Climate models fail in key test region

Reposted from The GWPF

Date: 07/06/21

Dr David Whitehouse, GWPF Science Editor

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.

The basic questions for climate models is whether they realistically simulate observations, and to what extent can future climate change be predicted? It’s an important concept as political and environmental action is predicated upon it.

new paper by Timothy DelSole of George Mason University and Michael Tippett of Columbia University looks into this by attempting to quantify the consistency between climate models and observations using a novel statistical approach. It involves using a multivariate statistical framework whose usefulness has been demonstrated in other fields such as economics and statistics. Technically, they are asking if two time series such as observations and climate model output come from the same statistical source.

To do this they looked at the surface temperature of the North Atlantic which is variable over decadal timescales. The reason for this variability is disputed, it could be related to human-induced climate change or natural variability. If it is internal variability but falsely accredited to human influences then it could lead over estimates of climate sensitivity. There is also the view that the variability is due to anthropogenic aerosols with internal variability playing a weak role but it has been found that models that use external forcing produce inconsistencies in such things as the pattern of temperature and ocean salinity. These things considered it’s important to investigate if climate models are doing well in accounting for variability in the region as the North Atlantic is often used as a test of a climate model’s capability.

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal. They also found institutional bias in that output from the same model, or from models from the same institution, tended to be clustered together, and in many cases differ significantly from other clusters produced by other institutions. Overall only a few climate models out of three dozen considered were found to be consistent with the observations.

Read the full article at The GWPF

NOTE: The link above to the paper seems to have been removed. Here is a local copy:

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June 8, 2021 6:13 am

despite all that math

Nick Werner
Reply to  billtoo
June 8, 2021 12:58 pm

… if a model can’t predict which years will be colder and which years will be warmer than average at any particular location, anyone can do as well by throwing blue and red darts at a map.

michael hart
Reply to  Nick Werner
June 10, 2021 7:28 am

Yup. It’s difficult to believe that they have data even approaching the quality and duration required to do the experiment.

Some new pharmaceutical may not show certain rare side effects until it has been widely used in far more patients than a clinical trial. When the data isn’t there, it just isn’t there.

Ian W
Reply to  billtoo
June 9, 2021 5:25 am

And when the modelers are taken to task about their models not apparently matching reality – they will ask you to show them the errors in their math. Whereas the errors are almost always in the base assumptions in the design of their models – in other words the modelers do not understand what it is they are modeling and what is important to the outcome of their models.
If you point out a mis-assumption though they will always go back and ask you to show them the errors in their math. It’s a blind spot in the way most modelers think.
ALWAYS check the assumptions on which the model is designed – that is usually where the errors are. As these assumptions are what the modelers rest their understanding of how the ‘world works’ expect either arguments or the reversion above to – where is the error in my math?

Last edited 15 days ago by Ian W
Reply to  Ian W
June 9, 2021 7:25 am

I would add that not only are the assumptions (ie. the actual text) important to understand but who defined them and why. Assumptions in IT area can often be called “cover your backside” documentation since it is the section you put things which will cover scenarios that are very often exceptions to the rule.

In climate terms the factors and scenarios are so immense it would be impossible to define accurate realistic mathematical models and so there will probably be a very large list of assumptions. What I don’t understand is if they constantly get incorrect results from the models based on observational data – then why the heck do they not change them!

The only answer to this is either they don’t know how to improve them or they do not want to improve them. I assume the latter but it might be wrong assumption!

June 8, 2021 6:14 am

If it doesn’t agree with observations, it’s wrong. Feynman.

Reply to  Chaswarnertoo
June 8, 2021 7:34 am

If it doesn’t agree with observations, “adjust” the observations until it does. Mann.

paul courtney
Reply to  Graemethecat
June 8, 2021 9:14 am

Those who are attacking me are attacking science. Fauci.

Reply to  Chaswarnertoo
June 8, 2021 9:56 am

So what exactly is the observation here?

Reply to  Nick Stokes
June 8, 2021 10:02 am

The huge and increasing divergence between the models and the actual temperatures?

Reply to  Graemethecat
June 8, 2021 10:27 am

By how much, do they say?
No, the fact is that they have a model concerning the variability, which disagrees with CMIP5 variability.

Last edited 16 days ago by Nick Stokes
Reply to  Nick Stokes
June 8, 2021 10:52 am

Do we need to point out if the variability is divergent then the raw results must diverge.

Reply to  LdB
June 8, 2021 11:00 am

Diverge from what?
Does anyone have numbers here?

