Reposted from Dr. Roy Spencer’s Global Warming Blog
Dr. Roy Spencer
I was asked by Heritage Foundation to write an article on the exaggerated global warming trends produced by climate models over the last 50 years or so. These are the models being used to guide energy policy in the U.S. and around the world. The article is now up at Heritage.org. As a sneak peek, here’s a comparison between models and observations for the U.S. Corn Belt near-surface air temperatures in summer:
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Living near Kansas City, Mo, I pulled the USCRN data for the three nearest cities covered. Surprise, no trend for any of those cities.
Damn the observations, it’s full speed ahead for Al, John, Greta, Bill…
AI? Or Al?
It’s probably both: algore and AI.
Al invented AI, didn’t he?
Since AL is an Ai, that can’t be? It’s possible that the same person who invented artificial intelligence also invented the Algor-bot, though.
No, the Internet
“People underestimate the power of models. Observational evidence is not very useful,” – John Mitchell, UK MET
“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” ― Richard P. Feynman
Presumably Mr. Mitchell has a STEM degree.
Can we request that his university revoke his credential?
He may have a STEM degree, but whether his brain stem is working or is in fact connected to anything further up is a different question altogether.
The CSIRO told me that as well. The only relevant comparison for climate models is other climate models. Surprisingly, they placed the GISS model as their go-to standard despite is being cooler than the consensus..
Who can be bothered with the complexity of real world when models perform so more as expected. Everyone knows that CO2 causes warming and the models prove it. Real world measurements are aberrations and cause so many arguments. Lets just stick to the perfection of models and avoid the messy arguments.
Notice where ACCESS-CM2 fits on the above chart. The consensus position. Right at the core of consensus science.
Ahhh that would be inclusivity of the diversity-
Diversity and inclusionWe’re working hard to build a safe and welcoming culture where people can bring their whole selves to work. Valuing and enabling difference empowers our people, unlocking their potential to innovate and shape the future for our customers, all Australians and the world.
Diversity and inclusion – CSIRO
instant explanation of why theyre worse than ever…thanks
No mention of competence or expertise, oddly.
I have never been able to bring my ‘whole self’ to work – the other workers would no doubt object if I let my psychopathic tendencies out of the cage in the back of my mind. 👹
The difference between reality and a model of it is like the difference between a real person and a manikin.
More like the difference between a real person and the Knave of Spades. The computer simulations of climate are SO unskilled that they don’t even come close to resembling reality (only when they are “tuned” to “fit”). Their results are: JUNK.
See Bob Tisdale’s excellent expose of the climate crooks’ simulations here:
Climate Models Fail
https://bobtisdale.wordpress.com/2016/05/23/three-free-ebooks-on-global-warming-and-climate-change/
Of course observations are not useful, the truth hurts the cause
The Magic Circle have been saying observational evidence is not very useful if you want to know how the trick was done.
Didn’t a powerful hockey stick trick capture the minds of and clearly impress meteorologists at the very beginning of the whole CO2 saga. Mr Mitchell should tell us how useful the trick was at that time if he had the courage.
““People underestimate the power of models. Observational evidence is not very useful,” – John Mitchell, UK MET”
The “power of models” to do what?
Project a virtual reality onto our “lying eyes”?
Even the Russians (INM-CM5-0) were high.
The INM model, like all the others, is incapable of producing sustained cooling trends anywhere. INM starts cooler and runs cooler over most of the globe. The high CO2 scenario does not get the tropical oceans to current temperature till 2075.
All the models are junk based on junk science made up by amateurs.
With a weed store on every corner, who isn’t high anymore?
There are a few of us.
Never touch the stuff. !!
By just a little.
Also, these wacko predictions are averaged to give them the appearance of legitimacy in the eyes of the kept-uninformed lay public.
The Russian prediction likely was by an unbought scientist
About 25% of all college and university graduates in Russia have science and/or engineering degrees
That means a lot of rational thinking
Chess is Russia’s national past-time
That means a lot of strategic thinking
That means NATO can’t win in Ukraine, no matter what you read in the Media
Example
Ukraine shoots down a Russian plane with 65 of its own POWs, even though Russia has proof of informing Kiev high command, and that command acknowledged receipt of the message, a full 15 minutes before the shoot down
That is called disorganized incompetence
That is called disorganized incompetence, or deliberate sabotage of an agreed POW swap
15 minutes is not a lot of time to communicate information sent to the head of the government in Kiev and have it transferred to the soldiers in the field.
