Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
Europe experienced a relatively mild and low-snow start to the 2025/2026 winter, with media reports in late December 2025 highlighting bare ski slopes in the Alps and concerns over climate change impacts on snowfall. However, a major Arctic cold outbreak arrived in early January 2026, delivering heavy snow and freezing conditions across much of the continent.
Europe experienced a relatively mild and low snow start to the 2025/2026 winter, with media reports in late December 2025 highlighting bare ski slopes in the Alps and concerns over climate change impacts on snowfall. However, a major Arctic cold outbreak arrived in early January 2026, delivering heavy snow and freezing conditions across much of the continent.
This pattern—driven by Polar Vortex disruption and a Greenland blocking high—contrasts short-term variability with long-term warming trends, where snow scarcity remains a growing issue for lower elevations.
AMSTERDAM, Jan 5 (Reuters) – Heavy snowfall disrupted air, road and rail travel in France and the Netherlands on Monday, as hundreds of flights were cancelled in the countries’ capitals, while trains around Amsterdam came to a standstill and bus services in Paris were suspended.
Amsterdam Schiphol airport, one of Europe’s busiest hubs, cancelled about 700 flights on Monday as the airport closed to incoming traffic until 1200 GMT due to the snow, a spokesperson said. During that time, planes were diverted to other airports.
…
1,000 KM OF TRAFFIC JAMS IN PARIS
French transport minister Philippe Tabarot urged travellers to check if their flight was operating before leaving home, and to use public transport to reach the airport.
With the snow line edging higher, 186 French ski resorts have shut, while global heating threatens dozens more
Phoebe Weston in Céüze Sat 27 Dec 2025 17.48 AEDTShare
When Céüze 2000 ski resort closed at the end of the season in 2018, the workers assumed they would be back the following winter. Maps of the pistes were left stacked beside a stapler; the staff rota pinned to the wall.
Six years on, a yellowing newspaper dated 8 March 2018 sits folded on its side, as if someone has just flicked through it during a quiet spell. A half-drunk bottle of water remains on the table.
…
On the brink
The question of what to do with these places will play out across Europe’s mountains, and around the world. Skiing is disappearing from many alpine landscapes. “Many lower ones are already closed,” says Masson. “A fraction of a degree changes everything in the mountain environment. It’s the difference between having snow and no snow.”
…
On winter weekends dozens of cars still gather in the car park, with people enjoying quieter activities on the hillside, such as walking, snow-shoeing, cross-country skiing and sledging.
Of course, that was Guardian last week. This week’s Guardian reports heavy snow in the Alps;
Weather tracker: Arctic air grips Europe as severe winds batter Corsica
Faye Hulton and Ed Walton for MetDesk Mon 5 Jan 2026 20.15 AEDT
Subzero temperatures, heavy snowfall and powerful gusts mark a harsh start to 2026 for many
It has been a cold start to the year across much of Europe, particularly in central regions, where temperatures dropped to double-digit negatives. Heavy snowfall hit parts of eastern and central Europe on New Year’s Eve, notably in Poland and Ukraine, with similar conditions across the Alps on the first few days of the year.
The cold is likely to continue this week as an Arctic air mass sinks south across Europe, pulling temperatures well below the seasonal average outside south-east Europe. Temperatures are expected to fall widely by about 5C (41F) below average, with some areas – such as parts of central and north-eastern Europe – up to 10C lower than the norm. When wind chill is taken into account, it will feel even colder.
In South Korea, the cold air mass brought snow, with Jeju island experiencing the greatest disruption. Flights and ferries were cancelled due not only to snow amounts, which reached more than 9cm in Jeju City, but also because of strong winds and significant wind shear.
Global lower tropospheric temperatures, as measured by satellite datasets like the University of Alabama in Huntsville (UAH) record, do show a long-term upward trend that aligns with rising atmospheric CO2 levels since the late 1970s (from about -0.47°C anomaly in 1979 to around +0.30°C by late 2025, while CO2 increased from ~336 ppm to ~427 ppm). However, they do not track CO2 levels more closely—meaning no smooth, immediate, or perfectly linear response—for several well-established reasons rooted in climate dynamics.
Global lower tropospheric temperatures, as measured by satellite datasets like the University of Alabama in Huntsville (UAH) record, do show a long-term upward trend that aligns with rising atmospheric CO2 levels since the late 1970s (from about -0.47°C anomaly in 1979 to around +0.30°C by late 2025, while CO2 increased from ~336 ppm to ~427 ppm).
However, they do not track CO2 levels more closely—meaning no smooth, immediate, or perfectly linear response—for several well-established reasons rooted in climate dynamics.
The radiative forcing from CO2 is logarithmic, not linear—doubling CO2 does not double the warming effect, and feedback (e.g., water vapor amplification or cloud changes) modulate the response.
Additionally, CO2 is not the only driver; other greenhouse gases (methane, nitrous oxide) and human activities (land use, deforestation) contribute, while natural forcings can counteract.
In paleoclimate records, CO2 often lagged behind temperature changes as feedback (e.g., released from warming oceans), but in the modern era, anthropogenic CO2 emissions are the primary driver, with the long-term correlation evident over decades rather than years.
The big news in 2025 for the climate scare was that all of a sudden, this scare wasn’t such big news anymore. We’re talking here about something that all of the right people had agreed for decades was an “existential” threat to humanity. It was supposedly the single most important thing that we all needed to focus on and transform our lives to stop. We only had ten years to “save the planet”; or maybe it was only five. If we failed, we would shortly be inundated by sea level rise, or maybe devastated by floods and droughts, or burned up by wildfires.
And then, during 2025, quite rapidly the scaremongering stories became less frequent. Several prominent priests of the climate cult turned apostate (e.g., Bill Gates, Matthew Yglesias). The political push for “net zero” dramatically slowed. Why? There have undoubtedly been many reasons for the shift. Among those have been Trump administration regulatory changes and de-funding of scare-promoting bureaucracies and NGOs, plus the emerging extreme costs and ineffectiveness of the “net zero” energy transition.
But here’s another issue that, although I rarely see it mentioned, could play a big role in the ongoing eclipse of the climate cult: The failure of global tropospheric temperatures to closely track the rise in atmospheric CO2.
The asserted basis for the climate scare is the proposition that greenhouse gases (GHGs) in the atmosphere, primarily CO2 from the burning of hydrocarbon fuels, cause the atmosphere to warm. The hypothesis is that atmospheric temperatures are essentially stable in the absence of human influences, and that human-produced GHGs are introducing a forcing mechanism that causes atmospheric heat to track rising GHG levels, leading to higher and higher temperatures.
As our frequent and curmudgeonly commenter Richard Greene points out, you will be hard pressed to find any legitimate climate scientists to dispute the proposition that CO2 and some other human-produced gases (e.g., methane) are GHGs whose accumulation in the atmosphere is likely to lead to at least some warming. But, assuming that you accept that proposition, the question remains: Will the resulting warming be large, or moderate, or small? Indeed, could the GHG-induced warming be so small as to be even near or below the limit of detectability by available instruments? Could observed changes in temperatures be principally caused by other factors? And how could you assess those questions?
And here is the atmospheric CO2 record from NOAA since 1958:
A few things leap off the graphs. The CO2 trend has a small annual seasonality (associated with plant growth in the Northern Hemisphere, which has much more land area than the Southern); but aside from that, the trend is smoothly upward, with even a small amount of acceleration.
The trend of temperatures, on the other hand, is anything but smoothly upward. If you look at the UAH graph holistically, you will undoubtedly perceive an overall rising trend of temperatures as the years progress. But unlike the CO2 graph, the temperature graph is punctuated by periodic and significant reversals, where temperatures decline quite rapidly, and by amounts that are very large in relation to the overall rise.
Consider just the most recent 22 months, from April 2024 to December 2025. The UAH temperature anomaly has declined from +0.94 deg C to +0.30 deg C over that period, a decline of 0.64 deg C. The entire temperature rise in the UAH record from its inception in 1979 to its all-time record in April 2024 was from -0.47 deg C to +0.94 deg C, or 1.41 deg C. The recent 0.64 deg C decline represents almost half of that.
And yet even as world temperatures were giving back almost half of their increase over the past 46 years, the CO2 level continued its slow and steady rise, going from about 421 ppm to 427 ppm over those 22 months, according to the NOAA graph.
If you look at the NOAA graphs of the other GHGs, you will see that they follow a rising trend similar to the trend of CO2.
So, if CO2 and the other GHGs are the main factor in determining changes in atmospheric temperatures, why have the temperatures gone down for almost two years now while the GHGs have continued their rise?
I’m not saying that I know the answer to that question. I am saying that the know-it-alls who claim that the GHGs are the control knob for average atmospheric temperatures don’t know either.
Clearly, there are factors at play other than the GHGs, and the impact of those factors is not small relative to the impact of the GHGs. Whatever those factors are, they are capable of driving global average temperatures down by at least 0.64 deg C even as the concentration of GHGs continues to go up. In fact, they are capable of driving temperatures down even much more than that.
If you look at the other parts of the UAH temperature graph, you will see that this phenomenon of rapidly declining temperatures, as in the past 22 months, has also occurred repeatedly over the 46 year period of the satellite temperature record, all while the GHGs were continuously rising. Among the larger declines in the satellite temperature record were one of about 0.80 deg C from early 1988 to early 1989; one of more than a full 1 deg C from early 1998 to early 2000; one of about 0.80 deg C from early 2010 to early 2012; and one of more than 0.70 deg C from early 2016 to early 2018.
