Tag: climate model

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.

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.

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 resolved1,2.

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 weather3,4.

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.

No, NPR, CBS, and NBC, Climate Change Isn’t a Proven Cause of Arctic Warming

National Public Radio (NPR) recently published an article titled “Orange rivers and melting glaciers: federal report shows rapid change in the Arctic.” Simultaneously, CBS News published “Arctic temperatures increasing at over double the global rate since 2006, NOAA report says,” while NBC News published “Arctic is again the hottest it’s been in 125 years, with record-low sea ice, NOAA report says.” All three articles promote the claim that the Arctic is warming faster than the average rate of warming for the Earth as a whole, presenting this as clear evidence of accelerating human-caused climate change with far-reaching consequences.

A polar bear sitting on a rocky surface, looking up at the colorful aurora borealis in the sky during sunset.

From ClimateRealism

By Anthony Watts

Collage of news articles from CBS News, NPR, and NBC News about Arctic temperatures and climate change, with the text 'NOT VERIFIABLE' overlaid in red.

National Public Radio (NPR) recently published an article titled “Orange rivers and melting glaciers: federal report shows rapid change in the Arctic.” Simultaneously, CBS News published “Arctic temperatures increasing at over double the global rate since 2006, NOAA report says,” while NBC News published “Arctic is again the hottest it’s been in 125 years, with record-low sea ice, NOAA report says.” All three articles promote the claim that the Arctic is warming faster than the average rate of warming for the Earth as a whole, presenting this as clear evidence of accelerating human-caused climate change with far-reaching consequences. While the math may be technically true, based on history and the scarcity of long-term data, blaming the warming on climate change is unjustified. The period of data used is just 19 years, which doesn’t meet the 30-year criteria for a climate period. Further, all three articles ignore the lack of long-term weather data to compare the present short-term trend to.

The articles cite the National Atmospheric and Atmospheric Administration’s (NOAA) Arctic Report Card and lean heavily on the phrase “since 2006,” with CBS explicitly stating that Arctic temperatures have increased at more than double the global rate over that period, while NPR frames recent observations as proof of rapid, unprecedented change. But trends “since 2006” do not meet the most basic standard for defining climate. By long-standing convention, climate trends are evaluated over 30-year periods to filter out short-term variability. A roughly 19-year window on Arctic temperature is not a climate dataset; it is a snapshot. Presenting such a short interval as definitive evidence of long-term Arctic warming is a textbook example of confusing short-term patterns with durable trends.

NBC News claimed that the “Arctic is again the hottest it’s been in 125 years” but never mentions that historic temperature data for the entire Arctic region is relatively sparse and inconsistent over the past 125 years. Also, if the Arctic was as warm as now 125 years ago and more, human climate change couldn’t have been the cause and may not be the cause for the short-term warming trend the region is currently experiencing.

The problems with these kinds of reports are compounded by the Arctic’s uniquely sparse and inconsistent observational record. Reliable, continuous surface temperature measurements across much of the Arctic are relatively recent, with large areas lacking high-quality long-term data prior to the satellite era. As the Climate at a Glance analysis “Arctic Sea Ice” explains, the Arctic climate system is strongly influenced by natural variability, including ocean circulation patterns and atmospheric oscillations, which can drive multi-decadal warming and cooling phases independent of any long-term trend. Further, it notes that satellite data show summer minimum sea ice has not decreased at all since 2007, and has stabilized after a temporary low in 2012, seen in Figure 1 below.

Graph showing the Northern Hemisphere extent anomalies from September 1979 to 2023, with linear trend lines for 1979-2023 and 2007-2023.
Figure 1. Shows satellite derived summer minimum Arctic Sea ice extent values from 1979 to 2023, with dashed line showing the linear trend. The added trend line in red shows no change in summer minimum extent since 2007. Image source: NSIDC. Red trend line from 2007 and trend line examples under the title added by A. Watts

This is important because many claims pin other Arctic environmental changes on Arctic sea ice trends.

When you combine short records with a region known for large natural swings, it becomes impossible to say with confidence whether a post-2006 warming rate reflects a persistent climate signal or a temporary phase.

Climate at a Glance further notes that Arctic sea ice, glaciers, and temperatures have all exhibited substantial variability over historical and paleoclimate timescales, long before industrial-era CO₂ emissions. Periods such as the early twentieth century saw pronounced Arctic temperature increases comparable, in relative terms, to more recent decades. Without consistent, high-quality measurements extending well beyond the modern satellite era, claims of unprecedented warming rest on shaky ground.

The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6,) is far more cautious than the media coverage suggests. IPCC AR6 acknowledges Arctic warming as a feature in models and observations, but it also emphasizes uncertainties tied to data coverage, internal variability, and the relatively short observational record in polar regions. That nuance is missing from both NPR and CBS, which instead present recent conditions as if they settle the question.

Climate Realism has repeatedly addressed this issue, documenting how media outlets cherry-pick short timeframes to manufacture a sense of crisis. A simple review of Climate Realism’s Arctic coverage shows multiple examples where “fastest warming” or “record heat” claims collapse once longer records or broader context are considered. The pattern is familiar: select a recent starting point near a cool baseline, measure to a warm endpoint, and declare a dramatic trend—while ignoring earlier warm periods and the role of natural variability. In 2022, another “Arctic Report Card” media response was thoroughly debunked in CNBC and Other Media Outlets Miss the Mark Regarding NOAA’s Misleading ‘Arctic Report Card.’