Just because the people in Russia may be well-educated and logical, it doesn’t mean they can act on their rationality. With their form of government, they are powerless to impact the decisions made by the ruling class.
Tell us how the national pastime saved the lives of all the Russian soldiers permanently sleeping on the ground.
Clyde, I would call into question how much power do those of us in the West have to impact the decisions made by the ruling class? Can we reverse net zero in the UK, or the rush to renewables in the USA? How successful have the citizens of Germany been in curbing the actions of their green-obsessed government?
It’s not a clear-cut ‘us and them’ argument any more.
Clyde,
The control room of grid operator ISO-NE resembles a war room in ANY modern army. I have been in both kinds.
Instantly a blip is picked up, and, per SOP, a field gun, such as a Patriot, is alerted of “incoming”, told to press a button. Aye, Aye, Sir. It takes seconds.
Giving a 15-minute-heads-up is like forever.
There were two planes, one was shot down, the other turned around with 80 POWs.
A negotiated 65 + 80 = 145 POW swap, for each side, is a big deal.
It is known by at least 25 top people within Ukraine and another 25 within Russia, and likely by some NATO countries, such as the US/UK.
Everyone knows what the other guy is doing, in real time, from numerous satellites.
At present, attrition of supplies, equipment and personnel is the name of the game, which favors Russia.
Ukraine is losing about 1000 (wounded + killed) each day along a 900-mile front, about 1/mile.
Ukraine is not advancing anywhere, but Russia is.
Ukraine is recruiting less people than it is loosing.
Ukraine’s population was about 40 million before the invasion in February 2022, now it is about 22 million in the areas under Kiev’s political control, about 10.5 million are pensioned people.
The EU and US are paying the pensions, government employee salaries, etc.
Ukraine did accept a peace agreement, signed by all the parties, in March 2022, but Boris Johnson, who never combs his hair, told Zelensky to keep on fighting, and they did.
About 99% of all dead and wounded occurred after March 2022
It was a political decision not made by Russia.
Very nice.
“I flipped a coin 36 times, and it came up heads every time. I believe it is a “fair” coin.”
Said no one with a knowledge of statistics, ever.
Actually the odds are not zero. The chance of heads occurring 36 times in a row for a fair coin is one in 2^36 or 68,719,476,736. It’s not zero, but it’s exceedingly rare. If it’s a fair coin, then the probability of heads on the next flip is still 1/2–fair coins do not have memory of past flips.
Compared with the odds that he coin is rigged, the odds are zero.
Vanishingly small.
It’s the difference between mathematics and engineering.
The former is technically correct. The latter is practically correct.
Years ago, we here at WUWT published posts comparing observations to global climate model (GCM) outputs to show how poorly global climate models simulated reality on a subset of the globe, such as individual continents or ocean basins. In response, climate modelers who support alarmism, like those who write posts at RealClimate, would state that global climate models were not designed to perform regionally. In other words, they confirmed that global climate models were not simulating Earth’s climate.
I suspect they’ll use that tactic again in response to this post by Roy Spencer.
Regards,
Bob
The predictable response will be that pointing out the absurdity of the GCM results for the ‘corn belt’ is ‘cherrypicking’. What’s left unsaid, of course, is that pointing out when the Antarctic Peninsula heats up a few degrees above normal, is not cherrypicking.
Correct. The AGW clowns usually fail to read the underlying article either accurately or completely. Using their contrived ignorance, they then slap together a little strawman and knock that pitiful wretch to the ground.
As a sneak peek, here’s … the U.S. Corn Belt
Full article here:
https://www.heritage.org/environment/report/global-warming-observations-vs-climate-models
You forget their argument: climate models are not predicting future climate; they are projecting future climate–whatever that means.
Jim, this is a discussion of how well models simulate the past, not the future.
Regards,
Bob
The misrepresentations are figments of corrupt, self-serving thinking
They have nothing to do with the past and the future
“. . . how well models simulate the past, not the future.”
They do neither, IMO.
A Prediction is a statement about the future that has an associated understanding of its chance of coming true.
It may not be a good understanding. It may be a good understanding that there is no way of knowing if it will come true. But the key point about a prediction is that it is a statement about the future with a rationale for its probability of becoming true.
This is a subtle concept.
It’s not a rationale for the truth value of a statement about the future. That would be hard to justify.
It’s not the probability of a statement about the future becoming true. That may be unknowable.
It’s an associated rationale for that probability of a statement about the future becoming true.
“It will rain tomorrow” is a prediction if:
1) It says something about the future (which it does, obviously).
