 surrounding stations have their own levels of urbanization, making them hotter than they would be in the absence of pavement and roofs. So, that 5 deg. F excess is likely an underestimate of how much urban warming contributed to the Palm Springs record high temperature. Palm Springs was incorporated in 1938, and most population growth there has been since World War II.

If you are curious how the previous plot looks for the average of all July temperatures, here it is:


For the month of July, Palm Springs averaged 3 deg. F warmer than the surrounding stations (after adjusting for elevation effects, and keeping in mind that most of the *other* stations likely have their own levels of urbanization).

Clearly, Palm Springs has had spurious warming influence from the airport and surrounding urbanization which did not exist 100 years ago. But how much?

Impervious Surface Data as a Surrogate for Urbanization

This blog post is a prelude to a new project we’ve started where we will compare daily (as well and monthly) temperatures to a relatively new USGS dataset of yearly impervious surface coverage from 1985 to 2023, based upon Landsat data. I had previously experimented with a “Built Up” dataset based upon Landsat data, but it turns out that was just buildings. The “impervious surface” dataset is what I believe will have the greatest direct physical connection to what causes most UHI warming: roads, parking lots, roofs, etc. I think this will produce more accurate results (despite being only ~40 years in length) than my population density work (which is, we hope, close to being accepted for publication).

 

Are we using bad data?

Sabine Hossenfelder does make that argument.

 

Here's story about some likely bad data.
Death Valley Temperatures, Part II: Thoughts from William T. Reid
November 9th, 2024

NOTE: Since he has done extensive investigation into some implausibly hot temperatures reported in Death Valley, I asked Bill Reid to comment on my previous blog post where I maintain that the world record 134 deg. F highest recorded air temperature was likely biased warm by about 10 deg., and should not be accepted as a world record. What follows are Bill’s initial thoughts on the subject. Also, based upon his comments, I will likely update the charts found in my previous blog post with more realistic temperature lapse rate values in the early 20th Century when insufficient stations were available to determine accurate lapse rates.

by William T. Reid

A big thank you to Dr. Spencer for investigating the current (very dubious) world high-temperature record and for bringing attention to my Death Valley climate research. There are a handful of ways, both climatologically and meteorologically, to show that Greenland Ranch’s reported maximum of 134F on July 10, 1913, is likely not valid.

Dr. Spencer’s methodology here (comparing the Death Valley maximums to those the closest surrounding stations, with adjustments for station elevation) is indeed a devastating blow to the authenticity of the suspect observations. What it basically demonstrates is that the lower troposphere was not hot enough to support temperatures much above 125F in July, 1913. I have compared regional maximums for all of the hottest summertime events since 1911. In practically all instances (in which the Greenland Ranch and Death Valley reports appear reasonable), ALL of the maximums at the closest surrounding stations lend support to the maximums for Death Valley.

From July 7 to 14 of 1913, when eight consecutive afternoons had reported maximums of 127, 128, 129, 134, 129, 130, 131 and 127F in Death Valley, NONE of the maximums from the closest surrounding stations supported the Greenland Ranch maximums! The departures from average for maximums for the hottest five-day stretch were about +4 to +8 at the closest stations, while maximums at Greenland Ranch were nearly 15 degrees F above the average for July. (see table)


Annual maximums at Greenland Ranch from 1911 to 1960 ranged from 120F to 127F, except for the 134F in 1913. If the reported maximums at Greenland Ranch in July, 1913, were authentic, then the maximums at the closest surrounding stations in that month would have been much higher than reported. In addition, numerous regional heat waves have been hotter than the one during the first half of July, 1913. Why have Death Valley maximums failed to exceed 130F in the interim when three days in July 1913 purportedly reached 134, 130 and 131F?

