Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
The recent article in Yale Climate Connections, “Six photos show how climate change shaped our world in 2025,” by Samantha Harrington, presents a photo-driven narrative asserting that wildfires, floods, heat waves, hurricanes, and droughts in 2025 were “made more devastating and deadly by climate change.” This is outright false. The piece relies on striking images and rapid attribution claims to imply a climate causation but offers no hard evidence to support such claims and measured trends refute such claims.
The recent article in Yale Climate Connections, “Six photos show how climate change shaped our world in 2025,” by Samantha Harrington, presents a photo-driven narrative asserting that wildfires, floods, heat waves, hurricanes, and droughts in 2025 were “made more devastating and deadly by climate change.” This is outright false. The piece relies on striking images and rapid attribution claims to imply a climate causation but offers no hard evidence to support such claims and measured trends refute such claims.
The article states that these images “show the consequences of our warming climate in action,” and repeatedly asserts that climate change “made” specific events more intense or likely, citing groups such as World Weather Attribution and Climate Central. The emotional power of the photographs is undeniable. But photographs are snapshots in time; they say nothing about the 30-year trends required to establish climate, nor do they substitute for long-term observational evidence. More importantly, climate is a statistical construct, an average of weather over 30 years, not a force of nature. Only weather is a force of nature, and weather events are what do the damage in these photos.
A single fire scar, a flooded living room, or a satellite image of storm damage cannot establish a climate trend any more than a single cold snap disproves warming. This basic standard is precisely why meteorological agencies use 30-year normals. By presenting six isolated events—each framed as “made worse” by climate change—Yale Climate Connections collapses weather into climate and invites readers to infer trends that the evidence does not demonstrate.
When you step away from photos and examine history and measurements, the story becomes far more nuanced. For hurricanes, long-term records summarized at Climate at a Glance: Hurricanes show no clear upward trend in U.S. landfalling hurricanes or accumulated cyclone energy that would justify claims of steadily worsening storms.
For floods, Climate at a Glance: Floods documents the lack of a consistent global increase in flood frequency or magnitude, a conclusion echoed by the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).
For wildfires, Climate at a Glance: Wildfires explains that fire extent is heavily influenced by land management, ignition sources, and fuel loads, not temperature alone, with long-term trends varying widely by region. Heat waves and heavy rain events likewise show mixed regional behavior rather than a uniform global escalation, as summarized across the Climate at a Glance extreme-weather pages.
The article leans heavily on rapid-attribution claims—statements that climate change made a given event “two to four degrees hotter” or “700 times more likely.” These claims are derived from models comparing a simulated present world to a simulated counterfactual world without added greenhouse gases. They are not direct measurements. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) makes clear that attribution confidence varies widely by event type and region, and that uncertainties remain substantial. Later references to IPCC AR6 often disappear from media coverage.
Climate Realism has repeatedly debunked World Weather Attribution’s and Climate Central’s attribution claims, showing both that the factual claims of an impact between climate change and specified extreme weather events can’t be discovered in the data and that the methodology used to make such a connection is flawed, here, here, here, and here, for example.
Equally important is what the article ignores. Disasters are driven by exposure and vulnerability as much as by weather. Population growth in floodplains, development in fire-prone landscapes, inadequate drainage, poor forest management, and aging infrastructure all magnify damage. A flooded Texas home photographed in July reflects zoning decisions, river engineering, or warning systems—or lack thereof—that determine outcomes, not, in this case, a changed climate. A burned hillside in California says is indicative of fuel buildup after decades of fire suppression and inaction to prevent fuel buildup, not worsening drought or heat – neither of which are in evidence.
Here’s the key point: photos can capture damage, but they cannot diagnose causes.
Case in point, below is a photo from the year 1900:
By looking at it, you can’t determine the cause of that damage. It might be an earthquake, a tornado, hurricane, or some other high wind event. It might even be a demolition in progress. You just can’t tell.
If the same storm happened today, rest assured that experts and media armed with rapid response “attribution science” would rush to use those same pictures to assert that climate change was somehow responsible.
Climate Realism has repeatedly documented how photo-centric storytelling and rapid attribution are used to oversell certainty. Readers can see a catalog of critiques of wildfire, flood, hurricane, and heat-wave attribution claims at its coverage of extreme weather, where media assertions are compared against observational records and IPCC findings. Likewise, Climate Realism’s many analyses of so called “attribution science” shows how model-based probability statements are routinely presented as settled fact, even when underlying data are sparse or contradictory.
