By Rud Istvan, edited by Charles Rotter
WUWT reader Max alerted us to a 1994 Naomi Oreskes et. al. paper published in the prestigious journal Science. Her paper was a critical analysis of Earth Science numerical models.
I asked Rud to take a look, since he had previously written on climate models both here and in the ebook Blowing Smoke. What follows is an edited version of what Rud sent us, approved for publication by him.
After a quick read of Oreskes’s paper, I felt a double whammy was in order:
1. Explain Oreskes ‘science’ per se.
2. And then explain her later duplicitous conversion to rabid climate alarmist.
This is evidenced by her books ‘Merchants of Doubt’ and ‘Why Trust Scientists’.
This post is also another opportunity to restate, yet again, (using Oreskes own early explanations) some of the key problems with IPCC climate models, specifically CMIP5 for AR5.
For those unfamiliar with Oreskes, she received a degree in geology and subsequently became a practicing geologist.
Later, she returned to Cal Berkley for a PhD in history of science. After this she then became a rabid climate change alarmist, as evidenced by her books noted above. She became famous for her Warmunism. I noted this in footnote 24 to essay Climatastrosophistry in my ebook Blowing Smoke, which I based on former Czech president Vaclav Klaus’ 2007 book “Blue Planet in Green Chains”.
Oreskes’s work led to a tenured Harvard professorship. Her intellectual abandonment of her previous work and conclusions about earth science models, as encapsulated in her earlier paper in Science, is indicative of her career/financial turn to the dark side.
Her 1994 Science paper on earth systems numerical models used hydrology and geochemistry examples. We shall quote her reasoning, but substitute climate model examples. This is fair, since her 1994 paper explicitly also included meteorology and oceanography, implicitly including climate models. For extra fun, this guest post uses her exactly worded paper major subheadings, albeit in a slightly altered sequence for exposition purposes.
Abstract
“Verification and validation of numerical models of natural systems is impossible. … Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.”
Uh Oh! Not good for IPCC reliance on climate model projections to prescribe drastic climate policies such as in the Paris Climate Accords. How did Oreskes later writings ‘forget/disavow’ her earlier writings? While she has not explained her transformation; the old adage ‘follow the money’ might.
Verification: The Problem of ‘Truth’
“To say that a model is verified is to say that its truth has been demonstrated, which implies its reliability as a basis for decision-making. However, it is impossible to demonstrate the truth of any proposition, except in a closed system. … Numerical models may include closed mathematical components that may be verifiable. … However, the models that use these components are NEVER closed systems.”
Earth orbits around the Sun, receiving its sunlight energy and reflecting back some 30% proportion based on albedo, This is the the climate model cloud problem, from AR5 WG1 chapter 7. Some further proportion is lost back to space via long wave infrared outbound warming caused by the incoming sunlight energy, modulated by greenhouse gasses including CO2 and H2O. This postulated electromagnetic radiation imbalance is the so-called greenhouse effect, or anthropogenic global warming (AGW). All Earth systems are open systems to space. Not good for ‘sophisticated’ IPCC climate models according to early Oreskes.
Validation
“In contrast to the term verification, the term validation does not necessarily denote an establishment of ‘truth’. … Rather, it denotes the establishment of legitimacy. … For all the reasons discussed above, the establishment that a model accurately represents ‘actual processes occurring in a real system’ is not even a theoretical possibility.”
That conclusion is another big problem for Warmunists. IPCC climate models may not represent reality, and Oreskes said there is no way of finding out if they might.
Calibration of Numerical Models
“In the earth sciences, the modeler is commonly faced with the inverse problem. The distribution of the dependent variables is the well-known aspect of the system (guest post comment, e.g. GAST or SLR). The process of tuning the model–that is, the manipulation of the independent variables to obtain a match between the observed and simulated distribution or distributions of a dependent or distributions of dependent variables—is known as calibration.”
This early Oreskes explanation goes to the heart of the climate model ‘parameter tuning’ problem. The typical CMIP5 grid is 280km x 280 km at the equator. But important processes like convection cells (thunderstorms) happen on a 2km to 4km grid. This problem is illustrated by observed/modeled Arizona Thunderstorms.
The large Arizona grid ‘smeared’ meteorological models are effectively useless.
The present climate model problem is that the typical CMIP5 grid square was about 280km x 280km at the equator. The CFL constraint on numerical solutions to partial differential equations means, according to an NCAR rule of thumb, that halving grid size requires a 10X, (one order of magnitude) increase in computational intensity.
