Climate Central claims in “Summer Warming (1970–2025) Driven By Climate Change” that summers have warmed in 97 percent of 243 U.S. cities and that human-caused climate change is the leading driver in 91 percent of them. This is misleading at best. The analysis relies on biased average temperature trends and model-based attribution while downplaying one of the most important and well-documented drivers of urban warming: the Urban Heat Island (UHI) effect.
Climate Central claims:
Summers have warmed since 1970 in 97% of 243 U.S. cities analyzed. A new Climate Central analysis shows that human-caused climate change is the leading driver of summer warming trends in 221 (91%) of these cities.
Digging deeper, we find that Climate Central’s findings are based on changes in average summer temperature, calculated from daily highs and lows. That matters. Because when you look at how UHI typically manifests, the strongest signal is not in daytime highs, it is in nighttime minimum temperatures.
The Urban Heat Island is not a fringe concept. It is a well-documented physical process, and it has been observed and mapped worldwide. Even the often-biased website Wikipedia gets it, saying, “[t]he urban heat island (UHI) effect is a meteorological and climatological phenomenon in which urban areas experience significantly warmer temperatures than surrounding rural areas. The temperature difference is usually larger at night than during the day.”
Roy Spencer Ph.D., a principal research scientist at the University of Alabama in Huntsville, and the U.S. Science Team leader for the Advanced Microwave Scanning Radiometer on NASA’s Aqua satellite, has globally mapped UHI, including the United States and published a peer reviewed paper on the subject. Just one look at the U.S. UHI map produced from that paper is all you need to understand the concept.
You can clearly see the UHI in major cities in the United States, Canada, and Mexico.
As summarized in Climate at a Glance: Urban Heat Islands, built-up city environments absorb solar energy during the day and release it slowly at night, suppressing natural nighttime cooling. The result is elevated overnight minimum temperatures, especially in cities with extensive pavement, buildings, and infrastructure.
This is precisely the pattern seen in the real-world measurement comparisons done by Climate Central.
In The Heartland Institute’s Global Open Atmospheric Temperature System project in Reno, Nevada, a properly sited reference station was installed just 1.1 miles from the official airport ASOS station. Both stations experienced the same regional weather. The only meaningful difference was microsite exposure. The official station sits amid runways and pavement. The GOATS station was placed over natural ground, more than 100 feet from artificial heat sources, consistent with National Oceanic and Atmospheric Administration guidance.
The results are telling.
Across two full years of side-by-side data, the official Reno station measured warmer than the reference station on the vast majority of days. The biggest difference was not in daytime highs. It was in nighttime lows. In 2024, the official station averaged nearly 3°F warmer in nighttime readings than the properly sited reference station. In 2025, the nighttime difference was still more than 2°F.
That is classic Urban Heat Island behavior.
It is also important to note that Urban Heat Island effects are most pronounced during summer, precisely when Climate Central is measuring its “summer warming” signal. UHI is fundamentally driven by solar insolation. Asphalt, concrete, rooftops, and infrastructure absorb the greatest amount of solar energy during the long, high-sun days of summer. That stored heat is then released slowly after sunset, elevating overnight minimum temperatures and suppressing natural cooling. The combination of peak solar loading and dense built environments means that summer is when UHI influence is strongest and most persistent. Therefore, analyzing summer averages in urban stations without fully isolating UHI amplification risks attributing a seasonally maximized urban signal to broader atmospheric change.
When Climate Central reports that summers have warmed by 2.6°F on average across 236 cities since 1970, and then attributes at least half of that warming to human-caused climate change in 91 percent of cities, it ignores entirely the UHI. It would be a safe bet to believe that most of those 236 cities fall within the cities on the UHI map produced by Spencer that have experience between 0.2 and 2.0 UHI measured warming. Also undercutting Climate Central’s claim is the fact that it uses a statistical attribution model that compares ERA5-based modeled climate with the built in assumption that human-emissions drive heating with counterfactual ERA5-based model with no anthropogenic forcing. In other words, it is model against model with a big dose of UHI thrown in. That’s not science, it’s pre-determining a result.
Although, the document admits that “secondary drivers” include natural climate variability and the urban heat island effect influence measured temperatures, it treats UHI, the prime factor in measured warming, as residual noise.
Using the daily average temperature as a metric amplifies this problem. Because the average is calculated as the mean of daily high temperature and nighttime low temperature, any systematic upward bias in nighttime lows directly inflates the average. The Reno case study demonstrates that station placement alone can introduce 1 to 3°F of warming into reported records, particularly via elevated overnight minimum temperatures. That is comparable to the multi-decade summer warming Climate Central is highlighting.
This is not a theoretical quibble, it is a measurement integrity issue.
Climate Central emphasizes that 22 more summer days are now “hotter-than-normal” compared to the early 1970s. But “normal” is defined relative to 1991–2020 baselines, and hotter-than-normal days are again based on average temperature metrics. Urban growth and densification has intensified around many of these stations over the past half century, thus part of what is being counted as climate signal is localized microsite warming or UHI.
This is especially relevant for airports, which host many official climate measurement stations. Airports are highly built-up environments dominated by concrete, asphalt, jet exhaust, and infrastructure. The Reno comparison is not an anomaly. It is a case study illustrating the systemic vulnerability in the U.S. surface temperature network as documented in the 2022 nationwide project, Corrupted Climate Stations.
None of this proves that all warming is artificial, the Reno report itself makes that clear. But it does demonstrate that before declaring that 91 percent of urban summer warming is primarily human-caused, one must rigorously quantify and account for station siting bias and UHI amplification of average temperatures. In fact, 91 percent or some similar number may be human caused, but caused by the UHI a result of human development and poor measuring site placement, not “global” climate change.
The Urban Heat Island is a primary factor in urban warming trends, not the footnote that Climate Central treats it as. Before attributing summer warming in 221 cities to human fossil fuel use, Climate Central should first ensure that what they are measuring is a long-term atmospheric temperature trend, not the bias from urban asphalt and artificial heat sources.
Anthony Watts is a senior fellow for environment and climate at The Heartland Institute. Watts has been in the weather business both in front of, and behind the camera as an on-air television meteorologist since 1978, and currently does daily radio forecasts. He has created weather graphics presentation systems for television, specialized weather instrumentation, as well as co-authored peer-reviewed papers on climate issues. He operates the most viewed website in the world on climate, the award-winning website wattsupwiththat.com.
Originally posted at ClimateREALISM
Why no mention of USCRN, which WUWT itself claims is…
USCRN has been running continuously since 2005, alongside nClimDiv, which includes data from less well-sited stations that are adjusted to take account of things like UHI.
The “…properly sited… state-of-the-art...” USCRN data are warming faster than the adjusted nClimDiv data!
Check it here. This link is also found on the side-panel of this very site.
If USCRN stations are “properly sited“, as WUWT claims, then adjustments made by nClimDiv to address issues such as UHI must, if anything, be adding a cooling bias to the record over this past 21+ years.
“Why no mention of USCRN”
Did you forget this blog is built around pushing a narrative?