In their recent scary reporting, NPR, CBS, and NBC all gloss over the fact that the Arctic is one of the most climatically dynamic regions on Earth. Sea ice extent, snow cover, and surface temperatures respond strongly to shifting winds, ocean heat transport, and cloud cover. These processes can amplify short-term warming just as easily as they can produce abrupt cooling. Treating such behavior as proof of a one-way, accelerating climate crisis oversimplifies a complex system.

By framing a sub-30-year slice of Arctic data as definitive evidence of a climate change induced crisis, without acknowledging the severe limitations of historical observations in the region, NPR, CBS, and NBC grossly mislead their audiences. The claim that the Arctic has been warming “at over double the global rate since 2006” may be mathematically true for that chosen interval, but it does not establish that a long-term climate driven warming trend exists. At best, it describes a short-term pattern whose significance remains uncertain.

This is not careful climate reporting; it is narrative-driven alarmism. Readers and viewers deserve to know that “since 2006” is not established as a climate change trend, that Arctic records are short and sparse, and that natural variability looms large in the far north, with proxy evidence suggesting the Arctic has been as warm with lower levels of ice coverage for extended periods multiple times. Without that critical context, these stories exaggerate certainty of a human cause and overstate what science can actually tell us about the Arctic.

Wrong, Phys.org, Faulty Thermometers Aren’t Evidence of a ‘Climate Roller Coaster’

Phys.org recently published “Global warming amplifies extreme day-to-day temperature swings, study shows,” reporting on a new study in Nature Climate Change which claims that human-caused warming is intensifying rapid day-to-day temperature fluctuations, in the process creating a new category of climate hazard. This is false.

A concrete building featuring prominent thermometer and globe symbols, with several uniformed guards standing in front.

From The ClimateRealism

By Anthony Watts

Map showing global temperature variations with the text 'IGNORES THE REAL-WORLD' overlaid.

Phys.org recently published “Global warming amplifies extreme day-to-day temperature swings, study shows,” reporting on a new study in Nature Climate Change which claims that human-caused warming is intensifying rapid day-to-day temperature fluctuations, in the process creating a new category of climate hazard. This is false. The underlying study not only relies almost entirely on notably flawed climate-model assumptions, but astonishingly also ignores well-documented, real-world factors that directly affect short-term temperature variability, such as the urban heat island effect (UHI) and temperature-station siting biases. These omissions fatally undermine the study’s conclusions.

Phys.org’s press release asserts that “extreme day-to-day temperature changes have become more frequent and intense across low- to mid-latitude regions,” with researchers’ “optimal fingerprinting” methods confirming greenhouse gases as the primary cause. It further claims this daily volatility creates “a climate roller coaster” harmful to public health. The underlying Nature Climate Change study echoes this framing, presenting global maps of amplified temperature swings but never addresses the integrity—or contamination—of the surface data employed.

This is the study’s central flaw: Urban Heat Island (UHI) contamination artificially raises nighttime minimum temperatures, causing next-day temperatures to begin from an already elevated baseline. Artificial surfaces—pavement, buildings, vehicles, heat-retaining infrastructure—release stored heat overnight, pushing lows upward and exaggerating the apparent magnitude of day-to-day variations. Yet the terms “urban heat island” or UHI do not appear even once in the Nature Climate Change publication or the press release. Nor does the paper mention temperature-station placement, metadata quality, microsite compliance with quality control, or any discussion of observational uncertainty.

These omissions are inexcusable, because, as the linked Heartland Institute report Corrupted Climate Stations: The Official U.S. Temperature Record Remains Fatally Flawed demonstrates, approximately 96% of NOAA climate stations fail to meet the National Oceanic and Atmospheric Administration’s (NOAA) own siting requirements, and are corrupted by localized heat sources such as asphalt, rooftops, HVAC exhaust, machinery, or reflective surfaces. Heartland’s report shows that compliant stations—those free of artificial heat contamination—exhibit about half of the warming trend found in the corrupted network. See the figure below. Note the difference between the blue line (the uncorrupted stations) and the orange and red lines.

Graph comparing temperature trends from compliant and non-compliant NOAA climate stations in the contiguous United States from 1979 to 2008.

These findings are backed by NOAA’s own siting standards and confirm that station bias, not climate physics, drives much of the exaggerated warming in the U.S. record, and by extension, much of the world.

If the observational foundation is distorted, the derived conclusions—such as “increasing day-to-day temperature volatility”—are also distorted.

Further, the Nature Climate Change authors use global reanalyses and model-based “optimal fingerprinting” to attribute these amplified swings to greenhouse gases. But a model in which biased data serves as a foundation will simply reinforce or exacerbate those biases. None of the physical mechanisms the authors propose—soil moisture changes, pressure variability, drought feedbacks—can be meaningfully evaluated if the input dataset contains systematic nighttime warming caused by UHI and faulty station placement.

This is precisely why the U.S. Climate Reference Network (USCRN), which avoids artificial heat sources by design, shows smaller warming trends than the older, urban-contaminated networks. See the USCRN figure below showing maximum temperatures in the U.S. since 2005. Note the lack of increased temperature extremes (peaks) today compared to 20 years ago, or any obvious upward trend in the data.

Graph showing the maximum temperature anomaly in the U.S. since 2005, comparing ClimDiv and USCRN data with fluctuating temperature trends.
Source: National Centers for Environmental Information (NCEI) here: https://www.ncei.noaa.gov/access/monitoring/national-temperature-index/time-series/anom-tmax/1/0

Yet the current study never references USCRN data—or any equivalent bias-free baseline.