An about 7 orders of magnitude computational intractability constraint means such crucial processes as convection cells and Thunderstorms must be parameterized. The official AR5 ‘Experimental Plan’ for CMIP5 required a model initialization at year-end 2006, followed by a mandatory temperature hindcast of three decades to 1976. The problem is that this hindcast covers the entire temperature rise from about 1975 to about 2000. That period’s rise is virtually indistinguishable (both visually and statistically) from an equivalent rise about 1920 to 1945, as MIT Prof. Emeritus Lindzen pointed out.
The Warmunists’ model problem is that the former period rise cannot be attributed to AGW; there simply was not enough increase in CO2. It must be mostly natural, not anthropogenic. The latter period climate model parameter tuning issue necessarily drags in the attribution problem, i.e how much of the latter period is natural instead of AGW. The CMIP5’s attribution to AGW necessarily means all climate models run hot.
Confirmation
“If the predicted distribution of dependent data in a numerical model matches observational data, either in the field or laboratory, then the modeler may be tempted to claim that the model was verified. To do so would be to commit a logical fallacy.”
It is worse than a logical fallacy if the climate models do not match observations. There are two very salient examples: the tropical troposphere, and ECS.
CMIP5 models produce a tropical troposphere hot spot where none exists in reality. This is best illustrated by Dr. John Christy’s 2017 Congressional testimony, oft reproduced in various forms here at WUWT
Several recent papers have covered the ECS observational/model discrepancy, the most rigorous being two from Lewis and Curry. Their first set out methodology and results, their second slightly modified their first based on several insignificant criticisms.
From this sad saga come two ineluctable conclusions:
First, Naomi Oreskes sold her scientific soul to the Devil by joining the Warmunism academic movement after first publishing the opposite in Science.
Second, climate models simply cannot deliver their promised Warmunist goods. Never could, never will.
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It is a pity more people do not acquaint themselves with basic logic and the philosophy of science.
Deductive logic…”IF AXIOM, THEN DEDUCTIONS” is a closed logical space. If the axioms or geometry are taken to be true than the deductions of Euclid/Pythagoras etc are demonstrably also true.
This is the only demonstrable truth there is, and in modern terms its a trivial truth. One can say that the truth of the deductions is implicit in the axioms. Doing the formal proof of Pythagoras is merely explicating , in simple-to-follow steps, what is implicit in the definitions of what straight lines, triangles and a plane surface are.
Inductive logic however is not like that, and science depends on inductive logic. We do not say IF this THEN that, we say well we have that, what kind of of this might explain it? And then test to see if an explanation that we literally dream up, fits the facts of the experience of the phenomena under question.
If it does, and it adds something useful, it becomes a theory, if not, its just bunk.
AGW is, these days, bunk.
Oreskes seems to be a dim bulb, in that she has got halfway towards a Popperian perspective on science (Karl Popper: Cf “Conjectures and Refutations”) before abandoning it in an orgy of career advancing pseudo scientific claptrap. That is, however, how the left works…Cf “Fools Frauds and Firebrands” by Roger Scruton, a catalogue of pretentious intellectual claptrap espoused by the Left.
I personally have a theory that the likes of Oreskes are third rate minds, and they resent the fact that they are not good enough to grapple with science and achieve something worthwhile, so their bitterness, jealousy and resentment is taken up in attacking science and making a career out of sounding clever to even stupider people.
The Left is the repository for failed intellectuals – Marx is the classic example – who, unable to be accepted by a scientific or intellectual community, dream up clever ways to destroy it instead. Making them useful tools in today’s asymmetric warfare.
What philosophy gives to science is two critical perspectives. The first is that science, and I myself would say all human knowledge, is in fact a model that explains, and in the case of science, accurately predicts, the behaviour of the world as it appears to us.
The second is that model can be invalidated by comparing it to the the behaviour of the world, as it appears to us. When all that is impossible has been eliminated, what is left by INDUCTIVE – not DEDUCTIVE – logic, (Sherlock Homes is all INDUCTION – there is not an ounce of deduction in his methods) is not THE truth but simply still only a possible truth. Among many.
