Bad Science – Climate- Science.press

The Sheer #%&@! Gall of Nature Magazine. EU Chief Science Advisers Say Democracy Depends on Trusted Science, and Ignore How the Trust Was Squandered

Nature ran a Comment on November 3 by the seven members of the European Commission’s Group of Chief Scientific Advisors, Martin Kahanec, Naomi Ellemers, Adam Izdebski, Rafal M. Łukasik, Dimitra Simeonidou, Rémy Slama and Mangala Srinivas, headlined “How democracy depends on science — and must protect it.”

Vintage-style Nature Magazine cover featuring a bold editorial headline about EU chief science advisers, public trust in science, and democracy. — ***AI generated by Meta***

From Watts Up With That?

By Charles Rotter

The standfirst sets the terms:

Verified information from trusted experts is crucial to help the public and governments navigate rapid changes and increasing complexity.

How democracy depends on science — and must protect it | Nature

The advisers are worried that the public has stopped listening:

But for science to strengthen democracy, researchers must be trusted, too. Moreover, the legitimacy of expertise has become increasingly contested over the past few years in Europe and elsewhere.

Scientific advice is subjected to politicization in many countries, with consensual insights and clear evidence (such as on climate change) being dismissed as ‘just another opinion’ or as being driven by a political agenda. Public trust in expertise has become polarized along political lines.

How democracy depends on science — and must protect it | Nature

In their telling the politicization is something done to science by outsiders, with a little help from “a small number of powerful technology companies,” and by people whose doubts spread “through networks of people connected by identity, grievance or distrust.” It doesn’t seem to occur to seven chief scientific advisers that the distrust was earned, or that the institutions they speak for did a good share of the polarizing themselves. They come close to saying it in one paragraph:

Expertise strengthens democratic legitimacy when it is transparent, diverse, independent and open to scrutiny — as prescribed by the rules of science. But it weakens legitimacy when it is perceived as opaque, unclear, politicized, elitist or disconnected from people’s lived experiences.

How democracy depends on science — and must protect it | Nature

“Perceived” is doing the heavy lifting there. The record of the last six years shows scientific institutions being opaque, politicized and elitist in plain view, in their own emails and their own journals, and the public noticed.

Start with lockdowns. On October 4, 2020, Martin Kulldorff, Jay Bhattacharya and Sunetra Gupta published the Great Barrington Declaration, arguing for focused protection of the elderly instead of blanket lockdowns. Four days later the director of the NIH, Francis Collins, wrote to Anthony Fauci, in an email Bhattacharya later put before Congress:

This proposal from the three fringe epidemiologists who met with the Secretary seems to be getting a lot of attention – and even a co-signature from Nobel Prize winner Mike Leavitt at Stanford. There needs to be a quick and devastating published take down of its premises. I don’t see anything like that online yet – is it underway?

https://www.congress.gov/118/meeting/house/115561/witnesses/HHRG-118-IF16-Wstate-BhattacharyaJ-20230328.pdf

Collins wanted a takedown rather than a debate, and he was the man who controlled most of the biomedical research money in the United States, aiming it at professors from Stanford, Harvard and Oxford. Bhattacharya now runs the NIH, which is one way to settle an argument about who was fringe.

The advisers write that “the opinion of a single scientist might result in partial understanding” or “reliance on a dissenting voice that differs from the majority view,” and that high-level expert groups do better. The highest-level expert opinion of early 2020 on where the virus came from was a five-author paper, “The proximal origin of SARS-CoV-2,” published in Nature Medicine, a Nature journal, on March 17, 2020. It said “we do not believe that any type of laboratory-based scenario is plausible.” The authors’ private Slack messages, released in 2023, say something else. Kristian Andersen, on February 1, 2020:

I think the main thing still in my mind is that the lab escape version of this is so friggin’ likely to have happened because they were already doing this type of work and the molecular data is fully consistent with that scenario.

Top Scientists Misled Congress About Covid Origins, Newly Released Emails And Messages Show

The channel was named “project-wuhan_engineering” until February 6, when it became “project-wuhan_pangolin.” The House Select Subcommittee on the Coronavirus Pandemic, in its December 2024 final report, found that the virus “Likely Emerged Because of a Laboratory or Research Related Accident” and that Proximal Origin was “‘Prompted’ by Dr. Anthony Fauci to ‘Disprove’ the Lab Leak Theory.” The subcommittee’s Democrats disputed the Fauci finding. The FBI (moderate confidence) and the Department of Energy (low confidence) lean toward a lab origin, and in January 2025 the CIA joined them, with low confidence. The origin is officially still unproven either way, which is exactly the point, since the paper that called the lab scenario implausible has never been retracted, despite the reams of evidence against it, and the journal that published it is the one now lecturing about “communicating disagreement” and “quantifying uncertainties.”

Then came the mRNA vaccines. In March 2021 CDC director Rochelle Walensky told MSNBC that vaccinated people “do not carry the virus,” and the CDC had to walk it back. Pfizer’s trial was never designed to measure transmission, which Pfizer executive Janine Small confirmed to the European Parliament in October 2022, while mandates and vaccine passports across Europe had been sold on the duty to protect others. In 2025 the FDA required stronger myocarditis warnings on the mRNA vaccines, with the highest risk in males aged 16 to 25, a group whose own risk from COVID was low and who were being told to get boosted to keep their jobs and their places at university.

Then there’s the Lancet. In July 2024 it published a three-author correspondence, not a peer-reviewed study, that took the Hamas-run Gaza Health Ministry’s count of direct deaths, applied a rule of thumb of four indirect deaths for every direct one, and floated a figure of up to 186,000. The number went around the world under the Lancet’s name. The statistician Mike Spagat, who has spent decades studying war mortality, took the method apart, and the Lancet later ran a letter of concern about it. Whatever one thinks of the war, a medical journal lending its name to a multiplier applied to figures from one of the combatants is not the “verified information from trusted experts” the advisers have in mind.

On climate, the advisers’ example of a consensual insight being unfairly dismissed is climate change itself. Their reference for the dismissing is Lewandowsky, Ecker and Cook, and their reference for the doubt-manufacturing is Oreskes and Conway’s Merchants of Doubt, which tells you which side of the argument the European Commission’s science advice listens to. For a decade the most extreme emissions scenario, RCP8.5, was described in papers and press releases as “business as usual,” until Zeke Hausfather and Glen Peters wrote in Nature itself in 2020 that the “business as usual” story is misleading. I’ve written about that at length. In 2023 Patrick Brown, a co-author of a Nature paper on wildfire, wrote in The Free Press that he had left out the non-climate drivers of fire because he believed a paper focused on climate change was what high-profile journals wanted. Nature disputed his account, but nobody disputed that he knew which story would sell.

Gender medicine is where the “majority view” of the expert bodies held up worst. In August 2023 the American Academy of Pediatrics reaffirmed its 2018 policy on puberty blockers and hormones for minors while commissioning, after the fact, a systematic review of the evidence for it. The Cass Review for NHS England, published in April 2024 after commissioned systematic reviews from the University of York, called the evidence base “remarkably weak.” Court documents reported by the BMJ and others show that WPATH, the professional body whose standards of care the American societies relied on, commissioned evidence reviews from Johns Hopkins and then claimed approval rights over what could be published. WPATH denies it prohibited publication. The children and their families were owed the evidence, whatever it said, and the people who were supposed to produce it treated it as a communications problem.

And then the journals decided to campaign. In October 2020 Nature published “Why Nature supports Joe Biden for US president.” Scientific American, which had never endorsed a candidate in its first 175 years, endorsed Biden the same year and Kamala Harris in 2024. Nature even measured what its endorsement did, in a preregistered experiment by Floyd Jiuyun Zhang published in Nature Human Behaviour in 2023:

The endorsement message caused large reductions in stated trust in Nature among Trump supporters. This distrust lowered the demand for COVID-related information provided by Nature, as evidenced by substantially reduced requests for Nature articles on vaccine efficacy when offered. The endorsement also reduced Trump supporters’ trust in scientists in general. The estimated effects on Biden supporters’ trust in Nature and scientists were positive, small and mostly statistically insignificant. I found little evidence that the endorsement changed views about Biden and Trump.

Political endorsement by Nature and trust in scientific expertise during COVID-19 | Nature Human Behaviour

The endorsement changed nobody’s vote and cost Nature, and scientists in general, the trust of half the American electorate. Pew found that 87% of Americans had at least a fair amount of confidence in scientists in April 2020 and 76% in 2024, and among Republicans the share fell from 85% to 66%. That is the polarization “along political lines” the advisers complain about, and Nature’s own journal published the experiment showing how a Nature editorial helped cause it.

The advisers’ remedy is more of what produced the problem. They want “high-level expert groups, which engage directly with senior decision makers,” protection from the “dissenting voice that differs from the majority view,” and “independent monitoring and assurance of algorithmic systems,” which in the European Commission’s vocabulary means someone deciding which information reaches the public. Every episode above was a high-level majority view, and in most of them the dissenting voices, the ones the “trusted institutions” set out to suppress and even censor, were the ones that turned out closer to right.