Perhaps most telling, the article never asks whether these kinds of images would have been possible in earlier decades. The answer is yes. History is filled with devastating floods, fires, storms, and droughts long before modern CO₂ emissions rose. What has changed most is not the existence of extremes, but the ubiquity of cameras, drones, satellites, and social media—ensuring that every disaster is now documented in high resolution but falsely and instantly framed as evidence of “climate change” by narrative driven media outlets.
By substituting photographs for trends and models for measurements, Yale Climate Connections misleads readers into believing that six images can “show how climate change shaped our world.” They cannot. Climate is measured over decades, not captured in a frame. Until media reporting consistently distinguishes weather from climate and imagery from evidence, readers will keep getting a powerful yet false visual narrative. Only long-term trends can suggest that climate change is making weather worse and photographs can’t capture trends.
Mad Miliband argues that exposure to global gas prices is the UK’s “Achilles’ heel,” causing bill spikes (e.g., after Russia’s 2022 invasion of Ukraine) and long-term instability. He positions the transition to clean power (renewables and nuclear) by 2030 as the solution for lower, more stable bills, energy security, and jobs.
The net zero policies (subsidies, grid investments) are a significant driver of current high bills, sometimes outweighing wholesale fossil fuel impacts. UK bills remain among Europe’s highest, reflecting this mix.
Mad Miliband argues that exposure to global gas prices is the UK’s “Achilles’ heel,” causing bill spikes (e.g., after Russia’s 2022 invasion of Ukraine) and long-term instability.
He positions the transition to clean power (renewables and nuclear) by 2030 as the solution for lower, more stable bills, energy security, and jobs.
The net zero policies (subsidies, grid investments) are a significant driver of current high bills, sometimes outweighing wholesale fossil fuel impacts.
UK bills remain among Europe’s highest, reflecting this mix.
“The truth is our country remains in the grip of fossil fuels markets, controlled by petro states and dictators. That’s why your energy bills are so high.”
Mr Ed is lying to you.
Your energy bills are high because of the billions added on for subsidising renewables.
Even before the year has ended, CfD subsidies have already exceeded £2.5 billion for 2025:
The average wholesale market price this year has been £80.62/MWh, which is well below the £113/MWh on offer for offshore wind in the new CfD auction, AR7.
Of course, this is not the first Miliband has told blatant lies about energy prices.
One reader sent me this clip from Sunday with Laura Kuenssberg in November 2022 just before COP24 in Egypt.
It is worth watching from the start of the interview at about 10 minutes in, as the moronic Miliband tells of his plans to press Egypt’s President Sisi on the release from prison of that notable peace activist, the “British citizen” Alaa Abd El Fattah.
Yes the same Mr Fattah who we now learn is an extreme, hard line Islamist! It reveals just how long our great and good have been deluded by this anti-semite.
A minute later though, Miliband states:
“Solar and wind power in Britain are nine times cheaper than fossil fuels”
It was a commonplace claim back then, but never was true. It is remarkable how many still believe it to be the case.
We know that wholesale electricity prices briefly spiked in August 2022 to £363/MWh. But at the time of the interview, they had already dropped back to well below £200/MWh.
In comparison, the average CfD price for offshore wind was £187/MWh at the time.
Market prices were still high by historical standards, but despite that CfD strike prices were even higher. CfD subsidies of £150 million were paid out in that October to December quarter:
Kuenssberg failed to challenge this self evident nonsense, just the same as she did with Chris Packham’s similar claims on the same show last month.
Porter’s analysis centers on a warning from the National Energy System Operator (NESO), issued around the time of the UK Autumn Budget in late 2025. NESO highlighted an “emerging risk” to gas supply security due to potential premature closures of offshore pipeline infrastructure in the North Sea. As UK Continental Shelf (UKCS) gas production rapidly declines—accelerated by factors like high windfall taxes (Energy Profits Levy extended to 2030), limited new licensing, and low exploration activity (2025 was the worst year on record)—key pipeline systems face low throughput.
Porter’s analysis centers on a warning from the National Energy System Operator (NESO), issued around the time of the UK Autumn Budget in late 2025.
NESO highlighted an “emerging risk” to gas supply security due to potential premature closures of offshore pipeline infrastructure in the North Sea.