Which leads on to Occam’s Razor. Simple does not mean true, Simple is merely a way of being lazy when confronted with half a dozen possible explanations none of which can or have been shown to be false. For example if Einstein had shown up at the same time as Newton with his theories, they would have been indistinguishable with the instrumentation available, and in fact the mathematics of relativity would not have existed either. As soon as Einsteins theory became more accurate and more useful tahn Newtomn we threw Newton out. In Occam’s terms Einstein was no longer ‘complicated beyond necessity’, it was as complicated as necessity demanded…
…The Great Confusion, even among eminent scientists of quality work, is that they have become bewitched by the success of science, into thinking that the models upon which it relies are therefore true. That Gravity is as much a ‘fact’ as a stone falling on your toe.
It isn’t. Gravity belongs in the same realm as the Gods. A mysterious force invented to explain why stuff happens the way it does. The only difference is the precision and success with which the model called Gravity can be used to predict certain things. Whereas God is a great explanation, but useless at predicting which way the cookie will crumble.
And that leads to a simple conclusion as to why science is better than religion. It predicts the future accurately, and if it does not, it is no better than religion.
AGW does not and has not accurately predicted the climate. As a theory it is neither true, nor useful, in that context.
Don’t knock religion however. Some religion enables people to live longer and be more content. It gives their lives shape and meaning. It allows large societies to coexist peacefully. It is very much useful for species survival.
Irrespective of whether it has any truth content or not.
So I would urge people to test ideas – new ‘knowledge’ – against two yardsticks: Forget Truth – which cannot be established for inductive propositions – Does it pass scientific scrutiny: That is does it actually predict results that actually happen? In the case of AGW and indeed Marxism, the short answer is that it does not.
The second test, which we my apply independently of the first, is whether it leads to a higher likelihood of species survival or not. This lead to kind of moral pragmatism that says its OK for kids to believe in Santa Claus because it makes them happy and healthy and it is pretty harmless. And teaches them that adults are not Gods of Truth but tricksters and deceivers. A good Life Lesson.
If we examine AGW and Marxism in THIS light, as good things to believe in regardless of whether they are true or not, or lead to the claimed futures, it’s a lot harder to see whether species harm results. In the case of Marxism it is true to say that in almost every case where it has been applied it has in fact killed vast swathes of populations and driven the rest into poverty. But in the case of AGW its a bit different. However we are beginning to see that, like Marxism, its an expensive way to fail to achieve the objectives for which it purportedly exists.
In short ClimateChange™ is not able to actually achieve any climate change or to halt it. And thus remains an expensive burden on society, not a benefit.
I do urge people to read a bit of Popper, Scruton – maybe Kant and Schopenhauer, to at least have a common vocabulary in discussing why ideas are true, or not true.
And to understand that knowledge is just ‘models that work’ and that Truth, while we assume it exists somewhere, is never directly accessible. Truth is, to us, relative to our experience.And to the language we use to describe the world.
Of course Marxism seeks to replace that language…
A quote: “Some further proportion is lost back to space via long-wave infrared outbound warming caused by the incoming sunlight energy, modulated by greenhouse gasses including CO2 and H2O. This postulated electromagnetic radiation imbalance is the so-called greenhouse effect, or anthropogenic global warming (AGW).”
Maybe my comment is based on my limited English language capacity, but I see several errors in this description of the greenhouse effect (GHE). Firstly, I am not sure if this statement is made by the blogger, the blogger’s understanding of the AR5’s description or the description of Oreskes. Anyway, it is not a description of the GHE of the IPCC.
The GH gases do not modulate incoming shortwave radiation but GH gases (and clouds) absorb the longwave radiation emitted by the surface. There is no essential radiation imbalance between the incoming solar radiation and the outgoing longwave radiation, but they are essentially in balance and this balance exists regardless of the magnitude of the GHE.
The real error of the GHE definition of the IPCC is that they define the downward LW radiation by the atmosphere to the surface to be the same as the LW absorption by the GH gases and clouds. They not even close the same because the numerical values are about 345 W/m^2 and 155 W/m^2.
Antero
I also had a problem with the description.
“Some further proportion is lost back to space via long wave infrared outbound warming caused by the incoming sunlight energy, modulated by greenhouse gasses including CO2 and H2O. This postulated electromagnetic radiation imbalance is the so-called greenhouse effect, or anthropogenic global warming (AGW).”
There is no imbalance after any modest amount of time. The idea that “heat is accumulating” in the system is great for scaring people, but that is not how radiative balance works. Just because the temperature rises in the lower atmosphere doesn’t mean there is an imbalance in radiation. The increase in absorption/re-radiation serves to increase the temperature, but within microseconds the photons are gone. There is no such thing as a permanent imbalance. Can’t happen, not in a real world.
