Why the Climate Models will never work
Mike Jonas
The climate models are among the most sophisticated computer models ever developed. Billions of dollars and countless man-hours have been spent on their development. The IPCC references about 70 computer models in its regular climate reports. So the idea that the models will never work may sound absurd. But it’s true, and this article shows why, and to do that it bypasses all the complexity and just goes to the heart of the matter – which is surprisingly simple.
I should point out that I am not the first person to say that the climate models will not work. Many people have done that, for example Robert L Bradley Jr wrote climate models can never work, published in AEIR (American Institute for Economic Research), but somehow the gatekeepers manage to prevent the message from getting through to the general public. The problem is that the proponents of the climate models can use climate’s complexities to obfuscate – for ever.
Cambridge Dictionary: Obfuscate – to make something less clear and harder to understand, especially intentionally.
So, how can I simplify the picture? Let’s start with the statement by the IPCC way back in 2001: “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.“. If we could look at another coupled non-linear chaotic system, and understand how the structure used in the climate models could never work for that system, then maybe we could understand why the IPCC made that statement, and why the current set of climate models can never work.
Let’s look at road traffic.
A road traffic controller can tell pretty well how the next few minutes of traffic will go at a particular set of traffic lights or roundabout by seeing what traffic is on the approach roads. But to predict even the next few hours, they must know much more – will people be leaving work, is there a football match starting. Even to look a few hours ahead, knowledge of how many cars are currently on the roundabout a block away is already useless. Larger factors are at work.
What about the next few days – are roadworks scheduled, are school holidays starting. You can see that a model that starts with the amount of traffic on the road at this minute, and then works forward minute by minute calculating how the traffic changes, is a complete waste of time. Sure, you can feed in data about when people will leave work, when football matches are scheduled, when roadworks are scheduled, when the school holidays start, and your model may give some respectable answers. But the reality then is that all the minute-by-minute calculations are meaningless, what really matters is whether you get the bigger picture items right. And if you get the bigger picture items right, you can give a reasonable overall forecast of next month’s or next year’s traffic without going through all the intervening days minute by minute. In other words, in order to forecast next year’s traffic at Thanksgiving you won’t need to work out along the way how many cars will be on each road at 11:30am next Paddy’s Day.
Well, that’s what climate modellers need to understand. They march their models through time, typically twenty minutes at a time, using a single number for the climate equivalent of the speed of all traffic at a point in time between the set of traffic lights in the middle of town and the roundabout several blocks away on the edge of town, and think that they can produce an accurate forecast for the next few decades for the whole planet. (There’s a description here). Even if they had 10,000 numbers instead of a single number, it wouldn’t help. The reality is that they need to know what the sun will be doing over the next few decades, and the clouds, and the Pacific ocean, and so on, and then they will be able to give a reasonable climate forecast without a climate model, ie, without calculating what the temperature will be in the mid troposphere over a particular patch of the Atlantic next Wednesday at 3:20pm.
If they don’t know what the sun will be doing a few decades from now, and the clouds, and the Pacific ocean, and so on, then they can’t make a credible forecast, with or without a climate model. And I shouldn’t have to add that if they do know, then they don’t need a 20-minute climate model.
Postscript
I have kept the above article deliberately simple, so that it is easy to follow. It uses analogy, so it has limits, and given the complexity of Earth’s climate there are necessarily some gaps. In this postscript I will try to address some of those limits and fill in some of those gaps.
1. This article is loosely based on Edward Lorenz’s Chaos Theory. But you don’t need to understand chaos theory in order to understand this article. You just need to understand one fact that comes from Chaos Theory: In a chaotic system, a tiny error will relentlessly increase in size until it has completely swamped the predictions. See Explainer: what is Chaos Theory?
2. I used traffic flow as an analogy for climate, because it is easier to understand how to predict traffic – and how not to predict it. Traffic is a legitimate analogy, because both traffic and climate are chaotic systems. From Chaos Theory: “Chaotic behavior exists in many natural systems, including fluid flow, heartbeat irregularities, weather and climate. It also occurs spontaneously in some systems with artificial components, such as road traffic.“. Obviously the actual data and equations used for traffic are completely different to those for climate, but the basic principles of chaotic behaviour still apply. It is pointless for the climate models to look at the climate equivalent of traffic lights and roundabouts, ie. trying to compute climate on a micro scale (see 5. below).
3. One of the features of chaotic systems is that models can predict behaviour reasonably well for a short period, and then they rapidly deviate. From Butterfly effect: “complex systems, such as the weather, [are] difficult to predict past a certain time range (approximately a week in the case of weather) since it is impossible to measure the starting atmospheric conditions completely accurately.”. I would argue that a lot more than just accurate starting conditions are involved, because the predictions for the end of the first day are the starting conditions for the second day. In other words, even if you get your starting conditions absolutely perfectly, you will still be a long way out in the second week. There’s more at What is chaos? A complex systems scientist explains, eg. “A hallmark of chaotic systems is predictability in the short term that breaks down quickly over time, as in river rapids or ecosystems.“.
5. The statements I made about how climate modellers “march their models through time, typically twenty minutes at a time, using a single number for the climate equivalent of the speed of all traffic at a point in time between the set of traffic lights in the middle of town and the roundabout several blocks away on the edge of town” is correct. Climate models really do that. The way they operate was described in Climate Models: “Climate models are a mathematical representation of the climate. In order to be able to do this, the models divide the earth, ocean and atmosphere into a grid. The values of the predicted variables, such as surface pressure, wind, temperature, humidity and rainfall are calculated at each grid point over time, to predict their future values.“.
6. Some modellers claim that they have ‘moved on’ from the difficulties of chaos theory, and that certain factors like viscous effects would “tend to damp out small perturbations” – from Butterfly effect again. Well, until they can prove it by forecasting weather a month ahead, say, that to my mind is just magical thinking. Like the IPCC said: “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.“, and the same applies to weather, where long-term is just a week.
7. There is even some argument about whether the climate is deterministic. Climate modellers are reportedly getting more confident that climate is deterministic, but don’t be fooled into thinking that means that their climate models can predict it. Edward Lorenz demonstrated that a deterministic system could be “observationally indistinguishable” from a non-deterministic one in terms of predictability. (That’s in Butterfly effect too). For example, the behaviour of a snooker ball is deterministic, but the outcome of the first stroke in a snooker game can’t be predicted. There’s a nice demo of the unpredictability of a deterministc system at demonstration of the butterfly effect.
Summary
In summary, a climate model that works in tiny time steps on a coarse grid can never work for more than a very short time. It can be useful for helping people to understand climate, but it is useless for predicting future climate. And it doesn’t matter how fine a grid is used, or how sophisticated the partial differential formulae are, or how carefully the boundary conditions and chaotic attractors are managed, etc, etc, it still can’t predict more than a short time ahead. Certainly, it might get lucky sometimes, but that’s not the same as being reliable. And even if all the models get their forecasts right for a short period, there is still no reason to suppose that they will continue to be right – remember, the nature of chaotic systems is that they are predictable only for a short time.
Coming back to the last paragraph of the body of this article, clearly what we need in order to be able to make reasonable predictions of climate is an understanding of the longer term factors like solar activity, ocean oscillations, clouds, storms, etc, and greenhouse gases too, of course. We still don’t know what the sun will do next, and we still don’t know how the sun affects our climate. We still don’t know exactly how all the other factors work and interact. And if we did know all of those things, we wouldn’t put them into a 20-minute model, we would say that in x decades time the sun would be doing this and the oceans and clouds and so on would be doing that, so the climate will be doing such-and-such. Approximately.
Mike
“when the school holidays start, and your model may give some respectable answers.”
That is not a bad analogy. But it doesn’t mean that long term traffic analysis is useless. It actually works, as does climate modelling and numerical weather forecasting.
You can’t predict a future traffic state, just as GCMs can’t predict a specific climate state. But the modelling is what enables you to relate the drivers (eg football) you mention to road grid performance. It is the only quantitative link that you have. It tells you how much traffic you can expect, in those circumstances. That is a statistical answer. It won’t get every traffic jam right. But it is what you need for road planning.
That’s exactly my point. You get a reasonable answer by estimating the future traffic then modelling it on a future date, not by modelling the traffic minute by minute from today.
“estimating”
That is my point. The only quantitative way of estimating what putting N cars on the road in all these surrounding circumstances is to do it and see what happens. Too hard with real cars, but micro modelling can do it.
Exactly. Estimate the future, then model it. Micro-modelling can’t tell you what N is going to be. You have to estimate the future N using macro data and methods. Then if you want you can micro model it over a very short time as at some future date. It won’t get detail right, but it can be useful for road planning. You can’t get to that future date by micro-modelling. As I said in the article, trying to do that is a complete waste of time.
And that’s the point. We waste enormous amounts of time and money for nothing.
I worked for a major telephone company for 30 years in various positions, including long-range planning.
One of our major tasks was to try and forecast where to install new central offices in order to obtain land, rights-of-way, etc in a timely manner. This was all based on trying to estimate where in a city expansion and population growth would occur. Not a lot different than doing road planning.
