Mike Jonas
… and wave as hard as you like. There’s no need to worry about disrupting Earth’s climate.
It has been a long time coming, but I have had another paper on Climate Modelling published – Chaos Theory is Void. It addresses the well-known and generally-accepted Butterfly Effect, aka Chaos Theory. I and others have felt for a long time that the Butterfly Effect exists only in models, and that Earth’s self-correcting weather/climate is too stable for the Butterfly Effect to exist to any meaningful extent in the real world. But how to prove that?
Two years ago, in GCMs Cannot Predict Climate, I described how GCMs (General Circulation Models) cannot predict climate because their iterative mechanism fails after a few weeks. Clearly the iterative mechanism itself is the problem. But how to prove that?
It all stayed on the back-burner for a while, but then I came across a paper that criticised an AI-based model for not being able to replicate the Butterfly Effect. In my previous paper I had argued that the structure of GCMs was upside-down: instead of working bottom-up from weather to climate, a climate model should work top-down with climate. Now, here was AI doing exactly what I argued for and being criticised for not having the same problem as GCMs!
That got me thinking about exactly how I could argue in favour of the AI-based model. The Butterfly Effect is difficult to argue against because it compares what is with what might have been. Since you can never tell what might have been, you can never disprove it.
And then the thought occurred to me: How large would a cloud have to be, to deliver the same global average temperature change as the adjustments to initial conditions used in the Kay et al paper (“Kay”) that demonstrated the Butterfly Effect and which I had cited in my previous paper. The answer: 1000 square metres. In just one hour, a single cloud, 25 metres by 40 metres, could change Earth’s global average surface temperature by as much as all the adjustments added together that Kay used to demonstrate the Butterfly Effect. Now we all know that the Butterfly Effect is not literally about a butterfly flapping or not flapping its wings, but this is a very small cloud (25×40 metres is about 30×45 yards). That butterfly is intangible, but Kay has given me a number to work with. Anyway, one thought led to another, and to the conclusion (carefully argued in the paper) that for all real world climate purposes, Chaos Theory can safely be ignored. It is scientifically inoperative. It is Void.
The two thoughts that led to this conclusion are:
1. There are a very large number of opportunities for a very small cloud to exist or not exist. We are not talking about just one cloud, we are talking about many millions of very small clouds that sometimes exist and sometimes don’t. Even if one very small cloud or non-cloud really can make Earth’s climate change as much as the Kay model does, it doesn’t have the planet to itself. There are all these millions of other potential clouds, and some of them, by existing or not existing, will pull Earth’s climate in one direction while other clouds and absent clouds will pull in the opposite direction. Maybe they all just cancel each other out?
2. GCMs try to model climate in small time steps through the daily cycle. At the end of each day, they return to a state that is often very close to the state of the day before. Any small model error during the day becomes a large error, relatively, at the end of the day. So you cannot model multiple days without knowing what makes the weather change over multiple days – after a few days, the GCM must fail. This is why weather forecasts become unreliable after a few days. The general wisdom is that there is an absolute 2-week limit for weather forecasts.
But what if we realise that the 2-week limit for weather forecasting is imposed by the models and not by the weather? There is then no intrinsic reason why a longer useful forecast cannot be made. Climate models can be improved similarly.
In fact, this is already happening. AI is increasingly being used for both weather and climate forecasting. AI is working top-down, and getting better results. We may soon be getting useful long term (eg, a month or two) weather forecasts. With top-down climate modelling and the death of RCP8.5 we might even get better climate predictions.
It probably can never be fully proved, but clearly the Butterfly Effect is a feature of GCMs that does not exist in the real world. Well, not quite “not exist”, but close enough. It can safely be ignored.
The Abstract of the paper:
This paper demonstrates that in the real world, Chaos Theory is Void. “Void” is used to mean that the theory is logically coherent and perhaps even true, but it can never contribute to scientific enquiry because it cannot be tested or applied. A Void theory is not necessarily false, it is just scientifically inoperative. Chaos theory does apply in General Circulation Models, but in this context there is no useful relationship between General Circulation Models and the real world. This has positive implications for both climate prediction and weather forecasting: by using a more appropriate model structure than General Circulation Models, it may be possible to improve climate prediction and to extend weather forecasting beyond the generally-accepted two-week prediction horizon.