Reply to  Nick Stokes
June 8, 2021 11:09 am

You said

>>>> No, the fact is that they have a model concerning the variability, which disagrees with CMIP5 variability. <<<

I merely remind you If that is true it follows the outputs must differ

I couldn’t give a rats about the numbers it’s statistical model junk which is a bit like believing in voodoo but less chickens die and is probably one up for attribution statistics.

Last edited 16 days ago by LdB
Richard Page
Reply to  Nick Stokes
June 8, 2021 11:11 am

Diverged from actual observations. They are using statistical analysis to try to quantify by how much the model output differs from observations of reality. All models will differ in some degree, presumably the ones that failed differed wildly from real world observations to the point where they were considered useless.

Reply to  Richard Page
June 8, 2021 12:17 pm

The paper is not analyzing the divergence from actual observations. It is analyzing the deviance of modeled variability with observed variability. As an illustration of the difference in concepts being discussed here consider that the authors determined that NASST variability deviates from itself by about 80 AMV units (4% significance) with the 1854-1936 period vs the 1937-2018 period. This compares with the modeled deviance of about 140 AMV units (7% significance) using 7 Laplacian eigenvectors.

Reply to  Richard Page
June 8, 2021 1:14 pm

From the start of their abstract, on what they actually did:

“This paper proposes an objective criterion for deciding if two multivariate time series come from the same stochastic
process. Importantly, the test accounts for correlations in both space and time. The basic idea is to fit each multivariate time
series to a Vector Autoregressive (VAR) Model, and then test the hypothesis that the parameters of the two models are equal.”

Mike McMillan
Reply to  Nick Stokes
June 8, 2021 2:00 pm

Reply to  Nick Stokes
June 8, 2021 5:20 pm

By how much, do they say?”

It matters not by how much.

Reply to  Nick Stokes
June 9, 2021 4:41 am

Nick writes “By how much, do they say?”

The paper is freely downloadable and contains graphs to answer that.

Their result doesn’t surprise me but on the other hand, I’d like to see their methodology applied to multivariate time series that really do come from the same stochastic process in different time periods to see that it works the way they think it will.

Reply to  Graemethecat
June 8, 2021 11:12 am

No. That is not what is being discussed in the paper. What is being discussed is the variability of the North Atlantic SSTs. 2nd order polynomial and 9th order polynomial trends are actually removed prior to analysis. This allows the authors to better assess the differences in variability between model and observation while minimizing contamination that might occur as a result of any warming/cooling trends embedded within the data. It’s not clear to me whether any long term divergence between modeled and observed temperatures would increase or decrease their deviance metric.

Last edited 16 days ago by bdgwx
Reply to  Nick Stokes
June 8, 2021 3:33 pm

It’s irrelevant. Anyone who thinks any computer model can predict what’s going to happen even just a few years into the future for a non-deterministic system with so many variables to any sensible level of confidence is fooling themselves. How these people can claim to be ‘scientists’ is beyond me.

Reply to  MarkW2
June 8, 2021 5:21 pm


Forrest Gardener
Reply to  Nick Stokes
June 9, 2021 5:04 pm

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.
And no I will not do your homework for you.

Richard (the cynical one)
June 8, 2021 6:15 am

No, no, no! It’s the climate that failed to live up to the models. Those model scientists put their absolute best into the work. By sheer willpower, they have tried wanting the climate into line.

Reply to  Richard (the cynical one)
June 9, 2021 8:26 am

Exactly. How could a dozen CHIMPS typing on model-generating computers possibly be wrong? 😉 😉

Last edited 15 days ago by beng135
June 8, 2021 6:27 am

To make matters worse, they still insist on detrending the long, irregular cycles of the AMO with less than three turning points. That may work to show a simple picture but it is statistical malpractice and a major source of climate modeling error if they generalize and incorporate that one. The same limited turning point issue goes for centennial low solar minimums or multi-cycle minimums.

climate4you welcome

Mark Fife
Reply to  ResourceGuy
June 8, 2021 7:31 am

Climate “science” is rife with statistical malfeasance and fails miserably as at its heart it is simply another version of The Big Lie.

CO2 levels do not follow human emissions in any way, shape, or form. The statistics to back this assertion are in fact trivial. Neither time series depicted are stationary. As the errors from regression are not stationary they are not cointegrated. Therefore any correlation is spurious. The results from testing for statistical significance by regression are invalid as the standard error depends upon the error terms being at least approximately normal.

Most importantly the error terms demonstrate beyond any doubt there is no linear relationship between the two time series.

The simple fact is Salby and several others are correct in asserting since human emissions are less than 5% of total CO2 emissions they cannot be greater than 5% of accumulated CO2.