BTW, Jun–Aug CRN temperatures are warming at a rate of 0.65°C/decade. Very rapid warming.
Jun-Aug catches the very top of the El Nino events.
But use them.. They are all you have.
Most of my favorite summer memories were made in Jun-Aug.
Bnice tries to deflect from the main point, which is the claim that UHI is the main driver of summer warming in the U.S.
However, USCRN (a network Anthony himself acknowledges as pristine) still shows substantial warming.
Maybe you didn’t examine the correct graph. Try this one from NOAA showing USCRN temperatures. I don’t see much warming in max temperatures.
USCRN CONUS Tmax anomalies show a warming trend of approximately 0.61°C/decade from 2005 to the present.
January should be compared with January, February with February, and so on.
There’s no reason to retain the seasonal cycle unless you like confounding variation.
Only warming in USCRN comes from the 2016 El Nino step and the 2023/4/5 spikes.
Please show us where the human caused warming is.
More deflection (as usual).
Silly me for expecting an honest reply.
Facts are as they are.. sorry if you can’t accept them.
He’s still trying to deflect from the main topic:
“The question was whether UHI is primarily responsible for the USA summer warming trend.
USCRN is a pristine network and still shows summer warming.
Whether that warming is caused by ENSO, anthropogenic forcing, or some combination is a separate attribution question.”
Changing the subject is one of the many ways deniers avoid confronting uncomfortable facts.
As we have seen time and time again,
Changing the subject is one of the many ways ALARMISTS avoid confronting uncomfortable facts.
Yet that is exactly what the so called “Climate Science” does.
“Yet that is exactly what the so called “Climate Science” does.”
Any particular instance? Or do you just expect people here to take your word for it?
No doubt the deniers here will. They’ll happily take any tiny breadcrumbs that point in their preferred direction.
Any particular instance?
Many of your posts.
How is “many of your posts” an example of the field of climate science retaining confounding variation in statistical analyses?
Was landing the rhetorical jab really worth sacrificing the logical structure of the discussion?
Apparently supporting their own claims and insulting their opponents simultaneously is one task too many for them.
I was commenting on the post:
“Changing the subject is one of the many ways deniers avoid confronting uncomfortable facts.”
There was no logical discussion.
By the way, the lead off post diverted from the article topic.
“I was commenting on the post:”
No, you weren’t.
The “logical” “structure” of your “discussion”??!!
Your employer reminds you to NEVER admit a jab landed, you’re gonna need some re-education at the CliSci Institute for diversionary trolling.
Are you sure that’s CRN data?
Regardless, I checked the data from the NOAA site
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/
and I get a trend for CRN, June – August of
TMax: 0.39 ± 0.38°C / decade
TMin: 0.33 ± 0.23°C / decade
Obviously too short a period to draw any real conclusions.
The trend for CliDiv since 1970 is
TMax: 0.21 ± 0.09°C / decade
TMin: 0.28 ± 0.06°C / decade
[deleted]
Wow. 0.39 +/- 0.38°C decade. That seems so nothing burger.
They have according to AI “Known limitations and caveats”
Do you want to attribute a causative factor?
Here is a graph of USA average temperatures straight from NOAA.
And basically all of that happened in a step change at the 2016 El Nino.
There is no sign of any human caused warming the the USCRN data.
Are you sure that’s CRN data?
Yes I am sure. See that “powered by Zingchart”. That is from NOAA. I don’t have a zingchart software package.
I also suspect from your chart, you are using anomalies. The chart I posted does not, it uses actual temperatures. Funny how anomalies can change the outlook of actual temperatures.
Do you have a specific link. The only graph I can find on the NOAA site that gives absolute temperatures is
https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/national/time-series/110/tmax/1/0/2005-2026
which looks just like your graph, but is not using CRN data.
This data gives a somewhat lower rate of warming since 2005 than the CRN data. 0.23°C / decade for TMax, and 0.36°C / decade for TMin, but again it doesn’t mean much over such a short period.
And this is what I get from NOAA using:
I also took the liberty of drawing an estimated (by me) trend line of the values. Funny how time series trends with various smoothing techniques always appear to have growth at the end isn’t it? Maybe you should just look at actual data before playing around with time series.
This article was about summer trends, not all months.
“I also took the liberty of drawing an estimated (by me) trend line of the values.”
A very bad estimate. Here’s my trend line, using OLS.
The trend is currently +0.61 ± 0.25°C / decade.
That trend has a lot to do with the record breaking temperatures in March. Taking the trend to the end of 2025, it’s just 0.52 ± 0.25°C / decade.
Forgot the graph.
Why would you expect anything less from an OLS. The starting point and ending point control the slope of the line. Why do you think that proper time series analysis starts with a stationary trend?
Your trend is similar to those of stock market traders and naive business folks. Oh, that stock is climbing tremendously, I’m going to buy it. Or, our revenues have been growing nicely, I’m going to forecast the next years revenue using this trend! LOL!
“Why would you expect anything less from an OLS.”
I keep forgetting all those times you were moaning at Monckton for his use of OLS.
“The starting point and ending point control the slope of the line.”
They do not.
“Why do you think that proper time series analysis starts with a stationary trend?”
I suspect what you mean by “proper” is just financial “make money on the stick market” forecasting techniques. I keep pointing out it makes no sense to talk about a stationary trend. By definition stationary data has no trend.
If you mean “trend stationary” then you need to explain why you don’t think this data is trend stationary.
And you keep forgetting how often you reminded everyone that he was cherry picking starting and ending points. If those don’t matter, why did you chose to castigate his process.
Sure they do. You choose the starting and ending point just like you accused Monckton of doing. If you choose a low value at the start and a high point at the end, you will obviously end up with an exaggerated slope. Point of fact, you never start your regressions in the 1930’s, why don’t you.
Lastly, OLS requires a model or functional relationship that allows predictions using the independent variable. You are trying to use it with a time series where time is not the independent value that determines temperature.
Here is a web site that summarizes the issues very succinctly. I’ve given you others in the past and you have never shown any references that refute them. You should show your references that refute the information on this page.
Why is ignoring temporal dependence a problem when performing OLS regression on time series data
Here is a summary from CoPilot that may also give you some guidance.
“And you keep forgetting how often you reminded everyone that he was cherry picking starting and ending points. If those don’t matter, why did you chose to castigate his process.”
Nice distraction. Being able to cherry pick a start pint does not mean that the start point controls the slope.
“You choose the starting and ending point just like you accused Monckton of doing.”
The starting point was 2005, the earliest point in NOAA’s data set. The end point was 2025, the most recent year to have had a summer.
I did not look back over every month in order to find the fastest warming trend. That was the equivalent of what I was accusing Monckton of doing.
“Point of fact, you never start your regressions in the 1930’s, why don’t you.”
Because CRN doesn’t go back that far. And note I used exactly the same period you did. I was simply pointing out your claim of zero trend was wrong.
“Lastly, OLS requires a model or functional relationship that allows predictions using the independent variable.”
Yes but all you are predicting is an average value. And it’s only reasonable to do this within the range of the data. You could predict what the expected temperature would have been on 2015, and then compare that with the observed temperature, but you are not predicting the future.
