If you cannot make a model to predict the outcome of the next draw from a lottery ball machine, you are unable to make a model to predict the future of the climate, suggests former computer modeller Greg Chapman, in a recent essay in Quadrant. Chapman holds a PhD in physics and notes that the climate system is chaotic, which means “any model will be a poor predictor of the future”. A lottery ball machine, he observes, “is a comparatively much simpler and smaller interacting system”.
Most climate models run hot, a polite term for endless failed predictions of runaway global warming. If this was a “real scientific process’” argues Chapman, the hottest two thirds of the models would be rejected by the International Panel for Climate Change (IPCC). If that happened, he continues, there would be outrage amongst the climate scientists community, especially from the rejected teams, “due to their subsequent loss of funding”. More importantly, he added, “the so-called 97% consensus would instantly evaporate”. Once the hottest models were rejected, the temperature rise to 2100 would be 1.5°C since pre-industrial times, mostly due to natural warming. “There would be no panic, and the gravy train would end,” he said
As COP27 enters its second week, the Roger Hallam-grade hysteria – the intelligence-insulting ‘highway to hell’ narrative – continues to be ramped up. Invariably behind all of these claims is a climate model or a corrupt, adjusted surface temperature database. In a recent essay also published in Quadrant, the geologist Professor Ian Plimer notes that COP27 is “the biggest public policy disaster in a lifetime”. In a blistering attack on climate extremism, he writes:
We are reaping the rewards of 50 years of dumbing down education, politicised poor science, a green public service, tampering with the primary temperature data record and the dismissal of common sense as extreme right-wing politics. There has been a deliberate attempt to frighten poorly-educated young people about a hypothetical climate emergency by the mainstream media, uncritically acting as stenographers for green activists.
In his detailed essay, Chapman explains that all the forecasts of global warming arise from the “black box” of climate models. If the amount of warming was calculated from the “simple, well known relationship between CO2 and solar energy spectrum absorption”, it would only be 0.5°C if the gas doubled in the atmosphere. This is due to the logarithmic nature of the relationship.
This hypothesis around the ‘saturation’ of greenhouses gases is contentious, but it does provide a more credible explanation of the relationship between CO2 and temperatures observed throughout the past. Levels of CO2 have been 10-15 times higher in some geological periods, and the Earth has not turned into a fireball.
Chapman goes into detail about how climate models work, and a full explanation is available here. Put simply, the Earth is divided into a grid of cells from the bottom of the ocean to the top of the atmosphere. The first problem he identifies is that the cells are large at 100×100 km2. Within such a large area, component properties such as temperature, pressure, solids, liquids and vapour are assumed to be uniform, whereas there is considerable atmospheric variation over such distances. The resolution is constrained by super-computing power, but an “unavoidable error” is introduced, says Chapman, before they start.
Determining the component properties is the next minefield and lack of data for most areas of the Earth and none for the oceans “should be a major cause for concern”. Once running, some of the changes between cells can be calculated according to the laws of thermodynamics and fluid mechanics, but many processes such as impacts of cloud and aerosols are assigned. Climate modellers have been known to describe this activity as an “art”. Most of these processes are poorly understood, and further error is introduced.
Another major problem occurs due to the non-linear and chaotic nature of the atmosphere. The model is stuffed full of assumptions and averaged guesses. Computer models in other fields typically begin in a static ‘steady state’ in preparation for start-up. However, Chapman notes: “There is never a steady state point in time for the climate, so it’s impossible to validate climate models on initialisation.” Finally, despite all the flaws, climate modellers try to ‘tune’ their results to match historical trends. Chapman gives this adjustment process short shrift. All the uncertainties mean there is no unique match. There is an “almost infinite” number of way to match history. The uncharitable might argue that it is a waste of time, but of course suitable scary figures are in demand to push the command-and-control Net Zero agenda.
It is for these reasons that the authors of the World Climate Declaration, stating that there is no climate emergency, said climate models “have many shortcomings and are not remotely plausible as global policy tools”. As Chapman explains, models use super-computing power to amplify the interrelationships between unmeasurable forces to boost small incremental CO2 heating. The model forecasts are then presented as ‘primary evidence’ of a climate crisis.
Climate models are also at the heart of so-called ‘attribution’ attempts to link one-off weather events to long-term changes in the climate. This pseudoscience climate industry has grown in recent years as global warming goes off the boil, and is largely replaced with attempts to catastrophise every unusual natural weather event or disaster. Again, put simply, the attribution is arrived at by comparing an imaginary climate with no human involvement with another set of guesses assuming the burning of fossil fuel. These days, every eco loon holding up traffic on the M25 to the grandest fear-spreader at COP27 is over-dosing on event attribution stories.
In his recent best-selling book Unsettled, Steven Koonin, President Obama’s Under-Secretary for Science, dismissed attribution studies out-of-hand. As a physical scientist, he wrote, “I’m appalled that such studies are given credence, much less media coverage”. A hallmark of science is that conclusions get tested against observations, and that is virtually impossible for weather attribution studies. “It’s like a spiritual adviser who claims her influence helped you win the lottery – after you’ve already won it,” he added.
Chris Morrison is the Daily Sceptic’s Environment Editor.
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This is an excellent summary of the kernel of what Climate Sceptics have been saying all along, notably since around 2011. Yet academic or politician who dissented from the Catastrophic Anthropogenic Warming Hypothesis (before it was rebranded as “Climate Change”) or any of the other sacred cows of their Liberal institutions risked being plunged into a pit of cockroaches in a jungle, or, worse, have the funding for their pet project being removed.
Clearly an experienced computer modeller knocking the back out of global warming computer models is not going to gain any traction. However this is a worthy publication and certainly helps our cause.