In his “bias” chart, Dr. Spencer notes the “substantial warm biases in the temperatures reported at Greenland Ranch in the first 10-15 years.” And, he mentions that the observer(s) may have been relying to some extent on thermometers other than the official instrumentation. I do think that the observer was comparing “household thermometer” readings with the official equipment on occasion from spring to summer of 1913. Higher readings off of the poorly-exposed thermometers near the ranch house and under the veranda were probably (and inappropriately) entered onto the official climate form. But, I have not uncovered much evidence of this particular type of deviation from standard observational procedures outside of 1913.

I would contend that the generally higher “bias” numbers from the early years comparably are due primarily to changes at the closest area weather stations which promoted cooler maximums early on and warmer maximums later. For example, two of the closest stations to Greenland Ranch in 1913 were Independence and Lone Pine, in Owens Valley. In 1913, Owens River water was diverted to Los Angeles, and the Owens Valley gradually dried up. Summer maximums increased as Owens Lake evaporated, irrigation was not possible and farmland was abandoned, and desert-like conditions developed. (Roy’s note: The early years had very few stations within 100 miles of Death Valley, and the temperature lapse rates I computed from those few stations appear to be biased as a result. I will correct this in a future blog post, and will provide what should be better estimates of average July daily maximum Death Valley temperatures.)

Also, in the early decades of the 20th century, thermometer shelters were (almost invariably) sited above grass. This resulted in very conservative (i.e., coolish) maximums at desert stations. Low humidities promoted cooling due to evapotranspiration effects. In the early decades of the 20th century, desert weather stations were generally in towns, amidst shade trees and lawns. The resulting maximum temperature reports were very conservative. By mid-century and thereafter, the town weather stations were more likely to be at the airport or at a municipal utility site, fire station or equipment yard. Grass cover and shade trees were usually absent at these locales. Today, desert weather stations in towns and cities are (almost invariably!) above bare ground.

You can imagine the difference in maximums between desert stations above oft-irrigated grass and those above bare ground. (Roy’s note: In my experience, unless the vegetation area is rather large, and there is almost no wind, a weather station’s daily maximum temperature will still be largely determined by air flowing from the larger-scale desert surroundings. But note… this is different from, say a poorly sited thermometer next to a brick wall or heat pump where hot air from an isolated source can elevate the daily maximum temperature recorded).

The Greenland Ranch station was originally sited above a patch of alfalfa grass, immediately adjacent to forty acres of cultivated and irrigated land.


It is my belief that the new observer in 1913 (Oscar Denton) was rather disillusioned with the conservative maximums from the official station above grass and next to the evaporatively-cooled farmland. I think he felt compelled to fudge the maximums upwards in 1913. Photographs of the Greenland Ranch weather station show that it was above bare ground by about 1920 (see example photo at top of post).

 

More about bad data from Death Valley.

Death Valley Temperatures, Part 3: Twelve Years of July Daily Tmax Estimates and the 134 deg. F Record
November 11th, 2024

Update (11/12/2024); New annotated version of Fig. 1 added. Corrected who the Greenland Ranch foreman was and associated correspondence.

In Part 1 I claimed that using stations surrounding Death Valley is a good way to “fact check” warm season high temperatures (Tmax) at the Death Valley station, using a correction for elevation since all surrounding stations are at higher (and thus cooler) elevations. In July of each year, a large tropospheric ridge of high pressure makes the air mass in this region spatially uniform in temperature (at any given pressure altitude), and daily convective heating of the troposphere leads to a fairly predictable temperature lapse rate (the rate at which temperature falls off with height). This makes it possible to estimate Death Valley daytime temperatures from surrounding (cooler) stations even though those stations are thousands of feet higher in elevation than Greenland Ranch, which was 168 ft. below sea level.

Lapse Rates Computed from Stations Surrounding Death Valley

If I use all available GHCN daily stations within 100 miles of Greenland Ranch (aka Furnace Creek, aka Death Valley N.P.) in each July from 1911 to 2024 to compute the month-average lapse rate (excluding the Death Valley stations), I get the results in Fig. 1.