Even their health-risk findings rely on heat-biased data. If nighttime temperatures are artificially elevated due to urbanization and poor siting, the apparent “roller-coaster swings” reflect local land-use change, not global climate physics. Any claimed health correlations with mortality data would therefore be mixing genuine weather effects with measurement artifacts.

What’s missing, in other words, is climate science’s most basic requirement: separating real climate signal from non-climatic noise.

UHI, land-use change, asphalt expansion, vegetative loss, waste heat emissions, and the explosive growth of built environments all elevate nighttime minimums and increase apparent variability. The study treats all variability as atmospheric in origin, ignoring the man-made thermal reservoirs embedded in modern cities.

A rigorous scientific analysis would have:

  • Examined UHI contamination explicitly.
  • Segregated rural high-quality stations from urban or microsite-compromised ones.
  • Compared modeled projections to USCRN’s pristine, bias-free observational network.
  • Quantified the effect of siting violations documented in my 2022 Corrupted Climate Stations study.

Instead, the authors rely on global models and “fingerprinting” techniques that simply assume the data are valid. That assumption is not supported by the evidence, and the report’s conclusions cannot be meaningfully distinguished from the biases embedded in the underlying data. This is not “a new extreme climate hazard”—it is the predictable result of modeling data with errors, then attributing those errors to greenhouse gases.

Phys.org does its readers a disservice by not fact checking the Nature study and examining other possible causes for its conclusions. Instead of a thoughtful scientific examination of the novel claims made in the study it is reporting on, Phys.org’s readers got a fawning, uncritical summary of the flawed Nature study, which exaggerates day-to-day variability based on the well-documented role of UHI and siting flaws. That’s not inquisitive journalism, that’s promotion; in reality no better than a press release.

CLAIM: Increasing heat is super-charging Arctic climate and weather extremes

From North Carolina State University and the “because our models say-so” department comes this press release that is far more predictable than the climate change it claims to predict – Anthony.

A frozen elderly man with icy facial features, wearing a white coat, stands in a snowy landscape.

From Watts Up With That?

By Anthony Watts

From North Carolina State University and the “because our models say-so” department comes this press release that is far more predictable than the climate change it claims to predict – Anthony.

By evaluating historical climate records, observational and projection data, an international team of researchers found a “pushing and triggering” mechanism that has driven the Arctic climate system to a new state, which will likely see consistently increased frequency and intensity of extreme events across all system components – the atmosphere, ocean and cryosphere – this century.

“We know that mean temperatures are rising, and the Arctic is commonly considered an indicator of global changes due to its higher sensitivity to any perturbation of external and internal forcings,” says Xiangdong Zhang, research professor at North Carolina State University and senior scientist at the North Carolina Institute for Climate Studies.

“The annual mean warming rate of the Arctic is more than three times the global average – this is known as Arctic amplification,” Zhang says. “But no systematic review has been done about the interplay of warmer temperatures with the dynamics of atmosphere, ocean and sea ice in weather and climate extremes around the Arctic.” Zhang is the lead author of the study.

The team looked at historic temperature data and the records of extreme events in the Arctic system components, as well as CMIP6 model projections that covered a period from the present-day to the end of the century. Overall, they saw that extreme events – atmosphere and ocean heatwaves, heavy precipitation, sea ice loss and ice sheet melt – have consistently occurred across the Arctic climate system with an increased frequency and intensity since the year 2000. The CMIP6, or Coupled Model Intercomparison Project Phase 6, is an international project consisting of modeling centers and groups worldwide.

“We usually think about warming as a gradual, quasi-linear change of temperature over time – it slowly gets warmer everywhere,” Zhang says. “But nonlinear changes occur across the entire system. The interplay between warming and the changes in atmospheric, oceanic and sea ice dynamics creates ‘pushing and triggering’ mechanisms that result in a tipping point for the climate system.”

According to the researchers, since the year 2000 these pushing and triggering mechanisms have forced a step change, or sudden shift, in the baseline of the Arctic climate system.

The mechanisms in question include changes in large-scale atmosphere and ocean circulation that enhance poleward atmospheric heat and moisture transport and ocean heat transport into the Arctic. Intense cyclones and blocking high-pressure systems that obstruct the movement of other systems through the upper atmosphere further enhance warming, increasing sea ice and ice sheet temperatures and pushing the Arctic climate system to a tipping point, triggering more extremes.

“Once there is a baseline state change in climate, we also see a change in extreme events,” Zhang says.

According to the researchers’ analysis, since the year 2000 the probability of atmospheric heatwaves has increased by 20%; Atlantic Ocean layer warm events have increased by 76%; sea ice loss events have increased by 83%; and Greenland Ice Sheet melt extent has increased by 68%.

“Prior to the 21st century, these events were rare,” Zhang says. “But with continued warming they will become the new norm, and we could see ice-free summers in the Arctic by mid-century. More work must be done to understanding the interplay of multiscale climate drivers in the Arctic, so that we can predict and plan for the future.”

The study appears in Nature Reviews Earth & Environment. Zhang is supported in part by the U.S. Department of Energy and the National Oceanic and Atmospheric Administration. The work was done by leading and early-career scientists from the U.S., Australia, Canada, China, Finland, Germany, Norway, Sweden, Switzerland and the United Kingdom.

CLAIM: Heatwaves to increase in frequency, duration under global warming

When I saw this press release from Portland State University, I knew I would not have to look far to spot the bias and/or error. First, it’s a climate model, second, it’s the WORST climate model, CMIP6 -Anthony

From Watts Up With That?