The authors, shamelessly circling the wagons of their failing institutions, of course declare no competing interests.

=====

Quotes from Kahanec et al., “How democracy depends on science — and must protect it,” Nature, November 3, 2026, https://www.nature.com/articles/d41586-026-03510-4.

When Experts Obfuscate! How Alarmist Scientists like D. Swain, J. Abatzoglou, A. Williams Falsely Attribute Wildfires to Climate Change.

In their Goss et al (2020) paper Climate Change Is Increasing the Likelihood of Extreme Autumn Wildfire Conditions Across California, those alarmist scientists clearly stated their intention was to make climate change the culprit by presenting an investigation that “only considers changes in climatic contributions to wildfire risk, irrespective of changes in fire ignitions, vegetation, land use or management strategies.”

From Watts Up With That?

By Jim Steele

In their Goss et al (2020) paper Climate Change Is Increasing the Likelihood of Extreme Autumn Wildfire Conditions Across California, those alarmist scientists clearly stated their intention was to make climate change the culprit by presenting an investigation that “only considers changes in climatic contributions to wildfire risk, irrespective of changes in fire ignitions, vegetation, land use or management strategies.” However, all those other factors can completely account for wildfire trends unrelated to climate change. For example, while much research has shown human ignitions have tripled the length of fire season, alarmist scientists dishonestly push a narrative that it is climate warming trends that have extended fire season. Papers not accounting for all confounding factors affecting a narrative, should never pass peer review. It is advocacy, not science! So, lets learn some science.

History shows that despite cooler and moister conditions pre-1800, an estimated ~4.5 million acres burned annually in California during the Little Ice Age (Stephens 2007). This far exceeds a 1979–1988 average of ~337,000 burnt acres/year, which then recently increased to a 21st century average of ~708,000 acres/year. However, California’s August 2020 fires did reach a similar burnt area of 4.4 million acres, when a rare swarm of thousands of dry lightning strikes ignited several small fires. Understanding the importance of vegetation analyses, reveals the great majority of lightning fires begin by igniting “fine fuels” like dead grasses, which then produce the fastest moving fires and burn the greatest area. Despite alarmists’ claims the recent increased burnt area is an indicator of climate change, vegetation changes are far more important. Burnt area is mostly determined by the presence and continuity of “fine fuels” and strong winds. But there were not any unusually strong winds when those 2020 fires began. But there has been an increase in invasive annual grasses creating greater fuel abunadance and continuity. For example, fine fuels (invasive annual grasses) have increased more than eightfold from 1990 to 2020 in the Great Basin which includes most of Nevada, half of Utah, and 10-20% of California, enabling a greater burnt area.

Understand The Critical Role of Fine Fuels

“Fine Fuels” are defined as dead vegetation less than ¼ inch in diameter. This includes cured grasses, weeds, pine needles, fallen leaves, and the small twigs of shrubs. They are also classified by fire experts as 1-hour fuels because they gain and lose moisture extremely quickly, gaining or losing 63% of their moisture in just 1 hour. Fine fuels can become highly flammable in just 60 minutes after a light rain. Climate change is totally irrelevant for fine fuel dryness and flammability.

Accordingly, 90+% of all wildfire ignitions happen in easily ignited fine fuels. Depending on the winds a grass fire, as seen in graphic A, will then rapidly spread the fire by advancing about 1 foot/second. The fire will keep advancing as long as flammable fuels are present near the flame front. However, a road may prevent the extent of grass fires, which partially explains why roadless areas experience the worst fires. Furthermore, statistics suggesting an increase in forest wildfires, obscure the fact that typically ~90–97% of the forest floor area in Sierra Nevada forests/woodlands is covered by 1-hour and 10-hour fine fuels. A grass fire’s flame front does not remain in one position for more than 5 to 15 seconds, so it moves rapidly through the forest floor without killing trees which require much more heat for a much longer time (graphic B).

However, if the fires are carried into the forests where poor land management has allowed shrubs and young trees and fine fuels to increase, the undergrowth provides ground fuels and enough fuels to release ample heat that can ignite ladder fuels and torch trees to create crown fires that emit embers that then spread the fire rapidly through the air. (graphic C & D)

Understand The Critical Role of Heat Source

Fine fuels require a heat flux (minimum rate of energy input) of 8,000–16,000 W/m² for several seconds. Higher fluxes ignite fine fuels more quickly. The flame front of an advancing grass fire releases 20,000–50,000 W/m², and that amount of heat easily dries and ignites 1-hour and 10-hour fuels, but not enough heat to burn live trees or dead logs with diameters greater than 3 inches. Multiple embers released by torched trees often collect on roofs with average heat fluxes of 15,000–80,000 W/m². Those heat sources are enough to ignite your house.

For heat flux comparisons, the sun provides at most about 1,000 W/m² of energy onto the earth’s surface on a clear day at noon. CO2’s current concentration adds less than 2.5 W/m² of energy since the Little Ice Age. However, a standard match provides about 25,000 W/m², but only for about 15 seconds. So, an arson’s match easily ignites any fine fuels and starts a wildfire. Depending on the electric current strength and other factors, a downed power line can produce 10,000 to 600,000 W/m² for several seconds to minutes. Thus, many major fires have been ignited when sparking electrical lines ignited the surrounding fine fuels. Although lightning can deliver up to a billion W/m², it only does so for less than a second. Such a short heat pulse rarely ignites a tree so primarily limits lightning’s wildfire ignitions to fine fuels.

Understand The Critical Role of Dryness

Flammability is high for most woody fuels when moisture content falls below 30%. Firewood is generally considered ready to burn efficiently below ~20–25% moisture content. Under 60% relative humidity, fine fuels commonly reach 10–20% flammable moisture content at air temperatures ranging from 30–100°F (–1 to 38°C). In contrast to claims by alarmist scientists, 1.5°C of global warming adds meaningless drying relative to the range of natural temperature fluctuations.

For example, during the rainless summer in California’s Mediterranean climate, relative humidity naturally falls and dries fine fuel’s moisture content to below 10% every day. Consider in California’s inland valleys and foothills during August, high relative humidity of 60% to 75% typically happens in the morning with temperatures in the 50s to mid-60s°F (10–19°C). Despite those cool temperatures, fine fuel moisture content reaches a very flammable 12% to 15%. As temperatures warm in the afternoon, commonly to the 80s and 90s°F (27–37°C), relative humidity falls to 30%-40%, drying fine fuels to a highly flammable 6-8% moisture content. More amazing, during the colder fall and winter temperatures when Santa Ana and Diablo wind events occur, the dry air descending from the deserts often lowers relative humidity to just 2%. Below ~10% moisture content, ignition probability from a single spark or discarded burning cigarette becomes extremely high. This evidence partially explains why despite 1.5°C cooler average temperatures during the Little Ice Age, 4.5 million acres had still burned annually in California.

To prevent rotting from fungi, exterior wood and building materials for houses range from 9% to 14% moisture content causing your house to burn more efficiently than dry firewood. Thus, we often observe neighborhoods burnt to the ground in a wildfire despite being surrounded by living trees with unaffected leaves and needles (graphic E in Paradise Camp fire and graphic F in the Dixie fire).

How to Protect Your Home from Wildfire

While so-called climate experts obfuscate wildfire science and claim you can reduce the threat of wildfire by reducing fossil fuel use, NOTHING could be further from the truth. Sadly, too many people have been lulled into wrongly thinking they are protecting their homes by buying electric vehicles or not eating meat. These obfuscating climate alarmists encourage people to stop critical thinking about how to honestly deal with wildfire threats, while useful idiots in social media advocate meaningless actions like eating bugs instead of beef.

To protect your home, simply prevent fine fuels and embers from carrying the fire into your flammable home. Create a defensible perimeter at least 5 meters away from your home by removing any fine fuels and any flammable vegetation. Don’t store flammable materials next to your home. Remove any tree branches overhanging your home and remove all low hanging branches that would provide der fuels that produce canopy fires and wind carried embers. Install fire resistant roofing and screens over all vents where embers could enter. If you understand the science of how wildfires spread, these precautions are just common sense. Alarmist climate experts be damned. Once you install these precautions, you can then guiltlessly enjoy a hamburger and fossil fuel powered heating and cooling systems.

Groan…Science Tries to Legitimize ‘Weather control’

In recent years, the frequency of weather-related natural disasters – cyclones, torrential rains, floods – has increased as a consequence of global warming.

Modeling weather interventions via computational modeling is the primary means by which weather interventions are studied.

From Watts Up With That?

By Anthony Watts

From the press releases “I never bothered to finish reading because it was too effing stoopid.” department.

Hiroshima University: In recent years, the frequency of weather-related natural disasters – cyclones, torrential rains, floods – has increased as a consequence of global warming. These disasters cause billions of dollars in damage and losses every year. As a result, there is great interest in weather control, the process by which human intervention can deliberately alter the weather.