We could do a very good job of estimating infrastructure growth over a short period (e.g. two years) by looking at things like construction permits and zoning. But it was a waste of time to try and do it on a month-by-month basis in order to do a lot of small growth extensions. It was actually less costly to do larger additions that would handle any actual growth profile.
New central office locating? An entirely different animal. There simply wasn’t any way to use existing population growth data to forecast where long term growth would occur. Something like a farmer deciding to sub-divide his 640 acre farm for residential/commercial growth would totally change growth profiles. The city planning commission trying to do zoning for future growth had the same problem – meaning everything from road planning to sewer infrastructure to water lines would be affected.
Our best source was experienced real estate companies with their hands on the pulse of the community and where they saw prime real estate coming on line in the future. Absolutely nothing to do with incrementing current data far into the future.
Very much like your factors of sun, ocean, and clouds. Trying to forecast the far future by incrementally extending existing conditions with “models” was, while not a total waste of time, very much a cloudy crystal ball.
I would be remiss if I didn’t point out that we had accountability for what we did in the form of actual monetary cost to the company. If we got it wrong we couldn’t just shrug our shoulders and walk away. That’s simply not true for climate science today. Their “predictions” are costing the common man terribly but climate science is not being held accountable for that. None of their predictions are coming true, from the Artic ice cap disappearing to mass starvation from heat related agricultural failure. The main defense from climate science – “just wait another 10 years, it *will* happen”. What *will* happen is that the common man will get poorer and poorer from failed predictions.
And not one of the climate prognosticators is on the hook for their failed predictions.
And absolutely NOTHING to do with climate modelling !!
N is used for number of particles while n is used for a fixed count of things. No “scientist” would ever confuse the two.
Making predictions is very very hard….especially about the future….Yogi said it years ago.
Yogi never said that.
It is not in any book by, or about, Yogi
He did say:
“I really didn’t say everything I said,”
Casey used to bounce a lot of his best lines 0ff Yogi and the press went along with it because it sounded a lot neater to blame them on Yogi.
“ as does climate modelling and numerical weather forecasting.”
Absolute BS….. .. “Climate” forecasting is absolute NONSENSE.
No-one has ever or will ever be able to forecast “climate (30 years) in advance.
Weather.. still stuck at a few days.
Climate models produce STATISTICAL GARBAGE. !!
Absolutely worthless for anything but rampant propaganda.
“Absolutely worthless for anything but rampant propaganda.“
We completely agree
Call the newspapers
This is front page news.
We agree about a lot of things actually.
It is remnants of your AGW cult brain-washing about CO2 causing warming, and your complete inability to produce any empirical evidence, that is the problem.
And, of course, your total inability to see that the three strong EL Ninos are the only warming source in the UAH atmospheric data.. clinging to some backward, ignorant lack of understanding of the effect of strong El Nino events..
And then using your abysmal lack of scientific understanding and comprehension to petulantly and arrogantly label others as “nutters”
Traffic analysis is based upon known knowns. Such as, traffic analysts go out and install traffic counters to measure the actual number of trips per day along some particular route. Traffic analysis also take into account other known factors, such as area population; economic drivers (a new business comes to town, or an old business shuts down; a new high density residential development just opened); approved development applications resulting in future population growth and changes in density; socio-economic factors such as demographic data (number of households; number of motor vehicles per household); etc. etc. etc.
There is a reason that traffic analysis is called “traffic engineering” and not “traffic alchemy”. It is firmly based upon engineering principles and defined data inputs.
So called “climate modelers” simply assert certain factors are in complete control of the earth’s climate, whatever “earth’s climate” may be defined as, and so just model the quantity and distribution of those known “climate knobs” and voila! there’s your climate prediction.
But that of course completely ignores the known complexity of the climate drivers in our astrophysical world, and pretends that known unknowns can be reliably predicted … let alone take into consideration the unknown unknowns.
Don’t even begin to go there and say that “climate science” is a real science, as opposed to “traffic engineering” which is a completely established practice of engineering.
And of course, even with traffic engineering, the chaotic nature of human and natural behavior (i.e., traffic accidents; parades; weather events; bridge failures; etc.) can completely destroy any predictive value of the engineering models, at least in the short term.
Modeling is what you do when you know how to build models and run computer programs but don’t have any depth of knowledge of the system you are studying.
Ouch. Actually not so in general, but definitely so it terms of climate…
Then you have a Black Swan Event, like the Key Bridge disaster, and it all blows up. Just ask the Maryland Department of Transportation how they are going to remodel the traffic flow through Baltimore.
Or more specifically, how they are going to model the traffic flow across the FSK bridge.
Better yet why didn’t they predict it and develop the needed work arounds for when it did happen. Hmmm. Something about unknown unknowns I think.
After every iteration, the uncertainty of the results increases, there is no way to escape this fact.
Cobblers. you don’t plan roads. You build them where the jams are. so you can move the jam to someone else’s bottleneck…
Mr. Stokes: I wonder if it’s wise to try to 1) post the first comment which 2) criticizes whatever point might be made by a skeptic. You show us that you really only give this surface take, and you never consider that he might have a point that shows your precious models are wrong-headed. The point of the article is that planning the future is not precise, and is not made more precise by minute-by-minute breakdowns of the “data”. Traffic planning for 100 years into the future is not made more precise by counting cars on a micro scale. You diminish your reputation by jumping in with easily discredited statements liike “it actually works, as does climate modeling.” Would you be willing to say that traffic planning 100 years out is a waste of time and money?
Nick: Just to be clear, are you saying the IPCC’s statement that: “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.“ is incorrect? Does that mean climate modelers have falsified Chaos Theory. Guess I missed the news.
So in your scenario, modeling would provide a refined estimate of climate sensitivity [to CO2]. Clearly it hasn’t. The range of sensitivity estimates is broad and not useful. And getting broader.
You’re talking about statistics based on past experience not modeling the future based on the underlying physics. In fact climate science specifically ignores the various warm and cold periods of the past in their calculations, pretending that the world has a thermostat good to fractions of a degree.
“modeling the future based on the underlying physics.”
You are not talking about “climate models”, then..
… they leave out most of the underlying physics and leave in a lot of baseless assumptions.
They are NOT science.
Please note that the recent WUWT article Artificial intelligence and weather forecasting…a quiet revolution is taking place in numerical weather prediction is a big step towards what I am advocating in this article. They are, at last, starting to look at the bigger picture instead of relying on the micro time-scale stuff.
They need to do that for climate too, starting now, but if the same people are the ones doing it then it still isn’t going to be much use. GIGO still applies. Or maybe it should be GOGO – Garbage Operator, Garbage Out.
Just imagine, if such AI got in the hands of climate realists. We’d let the model free to roam without it’s CO2 shackles, and there wouldn’t be any SLR acceleration, no increase in extreme weather, no melting glaciers.
Thus, I’m doubtful if it will happen. Since the pattern recognition AI will be unable to produce any tipping points or climate catastrophes based on the real climate history during e,g, the Holocene, it will be a politically risky project.
Or the AI could predict multiple different possible tipping points, none with any degree of certainty. The politicians would fear this most of all because it would mean having to actually having to justify why they would favor one over another.
AIs lack judgement, self-awareness, and imagination. The use of AI for advance computer algorithms really abuses the word intelligence.
But the focus lately has been on weather by the model people and that basically shows their desperation.
We know the temperature of earth has risen so we should expect more warm records than cold ones but that is a sign of modest warming not a catastrophe
The focus has been on extreme weather by the climate alarmists. It’s all they have to point to and it’s easy to do for them: An extreme weather event occurs and they claim it is caused by CO2. It’s the only talking point they have left besides the bogus, bastardized global temperature charts. Unfortunately for them, they can’t prove anything they say about extreme weather and CO2 being connected, which lets guys like me call them liars when they make such claims.
The temperatures of the Earth have *temporarily* risen. And thank the Good Lord for that! There is no guarantee temperatures will continue to rise. Going by history, it’s about time for a cooling phase. We’ve had two significant cooling phases already since the end of the Little Ice Age, and the third cooling may be on the way. What would the climate alarmists do then? Answer: They would do what they have done in the past when the question came up and claim that a decade or two of cooling would not mean that CO2 is not the control knob of the Earth’s temperatures. This is what liars do.
Based on solar magnetic field shifts, the approach of the grand solar minimum, and volcanism (there is a correlation), we might just be heading into that mini ice age that so worried people in the 70s.
Cooling temperatures don’t go on forever, either, as the Human-caused Global Cooling crowd found out.
Since the Little Ice Age ended, the temperature profile shows that we warm for a few decades and then we cool for a few decades, and the difference between the warmest and the coolest periods is about 2.0C.
I think the bastardized instrument-era Hockey Stick charts show a cooling of about 0.3C during the 1970’s amid the Human-caused Global Cooling era. Climate scientists would not get exercised over a 0.3C drop in temperatures and they wouldn’t be saying another Ice Age is in the offing for such a minor cooling.