“You are trying to use it with a time series where time is not the independent value that determines temperature.”
That’s exactly what time is in a time series. You are doing this in order to estimate a rate of change. It isn’t saying that time caused the change in temperature just that if you see temperature increasing over a given unit of time then that determines the rate of warming. Same logic as measuring the speed of a car. Time doesn’t cause the car to move, but if it moves 30km in half an hour then it’s average speed was 60km/hour.
“I’ve given you others in the past and you have never shown any references that refute them.”
What do you want me to refute? I’ve not had time to go through it in detail, but it seems correct. I keep telling you about the need to correct for autocorrelation. Yes it’s possible the confidence interval for the CRN trend should be larger. But incase you’ve forgotten, it was just a quick calculation to refuse your claim if a zero trend, with zero quoted uncertainty.
By the way, when you are accusing me of cherry picking, note that I specifically said that the tend will have been exaggerated by the record heat earlier this year and gave you the trend up to the end of 2025 to show how much difference it made.
Both you and NOAA should examine resolution uncertainty and significant figures requirements more closely also.
NOAA’s own documents provide that the resolution of CRN stations is 0.1°C. You need to explain, and NOAA should also, exactly how you can appear into the unknown and determine the one-hundredths digit when it isn’t recorded.
In your mind, look at a digital display with one decimal digit. How do you determine what the next digit is? It could be anywhere in the interval of ±0.05. Significant digit rules allow one to carry 1 or maybe two extra digits through calculations to help prevent rounding errors, but the result is always rounded to the correct number of significant digits.
Likewise, uncertainty should be rounded to one digit unless there is an excellent reason to specify two. What is your reasoning?
Basically your presentation should be:
TMax: 0.4 ± 0.4°C / decade
TMin: 0.3 ± 0.2°C / decade
The intervals are:
0.4°C [0.0°C to 0.8°C]
0.3°C [0.1°C to 0.5°C]
The range of uncertainty is so large one can not reliably determine the actual value with any confidence.
Jim did not provide any uncertainty estimates for the trend he manually calculated in his 5:52 AM comment.
Yet now that the actual trend estimate has been presented by Bellman (and it indicates substantial warming), it suddenly becomes necessary to discuss uncertainty estimates.
So what? Why don’t you refute it with some accurate information? I said it was an estimate. It is born from years of dealing with time series in business.
I’ve said it before, ordinary least squares (OLS) regression is one of the most widely used statistical methods for modeling the relationship between one or more independent variables and a continuous dependent variable. To use it properly requires using a model or functional relationship that allows predicting values of the dependent variable from different values of the independent variable. Unless time is a component of a functional relationship with temperature, then OLS is not of much use.
In this case, it ignores what has happened prior to the chosen starting point and what may happen after the chosen ending point. A proper trend of a continuous variable like temperature is one that does not rely on the starting and ending points yet has about as many points above the line as below the line.
Maybe you can provide a better trend and show us.
‘Be more precise’, says the man who estimates his own trend lines and hand-draws them onto his charts, lol!
At least I stated it was an estimate. That’s more than I have ever seen you do!
As I stated above, that estimate is based on years in business forecasting. You learn quickly that regressions such as being shown are frustratingly obtuse. Improper use of statistical procedures will cost you your job very quickly. These trends are not based on a proper model or relationship between time and temperature. Predictions made from them are worthless.
The equation used for the regression line would quickly show that using times in the future (x-value) becomes more and more unreasonable, just like the models used by the IPCC. Maybe Bellman can give us the equations used for his trends.
“The equation used for the regression line would quickly show that using times in the future (x-value) becomes more and more unreasonable”
Which is why you shouldn’t project outside the data range. As always you try to distract from the point with these strawman arguments. This has nothing to do with forecasting. It’s about seeing if temperatures have been warming.
Why then do you propound a degree/decade forecast. If you aren’t using the rate to project into the future, a simple statement of current warming would be sufficient. Such as, it has risen 1.5C over the last 100 years.
“Why then do you propound a degree/decade forecast. ”
It’s a rate, not a forecast.
” Such as, it has risen 1.5C over the last 100 years. ”
You could say that if you want, but I find a rate is easier to understand, as it’s not dependent on the length of the period.
I would also say that a total change is only an approximation if based on a linear regression. If you want the actual change it’s better to base it on the climactic average, as is done in the IPCC.
A rate of change, °/time, is the fundamental basis for making a forecast. If you think the rate is implausible for the future you should make that very clear. Otherwise, you are asserting that you expect it to continue.
” Otherwise, you are asserting that you expect it to continue. ”
I can’t help you with your delusions.
“You need to explain, and NOAA should also, exactly how you can appear into the unknown and determine the one-hundredths digit when it isn’t recorded.”
Not going through all this again. You know the explanation it’s been explained to you many times that an average of many things is not the same as a single measurement. You are either incapable of understanding this, or are just trying to cause a distraction.
“Likewise, uncertainty should be rounded to one digit unless there is an excellent reason to specify two.”
That’s your personal rule. I along with most sources I can find sat differently. The GUM says something to the effect that you should not use too many significant figures and that 2 is usually sufficient.
“Basically your presentation should be:
TMax: 0.4 ± 0.4°C / decade
TMin: 0.3 ± 0.2°C / decade”
And what do you gain by that? It makes no difference to the main point, but exaggerates the difference between the two trends arbitrarily.
“The range of uncertainty is so large one can not reliably determine the actual value with any confidence.”
Which was the point I was making. Twenty years over an individual season over a small part of the globe is unlikely to determine an exact trend.
If a daily average is not a single measurement, then stop treating it as one. Stop calling:
Temperature is an SI, internationally accepted unit of measure. If you use that unit, then you are also making the assertion that you have “measured” that value of temperature by using some model appropriate to determining its value.
When you say a daily average is 50°F, you have quoted a single temperature using an SI unit of measure. When you say a monthly anomaly is +2.2°C, you have quoted a single temperature using an SI unit of measure.
Per NIST, The kelvin (K) is defined by taking the fixed numerical value of the Boltzmann constant k to be 1.380 649 ×10−23 when expressed in the unit J K−1, which is equal to kg m2 s−2 K−1, where the kilogram, meter and second are defined in terms of h, c and ∆νCs.
The other units of temperature are derivations of this definition.
Perhaps you have a better caption to use than an SI defined unit of temperature.
“When you say a daily average is 50°F, you have quoted a single temperature using an SI unit of measure.”
You’re really confused..
“Temperature is an SI, internationally accepted unit of measure. If you use that unit, then you are also making the assertion that you have “measured” that value of temperature by using some model appropriate to determining its value.”
Complete nonsense. SI units don’t define how to use them, they just define what the units are.
You are really outside your wheelhouse trying to refute this one.
Kelvin is an SI base unit. It is defined using the Boltzmann constant of k_B = 1.381×10⁻²³J/K. It is an exact definition of the relationship between energy and temperature in Kelvin. This defines exactly what it is and how it must be used and interpreted.