2) The reason for making that statement with a known confidence is understood.
Point 2 could be that you’ve seen clouds coming from a satellite view of the ocean. Or it could be that the cows are defecating at the edge of the field. Or it could be that you have just blurted the first thing that came into your head. So long as the rationale is understood – whether good or not – it’s a Prediction. It has an associated means of being judged as to whether it should be trusted.
A Projection is just like a prediction but with no consideration as to why they are making the statement.
A projection still says something about the future. Obviously.
But it has no associated concept of estimating why it should be trusted.
It’s just not the sort of thing that applies. Like the colour of mass or the taste of a sphere.
For further study, see the concept of Faith.
To me, as a engineer, a projection is an extension of a linear trend line created from a piece of a cyclical process. It becomes a prediction when people act on that projection as if it was truth.
For all their complexity, the output of the climate models is a linear trend line. Pat Frank has proved that. Extending that linear trend line into the future is projection, acting upon that projection makes it a prediction.
I very much appreciate your views of reality.
If there is no concept of how much it should be trusted, then the conclusion must be that it should not be trusted at all. Calling it “science” is even less descriptive. A crystal ball used by a soothsayer may provide a projection of what might happen, but trusting it is not something science is based on.
I could say that Armageddon in the Bible is likely to occur tomorrow. However, I suspect that is unlikely. Similarly, GCM’s say CAGW is likely to occur in the future. But, that is also unlikely.
If you can’t predict the past, you certainly can’t predict (project) the future with any confidence at all.
Thank you Gormans. It think you are both correct.
Of course, choosing one’s next line of research is acting on the projection. Which would then make it a prediction.
But it is forbidden to test the projection, as Jim Masterson pointed out.
The climate misrepresentations, never call them models, serve the purpose of scare-mongering by others, the main reason the “others” are paying to have these misrepresentations
An archaic definition of “projector” should be resurrected.
From dictionary.com, “a person who devises underhanded or unsound plans; schemer.”
Likely response is “Yes, models still need work. More money please”
Bob Spencer separates the wheat from the chaff, aka bull manure.
“Bob Spencer???”
Well 😏 it really is kind of a nice compliment to both fine scientists, Bob Tisdale and Roy Spencer.
Thank you, Janice
Happy 2024 to you
My pleasure, Wil. May 2024 bring you much joy. 😊
Relevant literature…
Mueller et al. 2016 DOI 10.1038/nclimate2825 – Cooling of US Midwest summer temperature extremes from cropland intensification
Lin et al. 2017 DOI 10.1038/s41467-017-01040-2 – Causes of model dry and warm bias over central U.S. and impact on climate projections
Alter et al. 2018 DOI 10.1002/2017GL075604 – Twentieth Century Regional Climate Change During the Summer in the Central United States Attributed to Agricultural Intensification
Zhang et al. 2018 DOI 10.1002/2017JD027200 – Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site
Qian et al. 2020 DOI 10.1038/s41612-020-00135-w – Neglecting irrigation contributes to the simulated summertime warm-and-dry bias in the central United States
Coffel et al. 2022 DOI 10.1029/2021GL097135 – Earth System Model Overestimation of Cropland Temperatures Scales With Agricultural Intensity
excuses, excuses.
made up by twisted little cultists.
Made up by statisticians that have absolutely zero understanding of physics, biology, horticulture, etc. You don’t need to consider any factors other than the temp trend in predicting the future.
It’s all the other stuff, like greening of the earth and the resulting evapotranspiration, that causes the crystal ball to be fuzzy. But like a carnival huckster with a crystal ball, climate science can *see* the future in that cloudy mess.
Take the first one. What do you suppose is creating the possibility of crop intensification? Did you even bother to read the abstract?
If CO2 is greening the earth, what is that doing to evapotranspiration around the globe?
It’s an indication that Freeman Dyson’s criticism of the climate models not being holistic was right on the money. The climate models don’t take into consideration of the greening of the earth affecting maximum temps. Just like they don’t handle clouds correctly. Just like they don’t handle precipitation correctly.
The excuse that the models don’t look at short-term details, only long term trends is typical garbage from climate science. It’s the short-term details that create the conditions for the long term trends. Ignore the short term details and you’ll never get the long term correct.
Look at the second one. It’s the same thing warmed over. Projecting the future based solely on one input, past short-term temperature trends. Nothing holistic in the analysis at all. Nothing about other factors that will impact the future.