No, the climate scientists in the 1970’s were looking at a cooling of 2.0C or greater when they expressed concerns that the Earth might be entering another Ice Age.
The bogus, bastardized instrument-era Hockey Stick chart seriously deranged the thinking about the past. Too many people, when thinking of past temperatures, automtically see the Hockey Stick temperature profile in their heads and it throws them off the right track.
And, of course, this was intentional on the part of the temperature record bastardizers. They wanted to confuse the issue as a means of promoting their Catastrophic Anthropogenic Global Warming (CAGW) narrative/hoax.
The Temperature Data Mannipulators have been very successful with this propaganda effort. Too many here at WUWT assume the bastardized instrument-era Hockey Stick chart represents reality.
All the unmodified, regional, historic written temperature records refute the bogus Hockey Stick temperature profile. None of them have a “hotter and hotter and hotter” temperature profile like the bogus Hockey Stick chart.
One of these temperature profile versions, the benign written historical records. or the Hockey Stick creation, is wrong.
So are the written records wrong, or is the computer-generated Hockey Stick wrong? I’m going with the written record. The one that was made before Human-caused Global Warming/Climate Change became a politcal issue.
After it became a political issue, along comes the computer-generated Hockey Stick chart. The Hockey Stick chart is political propaganda masquerading as science. It doesn’t equate with the written, historical temperature records.
Here is a comparison between the written, historical U.S. temperature record profile and the bogus bastardized Hockey Stick chart profile:
All the unmodified, written, historical temperature records from around the world have a similar temperature profile to the U.S. profile, where it was just as warm in the Early Twentieth Century as it is today. And of course, this means that CO2 has no significant role in warming the atmosphere because it is no warmer today than in the recent past, even though there is more CO2 in the air today than then (280ppm then, verses 425ppm today).
None of the unmodified, written historical temperature records have a temperature profile that looks like the Hockey Stick temperature profile, which shows temperatures getting hotter and hotter and hotter from the 1930’s to today, and today is the hottest time in human history. A very scary picture indeed. But it is a bogus picture. It does not represent reality. it is a propaganda effort on the part of some unscrupulous scientists. The written, historical temperature records put the lie to the bogus Hockey Stick profile.
The bogus Hockey Stick chart is the BIG LIE of alarmist climate science. Without this Big Lie, they would have nothing to scare people with about CO2.
So-called “Warming” is probably caused by putting most of the world’s thermometers in cities around the world, which are growing because of the growing world population, causing more heat-holding asphalt and concrete to be used in new roads and construction and heated by the sun and then measured.
The thermometers in the US which are sited away from urban areas in the USCRN (US Climate Reference Network) don’t show any warming since March 2005 when it started.
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/0
story tip
*EXACTLY* what temperature on earth has risen?
Do we? Or has it just risen only where the thermometers were sited?
Most of the thermometers are in urban areas, which are growing because of the increasing population, requiring more heat-holding concrete to be used in construction and more asphalt to be used in heat-holding road construction
“And even if all the models get their forecasts right for a short period”. Then somehow the models have morphed to one. 😉
I could be picky and say it might not be the same short period for each model, but yes I should have said “a model” not “all models”.
You appear to be confusing the predictability of a trajectory and the predictability of the average of a trajectory. If you consider the Lorenz attractor then you cannot predict where a particle will be at some point in the future but you can predict with an arbitrary degree of accuracy the time average of the trajectory since the particle is confined to lie on the strange attractor which is fixed.
Climate is the average of weather and while the weather is chaotic there is not evidence that the climate is. You can fairly confidently predict that in 100 years time summer will be warmer than winter because climate is predictable.
“You can fairly confidently predict that in 100 years time summer will be warmer than winter because climate is predictable.”
That is probably the most idiotically meaningless and irrelevant statement ANYONE has ever made. !!
Even in the lowest temperatures of the last major ice age.. summer would have been somewhat warmer than winter.
The only person he’s fooling is himself.
Mr. 2000: I would say that it is a fine example of AGW propaganda. People experience summer vs. winter, but nobody “experiences” climate change, it happens too slowly. But propagandists understand how to palaver the masses, so they pretend summer v. winter (orbital caused, not CO2) is part of their theory. Mr. Walton is proving, once again, that he prefers propaganda, no matter how dumb we know it to be, because he thinks ordinary people are taken in by this. But you knew that.
The Earth is still in a 2.56 million-year ice age named the Quaternary Glaciation. By definition, the ice age the Earth is in won’t end until all natural ice on the Earth melts.
https://en.wikipedia.org/wiki/Quaternary_glaciation
Ah yes, the almighty average rears its ugly head again:
“Problems? Just invoke the mean and they all disappear in a puff of greasy green smoke!”
Averaging does not decrease uncertainty, it instead increases it.
Until climate science realizes this (among many other facts), it will remain in its present hopeless state.
“Climate is the average of weather”
Climate may be the average of weather but it is *NOT* the average of temperature. If it was the average of temperature then Las Vegas and Miami could be said to have the same climate when it is obvious that they don’t.
” You can fairly confidently predict that in 100 years time summer will be warmer than winter because climate is predictable.”
Really? Summer and winter are controlled by the tilt of the earth. Are you saying that tilt will remain constant for all time? That Jan in the NH will *always* be colder than July?
They need more and more graphics cards, accelerators, etc. for more and faster FORTRAN to figure this out?
Sorry, there is *every* evidence that climate is chaotic, and we are currently on the ICE AGE attractor.
Climate is only a long term average of weather, and if there are long term feedback mechanisms in weather, then climate will be chaotic.
And there are. Ocean current represent decades of delay, Melting of ice sheets, hundreds of years.
By definition a chaotic system has overall negative feedback that tends to constrain it to limits, otherwise it would cease to exist as a system. And that’s what defines those attractors. But there is no guaranteed constraint to say that you cannot move from one attractor to another, as in the current interstadial. Nor is there anything in theory that disallows decadal warm periods or cold periods, as evinced by the various pseudo oscillations in the ocean wind and current models.
Cf PDO, AMO, AO et al…
It is all very well to believe in Anthropic climate change, and dismiss all te counter evidence, but that makes you merely a bigot. Not smart at all.
‘Sorry, there is *every* evidence that climate is chaotic, and we are currently on the ICE AGE attractor.’
We’re in an Ice Age because of the closure of the Panamanian Seaway. I have no idea whether or not plate tectonics is a chaotic process.
Here is a study that says that the level of solar output is caused by nested double dynamos that rotate at slightly different speeds, which provides more output when they are synchronized.
‘Modern Grand Solar Minimum will lead to terrestrial cooling’
https://www.tandfonline.com/doi/pdf/10.1080/23328940.2020.1796243?needAccess=true
But predicting to a tenth of a degree and basing major disruptions to energy and economy on that prediction?
Not the tenth of a degree, the HUNDREDTH of a degree!
Isaak says:”…in 100 years time summer will be warmer than winter…”
Will CO2 be the cause of summer being warmer than winter or the earth’s orbit around the sun?
Those are called “seasons.” HTH
The IPCC references about 70 computer models in its regular climate reports.
And all give different
predictionsprojections of future climate.If climate models were remotely realistic, there would only be one.
“If climate models were remotely realistic, there would only be one.”
That would be the Global Operational Device or GOD for short…oh 🙂
“If climate models were remotely realistic, there would only be one.”
That would put 69 climate modeling teams out of business. The UN IPCC is interested in spending billions of taxpayer dollars on climate change modeling and this works much better for them if they have 70 teams to spend money on rather than just one.
You have to put your bureaucrat hat on to understand this line of thinking. The IPCC says, “the more, the merrier!”
Bureaucracies have one goal over all others – to grow.
I learned this in poly sci in 1970. And, damned if it wasn’t true.
The only change I would make is to define the growth rate to be exponential rather than linear!
Pournells Iron Law of bureaucracy states that in every case the group dedicated to the organization not its aims will gain and keep control of the organization.
And those bureaucrats in control don’t like others comng in with new ideas.
One must conform in order to get along in a bureaucracy. Making waves is frowned upon.
Knowing exactly how the sun effects earth climate would entail knowing how a large number of solar variations effect a great many different aspects of the climate system. However, those great many sun-climate factors would likely give no insight whatsoever into the chaotic functioning of the sun itself. Therefore knowing these relationships to climate responses would likely give very little long term predictability of the earth’s climate system. One could at most make a large number of if-then statements about how the climate system would react if the sun did such and such.
Furthermore, other in-system aspects such as ocean oscillations, clouds, and storms interact in non-linear ways in their own versions of chaotic systems so that even completely understanding each aspect separately would give only short term insight to their interactions. None of this is likely to improve long term predictability.
AndyHce – I think you are basically right. However, it is reasonable to question how far ahead a climate prediction can be useful, given that just about nothing in humanity’s existence can be forecast a century ahead, and given that the average lifespan of a house in the USA is only about half that. Nothing is for ever. With ocean oscillations typically having a cycle length of something like half a century, a forecast just one cycle ahead might be at least a bit credible – and might be a bit useful. Worth trying?
You are spiraling in on the basic point that this climate modelling effort lacks robust systems engineering.