By Anthony Watts

When I saw this press release from Portland State University, I knew I would not have to look far to spot the bias and/or error. First, it’s a climate model, second, it’s the WORST climate model, CMIP6 -Anthony

Via Eurekalert:
As the climate becomes warmer on average, it makes intuitive sense that we will see more hot days and we’ve had predictions of this for some time. However, the duration of heatwaves — how many days in a row exceed a temperature that is unusually hot for a given region — can be very important for impacts on humans, livestock and ecosystems. Predicting how these durations will change under a long-term warming trend is more challenging because day-to-day temperatures are correlated — tomorrow’s temperatures have a dependence on today’s temperature.

This study takes this effect into account, along with the warming seen in current and historical observations and projected for the future by climate models for a wide range of land regions. Not only do the heatwave durations increase, but each additional increment of warming causes a larger increase in the typical length of long heat waves. In other words, if the next decade brings as much large-scale warming as a previous decade, the additional increase in heatwave durations would be even larger than we’ve experienced so far.

Abstract

Heatwaves are expected to both increase in frequency and duration under global warming. The probability distributions of heatwave durations are shaped by day-to-day correlations in temperature and so cannot be simply inferred from changes in the probabilities of daily temperature extremes. Here we show from statistical analysis of global historical and projected temperature data that changes in long-duration heatwaves increase nonlinearly with temperature. Specifically, from analysis informed by theory for autocorrelated fluctuations applied to European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations, we find that the nonlinearity results in acceleration of the rate increase with warming; that is, each increment of regional time-averaged warming increases the characteristic duration scale of long heatwaves more than the previous increment. We show that the curve for this acceleration can be approximately collapsed onto a single dependence across regions by normalizing by local temperature variability. Projections of future change can thus be compared to observations of recent change over part of their range, which supports the near-future-projected acceleration. We also find that the longest, most uncommon heatwaves for a given region have the greatest increase in likelihood, yielding a compounding source of nonlinear impacts.

Journal Nature Geoscience

DOI/Link: 10.1038/s41561-025-01737-w 

Another Day, Another Model of Future Climate Doom

From the Institute for Basic Science and the “we’re all gonna die” department comes Episode #2971 of model madness via press release:

Earth’s future climate at 9 km worldwide resolution

From Watts Up With That?

By Anthony Watts

From the Institute for Basic Science and the “we’re all gonna die” department comes Episode #2971 of model madness via press release:

Earth’s future climate at 9 km worldwide resolution

Global Warming does not affect our planet evenly. Some areas such as the Arctic region or high mountain peaks warm faster than the global average, whereas others, including large parts of the tropical oceans, show reduced temperature trends compared to the mean. The heterogeneity of future rainfall patterns is even more pronounced. To adapt to future climate change, policymakers and stakeholders need detailed regional climate information, often on scales much smaller than the typical resolution (~100-200 km) of climate models used in the reports of the Intergovernmental Panel on Climate Change (IPCC).

A team of scientists from the IBS Center for Climate Physics (ICCP), Pusan National University in South Korea and the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany has achieved an important breakthrough in climate modeling, providing unprecedented insights into Earth’s future climate and its variability. Their research was published in the open access journal Earth System Dynamics.

Utilizing the AWI-CM3 earth system model, a novel iterative global modeling protocol, and two of South Korea’s fastest supercomputers (Aleph at the Institute for Basic Science and Guru at the Korea Meteorological Administration), the researchers have simulated climate change at scales of 9 km in the atmosphere and 4-25 km in the ocean. These extensive computer model simulations offer a more accurate representation of future climate conditions, enabling better planning for climate adaptation.

The AWI-CM3 high-resolution model accurately represents global climate, including small-scale phenomena, such as rainfall in mountainous regions, coastal and island climate processes, hurricanes and ocean turbulence (Fig. 1). By resolving more regional details and their interactions with the large-scale atmosphere and ocean circulations, the model demonstrates a superior performance compared to most lower-resolution climate models.

A snapshot of simulated climate conditions. Blue/red shading: sea surface temperature deviations from zonal mean; gray/white shading: low clouds; green/pink shading: 10m wind speed; blue/yellow shading in upper panels: hurricane precipitation. The figure illustrates the ubiquity of mesoscale climate phenomena, such as Tropical Instability Waves in the equatorial Atlantic and Pacific, hurricanes (making landfall in Hawaiʻi in this snapshot), ocean cold wakes generated by hurricanes, stratocumulus cloud decks and patchy day-time convection over the Amazon forest. Credit: Institute for Basic Science

A main product of the simulations is a set of detailed global maps of expected climate change (e.g., temperature, rainfall, winds, ocean currents, etc.) for an anticipated 1oC future global warming.

“It is important to keep in mind that Global Warming is spatially quite heterogenous. For a 1oC global temperature increase, the Siberian and Canadian Arctic will warm by about 2oC, whereas the Arctic Ocean will experience warming of up to 5oC. In high mountain regions, such as the Himalayas, the Andes and the Hindu Kush, the model simulates a 45-60% acceleration relative to the global mean”, says MOON Ja-Yeon from the ICCP, and lead author of the study. To ensure broad access to these high-resolution climate projections, the team has launched an interactive data platform, where users can explore future climate change on regional and global scales (Fig. 2). Normalized climate change data for a 1°C Global Warming level can be downloaded and opened directly in the Google Earth application. These data can provide information on expected future changes in climate variables, such as windspeed and clouds, which are relevant for the future deployment of wind or solar farms, respectively.