You are a great one for Monte Carlo studies. How do you do a good Monte Carlo simulation if you don’t know and consider *all* of the factors in the functional relationship?
I’m not paying for the article. Just what is meant by “agricultural intensification”? It points out that it doesn’t mean land conversion to crop land, so what does it mean?
This simply says that the current models as used are wrong and include a warm bias. No kidding. And you use this as an excuse as to why the author says models don’t accurately forecast observed temperatures.
I’m not going to bother with the rest. You might actually read them and post the reasons you thought they might be meaningfull.
‘Instead of admitting that natural processes could be at work in causing climate change, “energy equilibrium” is what is assumed by the mainstream climate research community for the natural state of climate system unaffected by humans’ (Roy Spencer).
That takes us right back where it all started in earnest:
Except that the NH proxy fabrication from 1000-1850 is TOTALLY CORRUPTED by agenda-driven bias.
And bears absolutely zero resemblance to basically zero individual proxies.
The graph is bogus, because it does not show
1) the temperature dip of the Little Ice Age from about 1400 to 1900; the low point was 1700, AND 2) the Medieval Warm Period from 1000 to 1400.
Present CO2 levels are near the lowest for the past 600 million years, which causes significant desertification in many areas of the world, due to a lack of flora
CO2 IS A LIFE GAS; NO CO2 = NO FLORA AND NO FAUNA
https://www.windtaskforce.org/profiles/blogs/co2-is-a-life-gas-no-co2-no-life
Furthermore, real temps in the 1940s were similar to around 2000..
.. so they are using massively corrupted urban surface temperatures that are totally meaningless as a measure of global temperature.
Mr. Hanley: I think at least 8 people missed your point… 🙄 😏 . Nice graph showing the history of observation-based science versus the Johnny-Come-Lately, unskilled simulations by the GCMs.
AGW is a big fat lie. CO2 EMISSIONS UP GREATLY –> WARMING NOT. Game over.
HOORAY!! THE EDIT TOOL WORKS!!! 😀
Wonderful of you to join us Janice. I don’t know if it was a staggered add-on with different devices but we’ve been mentioning this for over a week.
It is great, though, isn’t it?
Yes! 🙂
If I may flip Ronald Reagan’s joke —
The ClimateCult™ barn is full of horse-apples but there isn’t a pony anywhere to be found.
Meanwhile:
” the 2023 United States corn crop is the largest on record “
Of course, corn is a tropical plant and a C4 plant. It does better when the temperatures are higher and when the CO2 is higher (but not as good as C3 plants which really love CO2).
Yes not believing in terrifying children with the climate dooming would have been the last straw-
My Clinical License is as Good as Gone (youtube.com)
Re-education camp as the only sliver of a chance back into the shrink club for you heretic.
AbstractEvidence is presented that the recent worldwide land warming has occurred largely in response to a worldwide warming of the oceans rather than as a direct response to increasing greenhouse gases (GHGs) over land. Atmospheric model simulations of the last half-century with prescribed observed ocean temperature changes, but without prescribed GHG changes, account for most of the land warming. The oceanic influence has occurred through hydrodynamic-radiative teleconnections, primarily by moistening and warming the air over land and increasing the downward longwave radiation at the surface. The oceans may themselves have warmed from a combination of natural and anthropogenic influences.
https://link.springer.com/article/10.1007/s00382-008-0448-9
Ireneusz,
Yes. I am working on a subset around Bass Strait, between Tasmania and mainland Australia, to show that ocean drives coastal land temperatures, or if not, that another driver mechanism is missing.
Geoff S
“The oceans may themselves have warmed from a combination of natural and anthropogenic influences.”
What anthropogenic influences?
Seriously. Since CO2 lags warming by a quarter cycle in the ice core proxy observations, it couldn’t be human CO2 emissions. So……………….. *crickets* 🙂
What conceivable “human influence” could possibly warm the oceans !
Someone is totally delusional.
Someone once mentioned that roughly a third of the worlds population bathe or swim in the sea every year. That is an awful lot of people peeing in the sea…
The Saudi’s have desalination plants, for drinking water
The models are really clever and lovely – so what do they say about average barometric pressure?
Is it above or below 1013millibar and over the decadal timespan, is it rising or falling
What do the climate stations says about it?
i.e. For any given location, what is the Annual Average air-pressure obtained from readings taken at hourly (or less) intervals?
(I want to know how much water is in the landscape – is it increasing or decreasing)
Just as really clever people can and will tell us how much ‘precipitable water‘ there is in the sky at any given moment, I want to know how much ‘evaporatable moisture‘ is in the soil.