Cumulative Emissions.jpg
Reply to  Mark Fife
June 8, 2021 8:29 am

Salby thinks that my Guinea pig is a co2 source, whereas she is a co2 sink. This is valid for the entire biosphere. The difference lies in net and gross calculations for every co2 source.

Mark Fife
Reply to  Hans Erren
June 8, 2021 9:33 am

Math is hard.

Cumulative Emissions 4.jpg
Joseph Zorzin
June 8, 2021 6:27 am

from the GWPF site:

“Recently Michael Mann, in particular, has said there is no such thing as internal climate variability, maintaining that oscillations seen in proxies of pre-industrial temperature can be explained as an artifact of volcanic activity. The researchers find no evidence for this in the North Atlantic data.”

As if Mickey Mann knows for a fact that volcanic activity is the cause of the oscillations. Can he prove it? What’s his logic?

Be sure to read the reviews of Mickey’s late book on the Skeptical Science web site: and a video of Mickey being interviewed and his reading from the book at: I’d like to see a new discussion thread on that book so we don’t step on this thread too much. I’ve mentioned this book a few times but since my comments were off topic, they were removed.

The video is almost an hour and half- get your fill of Mickey!

Gordon A. Dressler
Reply to  Joseph Zorzin
June 8, 2021 6:44 am

I’d rather not.

Carlo, Monte
Reply to  Joseph Zorzin
June 8, 2021 6:53 am

And the last line of the article:

The researchers have a book being published by the Cambridge University Press later this year called “Statistical Methods for Climate Scientists.”

Apropos for Mann.

Right-Handed Shark
Reply to  Joseph Zorzin
June 8, 2021 8:11 am

My stomach is not strong enough.

Joseph Zorzin
Reply to  Right-Handed Shark
June 8, 2021 8:53 am

you need to be prepared with a bottle of Maalox and a vomit bag

Reply to  Joseph Zorzin
June 8, 2021 10:09 pm

Rather fill in Mickey ! 🙂

Coeur de Lion
June 8, 2021 6:42 am

Regarding Michael Mann, I still have enormous difficulty rejecting the opinion of 100 world class scientists that he is a disgrace to the profession. (Mark Steyn)

Joseph Zorzin
Reply to  Coeur de Lion
June 8, 2021 7:27 am

The Skeptical Science book review says of Mann:

“Michael Mann‘s book is essential reading for anybody who doesn‘t accidentally want to fall for the latest tricks utilized by the fossil fuel industry and other groups heavily invested in the status quo. He shines the spotlight on the various underhanded tactics with which these vested interests and inactivists try to drive a wedge into the climate movement or try to shift the blame for the climate crisis from them to us as consumers. Once you know what to be on the lookout for, you‘ll no longer fall prey to these methods and can also call them out when you see others falling for them, who haven‘t been made aware of the tactics yet. Forewarned is forearmed as the saying goes!”

mark from the midwest
Reply to  Joseph Zorzin
June 8, 2021 8:21 am

Kind of like Marx and Engels pointing out all those underhanded tricks that capitalists were using to increase productivity and raise standards of living. The worst trick of all was when Henry Ford started paying workers 5 dollars a day, almost twice the average wage for semi-skilled labor. Wow, what rat-bastards those capitalists were.

Reply to  Joseph Zorzin
June 8, 2021 8:22 am

ROFL so we start with the great oil company conspiracy and from that moment on you know you are not reading a review but a sermon from a true believer … yeah perhaps we might have bought that crap if it was a little less biased

Last edited 16 days ago by LdB
Gordon A. Dressler
June 8, 2021 6:42 am


June 8, 2021 6:52 am

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.

I could have told the researchers, if you do not understand how something works you cannot [successfully] model diddly squat.

It really is as simple as that.

Reply to  fretslider
June 8, 2021 7:48 am

Given enough knobs to twiddle, Climate “Scientists” can fit the outputs of their models to fit any temperature series they want. However, the models have no basis in Physics, and are utterly worthless for predicting future outcomes.

Ian W
Reply to  Graemethecat
June 9, 2021 5:43 am

But they can choose a subset of models and ‘average’ their completely mismatched results into an ‘Ensemble’ then by their choice get an average that is ‘less wrong’. Those meteorologists who read what was done will assume that the ‘ensemble’ is the forecasting method where ONE model is run with slightly differing inputs to see the spread of forecasts from the ONE model. It is not. It is a way of confusing the readers into thinking that a standard weather forecast system is being used – when all that is being done is obfuscation of the methods used to hide the errors and mismatches in the climate model outputs.