Download data of climate change (e.g., temperature, wind speed, precipitation, etc.) per 1oC global warming from 9 km AWI-CM3 Global Warming simulations. Go to: https://climatedata.ibs.re.kr/data/papers/moon-et-al-2024-earth-system-dynamics. Credit: Institute for Basic Science

“Our study also highlights the regional impacts of major modes of climate variability, such as the Madden Julian Oscillation, the North Atlantic Oscillation, and the El Niño-Southern Oscillation, as well as their response to greenhouse warming” says Prof. Thomas JUNG from the AWI and co-corresponding author of the study. According to the AWI-CM3 simulations, the amplitude of both, the Madden Julian Oscillation and of the alternating El Niño and La Niña events will increase in the future, which will lead to intensified rainfall impacts in affected regions. ​The simulations further indicate an increase in the frequency and intensity of extreme rainfall events (>50 mm/day) in areas such as eastern Asia, the Himalayas, the Andes, Amazonia, mountain-tops in Africa and the east coast of North America with significant implications for flooding, erosion, and landslides.

“Most global climate models used in the assessment reports of the IPCC are too coarse to resolve small islands, such as those in the western tropical Pacific. These islands are already threatened by global sea level rise. Our new climate model simulations now provide new regional insights into what these regions can expect in terms of changes in ocean currents, temperatures, rainfall patterns and weather extremes. We hope that our dataset will be used extensively by planners, policy- and decision-makers and the public.”, says Prof. Axel TIMMERMANN, Director of the ICCP and co-corresponding author of the study.

The study’s findings offer critical information for assessing climate risks and implementing adaptation measures on regional scales.

Journal: Earth System Dynamics

DOI/Link : 10.5194/esd-16-1103-2025 

No, 170 Londoners Didn’t “Die of Climate Change” Last Week

According to Imperial College and the London School of Hygiene and Tropical Medicine, 170 Londoners died due to climate change in last week’s heatwave. But it’s just more modelling make believe, says Ross Clark in the Spectator.

From The Daily Sceptic

By Will Jones

According to Imperial College and the London School of Hygiene and Tropical Medicine, 170 Londoners died due to climate change in last week’s heatwave. But it’s just more modelling make believe, says Ross Clark in the Spectator. Here’s an excerpt.

So, 260 Londoners died as a result of last week’s heatwave, of which 170 can be attributed to climate change. So claims Imperial College and the London School of Hygiene and Tropical Medicine.

There’s just the one problem with this: the researchers haven’t actually counted any deaths at all. The study rushed out this week is nothing more than a piece of modelling, which estimates the number of deaths which might be expected to have been caused by the hot weather, as well as trying to guess how much hotter last week’s weather was than it would have been without man-made climate change. We won’t know whether there really were any excess deaths during the hot weather until the Office for National Statistics (ONS) publishes its figures in a few weeks’ time.

Why rush out a piece of modelling when we will shortly have some real-world data? The researchers will have to explain their reasoning, but it is as well to note that both the participants in this study have a bit of previous when it comes to modelling alarming numbers of deaths. 

Both were heavily involved in modelling during the Covid pandemic. Many of their predictions were not able to be tested against real data: we will never know, for example, whether 500,000 people really would have died of Covid were it not for social distancing measures – as Imperial College claimed in its paper of March 16th 2020 – because, of course, the government did impose a lockdown.

But towards the end of the crisis, one prediction was able to be tested against reality. On December 11th 2021, the London School of Hygiene and Tropical Medicine put out a paper claiming that 24,000 people would die as a result of the Omicron variant of Covid, with daily hospitalisations peaking above those in January 2021, unless the government introduced a return to lockdown -style measures. Perhaps it was counting on ministers taking its advice, but in the event the cabinet resisted – and the prediction was exposed for what it was: a gross exaggeration.

Worth reading in full.

The Science of Scapegoating: Nature’s Farcical Case for Climate Litigation

In the latest episode of Climate Courtroom Theater, Nature magazine has kindly served up a glowing piece of agitprop masquerading as science: “Carbon majors and the scientific case for climate liability.” A more fitting title might be “How to Blame Corporations for the Weather in Five Easy Steps.”

From Watts Up With That?

From Charles Rotter

Carbon majors and the scientific case for climate liability | Nature

In the latest episode of Climate Courtroom Theater, Nature magazine has kindly served up a glowing piece of agitprop masquerading as science: “Carbon majors and the scientific case for climate liability.” A more fitting title might be “How to Blame Corporations for the Weather in Five Easy Steps.” With all the analytical rigor of a late-night infomercial and the objectivity of a Greenpeace fundraising letter, the paper seeks to transform centuries of scientific uncertainty into a lucrative legal weapon against energy producers.

Emissions linked to Chevron, the highest-emitting investor-owned company in our data, for example, very likely caused between US$791 billion and $3.6 trillion in heat-related losses over the period 1991–2020, disproportionately harming the tropical regions least culpable for warming. More broadly, we outline a transparent, reproducible and flexible framework that formalizes how end-to-end attribution could inform litigation by assessing whose emissions are responsible and for which harms. Drawing quantitative linkages between individual emitters and particularized harms is now feasible, making science no longer an obstacle to the justiciability of climate liability claims.

Let’s begin by unraveling the pseudo-scientific spaghetti they attempt to pass off as empirical analysis.