And I Do Not Want To Know what NASA or any of their garbage-can Sputniks have to say.
Are those who follow the models dumb or are they deliberate?
If they are not dumb, seeing they are all well educated, they must be deliberate.
They deceive people because of their ideological fixation, fixation on an idea.
A good scientist tests conclusions. They cannot because it may prove them wrong.
“The end justifies any and all means.”
very simple
VERY Clear
Read Dr Spencer’s conclusions. He makes three points:
Models and algorithms are a problem in climate science but they’re an even bigger problem for innocent citizens.
EXCELLENT ! Great article Roy.
Isn’t this article an exercise in pure cherry-picking? “Here we highlight places where climate models are overestimating warming trends, and ignore anywhere where they are not.” The article also presents graphics suggesting there is zero variation expressed in the range of model results, which is quite simply misleading. You at the very least need to present some kind of depiction of the spread, as RC does:
Averaging the outputs of multiple models is absurd, without any technical quantitative justification.
Why do you reckon that Roy Spencer did it in his article?
Leftists, never take responsibility for their own actions do they !
Roy averaged them to show just how incredibly GORMLESS and MEANINGLESS the models are.
You have a point – why bother keeping models that are extreme outliers – keep the best models, discard the rest – poof- heads exploding across academia at the thought that their cushy jobs and lucrative funding has to be earned with hard work and good results!
Actually the damning thing about the forecasts of all these climate models is that they disagree radically and the “ensemble” or averaging of the modelled results is scientifically and mathematically unsound.
Logically, there can only be one ‘best’ model or run. Averaging that with all the others, at the very least, increases the variance of the average. Considering that all the models show more warming than is actually taking place, averaging an ensemble also causes a drift away from that one best estimate.
It is actually not the case that there is a single “best” model. Different models are built for different purposes to fill different needs, and each is better at some thing or set of things than other models. So none of them is the best and none is the worst, and each can offer valuable information. It is true that the simple ensemble mean isn’t necessarily the best estimate, and it is true that the ensemble spread is not really a probabilistic distribution, thought it is often treated that way.
This is not correct, quite a few models are replicating surface trends quite closely, as you can see in the above graph. Particularly when screened for transient climate response.
NONE of them is accurate enough to be useful. They are ALL so far off from observations that they are unskilled.
See Climate Models Fail, by Bob Tisdale.
They are all PROVEN to be total garbage… just like your comments !!
NONE of the models get within cooee of real surface temperatures. (except maybe that Russian one)
Only a moronic fool would pretend that GISS is remotely representative of global temperature
Rusty can’t name even one prediction these linear extrapolations have gotten right.
Zip. Zero. Nada.
“Different models are built for different purposes to fill different needs”
Then how does it make any sense to “average” them?
That’s like saying a model of the horsepower output of a 350 in^3 can be averaged with a model of the horsepower output of an International Harvester 560 tractor and it will tell you something meaningful. They are built for different purposes for different needs as well!
And the average of the two models is useless!
Why are you lying so much?
Unsound should read a fraud
An average of a fraud is a fraud
The very fact that there are temperature series from local to global to models that drastically vary should tell you that no one has properly defined how the earth/sun interact. To call one correct and all the others incorrect only reflects a bias.
The very first conclusion that one must make is that all global temperatures regardless of their origin have uncertainty. Defining that uncertainty is a necessary function of science which climate scientists pointedly ignore. They do not want to admit that the decadal trends are at least ±2°F.
GISS is URBAN MAL-ADJUSTED garbage.
The very epitome of GIGO !!
It cannot remotely be considered a measure of global temperature.
It’s a very pretty 2D graph of something.
It doesn’t say what temperature scale it’s using on the Y axis.
The graph measures some temperature against an “anomaly” of some sort of temperature derived from 20 years of measuring of another number, the basis of which is not described.
The X axis in time is so squashed to present your Y axis numbers of a small temperature differential as important and to be taken as “real”.
It purports to reduce averaged measurements of a 4 dimensional field of “temperature” to a few 2D points.
The computer models of forecast temperature are simply code written by human beings. I imagine that the code for these models is not only mathematically complex but also runs into millions of lines of code.
The now infamous UK Post Office Horizon Project cost upwards of £4 Billion with estimated code of over tens of million lines. Some estimates are hundreds of millions of lines.
Compared to estimating Earths “temperature” over the next 100 years, Horizon was a simple project.