Reply to  Ian W
June 9, 2021 6:50 am

I have never understood how Climate “Scientists” can justify averaging the output of dozens of models, and get away with it.

mark from the midwest
Reply to  fretslider
June 8, 2021 8:22 am

Actually you can model diddly squat, but then the results are worth … wait for it … diddly squat!

Gerry, England
June 8, 2021 6:58 am

Would the few models that were close to reality be Russian by any chance?

Reply to  Gerry, England
June 8, 2021 7:05 am

That’s what I was looking for. Which models are tracking the sea temperature and what are they projecting?

June 8, 2021 7:10 am

Once again the question: why are we taking the mean of a bunch of models that are failing and one or two that are succeeding? What possible validity can the result have? Why is this a better procedure than throwing out the ones that fail and just using the ones that succeed?

And which, if I understand it correctly, are actually one, which comes from Russia!

Somebody help. Is this just a stupid question, and if so why? Is there some explanation someplace that everyone knows about except me?

Jeff Alberts
Reply to  michel
June 8, 2021 10:21 am

Totally agree. Just like averaging temperature readings from around the world is physically meaningless. But in the case of models, the “prevailing wisdom” seems to be that a million wrongs make a right.

Reply to  michel
June 8, 2021 1:49 pm

It doesn’t look like a “mean of a bunch of models” is being analyzed here. It looks like each model is treated separately. Are you seeing something different?

It looks like the result has applicability to the hypothesis that NASST variability is externally driven.

If we threw out the models that fail then you might erroneously conclude that NASST variability is externally driven and that the ECS is stupidly high.

If you interpret that publication differently let me know!

June 8, 2021 7:23 am

So the observations are wrong?

Reply to  TonyG
June 8, 2021 8:24 am

According to the paper cited above observations have a self deviance score of about 4%. The threshold for “failure” in the paper is 5%. This compares with the median score of 7% from models. If your use of “wrong” is equivalent to the OP’s contextual use of “fails” then we can say that observations are at least very close to being “wrong” per the paper.

Last edited 16 days ago by bdgwx
Reply to  TonyG
June 8, 2021 9:50 am

> …observations are wrong?

Just not sufficiently “adjusted.”

Reply to  Rob_Dawg
June 8, 2021 4:18 pm

Gee, Nick. Thanks for the down vote. Good to know you care enough to bother.

Reply to  TonyG
June 8, 2021 10:05 am

“So the observations are wrong?”
Which observations?
 Some information theory researchers have a model of what they think the models should have produced, and found a discrepancy between their models and the CMIP5 ones.

Reply to  Nick Stokes
June 8, 2021 10:54 am

ROFL nice strawman does he burn well?

Reply to  LdB
June 8, 2021 11:08 am

Do you have any idea what numbers we are talking about?

Richard Page
Reply to  Nick Stokes
June 8, 2021 11:19 am

No Nick. Bad.
They are using real world observations – actual data from reality. By comparing model output to observations, they determine the degree by which the models differ from reality – those that differ greatly from reality get a fail and should be considered useless for all purposes. Whether they will be depends on vested interests and how much traction this paper might get.

Reply to  Richard Page
June 8, 2021 12:07 pm

Also, my comment was a joke, (an old one around here, I’ll admit) but I guess some people have to take everything seriously.

Reply to  Richard Page
June 8, 2021 12:40 pm

As I posted below the concept of “failure” here is not as binary as you might think. Remember, NASST variability deviates from itself at 4% significance. This compares to the median CMIP5 variability deviation of 7% significance. And based on the context of your posts I think you may have a misunderstanding of what “failure” even means here. It does not man CMIP5 is inadequate for modeling NASST. It means that internally driven variability of NASST cannot be rejected.

BTW…note that models that do a better job of mimicking NASST variability suggest high equilibrium climate sensitivities. This paper predicts an ECS of 4.8C using a linear regression model of the analyzed deviances. In other words, this is not a paper I would be pushing as evidence for lower climate sensitivities.

Last edited 16 days ago by bdgwx
CD in Wisconsin
June 8, 2021 7:32 am

“Recently Michael Mann, in particular, has said there is no such thing as internal climate variability, maintaining that oscillations seen in proxies of pre-industrial temperature can be explained as an artifact of volcanic activity. The researchers find no evidence for this in the North Atlantic data.”


I’m not even a scientist, and yet I find it hard to believe that there is nothing internal to the Earth’s climate system that causes temperature variability over time in the proxy record.


“..Climate variability can also occur due to internal processes. Internal unforced processes often involve changes in the distribution of energy in the ocean and atmosphere, for instance, changes in the thermohaline circulation . Climatic changes due to internal variability sometimes occur in cycles or oscillations…”

I’m running out of words to describe Mann.

Doug S
Reply to  CD in Wisconsin
June 8, 2021 8:08 am

The issue of internal energy of the earth (or any planet being studied) was one of the questions I wondered about 10 years ago when first studying this climate religion. I asked the guys at realclimate where the Kinetic energy term is located in the model math and got a courteous response. At that time they claimed the global warming math/models is strictly based around radiation terms. They were polite and told me I was just not used to seeing the math expressed this way. They may be completely correct for all I know, I only completed the Bachelor level in Physics so I’m no where close to the PhD’s in Physics.

However, the question still intrigues me. What if you have two equal planets, all things equal. Both planets have fine silica dust covering the surface. The only difference is one planet has internal energy and the other does not. Not sure it matters where the energy is coming from, volcanoes? The planet with internal energy develops strong winds which carry the fine silica dust into the atmosphere. This in turn increases the albedo of the energized planet. The other planet has the settled dust on the surface and it never gets moved off the surface. My assumption would be that the energized planet would have a lower temperature (due to increased albedo) in it’s atmosphere than the non-energized planet.

So if my thought experiment is correct here, you would need a KE term in your model math to capture this effect.

Smart Rock
Reply to  Doug S
June 8, 2021 9:10 am

Doug, you should learn a bit of geology. This link has a decent summary of plate tectonics (but slightly out of date). Plate tectonics is the surface expression of heat flow from radioactive decay (and probably phase changes in the core as well), and is what drives all geological processes. Without that very small average heat flow of 0.09 W/m² the earth would probably be covered in one big ocean with no land.

Geothermal heat flow is too small to affect climate directly, but it does affect climate over the long term, by changing the configuration of oceans and continents. This is the “internal energy” that you are looking for. It’s not kinetic energy; it’s heat.

Zig Zag Wanderer
Reply to  Smart Rock
June 8, 2021 3:20 pm

It’s not kinetic energy; it’s heat.

Heat is kinetic energy too.

Doug S
Reply to  Smart Rock
June 8, 2021 3:36 pm

Thank you Smart Rock, I appreciate the link and will take a look.

Jeff Alberts
Reply to  CD in Wisconsin
June 8, 2021 10:23 am

Put simply, if it’s cold in one place and hot in another, that’s internal variability. Because the cold place will be warm at another time, and vice versa for the hot place.

Reply to  CD in Wisconsin
June 8, 2021 5:39 pm

”“Recently Michael Mann, in particular, has said there is no such thing as internal climate variability,”

Michael Mann is obviously an idiot, or a fraud. I’m going with idiot.

Climate variability on Mars….
”Martian climate variability on interannual time scales is an interesting topic from many points of view. Because of the short radiative time constant of the martian atmosphere and its lack of ocean heat storage, one would not expect the martian climate to undergo significant variations on interannual time scales. Yet, with detailed examination of the multidecade telescopic record of great dust storms,1 multiyear surface pressure records acquired at the Viking landing sites,2 multiyear orbiter observations of the appearance of the seasonal and residual polar caps,3 and evidence of large variations in atmospheric water, it becomes clear that the climate of Mars does exhibit distinct variations from one year to the next.

Volcanic activity on Mars….
MARS VOLCANOESMars today has no active volcanoes. Much of the heat stored inside the planet when it formed has been lost, and the outer crust of Mars is too thick to allow molten rock from deep below to reach the surface.

So much for ”no such thing as internal climate variability” and his stupid ”volcano theory”
Does anyone still take this bloke seriously?

Last edited 15 days ago by Mike
June 8, 2021 7:47 am

It is an interesting paper. I’d like to see this reviewed and replicated by other experts before drawing too many conclusions. However, one thing I noticed is that their test of “failure” is based on deviance between observed and modeled NASST with 2nd and 9th order polynomial fits removed with “failure” being defined as a deviance > 5%. With 7 Laplacians the deviance ranges from about 4-11% depending on model. With 10 Laplacians the deviance ranges from about 6-10%. It is important to note that the later half NASST series deviates from the early half series by 4% and 5% respectively. In other words, NASST deviance from itself barely passes the test using the 5% threshold for “failure”. And with the median model deviance being around 7% its a stretch at best to categorize model performance as a failure in such binary terms especially considering that the observational time series is just a couple of percentage away from the model time series and is itself close to “failure”. What would it even mean if observations were a “failure” anyway? It is also important to understand that “failure” in this case is isolated to variability of the North Atlantic SSTs. Pass/Fail results from this test have no relevance to model skill in terms of the observed vs modeled increase/decrease in SST of the North Atlantic region, global SSTs, or global near surface temperatures over long periods of time. If I have somehow misrepresented or overextended interpretation of this publication please let me know.

Last edited 16 days ago by bdgwx
Reply to  bdgwx
June 8, 2021 8:16 am

It doesn’t come to the right conclusion so it’s never going to be reviewed … the best you will get is a few drive by shots fired.

There is no room for any doubt as the Climate Scientists ™ head into the great money grab conference. The fact they are doomed is lost on them because they are true believers and think the public care.

I had rated historic reparations chance extremely low but post covid it is dead in the water as China would be asked for covid reparations under the same rules.

Last edited 16 days ago by LdB
Reply to  LdB
June 8, 2021 1:38 pm

It looks publication worthy to me. I’m not sure what a “right” or “wrong” conclusion would be here exactly Can you define what a “right” or “wrong” conclusion is?

Zig Zag Wanderer
Reply to  bdgwx
June 8, 2021 3:26 pm

I think LdB is referring to the dogma of Climate Scientology

Last edited 16 days ago by Zig Zag Wanderer
Reply to  bdgwx
June 9, 2021 12:24 am

Climate emergency = right
Anything else = wrong

As defined by what is needed by Climate Science ™

Last edited 15 days ago by LdB
Reply to  LdB
June 9, 2021 6:25 am

What equilibrium climate sensitivity is the threshold for climate emergency?

Ulric Lyons
June 8, 2021 8:22 am

The AMO was colder in the early to mid 1970’s, mid 1980’s, and early 1990’s, because of stronger solar wind conditions driving positive NAO/AO regimes, and the AMO warmed strongly since 1995 with the decline in solar wind temperature/pressure since then causing an increase in negative NAO/AO conditions.

Bill Powers
June 8, 2021 9:17 am

 “Overall only a few climate models out of three dozen considered were found to be consistent with the observations.”

I would like more detail on the models that proved to be consistent with observations.

Reply to  Bill Powers
June 8, 2021 7:43 pm

The three main contributors to average global temperature (agt) change since 1700 are: 1) Increase in water vapor (WV is a greenhouse gas). 2) The influence of variation of solar output. The effect of this on agt is quantified by a proxy which is the time-integral of sunspot number (SSN) anomalies. 3) The effect on average global temperature of the net of all ocean surface temperature (sea surface temperature, SST) cycles which, for at least a century and a half, has had a period of about 64 years. Optimum combination of these three factors closely matches measured agt for as long as it has been accurately measured worldwide. The match has been 96+% 1895 to 2020.

Aintsm 1850 2020.jpg
June 8, 2021 9:30 am

to quantify the consistency between climate models and observations using a novel statistical approach.”

A novel statistical approach? I guess that raises the warning flag right off the bat.

June 8, 2021 9:33 am

Science advances by strictly adhering to the scientific method, which means when hypothetical projections don’t statistically reflect reality for a statically significant duration, the hypothesis is either revised or discarded.

CMIP5 model projections have already exceeded the parameters required for disconfirmatiin, however, CAGW has always been a political phenomenon and not a physical one.

To keep the disconfirmed CAGW alive, “scientists” have relegated themselves to manipulating data and making ECS projections so ludicrously expansive (1.5C~5.0C), it’s almost non-disconfirmable.

More and more real scientists are catching onto the CAGW scam, and we’ll see more papers like this one being published exposing the sham that CAGW is.

Reply to  SAMURAI
June 8, 2021 1:40 pm

You do realize that this publication suggests the best estimate of ECS given the authors variability deviance model is 4.8C right?

June 8, 2021 9:46 am

> …almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.
You have a climate disease. We are sure. We have diagnosed it ourselves. We have here 107 different chemicals of which one, at most, is the cure. We’ve separated out 4 different averaged scenarios, all bad. We recommend the worst set RCP 8.5 just to be sure. Trust us.

Michael Jankowski
June 8, 2021 9:58 am

“…using a novel statistical approach…whose usefulness has been demonstrated in other fields such as economics and statistics…”

So it’s not “novel” at all.

Reply to  Michael Jankowski
June 8, 2021 4:17 pm

“Economics” exists so “climate science” doesn’t get lonely in the dunce corner.

Joel O'Bryan
June 8, 2021 10:24 am

Failing major elements of weather in observation is a well known problem for all climate models. They fail at everything from failure to re-create a single ITCZ, and a lack of QBO or ENSO internal variations that carry significant impacts to temperature trends to failure of precipitation rates and amounts. They are all junk science.