Modeling weather interventions via computational modeling is the primary means by which weather interventions are studied. However, as weather is a vast and complex system, state-of-the-art numerical weather prediction (NWP) models of interventions are highly limited, requiring enormous computational resources.

A team of researchers led by Hiroshima University have applied black-box optimization algorithms to rainfall control and have demonstrated that they can be used to model rainfall minimization through weather control.

Their findings were published in the Journal of Computational Science on April 10, 2026.

Modeling weather control is an application of control theory, an interdisciplinary field of engineering and mathematics that deals with the behavior of systems. NWP modeling of weather control takes the input, models every step of the control process, and yields the output. It is here that the need for enormous computational resources arises, as weather is a highly complex system that is very difficult to accurately model in full.

“Our study asks how to design effective weather interventions for reducing rainfall when weather simulations are highly nonlinear, expensive, and do not provide reliable gradient information,” says Masaki Ogura, professor at Hiroshima University’s Graduate School of Advanced Science and Engineering and corresponding author of the study. “This is important because … any practical intervention method must work under strict computational limits.”

The researchers chose to use black box optimization to tackle this issue. Black-box optimization is the process of optimizing a system or function where the internal workings are not known or are too complex to be modeled accurately. The term “black-box” signifies that the system’s input-output behavior is the only information available for optimization.

The researchers chose four black-box optimization algorithms: Bayesian optimization, random search, particle swarm optimization, and genetic algorithms, and applied them to two experimental settings with different scales and levels of complexity. They used the NWP model Scalable Computing for Advanced Library and Environment Regional Model (SCALE-RM), specifically developed for climate research, as the base model for their simulations. The two experimental settings were the smaller and limited warm bubble experiment and the larger and more complex real atmosphere experiment. Specifically, they tested how a weather intervention modifying wind-fields would reduce rainfall over a target region. Their models attempted the intervention to a small portion of the simulation, either only at the start of the simulation (one-step) or every 600 or 3600 seconds (multi-step).

“Black-box optimization is a practical approach for weather-intervention design, and Bayesian optimization performed best among the tested methods under the studied settings,” says Ogura, describing the results. “One striking aspect is that meaningful rainfall reduction was achieved even under a very limited budget of expensive weather simulations. This is notable because each candidate intervention had to be evaluated through a full numerical weather simulation, so the optimizer had to find effective actions with only a small number of trials.”

Further, Bayesian optimization exhibits sensitivity to hyperparameters (parameters that define any configurable part of a model’s learning process). This indicates that Bayesian optimization can be adjusted, potentially leading to flexibility and adaptability to different atmospheric conditions.

By demonstrating the potential to design effective weather interventions even under limited computational resources, this study accelerates future research and development in disaster-prevention technologies and climate engineering.

The researchers caution that their findings may not generalize to all scenarios of weather interventions. “The next step is to test the framework in more diverse atmospheric scenarios and better understand why the methods perform differently,” Ogura concludes. “The ultimate goal is to build a reliable computational basis for future weather-intervention design for disaster mitigation.”

Yuta Higuchi & Yang Bai at Hiroshima University; Rikuto Nagai & Naoki Wakamiya at The University of Osaka; Atsushi Okazaki at Chiba University co-authored the study.

This work was supported by the Japan Science and Technology Agency (JST) Moonshot R&D Program (JPMJMS2284).

###

About That Climate Emotions Study

Researchers turned the statement “It annoys me to watch people succumb to climate hysteria” into the emotion of climate contempt.

The study looked at 966 adults in Germany and tested whether different climate-related emotions predicted different kinds of environmental behavior.

From Watts Up With That?

By Charles Rotter

I read a lot of climate papers so you don’t have to. Most of them are about the physical world, and you need some background to follow them. Every so often one comes along that requires no background at all, because it is about feelings, and we all have those. This is another one of those.

The paper is “Do climate emotions matter? Investigating their role in pro-environmental behavior,” published this month in the Journal of Environmental Psychology by Paula Blumenschein and colleagues at two German universities. They surveyed 966 Germans about their climate feelings, ran the answers through a regression, and concluded that climate feelings are important and should get more attention. I am going to walk you through it, because the walk is the fun part.

Do climate emotions matter? Investigating their role in pro-environmental behavior – ScienceDirect

Let me start with the first sentence, because the first sentence does a lot of work:

The climate crisis is progressing at an alarming rate with serious implications for planetary and mental health.

That is the opening line. Not a hypothesis, not something the paper sets out to show. It is the floor the rest of the building sits on. The crisis is assumed, the alarming rate is assumed, and everything that follows is about how people feel inside that assumption. I want to flag this early, because if you do not share the premise that we are living through a planetary emergency, the study has already decided what that makes you. We will get to what that makes you. It is one of my favorite parts.

Here is the setup. The researchers used something called the Inventory of Climate Emotions, which measures eight separate climate feelings, each with four survey questions answered on a scale from strongly disagree to strongly agree. The eight feelings are climate anger, climate anxiety, climate guilt, climate sorrow, climate enthusiasm, climate powerlessness, climate contempt, and my personal favorite, climate isolation.

Climate isolation is defined as loneliness from other people not caring about climate change as much as you do. The survey measures it with statements like “I feel lonely because most of the people around me don’t care about climate change as much as I do.” This is now a measurable emotion with its own subscale. And in this study it turned out to be the single strongest predictor of whether someone goes to a climate protest. The lonelier you feel about caring more than your friends, the more likely you are to march. I could have told you that for free, but now it has a number, and the number is 0.30.

Now to my favorite part, which is climate contempt.

Contempt is the emotion the researchers built to capture climate skeptics. They define it as disregard for the issue of climate change. The actual survey question they use to measure it, and I am not making this up, is this:

It annoys me to watch people succumb to climate hysteria.

Sit with that for a second. Somebody who thinks a fair amount of climate alarm is overcooked, which describes most of the people I know and probably most of the people reading this, would agree with that sentence. The researchers have taken that view, which is a position about the world, and reclassified it as a negative emotion called contempt. You are no longer a person with an opinion. You are a person with a feeling, and the feeling has been entered into a statistics program.

And then a wonderful thing happened. When they ran the numbers, climate contempt came out predicting more climate activism, not less. The skeptics, in their model, were the ones going to the protests. This is the exact opposite of what the whole category was designed to show.

So what do you do when your denial-detector says the deniers are the activists? You call it a suppression effect. Here is their actual explanation:

This pattern should not be interpreted as evidence of a direct positive effect regarding those two outcomes, but emerges only when considered alongside other predictors and reflects suppression.

Suppression is a real thing in statistics. It is also what your results look like when you stuff eight nearly identical variables into the same model and the math starts arguing with itself. And these variables are nearly identical. The paper reports that climate anxiety and climate sorrow are correlated at 0.78. Anxiety and anger at 0.74. Anxiety and guilt at 0.70. These are not eight different emotions. This is one worried person being asked the same question eight slightly different ways, and the regression is now trying to tell the eight versions apart.

To their credit, and I mean that, the authors eventually admit it. They conclude that the contempt subscale “lacks construct validity,” which is the polite way of saying it does not measure what it was supposed to measure. They say the same about the powerlessness subscale, which also produced a backwards result. So two of the eight feelings are broken by their own account, and the other six are so tangled together the model is, in their own word, suppressing itself.

Now here is the part that I think actually matters, underneath the comedy.

The study has one outcome that is not a feeling. Most of what they measured was self-reported: how much activism do you do, how much do you nudge your friends. But for diet they used a carbon calculator on meat and egg consumption, which is at least an attempt to measure a real thing in the real world. And that is exactly where the whole apparatus falls apart. The feelings explained about 54 percent of the variation in self-reported activism, the squishiest measure, and about 17 percent of the variation in actual dietary emissions, the realest one. The closer they got to something physical, the less the feelings predicted it.

And climate guilt, which they expected would go with a cleaner diet, went the other way. The people who felt the guiltiest about their diet had the worst diets. Either guilt is a remarkable thing, or guilt follows your behavior instead of fixing it. The authors notice this, raise their eyebrows, and move along.

Which is the whole study, really. People who say they care a lot about climate change also say they do a lot about climate change. That is not a discovery. That is the same person answering two versions of the same question and the researchers drawing a line between the answers. The authors even admit, way down in the limitations, that the data cannot tell you which way the arrow points, whether the feelings drive the actions or the actions drive the feelings or both come from something else entirely. That is the correct thing to admit. It also quietly empties out the rest of the paper.

The conclusion, after all of this, is that climate groups should hold more sessions where people can share their feelings of climate isolation and climate enthusiasm. They reached that conclusion by surveying people in the climate movement and discovering the climate movement has feelings.

I do not doubt that people have real and powerful emotions about the environment. That is a genuine subject. But you cannot prove a crisis by assuming it in line one, you cannot turn one anxious mood into eight separate emotions by giving each its own subscale, and you certainly cannot take “it annoys me to watch people succumb to climate hysteria,” file it under contempt, and act surprised when the people who agree with it do not behave like the villains you cast them as.

