Essay by Eric Worrall
If you have a possible missing variable problem, the solution is to add more arbitrary adjustments to your model?
AI Exposes Accelerated Climate Change: 3°C Temperature Rise Imminent
BY IOP PUBLISHING
AI-enhanced research shows regional warming will exceed critical thresholds faster than expected, with most regions surpassing 1.5°C by 2040. Vulnerable areas like South Asia face heightened risks, urging swift adaptation actions.
Three leading climate scientists have analyzed data from 10 global climate models, utilizing artificial intelligence (AI) to enhance accuracy. Their findings indicate that regional warming thresholds are likely to be reached sooner than previously estimated.
…
Elizabeth Barnes says: “Our research underscores the importance of incorporating innovative AI techniques like transfer learning into climate modeling to potentially improve and constrain regional forecasts and provide actionable insights for policymakers, scientists, and communities worldwide.”
Read more: https://scitechdaily.com/ai-exposes-accelerated-climate-change-3c-temperature-rise-imminent/
…
The referenced study;
Combining climate models and observations to predict the time remaining until regional warming thresholds are reached
Elizabeth A Barnes*, Noah S Diffenbaugh and Sonia I Seneviratne
Published 10 December 2024 • © 2024 The Author(s). Published by IOP Publishing Ltd
Environmental Research Letters, Volume 20, Number 1 Citation Elizabeth A Barnes et al 2025 Environ. Res. Lett. 20 014008DOI 10.1088/1748-9326/ad91caAbstract
The importance of climate change for driving adverse climate impacts has motivated substantial effort to understand the rate and magnitude of regional climate change in different parts of the world. However, despite decades of research, there is substantial uncertainty in the time remaining until specific regional temperature thresholds are reached, with climate models often disagreeing both on the warming that has occurred to-date, as well as the warming that might be experienced in the next few decades. Here, we adapt a recent machine learning approach to train a convolutional neural network to predict the time (and its uncertainty) until different regional warming thresholds are reached based on the current state of the climate system. In addition to predicting regional rather than global warming thresholds, we include a transfer learning step in which the climate-model-trained network is fine-tuned with limited observations, which further improves predictions of the real world. Using observed 2023 temperature anomalies to define the current climate state, our method yields a central estimate of 2040 or earlier for reaching the 1.5 °C threshold for all regions where transfer learning is possible, and a central estimate of 2040 or earlier for reaching the 2.0 °C threshold for 31 out of 34 regions. For 3.0 °C, 26 °C out of 34 regions are predicted to reach the threshold by 2060. Our results highlight the power of transfer learning as a tool to combine a suite of climate model projections with observations to produce constrained predictions of future temperatures based on the current climate.
Read more: https://iopscience.iop.org/article/10.1088/1748-9326/ad91ca
If I have understood correctly, they are essentially using the AI as a complex black box polynomial correction to their rather imprecise climate models, to try to squeeze out better answers. The polynomial is trained by comparing observed temperature data to model output, then the resultant amalgamation of climate models and AI polynomial corrections is extrapolated to try to predict future events.
The problem with this approach is it creates the illusion of accuracy, without actually knowing if greater accuracy has been achieved. An AI used in this way applies complex arbitrary “corrections” to input data, to generate a near perfect match to any data used to train that AI. But the AI knows nothing about the underlying physical phenomena. The AI might be able infer physical phenomena if it has enough data – or the AI could just make stuff up, especially if unknown critical input data is missing from the set of data which is used to train the AI.
AI does have a role in scientific analysis. In fields like drug discovery and complex optimisation problems, AI can produce excellent results.
But AI also has a well known tendency to go off the rails, to “hallucinate” false results.
An AI malfunction is not a problem if you can test the quality of the AI results immediately. But using AI to try to figure out how to correct climate models, where nobody will know for years or decades whether the AI got it right, then using those AI corrections to project future events, this seems a dubious use of artificial intelligence.
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The word “intelligence”, artificial or otherwise…
… does not belong in a sentence with the words “climate model”.
____
“comparing observed temperature data”
Do they mean data from urban tainted, unfit-for-purpose, mal-adjusted, and agenda-manufactured surface sites??
Has A.I. Looked at the Data on Hurricanes, Typhons, Tornadoes , Floods, Droubts, Sea level rise, ETC. they are not getting worse!!!
Forget all that – apply their method to the stock market! If they’re so good at predicting the future they should all be billionaires in a couple years. If they’re still pandering for research grants in 2027 we’ll know that their methods are crap.