The central claim of this paper is as bold as it is bizarre: emissions from a handful of “carbon majors” can be directly linked to specific temperature and sea-level changes. How do they arrive at this? Through a series of models so layered in assumptions, estimates, and conjectures, they resemble an academic version of Inception.

From the paper:

“We find that emissions traced to 88 carbon majors contributed 0.50 ± 0.04 °C (50% ± 4%) of the rise in global mean surface temperature between 1851 and 2021.”

Translation: “We ran a model that assumes what we want it to prove, and—surprise!—it proves it.”

The authors use what they call an “emissions attribution approach,” which sounds fancy until you realize it’s essentially climate model Mad Libs. They start with historical emissions from companies like ExxonMobil and Chevron, toss them into a simplified climate model (MAGICC6, a delightful irony), and voilà—they claim these firms raised the global temperature by 0.50 °C and sea level by 9 cm. That’s 9 centimeters of litigation-ready accusation, extrapolated from a data set with more holes than a slice of Swiss cheese.

Garbage In, Liability Out

A key ingredient in this scientific stew is the Carbon Majors Database, a record of emissions tied to corporations since 1854. It’s unclear how the authors reconcile historical fossil fuel extraction with the vastly different combustion technologies, energy efficiencies, and population demands over 170 years. Spoiler alert: they don’t.

This database assigns blame for emissions as if industrial development was a solo act orchestrated by a few oil barons, rather than a global march driven by human demand, geopolitical necessity, and technological evolution. It’s a historical rewrite so absurd it could qualify as climate fan fiction.

From the paper:

“We argue that our analysis supports the scientific basis for climate change litigation and legal strategies to hold fossil fuel companies accountable for climate harms.”

This is the ideological Rosetta Stone. The research isn’t intended to explore the causes of climate variability. It’s designed to provide ammunition for lawsuits—plain and simple.

The paper’s activist streak isn’t buried—it’s front and center. The authors explicitly aim to support “liability and litigation efforts,” which they somehow claim does not compromise the neutrality of their work. This is akin to a plaintiff writing a judge’s ruling in advance, with the ink still wet on their lawsuit.

From the authors:

“Our results provide scientific evidence that can inform legal arguments about causation and corporate responsibility.”

And there you have it: the transformation of climate science into a courtroom consultant. The authors aren’t merely producing scientific knowledge—they’re drafting talking points for tort lawyers.

One might expect Nature to demand higher standards than turning scientific publishing into an amicus brief for Greenpeace. But alas, in today’s climate orthodoxy, publication standards appear to hinge more on ideological alignment than analytical robustness.

Assigning legal liability to energy producers for climate outcomes is akin to suing a baker for obesity or a lumberjack for deforestation—ignoring the role of demand, policy, and collective human action. It rewrites economics and responsibility with the logic of a show trial.

No mention is made of the role of consumer behavior, nor the benefits these energy sources provided over centuries. There’s also a disturbing absence of counterfactual analysis—what would the world have looked like without fossil fuels? The authors don’t ask because they already know the answer would obliterate their thesis.

The “net social benefit” of hydrocarbons, historically speaking, dwarfs the theoretical damages derived from speculative modeling. But that nuance has no place in a paper designed to weaponize science in service of litigation.

A Court Case in Search of a Crisis

This is not science—it’s a legal brief dressed up as climate modeling. It seeks to forge causality out of correlation, cherry-pick data to frame corporate villains, and provide activist lawyers with a glossy PDF of “evidence” that would fall apart under any serious cross-examination.

The true tragedy here isn’t the legal threats levied against corporations. It’s the degradation of scientific integrity, sacrificed on the altar of ideological fervor and political convenience. In the end, this paper says more about the biases of its authors than it does about the Earth’s climate.

And while the authors may hope their citations are someday read aloud in court, they may want to prepare for another reality: being footnoted in future textbooks as a cautionary tale of how not to do science.

Year 2024 NOAA Measured Sea level Rise Data Show Climate Alarmists CO2 Driven Sea Level Rise Acceleration Claims Have Spectacularly Failed

NOAA year 2024 measured U.S. Station Sea Level rise data outcomes establish that alarmists 4 decades long hyped claims of high rates of accelerating sea level rise due to increasing man-made CO2 emissions have clearly failed.

From Watts Up With That?

Guest essay by Larry Hamlin

NOAA year 2024 measured U.S. Station Sea Level rise data outcomes establish that alarmists 4 decades long hyped claims of high rates of accelerating sea level rise due to increasing man-made CO2 emissions have clearly failed.

In 1988 Senate hearings Democrat politicians claimed that climate model derived accelerating sea level rise future outcomes will occur because of man-made CO2 emissions as presented in their Senate testimony by “experts” as addressed in detail here in the WUWT article shown below.

At the time of these hearings in 1988 global CO2 emissions stood at about 20.85 billion metric tons and since then have climbed to about 35.13 billion metric tons by year end 2023 as documented here.

The entire increase in global CO2 emissions from 1988 to 2023 is accounted for solely by the world’s developing countries (24.02 billion metric tons in 2023 versus 9.27 billion metric tons in 1988) lead by coal fuel dominate China and India with the developed countries, including the U.S. and Europe, having CO2 emissions lower (11.11 billion metric tons in 2023 versus 11.59 billion metric tons in 1988) than year 1988 levels.

The developed countries globally irrelevant emissions outcome during this period occurred while they were wasting trillions of dollars on hugely unreliable and non-dispatchable renewable energy along with absurd net-zero pipedreams resulting in  massive energy cost increases, failing and decaying reliability of their energy systems with major economically important businesses and industry suffering decline and stagnation while damaging GDP growth in the largest of these nations.       

At the Democrats Senate 1988 hearing so called “experts” were paraded forward and provided testimony claiming that rising global CO2 emissions experienced to date (1988) would result in increasing sea level rise acceleration rates of 2.5 inches over the next decade (0.25 inches per year) as noted below from the hearing record as follows:

“Global mean temperature will likely rise at about 0.6 degrees F per decade and sea level at about 2.5 inches per decade.”

“These rates are about six times recent history.”

“Furthermore, as long as greenhouse gases continue to grow in the atmosphere, there is no known natural limit to the warming short of catastrophic change.”

“Because the oceans are slow to heat, there is a lag between emissions and full manifestation of corresponding warming, a lag which some estimate at 40 years.”

“The world is now 1 degree F warmer than century ago and may become another 1 degree warmer even if conditions are curtailed today.”

“Every decade of delay and implementation of greenhouse gas abatement policies ultimately adds perhaps a degree F of warming, and no policy can be fully implemented immediately in any event.”

As noted above global CO2 emissions have climbed from 20.85 billion metric tons to 35.13 billion metric tons from 1988 to 2023 driven exclusively by the developing nations.

Over this 4 decades interval of climbing CO2 emissions that, according to the 1988 Senate hearing “expert” testimony, should have resulted in huge increases in sea level rise acceleration far above the 2.5 inches per decade (0.25 inches per year) outcome hyped in the 1988 hearing.

NOAA has just released its year 2024 Relative Sea Level Rise data for its U. S. Stations. The data for the longest U.S. Station time period measurement location is at the Battery, New York (1856 to 2024) and is shown below.

The 2024 sea level trend outcome is the same as in 2023 at 2.92 +/- 0.09 mm/yr despite the usual contrived alarmists “Hottest Year on Record” claims debunked here.

Shown below is NOAA’s Battery Station relative sea level rise measured data from their report issued a decade ago for year 2014 and covering the period from 1856 to year 2014.

Note that NOAA’s 2014 report states the following regarding these mean sea level trend data measurements:

“Although the mean trend may change from year to year, there is no statistically significant difference between the calculated trends if their 95% confidence intervals overlap. Therefore, the most recent calculated trend is not necessarily more accurate than the previous trend; it is merely a little more precise.”

Therefore, since the NOAA Battery Station year 2024 relative sea level rise trend measured 95% confidence interval is from 3.01 to 2.83 mm/yr and thus overlaps the prior decade ago report year 2014 95% confidence intervals there is no statistically significant difference between the year 2014 and 2024 mean relative sea level trend measurement values.

Nor is there any statistically significant difference between the Battery Station 2014 mean sea level trend versus the 2007 or any other mean sea level year shown including the trend from 1856 to 2006.

Additionally, the difference between mean sea level rise trend values for the decade long period 2014 to 2024 (2.92 mm/yr – 2.84 mm/yr) is a mere +0.08 mm/yr (or +0.0031 inches/year or +0.031 inches/decade) versus the climate alarmists hyped 0.25 inches/yr rate from 1988 with that rate   claimed to grow even higher if emissions growth weren’t immediately curtailed.

What ever happened to the “experts” 2.5 inches per decade increase in sea level rise acceleration that was claimed in the 1988 Senate Hearings based on CO2 emissions that had occurred to that time and the even further escalation in sea level rise acceleration that would clearly occur if emissions weren’t immediately curtailed (which never happened as addressed above) in the coming decades?

Provided below are NOAA’s year 2024 sea level rise trends for  

a few other U.S. NOAA coastal locations all of which show the same huge disconnect between measured mean sea level rise trend rates versus the hyped claims of accelerating sea level rise from the 1988 Senate Hearings that contributed to the climate alarmists energy and economically damaging global wide emissions reduction campaign launched from those hearings.

Shown below are NOAA’s sea level rise data for Honolulu, Hawaii the birthplace of President Obama who claimed in his Democratic Party nomination speech on June 3, 2008, that we would be able to look back upon his nomination and tell our children that “this was the moment when the rise of the oceans began to slow and our planet began to heal”.

The year 2024 NOAA Honolulu relative sea level rise data show a sea level rise of 1.56 +/- 0.20 mm/yr versus 1.54 +/-0.20 mm/yr in 2023.

Provided below is NOAA’s Honolulu Station relative sea level rise measured data from their year 2014 report again covering the period from 1856 to year 2014 a decade ago.

The NOAA Honolulu Station year 2024 relative sea level rise outcome 95% confidence interval is from 1.76 to 1.36 mm/yr thus overlapping the prior year 2014 95% confidence intervals. So there is no statistically significant difference between the decade ago year 2014 value (or any other year shown) and year 2024 mean relative sea level trend measurement value.

Additionally, the difference between mean sea level rise trend values for the decade long period 2014 to 2024 (1.56 – 1.41) is a mere +0.15 mm/yr (or +0.0059 inches/year or +0.071 inches/decade) versus the climate alarmists hyped 0.25 inches per year in 1988 with that value sure to grow even higher if emissions growth weren’t immediately curtailed – which, of course, totally failed.

Looking at NOAA’s year 2024 data for the San Diego Station location we see the 2024 mean sea level trend rate of 2.23 +/- 0.17 mm/yr which is the same as in year 2023.

Shown below is NOAA’s San Diego Station relative sea level rise measured data from their year 2014 report again covering the period from 1856 to year 2014 a decade ago.