The 966 Germans were asked how they felt. They told them. The feelings went into the model, the model made numbers, the numbers went into the journal, and everyone got paid, the survey panel included. And that, as far as I can tell, is the rest of the story.

A Statistical Evaluation of the Whining of Climate Scientists as Political Activists

I read this so you don’t have to.

Most of 35 Dutch climate scientists interviewed for a new paper want to be activists, with one saying they’re “scared shitless” about the climate and comparing street protests favorably to briefing the EU.

From Watts Up With That?

By Charles Rotter

I read this so you don’t have to.

A new paper published late May in Environmental Communication presents what is described as a “two-dimensional framework” for analyzing how climate scientists experience the public sphere. The lead author is Victor Avramov of the Athena Institute at VU University Amsterdam. The co-authors are at the University of Amsterdam and the Rathenau Institute in The Hague. The work was funded by the Dutch Research Council through the Dutch Science Agenda, file number NWA.1397.21223.014, paid for by Dutch taxpayers.

https://www.tandfonline.com/doi/full/10.1080/17524032.2026.2674245#d1e354

ABSTRACT

Climate scientists are increasingly drawn into a polarized public sphere, challenging relations between science and society. In this study, we interviewed thirty-five climate scientists – diverse in discipline and seniority – working in the Netherlands about their perceptions of, and experiences with public engagement. Based on our empirical material, we construct an analytical framework with a politization and participation axis on which we position their statements. Demarcating their public activities along these dimensions, climate scientists highlight concerns for scientific credibility, political efficacy, normative responsibility and individual capacity. While there is a clear opposition between those compelled to advocate for stringent climate policies or tackle misinformation and those who believe their main role is to provide solid knowledge and leave the normative choices to activists or politicians, only few scientists collaborate with stakeholders. Letting different stakeholders speak and participate in knowledge productions, we argue, may provide a solution to the science vs politics stranglehold.

The sample size is thirty-five. The sample consists of thirty-five climate change researchers from the Netherlands. The statistical method is to interview them for fifty to ninety minutes, transcribe the interviews, code the transcripts inductively, and then plot the resulting codes on a two-dimensional chart. The axes are labeled “Politization” (sic, throughout) and “Participation.” The axes have no units. The chart contains no data points and no scale, only four softly colored blobs of varying size labeled Demarcation, Seeking Impact, Activism, and Co-Creation. The bigger blobs represent more interviewees having said roughly similar things. The placement of the blobs within their quadrants is determined by the authors having decided that is where the blobs go.

This is what social science research looks like in 2026.

What the thirty-five interviewees have to say

The interview quotes, which take up most of the paper, deserve to be read at length. The plotting is less informative than the quotes themselves.

Here is interviewee P23 explaining why scientific neutrality may be overrated:

Besides being a scientist, I am also a citizen, and I am scared shitless about what is happening. Why should I not be allowed to speak out about it and why would it hurt my credibility, if it is actually science, which helps me understand what is happening?

The paper does not consider whether being scared shitless is a sound foundation for professional conduct. The paper plots P23 in the upper-left quadrant, which is labeled Activism. This is presented as a legitimate role for a scientist to occupy.

Here is interviewee P28, an Earth sciences professor, comparing the effectiveness of formal scientific advice to the European Union with the effectiveness of street protests:

I’m going to Brussels soon to talk to the EU. I could be talking in a void … but sometimes I doubt the effectiveness of that role compared to what we reach when we just take to the streets.

The paper does not raise the question that suggests itself, which is whether a scientist who has concluded that street protests are more effective than scientific consultation is still a scientist in any meaningful sense. P28 appears in Activism. P28 supplies several other quotes the authors find illuminating.

Here is P28 on the burdens of being a moral exemplar among the unenlightened:

I do feel estranged from some colleagues that I was good friends with because they never wanted to apply the climate crisis to themselves, to their own political lives.

The paper treats this as evidence of institutional friction with activist scholars. The friction is real. The other reading, which the paper does not consider, is that the friends in question may have grown tired of being lectured.

Here is P15, in the same general region of the plot, on the indignity of being labeled what they evidently are. The interviewee, the paper reports, admitted to censoring themselves out of the fear of being called an activist. The paper presents this as a problem. The chilling effect of accurate labeling is preventing the activist from doing more of the activism. The accurate labeling is the problem. The activism is the data.

The chart

The methodological centerpiece is Figure 1, a two-dimensional framework on which the four clusters of interview statements are plotted by their relative degrees of politization and participation. The axes have no units. The blobs have no coordinates. The placement of the blobs in their quadrants was determined by the authors in collaboration. The figure caption notes that higher color density represents higher frequency, which gives the chart its only quantitative dimension: how often the interviewees said things that fell into each cluster, as determined by the authors’ own coding decisions.

Figure 1. Two-dimensional framework obtained through the inductive analysis of 35 climate change researchers’ engagement in the public sphere. The clusters represent interview statements plotted according to the degrees to which they indicated politicizing and/or participatory experiences and role perceptions in the public sphere. Higher color density represents higher frequency.

The chart has axes. The axes have labels. The chart has clusters. The clusters have positions. The chart has a color-coded legend.

This makes the chart scientific in the sense that it is shaped like science.

What the chart tells the reader, on inspection, is that out of thirty-five interviews, the largest cluster of statements fell in the upper-left quadrant labeled Activism. The next-largest fell in the middle, labeled Seeking Impact. A smaller cluster appears on the right, labeled Co-Creation. A thin sliver appears on the lower-left, labeled Demarcation. Demarcation is the position of scientists who believe their job is to do science and to leave politics to politicians and activism to activists.

The paper treats this thin sliver as the limiting baseline to be overcome. The activist blob, in the upper left, is presented as a more mature and developed position. The co-creation blob on the right is the position the paper ultimately recommends.

Where this is going

The discussion concludes that the way out of the “science-versus-politics stranglehold” is participation. Specifically, citizen assemblies in which scientists and members of the public deliberate together about climate research. The closing paragraph helpfully discloses:

In a current research project, we are exploring the potential of the democratic innovation of citizen assemblies in the domain of science communication.

The paper that diagnoses the stranglehold and prescribes citizen assemblies as the cure is written by people who are funded to study citizen assemblies. The diagnosis arrives matched to the prescription as if by appointment. The Dutch taxpayer is paying for both.

The discussion further commends intellectual humility and being an honest broker of knowledge and observes that scientists running citizen assemblies must be trusted as neutral. The thirty-five interviewees, plotted across the framework, contain almost nobody in the demarcation quadrant who could plausibly be described as trusted to be neutral. The neutral scientists, in the paper’s own data, are the small marginal cluster. The activists and the participation enthusiasts are the bulk. The paper’s recommended fix for the trust problem is that those activists and participation enthusiasts should run the citizen assemblies in which they are trusted to be neutral.

A note on the broader context

There is a serious point underneath the methodological theater. The thirty-five interviewees represent a profession that has spent the last fifteen years building public credibility on the strength of being scientists. The credibility has been pledged to scenarios that have now been formally retired (see our extensive recent coverage of the death of SSP5-8.5 and SSP3-7.0), to attribution claims downstream of those scenarios, and to a political program that has consistently overstated the certainty of the science. Public trust has shifted accordingly. The paper’s thirty-five interviewees are part of the system that produced that shift and have noticed.

The Avramov paper is what happens when the people inside that system look around, notice that things have gone sideways, and conclude that the problem must be the boundaries between science and politics. Not the substance of the science. Not the choice of scenarios. Not the alignment of published conclusions with the funding priorities of state research councils. Not the fifteen-year pattern of overstating findings to support pre-existing policy preferences.

The problem, in this paper’s framing, is that the scientists have not been doing enough activism.

The solution is more activism, conducted under a different name, with the participation of citizens brought along through deliberation. On the paper’s own evidence, the activist scientists want to be trusted while also being political. They want to be neutral while also being activists. They want to be heard while also lecturing. They want to be considered scientists while also taking to the streets. They want to be paid by the state while also speaking truth to it. They are caught, the title says, in the fray. They are also scared shitless. The paper plots all of this carefully.

The thirty-five interviewees of the Netherlands are now on record. The Dutch Research Council, having paid for the insight, will presumably commission more.

That is the grant cycle doing its job, which is what the grant cycle has always been for.

Flipping Out in a Warming World

From Watts Up With That?

Robert Vislocky, Ph.D.

An article published in Nature Communications (“Rapid Flips Between Warm and Cold Extremes in a Warming World”) has been making its rounds recently in several climate forums on social media even though it was published a year ago (see link below). In the article the authors claim that flips between warm and cold extremes are becoming more frequent and intense in a warming world.
According to the authors, rapid extreme temperature flips “allow a very short time for human and ecosystem preparedness to respond and adapt” and “can increase the risk of severe and even irreversible impacts on human health, infrastructure, air quality, and plant phenology.”