They made a complete arse of it and lied, lied and lied again about the bugs and significant accounting failings of the software.
The idea of accepting computer code which attempts to describe the Earth’s “average” temperature in 50 years time is ridiculously naïve.
“It purports to reduce averaged measurements of a 4 dimensional field of “temperature” to a few 2D points.”
It’s far more than a 4D field. It is truly multi-dimensional. Latitude, longitude, elevation, terrain, geography, pressure, humidity, clouds, microclimate (e.g. is the ground covered in grass or sand), etc. At least a 9D field based on this and I’m sure I could list out more. Even more difficult some of these are functions of others so you have inter-dimensional relationships to consider as well!
One should add that the Post Office is now going back to remove incorrect convictions for larceny and embezzlement of employees. Employees for whom the Horizon software mistakes ruined their lives.
Sound familiar about what GCM software may be doing to all our lives.
Another link free post thus worthless you are misleading as usual anyway.
The New Models, the grey area getting further and further away from the data, proves Dr. Spencer’s point.
I know some people are getting tired of me saying this, the truth simply is…
“The biggest problem with climate models is getting them to matchup to reality.”
Why would one want to match up to reality when models are better than reality?
Call them inventions, creations, charades, kabuki, anything but models
“Projecting the future”
I can just see the computing hall right now, a sleazy little cubicle on a wet & windy seafront, entrance through a pair of ragged curtains, to reveal the AI, wearing brass earrings, a headscarf, and a pair of gnarled interfaces gazing into a little glass ball!
BTW, I once gave a course in Ernie’s parlour!
😄 Yes. OR…. a little, rickety, caravan where Professor Marvel, wearing a satin turban and wearing checkered trousers, tells a little girl …..
Note: unlike the GCM code writers who are intentionally telling lies about human CO2 to make money (solar, wind, EV’s, bogus surface temp. data, and other scams), Professor Marvel really did a fine job of gathering data and handing out wise advice to a distraught little girl ….
As we know, “if Y then X” does not follow from the relation “if X then Y”. Thus, if X is a description of the physical processes affecting climate and Y is climate observation, the fact that climate observation is Z rather than Y proves that the description X is not true. But if climate observations were Y that would still not prove that X is true. That is why, in my opinion, the simple observation that climate models parameterize huge physical processes like cloud formation is more important than a demonstration that model outputs are not matching observations. The fact is we know that the X in a climate model is a very wild guess. And that means that even if a model happened to match observations, that result would be purely accidental. Pat Frank made this point very powerfully in his propagation of error paper. John Clauser also understands it well, even without mathematical demonstration. Clauser immediately knew model projections were unreliable because clouds and many other climate processes are too poorly understood for reliable projections to be made.
CO2 impact simply cannot be demonstrated from a calculation that includes as variables important climate processes that cannot be much more reliably quantified than is currently possible. And since we know that X is a wild guess, the fact that all modelers are making a guess at X that results in their models running hot shows that the modeling undertaking is a biased one. If I ask 100 people to pick a number between 1 and 100 and they all pick 17, I know they have colluded in their choices. So, for me one of the really interesting things about Dr. Spencer’s work is that it demonstrates the bias of the modeling community. Why are all of the modelers getting it wrong in the same direction?
Garbage in, garbage out.
Thanks Tim. Concisely put.
“Garbage in, garbage out.”
Which is why they can make them sort of match GISS, which is the absolute definition of GIGO. !
“Why are all of the modelers getting it wrong in the same direction?”
other people’s money.
Clearly, their unstated assumptions that control the design of the model are wrong. The question should be, “Why are the assumptions used by everyone, all leading to a positive bias?” Possible answers are that 1) the modeling approach is fundamentally flawed, 2) there is some forcing factor that is unknown and unaccounted for, such as submarine vulcanism, and/or 3) the models are purposely tuned to agree with the consensus paradigm, which leads to job security because of the impending threats created by unconstrained warming. Or, 4) all of the above.
Dr. Spencer, in Chart 3 of your Heritage Foundation article you show temperature curves based on datasets labeled Radiosonde (presumably baloons), Reanalysis and Satellite. What is the Reanalysis data set based on?
Separately, why don’t you consider changes in total solar irradiance as a major control on long-term earth surface temperatures?
Don Langmuir
No suprises. This papers drops ECS by 40% from IPCC
https://journals.ametsoc.org/view/journals/clim/36/18/JCLI-D-22-0708.1.xml
The lesson I get from this is that the truth is vastly outnumbered by the fantasies of elites and “academic” incompetents.