Here is a study from 2020 that predicts the output of the Sun over thousands of years.
‘Modern Grand Solar Minimum will lead to terrestrial cooling’
https://www.tandfonline.com/doi/pdf/10.1080/23328940.2020.1796243?needAccess=true
LTNs: Screws everything up
“”Bus takes two hours to travel three miles through congestion fuelled by new low-traffic neighbourhood””
https://www.standard.co.uk/news/london/transport-for-london-lambeth-low-traffic-neighbourhood-streatham-b1141569.html
Try modelling just one….
Nice one. It shows how important it is to get your assumptions right. All those fines should be given back.
The fines have already been spent….
“Lambeth Council debts rack up to £846m as number of staff earning £100k+ increases”
https://www.brixtonbuzz.com/2024/01/lambeth-council-debts-rack-up-to-846m-as-number-of-staff-earning-100k-increases/
Lambeth does its very best to ape somewhere like Harlem, without the globetrotters. As an employer, Lambeth says
“We believe in rewarding our staff. When you join us, you’ll gain access to a range of benefits, including:
Participation in our 5 Staff Networks: Black, Asian, and Multi-Ethnic, LGBTQ+, Disability, Young Professionals, and Women’s.
Lambeth: Senior Communications Manager – £58,197 pa rising in annual increment to £61,347 per annum
https://jobs.theguardian.com/job/8928323/senior-communications-manager/?LinkSource=SEOLandingPageListing
“The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.“
A penny for every time a contributor to WUWT clipped this IPCC quote and I’d be a rich man. It goes on to say, immediately after the above sentence:
In other words, no single model is expected to match exactly the variability of a non-linear chaotic system; but a range of models incorporating different onset dates for things such as ENSO, etc should be expected to provide a reasonable average of the direction of travel.
There’s a really simple way to judge the effectiveness of the CMIP model ensembles: compare them to observations.
Nice example of blind faith
“”a range of models””
And all useless.
Hardly blind faith when the multi-model average is quantifiably close to observations.
So, as I said, nice example of blind faith. Embedding a chart like that is a real insult to the intelligence.
Is that what they teach these days? Now pull up an accurate chart that shows the divergence – or have you got an attack of the Mann’s hide the decline|?
Absolutely blind faith when the lambda factors have been fudged to get exactly that result, for no other reason than the models include two assumptions. The dominance of CO2 and the presence of positive feedback.
And 50 years is a very short time in climate.
Here it is again: the almighty mean, courtesy of the CHIMPS.
Hindcasting is used as the validation method.
Hindcasting is simply curve fitting to known data.
I can create a model that accurately predicts all past lottery numbers.
Sadly, that does not work for future, random, picks.
That’s funny – to my eyes your graph looks to show an increasing DIVERGENCE of observed and modelled temperatures as the models run hotter and hotter.
Even if that chart was not a lie, r would be irrelevant. Not enough is known about the many causes of climate cange to construct a real model. If a climate confuser game appears correct, it is just a lucky guess
Honest Chart
It also matches the amount of concrete and asphalt used in cities around the world which gets warmed by the Sun and then measured by the thermometers in the world’s cities.
Thermometers in the USCRN network placed in rural areas in the US haven’t shown any warming since March of 2005 when it was started.
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/0
Blind Faith! Great group.
https://www.youtube.com/watch?v=JrttBm7r4JQ
My favorite of their’s.
Can’t Find My Way Home is also my favorite Blind Faith song
The original CD had poor sound quality for that song, especially cymbals. The remastered CD version was much better
That’s why model output and reality have some little differences 😀
Your example is a biased one.
You certainely remeber what several IPCC scientists told, the models run to hot.
Use of ‘too hot’ climate models exaggerates impacts of global warming
All three studies, published in the past year, rely on projections of the future produced by some of the world’s next-generation climate models. But even the modelmakers acknowledge that many of these models have a glaring problem: predicting a future that gets too hot too fast. Although modelmakers are adapting to this reality, researchers who use the model projections to gauge the impacts of climate change have yet to follow suit. That has resulted in a parade of “faster than expected” results that threatens to undermine the credibility of climate science, some researchers fear.
Love that comparison!
Nothing like a 13-year out-of-date chart of models of the tropical mid-troposphere to compare against global surface models up to the present.
Than take that 😀
Beautiful! Is there a version of this chart showing the ‘ensemble mean’ as a separate bar?
No, Dr. Roy Spencer didn’t publish any.
“Computer models are no different from fashion models: seductive, unreliable, easily corrupted and they lead sensible people to make fools of themselves”
Jim Hacker (Yes Minister)
Nothing like an UP-TO-DATE chart to show fungal up as a gormless twit.. !!
Are you saying the models are meant to represent adjusted URBAN surface temperatures … and NOT global temperatures.
WOW.. talk about shooting yourself in the foot with a 12 gauge !!
And no.. not the mid troposphere.
You are a total idiot, fungal !!
We are on the precipice replaced code red.
Has anybody else seen this video
https://youtu.be/BbSEC6VN4Rs?si=cTbXEsK-p8geE3Dq (about 4:30 in)
by Sabine Hossenfelder, where she explains that according to some study, the globe isn’t warming as fast as models predict, which would mean that the models would have to use a lower ECS to get current temperatures about right, but then the models would get the future predictions wrong (I think she meant on the cold side).
Or something like that. She goes on to say that it’s “bad news”.
I found it a bit disappointing to learn that even when the world is warming slower than the models it’s still bad news.
The IPCC has its premise backward. Warming is good for humans, not bad
About 4.6 million people die each year from cold-related causes, and about 500,000 people die each year from heat-related causes. When humans breathe in cool air the blood vessels in our lungs constrict to conserve heat, this causes our blood pressure to rise causing increased strokes and heart attacks in the cooler months.
‘Global, regional and national burden of mortality associated with nonoptimal ambient temperatures from 2000 to 2019: a three-stage modelling study’
https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00081-4/fulltext
The important question for policy makers is the reliability of predictions based on climate modelling. Your key remark is:
What is the evidence for this? My impression is that the spaghetti graphs so commonly displayed are a decisive counter-example. We have one model, the Russian one, which seems to be confirmed by observation, and a whole bunch more which seem to be falsified by it.
Your argument is, take the one that is confirmed, average it with the ones that have been falsified, and you get a more reliable prediction than if you just used the confirmed one.
Why would you think this is at all plausible? And, remember, its going to have to be not just plausible, its going to have to be well evidenced amounting to proof, because trillions of dollars or policy decisions rest on it.
There is an interesting connexion to the ‘precautionary principle’ here. Imagine you have a range of models compatible with observation and one or two far out in left field. The compatible model by itself doesn’t support much or any action. The argument now becomes, lets average them all, and we now have predictions which justify our proposed policies. The precautionary principle is different from this, it works by manipulating payoffs, but this is another vehicle to get to the same destination. In both cases the attempt is to find a way to use falsified theories and their predictions as the basis for policies, as if they were in fact validated.
Here.
A graph of anomalies? What is the measurement uncertainty for those anomalies? What is the variance of the anomaly data set? What is the variance of the data sets used to calculate the anomalies? What is the measurement uncertainty of the data sets used to calculate the anomalies?
You post a graph that implies that you know with 100% accuracy what those anomalies *are*, clear down to the hundredths digit. That is pure, unadulterated garbage.
How much has the dataset been adjusted from raw measurements prior to determining those anomalies?
Always ask this question.
That is very pretty, but its not evidence of anything.
I take it you are Nick stokes, because there is that same patient research into received wisdom coupled with a total inability to understand basic scientific and mathematical concepts
You ran away from giving a cogent answer by spamming your cherished CHIMPS graph (again).
To be expected.
Ensembles built up on ensembles are as bad as averages ,built upon averages. These are built by programmers that probably have never taken a calculus based junior or senior level physical science course.
Proponents of these models that are not prepared to discuss the measurement uncertainty of the inputs and how it affects outputs are not qualified to assess the quality of the outputs either.
Think about the logic. This is not evidence of the argument you are trying to make.
You take a collection of models, not assembled on any clear basis. You then take the mean of their forecasts and discover a reasonably good fit of this mean to observations.
Though you also discover that the spread of the models is very great, both upside and downside.
You then claim that this agreement validates the procedure. It doesn’t. You don’t know why these out of all possible models were selected. You don’t know why they are right or wrong in either direction. You cannot conclude that the mean of this arbitrary set is a valid source of predictions going forwards.
Always try to spell out exactly what the argument is, and as in this case, you often find that it doesn’t show what it is cited to claim.
But if you think it does, spell it out one line at a time, whatever the argument is, and lets see all the premises and why exactly they logically imply the conclusion.
My conclusion remains: get one working model. Don’t think that by averaging the predictions of a lot of models that are failing for unknown reasons you are going to get anything that will support trillions of dollars of investment. Try this with any Fortune 500 finance committee, you’ll get yourself fired.
“good fit of this mean to observations.”
Except that they are not “observations”…
They are manically mal-adjusted urban and airport temperatures kludged together to give meaningless numbers..