Of course, the news media swarmed all over the article like a fly to honey and pimped up the scare. Check out some of the dire headlines below:

“Sweating to Shivering: Study Finds Rapid Swings in Temperature Have Increased”

Sweating to Shivering: Study Finds Rapid Swings in Temperature Have Increased – The New York Times

“Study Finds Sudden Weather Whiplash Events on the Rise”

Study Finds Sudden Weather Whiplash Events on the Rise

“From boiling hot to freezing cold: Climate crisis sparks sudden temperature changes”

https://nation.africa/kenya/health/from-boiling-hot-to-freezing-cold-climate-crisis-sparks- sudden-temperature-changes-5051276

Now it’s time for the reality check. Figure 1 below from the article shows the trends in the frequency (a, b), intensity (c, d) and transition duration (e, f) of extreme warm-to-cold and cold-to-warm flips for the period 1961 to 2023. The global trend is shown in the smaller embedded charts while the regional trends are shown as shaded areas on the maps. Extreme flips are defined as going from at least one standard deviation above normal to at least one standard deviation below normal (or vice-versa).

Figure 1. **Trends in the frequency (a, b), intensity (c, d) and transition duration (e, f) of extreme warm-to-cold and cold-to-warm flips.** Wu, S., Luo, M., Lau, G.NC. *et al.* Rapid flips between warm and cold extremes in a warming world. *Nat Commun* 16, 3543 (2025).

Now brace yourselves, according to their charts the number of extreme warm-to-cold flips increased from about 1.64 flips per year in 1961 to a whopping 1.82 flips per year sixty-two years later in 2023 (Fig 1a). That’s an increase of 0.29 extra extreme flip days per century! The number of extreme cold-to-warm flips increased from about 1.43 flips per year in 1961 to a colossal 1.51 flips per year in 2023 (Fig 1b). That’s an increase of 0.12 extreme flip days per century. With that kind of change I don’t know how life could possibly adjust (tongue-in-cheek humor).

While the authors may be able to show statistical significance of these frequency trends, in reality they are meteorologically and physically insignificant to the point where no human, animal, or plant would even notice the change (even if such change was real).

In addition to the flip frequency, the authors claim that the transition duration is decreasing too. This is the time it takes in days to go from a warm extreme to a cold extreme (or vice-versa). In the graphs it appears the average flip between extremes takes a little over four days or roughly 100 hours. After a whole century of warming the average flip between extremes would be about 2.5 hours less based on their trend lines (Figs 1e, 1f). Again, this change is entirely meaningless as is the intensity increase of 0.15 standard deviations per century (Figs 1c, 1d).

On top of the meaningless trends in observed extreme temperature flips, there are several questionable aspects of the study that raise red flags for concern. For starters, why did the authors begin their analyses in 1961? The temperature datasets that they used in the study all go back much further than 1961 (e.g., 1940 for the ECMWF ERA5 reanalysis dataset, 1940 for the NCEP/NCAR reanalysis dataset, and 1850 for Berkeley Earth). So why not start the trend in 1940, for example, in line with when the reanalysis data sets begin? Maybe it’s because the trends disappeared or reversed when longer datasets are utilized? Interestingly, although the 1961-2023 data shows a tiny increase in flip frequency, the data since 1980 shows absolutely no trend according to their charts in (Figs 1a and 1b). So it’s quite possible the 1961 start date was cherry picked to show a positive trend in flip frequency.

But wait, there’s more! The authors apply CMIP6 climate model simulations using four different emission scenarios out to 2100 (see Figure 2a to 2f below). Under SSP5-8.5 and SSP3-7.0 scenarios the authors show roughly a 7% increase in the frequency and intensity of flips between extreme warm and extreme cold temperatures (and vice-versa) by the end of this century. Accompanying those results is the usual appeal to emotions that these flips will disproportionately affect poorer nations. Nonetheless, both of those high emission scenarios have been recently declared unrealistic by the IPCC. Under more realistic emission scenarios that were tested by the authors, no meaningful trends in extreme flips are shown out to 2100. What’s even more interesting is that the historical climate model simulation from 1961 to 2023 that they use as a baseline actually shows a slightly declining frequency of extreme temperature flips during this period (Figs 2a, 2b, gray curve), which is contrary to their observational study in Figure 1 that showed a tiny increase.

Figure 2. Time series of the frequency **(a, b), intensity (c, d) and transition duration (e, f) of extreme warm-to-cold and cold-to-warm flips based on CMIP6 simulations under various emission scenarios.** Wu, S., Luo, M., Lau, G.NC. *et al.* Rapid flips between warm and cold extremes in a warming world. *Nat Commun* 16, 3543 (2025).

The bottom line is this is a perfect example of another study from the climate community that presents very marginal results with significant red flags but is sensationalized by the news & social media who refuse to bat a critical eye at the study along with an apparently forgiving peer review.

Junk Science: “Loneliness caused by climate change kills people”

From the University of Sydney and the “this is what passes as peer-reviewed science these days” department come this load of rubbish. The paper argues that climate change erodes social connections, through heatwaves keeping people indoors, disasters displacing communities, and droughts reducing participation in rural community life. They further claim that this social isolation compounds mortality risk during climate events.

From Watts Up With That?

By Anthony Watts

From the University of Sydney and the “this is what passes as peer-reviewed science these days” department come this load of rubbish. The paper argues that climate change erodes social connections, through heatwaves keeping people indoors, disasters displacing communities, and droughts reducing participation in rural community life. They further claim that this social isolation compounds mortality risk during climate events. They contend that disadvantaged populations bear a disproportionate burden, that weak social ties reduce collective willingness to act on climate change, and that governments should therefore treat “social connection” as essential climate infrastructure alongside housing and transport. The British Columbia Heat Dome is cited as evidence, noting that people with schizophrenia made up around 8% of heat deaths, which the authors attribute partly to social isolation.

But here are the obvious things they miss. Schizophrenic people have paranoia and distrust of institutions can make people less likely to seek help from emergency services or cooling centers. Their condition puts them more at risk, not climate change. But, wait! There’s more…

Antipsychotic medications suppress sweating, impairing the body’s primary cooling mechanism.

Reduced interoceptive awareness means patients may not recognize dangerous overheating until it’s too late.

Poor housing and lack of access to air conditioning leaves patients physically exposed to heat.

Cognitive symptoms (disorganized thinking, avolition) impair practical heat-avoidance behavior.

Overrepresentation in homeless and precariously housed populations increases exposure risk.

Sedative effects of some antipsychotics reduce general alertness and responsiveness to environmental threats.

– Anthony

Climate change costs lives by breaking down social connection
Climate change is a social crisis, disrupting connection, community and belonging

Climate change is widely understood as an environmental and economic threat, but new research from the University of Sydney shows it is also a growing social crisis, weakening the relationships people rely on to survive.

Published in Nature Human Behaviour, the international study found climate change is eroding social connection at a time when those ties are most needed, creating a feedback loop where disruption to relationships reduces people’s ability to adapt and recover.

“Climate change isn’t just something happening ‘out there’,” said lead author Dr Marlee Bower from the Matilda Centre for Research in Mental Health and Substance Use. “It’s reshaping how we live, how we connect, and ultimately who has support when things go wrong.”

How climate change is breaking down social connection

The research draws together global evidence showing that climate pressures are reducing everyday opportunities to connect. Heatwaves and air pollution are pushing people indoors and away from shared public spaces, while interruptions to school and work make it harder to maintain relationships.

Evidence from China and Tuvalu shows these pressures can reduce social activity, increase depression, and in some cases lead people to withdraw from community life altogether.

When disasters like floods, cyclones and bushfires strike, the effects are more immediate, with displaced communities, damaged homes and public spaces, and disrupted daily routines. Case studies in the Dominican Republic and Japan show that even when relocation improves physical safety, it can leave many people more socially isolated and in poorer health.

Slower-moving impacts can have similar consequences over time. In rural Australia, prolonged drought has been linked to declining participation in community life and increasing isolation, while financial strain, food insecurity and misinformation create further barriers to connection and trust.

Climate change is widening the gap in who stays connected

The study finds these impacts are not shared equally. People already facing disadvantage, such as those on low incomes, in poor-quality housing, living with disability, or from marginalised communities, are more exposed to climate risks and less able to stay connected.

“Many face a double burden,” Dr Bower said. “They are more vulnerable to climate impacts because of where and how they live, and have fewer social and financial supports to draw on.”

As these pressures build, the researchers describe a widening “social health gap” between people who remain well connected and those who become increasingly isolated.

Social connection is critical for climate action

Experts say the strength of social ties shapes how communities respond to climate change, with higher trust linked to greater support for climate action and collective responses.

“When people feel disconnected, they are less likely to participate, cooperate or help each other,” Dr Bower said. “That limits our ability to respond to climate change in a meaningful way.”