I also introduce this general principle:
Any predictive system constructing cycles of variable duration and/or varying amplitude will fail after a few cycles if the mechanisms behind the variability are not fully understood and/or cannot be accurately replicated.
Before I wrote the paper, I used AI to do a thorough search of the scientific literature for anyone saying the things that I say in the paper. To my surprise I came up with nothing. The nearest was Krishnamurthy (2019) which said that “slowly varying components [..] provide a basis for predicting certain aspects of climate at longer range“. It got agonisingly close to limiting Chaos Theory to models, saying “imperfections in the models limit reliable predictability“, but there was still implicit acceptance that Chaos Theory applied in the real world. So I had to write my paper.
Footnote: I needed a clear and preferably short word to describe the non-applicability of Chaos Theory. I searched, and Grok searched, and we came up blank. “not falsifiable” is equated with “not science” by Karl Popper, “null” as in “a null hypothesis” is inaccurate, and so on. Then I thought of the legal expression “Null and Void” – “Null” and “Void” must have different meanings, otherwise they would only use one of them. Silly me, the law is not that logical, but in law there actually is a meaning of “Void” (without legal force or binding effect) which is analogous, so I used “Void”.
PS: I thought for a while about how to keep the paper’s title as short and clear as possible. At just 4 words, had I created the shortest scientific paper title ever? Alas, no, the record is still held by Professor Doron Zeilberger for a 2007 paper title 0 characters long. (The paper was about Nothing). That record will be hard to beat.
I’m not anywhere close to being a Real Scientist™, but I like this. It seems incredibly simple and intuitively correct. Willis has argued similarly, that tropical thunderstorms are nature’s regulators. Thermostats in homes are so powerful that it would take huge random fluctuations to override them. And the same goes for overriding thunderstorms, or even the 1000 sq meter clouds you mention. No butterfly, real or figurative, could override nature’s thermostats.
Exactly my thoughts when I read the article.
If the planet has survived this long- with all the forcings over the millennia, the system must be really stable….
Indeed. I have long maintained that if the climate and the planet in general were as sensitive and as delicate as the alarmists insist, we would not be here to argue about it.
Chaos is a powerful form of stability which may well be what makes the climate system stable. The kind of sensitivity the alarmists are claiming does not exist. Theirs is a deep misunderstanding of the butterfly effect.
Look at the transient response from an event such as a volcanic eruption. If it goes back to normal without any overshoot or ringing it’s stable. So much for tipping points.
Yes but chaos is a powerful form of stability called the strange attractor. The butterfly effect makes system behavior unpredictable within that stable range.
However it is likely to constrain or even negate perturbations because it is driven by negative feedback. That may well be why the satellite record shows no effect from the CO2 increase.
The paper is nonsense. All it does is assert without any evidence a general principle that the author claims invalidates chaos theory. Where is the evidence that this principle is true and under what conditions and where the evidence that it applies to climate modelling.
And it not surprising that it is published in the highly esteemed “World Journal of Advanced Research and Reviews” which appears to publish any rubbish without any evidence of peer review as long as you pay.
Yet you seem unable to articulate in detail what is wrong with it, just a sneering reaction was all got out of it from you….
But there is nothing in the paper to argue against. The author states their general principle, does not prove that it is valid in any way and then states
that the general principle proves that chaos theory is void without proving how it follows.
Not is there any definition of what is meant by “Chaos Theory” there is no single such thing as chaos theory. At best the author seems to think that it means sensitive dépendance on initial conditions. But then they are confused whether they are talking about numerical simulations or reality. The general principle appears to only apply to numerical models so it could be that the climate is chaotic but it can’t be simulated by numerical models.
There is not a single statement in the paper that has anything approaching a proof. It is just a bunch of assertions to come to the conclusion that the author wanted.
Logical arguments are typically composed of a set of assertions followed by a conclusion. One or more of the assertions must be proven to be wrong in order to question the conclusion. I’m not seeing where you are proving any of the author’s assertions to be wrong.