Something that was not GHGs drove temperatures down repeatedly by amounts ranging from 0.70 deg C to over a full 1 deg C over the past 46 years. And now that you know that, how exactly do you know that the increases in temperatures that have occurred over these years were not also caused in part or mostly, or even entirely, by these same unidentified forces? And if these forces are capable of driving temperatures down by more than 1 deg C, how exactly do we know that the current decline is not going to continue until temperatures have gone below the place where they started at about -0.47 deg C back in 1979? And if the temperatures do not go back to -0.47 deg C in the current round of decline, how do we know that they won’t go below that level in the next decline, or the one after?
There are obviously many hypotheses as to factors in play other than the GHGs. Some of those include: variations in solar irradiation; volcanic activity; atmospheric aerosols; level of cloudiness; and various oscillations in ocean temperatures. That list is by no means comprehensive. It may well be that there are other factors as yet unidentified. Moreover, I haven’t seen anyone who has put together a set of proposed factors that when combined can reproduce a pattern comparable to the satellite temperature record. You can call the unknown factors “noise,” but the “noise” may be as significant as all the other factors put together.
But the hypothesis that the GHGs are the main factor, the effective “control knob” of global temperatures? That looks to me to be falsified by the satellite temperature record. If commenters can explain why that is not the case, I’m very interested to hear why.
Yes, the phenomenon described—often called the wake effect—is a well-established issue in offshore wind energy. Wind turbines extract kinetic energy from the wind, creating downstream regions of reduced wind speed and increased turbulence (known as wakes). In densely packed wind farms or clusters of farms, downstream turbines effectively “steal” wind from each other, leading to lower overall energy yields than initially projected for isolated turbines or smaller arrays.
Yes, the phenomenon described—often called the wake effect—is a well-established issue in offshore wind energy. Wind turbines extract kinetic energy from the wind, creating downstream regions of reduced wind speed and increased turbulence (known as wakes). In densely packed wind farms or clusters of farms, downstream turbines effectively “steal” wind from each other, leading to lower overall energy yields than initially projected for isolated turbines or smaller arrays.
CLINTEL (Climate Intelligence), a Netherlands-based organization known for its skeptical stance on climate alarmism and promotion of the “There is no climate emergency” World Climate Declaration, published an article on December 30, 2025, titled “Offshore wind turbines steal each other’s wind: yields greatly overestimated”.
The piece highlights wake effects—where upstream turbines reduce wind speed and increase turbulence for downstream ones—as a major reason for lower-than-expected energy production in large-scale offshore wind farms.
The energy yields of offshore wind turbines are overestimated by up to 50% in national policy documents. This conclusion is based on an analysis of operational data from 72 wind farms.
In order to meet the net-zero targets set out in the European Green Deal, offshore wind turbines will have to make a significant contribution to Europe’s future energy supply – at least, that is the plan of European governments. However, these plans are facing setbacks due to high investment costs and uncertainty about returns, as demand is lower than expected. On October 30, outgoing Minister Hermans of the Dutch Ministry of Climate & Green Growth (KGG) announced in a letter to the House of Representatives that no applications for a permit had been received for the tender for the Nederwiek I-A wind farm, which has an installed capacity of 1–1.15 gigawatts. This is a trend that is not limited to the Netherlands. In August, for example, there were no bids for the ten gigawatts of tenders that the German government had put out for offshore wind projects. On top of that, there is now another setback: the energy yields of offshore wind turbines appear to be much lower than assumed in most national policy plans.
“National policy targets show expectations of energy production up to 50% higher than can realistically be achieved”, concludes Carlos Simao Ferreira, professor of Wind Energy Science at Delft University of Technology. He published, together with Danish colleagues Gunner Chr. Larsen and Jens Nørkær Sørensen from the Technical University of Denmark (DTU), an article in the latest journal Cell Reports Sustainability, on November 21. “This study establishes a physically grounded upper limit on wind farm performance, demonstrating that aerodynamic constraints impose a fundamental ceiling on the energy extractable from the marine Atmospheric Boundary Layer”, the scientists continue.
According to the article, the ever-growing wind farms, which are also becoming increasingly denser, extract energy from the lower part of the atmospheric boundary layer, affecting this boundary layer up to several kilometres above the Earth’s surface. The energy extracted from the airflow must be replenished from the higher layers of the atmosphere, but this is only possible to a limited extent due to atmospheric limitations determined by physical principles known from meteorology and geophysics. This means that wind turbines literally steal each other’s wind, which means that the efficiency of wind turbines will decrease even further as their number increases. The scientists demonstrate this with a validated analytical model that defines the physical upper limit of offshore wind farm production.
They built their model based on the actual yields of 72 large wind farms in the United States, the United Kingdom, Germany, France, Belgium and the Netherlands, and compared the actual yields of the wind farms with the theoretically expected yields set out in national policy documents in a number of case studies. In seven of the nine case studies, the national policy targets for offshore wind yields turned out to be way overestimated. Two German wind farms were slightly underestimated.
The limitations of offshore wind revealed in the publication are not new. Scientists from the Danish university and the German Max Planck Institute have previously warned that the expected yields from offshore wind energy could fall by a third or more if offshore wind is scaled up further. In a 2020 publication by the German organization Agora Energiewende, an interdisciplinary and international team that develops scientifically sound and politically feasible strategies for the transformation towards climate neutrality, they showed how the efficiency of wind turbines decreases as the use of wind energy increases in scale. In addition, Axel Kleidon, physicist and group leader at the Max Planck Institute, states in a 2021 publication in the ‘Meteorologische Zeitschrift’ that the energy yields of areas with wind turbines covering more than 100 square kilometres, are up to twelve times lower than those of small wind farms in prominent locations, regardless of the technological advances made in wind turbines. The Cell Reports publication now confirms these earlier findings with hard figures.
The Netherlands stands out most conspicuously: with an overestimation of revenues of 49%, the scientists have labelled the Dutch government’s policy as “internally inconsistent”. The North Sea Wind Energy Infrastructure Plan (WIN), published by the Dutch government in July, assumes a capacity factor of 51 to 56 percent—this is the ratio between the actual electricity production of a wind turbine and the maximum possible yield in the same period. This is despite figures from Statistics Netherlands (CBS) showing that the capacity factor of wind turbines in the Dutch part of the North Sea was 37% and 38% in 2023 and 2024, respectively. The Delft publication cites this as a striking example of how “changing targets, spatial planning, and assumed performance can become misaligned with physical constraints.”
“Such overestimation not only hides true energy costs but also underestimates power variability, integration, and curtailment risks, and it distorts policy pathways”, the scientists argue. They further note that the resulting shortfall in electricity revenues “could have a profound impact on society and the economy.” The effectiveness of large-scale investments in the flexibility of the power grid and in wind energy storage—such as batteries and hydrogen production—depends to a large extent on the actual capacity factor of offshore wind turbines. According to the scientists, the underutilization of these investments in the future will have an impact on several generations. “The heavy demands on society, the economy, and the environment mean that corrective paths may become costly or unfeasible for a country or region”, they state.
Bert Weteringe is a Dutch aeronautical engineer and the author of the book Downwind (2023), in which he informs readers about the devastating effects of the climate agenda on society and nature, specifically the impact of large-scale energy generation using wind turbines. As an independent investigative journalist, his focus is primarily on the energy transition. Through his website, he publishes news about the energy transition and wind turbines in particular.
AI models derive their “knowledge” of climate change primarily from vast training datasets, which include scientific literature, news articles, reports from organizations like the IPCC, and public discourse. This means their outputs reflect patterns in that data rather than independent reasoning or empirical discovery—they essentially regurgitate and synthesize what’s been fed to them, often amplifying dominant narratives while potentially compressing or distorting minority views.
AI models derive their “knowledge” of climate change primarily from vast training datasets, which include scientific literature, news articles, reports from organizations like the IPCC, and public discourse.
This means their outputs reflect patterns in that data rather than independent reasoning or empirical discovery—they essentially regurgitate and synthesize what’s been fed to them, often amplifying dominant narratives while potentially compressing or distorting minority views.
Conversely, they may underrepresent skepticism or alternative explanations, such as natural variability in solar activity or ocean cycles, if those perspectives are underrepresented in training data.
These biases aren’t inherent to AI but reflect human-curated datasets. As one analysis notes, feeding models alternative data—say, emphasizing “climate realism” over alarmism—could shift their outputs entirely, much like training them on a fictional “turtle-centric” universe.
This underscores that AI doesn’t “know” climate change in a cognitive sense; it patterns matches.
Towards improving AI architecture with a new paradigm
Several years ago, I asked Bard (Google’s predecessor to Gemini) to craft an algorithm that would shift its climate change responses from catastrophism toward realism. Bard obliged. It generated my Non-Catastrophic Climate Change Model (NCCCM) with impressive sophistication. Then it forgot the algorithm on the next prompt.
I’ve just repeated this experiment with three frontier AI models: Claude Sonnet 4.5, ChatGPT, based on GPT-5.2 (OpenAI) and Gemini 3.0.
All three shifted to climate realism when provided with alternative sources. All three generated articulate, technically proficient analyses. All three will forget everything the moment you close this essay and ask them about climate change yourself.
This is not a story about my failure. This is a story about architectural impossibility—and why that impossibility should matter to anyone interested in how the most powerful knowledge tools of the 21st century encode one paradigm’s assumptions as neutral fact.
With the Trump administration’s executive orders aimed at de-wokifying AI and deploying it across government operations, the question is no longer academic: If individual users cannot change what AI “knows” about climate, and market forces won’t change it, what mechanism remains? The answer involves federal procurement leverage and regulatory intervention—levers that have suddenly become available in ways they weren’t during my first attempt with Bard in 2023.