The consequences are more visible during disasters, where people with weaker social ties are consistently more likely to die than those with strong support networks.

During the 2021 British Columbia Heat Dome, which caused more than 600 deaths, people with schizophrenia – a group more likely to experience social isolation – accounted for around eight percent of heat-related deaths, highlighting how social isolation can compound risks.

“If we ignore social health, we miss a major driver in who survives climate events and who doesn’t,” said Dr Bower.

Social connection must be treated as climate infrastructure

Despite its importance, social health remains largely absent from climate policy. The researchers highlight that while governments invest heavily in infrastructure and emergency response, far less attention is given to the social systems that determine who receives help.

With social disconnection already affecting up to one in four people globally and impacts on mortality comparable to smoking and obesity, the authors argue that social connection should be treated as essential climate infrastructure, embedded in housing, transport, urban design and public spaces.

“If we keep treating climate change as a scientific or technical problem, climate policy will fail,” said Dr Bower. “Social health allows communities to survive and recover.”

Evidence from Australia’s Black Summer bushfires shows people with strong community ties experienced lower distress and greater resilience long after the fires, provided those connections existed before disaster struck.

“There’s often a surge of togetherness immediately after an event, followed by exhaustion months later,” Dr Bower said. “The people who fare best are those who were connected beforehand.”

-ENDS-

Journal

Nature Human Behaviour DOI 10.1038/s41562-026-02455-y

Models Gone Wild: The Ionosphere Triggers Earthquakes?

The idea that ionospheric disturbances could influence earthquakes is an emerging and speculative concept in geophysics, primarily explored in a recent theoretical model rather than through established empirical evidence.

However, this remains a speculative hypothesis rather than an established fact, and the mainstream scientific view continues to treat ionospheric changes primarily as consequences of earthquakes (co-seismic effects) or potential precursors from below-ground processes, not drivers from above.

From Watts Up With That?

By Anthony Watts

From the “anything goes in modeling these days” department. Here’s my take on it.

The paper proposes a capacitive coupling model in which a fractured crustal zone supposedly acts as a capacitor, storing charge that interacts with the ionosphere, and reciprocally, solar-flare–enhanced ionospheric charge induces electrostatic pressures in crustal voids sufficient to trigger rupture.

It is certainly an imaginative (and that is being generous) attempt to unify atmospheric electricity and seismology, but the central weakness is that it moves rapidly from correlation to causation without robust empirical validation.

The authors assume, for example, that descending ionospheric charge density can be treated as equal to total electron content (TEC) and that this charge forms an effective space-charge layer capable of inducing megapascal-scale pressures. That assumption alone carries enormous physical uncertainty: TEC is an integrated vertical column measurement over hundreds of kilometers, not a direct proxy for localized lower-ionospheric space charge. Likewise, the model presumes idealized dielectric behavior in fractured crust saturated with supercritical water, with specified permittivity, breakdown voltage, and uniform void connectivity, yet these parameters are not constrained by real-world field measurements at seismogenic depths. In short, the mechanism rests on layered assumptions rather than observational confirmation. It is a lot like climate science.

Moreover, the invocation of the 2024 Noto Peninsula earthquake and coincident solar flare activity as supporting evidence reads more like post hoc pattern matching than statistical demonstration . Solar flares are frequent; earthquakes are frequent; coincidences will occur. A convincing case would require rigorous statistical analysis across many events, controlling for flare magnitude, geomagnetic latitude, background stress state, and tidal forcing. Instead, the pressure estimates (e.g., ~5 × 10⁶ N/m² for 10 TECU) are presented as comparable to tidal or gravitational stresses , yet no sensitivity analysis is offered to show how stable those results are under more realistic conductivity, charge dissipation, or geometric heterogeneity. The Earth’s crust is not a laboratory capacitor with neatly parallel plates, and electric fields in conductive, fluid-rich rock are likely to dissipate rapidly. IMHO, the paper reads as speculative fiction.

Via Eurekalert:
Could ionospheric disturbances influence earthquakes?

Kyoto, Japan — Researchers at Kyoto University have proposed a new physical model that explores how disturbances in the ionosphere may exert electrostatic forces within the Earth’s crust and potentially contribute to the initiation of large earthquakes under specific conditions.

The study does not aim to predict earthquakes but rather presents a theoretical mechanism describing how ionospheric charge variations — caused by intense solar activity such as solar flares — could interact with pre-existing fragile structures in the Earth’s crust and influence fracture processes.

In the proposed model, fractured zones within the Earth’s crust are assumed to contain high-temperature, high-pressure water, potentially in a supercritical state. These zones behave electrically like capacitors and are capacitively coupled with both the ground surface and the lower ionosphere, forming a large-scale electrostatic system.

When strong solar activity increases electron density in the ionosphere, a negatively charged layer can form in the lower ionosphere. Through capacitive coupling, this space charge may induce strong electric fields inside nanometer-scale voids within fractured crustal regions. The resulting electrostatic pressure could reach magnitudes comparable to tidal or gravitational stresses known to affect fault stability.

Quantitative estimates in the study suggest that ionospheric disturbances associated with large solar flares — corresponding to increases in total electron content of several tens of TEC units — could generate electrostatic pressures on the order of several megapascals within crustal voids.

Ionospheric anomalies, such as increased electron density, lowered ionospheric altitude, and slowed abnormal propagation of medium-scale traveling ionospheric disturbances, have been repeatedly observed prior to major earthquakes. Traditionally, these phenomena have been interpreted as consequences of stress accumulation within the Earth’s crust.

The new model provides a complementary perspective by proposing a bidirectional interaction: while crustal processes may affect the ionosphere, ionospheric disturbances themselves may also exert feedback forces on the crust. This framework offers a possible physical explanation linking space weather phenomena and seismic processes without invoking direct causation.

The study discusses recent large earthquakes in Japan, including the 2024 Noto Peninsula earthquake, as examples that are temporally consistent with the proposed mechanism. In these cases, intense solar flare activity occurred shortly before the seismic events. The authors emphasize that such temporal coincidence does not establish direct causality but is consistent with a scenario in which ionospheric disturbances act as a contributing factor when the crust is already in a critical state.

By integrating concepts from plasma physics, atmospheric science, and geophysics, the proposed model broadens the conventional view of earthquakes as purely internal Earth processes. The findings suggest that monitoring ionospheric conditions, together with subsurface observations, may help improve scientific understanding of earthquake initiation processes and seismic hazard assessment.

Future research will focus on combining high-resolution GNSS-based ionospheric tomography with space weather data to clarify the conditions under which ionospheric disturbances may exert significant electrostatic influence on the Earth’s crust.

###

The paper “Possible Mechanism of Ionospheric Anomalies to Trigger Earthquakes: Electrostatic Coupling Between the Ionosphere and the Crust and the Resulting Electric Forces Acting Within the Crust” appeared on 3 February 2026 in International Journal of Plasma Environmental Science and Technology, with doi: 10.34343/ijpest.2026.20.e01003

https://ijpest.com/Contents/20/1/e01003.html

Turning “What If” into “How Many”: The Rhetorical Alchemy of Climate Modeling

The article uses a recent Nature study on malaria in Africa as a case study to argue that climate impact research often transforms highly uncertain, scenario-based modeling into seemingly precise, alarmist predictions through selective emphasis and framing.

An elderly wizard holding a flask with green liquid while surrounded by scientific charts and graphs related to climate models and malaria, with stacks of documents and question marks in front.

Charles Rotter critiques a 2025 or early 2026 Nature paper titled something like “Projected impacts of climate change on malaria in Africa.”

He argues that climate-impact studies often perform a kind of “rhetorical alchemy”.

Such papers reveal more about the state of climate-impact modeling than about actual future malaria burdens.

It’s presented as an example of how uncertainty gets rhetorically transformed into urgency to support policy narratives.

Climate science communication often converts speculative chains into seemingly precise catastrophe forecasts, inflating perceived certainty and risks.

____________________________________________________________________________________

A businessman in a suit stands on a reflective surface, observing a burst of floating white papers against a cloudy backdrop.

From Watts Up With That?

By Charles Rotter

Article titled 'Projected impacts of climate change on malaria in Africa' published in Nature on January 28, 2026, listing authors and access metrics.

A recent Nature paper, “Projected impacts of climate change on malaria in Africa,” provides a textbook example of how layered model uncertainty can be transformed—through careful framing—into quantified predictions that appear far more authoritative than the underlying evidence warrants.

The headline figures are stark: 123 million additional malaria cases and more than 500,000 additional deaths by mid-century, attributed to climate change.

These numbers are already circulating as if they describe a measurable future risk. A close reading of the paper itself tells a very different story.

Abstract

The implications of climate change for malaria eradication this century remain poorly resolved^1,2.

Many studies focus on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control and the socioeconomic environment, including disruption from extreme weather^3,4.

Here we integrate 25 years of African data on climate, malaria burden and control, socioeconomic factors, and extreme weather.

Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario⁵, we estimate the future impact of climate change on malaria burden in Africa, including both ecological and disruptive effects.

Our findings indicate that climate change could lead to 123 million (projection range 49.5 million to 203 million) additional malaria cases and 532,000 (195,000–912,000) additional deaths in Africa between 2024 and 2050 under current control levels.

Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50–94%) of additional cases and 93% (70–100%) of additional deaths.

Most increases stem from intensification in existing endemic areas rather than range expansion, with significant regional variation in impact.

These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress towards malaria eradication.

https://www.nature.com/articles/s41586-025-10015-z

This is not a study that reports new empirical discoveries about malaria transmission. It is an exercise in scenario construction. Its results emerge from a long chain of assumptions, models, and parameter choices, each, possibly, defensible in isolation, but collectively producing an impression of precision that the authors’ own caveats do not support.

The paper opens by positioning itself as a corrective to earlier work, arguing that prior studies focused too narrowly on ecological mechanisms while neglecting social and infrastructural disruption. That framing sets the stage for what follows: a shift away from biology and toward modeled institutional fragility.

“Nearly all existing projections share a central limitation: although they explore climate effects in isolation, they do not adequately account for non-climate determinants of malaria trends.”

This sounds reasonable, but what replaces that “limitation” is not observation. It is an expanded modeling framework that incorporates still more uncertain components.

The foundation of the analysis is future climate. The authors rely on downscaled CMIP6 global climate model outputs under the SSP 2-4.5 scenario, described as a “middle of the road” pathway. These models are known to diverge substantially in regional precipitation and extreme weather projections across Africa. The paper attempts to address this by using ensembles, but ensembling disagreement does not eliminate uncertainty—it simply averages it.

“Between-GCM uncertainty and variability were accounted for using an ensemble of CMIP6 members…”

That sentence does a great deal of rhetorical work. “Accounted for” sounds reassuring, but an ensemble mean is not a validation. It is a compromise among conflicting model structures, all of which share common assumptions and biases.

Those climate projections are then downscaled to a 5×5 km grid and fed into mechanistic models that translate temperature, rainfall, and humidity into mosquito and parasite suitability indices. These models are highly nonlinear and sensitive to thresholds.

Small errors in climate inputs propagate into large swings in transmission suitability. This is a property of the models—but it is rarely emphasized when results are summarized.

The suitability indices are then used as predictors in a stacked statistical framework combining linear models, generalized additive models, boosted regression trees, and a Bayesian geostatistical smoother. The machinery is complicated, but it is also opaque. Even the authors acknowledge that the uncertainty behavior of such systems is poorly understood.

“There is currently limited precedent for fully characterizing uncertainty in stacked models, particularly when applied to spatially correlated data.”

That admission should fundamentally constrain how the outputs are interpreted. Instead, it is buried in the Discussion, far removed from the headline numbers.

Up to this point, the results are relatively modest. When ecological effects alone are considered, the authors find that climate-driven changes in malaria transmission are small and mixed—some regions increase, others decrease, and the continent-wide average effect is close to zero.

“We project that, considered in isolation, the ecologically driven impacts of climate change on malaria transmission would lead to minimal overall change in Africa by 2050…”

This result is rarely highlighted, because it does not support the sense of urgency implied by the paper’s title.

The dramatic figures emerge only when the authors introduce a second layer: disruption from extreme weather events. Floods and cyclones are modeled to damage housing, disrupt vector control, and reduce access to treatment. According to the paper, these disruptive effects dominate the outcome.

“Extreme weather events emerge as the primary driver of increased risk, accounting for 79%… of additional cases and 93%… of additional deaths.”

This is the pivotal move. The paper is no longer primarily about climate and malaria ecology. It is about assumed disruption to institutions and infrastructure, projected decades into the future.

Here the evidentiary basis becomes especially thin. The magnitude and duration of disruption are not derived from large datasets or controlled studies. They are assembled from a literature review of heterogeneous case studies and 34 expert interviews, then translated into parameters describing how much housing is damaged, how many clinics close, and how long recovery takes.

“The paucity of data necessitated a more heuristic approach to quantifying likely impacts.”

“Heuristic” is doing a lot of work here. These parameters are not measured. They are judged to be plausible. Because the data are sparse, the authors apply uncertainty ranges spanning 50% to 150% of their central values.

“A broad uncertainty range was considered appropriate because the scarcity of observational data precluded a more formal quantification…”

These heuristic disruption parameters are then applied continent-wide, month by month, under simulated future floods and cyclones generated by climate-driven storm models. At this point, the model is several layers removed from anything that could reasonably be called an observation.

Yet the final outputs are presented as cumulative totals of cases and deaths, with specific numbers and ranges.

A bar graph showing the cumulative change in clinical cases (in millions) from 2025 to 2045, categorized by disruptions in access to treatment, housing, and vector control, along with ecological factors like temperature suitability and larval habitat availability. — a, Projected continent-wide cumulative impact on cases by year, with decomposition showing distinct contributions of the different ecological and disruptive drivers of climate change impact. Grey bars denote projection range, where this is calculated as the 10th and 90th percentile of the model ensemble across all GCMs and disruption parameter ranges. b, Mapped cumulative impact on cases with colour scale reflecting degree of GCM ensemble consensus. Large water bodies, national parks with extremely low or zero population, and out-of-scope countries are masked in dark grey. All cumulative impacts are relative to 2022 case counts, aligned to national totals reported in the 2023 World Malaria Report⁵⁰. Administrative boundaries were obtained fro
m the Malaria Atlas Project (https://data.malariaatlas.org)⁴¹, under a CC BY 3.0 licence.

https://www.nature.com/articles/s41586-025-10015-z/figures/3

“Our findings indicate that climate change could lead to 123 million additional malaria cases and 532,000 additional deaths in Africa between 2024 and 2050…”

What is rarely emphasized is that these figures depend critically on assumptions that freeze nearly all non-climatic progress. Malaria control coverage, housing quality, healthcare infrastructure, and socioeconomic development are held constant at present-day levels—except when they are damaged by climate events.

“We deliberately hold constant present-day levels of transport and healthcare infrastructure, housing quality and malaria control…”

This is not a neutral assumption. Historically, malaria burden has declined primarily because of improvements in drugs, vector control, infrastructure, and economic development. By suppressing those trends while allowing disruption to accumulate, the model structurally biases results toward worsening outcomes.

The authors also acknowledge that their projections are not forecasts.

“Our projections allow exploration of climate change effects but are not intended as forecasts of future conditions.”

They go further, stating explicitly that the uncertainty ranges are not statistical.

“The projection ranges are not… formal statistical intervals, providing indicative measures of uncertainty rather than probabilistic statements.”

These are not minor footnotes. They directly contradict the way the results are likely to be interpreted by policymakers, journalists, and advocacy groups.

This is where the rhetorical sleight of hand becomes apparent.

Deep in the paper, uncertainty is emphasized, limitations are acknowledged, and projections are framed as exploratory. In the abstract, figures, and conclusions, those same exploratory outputs are converted into quantified impacts with an unmistakable air of urgency.

The issue is structural. When multi-layered scenario models produce precise numbers, those numbers take on a life of their own. Caveats fade. Assumptions harden into facts. What began as “what if” quietly becomes “this will.”

The paper does not demonstrate that climate change will cause hundreds of millions of additional malaria cases. It demonstrates how easily such numbers can be produced when uncertain climate projections, sensitive ecological models, heuristic disruption parameters, frozen socioeconomic baselines, and long- time horizons are combined in a single framework.

The appropriate way to read this study is not as a prediction, but as a thought experiment—one that is highly sensitive to its assumptions and explicit about its limitations, if one reads far enough. The problem is not what the authors say in the fine print. It is how far that fine print is removed from the numbers that will be remembered.

In the end, this paper tells us less about the future of malaria than about the current state of climate-related modeling. It shows how uncertainty can be multiplied, smoothed, and translated into apparent precision. It shows how scenarios can be mistaken for forecasts. And it shows how, once numbers are published in a journal like Nature, they are treated as evidence—even when the authors themselves say they are not.

That is the sleight of hand worth paying attention to.

An elderly wizard in a dark robe and pointed hat is pouring a potion from one flask to another, creating glowing question marks that rise from a bubbling cauldron. Behind him are charts and graphs on the walls, along with a globe and various laboratory equipment. A mosquito image labeled 'MALARIA' is also present.

The Brain Microplastics Claim and the Collapse of Scientific Restraint

In early 2025, a University of New Mexico study published in Nature Medicine (February 2025). Researchers analyzed autopsy brain samples and found surprisingly high concentrations of tiny plastic particles, with levels roughly 50% higher in 2024 samples compared to 2016 ones. The brain showed higher plastic accumulation than organs like the liver or kidneys, and even more in cases involving dementia (though causation wasn’t proven).

Headlines often sensationalized this by noting the total plastic mass in an average brain could equate to about the weight of a plastic spoon (around 7–10 grams in some estimates), or roughly 0.5% of brain mass by weight.