Berkeley could put them together to produce whatever that wanted to fabricate.
Only thing they certainly CAN’T represent is anything real.
More of Berkeley’s FAKED URBAN DATA.
Nothing “global” about it.
Only evidence you have there is that Berkeley knew what they wanted to fabricate as their temperature series.
Evidence tantamount to deliberate fraud. Well done!
Notice that you are using SSP2-4.5…
…. and that even that is showing warming higher than any REAL warming.
Does that mean you are finally realising that higher levels of junk forcing are even more total garbage. !
The US has cooled slightly since March 2005 when rural locations started being used in a new network called the US Climate Reference Network(USCRN).
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/0
The other thing I wonder is how fungal thinks that Berkeley, using all the WORST urban and airport data in the world, and having zero clue of the actual places it comes from….
… could possibly come up with a temperature series fabrication that could be even remotely representative of any fanciful “global” temperature.. !
“Rather the focus must be upon the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions. “
Which they CANNOT POSSIBLY DO !!
Climate models are basically just meaningless computer games.
Chimps have been compared to REAL measurements and shown to be absolute GARBAGE.
Yes.. when you use massively adjusted and FAKED data.. they can get a match.
But this is not reality.
You mean like Remss? You know where the annual temperature is higher than the monthly temperatures? What special sauce is used in that?
The distribution of models’ future states is essentially random within a range determined by the assumptions and constraints applied to the models. If you run the same model many times, changing initial conditions by tiny amounts each time, you get an enormous range of results at local and regional level. They did this some years ago with one model, changing initial conditions by trillionths (yes trillionths) of a degree, and that is exactly what happened. But there was still a reasonable consistency for things like global average temperature because that was determined by the assumptions and constraints, not by the 20-minute steps. If you simply took their assumptions and constraints, you could tell what the model’s global average temperature was going to be. Basically, it was determined by the ECS they used, plus their assumption for future CO2, plus their assumption that the sun and other natural factors had little or no long term effect. You could get there very quickly just with a calculator.
I seem to recall that Willis did just that, some time back, here.
“You could get there very quickly just with a calculator.”
Pat Frank did just that by generating a simple linear equation that perfectly matches the “ensemble” output of the computer models.
What? Are you saying that ECS, not CO2, is the control knob of the Earth’s climate?
Pat Frank demonstrated that the GCMs are effectively nothing more than linear extrapolations of CO2 concentrations.
The amount of concrete and asphalt poured probably matches the measured temperature even better since almost all the thermometers are at airports or in urban settings with lots of concrete and asphalt to store the heat from the Sun.
You say “a range of models incorporating different onset dates for things such as ENSO” when you really mean ‘an accurate model run many times with different onset dates for things such as ENSO’. Running a set of bad models will never give good results.
You appear to have inadvertently linked to a Real Climate page with only a limited set of comparisons.
I think you meant to link to this RC webpage instead, titled “Climate model projections compared to observations“.
As “climate change” is often, but incorrectly, simply reduced to “an increase in GMST”, the least irrelevant comparison is probably provided by the following graphic from that webpage (last updated 3 Feb 2024).
Conclusion
The “latest and greatest” (CMIP6) climate model ensemble “runs hot”, and the PDF (Probability Distribution Function) needs to be manually filtered post hoc in order to even come close to “reality”.
GISS, Had, Berk represent “mal-adjusted” URBAN and AIRPORT temperatures.
They CANNOT be remotely representative of REAL global temperatures.
Sorry, I keep forgetting that is a local convention rather than a globally recognised one.
The word “reality” in my post is surrounded by “cynicism and/or irony alert quotes”, not the more widely used “scare quotes”.
You simply can’t get the right answer from incorrect factors. Very few of the models match observations. Therefore they simply can’t be averaged to get a right answer. You just wind up with an average that is wrong.
If each model exactly matched observations with the same set of inputs and parameterizations then you could use the 70 models to do a sensitivity analysis for each possible factor to find out which factor has the most impact on the model output.
But that is *not* what climate modeling does. The modeling is still caught up in the fact that very few actually match observations!
And with that one sentence they completely absolve themselves of any pretence of understanding models, or science.
That statistical averaging simply does not work with chaotic systems.
Consider a hundred snipers all shooting at Donald Trump. (or Joe Biden, it doesn’t matter). All at extreme range. The average of the scatter of the shots, applied to the intended targets, ends up with (both) being killed. But if the pattern is wide enough the chances that any shot will be on target is vanishingly small.
It is clear to me that that sentence was added in to justify funding to a bunch of people who have no business calling themselves scientists at all.
Which means that an average of all the shots won’t be close.
Why are political polls always ±3 or 4 percent. That tells me that the temperature range would be ±0.46 to ±0.6 degrees @ 15°C.
Does anyone believe temperature models are any better?
That’s why they average the models. To give that full range visibility. (/sarc)
The ensembles are within the uncertainty interval and therefore are meaningless for predictions!
The models are programmed to make CO2 the controlling factor. That is why they all end up as linear projections. That is, there is one linear variable in the equation.
Did you not read about errors causing unrestrained outputs? The models all have artificial boundaries built in to prevent that.
The earth has no artificial boundaries yet has stayed remarkably within boundaries. That should inform one that the models are not complete nor accurate.
That chart is a lie and you are a iiar
The average climate confuser game predicts warming at a rate about 2x the actual warming rate from UAH satellite data since 1979.
Your chart does not show that
Your chart must be a lie.
The whole IPCC premise that warming is bad is flawed.
Around 4.6 million people die each year from cold-related causes compared to about 500,000 that die each year from heat-related causes.
‘Global, regional and national burden of mortality associated with nonoptimal ambient temperatures from 2000 to 2019: a three-stage modelling study’
https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(21)00081-4/fulltext
Humans can tolerate heat much better than they can tolerate cold. That is why, outside of the Tropics, almost everybody lives in heated dwellings, uses heated transportation, and works in heated buildings.
Mike,
What about the Russian model? It seems to be doing what your piece argues is impossible, that is, it gives long term predictions which are verified by observation.
The Russian model uses, amongst other things, a much lower ECS than other models. If you take all their assumptions you can work out their future global average temperature without stepping through 20 minutes at a time.
What is the ECS in the Russian model?
Andy May puts it at 2.1. As he says, the ECS isn’t put into the Russian model as an ECS, but the assumptions and methods they use give it an ECS of 2.1. Volodin (https://agupubs.onlinelibrary.wiley.com/doi/pdf) puts it at 1.8, but that might be a different version of the Russian model. In any case, maybe ECS isn’t a constant.
The idea that it might not be a constant came to me a year ago – a mere woodsman. 🙂
Volodin is speaking specifically to INM CM5, the most recent version. I have Volodin’s important a paper where he also shows it is the only CMIP6 model that does NOT produce a spurious tropical troposphere hotspot.
Years ago there was an experiment where there were a number of cars being driven in a large circle. This was to look at bunching on motorways. Initially the cars were equidistant apart and all drivers were told to keep to a specific speed. After only a few orbits round the track a pattern developed where cars were bunching. This was due to slight variations in the speeds of the cars, caused either by variations in the speedometer or human intervention.
An apparently ordered system initially is anything but, and that is with only one variable, speed.
Yes, and what is really intriguing is that the bunching moves backwards through the traffic. Not that it isn’t obvious once you think about it – and I have been in heavy motorway traffic where I got a lot of time to think about it.
I saw a study once where a driver in heavy traffic applying his brakes for no apparent reason would cause a backup with all drivers behind him applying their brakes for over half an hour.
I can verify that. Driving in St. Louis you only needed a car to be on the shoulder for whatever reason. You would end up testing your reaction time and brakes 2 miles before and testing your cars horsepower as you go by.
Hmmm…the Indy 500 example?
Good example.
Traffic can be modeled as a wave packet, akin to quantum mechanics.
It seems that everyone has forgotten about Pat Frank’s paper on why models are useless. No amount if jiggery pokery will make them work.
JeffC – do you have a link to Pat Frank’s paper? As I said, many people have pointed out that the models are useless. What I wanted to do here was to relate it to something – road traffic – that lots of people could understand from their own experience, Otherwise ‘they’ can just go on hiding behind climate’s complexity.
Here’s one:
https://www.frontiersin.org/articles/10.3389/feart.2019.00223/full
Btw, notwithstanding your very fine article, ‘they’ will go on hiding behind climate’s complexity.
Citation:
Frank P (2019) Propagation of Error and the Reliability of Global Air Temperature Projections. Front. Earth Sci. 7:223. doi: 10.3389/feart.2019.00223
try here: https://www.frontiersin.org/articles/10.3389/feart.2019.00223/full#B97
Pat’s paper is quite detailed. People have tried to shoot holes in it but Pat has answered all criticisms rationally and logically. The basic thing to take away from it is pretty much what you said in another post about changing initial conditions by a trillionth of a degree changed the output of the model pretty drastically. Pat basically asserts the same thing, measurement error associated with an initial condition will be compounded at each incremental step in the model.