Lest you misunderstand a base unit. You can’t just measure something at 300K and say it has 1.381×10⁻²³J. That would be saying a 1 gram block of lead at 300k has 4.143×10⁻²¹J. While a 1,000 kg block at 300K also contains 4.143×10⁻²¹J. It is one reason temperature is intensive and has strict requirements to average. I’ll leave it up to you to study how and why this is.
In no sense does an SI unit define how it is used.
What you are describing is how the unit is defined, not how it has to be used. There is absolutely no requirement that using an SI unit means you have “measured” it using some model. Estimating, approximating, or any other use is perfectly legal. From the NIST website:
https://www.nist.gov/pml/owm/metric-estimation-game-sp-1336
And all this is another one of your distractions, with nothing to do with the fact that an average can have a higher resolution than the individual measurements.
There is absolutely no requirement that using an SI unit means you have “measured” it using some model.
You wouldn’t know a model if it bit you on the butt.
From the GUM:
This is for a mathematical model for a simple measurement of the width of a gauge block. The simple phrase you show from NIST describes nothing about how to define a measurement model and all the influence quantities that can affect a measurement.
I’ll bet you are the guy that goes out to a board pile, hooks the tape measure at one end and uses a 1/16th carpenters pencil to draw a mark, then cuts somewhere on the mark. You wouldn’t get away with that doing trim carpentry. Pin wear on the hook, temperature of the tape, humidity of the board are all influence quantities. The last thing I did every day when doing trim was to put the boards I would use tomorrow in the house so the humidity would stabilize. Otherwise you are lucky to end up with only a 1/32nd gap or larger gaps a week after installation.
In my younger days I raced motorcycles with 2cycle engines. When climbing a 70 degree hill you couldn’t afford to lose momentum so you raced the engine until it screamed like a banshee being rαped. Measurements meant something when that engine was turning 12,000 rpm.
Another deflection.
Your inability to stick to a subject and defend the claims you’ve made, either has to be deflection or trolling.
Your claim was
I interpret that as you saying that quoting anything using SI units, requires you to have used formal measurement procedures. That is your claim I am questioning.
So far you have done nothing to support your claim. Just thrown out your usual insults.
No insults, just statement of facts. If you claim that a number has an SI unit associated with it, then yes, you will have had to use some measurement model to arrive at its value. Using a measurement model requires one to specify both the estimate of its value and the uncertainty.
Just saying, well it is just an average does not cut it, especially when it is used to justify taking more of my money. I expect more from science than that.
From the GUM:
Let’s put it in just a few words. Are you trying to tell us that an average of two measurements is NOT a measurement?
“No insults, just statement of facts”
“If you claim that a number has an SI unit associated with it, then yes, you will have had to use some measurement model to arrive at its value.”
Only if you accept that the measurement model could just be a wild guess.
“Using a measurement model requires one to specify both the estimate of its value and the uncertainty.”
It really doesn’t. If I go for a walk of about 10km I do not have to state what measurement model I used, or what the uncertainty is it’s just a rough estimate of the distance.
Perhaps you would be so good as to post a reference that says a wild ass guess can be considered a decent measurement that meets scientific requirements.
You are making fun of sincere metrologists and scientists to say nothing of machinists, mechanics, surveyors, and yes, engineers. It is too bad that you consider measurements to be a joke. May your next purchase be manufactured using wild guesses for the parts.
“Perhaps you would be so good as to post a reference that says a wild ass guess can be considered a decent measurement that meets scientific requirements.”
I said nothing about donkeys, and you are missing the point – something you seem to be an expert in.
“You are making fun of sincere metrologists and scientists…”
No. I’m making fun of you and your inability to understand that using SI units does not require any special measurement models or uncertainty analysis – they are just everyday units of measurement.
And I am not taking any lessons in making fun of scientists from someone who constantly says scientists don’t live in the real world, don’t understand statistics, or whatever.
The rules of Scientific Notation include the result of any calculation can not have greater resolution (significant digits) than the parameter of least precision.
2.1 / 2 = 1
2.1 / 2.0 = 1.1
2.1 / 2.00 = 1.1
2.10 / 2.00 = 1.05
The much-heralded USCRN is hoisting WUWT by its own petard.
The Modis Operandi of this site for the past decade and a half has been to discount the surface temperature record because it is “contaminated” with UHI.
Along comes USCRN sited in pristine locations, to the great acclaim of the ‘skeptics’ and, lo-and-behold, it’s warming faster than the adjusted data!
Show us your graphs from which you are making this conclusion. Assertions without backup are worthless.
Jim,
When dealing with flame warriors a fun tactic is to treat them like kittens and tease them with catnip. 🙂
“Jun–Aug CRN temperatures are warming at a rate of 0.65°C/decade. Very rapid warming.”
I think that might be Fahrenheit, not Celsius. NOAA have this annoying habit of downloading °F, even after you’ve switched to °C.
Yes, it was. Thank you. My mistake.
No worries. I only guessed that was the problem because I’ve made the same mistake a number of times.
Bellman, what value did you calculate for TMAX CRN?
The attached image shows a trend of 0.0092F/month for the time series.
From there:
0.0092 * 120 * 5/9
= 0.61C/decade.
I just want to make sure I’m calculating this correctly.
Yes, that’s the value I got for all months. It’s currently 0.61°C
/decade for all months, and 0.39°C / decade for the Jun-Aug average.
So in 5 decades we should see a 3C rise and by 2100 about 4.6C. You are fast approaching the recently retired RCP 8.5 catagory.
so what?
that’s a good thing coming out of an ice age isn’t it?
So +21-years of ‘pristine’ temperature data as a faster warming trend than the data you guys all claimed was corrupted by UHI!
That’s quite a big ‘so what?’
Yes. And it is not proof of any kind that CO2 is the “control knob.”
nClimDiv is a FAKE data series, adjusted to match USCRN
Any difference is purely an artefact of the adjustments made.
Comparing an “adjusted” data set against a real data set is scientific nonsense.
USCRN shows no warming except at the 2016 El Nino and some spike related to the 2023/4/5 El Nino.
“nClimDiv is a FAKE data series, adjusted to match USCRN
Any difference is purely an artefact of the adjustments made.”
So are they matched, or is there a difference? It would be helpful if you got your narrative straight.
“Comparing an “adjusted” data set against a real data set is scientific nonsense.”
Not really. The comparison is done to determine whether the adjustments are actually working, believe it or not. Though we understand why you might not like the results.
LOL.. you really don’t understand much at all do you. 🙂 [<—-You are prohibited from insulting other commenters. it's blog policy, enforced more harshly on you, because you've earned it. Leaving this example up so you know AGAIN why you get deleted. Other reasons are shrieking demands for evidence from people who've given it to many times over, and harassing Nick. You've been told this. This is just a reminder~cr]
nClimDiv is homogenised at a regional basis to match “pristine” sites, then combined to give a whole of USA value.
Of course they don’t match exactly..
The graph below shows the evolution of their matching algorithm.
At least you understand that to ONLY reason for comparing the two, is to see how well the adjustments are working so you can “adjust” your “adjustment” algorithm
Otherwise , the comparison is totally meaningless.