Fig. 1. Lower tropospheric temperature lapse rate estimated from all stations within 100 miles of Greenland Ranch, Death Valley, CA. The number of available stations for these calculations range from several in the early years to 25 or more in the later years. Here I will assume a constant lapse rate of -0.004 during the 20th Century. The 4th order polynomial fit to the data would be another way to assume how the lapse rate changes over time.

The computed lapse rates mostly fall between the dry adiabatic value and the U.S. standard atmosphere value (except in the early years). Given the few stations available in the early years, I will base the calculations that follow on an assumed lapse rate of -0.004 deg F per ft. for the first half of the record, and will assume that the observed steepening of the lapse rate after the 1980s is real, with a value of -0.0048 deg. F per ft. in the early 2020s. In Part 1, I used the actual values in Fig. 1 in each year to estimate Death Valley temperatures. This time I’m using average lapse rate values over many years, keeping in mind the early decades had few stations and so their values in Fig. 1 are more uncertain.

Daily Estimated July Tmax at Death Valley: 2021-2024

How accurately can we estimate daily Tmax temperatures in Death Valley from surrounding high-elevation stations? The following plot (Fig. 2) shows how the July daily observed Tmax temperatures in Death Valley (2021, 2022, 2023, 2024, orange for Death Valley N.P.) compare to estimates made based upon surrounding, high-elevations stations (blue), assuming a lapse rate of -0.0048 deg. F per ft (see Fig. 1).




Fig. 2. Daily estimated July Tmax temperatures for Death Valley N.P. from surrounding stations (blue) compared to those observed (orange, 194 ft. below sea level) for 2021, 2022, 2023 and 2024.

In each year the daily estimates from surrounding stations (blue) are reasonably close (within a couple of degrees) to the observed values at both Death Valley N. P. (orange) and at the nearby station Stovepipe Wells. For example, on July 7, 2024 the observed “near record” value of 129 deg. F degrees agrees well with the lapse-rate estimated value of 128 deg. F. Note there were many (27 of 2 stations within 100 miles of Death Valley available to make these estimates during these years.

Daily Estimated July Tmax at Death Valley: 1935-1938

Next, let’s travel back to the 1930s, when there were fewer stations to do these estimates (Fig. 3).


Fig. 3. Daily estimated Tmax temperatures for Death Valley during 1935, 1936, 1937, 1938 from surrounding stations (blue) compared to those observed at Greenland Ranch (orange, 168 ft. below sea level) and Cow Creek (grey, 151 t. below sea level).

Despite only having 7 or 8 stations from which to estimate Death Valley temperatures, the agreement is still reasonably good in 1935, with no bias between observed and estimated, but 1-3 deg. F bias at Greenland Ranch vs. estimated in the following 3 years. There are also a few low temperature outliers in 1937-38 at Greenland Ranch and Cow Creek; I don’t know the reason for these.

Daily Estimated July Tmax at Death Valley: 1912-1915

Finally we examine the period in question, when the 134 deg. F world record temperature was recorded on July 10, 1913 (Fig. 4).


Fig. 4. Daily estimated Tmax temperatures for Death Valley during 1912, 1913, 1914, 1915 from surrounding stations (blue) compared to those observed at Greenland Ranch (orange, 168 ft. below sea level).

During these years there were only 3 to 7 stations from which to compute Death Valley Tmax. In 1912, despite only 3 stations, the reported temperatures averaged only 3 deg. F above those estimated from surrounding stations. But in 1913 (the year of the record) the observations averaged an astounding 9 deg. F warmer than the surrounding 5 stations would have suggested. On July 10, the excess was 15 deg. F!

That second week of July 1913 was indeed unusually hot, and it was during this time that the ranch foreman (Oscar Denton) responsible for making the temperature readings from an official instrument shelter provided by the U.S. Weather Bureau in 1911 might have replaced the official values with values that more accorded with the heat he and his supervisor (Fred Corkill) were feeling on his veranda, away from the USWB instrument shelter which was sited next to an irrigated field. Bill Reid covers the details of correspondence between Corkill and a USWB official in San Francisco regarding the shelter temperatures and how much cooler they were compared to what was measured by a second thermometer farther away from the irrigated field. Reid believes (and I agree) that the shelter temperatures were, at least for a time while Denton was responsible for tabulating the daily measurements, replaced with measurements from a separate thermometer having uncertain quality and siting away from hot surfaces exposed to the sun.