A hyper-realistic model of a brain covered in colorful pieces of plastic, set against a backdrop of litter and waste.

A realistic model of a human brain, decorated with colorful plastic pieces and confetti, set against a background of newspapers highlighting environmental issues.

_________________________________________________________________________________________________________

From Watts Up With That?

By Charles Rotter

An abstract illustration depicting a brain silhouette with wavy lines suggesting sound or waves radiating from it.

For a brief and revealing moment, the public was told—without irony—that the human brain is now “full of plastic.”

Screenshot of a New York Post article titled 'New study finds entire spoonful of microplastics in people’s brains — and 3 times as much in those with dementia' with an embedded video showing a gloved hand holding a syringe.

The phrase was not metaphorical. It was literal, repeated across major outlets with the confidence usually reserved for gravity or photosynthesis.

Readers were assured that microplastics, and even nanoplastics, had accumulated in brain tissue at concentrations high enough to be weighed, compared, and graphed over time.

Some reports helpfully translated this into household imagery, offering estimates equivalent to spoons or teaspoons of plastic lodged in the mind. The implication was unmistakable: modern life had quietly transformed the brain into a synthetic landfill.

The study responsible for this frenzy appeared in a prestigious medical journal, which lent it instant authority.

Screenshot of a research article titled 'Bioaccumulation of microplastics in decedent human brains' published in Nature Medicine. Includes author names and publication details. — Bioaccumulation of microplastics in decedent human brains | Nature Medicine

The press release did the rest. Few journalists paused to ask the most basic question in analytical science: how, exactly, was “plastic” identified inside one of the most lipid-rich organs in the human body?

Fewer still asked whether the methods used were capable of distinguishing polymer fragments from ordinary biological molecules that share similar chemical signatures when thermally decomposed. The story was too good to slow down for that.

And that is precisely where the embarrassment begins.

The central claim of the paper was straightforward enough on its face. Using pyrolysis gas chromatography–mass spectrometry (Py-GC-MS), the authors reported detecting polymer signatures-predominantly polyethylene-in samples of human brain tissue collected post-mortem.

Concentrations were reported as higher than those found in other organs, and higher than those reported in earlier decades, suggesting accumulation over time. This was framed not merely as detection, but as evidence of increasing exposure and retention in neural tissue.

That framing collapses under even modest scrutiny.

Py-GC-MS is not a magic “plastic detector.”

It is a powerful but blunt instrument. The method works by heating a sample until it decomposes, then analyzing the resulting fragments.

Those fragments are matched to reference spectra to infer what compounds were present before pyrolysis. This works well when the target material has a distinctive decomposition pattern and the surrounding matrix is simple or well-controlled. Brain tissue is neither.

Human brain tissue is composed largely of lipids- cholesterol, phospholipids, sphingolipids, long-chain fatty acids-many of which, when pyrolyzed, generate hydrocarbons and alkene fragments that overlap substantially with those produced by polyethylene.

This is not an obscure technical footnote. It is a known, documented limitation of the method. In lipid-rich matrices, pyrolysis products from biological material can mimic polymer signatures unless digestion, cleanup, and marker selection are exceptionally rigorous.

The critique published in response to the study did not argue about policy, health impacts, or ideology. It argued about chemistry.

Screenshot of an article titled 'Challenges in studying microplastics in human brain' published in Nature Medicine on November 13, 2025, listing authors and metrics. — Challenges in studying microplastics in human brain | Nature Medicine

Specifically, it pointed out that the reported dominance of polyethylene in brain samples is exactly what one would expect if residual lipids were being misidentified as polymer fragments. The markers used to infer polyethylene presence are not uniquely diagnostic in the context of partially digested brain tissue.

Without exhaustive validation- using isotopically labeled controls, orthogonal analytical methods, and demonstrated exclusion of lipid interference- the conclusion that these signals represent plastic rather than biology is speculative at best.

Speculative is not the word used in headlines.

Equally troubling was the handling of contamination controls.

Microplastics research lives and dies on blanks. Airborne fibers, laboratory tubing, solvents, filters, gloves, and even lab coats are all sources of polymer contamination.

Serious studies devote enormous effort to procedural blanks, field blanks, recovery experiments, and transparent reporting of background levels.

The brain paper’s quality control reporting, while present, was insufficient to support the extraordinary claims made on its behalf. This is a methodological shortcoming. But when methodological shortcomings underpin claims of plastic-laden brains, the distinction matters.

Another red flag was the remarkable uniformity of polymer type. Across samples, across organs, across time, polyethylene dominated.

Real-world exposure does not work that way. Environmental microplastic mixtures are chemically diverse. Polypropylene, polystyrene, PET, PVC, and various copolymers are ubiquitous.

A biological accumulation process that somehow filters out nearly everything except polyethylene would require a mechanism so selective that it would itself be a major discovery. No such mechanism was demonstrated or even proposed.

The simpler explanation- that the analytical method preferentially “sees” polyethylene- like pyrolysis products in lipid-rich tissue- was never meaningfully addressed in the public narrative.

This is where the episode shifts from merely flawed to genuinely embarrassing.

Because once the technical critique appeared, it became clear that the most sensational aspect of the claim- the brain itself- was also the most analytically vulnerable.

The very property that makes brain tissue interesting for alarmist storytelling, its centrality and sensitivity, is what makes it uniquely difficult to analyze using pyrolysis-based methods. Fat looks like plastic when burned. That is chemistry.

None of this means that microplastics cannot, in principle, reach brain tissue. It means that this particular evidence does not establish that they have, let alone that they accumulate in meaningful quantities, let alone that they cause harm. Detection is not dose. Dose is not pathology. Pathology is not inevitability. Each step requires its own evidence, and skipping steps does not make the staircase shorter; it just makes the fall harder.

The media treatment of the study followed a depressingly familiar pattern. A technically complex paper with severe limitations was distilled into a single shocking sentence.

That sentence was repeated, embellished, and illustrated with stock photos of plastic waste.

Caveats, where they appeared at all, were buried deep in the text or outsourced to future research. By the time critical responses emerged, the narrative had already done its work. The image of plastic-stuffed brains had entered the cultural bloodstream, immune to correction.

To its credit, some mainstream outlets eventually noticed the problem.

Articles began to appear noting that key findings were being challenged, that analytical methods might be misfiring, and that false positives were a serious concern. This was presented as an update, not a reckoning. The original headlines were not retracted. The damage was simply allowed to age.

This is how advocacy ecosystems behave when the conclusion precedes the measurement.

The deeper issue exposed by the brain microplastics episode is a structural incentive problem.

There is enormous prestige in being first to claim that a new part of the human body has been “invaded” by modern pollution.

There is far less prestige in publishing a careful null result or a paper explaining why a method cannot yet answer the question being asked.

Journals reward novelty. Media rewards fear. And so, the system reliably produces dramatic claims perched on fragile analytical scaffolding.

Once that scaffolding is examined, the claims wobble.

The embarrassment, then, is not that scientists are studying microplastics in the body. That is a legitimate research question.

The embarrassment is that a claim as extraordinary as widespread plastic accumulation in the human brain was allowed to escape into public discourse without being anchored to analytical certainty.

The embarrassment is that chemistry was treated as a mere technicality rather than the foundation of the entire conclusion. The embarrassment is that skepticism- real skepticism, the disciplined refusal to accept claims without robust evidence- was framed as denial rather than diligence.

If the brain study had been reported honestly, the story would have been mundane: preliminary measurements using a challenging method in a difficult tissue type suggest possible signals that require extensive validation and independent replication.

That story would not have gone viral. It would not have justified sweeping narratives about modernity poisoning the mind. And it would not have served as rhetorical fuel for policy arguments that were already queued up in advance.

Instead, the public was handed a conclusion masquerading as a discovery.

When future readers look back on this episode, they are unlikely to remember the technical details of pyrolysis markers or lipid interference. They will remember the image. And when the image eventually dissolves under the weight of better measurement- and it will- the episode will stand as another example of how easily scientific restraint is abandoned when a frightening story is available for the telling.

That is the cost of ignoring it.

Tag: Bad Science

The Sheer #%&@! Gall of Nature Magazine. EU Chief Science Advisers Say Democracy Depends on Trusted Science, and Ignore How the Trust Was Squandered

Like this:

When Experts Obfuscate! How Alarmist Scientists like D. Swain, J. Abatzoglou, A. Williams Falsely Attribute Wildfires to Climate Change.

Like this:

Groan…Science Tries to Legitimize ‘Weather control’

Like this:

A Statistical Evaluation of the Whining of Climate Scientists as Political Activists

ABSTRACT

Like this:

Flipping Out in a Warming World

Like this:

Models Gone Wild: The Ionosphere Triggers Earthquakes?

Like this:

Turning “What If” into “How Many”: The Rhetorical Alchemy of Climate Modeling

Abstract

Like this:

The Brain Microplastics Claim and the Collapse of Scientific Restraint

Like this: