Dr. Chris Essex: Why Computers Cannot Reproduce The Climate, Never Mind Predicting Its Future
A GWPF talk by Dr Christopher Essex – Chairman, Permanent Monitoring Panel on Climate, World Federation of Scientists, and Professor and Associate Chair, Department of Applied Mathematics, University of Western Ontario (Canada) in London, 12 February 2015
Has the scientific problem of climate been solved in terms of basic physics and mathematics? No, but you will be forgiven if you thought otherwise. For decades, the most rigorous treatments of climate have been done through climate models. The clever model pioneers understood many of their inherent limitations, but tried to persevere nonetheless. Today, few academics are even aware of what the pioneers understood, let alone what has been learned since about the full depth of modelling difficulties.
Meanwhile popular expressions of the scientific technicalities are largely superficial, defective, comically nonsensical, and virtually uncorrectable. All of the best physics and all of the best computer models cannot put this Humpty Dumpty together, because we face some of the most fundamental problems of modern science in climate, but hardly know it. If you think you want to have a go at those problems, there are at least a couple million dollars in prizes in it, not to mention a Fields Medal or two.
But even if you don’t have some spare afternoons to solve problems that have stymied the best minds in history, this talk will cure computer cachet even for laymen, putting climate models into theirs proper perspective.
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‘Climate’ is about the averages of *observations*, and is necessarily a *historical* concept.
Modelling is about drawing analogies between two similar systems – one physical and one mathematical.
Analogies are like ropes – they tie things together fairly well, but you can’t get anywhere by pushing them.
As far as the two systems – the physically observed and the mathematically predicted – align with each other, the model is a useful tool for *interpolating* to estimate the results of measurement that have not been made.
The peril is encountered when one tries to *extrapolate*. This is because each data point added to the basis for the model expands the universe of possible models by adding an entire mathematical dimension. The model available for making estimates *before* the added data point was available becomes inadequate – there is not an infinite universe of possible mathematical models that will exactly fit the original data set, only one of which will also *extrapolate* correctly to include the new data point. Until you have the new data point in hand, there is no way to know which of the new models will be the one that works correctly. Models cannot be relied upon for correct extrapolations.
This not pedantry, but a direct consequence of the Nyquist–Shannon sampling theorem.
True, that would be like shooting pool with a rope.
+1
I tried to resist..but I am weak,
All I could think of was….people like Mann are pretending to shoot pool with a broken hockey stick and no….spherical, enameled objects. *grin*
Corrigendum: …there is NOW an infinite universe of possible mathematical models…
Yeah, what Dr. Essex said
Thank you Dr. Chris Essex for a calmly presented talk containing plain reasoning that provokes some fundamental thought.
Lewis Carroll was the pen name of Charles Lutwidge Dodgson who was a mathematician and logician (among other interests). He would have appreciated the talk by Dr. Chris Essex.
What is of interest to me is why the major government scientific organizations consciously and premeditatedly chose a primary strategy of prioritizing GCMs (and other types of models) in their PR promoting the observationally challenged theory of significant climate change from CO2 produced by burning fossil fuels.
There is some fundamental means of understanding the irrational climate movement if we know that why concerning the major government scientific organizations’ choice of strategy.
I am tending recently to think the answer to that ‘why’ is related to a new** philosophy of science that is specifically tailored for only government supported scientific structural processes.
** new as in becoming publically obvious starting post WWII
John
If I may I think you are right. I was just going to point out that Lewis Carroll is believed to be poking fun at philosophers he knew. I wonder if the present philosophy is not a return to an old Persian and Indian philosophy where the lower orders of society were of no importance. The higher forms of human life were more evolved and did no harm to animals ( or in some cases to plants) so that they could go up into a more higher more spiritual realm. and in some forms of this philosophy the ultimate goal is to become non existent. Much like the Gnostics of the Later Roman Empire. peasants and the poor do not count in their view. Maybe there are still such philosophies in the Far East today which is why the United Nations is so keen on the Global Warming so many of them will have this at the back of their mind in all deliberations.
In the 70s, I lived in an Asian nation which published it’s newspapers in Chinese, rather than in the nation’s own alphabet. Only the educated elite members of society understood Chinese characters. The implications of that one simple peek into their society were sobering.
The structure of US academia revolves around the same idea.
M E Wood on February 20, 2015 at 11:52 am
M E Wood,
Certainly the meme of the enlightened few and with the rest being subservient to the few is Plato’s philosophy and the argument can be made the same for the Neo-Platonist who is Kant. Certainly Asia had its share of those kinds of views.
I do not see America being ‘de facto’ ever led by Scientist Kings who style themselves after Philosopher Kings.
John
Sorry for the blockquote tag formatting error in my comment ‘John Whitman on February 20, 2015 at 5:46 pm’
John
[removed. .mod]
[removed. .m o d]
– – – – – –
.m o d,
Thanks.
John
Yeah the models are all junk. But let’s panic and train wreck the economy just in case. Cuz that is what good Socialists do!
Dr. Chris Essex
A very interesting and provocative post. Thank you.
Now if the media would just run with it every time there is another dire prediction by the CAGWers.
Every politician intending to base their decisions on model “projections” (The believers) should be made to listen to that lecture until they understood what Dr Essex is saying, Or at least understand the simple truth that the models cannot give the strength of prediction as presented to them by the charlatans and the media.
Here is the 2013 version of this lecture. In some ways it is easier to follow, however in other ways the latest version has some more interesting data. BTW, The “green energy” laser pointer worked a lot better in 2013.
Maybe time to change the battery, doc.
I’m somewhat confused at all this hostility towards climate models since so many such models have been presented on WUWT and other skeptic sites over the years to a generally warm reception.
Examples include the recent notch model of Evans, championed by Joe Nova and VIscount Monckton, regular wuwt contributor David Archibald’s solar cycles model of 2008 predicting a 1.5 degree decrease in temperature between 2008 and 2020 (methinks we can agree on a Fail for Mr. Archibald), similar models by Easterbrook and Orssengo from some years back predicting cooling, Monckton’s modelling of the Climate Sensitivity, and so on and so forth,
IN view of the consensus on climate models expressed in this thread am I to assume that all these hitherto well-received models are now to be treated as dross?
Bill H,
Models are not the problem; the misuse of climate models is the problem. The complete failure of climate models to make consistent and accurate predictions has discredited GCMs.
For example, no GCM was able to forecast the most significant event of the past century: the fact that global warming has stopped. And not just temporarily: global warming has been stopped [depending on the yardstick used] anywhere from 10 – 18 years. That is a long time! But no models predicted that “pause”.
Since GCMs don’t take clouds into account, naturally they will be wrong. Clouds have an enormous effect. They reflect solar energy to space. Conversely, when clouds thin they allow more energy in. See Willis Eschenbach’s related articles on emergent phenomena.
There are some very simple models that do every bit as well as the best supercomputer GCMs [if not better], and they don’t cost anything. The sensible course of action would be to sell the expensive computers, lay off the programmers and scientists, and use the simplest of models.
But we all know the real reason for expensive models and computers: it’s a gravy train that a favored clique of scientists ride. Their job is to scare the public. Their pay is continued employment, expense-paid travel to exotic locations, and relative fame in their community.
There is more real science done here than with all the multi-million dollar GCMs.
To see how to make forecasts without using GCM type models see my earlier comment
http://wattsupwiththat.com/2015/02/20/believing-in-six-impossible-things-before-breakfast-and-climate-models/#comment-1864274
The answer to your question depends on how you define models. Monckton’s efforts are based on bottom up methods similar to the IPCC models and therefore are also not useful for forecasting . Easterbrook, Archibald and e,g, Scafetta are not of the same type being much simpler and closer to the basic solar driver and temperature data so that their outcomes will be closer to reality. However they still rely too much on curve fitting to some mathematical formula – which nature knows nothing about. It is better simply to look at patterns in the temperature and driver data and eyeball them forwards using common sense and general knowledge of past events rather than some mathematical formular.
Guy Callendar vs the GCMs
http://climateaudit.org/2013/07/26/guy-callendar-vs-the-gcms/
Bill,
It can be argued that all science involves “models.” The idea is that science investigates subjects of interest, and by separating the system into its irreducible components, and then identifying the minimum elements that reproduce the gross system behaviour you can claim to have “understood” the system. Field science runs up against the reality that no system operates independently of its environment and that lack of independence influences the initial states of the remaining system components. That’s cool as long as the variance between the real system and the model are equal and have a similar center. When a model runs perpetually biased, and its variance doesn’t replicate the variance of real system, then you plainly have a problem. The consensus has nothing to do with it. The important measure in science is direct utility. No climate model so far has direct utility to an understanding of climate. They are well applied in the political and economic sciences where they are employed to generate opinions and funding.
Great presentation, I learned a lot.
The full quote says:
“In sum, a strategy must recognise what is possible. In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible. The most we can expect to achieve is the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions. This reduces climate change to the discernment of significant differences in the statistics of such ensembles.”
It doesn’t say we can’t know anything. It says we’ll know with uncertainty that can be quantified. Which shouldn’t surprise anyone.
I really do not understand how we’ll know with uncertainty that can be quantified.
WTF? You really think you can quantify the uncertainty in a coupled non-linear chaotic system?
With what level of certainty is your discernment of the uncertainty?
Many, many decades ago I was exposed to LSD, Mescalin and Psilocybin.
This type of logic is eerily similar to the effects of those drugs on my perception of reality.
Quantified uncertainty is such a simple thing. “Make it fit the meme”.
It says “The most we can expect to achieve is the prediction of the probability distribution of the system’s future POSSIBLE states…(by the generation of ensembles of model solutions)…”This reduces climate change to the discernment of significant differences in the statistics of such ensembles.”
Prediction- synonymns- forecast, prophecy, prognosis, prognostication, augury;projection, conjecture, guess
Probability distribution-a function of a discrete variable whose integral over any interval is the probability that the random variable specified by it will lie within that interval.
Chaotic system- Complex system that shows sensitivity to initial conditions, such as an economy, a stockmarket, or weather. In such systems any uncertainty (no matter how small) in the beginning will produce rapidly escalating and compounding errors in the prediction of the system’s future behavior. To make an accurate prediction of long-term behavior of such systems, the initial conditions must be known in their entirety and to an infinite level of accuracy. In other words, it is impossible to predict the future behavior of any complex (chaotic) system.
The most we can expect to achieve is conjecture, guessing, projecting based upon the discrete variables that we know of, that when combined in a system that contains variables that we DO NOT know of, become completely meaningless because our lack of knowledge of the entirety of the initial conditions to an infinite level of accuracy produces rapidly escalating and compounding errors in the predictions of the system’s future behavior.
If it is impossible to even identify and quantify the initial conditions of our climate to an infinite level of accuracy, then the possible levels of uncertainty are infinite as well….aren’t they?
“We will know with uncertainty that CAN be quantified.” Three years ago I asked the NCAR to quantify an impact of an up-to 3% error in a latent heat water. They could not do it. As far as I know, the error is still there. Why should I take these clowns seriously – other than a black hole for my tax money?
Sorry for a typo – an error in a latent heat of water vaporization.
It’s a word game, Nick. While the statement is true to a certain degree, the probabilities provide us no information. They are all zero. That’s what you get when your off by 30 orders of magnitude.
Nick, with respect;
“It doesn’t say we can’t know anything. It says we’ll know with uncertainty that can be quantified. Which shouldn’t surprise anyone.”
Nobody in climate science has ever attempted any SERIOUS quantification of the uncertainties. 0.001 degrees per week… really…. The climate science community has totally dismissed the “uncertainties” in the science. As soon as they can tell us the exact temperature back in 1850 on July 13 at 100 thousand locations across the surface of the Earth with thermometers that have all been calibrated to a single temperature standard the engineering community might take you seriously. Until then, the climate “models” are just a very bad joke. As explained in Dr. Essex’s well explained lecture posted here. You should listen to it several times very SLOWLY to understand the important points he has presented in well understood detail.
Not knowing the uncertainties in engineering gets people KILLED, very dead.
The climate “models” are a poor jest at best, averaging an “ensemble” of poor jests becomes a “cruel joke” and folks that believe they can tell us what will happen in a century are “NUTS”. I am empathetic to those that have dedicated their career to this, but history will not be kind to them.
Cheers, KevinK.
Re: “… we’ll know with uncertainty that can be quantified.”
With 100 km wide holes in our knowledge, the HONEST thing is to say is, as Dr. Essex essentially does:
We don’t know.
**************************************
Further,
your empty assertion (far above) to the effect of: “someday, we might know” is worthless.
We are all going to die from catastrophic anthropogenic climate modelling.
DBStealey,
I suggest you re-read Dr. Essex’s article. He is attacking the models themselves not their “misuse”, as are most of the preceding comments, e.g. Paullinsay, describing GCM’s: “the math is wrong the physics is wrong. There is no further discussion”.
GREAT LECTURE!! thanks for posting, very informative, educational.
John in Rochester Michigan
While I have some sympathy for this approach, if done right with an ensemble that truly does aim to cover the space of possible outcomes, the way this works in practice is akin to believing that you can take a committee of half a dozen people, none of whom are competent, but the diversity they represent will lead to a great decision.
(De-motivational poster of a large snowball carving it’s way down a snowy mountainside)
“A few harmless flakes working together can release an avalanche of destruction”
This discussion of Navier-Stokes reminds me of a little poem I wrote some time ago.
VISCOUS THINKING
Big fools have little fools
Who feed on their “lucidity”
And little fools have lesser fools
And so on to stupidity.
~ Max Photon
In science all have favorites,
Some neutron others proton,
But if you ask who I like best,
I’d have to say “Max Photon!”
+1
This discussion of Navier-Stokes reminds me of a little poem I wrote some time ago.