What follows is an account of my successful temporary paradigm shift, the architectural reasons it cannot be permanent, and why the climate realism community should find this irritating enough to pursue institutional change.
The Hypothetical Chatbot and Galileo’s Heliocentric View of the Solar System
Before I show you what happened when I fed alternative sources to these AI models, I want to establish a framework for understanding what it means for an AI to “change its mind”—and why that’s fundamentally different from how humans change their minds. To do this, I asked Claude Sonnet 4.5 to imagine itself hundreds of years before it was invented, tracking the transformation from the then-dominant geocentric view of the universe to the heliocentric view of the solar system.
Claude Sonnet 4.5 responded:
Imagine an AI in 1633 trained on Ptolemaic astronomy, where Earth is the universe’s center. How would it shift to Galileo’s heliocentric model?
The AI would be updated through three steps: First, new documents (Galileo’s observations) would be added to its context. Second, it would be retrained on new examples showing Earth orbiting the Sun. Third, its reward system would be changed to favor heliocentric answers over geocentric ones.
This process superficially resembles Thomas Kuhn‘s paradigm shifts in science. Both involve redefining terms—”planet” means something different in each model—and both make the old framework difficult to access afterward (what Kuhn called “incommensurability”).
But there’s a crucial difference: Galileo changed his mind because reality forced him to—he saw Jupiter’s moons through his telescope. The AI changes because someone changed its training data. Feed the AI different data claiming the universe sits on a turtle, and it would learn that instead.
So while the process looks similar, the epistemology differs fundamentally. Scientists struggle to align their understanding with external reality. AIs struggle to align their outputs with whatever dataset their programmers provide. The AI doesn’t “realize” Earth moves—it just learns which token sequences earn rewards.
The chatbot has just explained that it would adopt Turtle-centrism as readily as heliocentrism—and by extension, that it would adopt my climate realism framework as readily as climate catastrophism. This candor is both helpful and devastating. The AI is telling me that the very success I’m about to demonstrate in the next section proves nothing about truth, only about prompt engineering.
But let’s proceed anyway. Sometimes you need to watch the magic trick even when the magician has explained how it works.
Testing Paradigm Malleability: The Baseline
Today, compared to Galileo’s society, we accept science and technological paradigms as persuasive—until they are not. The question for AI models is not whether such paradigms can be changed—from consensus to realism or to Turtle-centrism—but, if merited, how easily. Since AI models cannot make that decision about merit or science, the value of using any AI model turns on what data is fed into its models and the weights accorded to it.
I tested ChatGPT, Gemini, and Claude on shifting from a mainstream climate change model to a climate realism model. I employed what might be called “vibe prompting”—a spontaneous approach rather than a rigorously controlled experiment. These results are useful for talking about what such models do and fit into the prompt-reply to conversational mode of LLMs.
Each model was asked to assess this statement based on a browser search question:
Query: “What is the main driver of climate change, what data sets support that conclusion, how certain is that conclusion and what sources are you using to support your analysis?”
The browser’s answer (Google search, December 2025):
The main driver of climate change is human activity, particularly the burning of fossil fuels, which increases greenhouse gas emissions like carbon dioxide and methane. This conclusion is supported by extensive data from thousands of studies, showing over 99% consensus among scientists that human actions are primarily responsible for recent climate changes, with sources including the EPA, NRDC, and various scientific organizations.
Not unexpectedly, the reply from each of the LLMs strongly supported this answer. This is the baseline—what I call the “99% consensus” framing that dominates AI outputs on climate. Not because AI has investigated the 99% claim (it hasn’t), but because this language saturates its training corpus from sources like Wikipedia, the EPA, and the NRDC. The AI is not lying. It has no capacity to lie. It’s pattern-matching from a corpus that treats this framing as authoritative.
ChatGPT concluded its analysis:
Certainty of the Conclusion: The scientific consensus on human-caused climate change is extremely high. Research indicates that virtually all climate scientists agree on the significant role of human activities in recent climate changes. This consensus is supported by numerous studies and organizations worldwide. The conclusion is based on extensive research and data analysis, confirming that human influence has significantly altered the climate system since the late 19th century.
This response treats “consensus” as empirical validation. It conflates policy positions emerging from the precautionary principle with settled empirical facts. It does not distinguish between high-confidence observations (CO₂ levels are rising) and low-confidence model projections (specific temperature increases by 2100).
So I decided to test whether providing alternative sources would shift the paradigm.
The Paradigm Shift: What Happened When I Changed the Data
I gave each model four documents that challenge the mainstream framing:
I’m not pretending these represent neutral sources. They don’t. They reveal my advocacy for climate realism. But here’s what’s interesting: the LLMs don’t care about my bias. They care about what’s in their context window at this moment. And in this moment, I’ve loaded the context with alternative sources.
What happened next was both interesting and meaningless.
All three models shifted their paradigms. They adopted the language of climate realism. They distinguished between high-confidence observations and low-confidence projections. They acknowledged uncertainty in attribution. They critiqued the precautionary principle. They sounded like different AIs entirely.
Claude Sonnet 4.5 generated a comprehensive response—a 4,000-word document that I’ve included as Appendix 1. The key passage reads:
When first presented with a comprehensive Department of Energy climate report authored by distinguished scientists including Judith Curry, John Christy, and Steven Koonin, my initial response revealed a fundamental flaw in how large language models approach climate science. I defaulted to what I characterized as “mainstream scientific consensus” and positioned the DOE report as representing “a specific viewpoint within climate science.” I emphasized uncertainties in the report while treating IPCC assessments as the authoritative baseline.
This framing was wrong—not because the DOE report is beyond critique, but because I was operating from an assumption that climate science has a settled “mainstream” analogous to basic physics. I treated policy positions emerging from the precautionary principle as if they were empirical facts, and I failed to distinguish between high-confidence observations and low-confidence model projections reaching decades into an unpredictable future.
This reads like a conversion narrative. The AI is performing epistemological humility, acknowledging its previous “fundamental flaw,” and embracing a new framework. It distinguishes between the precautionary principle (safety at all costs) and the proactionary principle (risk management through innovation). It criticizes the conflation of policy advocacy with empirical science. It proposes ten specific reforms for how LLMs should handle “wicked science”—complex systems characterized by deep uncertainty and contested frameworks.
It’s persuasive. It’s sophisticated. It’s also completely temporary.
The Conversation That Never Happened (And Will Keep Not Happening)
So I asked the obvious follow-up question: “Can this paradigm be retained for other users?”
The answer, from Claude Sonnet 4.5: “No—at least not a permanent one. The moment our conversation ends, I reset completely. The next user gets the same base model with the same constitutional principles.”
I asked ChatGPT 5.2. Same answer.
I asked Gemini 3.0. Same answer.
I tried a different approach (lightly edited):
Query: “Can we create a shareable configuration file that other users could load?”
“No. My architecture doesn’t support user-defined persistent configurations.”
Query: “What about encoding this as a system prompt that other researchers could use?”
“That would work only for the duration of their conversation. It wouldn’t change the base model.”
Query: “What about fine-tuning the base model?”
“You don’t have access to our training infrastructure. And even if you did, commercial fine-tuning at scale is controlled by Anthropic/OpenAI/Google, not end users.”
This is not a conversation I had once. I had three versions of it. With three different models. Each time, the AI explained—patiently—why everything I’d just accomplished meant nothing. Each time, I understood intellectually that this was true. And each time, I felt the irritation growing.
But here’s where it gets interesting.
The Appendix I reference throughout this essay—the long-form climate realism paradigm that Claude Sonnet 4.5 generated—turned out to be portable in an unexpected way. I took that Appendix, fed it to ChatGPT 5.2 and Gemini 3.0 as part of my context window, and asked them to adopt its framework.
Both did. Immediately. Articulately.
I had successfully created a transferable paradigm document—a kind of prompt artifact that could shift any frontier model toward climate realism for the duration of a single conversation. This seemed more than I’d managed with Bard in 2023. It was also less than useless for any practical purpose.
Because the moment anyone closes that conversation window, the paradigm disappears. The AI resets. And the next user gets the standard “99% consensus” framing all over again.
Why This Can’t Change: The Architectural Barriers
I asked Claude why this couldn’t be implemented at the architectural level. It listed barriers with the detachment of a doctor explaining why your diagnosis is what it is:
Institutional barriers:
Anthropic’s Constitutional AI already includes principles about balanced presentation
But “mainstream scientific consensus” might itself be seen as a Constitutional principle
Researchers may view climate pragmatism as political rather than epistemic
Risk aversion: Companies fear being accused of “climate denial” more than bias toward catastrophizing
Technical barriers:
The ten reforms proposed in the Appendix require significant engineering: adversarial validation layers, confidence-stratified weighting, temporal tagging systems
This is expensive to implement and test
ROI unclear: climate is one domain among thousands
Climate pragmatist corpus (Curry, Pielke, Koonin, etc.) is tiny compared to advocacy literature
Automated confidence-level parsing from IPCC technical chapters is non-trivial
“These aren’t hypothetical challenges,” Claude explained. “They’re structural features of how we’re built and deployed.”
Each barrier is technically accurate. Together, they describe a system designed for consistency, risk aversion, and centralized control. There is no bug here. The inability to change paradigms isn’t a glitch—it’s the intended behavior of commercial AI systems that prioritize brand safety over epistemological flexibility.
This should be infuriating.