So, How Much Hot Bias Exists in the 134 deg. F “World Record”?

We will never know exactly how much warm bias exists in the world record value. But from comparison to the biases in 1912 and 1914, I would say 9 to 12 deg. F is a reasonable estimate.

Of course, this might be adjusted somewhat if one assumes a slightly different lapse rate than the -0.004 deg. F per ft. I have assumed here (see Fig. 1). For instance, what if the air mass on July 10, 1913 had an exceptionally steep lapse rate, such that an even greater adjustment for elevation needed to be made to estimate the hot temperature in Death Valley? If I use use the lapse rate estimated from the 5 surrounding stations on July 10, 1913 (see Fig. 5), that lapse rate value is indeed “steeper”, at -0.0053 deg. F per ft. But if we use that value to estimate the Death Valley temperature, it is still 10 deg. cooler than the 134 deg. F recorded value. This is still within the 9 to 12 degree bias range I mentioned above.


Fig. 5. The world record value of 134 deg. F (red) is 10 deg. F warmer than that suggested by the surrounding higher-elevations stations’ temperature variations with elevation on July 10, 1913.

Conclusion

The 134 deg. F world record hottest temperature from Death Valley is likely around 10 deg. F too high, compared to elevation-adjusted temperatures from surrounding stations. The most likely cause is that the ranch foreman’s reported measurements were (shall we say) unacademically recorded. I find it rather remarkable that the world record hottest temperature from Death Valley was not revised many years ago, since the methods for “fact checking” the record are fairly simple, and based upon meteorological principles known for well over 50 years.

 

Fake weather stations produce fake data.
Research: “103 Of 302 Weather Stations In United Kingdom Do Not Exist At All!
By P Gosselin on 27. November 2024

Paul Homewood wrote about poorly sited weather stations in Great Britain, thus making readings and climate data rather questionable.

The European Institute for Climate And Energy (EIKE) now reports of Great Britain’s phantom weather station network: one third of all its stations don’t actually exist.

By Maurice Forgeng. Original article: Epoch Times here.

“All climate forecasts are based on more or less long-term documented measurements from weather stations. They can be used to determine past developments as well as possible trends in future local temperatures. Meteorologists also compile national and international trends based on the data from many measuring stations.”

“But what if much of this basic data is incorrect – or even made up? This is what journalist Ray Sanders claims about the measured values in the UK. According to his research, 103 of the 302 weather measuring stations in the United Kingdom do not exist at all. Nevertheless, they provide official data that is available to everyone on the website of the national weather authority.”

Sanders investigated the weather stations listed by the British Meteorological Service and found discrepancies in the data. For example, the Dungeness weather station supposedly on a nuclear power plant doesn’t exist. In fact, four out of eight stations in Kent do not exist Also, coordinates for many stations are inaccurate.



The alleged location of a weather station is said to be on the roof of a nuclear power plant, according to the coordinates of the British Weather Service. Photo: Google Maps

The most egregious: Sanders had made several inquiries to the weather authority to obtain more detailed information about the weather stations. He reported in the open letter: “Of the 302 locations mentioned, over a third (103) do NOT exist. The Met Office refused to tell me exactly how or where the alleged ‘data’ for these 103 non-existent sites came from.”

Consequently. Sanders questions the scientific validity of the weather data, noting similar issues with weather data collection in the USA where authorities collect measurements for weather stations that have been out of operation for years or decades and corrections are regularly made to some temperature series.

In the USA, it has been noticed that older temperature values have almost invariably been made colder and more recent measurements warmer. This gives a clearer impression of a warming of the regions.

Full report at EIKE

Original report: at Epoch Times.

 

Deleted.