Climate science answer: “All measurement uncertainty is random, Gaussian, and cancels”. Thus they say that over multiple increments any measurement uncertainty of the output will disappear and the output can be considered to be 100% accurate. I.e. what we see in our cloudy crystal ball is 100% accurate and will come tp pass”.
Pat showed in a subsequent paper that temp data from liquid-in-glass thermometers have an in-built, inherent measurement uncertainty due to their design. This is a systematic uncertainty and systematic uncertainties are not random, not Gaussian, and do not cancel. Any model using past temps from LIG thermometers as an initial condition will have an inherent measurement uncertainty that can’t cancel and which will compound through all the incremental steps.
Pat’s derivation of a simple linear equation matching the ensemble output of the models is just more confirmation that, for all their complexity, the models are in essence just linear equations that output a line with a slope – i.e. an ECS. All the model outputs are different because they all boil down to a different ECS. The ensemble is just an average of those ECS values. And most of the models pick an ECS that is way too high.
Variance of a data set is a metric for the uncertainty of the average calculated from the data. The wider the variance the smaller the “hump” is around the average. Think standard deviation and measurement uncertainty. Yet climate science NEVER shows any variance of any data sets they use. They don’t show the variance of any subsequently calculated values using the base data sets. Think NH summer temps and SH winter temps being averaged together. Winter temps have higher variance than summer temps – so how do you just average the two data sets together without compensating for the different variances? Climate science does it by just ignoring the variances of the data! It all goes back to the meme of all measurement uncertainty is random, Gaussian, and cancels. Therefore all averages of everything is 100% accurate.
‘Therefore all averages of everything is 100% accurate.’
Tim, it’s even worse than that – they’d have you believe that E((T_i)^4) = (E(T)^4), which per Jensen’s Inequality, ain’t necessarily so.
The biggest problem with averages is almost nothing is actually average.
This is the one I think.
https://www.frontiersin.org/articles/10.3389/feart.2019.00223/full
Has anyone considered the Stock Market as an analogy for climate?
The “Climate Change” fad is like the US 1928 stock market fad which all came crashing down in 1929 when the truth came out and it crashed.
Technically, models in and of themselves are not worthless. What is worthless is how the results of modelling are applied. In engineering, models are very useful, but the results are always validated by test and they fail the test, the models are reassessed and corrected. There are no knobs turned. The flaws are identified and fixed. The models rerun. The tests rerun. it is an iterative process until complete. And complete means getting to the correct answer, aka, the truth.
Correcting a model based on a concept of a how a system works when the concept is wrong is just mental masturbation. At some point you have to start over with a new, better concept. This point is ignored by climate science. They believe that by adjusting their parameterization constants they can “correct” their models to match reality. That belief all by itself should tell an objective observer that they are developing an ever more complex data matching algorithm and do *not* have a valid concept of how the system we know as Earth actually works.
Interesting article Mike. But you could use the same arguments to “demonstrate” that weather forecasting models can’t work beyond a few hours or, at most, days. In point of fact weather forecasters currently make pretty good predictions out to 10 to 14 days. But the weather forecasters — unlike climate “scientists” — actually check their predictions against reality with some degree of rigor. And — again unlike climate scientists — the weather folks are up front about the limitations of their modelling.
I misspent much of my youth hanging out in (generally frigid) mainframe computer rooms at weird hours of the night working with (testing, debugging, and at times coding) orbit determination software. So I reckon that I can claim considerable practical experience with numerical modelling. It can work amazingly well. But to get good results, you need to be able to model every significant force involved with great accuracy. And you need to recalculate everything whenever any force changes significantly. I agree with you that climate forecasters can’t currently do that. Their demonstrably awful results demonstrate that.
I do think that someday we might well have climate models that actually make meaningful predictions. But that someday isn’t just one or two algorithm tweaks away. Most likely, it’s many decades or even centuries of hard work away.
don k – the limit for the current weather models is generally acknowledged to be about a week. But now they are starting to use pattern-matching – Artificial intelligence and weather forecasting…a quiet revolution is taking place in numerical weather prediction – they should get a lot more than a week reasonably well. Commercial weather forecasters have been doing it that way for a long time.
Mike: “About a week” is using rather rigorous standards. Cliff Mass had an article about that a few months ago, but I haven’t been able to find it this morning. I find published ten day forecasts to be perfectly OK for things like planning outdoor activity even if they sometimes don’t get temperature within 3 degrees or get the time of arrival of weather fronts within a few hours. Would that climate models were remotely that good.
As for AI (aka Machine Learning). AI certainly might surprise me. But my bet would be that in the long run it will find a few serious uses, but mostly it will seriously disappoint. At this time the only really mature attempt at AI/ML backed by serious effort and money seems to be IBM’s Watson. I think that Watson’s failure to find any useful application other than whomping humans at Jeopardy ought to serve as a warning to those who expect AI to solve all the world’s problems while making investors in Nvidia wealthy beyond the dreams of avarice.
“Commercial weather forecasters have been doing it that way for a long time.”
The Farmers Almanac has been doing it for even longer. And with “reasonable” accuracy.
Pattern matching isn’t incremental modeling as I believe you have pointed out already.
Weather forecasters do not make a 10 day prediction then go on vacation. They are constantly updating the predictions based on observations, sometimes as quickly as 15 minutes, but usually in hours or half day increments.
I follow the NOAA weather website for my town and notice the projection graphs change. I have not thought to sample the time rate of change, but they change and not on a 10 day increment.
If you make a 10-day prediction and change it 15 minutes later, you now have two predictions – a 10-day prediction and a 9.99-day prediction. The 10-day prediction hasn’t changed and is still just as accurate or inaccurate as when it was first made.
“Climate” has also been redefined by the World Meteorological Organization to be only 30 years now instead of the thousands to millions of years it used to be.
They need to make a prediction of the next 30 years and see how accurate their model was before spending hundreds of $US trillions.
Actually, the bottom line is future climate cannot be predicted, period.
Because the known unknowns are, well, unknown, not to mention the unknown unknowns.
Sure we can make gross macro-predictions of climatic cycles that are based only by looking at past geohistory … we can say that there is a significant probability that the earth will enter another glaciation phase (“ice age”, colloquially) eventually, but we actually cannot predict one … because it is entirely plausible that the earth has experienced its last glaciation in the late Pleistocene, and we’ll never see another. Even if we could be certain that there will be another glaciation event, we cannot know when that will occur – next year? 1,000 years from now? 10,000 years from now?
For practical considerations that actually affect how people live on this planet, it’s best to simply understand the weather and have forecasters do the best job they can using data available in real time to reasonably forecast weather. They’re doing that now, and generally getting better and better at it over time.
The earth is slowing. Orbital mechanics are constantly changing. The slowing is due to gravitational forces. Slowing means an increasing distance to the sun.
On a day to day or even annual basis, the effects are numerically insignificant. But the changes are real.
Good points in this article! It seems no one was worried too much about projecting future climate states until the attribution of warming to emissions of CO2 became fashionable.
It may take a few more years for the reasons to be more widely re-discovered, but the large-grid, discrete-layer, step-iterated, parameter-tuned climate models should NEVER have been thought capable of reliably demonstrating, diagnosing, or projecting a climate system response to incremental non-condensing GHGs.
But now we have observing platforms in space that look at the longwave emitter performance of the surface+atmosphere system itself in near-real-time and relatively high resolution. It’s obviously not a passive radiative “trap.” We see powerful dynamic self-regulation – motion, clouds, overturning circulation, highly variable emitter output. The minor effect of incremental GHGs only produces a bit stronger radiative coupling between the surface and the lower atmosphere. This does not mean that the land + oceans MUST get warmer (i.e. accumulate heat energy) as a result.
This is why I made this time-lapse video last year. More explanation in the description text box.
https://youtu.be/Yarzo13_TSE
You can watch in near-real-time here.
https://www.star.nesdis.noaa.gov/GOES/fulldisk_band.php?sat=G16&band=16&length=12
‘It seems no one was worried too much about projecting future climate states until the attribution of warming to emissions of CO2 became fashionable.’
I don’t think it became ‘fashionable’, as much as it became obvious to the Left that climate alarmism could be useful in collapsing liberalism.
PS – Your links are always worth a look.
A fair point.
“coupled non-linear chaotic system”
Not being a scientist or engineer- I am curious about the meaning of “coupled” in this context.
An example would be the “coupling” of ocean and atmosphere. Surface evaporation > condensation adds heat to the atmosphere > shading and precipitation cools the ocean surface > dissipation of clouds allows more sunlight to warm the ocean > etc. In other words, large two-way effects and dependencies.
More than just two-way connections. It’s a system with multiple connections between multiple pieces, and the relationships are variable. It is why predictions are impossible as you go into the future. Climate science has glommed onto a variable that can be considered pretty stable, i.e., CO2, as an overall control knob of temperature.
Ask yourself what was the last CAGW paper anyone has seen that has predicted what changes the GCM”s show for northern Africa or Canada in terms of CLIMATE? Not just temperature but Koppen climate assignment. Why won’t ant CAGW advocate go out on a limb and make actual forecasts of climate in Europe? What area is going to move from forest to tundra? Grassland to desert?