I find these sentences to be interesting questions:
Remember, the author’s conclusion includes variability … “that cannot be replicated”. That pretty much covers both weather and “global climate” and how they are modeled today. Most models just ignore the variability, i.e. the variance, associated with almost all factors and use “averages” as if that is sufficient. It’s impossible to replicate variance if you ignore it at the start.
The point is that while I have not proved any of the assertions to be wrong neither has the author provided any evidence to show that they are right. At the moment they remain as plain assertions from which the conclusion might logically follow but unless there is reason to believe the assertions there is no reason to believe the conclusion.
“unless there is reason to believe the assertions there is no reason to believe the conclusion.”
WOW you really are exposing climate science today, aren’t you Izzy !
..
If you can’t show where the assertions are wrong, then normally they are assumed to be true in a logical argument.
The burden is on the other person to falsify the assertions. Just like the skeptics of climate alarmism have been doing for 20 years with the assertions of pending doom.
E.g. polar bear populations have grown. Poles still have ice. Glaciers still exist. Storms aren’t worse. New York and Miami haven’t flooded yet. Radiative flux balance at the TOA is impossible. And on and on and on ….
The ‘chaos’ theory was developed by Lorenz in the 60s to explain some of the extreme sensitivity experienced in nonlinear dynamical systems. I demonstrated it both experimentally and using chemical kinetic models in my PhD thesis about 10 years later.
Try reading the paper more carefully – I do not state or even suggest that the general principle proves that chaos theory is void. Yes, there is a difference between numerical simulations and reality, but they are not confused, the point is that they are different.
“All it does is assert without any evidence a general principle that the author claims…….”
Wow !!! sounds just like climate science. !!
I think you should stick to angling.
Yes, as it was back in 1653, when there were plenty of fish in the streams and no government-imposed restrictions, licenses or fees.
(And of course, apparently climates were just purrrfect back then, not like now, when the oceans are boiling)
Izaak Walton, see my just posted below middle of the night long knowledgable comment. Boy, you out yourself as both climate biased AND mathematically ignorant. This paper is NOT the nonsense you assert—displaying only your own ignorance. And your deliberate aspersion of the publishing journal follows what your ilk always attempt but as here usually fail at. What matters is not the publishing journal (often climate religion gatekeeping). What matters is what the published paper actually says.
The general principle takes aim at the GCM mechanism, not Chaos Theory itself. Chaos Theory is addressed partly by the bit about clouds but mainly by its untestability.
The general principle is “proposed”. My understanding is that this means that it is put forward for discussion, not that it is claimed to be proven. Certainly that is what I meant. I do explain it, and I point out that GCM behaviour matches its implications.
Your comment about the journal is just an ad periodicum – that’s like an ad hominem in that it deflects away from the subject under discussion. All the journals are indexed equally by Google Scholar, which is how it should be.
Again the general principle doesn’t make any logical sense. It states
“Any predictive system constructing cycles of variable duration and/or varying amplitude will fail after a few cycles if the mechanisms behind the variability are not fully understood and/or cannot be accurately replicated.”
Now nowhere is “predictive system” defined, nor is it explained what “constructing” means here. How does a predictive system construct anything? What agency does it have? Then “fail” is also not defined? All models have some error which grows with the length of the simulation and/or step size and that doesn’t mean that they fail it just means that they are not an approximation of reality. The in the second half of the sentence it switches from models to “mechanisms” which for some reason need to
be accurately replicated. Again what does “accurately replicated” mean — is it in the model or in reality? The general principle at best appears to be saying that if you use the wrong equations and/or the wrong boundary conditions to simulate a physical problem then you will get the wrong answer — which is hardly surprising and is something that has been well known for centuries.
“if you use the wrong equations and/or the wrong boundary conditions to simulate a physical problem then you will get the wrong answer ”
Again.. you are describing climate science to a “t”. !!
“that doesn’t mean that they fail it just means that they are not an approximation of reality.”
If they are not an approximation of reality then they are nothing more than mathematical masturbation.
How do you validate a model’s output if you can’t compare it to reality?
You and I both use words without defining them, because we expect others to understand normal English. Under those rules, I find your notion that my general principle is “saying that if you use the wrong equations and/or the wrong boundary conditions to simulate a physical problem then you will get the wrong answer” when it makes no mention at all of any of those things and says quite clearly that variable cycles are the issue.
I would say, as a general rule, that one cannot model something that one does not have sufficient understanding of in the first place. Invoking the butterfly to justify climate alarmist assumptions (GCMs) seems to be much like this cartoon ( I think I found it here originally).
If a butterfly flaps its wings at the wrong time.. it will get eaten. !
That is nature. !
more UtterBollox
The other reason GCMs fail is that they are based on the false notion that CO2 drives climate.
First of all, the “butterfly effect” is just one aspect of broader chaos theory. And the original discovery by Ed Lorenz was essentially what you were mentioning, analogous to thousands or even millions of small clouds that either exist or don’t exist: the original finding by Lorenz was that if a weather forecast model was run first where the calculations at each model gridpoint are done with very high mathematical precision producing one weather pattern state, say, 3 months into the future, but then is run again and stopped at some point in the run with the gridpoint numbers being stored at lower precision, and then the model is re-started, the final weather patterns in the 2 runs will eventually diverge into totally different states after those 3 months. This is called “sensitive dependence on initial conditions”. So, the small differences at every gridpoint can indeed be thought of as analogous to clouds that either exist or don’t exist. And everyone agrees that this effect (which indeed can be random with those *initial condition* errors canceling out in an area-average sense) is why a climate model run with changing *boundary conditions* (e.g. a change in solar input or greenhouse gas concentrations), even if it were to accurately represent the long-term global-average or regional-average effect of those changes, can never predict the weather patterns on a specific day (or even month) many years into the future. The climate model-predicted climate changes are only in a time-averaged sense, say several years or longer. So, a boundary condition change (say, increasing the sun’s brightness by 10%) must produce some sort of climate change, but the butterfy effect (which is just an extreme metaphor for what is actually happening) keeps us from ever knowing what the weather patterns will be on July 15, 2050. So, I believe what you are describing is already known and actually is an example of the (somewhat misleadingly named) butterfly effect.
Maybe TPTB have chosen climate as vehicle because of Lorenz ‘ discoveries using weather models,
knowing very well that they can get any outcome they want to with small changes/manipulations.
Let the Butterfly flap a bit here and flap a bit there.
Use the professional butterfly catcher Paul Ehrlich for ironical purposes
and go to extremes
as mild-moderate scenarios won’t scare anyone.
But it would be interesting to know how many butterflies they actually needed to flap science out of the ice age into global warming.
Roy, everything you say relates to models, and is correct. But there is a general understanding that Chaos Theory applies to the real world, and Edward Lorenz has framed it this way, but every attempt to demonstrate it has been model-based and cannot show anything beyond the fact that it does apply to models. My claim, which I think is reasonably if briefly argued, is that Chaos Theory applies to models (“By contrast, Kay has demonstrated that Chaos Theory does apply in GCMs.“) but does not apply in the real world. It may be tempting to think that the fact that Earth’s weather/climate varies in ways that we don’t understand and find hard or impossible to predict supports Chaos Theory applying to the real world, but it doesn’t. It cannot, because we can never test the difference between what is and what might have been. What I am trying to indicate is that the more we learn about the weather/climate the better we can predict it, but to do that we must move past GCMs. Interestingly, this is now being done.
Chaos is a feature of a certain sort of equations, typically those with a strong nonlinear feedback component. The scientific question as with all math is where do these equations describe reality. (Even arithmetic has its limits.)
Modeling is one way to do this because a model is equations in action. If the model demonstrates chaos and we think the equations are correct for reality then we are talking about reality being chaotic not just the model. That is what Lorenzo did.
Another approach is to look for aperiodic oscillations in natural systems that likely have the necessary features for chaos. Weather certainly fits this condition. So do lots of other natural systems, wild animal populations for example.
Of course this is all just evidence not proof. Science is about evidence. Note too that nonlinear equation systems are often only chaotic within a certain range of values, called the chaotic regime.
Surely the issue is that we don’t know for certain what is happening within the climate/weather/atmospheric system, how the different components interact and the causal relationship between the components rather than correlation. We can measure an outcome but we don’t know exactly how it arose. We can observe events at a macro level and using prior observations of similar events make an educated guess of what is likely to happen, but there’s no certainty.
The current alarmist view has taken a correlation and created a causal relationship.
Averages ignore the variance of the input factors and thus can’t get the variance of the output correct – unless it is by pure happenstance. Time averaging “averages” doesn’t help.
Chaos theory is the math that deals with non-linear dynamic systems that are governed by mathematically fixed rules but are sensitive to initial conditions, such that small divergences from initial conditions accumulate uncertainty over time. However the systems stay within mathematical boundaries (so that endpoints fall within the bounds of “strange attractors”) but prediction of long term outcomes is not practical because the uncertainty of any specific outcome increases exponentially over elapsed time. The idea of the “Butterfly Effect” is that small displacements of starting points result in potentially very large displacements of the potential endpoints. But the endpoints are still mathematically bounded and fall within the pattern of the strange attractor. Chaos Theory is valid, but the “Butterfly Effect” metaphor has metamorphosed over time to become an urban myth suggesting minor perturbations can make systems collapse into chaos. That’s wrong. Think of it this way: If two migrating Monarch butterflies start out side-by-side in Connecticut but one goes left around a tree and the other goes right, they still both end up in the Monarch Butterfly Biosphere Reserve in Mexico (the “strange attractor”), but one might end up in the Michoacan side and the other in the Estado de Mexico side. Or vice versa. The first tree they diverted around was their own “butterfly effect” – the first tiny perturbation in chains of hundreds of subsequent tiny diversions, each of which made precisely predicting the final destination of each butterfly more and more unlikely, and after thrice the Lyapunov time had passed, quite impossible. But they both end up in the Butterfly Preserve.
Chaos Theory is valid in models. Yes, that is correct. But in the real world we can see variability, that’s all. Every attempt to model the variability by definition takes place in a model. I would argue that each time we gain more understanding of our weather/climate and add it to the models, so a little bit of Chaos Theory is replaced. But the real world didn’t change, only the models changed.
If I’m understanding this correctly. Models can (sort of) predict climate because we use Chaos Theory in the model, not because Chaos Theory actually exists in the real world. Kind of like models can (sort of) predict temperature changes because they use CO2 as a control knob, not because CO2 is actually a control knob.
No quite the opposite is the case. Chaos is intrinsically unpredictable so the models minimize it. Weather and hence climate have a great deal more than the models.
The models just have small scale chaos. Reality has a lot more.
Maybe not. In some places, in the Kay study, the variability resulting from the initial tiny perturbations in the model greatly exceeded those places’ natural variability.
Propagation of perturbation has nothing to do with chaos. If anything chaos would have constrained these effects because chaos is a form of stability.
Dr. Spencer, would you care to offer a response to a comment I make below concerning what kinds of physical observations and follow-on analyses should be done to gain a better understanding of how the earth’s real-world climate system actually operates.
Nicely put Nevada.
Three (very late my time due to sleep inability) ‘clarifying’ supplemental reactions.
A thermoregulatory mechanism is by definition nonlinear. It does not follow superposition since the strength of the feedback depends on the size of the perturbation.
And just because something is nonlinear it doesn’t mean that it is chaotic. A simple pendulum is nonlinear since the restoring force depends on sin(thêta) which is a nonlinear function. But the pendulum itself is not chaotic but highly regular. The climate could be the same — highly nonlinear and regular. Or it could be chaotic but the size of the attractor could be small enough so that it appears regular.
That is a novel criticism of the climate models.
I agree with you. It is quite damning.
They cannot be shown to have physical meaning. Or even what the physical meaning might be.
I’m not sure about “is by definition nonlinear” as negative feedback can be linear. Having said that, the thermoregulatory effect clouds is likely very non-linear as the vapor pressure of water around normal ambient temperatures approximates an exponential. That is it roughly doubles for every 20°F increase in temperature and combined with water vapor being less dense than air makes for a strong driver of convection.
I think it would be a stretch of the imagination to say chaos theory can be dismissed when looking at weather and ultimately climate evolution.
As Donald Rumsfeld so famously said, paraphrasing, we have known knowns, we have known unknowns and then we have unknown unknowns.
The unknown unknowns by their very nature tell us we don’t know what we don’t know and thus we can not know every detail of any system that is chaotic.
A note of balance.
We are told, our best scientific studies by some of the finest brains ever dedicated to explain the physics involved in the universe and our existence within it are baffled. They tell us, they have no idea where the missing 80+% of matter is, that is needed to explain why we exist.
I suspect our capacity to know where every last cloud is, and be able to feed a model that detail is beyond our ability. Even being able to assert or define when a cloud is or is not a cloud, will remain unknown and unknowable.
Well put. Chaos Theory as a mathematical system is entirely useful to help us analyze well bounded systems with inherently unstable parameters. It helps us understand why even if the universe were deterministic (an unsettled question), once the cosmic billiard balls were broken at the moment of the Big Bang, it became impossible to predict where all those hydrogen atoms would end up by, say, next Tuesday.
Chaos is a form of stability due to a nonlinear negative feedback. The price is intrinsic unpredictability. It has nothing to do with small changes becoming big or accumulating iterative errors. It is a math property.
Chaos is an irregular oscillator. A parameter wants to grow but a nonlinear feedback constrains it. The struggle creates an irregular oscillator that is sensitive to infinitesimal differences in initial conditions. The scientific question is where this math applies? Most climate data oscillates irregularly so is likely chaotic.
The ocean tides are an “irregular oscillator” with effects not considered in recent decades within all the wide-ranging programs of “climate” simulation. Keeling and Whorf 2000.
https://www.pnas.org/doi/full/10.1073/pnas.070047197
OK–“We propose that strong tidal forcing causes cooling at the sea surface by increasing vertical mixing in the oceans.” Heat source??– “The 1- to 2-kyr Ice-Rafted Debris (IRD) Cycle.” ??
Ecologists dealt with this a long time ago. Ecosystems, and components, are either fragile, resilient, or a joker. Joker is like complex, complicated, (chaos?) etc., meaning that we really don’t understand it. Holling, C. S. 1973. Resilience and stability of ecological systems. Annual Review Ecology and Systematics. 4:1-23.
Temperature is a (measurable) parameter. A substance receiving energy will generally increase temperature proportionate to the energy received. But any substance above zero K will radiate energy away at roughly the fourth power of its temperature, thus we have the basic negative “nonlinear feedback” that stabilizes the Earth’s “thermometer reading” against “infinitesimal differences”.
Very interesting and insightful.
From the paper:
“A General Principle
The following general principle is proposed:
Any predictive system constructing cycles of variable duration and/or varying amplitude will fail after a few cycles if the mechanisms behind the variability are not fully understood and/or cannot be accurately replicated.
The underlying reason for proposing this principle is that by definition the situation at the end of a cycle is close to the situation at the start of the cycle, so that every small prediction inaccuracy during a cycle becomes a large error, proportionately, at the end of the cycle. Without the usage of factors that transcend the cycle, the predictive system must break down after a few cycles. “Few” is not quantified, and would clearly depend on the specifics of each situation.”
Suppose that there is unresolvable measurement uncertainty in an external value which MUST apply as the primary energy input into the dynamic system at every time-step. Suppose further that a physics-based model has been perfected in every detail to respond to energy flows, and to simulate cloud formation and dissipation, so as to represent the real climate system in high fidelity. How useful might this model ever be for diagnosis and prognosis of the climate system response to rising concentrations of CO2?
It will have no such value at all. Why not? Because the rapid buildup of uncertainty within the model as the time-stepped iteration proceeds cannot be avoided. It’s not because of non-linear dynamics or the susceptibility to slight changes in initial conditions. A time-step-iterated “climate” computation is characteristically incapable of reliable prediction if only because of the uncertainty in an external value (TSI in this exercise linked below.)
https://www.regulations.gov/comment/DOE-HQ-2025-0207-0371
Thank you for listening.