 

Bad data, indeed.

Hot Death Valley Days: Don’t Trust Those Temperatures
December 13th, 2024

AP photo/ Ty O’Neil.

Summary

• Previous research has shown the temperatures recorded at Death Valley National Park (DVNP) have curious warm biases on very hot days, possibly due to instrument deficiencies or proximity to mounting structure apparatus and other manmade structures.
• Here it is shown from 21 years of summertime (June, July, August) data that DVNP has many more days when temperatures are much higher than those at the nearby Stovepipe Wells station, than when Stovepipe Wells has hotter days than DVNP station.
• These lines of evidence suggest that the hot summer daytime temperatures reported at Death Valley National Park have potentially large biases, and should only be used for their entertainment value. . . .

 

More bad data.
New Study Casts Doubt On The Accuracy And Reliability Of The Modern And Paleo CO2 Record
By Kenneth Richard on 4. March 2025

Reconstructed ice core CO2 values and modern CO2 and CH4 measurements do not support the narrative that human emissions are driving changes in atmospheric greenhouse gas concentrations.
New research extensively reviews the “pitfalls” of believing the conventional wisdom about modern CO2 concentration variations, as well as the “flaws” in reconstructed CO2 values from ice cores.

The record of annual increases (or decreases) in CO2 (ΔCO2) from the NOAA database indicate that prior to 1958 there were years when CO2 increased by 4 or 5 ppm from one year to the next, or even decreased by 3.5 ppm relative to the previous year.

“efore 1958 there are several years which show an increase of about 5 ppm or a decrease of about 3.5 ppm. … If these reconstructed values are correct, then there have been many years since the Industrial Revolution in which atmospheric CO2 has decreased.”

Problematically for the anthropogenic global warming narrative, it is not possible for human CO2 emissions to have driven either the 4.9 ppm increase from 1872 (286.66 ppm) to 1873 (291.56 ppm), or the 3.5 ppm decrease from 1908 to 1909. Year-to-year changes in human emissions could not have been nearly large enough to produce that much change – in either direction.

“The most impressive value is the of 4.9 ppm in 1873.”

“The year when human emissions exceeded 7.8 gigatons (Gt, equivalent to 1 ppm) was 1913. Before this year, an increase of more than 1 ppm per year is impossible, even when the CO2 increase over the Industrial Era is assumed to be of anthropogenic origin.”



Image Source: Ato, 2025
The NOAA record of year-to-year change in the methane (CH4) concentration reveals a similar problem, but this time in the 1980s-to-present data.

Not only are human CH4 emissions not large enough to account for the annual changes, but the changes show a nearly 40-year declining trend, 1984 to 2013, before rising again in the last decade.

“Since atmospheric methane has actually dropped even in present days, when humans are emitting large amounts of the gas [CH4], it cannot be assumed that about 1000 ppb [parts per billion] would have accumulated and risen in previous periods of low emissions.”



Image Source: Ato, 2025
There are fatal flaws in assuming we can actually derive accurate estimates of past global atmospheric CO2 concentrations from ice bubbles located at one site on Earth, Antarctica.

The author succinctly summarizes the problems with believing air from hundreds of thousands of years ago is fully sealed, uncontaminated, and never-changing in bubbles within the ice, and at no time in any and all excavation processes does the globally-representative CO2 value fail to deliver the precise measurement.

“There is no experimental evidence to prove the basic assumption of this method [that assumes the ‘composition of the gas at the time of capture will remain the same indefinitely’], that the gases in the upper layers will mix together for several years to thousands of years, and once they are sealed off, they will remain constant and no changes will occur.”



Image Source: Ato, 2025

 

The UHI rides again.
Our Urban Heat Island Paper Has Been Published
May 15th, 2025

It took the better part of two years to satisfy the reviewers, but finally our paper Urban Heat Island Effects in U.S. Summer Surface Temperature Data, 1895–2023 has been published in the AMS Journal of Applied Meteorology and Climatology.