When I can drive 200 miles south and have a “climate change” of 3°, is 1 or 2 degrees going to worry me.?
A simple analogy. You and your wife are walking down the street. Now you hold hands, which couples you such that the motion of you affects your wife and the converse.
I both taught computer modeling as a professor and did computer modeling as a systems analyst.
In an attempt to model control of heating and air conditioning in a skyscraper I found, predictably, that weather mattered. The schedule of population of meeting rooms mattered. Which days were religious holidays for some locally practiced religion mattered. The local football schedule mattered. The probable dress of workers by season mattered. A myriad of tiny effects made it so complex that automated control was impossible.
Like the IPCC said: “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.“, and the same applies to weather, where long-term is just a week.
Let’s say we want a computer program to predict the make and model of the first car to pass a certain point at Noon on a busy road complex where traffic merges from several routes.
Well what information does the computer need if not a complete list of every make and model car that is still on the road? That is all we can rely upon even if we have reams of complex computer data about what occurred at the required time and the required day of the week at the point we are watching … unless we have eliminated randomness.
How do you eliminate randomness in the example given? The answer is you cannot since there could be one or more random traffic events (e.g. accidents, emergencies or just random hold ups) on all but an infrequently used route onto the busy road we are monitoring. At the point we are interested in at twelve noon the 1915 classic car using this obscure route was neither expected nor had it been included in our list of makes and models.
Now think about climate and admit its unlikely to be tamed in the ways we would like for far too many varied and interesting reasons. At heart humans and their computers don’t do randomness because if we could there are a lot of things we would immediately find stopped happening. Bookmakers wouldn’t exist but that might be a good thing. But wouldn’t nature give up with its secrets exposed and life as we know it would end? It’s randomness that makes the universe work and the reason why infinity can never be measured. Humans must always be kept in the dark about certain things.
Just as the circle is that familiar shape that has no beginning and no end and cannot be squared nature has much stuff that we are never supposed to know about in any depth.
And we all saw what randomness achieves with COVID-19 and how nature walked all over our attempts to try to be too clever by half instead of sticking to doing what we knew already and staying sensible.
The spherical grid drawing in the article neatly illustrates another problem that is just ignored AFAIK: the grid uses equal latitude-longitude increments. What this means is that the area of each grid is a strong function of latitude. It is easy to show that the areas at the equator are an order-of-magnitude larger than those near the poles. The UAH data also has this problem.
Averaging, homogenization, and infilling will fix this problem — according to climate science anyway!
“. . . spherical grid drawing in the article neatly illustrates another problem . . . .”
There are ways around that problem. If you divide the sphere up using an icosahedral grid, then all (triangular) areas are equal in size, Another is the conformal cubic grid, but that’s not as good. However, if a model uses a lat-long grid, then you’re correct.
Yes, the lat-long grid points are essentially trapezoids.
As I hope I made clear in the article, you can divide the globe up any way you like and it still won’t make a 20- minute model reliable. Shortening the 20 minutes also won’t help.
You did, it was clear.
Mike Jonas:
Our climate is completely unpredictable because it is controlled by the amount of dimming SO2 aerosol emissions there are in our atmosphere, primarily from unpredictable volcanic eruptions.
I find it incredible that everyone overlooks this simple fact!
However, there is a caveat to the above. It CAN be predicted, historically, that the fewer SO2 aerosols there are in the atmosphere, the HOTTER it will get.
While aerosols, like many other things, can have an influence, the major climate factors surely are the sun and Earth’s relationship to it, clouds and oceans.
You deliberately forgot CO2 emissions as a cause of climate change because you are a Warming is Natural Nutter.
The evidence of the causes of warming since 1975 favors manmade causes, but you are SO biased that you won’t even mention CO2.
Reduced SO2 pollution is another cause of AGW.
TOA solar energy has not increased since the 1970s so could not directly cause any warming after 1975
Planetary geometry is a major cause of climate change but irrelevant for a 50 year period.
There has been a decline in the percentage of cloudiness but that is not an accurate proxy for the the exact amount of sunlight blocked by daytime clouds, or the exact effect of night clouds on Earth’s ability to cooling itself.
A smaller percentage of cloud cover suggests that is a natural cause of some of the warming after 1975.
Evidence of other natural causes of the warming since 1975 is weak.
The rural US has cooled slightly since March 2005 according to the US Climate Reference Network (USCRN) which is all rural.
https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tavg/1/0
Mike Jonas:
You overlook the fact that the intensity of the solar radiation striking the Earth’s surface is moderated by the amount of dimming SO2 aerosols that there are in the atmosphere, making them the actual control knob of our climate.
THEY regulate the temperature of the ocean, and the amount of cloud formation.
What accounts for the cyclical nature of the temperature profile since the end of the Little Ice Age then?
Random SO2 emissions wouldn’t cause cyclical warming and cooling.
Tom Abbott:
Attached is a WoodForTrees.org plot of land-ocean global temperatures since the end of the LIA (1850). Every temperature increase or decrease can be associated with an increase or a decrease in atmospheric SO2 aerosol levels.
You are using a bastardized version of the temperature record to prove your case.
This bastardized chart does not represent reality, so your claim doesn’t represent reality, either.
Tom Abbott:
The vast majority of the temperature fluctuations shown are due to volcanic eruptions. That IS reality.
It does NOT matter how bastardized the the temperatures associated with those events are, what matters is the CAUSE of the events!
You had spoken of random SO2 emissions not causing cyclical warming or cooling, but that is exactly what volcanic eruptions do (as well as industrial SO2 aerosol emissions) do.
Take your blinders off..
Approximately.
Weather forecasting is a bit like driving a car in reverse without mirrors or looking backwards. You can only predict where you’re going by looking at where you’ve been. If the road you see out the windshield is straight keep the wheels straight. If you’re on a steady curve try to maintain that curve. But you will not get very far before you drive into the ditch, even if you are on roads you’ve driven every day and know every twist and turn.
The author is a conservative living in a normal world of facts, data and logic. He has presented a 40 year old argument that climate models don’t work. From a conservative point of view that is correct.
But these are not conservative models. They are leftist models and they work VERY WELL for the leftist goal: Creating fear of the future climate, with manmade CO2 emissions as a boogeyman, and a goal of increasing leftist government power, and control of the private sector.
There are no climate models
There are only Climate Confuser Games, used as leftist propaganda with no intention of making accurate predictions. They are intended to make scary climate prediction, with the exception of the Russian INM
Not enough is known about every climate change variable to construct a real model. And even with that knowledge, it does not appear that the future climate could be predicted, not even for the next year
Climate confuser games are expensive leftist climate propaganda
We know they are propaganda for two reasons:
(1) Predictions are getting less accurate over the decades rather than more accurate. The average wild guess of the ECS of CO2 keeps increasing.
(2) The Russian INM computer game, which least overpredicts warming, should get 99% of the attention, but probably gets 1%
Any computer game that appears accurate has just made a lucky guess.
Climate change is merely a prediction of CAGW. I realized that in 1997 hen I first examined “global warming.
There were 100 years predictions. I immediately invented the term Climate Confuser Game and decided people would never fall for scary predictions of doom using climate confuser games. I was completely wrong.
As a child my parents taught me to ignore predictions because they are almost always wrong. They had no idea that someday leftists would try to control the world with predictions of climate doom, still believed by most people after being wrong for the past 44 years, since the 1979 Charney report (the +1.5 to +4.5 degrees C. report).
My 1997 climate prediction was:
“The future climate will be warmer,
unless it is colder.”
I’m sure of that.
Richard Greene
(BS, MBA, DRCS)
Don Rickles Charm School
WUWT Downvote Champion
The Honest Climate Science and Energy Blog
You really do have deep-seated irrelevance and insecurity issues, don’t you, you poor thing !
And you have deep seated anger and are a junk climate science El Nino Nutter
You need to be sedated!
Poor petal.
Still stiving for some relevance in an otherwise irrelevant existence.
So long as you don’t give up. That’s ok.
Keep being a try-hard. !
“You need to be sedated!”
I’m not the one with massive child-like ADHD symptoms !!
You have chronic MADS
Mental
Abilities
Deficit
Syndrome
And you have chronic AGW-cult-zero-evidence-syndrome.
Your mental abilities aren’t just deficit.. They are non-existent. !!
Two-thirds of Republicans under the age of thirty agree with the so-called “Climate Change” viewpoint.
As the surface is always warmer than the cold upper troposphere (which is what the IPCC claims warms the climate), no gas at any concentration in the atmosphere can warm the climate It’s simple. We can stop even pretending that greenhouse gases exist.
“One of the features of chaotic systems is that models can predict behaviour reasonably well for a short period, and then they rapidly deviate.”
I think the term the author is looking for is “The Horizon of Predictability.”