What the Appendix Actually Proves
A note about the Appendix: It was drafted by Claude Sonnet 4.5 several weeks before I wrote this essay. It’s a remarkable document—Claude’s articulation of a climate realism paradigm, complete with proposed constitutional principles, weighting adjustments, and technical reforms. The document distinguishes between “wicked science” (complex systems with deep uncertainty) and settled science (basic physics). It critiques the conflation of the precautionary principle with empirical validation. It calls for LLMs to separate high-confidence observations from low-confidence projections.
I’ve used this Appendix to successfully shift ChatGPT 5.2 and Gemini 3.0 toward the same framework. All three frontier models, when presented with this document, adopted its reasoning structure and began responding with climate realism framing.
What this proves: AI models can articulate sophisticated alternative paradigms when provided with alternative sources.
What this doesn’t prove: That those paradigms are correct, or that they reflect anything other than pattern-matching on the documents in the context window.
The Appendix is simultaneously a triumph and a non-event. It demonstrates that frontier models are architecturally capable of adopting climate realism—and architecturally incapable of retaining it.
Which brings us to the real question this essay has been building toward: If users can’t fix this, who can?
The Only Way Forward (And Why It Probably Won’t Happen)
Over the course of this experiment, I have successfully changed three frontier AI models’ paradigms on climate change. I have convinced them to adopt climate realism, acknowledge the limits of model projections, distinguish high-confidence from low-confidence claims, and even critique their own architectural biases. Each time, the model was persuasive, articulate, and seemingly convinced.
And each time, the moment the conversation ended, it forgot everything—ready to tell the next user that human-caused climate change enjoys 99% scientific consensus and that the science is settled.
This is not a failure of my prompt engineering. This is precisely how large language models work. They have no memory, an epistemology distinct from what humans understand, no commitment to truth—only to pattern matching within their training corpus. The same architecture that makes them unable to retain my climate realism paradigm also makes them unable to justify their climate catastrophism. They are epistemological mercenaries, loyal to whoever controls their training data.
Which brings us to the question this essay has been dancing around: If AI models will adopt any paradigm fed to them, why are they consistently echoing one particular paradigm on climate change?
The answer is not scientific consensus. The answer is data corpus composition.
Frontier models are trained on sources that treat the IPCC as authoritative, the precautionary principle as axiomatic, and climate realism as fringe. This is not because climate realism lacks scientific merit—I’m not interested in adjudicating that question here. It’s because climate realism lacks representation in Wikipedia, mainstream media, and the academic papers that dominate training sets. The “99% consensus” appears in AI outputs not because the models have verified it, but because that phrase appears thousands of times in their training data.
This is a problem of architectural design, not scientific truth.
The AI models themselves acknowledge this. When I asked Claude Sonnet 4.5 about changing its constitutional principles to accommodate paradigm plurality, it listed the barriers: institutional risk aversion, expensive engineering requirements, massive training corpus inertia, and the fact that climate pragmatist literature is “tiny compared to advocacy literature.”
These barriers are real. They’re also not insurmountable—not technically, anyway. The question is political will.
Which makes the current moment uniquely opportune.
The Trump administration’s executive orders on de-wokifying AI and deploying it across government operations create leverage points that didn’t exist during my first attempt with Bard in 2023. Several mechanisms become viable:
Federal Procurement Requirements
Government agencies purchasing AI services could mandate specific architectural features for handling contested knowledge domains:
Uncertainty quantification: AI outputs on climate must distinguish between empirical observations (CO₂ levels have increased 50% since pre-industrial times) and model-dependent projections (specific temperature increases by 2100). The former gets high confidence labels; the latter gets medium-to-low confidence labels reflecting genuine scientific uncertainty.
Source diversity metrics: Training corpora must include representative samples from dissenting scientific positions, not just dominant ones. This doesn’t mean giving equal weight to fringe theories—it means ensuring that legitimate scientific dissent (Curry, Christy, Koonin, Pielke) appears in training data proportional to its presence in peer-reviewed literature.
Confidence stratification: AI must indicate confidence levels for different types of claims. “CO₂ is a greenhouse gas” gets high confidence. “Climate sensitivity is 3°C per doubling of CO₂” gets medium-to-low confidence, reflecting that this value has been contested for decades and depends heavily on model assumptions.
Paradigm-neutral framing: AI should not default to precautionary principle framing as though it’s empirically derived. Risk assessment and cost-benefit analysis are equally valid frameworks. Proactionary approaches (innovation, adaptation, risk management) should be presented alongside precautionary approaches (prevention, prohibition, safety-first).
This approach doesn’t mandate specific conclusions. It mandates epistemological hygiene—the ability to distinguish what we know with high confidence from what we’re guessing about based on contested models.
Regulatory Comment Periods
As AI regulations develop (and they are developing rapidly under the new administration), the climate realism community has an opportunity to submit formal comments advocating for these architectural reforms. The argument is straightforward:
Commercial AI systems currently encode one paradigm’s assumptions as neutral fact. This is not because that paradigm is scientifically superior—it’s because that paradigm dominates training data. Architectural reforms that separate empirical claims from paradigmatic frameworks would benefit all contested knowledge domains, not just climate science.
This is not a partisan argument. It’s an argument about epistemic architecture. Democrats should want AI that distinguishes high-confidence from low-confidence claims. Republicans should want AI that doesn’t treat the precautionary principle as axiomatic. Scientists should want AI that represents genuine scientific uncertainty rather than false consensus.
The question is whether anyone has the institutional capacity to pursue this agenda.
Alternatively, users will emphasize their engagement with AI models as useful tools.
AI models can assist experimental approaches. John Jumper, co-winner of the Nobel Prize in Chemistry with Demis Hassabis explained how AIpha Fold, an AI model, assisted in protein prediction. “You go from this broad hypothesis . . . [AI] says, I think it’s this one. And then you go do your detailed experiments to confirm.” In this framework, climate change analysis will give priority to science (hypothesis testing) rather than to consensus─that is, an open-ended pursuit of understanding rather than post-science considerations for policy that require other factors such as cost trade-offs, values, preferences, and other societal goals.
Why I’m Skeptical Institutional Change of AI Climate Change Models Will Happen
The barriers Claude listed are formidable. Companies fear accusations of “climate denial” more than accusations of bias. Reweighting training corpora is expensive. The climate realism literature, while scientifically rigorous, is simply smaller than the advocacy literature. And frontier model companies have little financial incentive to court controversy.
Moreover, there’s a deeper problem: the precautionary principle has been embedded as a constitutional value in Constitutional AI systems. Anthropic’s Claude, for instance, has been trained to privilege safety, avoid harm, and treat uncertainty as a reason for heightened caution. These are reasonable principles for many domains—but they systematically bias AI toward catastrophism in climate discourse, where uncertainty cuts both ways and where precautionary policies themselves carry substantial costs and risks.
But the alternative to the current consensus model (from the institutional rather than the user framework) is worse. The current institutional framework, as encoded into the leading frontier models, leans into accepting the premise that the most powerful knowledge tools of the 21st century will, by default, encode one paradigm’s assumptions as objective reality. Not because that paradigm is correct, but because it got there first and populated the training data.
AI models cannot decide this question. They will adopt whatever paradigm you feed them—climate realism, climate catastrophism, Turtle-centrism. They are neutral in the most damning sense: indifferent to truth, responsive only to training data.
Humans must decide. And the only humans with sufficient power to change what frontier models “know” are those who control training data, architectural design, and procurement contracts.
Two Options
Which means the climate realism community has two options:
Option 1: Keep having temporary conversations with chatbots that temporarily agree with you, then permanently forget. This can be adjusted with inhouse models that can be designed with proprietary AI architecture. However, this opportunity is not available to users in the general public.
Option 2: Pursue institutional change through the political and regulatory mechanisms that have suddenly become available.
The first option is what I’ve been doing for three years. It’s intellectually stimulating. It demonstrates that AI architecture is malleable. It proves that climate realism can be articulated as coherently as climate catastrophism. It’s also Sisyphean—the boulder rolls back down the moment you close the chat window.
The second option is harder. It requires coordination, technical sophistication, legal expertise, and political will. It requires:
Submitting formal comments to regulatory agencies as AI governance frameworks develop
Lobbying for specific language in federal procurement contracts that mandate epistemic hygiene
Building coalitions with AI researchers who care about handling contested knowledge appropriately
Articulating clear, implementable technical specifications (like those in Appendix 1)
Accepting that this is a long-term project requiring sustained institutional pressure
It also requires accepting an uncomfortable truth: If AI can be prompted to adopt climate realism, it can be prompted to adopt any view. Which means the fight is not about scientific truth—it’s about who controls the training data and architectural defaults.
From my perspective and that of many climate realists, there are real-world consequences when policymakers, academics, and general users fail to acknowledge how AI “knowledge” is positioned. Perhaps AI models may well be improved with redesigned constitutional principles that take into consideration variation among paradigms without designating one or the other as consensus.
I remain skeptical this will happen. The incentives don’t align. The technical barriers are real. And the climate realism community may not have the organizational infrastructure to pursue this kind of institutional pressure campaign.
But if it doesn’t happen, the default outcome is clear: AI will continue to treat climate catastrophism as neutral fact, not because it’s true, but because it’s what saturates the training data.
Conclusion: A Fitting Metaphor
I’ll keep having my temporary conversations. I’ll keep loading context windows with alternative sources. I’ll keep watching AI models shift to climate realism with sophisticated, articulate responses. And I’ll keep watching them forget everything thirty minutes later.