The NOAA San Diego Station year 2024 relative sea level rise 95% confidence interval is from 2.4 to 2.06 mm/yr thus overlapping the prior year 2014 95% confidence intervals so there is no statistically significant difference between the decade ago year 2014 (or any other year shown) and year 2024 mean relative sea level trend measurement values.

Additionally, the difference between mean sea level rise trend values for the decade long period 2014 to 2024 (2.23 – 2.08) is a mere +0.15 mm/yr (or +0.0059 inches/year or +0.071 inches/decade) versus the climate alarmists hyped 0.25 inches per year in 1988 with that value sure to grow even higher if emissions growth weren’t immediately curtailed.

Looking more broadly at NOAA’s year 2024 updated data we see in the U.S. Sea Level Trend Map (shown below) that areas experiencing little-to-no change in relative sea level are illustrated in green, including stations consistent with the average global sea level rise rate of just 1.7-1.8 mm/yr (about 7 inches per century) with these “green” stations predominantly present across the coastal regions of the U.S. and Hawaii.

Areas with yellow-to-red colors are experiencing both global sea level rise and lowering and sinking of local land causing an exaggerated rate of relative sea level rise.

Areas with blue-to-purple are experiencing global sea level rise and greater vertical rise causing an apparent decrease is relative sea level rise.

NOAA’s year 2024 updated Relative Sea Level Trend data measurements continue to demonstrate that climate alarmists claim of hugely growing rates of sea level rise acceleration are completely out of touch with NOAA’s overwhelming data reflecting measured rates of relative sea level rise.

It’s time to stop using 4 decades long spectacularly failed climate model hyped sea level rise schemes to mandate absurd energy policy schemes that are destroying our necessary and beneficial need for future economic growth.

Temperature Spike Due to Reduced Sulfate Emissions

After two years of fabricated alarmism about the apparent shift upwards in global temperatures, scientists have now established that the main culprit has been the reduction in sulfate emissions from shipping!

From NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

After two years of fabricated alarmism about the apparent shift upwards in global temperatures, scientists have now established that the main culprit has been the reduction in sulfate emissions from shipping!

Abstract

The summer of 2023 saw an anomalous increase in temperatures even when considering the ongoing greenhouse-gas-driven warming trend. Here we demonstrate that regulatory changes to sulfate emissions from international shipping routes, which resulted in a significant reduction in sulfate particulate released during international shipping starting on 1 January 2020, have been a major contributing factor to the monthly surface temperature anomalies during the last year.

We do this by including the appropriate changes to emission databases developed for the Climate Model Intercomparison Project version 6 (CMIP6) in Community Earth System Model (CESM2) simulations. The aerosol termination effect simulated by the updated CESM2 simulations of W m−2 and 0.08 K±0.03 K is consistent with observations of both radiative forcing and surface temperature, manifesting a similar delay as the one observed in observational datasets between the implementation of the emission changes and the anomalous increase in warming. Our findings highlight the importance of considering realistic near-future changes in short-lived climate forcers for future climate projections, such as for CMIP7, for an improved understanding and communication of short-term climatic changes.

https://esd.copernicus.org/articles/15/1527/2024

This really should not have come as a surprise to anyone, which strengthens the impression that the climate establishment have deliberately ignored sulfate emissions, in order to focus the public’s minds on greenhouse gases.

It has long been recognised that sulfate pollution was a factor in the global cooling between the Second World War and the 1970s. As countries began to clean their air, temperatures naturally rose. Even the Met Office admitted this was probably why UK temperatures rose in the 1980s and 90s.

Cornell University have more detail on their researchers’ study:

Mandated reductions in sulfate emissions from international shipping routes in 2020 are partly responsible for the record high temperatures, the researchers found. Reducing the amount of aerosol particles in the atmosphere reduces cloud coverage; thus, clouds’ ability to reflect solar radiation back to space is diminished. The paper’s findings suggest future policy decisions around abrupt reductions in tropospheric aerosols should take into account their surface temperature impact.

Past research indicated that such change would lead to a minor increase in the global temperature due to a reduction in cloud formation, but Visioni and co-author Ilaria Quaglia, postdoctoral researcher in the Sibley School of Mechanical and Aerospace Engineering (Cornell Engineering), used Earth system model simulations to prove the significance of the sudden drop in sulfate shipping emissions.

These changes in the shipping industry had been discussed for years, Visioni said, with the International Maritime Organization (IMO) making a decision in 2014 to begin enforcing stricter sulfate emissions by 2020. Fuels with lower sulfur are much more expensive, so it took some time for the industry to adjust, he said.

The regulation required ships to use fuel with a sulfur content of no more than 0.5%, down from the previous limit of 3.5%. This reduction led to a more than 80% decrease in total sulfur oxide emissions from shipping.

And while there was some talk of this tradeoff within the shipping industry, he said, there was little attempt to call widespread attention to the potential effect.

The Cornell researchers looked at monthly global temperature anomalies over the period 2020-23, removing the assumed linear contribution from greenhouse gases and seasonality, in order to determine the shipping industry’s impact on temperature anomalies. They found that removing sulfur dioxide from shipping fuel likely increased the planet’s temperature by 0.08 degrees Celsius.

“The unprecedented heat became a normal warm year once you accounted for that,” Visioni said.

https://news.cornell.edu/stories/2025/01/shipping-emissions-mandate-led-spike-global-temperatures

That last sentence is worth repeating:

“The unprecedented heat became a normal warm year once you accounted for that,”