The oceans can store a great deal of energy, but not all that much. There are so many cases where the radiation outgoing could exceed (or not) the incoming amount it is pointless to be concerned about what false precision is contained in estimates from the IPCC. If insolation dropped and clouds decreased, the lower atmosphere would warm. If the clouds increased, it would not matter if the insolation went up or down – it will cool. Until they can model clouds correctly, there are no valid climate model outputs.
Antero Ollila December 29, 2019 at 6:25 am
A quote: “Some further proportion is lost back to space via long-wave infrared outbound warming caused by the incoming sunlight energy, modulated by greenhouse gasses including CO2 and H2O. This postulated electromagnetic radiation imbalance is the so-called greenhouse effect, or anthropogenic global warming (AGW).”
Maybe my comment is based on my limited English language capacity, but I see several errors in this description of the greenhouse effect (GHE). Firstly, I am not sure if this statement is made by the blogger, the blogger’s understanding of the AR5’s description or the description of Oreskes. Anyway, it is not a description of the GHE of the IPCC.”
Guess whose:
“By Rud Istvan, edited by Charles Rotter”
To any experienced modeler worth his salt, Oreskes earlier claims are almost tautologies.
I have to say that I have long been perplexed by Orsekes’ change in perspective. I quite admired her and her work back in 1994, and was in strong agreement with her 1994 article. I have no insights into why she did a 180 on the uses of mathematical models, except that, as many have postulated above, politics inserted itself into her personal perspective on the effectiveness of predictive models. With these climate models always somehow indicating the remediative effects of desired Marxist outcomes, they now became “scientifically reliable’ predictors. So sad that she sold out – nay, more than sold out, as she is vituperative in her scorn of those who have failed to genuflect to her (s)creed.
I remember years ago finding a quote on a NASA climate page from a scientist saying that clouds were impossible to model with any fidelity because computing power would have to be many magnitudes higher than it currently was. Additionally, he also added that it made things even more difficult due to the fact that they didn’t even know how some clouds affected climate. I didn’t save that link and tried to find that page again. From what I can tell that page has been removed. It certainly didn’t help the CAGW narrative so I’m sure they disappered that page on purpose and now it only exists in my memory, which means I can’t prove they actually said what I said they said despite knowing it’s the truth.
Joz Jonlin :
I remember years ago finding a quote on a NASA climate page from a scientist saying that clouds were impossible to model with any fidelity because computing power would have to be many magnitudes higher than it currently was. Additionally, he also added that it made things even more difficult due to the fact that they didn’t even know how some clouds affected climate. I didn’t save that link and tried to find that page again.
____________________________________
– would be here –
https://www.google.com/search?client=ms-android-huawei&sxsrf=ACYBGNQpRangQPHg8CZ2C1Aic9rkm3QooQ%3A1578577679543&ei=Dy8XXqvjIMytrgS4yY3YDg&q=NASA+scientists+clouds+are+impossible+to+model+with+any+fidelity&oq=NASA+scientists+clouds+are+impossible+to+model+with+any+fidelity&gs_l=mobile-gws-wiz-serp.
– here:
https://www.google.com/search?q=clouds+are+impossible+to+model+with+any+fidelity&oq=clouds+are+impossible+to+model+with+any+fidelity&aqs=chrome.
– or here:
https://www.google.com/search?client=ms-android-huawei&sxsrf=ACYBGNQpRangQPHg8CZ2C1Aic9rkm3QooQ%3A1578577679543&ei=Dy8XXqvjIMytrgS4yY3YDg&q=weather+clouds+are+impossible+to+model+with+any+fidelity&oq=Watherclouds+are+impossible+to+model+with+any+fidelity&gs_l=mobile-gws-wiz-serp.
Re: Nick Stokes and the statemens about models.
Pretty much everything you wrote about wind tunnel models is wrong. A wind tunnel model is based upon empirical equations that were developed by measurement, not theory. When a wind tunnel is used to create an aerodynamic design, that design is built into a plane and flown. The flight of the plane then “validates” the results from the wind tunnel. If the wind tunnel did not perform as expected, it is tweaked or the transfer functions are tweaked. Eventually the engineers can gain full trust of the wind tunnel model. Your general statement equating this to climate models are just flat wrong.