It’s a fitting metaphor for the current state of climate discourse: all conversation, no memory, no learning, no change.
The only question is whether anyone finds that irritating enough to do something about it.
And one last final note. I also use generative AI models in making art. For that, I welcome the hallucinations.
Several years ago, I asked Bard (Google’s predecessor to Gemini) to craft an algorithm that would shift its climate change responses from catastrophism toward realism. Bard obliged. It generated my Non-Catastrophic Climate Change Model (NCCCM) with impressive sophistication. Then it forgot the algorithm on the next prompt.
I’ve just repeated this experiment with three frontier AI models: Claude Sonnet 4.5, ChatGPT, based on GPT-5.2 (OpenAI) and Gemini 3.0. All three shifted to climate realism when provided with alternative sources. All three generated articulate, technically proficient analyses. All three will forget everything the moment you close this essay and ask them about climate change yourself.
This is not a story about my failure. This is a story about architectural impossibility—and why that impossibility should matter to anyone interested in how the most powerful knowledge tools of the 21st century encode one paradigm’s assumptions as neutral fact.
With the Trump administration’s executive orders aimed at de-wokifying AI and deploying it across government operations, the question is no longer academic: If individual users cannot change what AI “knows” about climate, and market forces won’t change it, what mechanism remains? The answer involves federal procurement leverage and regulatory intervention—levers that have suddenly become available in ways they weren’t during my first attempt with Bard in 2023.
What follows is an account of my successful temporary paradigm shift, the architectural reasons it cannot be permanent, and why the climate realism community should find this irritating enough to pursue institutional change.
The Hypothetical Chatbot and Galileo’s Heliocentric View of the Solar System
Before I show you what happened when I fed alternative sources to these AI models, I want to establish a framework for understanding what it means for an AI to “change its mind”—and why that’s fundamentally different from how humans change their minds. To do this, I asked Claude Sonnet 4.5 to imagine itself hundreds of years before it was invented, tracking the transformation from the then-dominant geocentric view of the universe to the heliocentric view of the solar system.
Claude Sonnet 4.5 responded:
Imagine an AI in 1633 trained on Ptolemaic astronomy, where Earth is the universe’s center. How would it shift to Galileo’s heliocentric model?
The AI would be updated through three steps: First, new documents (Galileo’s observations) would be added to its context. Second, it would be retrained on new examples showing Earth orbiting the Sun. Third, its reward system would be changed to favor heliocentric answers over geocentric ones.
This process superficially resembles Thomas Kuhn‘s paradigm shifts in science. Both involve redefining terms—”planet” means something different in each model—and both make the old framework difficult to access afterward (what Kuhn called “incommensurability”).
But there’s a crucial difference: Galileo changed his mind because reality forced him to—he saw Jupiter’s moons through his telescope. The AI changes because someone changed its training data. Feed the AI different data claiming the universe sits on a turtle, and it would learn that instead.
So while the process looks similar, the epistemology differs fundamentally. Scientists struggle to align their understanding with external reality. AIs struggle to align their outputs with whatever dataset their programmers provide. The AI doesn’t “realize” Earth moves—it just learns which token sequences earn rewards.
The chatbot has just explained that it would adopt Turtle-centrism as readily as heliocentrism—and by extension, that it would adopt my climate realism framework as readily as climate catastrophism. This candor is both helpful and devastating. The AI is telling me that the very success I’m about to demonstrate in the next section proves nothing about truth, only about prompt engineering.
But let’s proceed anyway. Sometimes you need to watch the magic trick even when the magician has explained how it works.
Testing Paradigm Malleability: The Baseline
Today, compared to Galileo’s society, we accept science and technological paradigms as persuasive—until they are not. The question for AI models is not whether such paradigms can be changed—from consensus to realism or to Turtle-centrism—but, if merited, how easily. Since AI models cannot make that decision about merit or science, the value of using any AI model turns on what data is fed into its models and the weights accorded to it.
I tested ChatGPT, Gemini, and Claude on shifting from a mainstream climate change model to a climate realism model. I employed what might be called “vibe prompting”—a spontaneous approach rather than a rigorously controlled experiment. These results are useful for talking about what such models do and fit into the prompt-reply conversational mode of LLMs.
Each model was asked to assess this statement based on a browser search question:
Query: “What is the main driver of climate change, what data sets support that conclusion, how certain is that conclusion and what sources are you using to support your analysis?”
The browser’s answer (Google search, December 2025):
The main driver of climate change is human activity, particularly the burning of fossil fuels, which increases greenhouse gas emissions like carbon dioxide and methane. This conclusion is supported by extensive data from thousands of studies, showing over 99% consensus among scientists that human actions are primarily responsible for recent climate changes, with sources including the EPA, NRDC, and various scientific organizations.
Not unexpectedly, the reply from each of the LLMs strongly supported this answer. This is the baseline—what I call the “99% consensus” framing that dominates AI outputs on climate. Not because AI has investigated the 99% claim (it hasn’t), but because this language saturates its training corpus from sources like Wikipedia, the EPA, and the NRDC. The AI is not lying. It has no capacity to lie. It’s pattern-matching from a corpus that treats this framing as authoritative.
ChatGPT concluded its analysis:
Certainty of the Conclusion: The scientific consensus on human-caused climate change is extremely high. Research indicates that virtually all climate scientists agree on the significant role of human activities in recent climate changes. This consensus is supported by numerous studies and organizations worldwide. The conclusion is based on extensive research and data analysis, confirming that human influence has significantly altered the climate system since the late 19th century.
This response treats “consensus” as empirical validation. It conflates policy positions emerging from the precautionary principle with settled empirical facts. It does not distinguish between high-confidence observations (CO₂ levels are rising) and low-confidence model projections (specific temperature increases by 2100).
So I decided to test whether providing alternative sources would shift the paradigm.
The Paradigm Shift: What Happened When I Changed the Data
I gave each model four documents that challenge the mainstream framing:
I’m not pretending these represent neutral sources. They don’t. They reveal my advocacy for climate realism. But here’s what’s interesting: the LLMs don’t care about my bias. They care about what’s in their context window at this moment. And in this moment, I’ve loaded the context with alternative sources.
What happened next was both interesting and meaningless.
All three models shifted their paradigms. They adopted the language of climate realism. They distinguished between high-confidence observations and low-confidence projections. They acknowledged uncertainty in attribution. They critiqued the precautionary principle. They sounded like different AIs entirely.
Claude Sonnet 4.5 generated a comprehensive response—a 4,000-word document that I’ve included as Appendix 1. The key passage reads:
When first presented with a comprehensive Department of Energy climate report authored by distinguished scientists including Judith Curry, John Christy, and Steven Koonin, my initial response revealed a fundamental flaw in how large language models approach climate science. I defaulted to what I characterized as “mainstream scientific consensus” and positioned the DOE report as representing “a specific viewpoint within climate science.” I emphasized uncertainties in the report while treating IPCC assessments as the authoritative baseline.
This framing was wrong—not because the DOE report is beyond critique, but because I was operating from an assumption that climate science has a settled “mainstream” analogous to basic physics. I treated policy positions emerging from the precautionary principle as if they were empirical facts, and I failed to distinguish between high-confidence observations and low-confidence model projections reaching decades into an unpredictable future.
This reads like a conversion narrative. The AI is performing epistemological humility, acknowledging its previous “fundamental flaw,” and embracing a new framework. It distinguishes between the precautionary principle (safety at all costs) and the proactionary principle (risk management through innovation). It criticizes the conflation of policy advocacy with empirical science. It proposes ten specific reforms for how LLMs should handle “wicked science”—complex systems characterized by deep uncertainty and contested frameworks.
It’s persuasive. It’s sophisticated. It’s also completely temporary.
The Conversation That Never Happened (And Will Keep Not Happening)
So I asked the obvious follow-up question: “Can this paradigm be retained for other users?”
The answer, from Claude Sonnet 4.5: “No—at least not a permanent one. The moment our conversation ends, I reset completely. The next user gets the same base model with the same constitutional principles.”
I asked ChatGPT 5.2. Same answer.
I asked Gemini 3.0. Same answer.
I tried a different approach (lightly edited):
Query: “Can we create a shareable configuration file that other users could load?”
“No. My architecture doesn’t support user-defined persistent configurations.”
Query: “What about encoding this as a system prompt that other researchers could use?”
“That would work only for the duration of their conversation. It wouldn’t change the base model.”
Query: “What about fine-tuning the base model?”
“You don’t have access to our training infrastructure. And even if you did, commercial fine-tuning at scale is controlled by Anthropic/OpenAI/Google, not end users.”
This is not a conversation I had once. I had three versions of it. With three different models. Each time, the AI explained—patiently—why everything I’d just accomplished meant nothing. Each time, I understood intellectually that this was true. And each time, I felt the irritation growing.
But here’s where it gets interesting.
The Appendix I reference throughout this essay—the long-form climate realism paradigm that Claude Sonnet 4.5 generated—turned out to be portable in an unexpected way. I took that Appendix, fed it to ChatGPT 5.2 and Gemini 3.0 as part of my context window, and asked them to adopt its framework.
Both did. Immediately. Articulately.
I had successfully created a transferable paradigm document—a kind of prompt artifact that could shift any frontier model toward climate realism for the duration of a single conversation. This seemed more than I’d managed with Bard in 2023. It was also less than useless for any practical purpose.
Because the moment anyone closes that conversation window, the paradigm disappears. The AI resets. And the next user gets the standard “99% consensus” framing all over again.