Somehow the idea has apparently been inculcated in climate science that “uncertainty” is bad so it is ignored and/or assumed away. Uncertainty isn’t “bad”, it helps describe the real world – it is a way to establish what MIGHT HAPPEN as opposed to trying to predict what *will* happen. The idea is to lower the uncertainty in order to limit the boundaries of what might happen, not to just ignore it.
IPCC TAR Chapter 14 Page 771 pdf3
The climate system is a coupled non-linear chaotic system, and therefore
the long-term prediction of future climate states is not possible.
You know what? The IPCC reports are abbreviated: First Assessment Report (FAR), Second Assessment Report (SAR), Third Assessment Report (TAR), and then they must have finally figured out that they couldn’t call the Forth Assessment report the (FAR) because that was already taken. So it continues as the AR4, AR5, AR6, etc,
And these geniuses who couldn’t figure out their nomenclature four reports into the future want us to believe they can predict world climate 100 years from now.
“The Butterfly Effect is difficult to argue against because it compares what is with what might have been. Since you can never tell what might have been, you can never disprove it.”
but I say onus is on claimer to prove it.
As Einstein said, no amount of experimentation could prove him right, but a single experiment could prove him wrong.
Can you prove that every particle in the universe attracts every other particle in the universe as Newtons Law of Universal Gravitation hypothesises?
I thought not. Gravity seems to exist regardless.
“A Sound of Thunder” by Ray Bradbury, is a SF story where a time traveler named Eckels steps on a butterfly during a hunt in the past, leading to significant changes in the future.
{first published in Collier’s magazine on June 28, 1952}
I read that ages ago. A different kind of “Butterfly Effect”.
I read that about 60 years ago. Eckels noticed the changes because he had a time machine. Those around him didn’t.
Without a time machine, how could Bradbury have known the phrase “Butterfly Effect” would be so widely used today?
I’m not sure I’m grokking all the subtlety, but doesn’t a compound pendulum exist in the real world? Do they not exhibit chaotic behavior?
Chaotic behavior…. That makes me recall my grade school report cards 😉
Our grade school report cards had a line for “Deportment”.
My mother was not impressed.
Alas, The Butterfly Effect is a mathematical idea — and clearly applies to computer modelling of almost anything. When an iterated computer program uses non-linear equations it is almost certain to run into the problems described (incorrectly) as Chaos Theory.
The Butterfly Effect is easily demonstrated in a few lines of code on even the most basic computers (I did so on a Commodore in the late 1980s). Non-linear results are found almost everywhere in the real world in natural systems.
In its most basic form, The Butterfly Effect states that very small changes in initial conditions (in iterated systems, each step is itself a “new” initial condition) can lead to very large effects in the results after a large number to iterations. But, usually overlooked, is the equally true idea that LARGE changes in initial conditions can lead to very SMALL changes down the line. Because … well, it depends on the system.
The climate system has long been recognized, even by the IPCC et al, as a “coupled non-linear chaotic system.” But it demonstrates this concept as I wrote 11 years ago in an essay titled “Chaos & Climate – Part 2: Chaos = Stability“. ( I recommend reading the entire series, but this one for this exact point).
The misunderstanding is that The Butterfly Effect only works one way — small changes to big changes — and ignores that case that even BIG changes can lead to small effects or stability.
My opinion is that the multiple non-llinearities combine in the climate system to tend to — maybe even demand — stability.
So what we see as a self-regulating climate system very well may be the result of The Butterfly Effect.
“My opinion is that the multiple non-llinearities[sic] combine in the climate system to tend to — maybe even demand — stability.”
It would have to, otherwise, as someone else pointed out, we wouldn’t be here to discuss it.
I’ve been asking this question for a decade and more on several climate forums, and no one has yet offered a satisfying answer:
What kinds of things should we be doing in terms of gathering physical observations of the earth’s real-world climate system as it operates up there in the sky, out there on the land, and down there in the oceans — and then performing a series of follow-on analyses of these physical observations in order to gain a fuller understanding of what’s actually going on inside this very complex system?
Said another way, when it comes to finding out how the real-world climate system operates minute-by-minute in real time up there in the sky, out there on the land, and down there in the oceans, what kinds of things should we be doing that we aren’t now doing to figure out how it all works?
