By P Gosselin on 7. October 2026
“Not a Single Heat Record was Broken in September in the United States. Some Records Going Back to 1881.
These Actual Facts Do Not Correlate With the Hottest Month Manipulated Averages the Government is Trying to Feed You!”
Many of the heat records occurred decades ago and still hold.
Many were set in the 1880s, 1930s, 1950s and 1980s.
I am surprised to see none of the high temperatures measured during the Dust Bowl of the 1930s surviving as records.
They do. They’re just in all the grey areas between the cherry-picked temperature records shown.
Self-deleted. Original comment didn’t make sense after I reread it.
Ahh. Dust bowl records as high as 121F were set in June and July, 1936. By September, the heat wave had passed.
The dust bowl was more about plowing land that shouldn’t have been plowed, rather than heat waves.
My mother-in-law told me about when living in Oklahoma during the dust bowl that the trees lost their leaves. Dust didn’t help but that was severe drought, check the records. 1950s drought was worse for Texas but 1930s was still bad.
I don’t doubt there was severe drought- just saying the drought alone didn’t cause the dust bowl. The high plains shouldn’t have been plowed up.
Actually the removal of hedgerows and wind breaks probably caused more problems than the plowed ground. Wind hadn’t scoured the dirt like in the 30’s at any time prior, that was recorded at least, even though high winds had been encountered before.
That’s a problem that is reoccurring today. Lots of farmers are removing hedgerows and windbreaks to increase planted ground. The only difference today is the use of no-till which still leaves some ground cover to prevent the scouring of the topsoil.
Long term impacts are, as usual, unknown. It has certainly reduced habitat for the wildlife for one.
It wasn’t the land shouldn’t have been plowed, a lot of it was plowing straight furrows miles long, mostly parallel to prevailing wind directions. The Soil Conservation Service that was started to address the problems introduced techniques like contour plowing and plowing perpendicular to prevailing winds. They also introduced planting trees in shelter belts to slow and break up surface level wind. The changed methods kept the wind from drying up the soil then blowing it away.
Some people like to claim that livestock “destroy the land” and strip away the vegetation. Um, yeah, that’s a no. How do they think the Great Plains supported millions of bison? Cloven hooved ungulates – they poop out the seeds, neatly packaged in fertilizer. Their hooves don’t “compact the soil”, they continuously churn it up, tilling the seeds and their poop into the ground.
If grazing animals destroyed what they depend upon to live, there wouldn’t be grazing animals.
Alan Savory mis-identified the reason that Elephant territories in Africa were turning to desert, as the Elephants themselves. His “fix” was to have 40,000 of them shot. Their grazing lands started drying up and dying off even faster.
He goes into detail in this TED talk, where he takes responsibility for the Elephant culling. He also goes into detail on his projects to restore desertified land, by grazing cattle on it. https://www.youtube.com/watch?v=vpTHi7O66pI
The Denis and Joseph comments are spot-on. An award-winning book tells the sad story of the “dust bowl” years. by Timothy Egan: The Worst Hard Time: The Untold Story of Those Who Survived the Great American Dust Bowl
Keep in mind the data is for selected cities.
I tested the Map against this webite for Denver, Kansas City, and Chicago.
https://recordtemps.in/denver-colorado/on/09-01
https://recordtemps.in/kansas-city-missouri/on/09-01
https://recordtemps.in/chicago-illinois/on/09-01
each website gives the Max temp for the month
The map with the record temp for Sept match to these web sites I supplyed.
Each website provides the ability to find max temp for any date of the month Back to all dates or map of all stations and step thru temps day to day each month.
So I guess the map is accurate or they used the same data
There are 865 “official” weather stations that make up the US surface temperature history (except for the Climate Reference System which is rarely cited by anybody.) Of these 26 (3% of all) are “Class 1” stations, the best located. Class 1 stations must be at least 100 meters away from any human heat source such as a building, road, runway and so forth. The remaining 97% of the stations are Class 2 through 5. The worst, Class 5 at 11%, are from airports. NOAA claims to “adjust” these poorer thermometers to correct for their flaws. To determine whether any claimed city temperature measurement is meaningful, one needs to know the class of the reporting thermometer, where specifically it is located, the reported value recorded by the thermometer, what the NOAA “correction” was and why it was made. Note that the Class 5 thermometers have an error band of 5C (9F). That is a lot to correct especially if it is an airport thermometer subject to jet exhaust which can have a temperature of well over 1000F. One also needs to know the thermometer technology, liquid in glass (slow response time to sudden changes) or electronic (near instantaneous response time to sudden changes.) I like to think that NOAA does the best they can with crummy data, but when your job relies on increasing temperatures……
Denis,
Can you please link to any US or other good international comparisons between temperatures from early glass thermometers and modern thermocouple electronic devices?