Anyone who thinks that somehow AI will achieve accurate predictions of future events should bet their future on it. Coming soon – AI predicts the outcome of the next PowerBall Loto drawing.
Yes, AI has all that data about how hurricanes, and tornadoes and floods and droughts are not getting worse. It says so right in the IPCC documents. Surely the AI has perused the IPCC documentation. I would think that is one of the first things the programmers would feed the AI.
It’s all a Fraud. The whole Human-cause Climate Change narrative is a fraud.
They selected 10 climate models. Out of how many? Seems like is 37 or something like that. What criteria were used to choose which models to use?
Lots of questions. No anweres.
The abstract is a meaningless word salad, full of hype and fuzzy jargon:
What committee defines these “thresholds”? (Dare I ask.)
Large Language Models, miscalled AI, so far seem to be an idiot savant version of the programming team. Someone gave the program the training data, so it comes with the same sort of prejudices as Wikipedia.
What I expect are more subtle versions of Google’s AI, when asked to show 1943 German troops, showed East Asians and Blacks in Waffen SS uniforms.
At least Google’s AI was DEI.
GIGO is always a problem and currently no AI can discern what’s garbage and what isn’t.
More Garbage, Piled higher and Deeper Upon the Garbage In, Garbage Out!
MGPhDUGIGO for government applications!
So has AI got beyond the 3-year-old child mentality yet?
More like an adolescent with really profound autism spectrum. Machine vision is also something the programmers have not settled yet, as the programs could not solve the “rotate these stacks of blocks” type puzzles.
AI is pattern recognition software. Therefore it is going to respond with the consensus version of whatever part of “the cloud” it searched. We have so many cases of the consensus being shown to be wrong after a few years that it can’t be considered anything other than a cursory review….and letting AI make consequential decisions could well be worse than allowing politicians to make them….
comment cancelled because I didn’t mean to post it yet.
What I was going to say is that climate models are built on erroneous not-real physics from the ground up. (see Shula/Ott video further down)
Doesn’t matter if they try to implement AI… just compounding the junk that is already there.
Digesting the bogus, bastardized Hockey Stick data will start AI down the wrong track.
AI will be trying to make sense from bogus, made-up temperature data. And, as we see, if it is projecting a 3C rise in temperatures, then it is not dealing with reality already. It’s been fed a bunch of crap and then it comes to erroneous conclusions.
If you write a comment that you decide not to post, you can just erase the comment from the comment box, and then click on “reply” and the comment box will close and nobody will be the wiser. 🙂
I tried that and it kept coming back.. wordpress can be odd sometimes.
My mind glazes over reading such gobbledegook
My limited experience with neural nets (6 months) tells me they should have trained it on old models predicting their future, our past, and tested it similarly (we used a random half do train it, the other half to test it, and ran it many times with different random halves). My brief skim saw nothing about what they trained and tested it on. I did note this confusing line:
“Limited” observations? They threw in arbitrary cherry-picked corrections? I have no idea what it really means, but it sure doesn’t sound objective.
Then there’s this:
More cherry picked data, and only temperatures? What about humidity, rainfall, and other aspects of weather?
Eh, it’s all pointless anyway. They start with the assumption that something bad is going to happen, and only ask “how soon?”
2023 just happened to have a strong, persistent El Nino event, exacerbated by the HT effect and decreased cloud leading to increase absorption of solar energy.
Do you think they programmed that in !!!
“This is clearly part of a propaganda campaign intended to ride on the current AI coattails.”
Yes, it is.
“‘Limited’ observations? They threw in arbitrary cherry-picked corrections?”
It sounds suspiciously like the tuning to agree with historical data, which is already done.
There’s historical data, and then there’s “historical data”.
Some of that historical data has been bastardized to the point of being unrecognizable.
I am reminded of my first interaction with a computer, a vast mainframe housed in a university building basement behind a glass panelled wall through which the operators, fully gowned and hairnetted could be observed. We mere mortal students could only approach as far as the hatch through which we passed our stacks of punched cards, reporting back the following day to collect the inevitable list of programming errors.
Late sixties, of course, and our lecturer started every talk by writing on the blackboard (yes, blackboard!) “GARBAGE IN, GARBAGE OUT”. Some things are constant.
Don’t drop that stack of punch cards!
I did that once. I learned my lesson. 🙂
I recall the hard-core computer jockeys always being armed with a big fat Magic Marker for marking the tops of decks.