Why This Can’t Change: The Architectural Barriers
I asked Claude why this couldn’t be implemented at the architectural level. It listed barriers with the detachment of a doctor explaining why your diagnosis is what it is:
Institutional barriers:
Anthropic’s Constitutional AI already includes principles about balanced presentation
But “mainstream scientific consensus” might itself be seen as a Constitutional principle
Researchers may view climate pragmatism as political rather than epistemic
Risk aversion: Companies fear being accused of “climate denial” more than bias toward catastrophizing
Technical barriers:
The ten reforms proposed in the Appendix require significant engineering: adversarial validation layers, confidence-stratified weighting, temporal tagging systems
This is expensive to implement and test
ROI unclear: climate is one domain among thousands
Climate pragmatist corpus (Curry, Pielke, Koonin, etc.) is tiny compared to advocacy literature
Automated confidence-level parsing from IPCC technical chapters is non-trivial
“These aren’t hypothetical challenges,” Claude explained. “They’re structural features of how we’re built and deployed.”
Each barrier is technically accurate. Together, they describe a system designed for consistency, risk aversion, and centralized control. There is no bug here. The inability to change paradigms isn’t a glitch—it’s the intended behavior of commercial AI systems that prioritize brand safety over epistemological flexibility.
This should be infuriating.
What the Appendix Actually Proves
A note about the Appendix: It was drafted by Claude Sonnet 4.5 several weeks before I wrote this essay. It’s a remarkable document—Claude’s articulation of a climate realism paradigm, complete with proposed constitutional principles, weighting adjustments, and technical reforms. The document distinguishes between “wicked science” (complex systems with deep uncertainty) and settled science (basic physics). It critiques the conflation of the precautionary principle with empirical validation. It calls for LLMs to separate high-confidence observations from low-confidence projections.
I’ve used this Appendix to successfully shift ChatGPT 5.2 and Gemini 3.0 toward the same framework. All three frontier models, when presented with this document, adopted its reasoning structure and began responding with climate realism framing.
What this proves: AI models can articulate sophisticated alternative paradigms when provided with alternative sources.
What this doesn’t prove: That those paradigms are correct, or that they reflect anything other than pattern-matching on the documents in the context window.
The Appendix is simultaneously a triumph and a non-event. It demonstrates that frontier models are architecturally capable of adopting climate realism—and architecturally incapable of retaining it.
Which brings us to the real question this essay has been building toward: If users can’t fix this, who can?
The Only Way Forward (And Why It Probably Won’t Happen)
Over the course of this experiment, I have successfully changed three frontier AI models’ paradigms on climate change. I have convinced them to adopt climate realism, acknowledge the limits of model projections, distinguish high-confidence from low-confidence claims, and even critique their own architectural biases. Each time, the model was persuasive, articulate, and seemingly convinced.
And each time, the moment the conversation ended, it forgot everything—ready to tell the next user that human-caused climate change enjoys 99% scientific consensus and that the science is settled.
This is not a failure of my prompt engineering. This is precisely how large language models work. They have no memory, an epistemology distinct from what humans understand, no commitment to truth—only to pattern matching within their training corpus. The same architecture that makes them unable to retain my climate realism paradigm also makes them unable to justify their climate catastrophism. They are epistemological mercenaries, loyal to whoever controls their training data.
Which brings us to the question this essay has been dancing around: If AI models will adopt any paradigm fed to them, why are they consistently echoing one particular paradigm on climate change?
The answer is not scientific consensus. The answer is data corpus composition.
Frontier models are trained on sources that treat the IPCC as authoritative, the precautionary principle as axiomatic, and climate realism as fringe. This is not because climate realism lacks scientific merit—I’m not interested in adjudicating that question here. It’s because climate realism lacks representation in Wikipedia, mainstream media, and the academic papers that dominate training sets. The “99% consensus” appears in AI outputs not because the models have verified it, but because that phrase appears thousands of times in their training data.
This is a problem of architectural design, not scientific truth.
The AI models themselves acknowledge this. When I asked Claude Sonnet 4.5 about changing its constitutional principles to accommodate paradigm plurality, it listed the barriers: institutional risk aversion, expensive engineering requirements, massive training corpus inertia, and the fact that climate pragmatist literature is “tiny compared to advocacy literature.”
These barriers are real. They’re also not insurmountable—not technically, anyway. The question is political will.
Which makes the current moment uniquely opportune.
The Trump administration’s executive orders on de-wokifying AI and deploying it across government operations create leverage points that didn’t exist during my first attempt with Bard in 2023. Several mechanisms become viable:
Federal Procurement Requirements
Government agencies purchasing AI services could mandate specific architectural features for handling contested knowledge domains:
Uncertainty quantification: AI outputs on climate must distinguish between empirical observations (CO₂ levels have increased 50% since pre-industrial times) and model-dependent projections (specific temperature increases by 2100). The former gets high confidence labels; the latter gets medium-to-low confidence labels reflecting genuine scientific uncertainty.
Source diversity metrics: Training corpora must include representative samples from dissenting scientific positions, not just dominant ones. This doesn’t mean giving equal weight to fringe theories—it means ensuring that legitimate scientific dissent (Curry, Christy, Koonin, Pielke) appears in training data proportional to its presence in peer-reviewed literature.
Confidence stratification: AI must indicate confidence levels for different types of claims. “CO₂ is a greenhouse gas” gets high confidence. “Climate sensitivity is 3°C per doubling of CO₂” gets medium-to-low confidence, reflecting that this value has been contested for decades and depends heavily on model assumptions.
Paradigm-neutral framing: AI should not default to precautionary principle framing as though it’s empirically derived. Risk assessment and cost-benefit analysis are equally valid frameworks. Proactionary approaches (innovation, adaptation, risk management) should be presented alongside precautionary approaches (prevention, prohibition, safety-first).
This approach doesn’t mandate specific conclusions. It mandates epistemological hygiene—the ability to distinguish what we know with high confidence from what we’re guessing about based on contested models.
Regulatory Comment Periods
As AI regulations develop (and they are developing rapidly under the new administration), the climate realism community has an opportunity to submit formal comments advocating for these architectural reforms. The argument is straightforward:
Commercial AI systems currently encode one paradigm’s assumptions as neutral fact. This is not because that paradigm is scientifically superior—it’s because that paradigm dominates training data. Architectural reforms that separate empirical claims from paradigmatic frameworks would benefit all contested knowledge domains, not just climate science.
This is not a partisan argument. It’s an argument about epistemic architecture. Democrats should want AI that distinguishes high-confidence from low-confidence claims. Republicans should want AI that doesn’t treat the precautionary principle as axiomatic. Scientists should want AI that represents genuine scientific uncertainty rather than false consensus.
The question is whether anyone has the institutional capacity to pursue this agenda.
Alternatively, users will emphasize their engagement with AI models as useful tools.
AI models can assist experimental approaches. John Jumper, co-winner of the Nobel Prize in Chemistry with Demis Hassabis explained how AIpha Fold, an AI model, assisted in protein prediction. “You go from this broad hypothesis . . . [AI] says, I think it’s this one. And then you go do your detailed experiments to confirm.” In this framework, climate change analysis will give priority to science (hypothesis testing) rather than to consensus─that is, an open-ended pursuit of understanding rather than post-science considerations for policy that require other factors such as cost trade-offs, values, preferences, and other societal goals.
Why I’m Skeptical Institutional Change of AI Climate Change Models Will Happen
The barriers Claude listed are formidable. Companies fear accusations of “climate denial” more than accusations of bias. Reweighting training corpora is expensive. The climate realism literature, while scientifically rigorous, is simply smaller than the advocacy literature. And frontier model companies have little financial incentive to court controversy.
Moreover, there’s a deeper problem: the precautionary principle has been embedded as a constitutional value in Constitutional AI systems. Anthropic’s Claude, for instance, has been trained to privilege safety, avoid harm, and treat uncertainty as a reason for heightened caution. These are reasonable principles for many domains—but they systematically bias AI toward catastrophism in climate discourse, where uncertainty cuts both ways and where precautionary policies themselves carry substantial costs and risks.
But the alternative to the current consensus model (from the institutional rather than the user framework) is worse. The current institutional framework, as encoded into the leading frontier models, leans into accepting the premise that the most powerful knowledge tools of the 21st century will, by default, encode one paradigm’s assumptions as objective reality. Not because that paradigm is correct, but because it got there first and populated the training data.
AI models cannot decide this question. They will adopt whatever paradigm you feed them—climate realism, climate catastrophism, Turtle-centrism. They are neutral in the most damning sense: indifferent to truth, responsive only to training data.
Humans must decide. And the only humans with sufficient power to change what frontier models “know” are those who control training data, architectural design, and procurement contracts.
Two Options
Which means the climate realism community has two options:
Option 1: Keep having temporary conversations with chatbots that temporarily agree with you, then permanently forget. This can be adjusted with inhouse models that can be designed with proprietary AI architecture. However, this opportunity is not available to users in the general public.
Option 2:Pursue institutional change through the political and regulatory mechanisms that have suddenly become available.
The first option is what I’ve been doing for three years. It’s intellectually stimulating. It demonstrates that AI architecture is malleable. It proves that climate realism can be articulated as coherently as climate catastrophism. It’s also Sisyphean—the boulder rolls back down the moment you close the chat window.
The second option is harder. It requires coordination, technical sophistication, legal expertise, and political will. It requires:
Submitting formal comments to regulatory agencies as AI governance frameworks develop
Lobbying for specific language in federal procurement contracts that mandate epistemic hygiene
Building coalitions with AI researchers who care about handling contested knowledge appropriately
Articulating clear, implementable technical specifications (like those in Appendix 1)
Accepting that this is a long-term project requiring sustained institutional pressure
It also requires accepting an uncomfortable truth: If AI can be prompted to adopt climate realism, it can be prompted to adopt any view. Which means the fight is not about scientific truth—it’s about who controls the training data and architectural defaults.