This sort of information will of course be ignored by TPTB given that the whole climate fiction was cobbled together when the Club of Rome was initiated and was a deliberate screen behind which the control and depopulation agendas could be hidden. Welcome nevertheless.
One pedantic quibble I have with people who use the term “chaotic” (in its mathematical sense) is that the adjective should be applied to the mathematical model, not to the physical system that is being mathematically modelled. “Chaotic” in the mathematical sense implies unpredictability, which, astonishingly, is at the heart of predictable arithmetic operations (and runs contrary to the spirit if not the letter of Newtonian mechanics, which was earlier mistakenly thought to be “deterministic”). However, in the everyday use of the word, it jut means “unpredictable”, or “I can’t see the pattern”, or “it is absolutely random”. The alarmists often deliberately or naively conflate “chaotic” with “unpredictable”, “unstable”, “exponentially worsening”. It is quite conceivable, but unproven in maths/science, that the climate is stable in the big picture (“over millions of years the global temperature fluctuates within a narrow band”), but unpredictable in the fine detail (aka “rainfall outside my front door tomorrow”). For comparison, the exact position after an hour of all the individual caffeine molecules in the cup of coffee in front of me is unpredictable, and the starting conditions are unrepeatable. However, I can still invent meaningful big picture terms like “concentration of caffeine molecules” because they are bulk quantities, not fine detail. We are simply not, in 2022, in the same position of characterisation of the climate as we are of my coffee.
Human response to lack of knowledge or comprehension leads to a mixture of fear, worship and socialisation (aka “birds of a feather” or “echo-chambers”), a trait which politicians are quick to exploit.
Open ended, non-linear, chaotic system.
Of course it cannot be “modelled”.
They can make models of anything, but in reality they’re guessing. Betting on the horses is probably more predictable; ask the bookmakers.
There are valid places for modelling – such as engineering, modelling stress on buildings and bridges, and such like. Physics is of course a much more precise science than climate “science”, which is in reality maybe upwards of one hundred different disciplines from atmospheric flow to dendrochronology to astrophysics.
There is no such thing as “climate science”. And the models. Just a way to empty our bank balances.
From 2017
https://principia-scientific.org/top-nasa-climate-modeler-admits-predictions-mathematically-impossible/
“Top American Climatologist, an expert in climate modeling, exposes the fallacy that current climate models provide a realistic or reliable prediction of future climate change. In a 1-2-3 step guide to disposing of the global warming debate Dr. Duane Thresher says successful modeling with modern computers is “mathematically impossible.””
2019
https://notrickszone.com/2019/08/29/nasa-we-cant-model-clouds-so-climate-models-are-100-times-less-accurate-than-needed-for-projections/
NASA: We Can’t Model Clouds, So Climate Models Are 100 Times Less Accurate Than Needed For Projections
And how can they possibly ever be correctly initialised?
How true – models are junk in, junk out. I work in IT, can build a ‘model’ for you with predetermined answers. Steve McIntrye ripped apart Mikey Mann’s fake hockey stick application by discovering that no matter what data set you entered, you always got the same answer.
‘the intelligence-insulting ‘highway to hell’ narrative – continues to be ramped up’…I predicted to some friends at our CC that the sermon on Sunday would be on this and guess what? Priest Idiot Climate Change, who has never spoken about Christian Hell, did not disappoint, was almost in tears, lamenting the UN’s annoucement of impending hell. Priest Idiot talked of penance of riding a train to a city, and cycling there instead of bussing or car driving. The clown has never discussed Christian penance.
So I challenged him to a public debate – he accepted. He knows he will be scorched. But as I said to him, ‘your religious views on Climate fascism have no basis in reality, so let’s debate and see where reason exists….’ I asked a friend if maybe he and others are really that retarded. I think that yes they are. A millenarian cult of doom mongers, who have an emotional need to self-flagellate.
I didn’t realise until last week that Morcott’s Uptick was a deliberate fabrication designed to be compatible with Mikey Mann’s fake Hockey Stick, Al Gore’s fake slideshow and Bill McKibben’s fake carbon dioxide graph. I had assumed it was a partially sincere mistake, but triggered when an activist journal editor refused to accept the original paper (with no uptick). (Using Morcott’s method you can generate random upticks and downticks, and you can then select, deliberately or accidentally, whichever one is ultimately published.)
Climate Science’s big error was ostensibly that the modellers assumed that the experimenters were supplying them with raw data, but the experimenters were nudging (“adjusting”) the data to it the model, which is the reverse of science and an indicant of fraud. The more this issue unravels, the more it seems it wasn’t so much Climate Science’s big error but more Climate Science’s big fraud. Many people got poor from it; a few got rich. Ditto Covid.
Climate Models Never Work
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Coming up with a theory or a prediction isn’t science.
Science is formulating a theory or prediction and then validating it or disproving it with real life data.
Climate “scientists” and epidemiologists seem to want to skip the second crucial part of the scientific process.
They want their theories to be categorised as scientific knowledge, which is totally fraudulent.
I refer you all to Dr Roger Pielke’s very clear explanation of why the IPCC simply isn’t worth listening to: (15) Dr Roger Pielke, Jr -What does IPCC AR6 say on Scenarios and Extreme Weather? – YouTube
Ian Plimer’s contributions are always worth reading or watching. Common sense allied to scientific knowledge overlaid with a wicked sense of Australian humour.
Keep it up Chris. Unfortunately too few people see this and too many believe the net zero propaganda because they are lacking the intelligence to understand what is really going on. Surely after the rubbish models about Covid and the propaganda about that, more people should realise the same thing is happening about climate. Is there nobody in the mainstream media with the guts to report this.
Absolutely spot on critique of climate modelling. Anyone who claims to be an applied mathematician or mathematical physicist, and that includes capable climate scientists, who disagree with this article, are nothing more than charlatans.