This is a key comparison which seems to suffer from “must not be spoken about” syndrome. Geoff S
CRN Doumentation
Documentation
ASOS
aum-toc
See Page 12
Current rate of global warming is 0.0156 C/y per UAH data.
Hardly a cause for concern.
It’s the 4 decimal places that’s a concern.
C’mon man, nothing we humans possess can possibly accurately, consistently measure the temperatures of all localities around this planet 24×265 to come up with an “average” change of 0.0156 from one year to the next.
But we have a consensus. What more proof do yo9u need? /sarc
Reminds me how the state of Wokeachusetts forestry authorities once gave a figure to 4 decimal places the growth of board footage in the state/year based on the heavy sampling of one plot per 600 acres (nationally based FIA data). Of course I foolishly challenged them over this- and as usual, they just ignored me as if I wrote my comments in hieroglyphics. 🙂 After all, they had a consensus and I only had my own single brain. 🙂
I remember very well the one-man Fauci consensus that became mandatory for the rest of the world, and also the 8.5 mio he paid to Dr Anderson and Dr Gerry to do a 180 and join his consensism.
*
365 even 🙁
The funny thing is, it’s not even a temperature measurement. At minimum, it’s an average of an average subtracted from another average. At best, one might call it an index, but that wouldn’t scare anybody.
The trendology trolls will be along shortly to correct this heresy.
They need to buy new calculators if 4 decimals is all they can get.
They should go to 11.
NOW we’re talkin’
👍😃
I believe that the value is simply the numerical average of all of the satellite measurements made at each measured elevation each month. Thats a lot of measurements. I don’t know what the +/- values are nor the precision.
When you consider that the global atmosphere is actually a time/space FIELD that varies every second of every day and at every point in space at the same time, and not a set of isolated, independent objects, the potential parent distribution is literally beyond count. Even with 10,000 observations in a data set that is still *sparse* sampling compared to the size of the parent. A single value known as the mean of those observations can hardly be considered an accurate measurement of that time/space rapidly varying field. Especially when the observations are taken at different points in time and space. That is not conducive to finding a STATE value for the field that can be compared to another STATE value at a later point.
It gets even worse when you consider that the climate at any point in space is directly related to the variances of the temperature at that point in space and not to the mid-range temperature.
It’s all a bunch of voodoo statistics done without any regard to the actual physical reality behind the data. Numbers is just numbers, all measurement uncertainty is random, Gaussian, and cancels, plus temperature variance isn’t climate, mid-range temperature is.
So, how ’bout this?
0.0000000567.
Recognize it?
This is the most apt number for these constructs –
5/8
(of sweet fvck all)
And the sea is rising the tremendous rate of 2mm/yr. Surely, sufficient for panic. 🙂
1993-present data, including acceleration calcs a current rise rate of ~4.2mm/year. If it kept up ’til 2100, another 18″ of SLR.
I agree that this can be dealt with, via trillions of USD. Less of a worry than the fungilence, pestilence, drought, flooding, extreme temps already evident from ACC.
That’s not the statistic I’ve read. Others here know more about this than I do as I don’t claim to be a climate scientist or expert on anything beyond forestry. I just think the correct figure will be closer to 2mm than 4.2.
Download the 1993-present satellite GMSL data. Perform a quadratic best fit, and extract the acceleration, rate, intercept coefficients. Instant rise rate is the first derivative of the time/level equation, ax^2+bx+c, or 2ax+b. Solve that for Now, and you get an “instant slope” (remember precalc?) of ~4.2mm/year. Now, the average, might be close to 2mm/year, but acceleration changes that.