Also remember going to the Student Center to buy cards — the serious players bought them in the big cardboard boxes. Don’t remember how much they cost.
The diagonal lines, Vs and Ws in Texta along the edges of the cards were a good idea, but had problems if you had to insert lines of code.
That’s why BASIC had line numbers. The first few columns of FORTRAN were reserved for comments, which tended to be used for card numbers as well. I think COBOL may have done the same. And you numbered the original cards in increments of 10 so you could add extra lines.
Then, of course, there were the prefix job control cards before getting into the program itself.
We thought we’d died and gone to heaven when we got to use the teletypes on the PDP minicomputer.
But you try and tell the yoong people today that; and they won’t believe ya.
What this new paper proves is that climate researchers are devoid of intelligence, either artificial or natural.
AI cannot ‘improve’ climate models easily shown wrong both in ‘best tuned parameter’ hindcasts, and by predicting a non-existent tropical troposphere hotspot. AI trained on past falsehoods merely reproduces those falsehoods. A ‘convolutional neural network’ just reproduces convoluted falsehoods.
Convoluted has two textbook definitions:
Both apply here.
And convolution has a technical meaning that has nothing to do with the “textbook definitions” above. See
https://en.wikipedia.org/wiki/Convolution
https://wattsupwiththat.com/2024/12/31/claim-artificial-intelligence-can-improve-climate-models/#comment-4015081
Whilst this is true. Its also true to say
In principle, AI is great at interpolation but not extrapolation. There are exceptions of course, but not with GCMs and climate. It just not that kind of problem.
In other news, ChatGPT retires after winning Lotto.
Con-volution: A Con used to support a Socialist Revolution
Please explain how the mathematical definition of ‘convolution’ as provided by you applies to the use in association with “convolutional neural network,” or as used by the referenced article, “incorporating innovative AI techniques like transfer learning into climate modeling.”
Yes, good point. I’d taken Izaak’s statement “nothing to do with the “textbook definitions” above” at face value and didn’t look at his link. It turns out neither of them is even close.
https://en.wikipedia.org/wiki/Convolutional_neural_network
The wikipedia article states that “A convolutional neural network(CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization.” The use of a filter means that essentially the output can be considered as a convolution between the input and the filter or kernel. The machine learning comes from the fact that the precise properties of the filter are unknown to begin with and has to be set by training the neural net. It is not identical to a mathematical convolution but similar enough.
Its not even close Izaak. That’s like saying neural networks are nothing but mathematics. Its so far away, its not even wrong.
Of course neural networks are nothing but mathematics. They only exist inside a computer program. All programs are nothing but mathematics. Turing showed that decades ago.
Congratulations. You have exceeded my expectations.
And would care to explain why programs are not mathematics?
Firstly your brain is a neural network and its not mathematics in a computer. Secondly this was about describing what a convolutional neural network is and its not even slightly described as just “mathematics” even though there is mathematics used in its creation.
But now you have a new claim that “programs” are mathematics and that’s not true either.
You may as well claim a flower pot is mathematics because it looks like anything with mathematics describing it or underlying its creation is just “mathematics” in your definitional world.
Tim,
it is well known that programs are independent of the hardware running them. And the algorithms themselves are all instances of Turing machines. As such they correspond to functions from the integers to the integers and nothing more. And as Turing machines are definitely part of mathematics so is all of computing.
Similarly the brain can be thought of as a computer and again the Church-Turing thesis applies, computability means able to be computed using a Turing machines. Thus the human mind is nothing more than a particularly complicated Turing machine. Alternatively the physical processes that occur within a neuron can be simulated to an arbitrary degree of accuracy using a digital computer and thus again the entire human brain could in principle be simulated and so is nothing more than a Turning machine.
And a flowerpot can be represented mathematically to whatever level of detail you require but it doesn’t make it a mathematical construct.
A trained neural network, whether that be a convolutional neural network or biological one, contains information which is abstractly represented and an emergent property.
Describing the “convolutional” part of a CNN separately with its mathematical meaning is about as useful as defining yellowcake as being uranium and trying to define cake in any common usage.
In the case of CNNs, convolutional is part of the name but only very obliquely has anything to do with mathematics.
How do you describe “intuition” using mathematics? How do you describe “creativity” using mathematics?
The human brain may be partly a neural network but it is *more* than that. Show me a Turing machine that can “create” something new.
A euphemism for data torture….. OK!
So it is going to be useful as a “climate” thing !
All climate data has been “convoluted”…
… that’s where GISS et al come from.