That is the reason why the modelling community went to the Intercomparison Project (CMIP) sleight of hand to fool the average person into thinking the models have been verified in some way. They only get “verified” with other simulations (models), not reality (observation).

June 8, 2021 11:16 am

The More Alarmists Talk, The More We Know Global Warming Is A Scam

While politicians, activists, and earnest-faced “journalists” spread terror as fast and hard as they are able, there continues to be a steady stream of counter information:

Tim Ball, a former University of Winnipeg climatology professor who unmasked Michael Mann, assures us that carbon dioxide, the target of climate alarmists’ crusade, isn’t actually a greenhouse gas.

Scientific research is plagued by a “replication crisis.” Economists have “found that studies that failed to replicate since their publication were on average 153 times more likely to be cited than studies that had,” says Science Alert, “and that the influence of these papers is growing over time.” And we all know that some of the most-cited reports by political and media activists are climate papers predicting doom.

There’s good news for polar bears, the warming zealots’ poster animal, due to unexpected (by alarmists) “sea ice thickness across the Arctic.”

Former Obama undersecretary of energy Steve Koonin, whose work we’ve highlighted, points out in his new book “Unsettled: What Climate Science Tells Us, What It Doesn’t, and Why It Matters” that: 
1) “the warmest temperatures in the U.S. have not risen in the past 50 years”; 
2) “the rate of global sea-level rise 70 years ago was as large as what we observe today”; and 
3) instead of famine, “in the 50 years from 1961 to 2011, global yields of wheat, rice, and maize … each more than doubled.” 
He pulls these facts not from the thin air that gave us the 1.5 degrees Celsius boundary but from U.S. government and United Nations reports.

Bjorn Lomborg, professor and author, and former director of Denmark’s National Environmental Assessment Institute, notes that climate-related deaths have fallen from nearly a half million to fewer than 25,000 in less than a century.

The most interesting is the stat that papers study findings that cannot be replicated are cited 153 times more often than confirmed studies. This means those that are fraudulent, propaganda or mere opinion are given way more weight than actual knowledge. Reminds me of the FDA/CDC/NIH/WHO

Pat Frank
June 8, 2021 12:28 pm

Perfect model tests have indicated that the climate of the North Atlantic is where climate models are most likely to be successful in a 5-year prediction.

That inference of success may be why DelSole and Tippett chose the North Atlantic region to test the models.

But the models failed where they were purported most likely to succeed. Will the IPCC see the light and disband? Will the cries of alarm fade? Fat chance.

Reply to  Pat Frank
June 8, 2021 12:53 pm

It looks like they chose the North Atlantic region because that is the region in focus for the AMO index and because of recent publications concerning the debate regarding whether variability in the North Atlantic region is internally or externally forced. I believe the most relevant publications are Mann 2020 and Mann 2021.It is interesting to note that Mann is the actor must influential in both the rise and fall of the AMO.

June 8, 2021 2:05 pm

Models are software. Software must be validated and verified by disinterested third parties, then tested extensively before it can be considered fit for use. This is one standard for any software used in life-sustaining applications, and even after decades of practice NASA gets it wrong sometimes for manned spacecraft, and often gets it wrong for other applications. It should be one standard, of many, for any system used to advise public policy decisions.

For the most part, model software source code is kept closed and hidden. This is formulaic for hiding incompetence and fraud. Government paid software source should not be exempt from freedom-of-information act or similar disclosure at common request.

Software can’t tell the future.

We know all this in other fields of endeavor and research. Somehow climate fraudsters don’t have to follow the rules, and it is built into their crooked game that model software is out of bounds for scientific verification.

Until they are open to detailed scrutiny and audit, and sufficiently understood in each instance, software models are indistiguishable from magic. A single instance of performance adequacy under test is insufficient to determine fitness for use.

Izaak Walton
June 8, 2021 2:39 pm

The paper appears to be nonsense. The authors set an impossible task and then they act surprised that climate models fail. What they did was take a time series derived from patchy experimental observations (there have been numerous posts on this blog about how sea surface temperature measurements can’t be relied upon) then subtracted off from that time series a 9th order polynomial fit and then demanded that climate models accurate reproduce what is left.

What the authors fail to do is to show the the residues they want climate models to fit are real and not just experimental error or numerical noise. There is no discussion about the size of the errors in sea surface temperatures and how this relates to the size of the residues. In any set of experimental measurements there is some amount of noise and unless you can characterise the size of that you have no way of knowing what is signal and what is noise.