Except they haven’t given any scientific evidence.. they squirm and worm and link to propaganda sites.. But no evidence.
Does not the BoM do much adjustment and homogenization of Oz temperature data? I recall there was file that listed all the adjustments for every weather stations and any station’s relocations.
The BoM makes lots of “homogenisation” adjustments.. usually using “unfit-for-climate-purposes” badly sited and urban affected sites.
A toss up as to if they are as bad as the Met Office, though.
Ken at KensKingdom has a good round-up of many of these “adjustments”
yep.
Apparently weather only started in 1910 in Oz, according to the BoM.
There were a lot of very warm temperature measured, in screens, before 1910 !
Yep, for hundreds of miles. And it also relies on “expert judgment” of those doing the homogenisation. How not to do science in a nutshell.
Good quality country sites, homogenised to match urban sites…
BoM and Met do much the same things..
In fact, the methodology came from the same person.. a guy called Stott from CRU, iirc.
What would be far more instructive and interesting, is if they DARED to calculate nClimDiv WITHOUT attempting to “adjust” for the urban warming and bad site placement etc etc.
bnice2000 pretty funny.
Just think about it..
Deliberately adjusting data to attempt to match another set of data is a manifest waste of time… It has no meaning.
Why not just use the reference data in the first place.
What it does prove is that can take ANY data they like and “adjust” it give whatever result they want.
Its a “climate agenda” thing.
That only happens in your head, to be fair.
Can you see the WUWT headline?
“NOAA adjust surface temperature data to match the rapid warming seen at the pristine sites!“
If it is adjusted, it is not data.
Did you read the article? The link you defaults to an average anomaly. It is no longer possible to make conclusions from an average temperature. Are you claiming that night time temperatures are not causing higher averages? If so you need to reeducate yourself.
There are too many agricultural studies that verify that Last Frost Dates have moved earlier and that First Frost Days occur later. Frosts occur at night, extremely rarely during daytime. This also indicates that night temperatures are causing higher averages.
I have been studying individual CRN stations. (AI helps with python scripts) Many CRN stations on the East and West coasts show larger rises in temperature than plains and mountain stations. Looking at the map above, it is not unreasonable to think UHI may have an effect over large areas as wind moves hot air around.
The conclusions you are jumping to have not been given adequate assessment by you. Science requires looking at details, not general averages that end up hiding important information.
Of course it is. It’s the metric used by every single local, regional and global temperature data producer. That includes UAH.
You’re entitled to your opinion; just as long as you know that’s all it is.
It is not an opinion dude. You have said nothing to refute what I have said.
Here is an ag study you should read.
https://www.nature.com/articles/s41598-018-25212-2
Here is study that discusses UHI encroachment causing a CRN station in Rhode Island to have questionable temperature readings. And there are others.
https://journals.ametsoc.org/view/journals/apme/58/6/jamc-d-19-0002.1.xml
I’ll tell you the old adage that I have often told my kids. If everyone was jumping off a bridge would you follow or ask questions?
A daily average temperature exists for only one reason, meteorologists used it starting in the 19th century and current use follows tradition.
Using it when we currently have 1, 2, 5, 10, 60 minute data is a travesty.
Show us a study that has a method to reliably separate a daily average into correct Tmax and Tmin.
If you can’t find one, you should try to figure out why
“Here is an ag study you should read.”
You keep ignoring the fact that that paper uses mean temperature to determine GDD.
How many data points for the mean temperature?
It’s just (max + min) / 2.
Tave = (Tmax +Tmin) /2
It is not the average over 24 hours.
The planet rotates. The cycle is partial sinusoidal and partial exponential.
The Tave calculation is not accurate, but it is easy.
Not just easy – it is all we have historically.
Ever since thermometers were invented we have measured the max and min.
An integration over the diurnal 24 hrs – not so much.
LOL. So tradition should control science. Forget Newton, Einstein, Galileo. None of their REVOLUTIONARY work was worth the paper it was written on because of – tradition?
Luddite
“Time marches on” is a reminder to adapt, grow, and keep moving forward.
Automated stations started in the U.S. in 1980 with the MMTS stations and in 1990 with ASOS and 2005 with USCRN. ASOS and CRN stations provide minute level data. That is 36 years of data. It is time to stop infilling, homogenizing, and other data changing to old temperature streams to maintain the fiction that somehow the changed data is as accurate as the newer data. It just isn’t.
Jim Gorman: we should ignore all old measurements and only use modern instruments.
Also Jim Goreman: “Point of fact, you never start your regressions in the 1930’s, why don’t you.”
That adequately expresses my thoughts. As an electrical engineer I have progressed from analog equipment to digital voltmeters, frequency counters/generators, and digital oscilloscopes. They all give better accuracy, precision, and resolution. VNA analyzers that fit into your palm have replaced whole benches of equipment.
You can plot Tavg from 1850 to 1990 all you want. You can use homogenized, infilled, and adjusted data in that period all you want. It will never compare to 5 minute data from ASOS and CRN type stations summed over a day.
I have already started using MSN CoPilot to create python scripts that allow me to download CRN 5 minute data from stations and graph it. I can do from 2005 to present in about 5 minutes. I can get a daily sum of 5 minute data for every day in that period. I am still working on algorithms to deal with missing or wrong data.
The data will have the original significant digits, i.e., 0.1°C. No adding significant digits through mathematical techniques that are questionable as to the effect on measurement uncertainty. No anomalies needed, just add everything up and see if it has grown or fallen.
As time permits, CRN also has insolation, surface temperature, and humidity available. Enthalpy can be calculated every 5 minutes which will revolutionize the comparison of heat values at all locations.
It is coming. It should be used now. 36+ years of data going to waste so we can maintain tradition is stupid.
“You can plot Tavg from 1850 to 1990 all you want. You can use homogenized, infilled, and adjusted data in that period all you want. It will never compare to 5 minute data from ASOS and CRN type stations summed over a day.”
So why did you recommend starting a linear regression at 1930?
I would have thought that you would know the answer to that since you are obviously into trends?
Do you think starting a trend in 1930 would lower the slope of the regression line?
If you agree, then you must also agree that the starting and ending point has a significant effect on the regression slope which Bellman denies.
Here is the string:
If you agree that they do not, then show us a trend starting in 1930 since Bellman won’t do it.
The problem is that we cannot show a USCRN trend beginning in 1930 because the USCRN record only extends back to 2005, as Bellman noted.
It would be very useful if we could.
You can’t make the argument that ClimDev and USCRN match and then turn around and say they don’t.
ClimDev has temperatures back to 1900 or further. If they match, use ClimDev for the trend or just append CRN as of 2005.
The agreement is from 2005 onward. No one has claimed that the datasets match prior to that period.
The reason this agreement is important is that it increases confidence in the accuracy of the official ClimDiv temperature record before 2005.
If two independent datasets closely track each other during the period in which they overlap, that provides evidence that the longer ClimDiv record is reliable over its earlier years as well.
Really? So how exactly did they begin to suddenly match in 2005, whereas prior to that, they don’t match.
All you are telling me is that you are unable download prior data and along with the CRN data in csv format put it into excel to graph it. If you can’t or haven’t done this, how do you make comments about the quality?