Izaak,
You need to reread Rud’s comment and not mix up “convolution” with “convoluted”
AI cannot improve models that are using erroneous physics. Here is a link to a video where Tom Shula falsifies the greenhouse effect hypothesis and shows a good analysis of the processes in the atmosphere that have produced the data that, even by the best, has been misinterpreted as evidence of that effect.
It really is a very good idea to watch this video at least a couple of times. !
I second that.
At an average radiating power of 240W/m^2, the radiating temperature of water is 260K. At that average temperature, the vast majority of the water is ice.
These guys do a good job on demising the “greenhouse effect” but talking about water vapour and gases is not very relevant to the radiation heat balance in Earth’s atmosphere. No one can grasp what is happening in the climate system unless they first understand the basics of ice formation; whether that be on land; on water or in the atmosphere.
Ice knocks out 29% of the incoming solar EMR before it even gets a change to thermalise. Ice is far more important than any of the gasses.
AI heterodynes AI results as if it were data.
0 x N = 0, however big, large or intelligent N is.
Nice picture
What would be hilarious is if the AI analyzed the data, set TCR=ECR=1.00, faithfully modeled temperatures and declared no problem.
Yeah, like that solution would ever have a chance of seeing the light of day.
It has long been my hope that true artificial intelligence will blow the lid off this entire climate scam.
Seems appropriate seeing the whole model thing is artificial anyway. The only thing I would question is the use of the word “intelligence”
Gee, I thought climate models already were artificial intelligence
NO, they may be totally artificial…..
… but they are also totally NON-intelligent.
(like their authors)
Also they used a year that is the tip of a big transient El Niño driven temp spike to define the climate state. The models do not see such spikes so the AI will “correct” these already unrealistically hot models even hotter. No wonder they get unrealistically fast warming.
Climate is chaotic. They can’t possibly give reasonable, long range, regional predictions no matter how much AI is used. It’s 100% Snake oil.
The models use unproven assumptions on how the climate system works and the datasets are incredibly dirty. AI can’t overcome that.
The expression “Echo Chamber” comes to mind. The AI learns from the internet, which prioritizes climate alarmism, and viola! provides predictions of calamity.
Who da thunk?
Hasn’t the 1.5 C degree ∆ already been surpassed? Looking at a chart of Little Ice Age (estimated) temperatures makes it look that way. For what it is worth!
Current temperatures are cooler than both the 1998 high point and the 2016 high point.
Looking at the UAH_LT chart on the sidebar displaying UAH Global Temperatures,
?resize=1320%2C594&ssl=1
From whatever 30 year period the baseline is set to, back in 1984 the low was -0.68°C below the baseline and the 2024 high point was +0.95°C above the baseline. So from 1984 to 2024 (40 years) Global temperatures increased a total of 1.63°C. The increase would be well over 2°C since pre industrial times…AND…no tipping points passed the world goes on and in fact temperatures dropped From 0.95°C to 0.64°C (3/10)
Still no tipping
No runaway Warmageddon
Guam is still upright
The oldest living US president is still in office (somewhere)
Nancy Lugosi still sits in dark corners during the day and is seen out at night
“The oldest living US president is still in office;”
I think they have him propped up in the janitor’s closet until they need him for a photo op!
Very nice Eric. A perfect misuse of AI. We start out with the last miracle tool the climate model. A system so complex and dealing with so much information only the newest most powerful computers can be expected to do a credible job. But even these powerhouses can’t produce results that agree with one another or accurately match observations. Clearly the information input is not correct or the algorithms aren’t up to snuff. Rather than admit their theory may be mistaken or change the input or change the algorithm these geniuses clamber onto the newest computer wonder, AI. They figure if they add some observations and AI to their models the combination will predict when the answers they want may come true. This is a stinking pile of bull feces.
Yep – piling unphysical corrections on top of an unphysical model which likely uses incomplete data, given the failure to produce a model which matches observations.
Accuracy. I don’t think that word means what they think it means…
Also climate models do not produce data.
Chuckle.
Next, we will be told the heat islands surrounding AI centers are the cause.
Some projections for the coming technological singularity hypothesise the entire Earth will be converted into a computer, and not just the Hitch Hiker’s Guide to the Galaxy :-).
43 is good enough for government work.
If AI is instructed to show future global cooling, it will do just that – with ease.
There have already been instances of A.I.’s showing bias. So I’m sure they can support any climate position their programmers put into them. The real question is – Why would we listen to them?