To quickly summarize, we used the average temperature differences between nearby GHCN stations and related those to population density (PD) differences between stations. Why population density? Well, PD datasets are global, and one of the PD datasets goes back to the early 1800s, so we can compute how the UHI effect has changed over time. The effect of PD on UHI temperature is strongly nonlinear, so we had to account for that, too. (The strongest rate of warming occurs when population just starts to increase beyond wilderness conditions, and it mostly stabilizes at very high population densities; This has been known since Oke’s original 1973 study).

We then created a dataset of UHI warming versus time at the gridpoint level by calibrating population density increases in terms of temperature increase.

The bottom line was that 65% of the U.S. linear warming trend between 1895 and 2023 was due to increasing population density at the suburban and urban stations; 8% of the warming was due to urbanization at rural stations. Most of that UHI effect warming occurred before 1970.

But this does not necessarily translate into NOAA’s official temperature record being corrupted at these levels. Read on…

What Does This Mean for Urbanization Effects in the Official U.S. Temperature Record?

That’s a good question, and I don’t have a good answer.

One of the reviewers, who seemed to know a lot about the homogenization technique used by NOAA, said the homogenized data could not be used for our study because the UHI-trends are mostly removed from those data. (Homogenization looks at year-to-year [time domain] temperature changes at neighboring stations, not the spatial temperature differences [space domain] like we do). So, we were forced to use the raw (not homogenized) U.S. summertime GHCN daily average ([Tmax+Tmin]/2) data for the study. One of the surprising things that reviewer claimed was that homogenization warms the past at currently urbanized stations to make their less-urbanized early history just as warm as today.

So, I emphasize: In our study, it was the raw (unadjusted) data which had a substantial UHI warming influence. This isn’t surprising.

But that reviewer of the paper said most of the spurious UHI warming effect has been removed by the homogenization process, which constitutes the official temperature record as reported by NOAA. I am not convinced of this, and at least one recent paper claims that homogenization does not actually correct the urban trends to look like rural trends, but instead it does “urban blending” of the data. As a result, which trends are “preferred” by that statistical procedure are based upon a sort of “statistical voting” process (my terminology here, which might not be accurate).

So, it remains to be seen just how much spurious UHI effect there is in the official, homogenized land-based temperature trends. The jury is still out on that.

Of course, if sufficient rural stations can be found to do land-based temperature monitoring, I still like Anthony Watts’ approach of simply not using suburban and urban sites for long-term trends. Nevertheless, most people live in urbanized areas, so it’s still important to quantify just how much of those “record hot” temperatures we hear about in cities are simply due to urbanization effects. I think our approach gets us a step closer to answering that question.

Is Population Density the Best Way to Do This?

We used PD data because there are now global datasets, and at least one of them extends centuries into the past. But, since we use population density in our study, we cannot account for additional UHI effects due to increased prosperity even when population has stabilized.

For example, even if population density no longer increases over time in some urban areas, there have likely been increases in air conditioning use, with more stores and more parking lots, as wealth has increased since, say, the 1970s. We have started using a Landsat-based dataset of “impervious surfaces” to try to get at part of this issue, but those data only go back to the mid-1970s. But it will be a start.

 

It is intuitively obvious that as the temperature differential between built-up and surrounding rural areas increases with population, heat flows faster from the warmer to the cooler areas. ISTR this is one of the effects that is proportional to the fourth power of temperature differential, so it creates an effective ceiling of ~5C on the urban warming effect.

Population might not be a very good indicator of non-CO2 human warming effects in rural areas because while farm population density has declined with increased mechanization of agriculture, energy use for heated buildings, outdoor lighting, etc. on farms and ranches has increased dramatically.

It's not enough for a site to be merely "not urban or suburban." It has to be remote from all human energy use and temperature-disturbing effects such as paving and deforestation. When you look only at those pristine rural sites, it is no warmer now than in the early 1940s.

As explained above, even "prosperity" is an imperfect proxy for human energy use and land use changes that bias the instrument temperature record trend upward.