Also for a climate model to reproduce the residue left after subtracting off the trend would require that the inputs be known to a much higher level of precision than what is possible. So again it is not surprising that climate models don’t agree with the finer details of observations since the forcing is not specified to the required precision for that to be possible.

Lastly the authors ignore the most important details. Can the models reproduce the overall trends? This is a first order effect and surely the most important. Asking instead whether models can reproduce a 10th order effect (what is left after you subtract of a 9th order polynomial fit) without asking about higher order terms is pointless.

June 8, 2021 3:06 pm

Start with part one:

Comparing climate time series – Part 1: Univariate test
Timothy DelSole1 and Michael K. Tippett2
1Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University,
Fairfax, Virginia, USA
2Department of Applied Physics and Applied Mathematics, Columbia University.
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, 2020
Published 22 October 2020.

From the abstract:

“ The procedure is illustrated by comparing simulations of an Atlantic Meridional Overturning Circulation (AMOC) index from 10 climate models in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). Significant differences between most AMOC time se- ries are detected. The main exceptions are time series from CMIP models from the same institution. Differences in stationary processes are explained primarily by differences in the mean square error of 1-year predictions and by differences in the predictability (i.e., R-square) of the associated autoregressive models.”

Tony Sullivan
June 8, 2021 4:47 pm

John Stossel is fighting the good fight, but he’s being censored…..errr, fact checked and it’s a joke.

June 8, 2021 5:17 pm

 Overall only a few climate models out of three dozen considered were found to be consistent with the observations.”

Blind luck

June 8, 2021 5:36 pm

Models are Models
I remember when computers were first used
there was a anachronym GIGO (garbage in garbage out)
unfortunately these humanities (climate change scientists – who are paid prostitutes of the anti society rich and famous) dont know how to do proper experimental design
anyone who builds simulation models for production plants knows you start simple and test against physical experimental results
Obviously they dont care as they get the money without having to prove that there study was worth our tax payer money
Maybe we need a KPI for institutions and if they fail no more gold

June 8, 2021 7:59 pm

The GCMs are fundamentally wrong. One mistake is revealed by Dr. Christy’s graph showing GCM calculated temperature increase rates averaging about twice measured. Another mistake is the way they handle water vapor (WV). It is calculated within the GCMs with the result being that calculated relative humidity (RH) is approximately constant as the temperature increases (some models simply assume constant RH as the temperature increases). The GCMs should use measured WV.
WV has been accurately measured globally using satellite instrumentation and reported as Total Precipitable Water (TPW) since Jan 1988. The measured WV increase has been about 1.49% per decade. The measured WV trend has been about 43% more than possible and is more than the trend calculated by the GCMs. This is shown graphically at which also has links to supporting data and analyses.
Since both have been accurately measured worldwide, more than 7 WV molecules have been added for each added CO2 molecule.
WV is a greenhouse gas (ghg). The part of the WV increase that is not accounted for in the GCMs is approximately the amount above that which would result from just temperature increase. This ‘extra’ WV is enough to account for all of the average global temperature increase attributed to humanity. The ‘extra’ WV comes mostly (about 90%) from increasing irrigation.
Another mistake in the GCMs is failure to account for the delay between the time a ghg molecule absorbs a photon and when it emits one. This delay is called relaxation time. It allows radiation energy absorbed by CO2 molecules in the troposphere (the troposphere is below about 8-16 km, depending mostly on latitude; higher at equator) to be ‘redirected’ to WV molecules which emit it at a longer wavelength. Much of the outward directed radiation from WV molecules in the troposphere makes it all the way to space. Details are in the links at the above graph.
Regardless of the WV increase that might result from any and all feedbacks, human activity has added more. Failure to account for actual measured WV is a mistake. Thermalization and the huge gradient in WV of about 1200 to 1 on average, ground level to tropopause, are what allow much of the energy absorbed by CO2 in the troposphere to be redirected to WV molecules and radiated directly to space.

TPW meas & calc H4 &5 29 RH thru Jan 2021.jpg
Last edited 15 days ago by Dan Pangburn
ray g
June 8, 2021 9:45 pm

If you’re in front behind do some adjustment and you can be behind in front

June 9, 2021 4:24 am

A clever programmer could make a ‘ model ‘ work backwards ,
but highly unlikely to work forwards ,
No science involved , just computer language .

June 9, 2021 7:51 am

The paper seems to have been removed from the GMU FTP website. I suspect Mann had something to do with the removal, by reporting it to the journal. I have restored a local copy and added a link to it at the end of the article.

Reply to  Anthony Watts
June 9, 2021 10:04 pm

I downloaded a copy from GMU just now, using download.file in R.

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