“The butterfly effect does not imply that chaotic systems are unpredictable. They in fact are predictable in the short term because of their deterministic character. But they become unpredictable after a certain amount of time, called the horizon of predictability. It’s the time required for tiny errors to double in size. For a chaotic electrical circuit, the horizon is something like a thousandth of a second. For the double pendulum, it’s a few tenths of a second. For the weather, it’s unknown but seems to be roughly a week or two, and for the entire solar system, it’s about 5 million years (as determined by very careful computer simulations).”
–Professor Steven Strogatz–Cornell University
The comparison to the traffic model does not include one serious risk factor. Climate models are “hindcast” as their validation mechanism. This is fundamentally curve fitting to a known data set. Would the traffic models hindcast to traffic data work any better and projecting future trends?
Simple answer: No.
Yes, nice succinct expression of a critical problem.
Models predict what they are programmed to predict
They are programmed to predict whatever the model owner(s) want predicted
The owners of the models present themselves as climate experts.
Studies of predictions in the past have shown that experts make MORE wrong predictions than the general public, and the batting average is very low for both groups.
Even a simple extrapolation of the past 30 to 50 year climate trend does a poor job predicting the climate trend for the next 30 to 50 years.
In fact, since 1900, more accurate predictions would have been the OPPOSITE of the prior 30 to 50 year trend.
Sort of a reversion to the mean prediction. That may not apply to the next 120 years, but it was an interesting pattern.
Never forget:
The global cooling predictions peaked in 1974 and then a global warming trend, still in progress, began in 1975.
With “Climate” being only 30 years now, according to the World Meteorological Organization, it is always, like the weather, changing.
Excellent article. However one must consider a major factor. I spent the better part of my career in the semiconductor business where all products are initially designed with models. One thing we learned fast is that with the best of intentions models never work. The problem with climate modeling is, I don’t think they have the best intentions in the creation of their models. The biggest problem in creating a model is being able to estimate all the possible conditions that may occur, and in what order. Nobody can do that. Complex models only eventually work at least to a useful state after many iterations and comparisons with reality. After the reality is checked the models are then tweaked to agree with reality. Climate models, given the climate is defined as weather over a large number of years, have never been checked against reality, and cannot be. Ergo they don’t and can’t work. Models are generated by the SWAG and “whoops, shit ” approach. In the beginning of model creation all the engineers make some estimates and when one doesn’t know what a particular parameter is, the agreement is , “well let’s SWAG it.” then after the model is built and a product produced against the model, it’s tested and all the “whoops, shit” are found (hopefully), and the model is corrected. Then the process continues.
For those unfamiliar with the term, a SWAG is a Scientific Wild Ass Guess. I think the climatistas just use WAGs.
In my days in IT, we used cardboard programmers to locate bugs. A cardboard programmer is a programmer who volunteers to listen while the one with the bug explains what is going wrong. Often, the bug is found without the volunteer saying a word. ie,a cardboard model of a programmer would have worked just as well.
Funny how things change but still stay the same. I (and my son) know that as “rubber ducking” as in you can do the same thing with a rubber duck. I’ve never heard of “cardboard programmers”
It’s amazing how well it works, too.
Back in the day, we used walk-throughs to locate bugs. It’s amazing how a programmer trying to walk-through his (or her) code would discover several errors. Unfortunately, management usually cut short error detection because of the schedule. In many cases, the testing was outsourced to the users.
When I was a system analyst, there were several programmers who didn’t know how to test their code. The way you test code is to add instrumentation in the code so testing is possible. That instrumentation is usually left out.
Very nice Mike.
“Climate models are a mathematical representation of the climate.”
This is what I have always thought but I am not as eloquent as you. Numbers and math are a wonderful thing, we can accomplish so much with them. They are not the only thing and can’t always give us the answers we need. Many times they can give us the answers we desire even if they aren’t exactly correct.
The CAGW crowd has been at it for decades and all they have given us is models and anecdotal evidence, it is time for them to move on and offer up some proper explanations of their hypothesis.
No one would be surprised if it were revealed that these climate modelers are being paid under the table by governments, environmental groups and companies who stand to benefit from pushing bad-case climate scenarios If it’s the governments that are responsible, it’s so that they can justify carbon taxes and subsidies for green products. If it’s the environmentalists, their motives would be to get more government handouts and more donations from the public .If it’s businesses, they’re looking for not only taxpayer-funded contributions but also a form of advertising for new green products. And let’s not for get the academics who are also looking for money to prolong their research, particularly when they haven’t been able to prove anything yet.
They are being paid over the table by governments. It’s called ‘grants’.
The chaotic atmospheric components tend to mitigate the effects of the solar forcing. For example, during a low solar period in a UK summer, overly wet conditions could be relieved by a hot dry Saharan plume, or conversely a hot and dry high solar period could be relieved by a thundery breakdown.
Nature designs well.
But the changes in solar forcing are not chaotic as they are ordered by the planets and are predictable at any range. My solar based predictions for UK weather last summer were a hot dry June, a very wet July (deeper -NAO), a mixed August, and a heatwave in the first third of September. That kind of detail can be done decades ahead.
The next 1934-1949-1976-2003-2018 type heatwave is in 2045, and the next 1540-1757-1936-2006 type super-heatwave is in 2116.
https://docs.google.com/document/d/e/2PACX-1vQemMt_PNwwBKNOS7GSP7gbWDmcDBJ80UJzkqDIQ75_Sctjn89VoM5MIYHQWHkpn88cMQXkKjXznM-u/pub
I do have one problem with this analogy. What the road network in future decades will look like is 100% dependent on what humans do.
The climate in future decades has very little, if any, dependence on what humans do.
EDIT: That is “global” climate. Humans can have a rather large effect on very local climates – such as urban heat islands. But essentially nothing where humans are not (which is the vast majority of the globe).
Roundabout’m, Roundem’
Confuse’m where they’re going .
Roundabout’m, Roundem’!
Confusem!
(I hate roundabouts. The directions say , “Turn right”.
How do you which is the right “turn right” in a circle if you missed the first right?
At a 4-way stop, “the first right turn” is pretty clear.
(Poor attempt at spoofing “Rawhide!”. My wife cut her finger and she needed my help more than I first realized.)
My wife’s finger is fine.
My comment will remain un-fine.
The great nuclear scientist Enrico Fermi and 10,000 others said this: “We can predict almost everything, except the future.”
For purposes of public policy decision making, policy analysts must predict the future under conditions of uncertainty. It goes with the territory of being a policy analyst and cannot be avoided.
More often than not, a small collection of simplified analysis assumptions better serves the needs of policy decision makers, as opposed to masses of highly detailed facts and information which, in the aggregate, only serve to obscure the fundamental truths of the policy issue debate.
For purposes of public policy decision making concerning climate change, I make these assumptions about the earth’s climate system:
The earth’s climate system is a huge collection of mostly natural and some anthropogenic physical processes whose aggregate effects can be represented by the earth’s global mean temperature.The earth’s current global mean temperature represents the current state of the earth’s climate system.Trends in the global mean temperature record represent trends in the state of the earth’s climate system. Today’s computer-driven climate models offer no better predictive ability that do simple visual analyses of obvious past and current GMT trends.
Once a year, usually in the spring, I post my graphical GMT prediction envelope, first presented here on WUWT in the spring of 2020. The prediction envelope embodies the above four assumptions.
Based on my 2020 graphical analysis, my prediction is that global mean temperature in the year 2100 will be in the range of +0.3C to roughly +3C above 1850 pre-industrial, with the most probable outcome being +2C above pre-industrial by 2100.
Four years on in the year 2024, GMT is staying well within the prediction envelope I produced in 2020. (After just four years, how could GMT not be staying well within my prediction envelope from 2020?)
What about the public policy decisions themselves concerning climate change?
Regardless of what actions western nations take in attempting to decarbonize their economies, the rest of the world will not decarbonize.Whether the rise in GMT by 2100 is +0.6C, or +2C, or +3C, the actual impacts of a probable rise in GMT are over-hyped and are largely uncertain in their actual long-term effects.If policy decision makers conclude that a steady rise in GMT represents a serious threat to mankind and to the earth’s environment, they have the option of funding solar geo-engineering through SRM, an approach which can quickly reduce GMT by 2C at a long-term cost which is far less than a futile attempt to decarbonize the world’s economy.
For those of you who say we can’t predict the future, and therefore shouldn’t try, my response is this:
If public policy decision makers ask you to predict the future, then step up to the plate and take your best shot at it. Document your analysis and your supportive material on the public record and you’ve then met your obligation to those in a position of public trust who asked you for your opinion.
Here is a forecast of the Sun’s output for the next 1,000 years.
‘Modern Grand Solar Minimum will lead to terrestrial cooling’
https://www.tandfonline.com/doi/pdf/10.1080/23328940.2020.1796243?needAccess=true
WUWT Moderators:
An hour ago, I put up this comment which was formatted using the tools at the bottom of the text entry box.
The formatting I used stayed present as I had originally posted it for about twenty minutes. And then something (or someone) destroyed the formatting that had been used in the comment text box when I entered the comment.
Was the removal of my comment’s formating the result of a bug? Or was it a moderator action? Or is removal of original formatting an ‘undocumented feature’ of the WUWT commenting software?