Oh, yuh, right- I’m gonna do that. 🙂 Not that I couldn’t but I’ve been out of college since the early ’70s. 🙂 I’m sure there are people here who know the correct answer. Or I could look it up. In fact, I will. I’ll ask ChatGPT. It knows everything about everything. 🙂
Your ChatGPT claim is obviously sarc. But I’m sure you can get AI to do what I recommended. And if your math is rusty or you learned other things instead, I did not mean to condescend. Your current work contributes lots to the public good.
I’ll skip much of what Hal, my AI guy said but his conclusion:
Maybe so, maybe not. Others may differ. Regardless, I’m not terrified. We’ll survive. We’re not going to spend trillions to stop using fossil fuels to avoid this- that’s for sure. I’m interested what others here may say, though they’ve probably moved on to the next exciting topic. 🙂
Those are important, different, issues. But if you start with a bogus value for current, you’re off the rails from the get go.
As I posted seconds ago, I used your figure of 4.5mm. See that comment below. I’m more horrified by the minute. I better have a stiff drink to calm me down. 🙂
Made me look. AI says 4.5mm/year. The diff between them and me might be from them using a rollling 60 day average, or because I {lazily) used the last measured date, instead of today.
Hal just said that 4.5 mm is about 3/16″. Now I’m truly terrified. Good thing I’m at 600′ elevation!
It’s every year. And if kept up, that rate would 2* by 2100. We would have another 18″ of SLR between now and then, and twice that if you did the same calcs since 1980.
Check population density maps. Much higher near coast lines. Not biblical, but it will cost trillions to accommodate. You should instead try and help out with upcoming fungilence, pestilence, since forestry is your livelihood.
Land mass can also be shrinking so maybe the amount of water is the same?
Some land mass is “shrinking” from aquifer depletion, clay dehydration, etc.. Some is isostatically rebounding. Satellite measurements don’t care.
The satellite measurements for sea level have the very same problem satellite measurments of temperature have – measurement uncertainty.
The measurement uncertainty for sea level due to slight orbital variation over both the long term and the short term are large enough to make the 2mm value part of the Great Unknown let alone >4mm!
The measurement uncertainty for satellite temperature measurements is at least +/-4C, driven mainly by path loss uncertainty for any specific observations plus long term drift in the hot calibration source, orbital drifts, and self-calibration. I didn’t realize that one of the main sources of “calibration” for the satellites is self-calibration of the network of satellites. It’s like going to a bunch of construction sites, grabbing a tape measure from each, and then saying the average from each measuring the same thing means they are all *calibrated” – no external calibration against a standard reference.
My guess is that the sea level satellite network is “calibrated” the same way!
Every eval ever performed has “measurement uncertainty”. The question is, does it materially matter to the eval being performed. And in virtually every case you dispute, temp, GMSL, whatever, it’s hell no. In this case, both the rise rates and accelerations of same vastly outdistance the Gordonian nitpicks.
Folks, the difference between real engineers and those who just play one on WUWT…
Actually, in engineering a sensitivity analysis should be performed. What does a plus or minus 1 mm (3.5 to 4.5 to 5.5) cause? What is the best mitigation? Sea walls? Fill in 1st floors? Raising those building that are amenable? Just screaming we are all going to die from CAGW if we don’t stop fossil fuels is not the answer.
Agree. SLR is not a top tier worry with me. If I recall, Nick Stokes agrees as well.
“The question is, does it materially matter to the eval being performed. And in virtually every case you dispute, temp, GMSL, whatever, it’s hell no.”
You also need to apply the same logic to the actual absolute values. Is a change from 0F to 0.1F a material matter? How about from 91F to 92F? Is that a material matter?
Actually it might make some ancient port cities actual port cities again.
Where’d you pick up those motorized goal posts? Alibaba?
What’s the R^2 for your fit? 0.2?
Fair question. Awful guess. The absolute ratio of expected value/standard deviation for the acceleration coefficient was 17.9. For the rise rate coefficient, 17.4, For the intercept, 16.9. The money value – the question you aksed – was slightly higher than your 0.2 guess. 0.97 – the square of the correlation coefficient between the reported and quadratically calculated 33 years of monthly values.
Sorry for the delay – I erased the old eval and had to spend 10 minutes to resurrect it. I’ll save it this time.
I suspect you entered disparate sets of data into an Excel spreadsheet, ran the trend option through all of them and selected second order ’cause it fit your acceleration agenda even though it was little different from the basic linear fit.