Freeman Dyson made this criticism of climate science long ago. It is not holistic at all. It uses a few forcings to generate a limited output that has little to do with climate (temperature is *NOT* climate). As Pat Frank has shown, climate science models can be reproduced by a simple linear equation. It doesn’t matter what kind of complex noodling is done inside the black box, what matters is what is input and what comes out.
The Earth system has been remarkably stable since it was formed. If it didn’t have some form of overall negative feedback it would be either a molten rock or a frozen rock by now. Looking at even 100 years worth of data today just isn’t sufficient to characterize either the variance of existence that the Earth is capable of or to distinguish all of the negative feedbacks that exist in the system. People like Willis E. have introduced some physics, that climate science seemingly just ignores, that have started down the path to better understanding but we have LONG way to go.
I have several of Freeman Dyson’s books on my shelf. For example, this one from 1979:
Were he alive today, I would ask him this question: Do we know more about the physics of quantum mechanics than we do about the physics of how the earth’s climate system actually operates?
We may not be able to do that. To the extent that explanation is retroactive prediction the butterfly effect makes chaotic events unexplainable.
I’m not talking about butterfly effects and chaotic events. I am talking about gaining more knowledge of the raw physical processes themselves as they operate minute-to-minute inside the earth’s climate system.
These processes will experience forms of variation which will push the climate one way or another over some longer periods of time.
That said, the question I’m asking has this as its basis: Do we even understand how these physical processes operate minute to minute — let alone hour-to-hour, day-to-day, week-to-week, month-to-month, and year-to-year?
I think we understand the basic physical operations at these scales. What we can never know is how they are operating in detail at any specific time.
So, volcanism is chaotic just in theory? Good to know!
Chaos bifurcations are low dimensional .
Global climate is more like a billion butterfly wings all flapping and cancelling each other out into a still chaotic average , but with no one flap dominating the entire swarm .
I don’t know…
Aren’t “Attribution Scientists” claiming they can identify the guilty butterfly?
That is not how it works. The sensitivity is in the math it the physics. The way the system trajectories are packed into the strange attractor the fact that two trajectories are infinitesimally different at a given time is enough for them to rapidly diverge. The difference does not dominate the system.
Back in ’92 I was lecturing on chaos theory at the Naval Research Lab when someone asked me about the new Treaty on Climate Change. I had not heard of it so started tracking it since weather is chaotic and climate is average weather. Chaotic systems have chaotic averages called strange statistics.
In Gleick’s famous book around page 160 he says in effect that climate modelers go to great lengths to avoid large scale chaos because it makes the results too unpredictable. I think this amounts to scientific fraud. The models are a little bit chaotic but not enough to get in the way.
Things like the MWP and LIA could easily be large scale chaotic oscillations.
You can’t “average” all inputs and expect to get appropriate outputs. Even the assertion by climate science that climate is average weather is questionable because of the time factor in the functional relationships. You have to integrate the weather over a sufficient length of time to include all oscillatory factors. Nyquist doesn’t just require a sampling frequency to identify high frequency components; it also requires a long enough observation period to identify low frequency components. When you are looking at “average” weather over two or three decades and trying to predict “average” weather 10 decades into the future, the adage of “the past is prologue” may or may not be a good assumption. Using present averages masks the very variance that combines into a future “average”.
Hurricanes and tornados grow stronger by shrinking from large to small (low pressure areas to hurricanes and supercells to tornados). It therefore should be called the pirouette-effect, and not the butterfly-effect.
The prime cause of rotation is the coriolis-effect.
The butterfly effect does not make these things happen. It merely tells us that we cannot know enough about the physical conditions to accurately predict their occurrence or strength. It is epistemic not physical.
Best. Post. Ever. IMHO.
(Comments section is also hilarious.)
A minor suggestion — readers of your new publication will need direction to your prior writing on the same subject:
By which is meant these:
Michael Jonas. General circulation models cannot predict climate. World Journal of Advanced Research and Reviews, 2024, 22(1), 1313-1318. Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.1235M. Jonas, WUWT (2024)