“Really? So how exactly did they begin to suddenly match in 2005, whereas prior to that, they don’t match.”
They cannot be compared before that because the CRN time series does not extend prior to 2005.
“All you are telling me is that you are unable download prior data and along with the CRN data in csv format put it into excel to graph it.”
I did graph it. Look at my comment dated May 29, 2026 6:03 AM.
“If you agree, then you must also agree that the starting and ending point has a significant effect on the regression slope which Bellman denies.”
I didn’t say points near the start end of a trend don’t have an effect, but they don’t “control” the slope.
“If you agree that they do not, then show us a trend starting in 1930 since Bellman won’t do it.“
“That adequately expresses my thoughts. As an electrical engineer I have progressed from analog equipment to digital voltmeters,”
But the question remains, what happens if you need to compare results when all you have is analogue data?
You want to argue it was hot in the US in the 30s yet say you can’t use any day from the period because it’s old fashioned.
CRN data is great, though it could be better, but it isn’t a time machine. It can’t tell what the temperature was in 1936, not can it tell you what the temperature is outside a small geographical region.
“36+ years of data going to waste so we can maintain tradition is stupid.”
You don’t have 36 years of CRN data, and none of it is going to waste.
I will examine it and if it is comparable to current measurements I will use it. If not, I will declare it unfit for purpose.
Do you think I have never dealt with data for call centers that was suspect? The number of circuits, people, expenses, etc. don’t come with centuries of data. You live with what you have and move forward.
You have claimed before that ClimDev and USCRN are comparable. Use ClimDev for 1930 to 2005 then add USCRN. That is what NOAA does with their small graphs on climate_at_a_glance. Do you have a problem with that?
“Do you have a problem with that?”
You are arguing so much, you are losing track of which side you are on.
No. I don’t have a problem with using old data when it’s all that’s available. You were the one objecting to “tradition”. As to mixing different instruments – yes that’s what has to be done. But you need to be careful about the calibration. It would be problematic to compare an average based on continuous readings, with averages based on the average of min max. They are measuring different things and could introduce a systematic bias.
“You are arguing so much, you are losing track of which side you are on.”
Sounds highly plausible.
I’ve seen a lot of WUWT commenters give Jim Gorman a virtual high five for the comments he posts.
Apparently, this is the kind of ‘hero’ they admire and cheer on.
I don’t ignore it because it is not germane to the issue of Last Frost Day and First Frost Day which define the growing season. If you have evidence that frosts mainly occur during daytime when the sun is shining, please present it. Otherwise, the study’s conclusion that nighttime temperatures are rising still stands.
As to the use of an average temperature for determining if a given day was a Growing Degree Day (GDD), the actual temperature value is only useful for assigning whether the day was a GDD or not, that is, when the average temperature exceeded a predefined base temperature. A GDD does not inform one as to the temperature value because that is not what is tracked.
If you want to argue that the uncertainty in the Tbase or Tavg contributes significantly to the total uncertainty of the number of GDD, then knock yourself out showing that.
You are still wanting to make the case that Tavg is a useful measurement. In this case it is useful because its actual value is not tracked nor projected. The uncertainty in Tavg may have an effect at the margin of determining if a day is a GDD but that is all. In this case, I suspect that there will be as many errors above the trigger point as there are below ending up with little effect.
Jim,
You made a language error.
Tmin and Tmax are measurements.
Tavg is not a measurement.
It is the result of a calculation.
It can be a useful in specific contexts as you point out.
You are correct. It is a calculation of a trigger point for declaring a Growing Degree Day. It is not used as a measure of temperature, although it uses temperature in the calculation.
I have no problem with declaring Tavg as a measurement if the GUM followed with all the correct determinations. Since the individual temperature measurements are non-repeatable, they are single measurements that cannot be analyzed statistically. Consequently, one must develop an uncertainty budget to obtain a reliable single measurement uncertainty value. Each Type B evaluation of each single measurement will be added via RSS to the combined uncertainty.
In addition, what is being measured is a “property” which requires the standard deviation between the measured values must be included in the combined uncertainty also.
Then when one uses Tavg ±u(Tavg) in a monthly average. The process repeats and the uncertainty only grows larger.
It is telling that Bellman, TheFinalNail, among others never, ever post about how the uncertainty of their favorite trends obviates any statistical significance at all. I cannot recall any of them ever posting a sample uncertainty budget ever, yet they want to tell us how wrong we are without showing any math.
“It is a calculation of a trigger point for declaring a Growing Degree Day.”
You keep saying that with no explanation. The paper specifically says that GDD is calculated as Tavg – base. Presumably negative values are treated as zero, which might be what you mean as a trigger point, but beyond that GDD is entirely dependent on the daily average temperature.
“Each Type B evaluation of each single measurement will be added via RSS to the combined uncertainty.”
You just keep ignoring the partial derivatives.
“In addition, what is being measured is a “property” which requires the standard deviation between the measured values must be included in the combined uncertainty also.”
You’re just making things up now.
“The process repeats and the uncertainty only grows large.”
It’s almost as if you’ve ignored everything we’ve been trying to explain to you over the last five years. And you still can’t see why it makes no sense to think that the uncertainty of an average grows larger with more measurements.
“It is telling that Bellman, TheFinalNail, among others never, ever post about how the uncertainty of their favorite trends obviates any statistical significance at all.”
What it should tell you is that I don’t think it. It’s just another of your strawman.
“yet they want to tell us how wrong we are without showing any math.”
This just has to be you trolling.
It not that hard to read the study. Understanding may take longer if you haven’t experienced farm life.
Look at figure three. See that graph in red? That is the accumulated GDD deviation from the mean of 1900 – 2014. Notice how it is actually going down over the century+ time frame. Also, note that in the 1930’s and 1940’s there is a significant peak. Maybe you could work to see if your temperature trends refute this.
I’ll post the graph here for all to see.
Jim,
Science has clear and precise definitions that facilitate communications.
That is lost with context derived definitions unless one spends the extra time to precisely define the context.
Words matter.
Control the language, control the ideas.
I have no issues with you or the concepts you address.
The whole of science is being corrupted and I am doing my small part to slow that tragedy down.
“I don’t ignore it because it is not germane to the issue of Last Frost Day and First Frost Day which define the growing season.”
I wasn’t talking about the growing season, but GDDs, and specifically your claim that it is not possible to draw any conclusions from an average temperature.
“As to the use of an average temperature for determining if a given day was a Growing Degree Day (GDD), the actual temperature value is only useful for assigning whether the day was a GDD or not, that is, when the average temperature exceeded a predefined base temperature.”
You are just wrong. The paper says that they calculate GDD as average minus base. Themore thevaverage temperature is above the base the more GDDs you have. That’s why they are called degree-days. If you only counted days it would be growing days.
“You are still wanting to make the case that Tavg is a useful measurement.”
I don’t need to make the case. Averages are used in many situations. They are by definition “useful”.
Accumulated Growing Degree Day Products | USA National Phenology Network
Please note, the Tbase is a trigger point for the accumulation of GDD values. GDD is the number of degrees the Tavg is ABOVE the trigger point. It is not an average in and of itself.