That’s how that statistical numbskull at the College of London produced the doubling, x^2, exponential global C-19 pandemic.
Even a linear equation w m=2 doubles over time.
2 4 6 8
vs
2 4 16 256
In the first several months there is little difference between the two.
Only after several weeks of data was it obviously second order not “exponential.”
“little different”. It’s the best fit available. And:
Easy to actually do the work.
“That’s how that statistical numbskull at the College of London produced the doubling, x^2, exponential global C-19 pandemic.”
Unlike them, I didn’t extrapolate. I just pointed out the best trend of existing data. I you want to know if existing data shows interpolated acceleration, it’s the definitive way to do so.
Well, interesting but beside the real points.
Earth is cooler with the atmosphere, water vapor and 30% albedo not warmer.
Ubiquitous greenhouse effect energy balance graphics do not balance and violate both GAAP and the Laws of Thermodynamics.
Kinetic heat transfer processes of the contiguous atmospheric molecules render a black body surface and “extra” greenhouse effect energy impossible.
GHE = bogus & CAGW = scam.
More motorized goal posting. Way back when, I responded to your question. Many here should heed Mr. Eischenbach, who, until recently, was a frequent poster here.
Respond to what I said, and not what you think I said.
No “disparate sets”. Just the one*. If you think you can find a better fit with any other than a nonsensical polynomial, KYSO.
FYI, here’s the chance that the acceleration is negative.
5.65E-72. 3 sig figs to expected value/standard deviation, so that’s what I went with. Rat me out to the sig fig police if you differ….
Pro tip. Use linest, choosing the y data for y argument , and date and date^2 for x argument. House all in a 3*5 array, and your coefficients will be identical to the coefficients you get when you scatter graph it and aks for a 2nd order trend line. The advantage is that you get coefficient standard deviations as well.
Just because “alarmists” use hyperbolic language doesn’t mean it’s OK for “our side” to do the same thing.
Start at NOAA’s “Daily Weather Records” website (direct link).
Switch to the “View Selected Records” tab, and select the following options :
Timescale = Monthly Records
Record Type = Broken
Parameter = Highest Max Temperature [ default option ]
Location Category = Country [ default option ]
Date Range = 2026-09-01 to 2026-09-30
Country = United States [ default option ]
Clicking on the “Show Records” button gave me 75 entries, and then clicking on the “Prev. Date” header listed them in “oldest first” order.
Looks like Casey, Illinois broke the longest-lasting September “heat record” by a whole 3°F, but there are 74 others to look through as well.
Checking the notrickzone it seems to link to a Facebook site. This is shown as –Climate Change — the Scam.
It would be inappropriate for Pierre Gosselin or WUWT to alter the heaadline text without an explanation.
“Date Range = 2026-09-01 to 2026-09-30″
A monthly record for only one month? Typo?
[ … Sigh … “More in sadness than in anger” I respond … ]
You have the ability to not just read my post and “Reply” to it, but cannot click on the provided link and look for yourself at what the “context” of that date range is ?
The “2026-09-01 to 2026-09-30” range filtered the NOAA database for the new “Highest Max Temperature” records that were set (/ “Broken”) during the specified period, i.e. the month of September 2026.
NB : Selecting the “Monthly Records” option was, in my opinion (you and other readers may disagree), appropriate for the ATL headline that started ” US September “Heat” Was No Record … ” and the sub-header on the AccuWeather graphic just 4 lines of text (and a large gap, for some unknown reason) later.
In case you choose to persist in your laziness I include a screenshot of the first 10 entries in the table resulting from following my suggested parameter configuration of the supplied NOAA link.
After sleeping on the issue it occurred to me that not all records are Tmax.
Were any Tmin records set in CONUS for the month of September in 2026 ?
Modifying the parameters on the NOAA webpage to the following gave me 14 rows of data (screenshot copied below for the lazy) :
Record Type = All [ “Tied” or “Broken” ]
Parameter = Lowest Min Temperature
Note that three of those rows are Sonora (California) breaking its (September) 1985 record on the 16th (by 1 F°), equalling the new record on the 17th, and then breaking it again on the 18th (by a second F°).
Two more rows are “Hastings 4 NE” (Florida) equalling its 1981 record on the 27th before managing to break it on the 28th (again by a whole 1 F°).