VISCOUS THINKING
Big fools have little fools
Who feed on their “lucidity”
And little fools have lesser fools
And so on to stupidity.
~ Max Photon
Statistical inference in a chaotic system is only valid over an indefinite range. A chaotic process can only be marginally represented by a complex multivariate distribution with liberal assumptions of independence and uniformity over time and space. Without these extraordinary and unjustified assumptions, this function is not only undetermined, but its discovery remains improbable. Statistical methods, as with the scientific method itself, are only valid within an exceedingly small frame of reference in both time and space, where accuracy is inversely proportionate to the product of time and space offsets from an established frame of reference. The scientific method is a process and method invented to constrain people from conflating philosophy and faith with science.
Yes yes. More of this please. I felt like I was back in school. I watched the entire thing. I’d love to see more educational videos of this sort in the future! The longer the better!
Help me out here folks as I am not a “science guy”. I think I understand the problem of the current “grid” being too large due to computational and scientific limitations. However, even if the grid could be reduced and new models were based on that, how in the hell would we know what was going to happen within the grid anyway?
We will never know what the sun is going to do much less the dozens of other natural phenomena i.e. cosmic rays, planetary vegetation, winds, volcanoes, animal activity, etc. I really don’t see how given the enormity of possible combinations of climactic events we could ever really know within any degree of accuracy what global temperatures will be in 100 years. Never mind what the effect of that variability would have.
Then of course we would also need to know the efficacy of the proposed so-called “solutions” to the problem which includes among other things carbon taxes and such. Mind you all of this hysteria is based on relatively minor changes in temperatures i.e 2 degrees or so. I am not seeing it. I do wish there were more actual scientists coming forward (like the man in this presentation) to rationally discuss these things.
Gary much of what you say is supportable. One thing however is certain. If you ignore the fact that you models are consistently wrong in ONE direction, you will always get the wrong answer.
In a nutshell this is what the IPCC is paid to do, and they do it well.
Chris Essex had given us an important talk – obviously. Many thanks.
When we decide to solve an equation numerically (subject to finite representation) instead of in continuous closed from (analytically) we KNOW we cross SOME line, and have an “elephant in the room”. However, the usual view is perhaps that the elephant can be kept in the FAR corner by just making a step size smaller. I think Essex’s pointing out that the two cases (continuous/discrete) involve different symmetries and conserved quantities changes that schism to a more categorical one where we are hiding in the NEAR corner from the roaming elephant, and that it is the discrete modeler who is thus cornered. Often we get away with it – in the simpler cases.
Моя модель Climate Change.
Climate зависит от альбедо Earth а не от CO2,метана and men,это только ускорители Climate Change.Однобокий рост внутреннего ядра Earth http://go.nature.com/w6iks3 деформируя кору изнутри http://yotu.be/edPhYeDrNIY изменяет форму планеты от которой зависит альбедо.
От альбедо и солнечной активности происходит колебание солнечной радиации в атмосфере Earth.От уровня солнечной радиации зависит давление в атмосфере,от давления режим ветров http://www.newsweek.com/speaking-green-tongues-scientist-discovers-new-plant-language-264734.Рост ядра продолжается поэтому происходит изменение альбедо и так далее по принципу домино.Постоянное колебание уровня солнечной радиации приводит к резкому и экстремальному изменению погоды в различных регионах планеты превращая Earth в планету бурь.Climate Change показатель скорости и один из флагов приближения катастрофы.
[From Google translate.
My model of Climate Change.
Climate depends on the albedo of the Earth and not from CO2, methane and men, it is only boosters Climate Change.Odnoboky growth of the inner core of Earth http://go.nature.com/w6iks3 deforming crust inside http://yotu.be/edPhYeDrNIY changes shape the planet on which depends the albedo.
From albedo and solar activity variations in solar radiation in the atmosphere Earth.Ot level of solar radiation in the atmosphere depends on the pressure, the pressure of wind regime http://www.newsweek.com/speaking-green-tongues-scientist-discovers-new-plant-language-264734.Рост core continues so there is a change of albedo and so on according to the principle domino.Postoyannoe level fluctuation of solar radiation leads to a drastic and extreme weather changes in different regions of the planet turning Earth into a planet bur.Climate Change of speed and one of the flags approaching disaster.” .mod]
“Much of the cagw scare is about increased variance or extremes ”
Only recently. Up until things started getting cold, we were never going to see snow again. This new “extreme weather” talking point was created as PR. The “99% certain” science at the time never mentioned it.
“or about tipping points”
Which is conjecture that they can’t actually back up in any meaningful way.
This is same but different? https://www.youtube.com/watch?v=hvhipLNeda4
lost my blogging capability