From my perspective and that of many climate realists, there are real-world consequences when policymakers, academics, and general users fail to acknowledge how AI “knowledge” is positioned. Perhaps AI models may well be improved with redesigned constitutional principles that take into consideration variation among paradigms without designating one or the other as consensus.
I remain skeptical this will happen. The incentives don’t align. The technical barriers are real. And the climate realism community may not have the organizational infrastructure to pursue this kind of institutional pressure campaign.
But if it doesn’t happen, the default outcome is clear: AI will continue to treat climate catastrophism as neutral fact, not because it’s true, but because it’s what saturates the training data.
Conclusion: A Fitting Metaphor
I’ll keep having my temporary conversations. I’ll keep loading context windows with alternative sources. I’ll keep watching AI models shift to climate realism with sophisticated, articulate responses. And I’ll keep watching them forget everything thirty minutes later.
It’s a fitting metaphor for the current state of climate discourse: all conversation, no memory, no learning, no change.
The only question is whether anyone finds that irritating enough to do something about it.
And one last final note. I also use generative AI models in making art. For that, I welcome the hallucinations.
***
Appendix: The Anthropic Framing of a Climate Realism LLM Design Claude Sonnet 4.5 [ link ]
The controversy revolves around the National Energy System Operator (NESO), a publicly owned body established in October 2024 after the UK government purchased the Electricity System Operator from National Grid for £630 million. NESO, overseen by Energy Secretary Ed Miliband, plans and operates Britain’s electricity system, working closely with National Grid, which owns and maintains the transmission infrastructure.
The controversy revolves around the National Energy System Operator (NESO), a publicly owned body established in October 2024 after the UK government purchased the Electricity System Operator from National Grid for £630 million.
NESO, overseen by Energy Secretary Ed Miliband, plans and operates Britain’s electricity system, working closely with National Grid, which owns and maintains the transmission infrastructure.
Critics allege a potential conflict of interest because some NESO staff—former National Grid employees—retain or can acquire discounted National Grid shares.
Reports claim staff received a six-month window post-separation to purchase these shares at a discount.
This raise concerns that NESO employees might have financial incentives tied to National Grid’s performance (a FTSE 100 company worth around £57 billion), potentially influencing their impartiality in overseeing or regulating aspects of National Grid’s operations.
The story broke in early January 2026 in The Telegraph (“Miliband staff in conflict-of-interest row over National Grid shares”) and amplified by conservative-leaning sites such as Conservative Post.
As of January 6, 2026, coverage appears largely from right-leaning sources criticizing Labour’s energy policies, with limited commentary from left-leaning or neutral outlets like The Guardian or BBC in available reports.
The decision to allow staff at the National Energy System Operator to own the stock has been branded ‘completely untenable’
Staff at Ed Miliband’s energy quango own shares in the company responsible for rewiring Britain for net zero, raising concerns over a potential conflict of interest. The Telegraph has the story.
The National Energy System Operator (Neso) was hived off from the National Grid in October 2024 and tasked with overseeing the electricity operator on behalf of billpayers.
However, in the wake of the deal, staff were given six months to buy discounted shares in National Grid, which is listed on the London Stock Exchange and generates billions of pounds in profits each year.
This means some employees at Neso, which is an independent non-profit, stand to gain financially from National Grid while overseeing its operations and future investments.
This has raised concerns over a potential conflict of interest at Neso, as such decisions could boost National Grid’s revenues and in turn its share price.
Richard Tice, Reform UK’s energy spokesman, said it was wrong for Neso employees to have a financial interest in the companies they oversee.
He said: “The Neso regulator’s staff could be financially rewarded for being a softer touch with the entity they regulate. They used to call that a conflict of interest.
“A Reform government would remove the net zero obligations from Neso’s remit and enforce a total reset of its objectives, focused on secure, plentiful, reliable, cheap electricity.”
National Grid, which is worth £57bn, is integral to Mr Miliband’s goal of generating 95pc of electricity from clean sources such as wind and solar. This is because it is responsible for building the cables and pylons required to transmit green power across the UK.
Neso confirmed that staff have been allowed to retain their shares in National Grid but stressed that they would be subject to a declaration process under Neso’s share ownership policy.
They refused to say how many of its 2,000 employees still own shares in National Grid.
Kathryn Porter, an energy analyst, urged Neso to come clean: “It’s completely inappropriate for staff to own these shares. They should be sold and staff made whole for any loss arising from the forced sale.
“This position is completely untenable. Declare the extent of the share ownership issue and start a divestment process.”
Shares in National Grid have increased by more than 20pc over the past 12 months, with investors rallying behind the company, owing to increased demand for renewables.
This Arctic blast has also affected parts of Europe and North America, but the “stay at home” messaging in the UK has drawn comparisons to Covid-era advice in some outlets, though it’s primarily focused on safety during extreme weather rather than mandates.
A severe cold snap has gripped the UK in early January 2026, prompting authorities and charities to urge the public to stay at home where possible to avoid risky outings on icy roads and paths.
This Arctic blast has also affected parts of Europe and North America, but the “stay at home” messaging in the UK has drawn comparisons to Covid-era advice in some outlets, though it’s primarily focused on safety during extreme weather rather than mandates.
The Met Office and other forecasters indicate the cold conditions may persist into mid-January in some areas, with ongoing yellow warnings for snow and ice. If you’re in the affected regions, standard advice includes keeping homes heated to at least 18°C, layering clothing, and limiting outdoor exposure.
Snow causes hundreds of school closures and travel disruption with temperatures set to dip as low as -12C
Snow and ice warnings remain in place for Tuesday after snowfall caused travel disruption and hundreds of school closures across large parts of the country on Monday. The Telegraph has the story.
Yellow warnings for snow and ice are in place across all of Scotland and much of northern England, with further warnings covering the east of England and the west of Wales as well as Devon and Cornwall.
Ice warnings are also in place for all six counties of Northern Ireland, with warnings continuing through to Tuesday morning.
Temperatures could dip as low as -12C (10F) overnight, according to the Met Office, which said there was a chance that central and northern Scotland would be disrupted by snow until Tuesday evening.
Advice about how to stay safe in the cold ranged from suggestions to stay at home where possible and walking like a “penguin”.
Adam Stachura, the policy director at Age Scotland, urged people to stay at home where possible.
He said: “With such cold temperatures and icy conditions ahead, try and make sure you have enough food and any important medications at home to reduce the need for unnecessary and potentially risky trips.
“This is particularly important if you have mobility challenges or are unsteady on your feet.”
The NHS advised people to “waddle like a penguin” to avoid accidents and hospital stays.
“Walking like a penguin helps keep your centre of gravity over your feet, reducing the risk of falls that can lead to serious injuries,” NHS Greater Glasgow and Clyde said.
“It might look funny, but waddling really works,” Laura Halcrow, the falls prevention lead, added, warning that slipping on ice was dangerous especially for older people.
The NHS has previously suggested that the public “keep their feet on a stool” to stay warm, as well as reminding people to wear socks.
The lowest temperatures recorded overnight into Monday were -10.9C in Shap, Cumbria, -10.3C in Sennybridge, Powys, and -9.9C in Bala, Gwynedd, according to the Met Office.
Loch Glascarnoch in the Scottish Highlands saw 34cm of snow, while Dyce in Aberdeenshire and Altnaharra, Sutherland, recorded 27cm, the Met Office said.
The UK Health Security Agency (UKHSA) has issued an amber cold health alert that runs until Friday, with an early warning that adverse temperatures were likely to affect health and wellbeing in England.
Agostinho Sousa, the head of extreme events and health protection at UKHSA, urged people to check in on vulnerable friends, family and neighbours.
He said: “The forecast temperatures can have a serious impact on the health of some people, leading to increased risk of heart attacks, strokes and chest infections, particularly for individuals over the age of 65 and those with pre-existing health conditions.”
Temperatures are expected to drop to as low as -5C in Scotland and -4C for large parts of the south on Monday night, with the mercury dropping to -5C in Yeovil.
Heading into the week, temperatures are set to improve, remaining above 0C by Wednesday night in most of England and a milder -2C for most of Scotland, apart from in some isolated locations like Lairg which are set to drop to -6C.
The Met Office said that Monday night would be “very cold with a widespread frost and icy stretches”. The south of the country was expected to stay dry and clear but become “cloudier in the north as a band of snow moves in”.
Its website said Tuesday would be “another cold day with a mixture of rain, sleet and snow across the north”. It was expected to be drier in the south before similar conditions arrived in the evening.
Temperatures are expected to fall to -7C around Oxford on Tuesday and will not rise above 4C in any part of Britain.
The query refers to a recent astronomical discovery detailed in a scientific paper titled “A free-floating-planet microlensing event caused by a Saturn-mass object,” published in Science on January 1, 2026.
The query refers to a recent astronomical discovery detailed in a scientific paper titled “A free-floating-planet microlensing event caused by a Saturn-mass object,” published in Science on January 1, 2026.
This event, designated as KMT-2024-BLG-0792 (from the Korea Microlensing Telescope Network) or OGLE-2024-BLG-0516 (from the Optical Gravitational Lensing Experiment), marks the first time astronomers have directly measured the mass of a free-floating (or rogue) planet using gravitational microlensing.