NB : The “Tamarac Wildlife Ref” weather station in Minnesota broke its (September) 1984 record by a “massive” 8 F° on the 21st. Are there any readers in that state that can confirm very cold nights around 3 weeks ago ?
.
A total of six new Tmin records were set (/ “Broken”) in CONUS during September 2026.
This does not constitute “proof” that the Earth has exited the Holocene interglacial.
This emphasizes the importance of the variability of temperature. That is never included in climate science since it would make people realize that within day and between days variance is CLIMATE. How close you can calculate a mean is totally worthless when compared to real climate change over whichever time period you want to look at.
As someone born in the first half of the last century I’ve seen a lot of different weather come and go. I sneer at the born-yesterday Chicken Littles who are forever crying either or both “The sky is falling!” and “Wolf!”
I know I’m supposed to be getting wiser as I age but I remain puzzled as to how so many can be so gullible in this age of instant and ubiquitous information…and how so many are apparently unable to separate the wheat from the chaff and the sheep from the goats…if I can think of any other trite folk expressions I will include them later…
Amen to that, and ditto on age. Well, experience is a great teacher and a long life gives lots of experience. The problem with instant and ubiquitous info is that nobody has been taught how to think.
“Not a Single Heat Record was Broken in September in the United States. “
The graphic posted shows all-time September record max temps from US cities for a single day, and not the record average September max for those cities.
NOAA was not referring to maxes, they were referring to mean temps (max+min/2) for the whole month.
A very different thing.
“The average temperature for the CONUS in September was 69.4°F, 4.6°F above the 20th-century average, making it the warmest September in the 132-year record, surpassing the previoups record set in 1998 (68.95°F).”
https://science.nasa.gov/earth/earth-observatory/summer-goes-out-with-a-heat-dome/
“Several locations set new daily high temperature records on September 15. These included Dallas, Texas, at 101ºF (38ºC), Memphis, Tennessee, at 99ºF (37ºC), and Nashville, Tennessee, at 100ºF (38ºC). The cities were all at least 12ºF warmer than normal that day, with Nashville breaking its daily-high record from 1927. The day before, Nashville also set a record-high minimum temperature of 75ºF (24ºC).”
69°F in 2026 was hotter than 69°F in 1998. got it…
The Mean Temp anomaly for September 2026 was 3.45F for Climdiv stations and 3.91F for USCRN stations.
In September 1998 the ClimDiv anomaly was 2.98F (USCRN not yet established).
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/9
“The average temperature for the CONUS in September was 69.4°F, 4.6°F above the 20th-century average, making it the warmest September in the 132-year record, surpassing the previoups record set in 1998 (68.95°F).”
Whether you said this or NOA said this, it’s still stupid. They’re both 69°F. Didn’t bother to read the rest of your stupid comment.
Anomalies are a waste of time. They only measure a temperature change, but totally miss the energy content at any location. Heat energy is what we need to investigate, not temperature! You can’t convert an anomaly into enthalpy!
As I continue to look into heat transfer form soil to air, I keep running into the fact that enthalpy must be used to evaluate how “hot” it actually is for any given temperature. Tmax, Tmin, Tavg, Tmonth are not good proxies.
I know everyone uses temperature by tradition, but heat energy is not an easy subject and requires more difficult analysis. Which has more heat energy in the atmosphere at 80 degrees? Miami or Las Vegas. How much IR from the soil is trapped by H2O at each location?
Don’t be fooled by climate science’s short-sighted view of insolation warming the air, it doesn’t. Insolation warms the soil and soil warms the atmosphere.
So what happens to the ~20% of insolation that is absorbed by the atmosphere?
Some is absorbed by scattering. Some is absorbed by CO2. Some is absorbed by H2O.
Some of the absorbed is radiated toward the earth but that is not “back radiation”. It is just “original” radiation in a different form and CO2 is not the original source.
You said: “Don’t be fooled by climate science’s short-sighted view of insolation warming the air, it doesn’t. Insolation warms the soil and soil warms the atmosphere.”
Which false, ~20% of the insolation is absorbed by the atmosphere thereby warming it.
About 50% of the insolation is absorbed by the surface.
“These Actual Facts Do Not Correlate With the Hottest Month Manipulated Averages the Government is Trying to Feed You!”