I should note that Agriculture and other disciplines are abandoning this simple calculation as is HVAC is abandoning Heating/Cooling degree days based on Tavg. As automated stations grow, hourly and even minute data allows for more accurate calculations.
Here is a study that has good information on using hourly data to determine GDD. GDD is still determined by the degrees above a trigger point but is not limited to a single average value.
You should ask yourself why you think averages are meaningful as other science disciplines are moving away from that. Tradition is not a good answer.
You keep repeating what I’ve just told you. GDD is here calculated as average – base, and treating negative values as zero. (They don’t actually state that they do that, but hopefully they do.)
This is not the same as what you claimed, that they simply counted the number of days over the base.
“Tradition is not a good answer.”
You are obsessed with that joke. It has nothing to do with tradition it’s a question of how far back you want your records to go. The paper is tracing the figures back to 1900, they do not have access to automated hourly data.
As so often it seems you will use any argument as long as it allows you to dismiss the evidence. You’ll insist that only the most recent data is used, but then complain that it’s starting in 1979 and so ignores the 1930s. You’ll insist on only using CRM data, then point out it’s impossible tell anything from a 20 year trend, and I dare say by the time we have enough CRN data you’ll reject it, and insist we go over to whatever the latest technology is.
Forgive me, sometimes I assume people understand after I have given them the resource to study.
The fact is that GDD is not an average. GDD is the number of degrees above a base value determined by using a temperature where growth starts. Because it is using a simple average, it is vulnerable to large errors, especially at the beginning of the growing season when low temperatures at night can slew the distribution.
In a way, GDD are similar to anomalies. However, the similarity ends there. GDD days are not averaged, they are summed/accumulated throughout the growing season.
You also have failed to address Figure 3. Why? It shows GDD falling while you show increased temperatures over the same period. The two don’t seem to be correlated, why?
“Forgive me, sometimes I assume people understand after I have given them the resource to study.”
Why? You never do.
All you keep doing is repeat what I’ve told you whilst ignoring what you originally said. Ii take it you are now accepting you were wrong to claim that the paper is not using average temperatures. That was the simple point I was trying to get you to acknowledge.
” It shows GDD falling while you show increased temperatures over the same period.”
Yes, it’s a puzzle given that they also claim GDD has been increasing at 50°C days, per century. It does demonstrate that HDD isn’t a very useful metric as far as agriculture is concerned.
As to why there graph shows a cooling trend when every other data set shows warming, I couldn’t say. Maybe the graph is wrong, maybe their method is wrong.
Yep! Everything is wrong. An AG study can’t possibly be correct.
Here is what was repeated twice in the study about GDD.
Hmmm. Some of us have been saying the same thing about temperature aggregation
“Yep! Everything is wrong. An AG study can’t possibly be correct.”
Stop with the strawmen. You asked why this one paper seemed to be at odds with other data sets, I suggested one possible explanation. Unlike you I don’t assume that scientists are incapable of mistakes.
If you think there is no possibility of a mistake, you need to address the difference between their statement that AGDD has been increasing at 50°C / century, and their graph purporting to show AGDD which seems to show a decline since the start of the century.
One possibility is that they are confusing the labels. Compare table 1 and 2. Table 2 shows “Trends in agroclimate indices for the CONUS climate regions.”, and this shows for CONUS as a whole, annual AGDD has increased by 50.2 °C / century.
But table 1 – “Trends in agroclimate indices for the major CONUS agricultural belts.” shows most belts as having a negative trend. But this is the trend in “Crop Growing season AGDD”, not annual AGDD. Possibly this is the data used in table 3, rather than the annual AGDD data.
Also, there is a mistake somewhere here. According to the text
Yet the table shows a negative trend for cotton. This is probably a mistake as all the monthly trends, except January, show positive trends.
I couldn’t help but thinking that there’s another possible factor in large cities, i.e. the growth of air conditioning since 1970.
In large cities there are huge ‘cooler pockets’ inside the buildings and that, in order to get those cooler pockets a lot of heat is pumped from the inside to the outside environment.
I’m not sure how large a contribution that might make… although I’d think it’d be greatest where you have the largest volume of interior space, typically high rises, but… it seems to me that while the pavement and other heat stores slowly releasing heat into the urban area’s environment, the once rare air conditioning should at least be looked at.
All of these factors, air-con, etc, are accounted for and adjustments made to compensate per site. We know it’s not perfect, because, as mentioned above, if anything the adjustments appear to be adding a slight cooling bias versus data from the ‘properly sited‘ US stations.
“and adjustments made to compensate per site”
LOL.. How do they know they have made the correct adjustments ?
Yes, the adjustments started out giving higher values, now they give very sightly lower values, as they have “adjusted” their adjustments.
As you just said, in total agreement to my first post.. It is all down to adjustments made.
NONE OF IT IS REAL !!
Your chart (which stops over a year and a half ago) shows the increasing divergence between the warmer-running ‘pristine’ sites and the adjusted data, supporting the view that the adjustments are lending a spurious cooling bias to nClimDiv.
That’s the point I made. I don’t see how you think pointing out that the “properly sited… state-of-the-art” temperature stations are warming faster than those that require UHI adjustment helps your argument?
The “adjustments” are nothing more than estimates, best guesses, or potentially biased factors.
That’s what I’ve always thought. To accurately adjust temps, every single site must have before/after temps tracked, accounting for every change in the environment (more/less A/C, more/less concrete, how far that concrete is from the thermometer, etc.). And the deviation from a ‘correct’ value will also depend on the wind direction that day, cloudiness, did anyone park in that parking lot that day, etc, etc.
Absolutely no way anyone can estimate these things for all temp stations with anything remotely resembling accuracy without a detailed analysis of the individual site, and even then I would think the error bars can go up to easily several degrees.
When the temperature difference between downtown and a suburb 10 miles away can easily be 10 degrees, I have no confidence in ‘adjustments’.
This is why many of groan when we see “anomalies” with uncertainties in the one-thousandths digit. The uncertainties generated from different instruments and housings over the last century are just waved away as a meaningless statistical consideration. The microclimates surrounding different stations can generate significant differences also. Again, just waved away.
People are beginning to question what is not happening. Out here on the Great Plains, little has changed over the last century other than longer growing seasons and better grain harvests. Weather is like it has always been, dry/wet, hot/cold, tornadoes, etc. Ho Hum.
Climate Central “Misleading at best”.
That is too kind. They are deliberately misleading ‘at best’, which has a number of specific other English names. Some of the politest of which include prevarication, disingenuous, and just plain lying. Or as Trump47 also politely says, “fake news media”.
For a Reno temperature check, I went to:
https://www.extremeweatherwatch.com/cities/reno/average-temperature-by-year.
The Thi and Tlo data from 1938-2025 are displayed in a long table. Here is the data for these two years:
Year——-Thi——-Tlo——-Tav Temperatures are ° C
2025——21.3——6.2——-13.8
1838——19.1——-0.7——-9.9
Change—+2.1—-+5.5——3.9
Range Thi: 17.3—21.4
Range Tlo: -1.4—-6.3
Weather Station: Reno Tahoe International Airport 1937-2025
Note that the “airport heat island effect” is 5.5° C for Tlo.