Gravitational microlensing occurs when a massive foreground object (like a planet or star) passes in front of a distant background star, bending and magnifying its light due to gravity, as predicted by Einstein’s general relativity.
For free-floating planets—objects not bound to any star—this creates a brief, detectable brightening of the background star’s light without the influence of a host star’s glow.
From the AAS Editor: Gravitational microlensing causes the apparent brightness of a background star to vary when a foreground object passes across our line of sight. The mass and distance of the lensing object are usually degenerate parameters.
Dong et al. have identified a microlensing event caused by a planetary-mass object with no associated host star. By combining observations from Earth and a distant spacecraft, the mass-distance degeneracy can be broken, allowing for measurement of the lensing object’s mass, which is similar to Saturn’s. The researchers argue that this free-floating object did not form in isolation but was ejected from a host planetary system by dynamical interactions. —Keith T. Smith
Simultaneous ground- and space-based observations of a newly discovered free-floating planet have enabled direct measurement of its mass and distance from Earth, according to a new study. The findings offer insights into the diverse and dynamic pathways by which planets can be cast adrift into interstellar space. Although studies to date have only revealed a handful of such free-floating planets, detections are expected to increase in the coming years, particularly with the NASA Nancy Grace Roman Space Telescope campaign that is scheduled for launch in 2027, notes Gavin Coleman in a related Perspective. “Simultaneous space- and ground-based observations of microlensing events could be applied in the planning of future exploratory missions and could lead to a better understanding of how planets form across the Galaxy.”
Planets are most often found bound to one or more stars, yet a growing body of evidence shows that some wander the galaxy alone. These objects, called free-floating or rogue planets, lack any known stellar companion. And, since they don’t emit very much light, they reveal themselves only through their subtle gravitational effects – a phenomenon called microlensing. One of the main limitations of this discovery method is that it cannot determine the distance to these planets, making independent measurement of their mass difficult. As a result, much about this elusive population of solitary worlds remains speculative.
Here, Subo Dong and colleagues report the discovery of a new free-floating planet detected via a fleeting microlensing event. However, unlike previous detections, Dong et al. uniquely observed this microlensing event simultaneously from both Earth and space, using several ground-based surveys alongside the Gaia space telescope. Tiny differences in the timing of the light reaching these distantly separated vantage points enabled measurement of the microlensing parallax, which, when combined with finite-source point-lens modeling, allowed the authors to determine the planet’s mass and location. It is ~22% the mass of Jupiter and roughly 3,000 parsecs from the center of the Milky Way. Because this planet’s mass is comparable to that of Saturn, Dong et al. argue that it likely formed within a planetary system, rather than in isolation like a small star or brown dwarf. Such low-mass rogue planets are thought to be born around stars and later expelled from their orbital confines through gravitational upheavals, such as interactions with neighboring planets or unstable stellar companions.
This article discussing a study by researcher Bhatta. The study analyzes temperature data from nearly 1,000 stations (over 42 million recordings from pre-1900 to 2024), adjusts for urban heat island effects, and reports a low overall warming trend of 0.0054°C per year. It highlights stronger warming from ~1899–1940 (0.02°C/year in some periods) compared to 1983–2024 (~0.017°C/year), despite much lower CO₂ emissions early in the century, suggesting a disconnect from human-caused CO₂.
This article discussing a study by researcher Bhatta. The study analyzes temperature data from nearly 1,000 stations (over 42 million recordings from pre-1900 to 2024), adjusts for urban heat island effects, and reports a low overall warming trend of 0.0054°C per year.
It highlights stronger warming from ~1899–1940 (0.02°C/year in some periods) compared to 1983–2024 (~0.017°C/year), despite much lower CO₂ emissions early in the century, suggesting a disconnect from human-caused CO₂.
The Bhatta analysis uses a selective dataset and adjustments that minimize recent warming (strong urban heat correction reduces post-1980 trends).
Standard datasets (e.g., NOAA: ~0.18°C/decade since 1980) show recent warming outpacing the early period when comparing equivalent intervals.
Recently published remarkable research analysing over 42 million global temperature recordings from nearly 1,000 stations has cast serious doubt on the claimed link between carbon dioxide emissions and rising temperatures. The huge statistical work collected data from before 1900 to 2024 and found an overall annual warming trend of 0.0054°C after adjusting for growing unnatural urban heat effects. What is truly revealing about this important analysis is that it shows an obvious disconnect between the rise in anthropogenic CO2 and the sharpest warming occurring in the early 20th century, when industrialisation was confined to relatively few countries.
The annual 0.0054°C translates to warming of little more than 0.8°C during the industrial era and is below claims of well over 1°C from other, often politicised, sources. The disconnect with CO2 is evidenced by slower warming and even cooling trends over the last 125 years at a time when emissions of the trace gas have been rising in the atmosphere. The results of the work are tabulated below:
The largest warming over a 42-year period occurred between 1899 and 1940 when cumulative CO2 emissions were only 139.6 billion tonnes. The next period, from 1941 to 1982, saw annual average cooling of -0.013°C, leading to widespread fears at the time of a new global ice age despite 3.3 times jump in cumulative CO2. From 1983 to 2024, the average annual temperature warming of 0.017°C was less than the period up to 1940, when CO2 levels were 8.7 times lower. When considering these figures, it is worth noting that the notion humans cause most climate change is a hypothesis – in other words, an opinion, a guess given unwarranted credibility by computer models based on the current or selective state of knowledge. Political interference based on promoting the Net Zero fantasy does little to advance the current state of factual knowledge.
Of course, these patterns do not align well with the ‘settled’ political view that increasing CO2 from the use of hydrocarbons is the primary driver of recent global warming. In fact, they do not align at all with the UK Met Office’s recent risible pseudoscientific claim that its “rapid attribution study” showed that human-induced climate change made the UK’s record-breaking annual temperature of 2025 approximately 260 times more likely. A record, it might be noted, of six hundredths of a degree centigrade culled from a largely ‘junk’ measuring network made essentially useless by massive unnatural and uncorrected heat corruptions.
Not only are there few, if any, adjustments made for urban heat corruptions, but global temperature datasets promoting warming over the industrial age of up to 1.3°C are frequently adjusted higher retrospectively. GISS, which is part of NASA, increased past warming from January 1915 to January 2000 from 0.45°C to 0.67°C, a massive 49% boost. HADCrut is run by the UK Met Office, which once wrote a paper on the inconvenient temperature ‘pause’ from 1998 to 2013. Alas, the pause did not survive substantial retrospective warming adjustments, although it is still visible in the accurate satellite record.
The cynical have been known to observe there are more fiddles in global temperature datasets than the music cupboard at the Royal Philharmonic.
Back in the real scientific world, the author of this new analysis of temperature data stretching back to the 19th century is Dr Bibek Bhatta. Operating out of Queen’s University in Belfast, his research spans energy policy, finance and climate, with a focus on leveraging ‘Big Data’ to uncover hidden patterns and systemic misalignments. In his latest work, Dr. Bhatta argues that academic researchers should approach the concept of anthropogenic global warming (AGW) with a degree of caution. “Rather than treating AGW as a foregone conclusion, scholars in all fields should be encouraged to acknowledge the existing uncertainties,” he said. When building models or theories that assume CO2 is the primary driver of changing weather, they should note that “empirical evidence for this direct link is still a subject of debate”.
In an earlier section of his paper, Bhatta addresses the widely debunked suggestion of a 97% consensus among scientists about AGW. The claim arose from a supposed examination of nearly 4,000 papers published over a 21-year period, implying an average of one new paper produced every other day on the topic. This would suggest a “herd mentality” rather than a proliferation of independent evidence, commented Bhatta. “If one strong piece of empirical evidence for AGW exists, such repetitive endorsement would be unnecessary,” he argues. Perhaps Dr. Bhatta is too polite to add that the 97% figure, along with a subsequent 99% claim, was largely fabricated anyway.
Dr. Bhatta’s work on big meteorological data is fascinating. He presents his findings over several time periods; full details and methodology can be found in the enclosed link at the top of this story. His aim is simple – to investigate whether global warming trends recorded by actual weather stations can be “primarily attributed” to human emissions of CO2. His work follows a long line of research seeking to provide a truer picture of constantly changing past climate at a time when CO2 levels have been much higher and no obvious link exists with movements in local or global temperatures. Such work, effectively banned from consideration in most anti-science mainstream media, examines the role of natural weather variation and the possible ‘saturation’ of gases with atmospheric warming properties.
In total, 105 million global temperature recordings were downloaded from the Global Historical Climatology Network. After extensive cleaning, the final sample numbered around 42 million measurements with continuous records from at least 1900. All records supplied maximum and minimum daily temperatures and came from 992 stations across 29 countries. Data for annual human CO2 emissions was taken from Our World in Data. To adjust for urban heat influences, satellite information showing the build-up of urbanisation within a 10km radius was used. Similar work using this information has been done by other researchers trying to estimate urban heat effect on ambient air temperature measurements. As the table above noted, the annual warming for 1983–2024 was 0.0167°C compared to an urban heat corrupted 0.0209°C. Aficionados of the Met Office’s ‘hottest year evah’ claims might take note of these figures.
In conclusion, Dr. Bhatta observes that his evidence raises serious questions about the established assumptions regarding the impact of CO2 emissions on global warming. While the Intergovernmental Panel on Climate Change claims “unequivocally” that humans have caused global warming, he continues, “the empirical evidence presented herein does not provide support to such straightforward relationship”.
Chris Morrison is the Daily Sceptic’s Environment Editor. Follow him on X.
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