September 2026 was the hottest September on record according to USCRN:
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/9
Anthony Watts himself supports these data and does not believe that the averages derived from the CRN are manipulated. He states here:
“The graph shows the Average Surface Temperature Anomaly for the contiguous United States since 2005. The data comes from the U.S. Climate Reference Network (USCRN) which is a properly sited (away from human influences and infrastructure) and state-of-the-art weather network consisting of 114 stations in the USA.
These station locations were chosen to avoid warm biases from Urban Heat Islands (UHI) effects as well as microsite effects as documented in the 2022 report Corrupted Climate Stations: The Official U.S. Surface Temperature Record Remains Fatally Flawed.”
https://wattsupwiththat.com/u-s-surface-temperature/
“September 2026 was the hottest September on record according to USCRN:”
And also USCRN is warmer than the supposed UHI affected Climdiv.
USCRN is ~ 0.6F higher than ClimDiv for mean maxima,
It wasn’t any hotter than 1998, so you’re wrong.
The Mean Temp anomaly for September 2026 was 3.45F for Climdiv stations and 3.91F for USCRN stations.
In September 1998 the ClimDiv anomaly was 2.98F (USCRN not yet established).
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/9
3°F in 2026 is not hotter than 3°F in 1998. If you have to record it out to 100th’s of a degree (or more) to try to discern a difference then it is meaningless.
Right on point. Heck, it tells me they need a calculator with more display digits. Once you go beyond the resolution of the measuring device, you are just reading bones scattered on the table.
And here I was thinking they were reading chicken entrails.
September 2026 has a ClimDiv of 3.45 degF
September 1998 has a ClimDiv of 2.98 degF
That is difference of 0.47 degF or 0.5 degF to 1 sig figure
Far from “meaningless”.
What’s the range of the data that went into each of the averages? If you can’t give a range (or an estimate of the range) then a difference of 0.5 °F is meaningless, or at least you haven’t demonstrated that it is meaningful (or significant, as some like to call it).
Err, the range is the whole ClimDiv network over the period specified.
That year’s September temperatures – daily maxima and minima
The range is not the whole ClimDiv network! There isn’t one climate everywhere!
Anomalies tell you nothing be temperature change. Anomalies can’t tell you heat energy changes.
Convert those into enthalpy and do you see the same thing in all locations?
Just one of your “six impossible things before breakfast” Jim.
Pray tell me how I can discover the partial pressure of WV of the air at the moments those many temperature values were measured and then when you have we can go and average them all together to give a mean enthalpy for the Septembers of 2026 and 1998.
FFS.
The specific enthalpy of moist air can be calculated as h = 1.006 × T + W × (2501 + 1.86 × T), where T is dry-bulb temperature in °C and W is the humidity ratio derived from relative humidity.
Convert Temperature and Pressure
Ensure the dry-bulb temperature (T) is in °C and the atmospheric pressure (P) is in kPa.
Calculate Saturation Vapor Pressure (Pws)
Use the Magnus formula:
Pws = 0.61094 × exp(17.625 × T / (T + 243.04))
This gives the maximum water vapor pressure at the given temperature .
Determine Actual Vapor Pressure (Pv)
Multiply the saturation pressure by the relative humidity (RH) fraction:
Pv = (RH / 100) × Pws
Compute Humidity Ratio (W)
The humidity ratio represents the mass of water vapor per mass of dry air:
W = 0.621945 × Pv / (P − Pv)
Here, P is the total atmospheric pressure.
Calculate Specific Enthalpy (h)
Finally, the specific enthalpy of moist air in kJ/kg of dry air is:
h = 1.006 × T + W × (2501 + 1.86 × T)
The first term (1.006 × T) represents the sensible heat of dry air.
The second term accounts for the latent heat of water vapor plus its sensible heat.
Pressure can be easily calculated using altitude. CRN hourly data has Tmax and RH .
Science isn’t easy. I’ve spent hours with CoPilot getting Python scripts to download the data and calculate various values.
If you want to be more knowledge than most climate scientists you need to do the work to have the correct information to do a correct thermodynamic analysis.
“If you want to be more knowledge than most climate scientists you need to do the work to have the correct information to do a correct thermodynamic analysis.”