At the above URL, there a selector for obtaining data for each month.
Also at the above URL, if the “average-temperature-by-year” option is not used, all temperature, weather and climate data from NOAA’s database are displayed. At the end of home page under “More” there additional options for display of data.
Be sure to check out the home page for this new website at:
https://extremeweatherwatch.com. There are links in light blue to weather stations located all around the woirld.
Urban warming or not, you’re are hotter now than without Global Warming. Simple math.
Natural solar /albedo forced warming.
Only evidence of human caused warming is at urban sites.
And we have a long way to go , warming from the COLDEST period in 10,000 years, to reach the Medieval warm period or the Roman warm period or the Holocene optimum.
No. We are warmer now than during the Little Ice Age.
Regardless, warmer or hotter does not prove CO2 is the “control knob.”
It just means weather happens.
Nor does any of this deal with what the optimal temperature should be. It is always warming of any amount is bad. I would like to some of them discuss what the optimum GMST should be so we can have a pertinent argument over whether we have reached it or not.
I believe you have seen me post the same thing.
What is the optimum climate? (Not just temperature).
Until the optimum is defined in specific metrics measurable and testable by anyone, we do not know.
How do you define a global climate when there are so many micro climates that are radically different? Averaging the Sahara with Siberia?
How do we know if we are departing the optimum or moving towards it?
All of this Net Zero and other policies could actually be keeping us from achieving an optimum.
I have done some research and found papers advocating for 20C to 22C. That means we have 4 to 6C yet to go. Will there be problems? Sure, but overall a much better and more productive world.
That is mitigation. It has occurred many times before on this old world. A quick search showed 8 major cities that used to be major ports but are now land locked. Maybe they will become major ports once again. Wouldn’t that be nice?
The “optimum” you speak of must be for human civilisation – and nothing else.
Humans developed that civilisation when the global ave temp was 15C and the climate was stable (Solar absorbed = LWIR out)
That is no longer the case.
You did no research did you? Here is a good paper.
Scientists Identify a Universal Optimal Temperature For Life on Earth : ScienceAlert
You didn’t read that article, or didn’t unstand what you read.
It is not saying a global average temperature of 20°C is optimal. It’s saying that 20°C is a local temperature that most species can survive in. It specifically says that above 20°C becomes less optimal for most species.
If we increase the global to 20°C much of the world will be too hot.
Back to cherry picking I see. What does this paragraph from the study tell you.
This doesn’t say that some species won’t have problems. Some species may very well go extinct. That has been happening since life began on this planet. However, overall 20°C should maximize richness.
The point is that whether it is 20°C or 17°C or 22°C we have not reached it yet. Are you going to go on the record that 14°C at the end of the Little Ice Age is the optimal temperature for life on the earth? Science is begging for someone to stand up and say what the optimal temperature should be, why don’t you be the first?
To you, the parts of a text that demonstrate you are misinterpreting it are always just dismissed as “cherry picking”.
Have you reconciled the fact that you are quoting an average temperature of 20°C as optimal, when you were recently insisting that average temperatures were meaningless?
Regardles, the problem you keep ignoring is that the 20°C figure is for the local average not the global. It’s saying you get most species in the parts of the world with an average temperature of 20°C, and this falls of quickly where the average is higher.
Now, if you achieve your goal of increasing global temperatures by 5°C, what do you think happens to the parts of the earth that are already 20°C?
“However, overall 20°C should maximize richness. ”
You’re making a lot of assumptions there. That an average of 20 means you have more land at 20, that this will compensate for the land that is now too hot, that the only thing that matters to life is temperature, that there won’t be any bad climactic effects, that life can quickly adapt to it’s new climate, etc.
And all this is assuming that “optimal” just means how much animal life there is. You don’t seem to care what this means for humanity or civilization.
” Science is begging for someone to stand up and say what the optimal temperature should be…”
Is it? If it is, I don’t think it’s going to get a sensible answer because as I keep saying it’s an I’ll defined question. It’s impossible to answer unless you define optimal. Optimal for whom, optimal in what way?
If you don’t believe what this article says, good for you.
I have given you a resource that has a certain position. Your questions are not a refutation, they are just showing your opinion is negative.
Why don’t you reciprocate and post an article or paper that shows a GMST of 14°C is optimum for life on the planet..
“If you don’t believe what this article says, good for you.”
Enough with your pathetic strawman arguments. I am neither disagreeing or agreeing with the article – I am pointing out that you don’t understand what it says.
Are the black & white photos fake of children playing in streams of water from fire hydrants. I’m 97% sure heat in summer in large cities has always been a thing. Recently, it is only more so in 91% of places; not global then.
There are no photos, but consider:
2 Samuel 4:5 (KJV)
“And the sons of Rimmon the Beerothite—Rechab and Baanah—went, and came about the heat of the day to the house of Ish-bosheth, as he was lying on his bed at noon.”
Siesta!
During hot days in cities, fire hydrants were opened up to let the kids play and keep cool.
We now have misters to do the same at festivals and such.
Still waiting on Tony’s paper on UHI 😂
Denial of UHI effect? That is funny. 🙂
There are plenty of papers out there, and much other information, on the effect of urban warming.
The map by Roy Spencer shown in the main post, is a great example.
Our esteemed host has put out a couple of papers on the effect of bad surface station placement.
You should find them and read them.
Very nice Anthony.
For what it’s worth, here’s the UAH trends over summer months, starting in 1979.
The USA shows little to no warming over much of the East, but more rapid warming over the North West.
Using my country mapping, I make the USA as warming at the rate of +0.14 ± 0.06 °C / decade in the summer. (Note this is the whole of the USA, including Alaska.)
Fastest warming NH countries in summer are all in Central / Eastern Europe.
Wow! I’ll bet those don’t have any UHI either. Have you investigated the locations used to obtain those values for their proper siting?
It’s satellite data.
1979 was the COLDEST period since the warmer 1930s, 40s.
Peak Arctic sea ice.
Great time to use as a reference for propganda. 😉
Well, I suppose that one could claim the UHI effect is human caused warming, After all, it would not exist if humans hadn’t built the city in the first place. /s
The UHI effect also includes thermal energy released during heating, cooling, lighting, transportation, and population density. Glass, steel, concrete, asphalt are indeed part of the baseline that controls the storage and release rates of thermal energy.
In addition, it is known that plants shield the ground causing a reduction in thermal energy sequestered below the surface during the day and resulting is less thermal energy released at night. Cities are famous for not having nearly the same level of plant coverage as rural areas.
Another serious factor is the massive increase in surface area in urban settings compared to urban. A 1 meter cube has 5 times the surface area as the 1 m^2 it sits on. How much does a 14 story building add to the base in terms of exposed surface area? More surface area means more thermal energy transfer from the structure to the atmosphere per unit of time when the surface is cooling (aka at night).
Love the Nevada demonstration. Well done.
“compared to urban” should read “compared to rural”