Thing is Jim, you seem not to be good at practicality.
We have what we have in terms of historic temperature data, and use it the best we can.
Humidity data not so much especially as wet bulb temp is even more prone to user error (drying out of the wick).
Given that WV turns over in a matter of 9-10 days in the atmosphere from evaporation to condensation and fall-out, there is regression to a mean value for the WV content.
Thus the inclusion of it with an enthalpy metric is irrelevant.
You are aware that WV increases at the rate of ~ 7% per 1C rise in temp, and therefor so does enthalpy.
So you are satisfied with tradition. Why am I not surprised? Trendologists aren’t interested in science, only in the ability to draw nice charts. Just like climate science. All they want is to be able to determine a nice temperature that can be charted.
An actual wet bulb temperature is not needed in the procedure I gave you. ASOS stations in the U.S. since about 1980 have sufficient data to calculate a relative humidity using the Magnus formula. All you need is Dew Point and Temperature.
“Thus the inclusion of it with an enthalpy metric is irrelevant.” Exactly how do you come to this conclusion? What is your definition of an enthalpy metric? WV is what holds latent heat. It is what is warmed by the earth’s IR radiation.
If you want to limit your calculation to a 10 day average at Tmax and Tmin, be my guest. It still needs to be included.
As I said, doing science is hard. Believing climate science that deals only with a part of the whole, doesn’t tell you the whole story.
There are Python distribution for Windows, Android, IOS, Linux, etc. Use you favorite AI to produce scripts that will compile the information you need. You will begin to see how much information climate science ignores in their pursuit of temperature trends.
Ignores by making garbage assumptions about the real world.
You and Teyve in “Fiddler on the Roof” would get along *splendedly*.
And you’d be doing it by candle light!
Huh? That might be true if evaporation and condensation ever actually STOPPED for that 9-10 days!
“You are aware that WV increases at the rate of ~ 7% per 1C rise in temp, and therefor so does enthalpy.”
Perhaps you should look at PV = nRT someday.
Tell us exactly what “hot” means. Does hot mean highest temperature? Does hot mean the highest heat energy content? It does matter. Temperature misses a lot of the energy being moved around in water vapor. Consequently, you can’t really define what is happening to all the energy reaching the earth.
Here in Australia we also have a problem with historic “records” in our official Bureau of Meteorology historic daily temperature data. There are two main data sets, labelled CDO and ACORN-SAT.
CDO is what the public can download from the BOM site Climate Data Online that BOM states to be raw, unadjusted and much the same as original observers wrote down.
ACORN-SAT is the BOM adjusted set, for Australian Climate Observations Reference Network – Surface Air Temperature. Acorn, the subjectively adjusted set, is what our BOM sends internationally to places compiling global temperature pictures.
There are several problems such as failure to properly estimate uncertainty, but the main one here is double dipping. The CDO set is NOT raw and unadjusted. It is riddled with undisclosed adjustments. We have adjustments of adjustments. Also, a recent test has retrieved, with difficulty, some views of original records for Sydney that are very different to those being used as “much the same as what the original observers wrote down”.
The more we study these BOM temperature data sets, the more we find a standard of science below expectations, below the standards used for comparable data sets in other disciplines like geochemistry.
Action is being taken in Australia. Readers in other countries might benefit from a forensic audit of your own fundamental temperature data. Geoff S
Keep the faith Geoff.
I’ve spent the last two days doing a deep dive int CoPilot about temperature averages. Try this on for size.
If climate science really wants to examine climate change in temperature, they should be using the variance in a day, month, or annual data set of temperature. THAT IS the “climate change” if you will. The uncertainty of a temperature average should include three items.
You can’t really support using the GUM for uncertainty of the average of temperatures because it requires you measure the same thing, i.e., the monthly average. The measurements are actually daily temperatures, not monthly average temperatures, so the GUM does can’t be used. The same thing is not being measured. You can determine the total uncertainties through a measurement model, but not through a random variable that is supposed to have monthly temperatures in it.
Googled “SLR”.
Wow!!!
Everybody and his cousin has papers, data sets, colored graphic w notes and arrows, interpretations, predictions…….
Heavily papered & published PhD’s (Certified to know everythang about everythang and not possibly wrong.) cranking numbers out they butts through GIGO software financed by the CAGW grant mills.