As UK Continental Shelf (UKCS) gas production rapidly declines—accelerated by factors like high windfall taxes (Energy Profits Levy extended to 2030), limited new licensing, and low exploration activity (2025 was the worst year on record)—key pipeline systems face low throughput.
On the same day that Rachel Reeves was announcing her Autumn Budget, the National Energy System Operator, NESO, quietly issued a bombshell warning about the risks the UK might start running out of gas on cold winter days as a result of the possible closure of offshore pipeline infrastructure. National Gas has been warning about these risks for some time, saying privately they may manifest as early as next winter. NESO is more optimistic. I sit somewhere in between.
Britain’s gas pipelines are at risk
The gas system in GB today depends on four main supply routes into the National Transmission System (“NTS”):
The UK Continental Shelf (“UKCS”): gas from North Sea and East Irish Sea fields enters the NTS via onshore terminals at St Fergus, Teesside, Bacton, Easington, and Barrow. UKCS production has fallen from around 100 bcm /year in 2000 to around 30 bcm more recently – roughly 35–40% of GB annual demand
LNG imports: Milford Haven (South Hook, Dragon) and Isle of Grain can deliver a combined 130–150 mcm /day, equivalent to around half of GB’s theoretical peak-day demand, but only if tanks are full and send-out is not constrained by regas capacity, shipping delays or NTS constraints
Norwegian pipelines: Langeled, Vesterled, and the Tampen Link bring Norwegian gas mainly to Easington and St Fergus. Combined they can supply roughly 80–100 mcm /day, though actual flows vary with continental nominations
Interconnectors: the Bacton–Zeebrugge (“IUK”) and Bacton–Balgzand (“BBL”) pipelines connect GB to northwest Europe. In theory they allow imports of 60–70 mcm /day. They typically export gas to Europe in the summer and import in the winter (effectively using European gas storage facilities although there are no explicit contracts behind that)
The North Sea Transition Authority’s (“NSTA’s”) latest projections show accelerating UKCS decline through the 2020s, and 2025 was the worst year for exploration on the UKCS since the basin began to be developed. According to Wood Mackenzie, no exploration wells were drilled in UK waters this year, the first time there has been no fresh exploration activity in the basin since oil and gas was found there in the 1960s.
The UKCS is in a much sharper decline than had previously been expected, primarily as a result of a punitive fiscal regime and current Government policy against new exploration. This not only threatens energy security in terms of access to molecules, it also threatens the viability of offshore pipeline infrastructure. The key issue isn’t simply that UKCS output is declining, but that the offshore gathering and transmission system has fixed costs and physical interdependencies – as throughput falls, it becomes progressively harder to maintain and justify these assets.
These pipelines were designed for high throughput and depend on tariffs from producers for their economic viability. As volumes decline, unit tariffs rise to recover fixed costs. At some point, remaining producers face uncompetitive transportation tariffs which accelerates field decommissioning, leading to a “death spiral” where loss of one anchor field undermines the economics of the whole system.
“We think Neso forecasts are too optimistic. We forecast gas production to decline by 70pc by 2030 due to the impact of the windfall tax. That suggests the UK will run out of LNG import capacity as early as 2031, as the current import terminals reach the maximum rate they can deliver gas into the UK grid during winter. The best way to ensure security of gas supply is to maximise domestic gas production,” – Chris Wheaton, analyst at Stifel
The Atlantic Multidecadal Oscillation (AMO), also referred to as Atlantic Multidecadal Variability (AMV), is a natural, large-scale climate pattern characterized by long-term fluctuations in sea surface temperatures (SSTs) in the North Atlantic Ocean. It alternates between warm (positive) and cool (negative) phases, each typically lasting 20–40 years, with a full cycle of about 60–80 years. The AMO index is derived from detrended, area-averaged SST anomalies in the North Atlantic (usually 0°–70°N).
I have been meaning to write this post for a long time because I think there is an important distinction about climate change that could potentially be affected by reducing GHG emissions that is not generally recognized. I have postponed this article because I did not want to try to explain the driving factor for my concern – ocean and atmospheric oscillations. Andy May is a petrophysicist who has a climate blog that recently published 14 articles about atmospheric oscillations that I have used in this post.
I am convinced that implementation of the New York Climate Leadership & Community Protection Act (Climate Act) net-zero mandates will do more harm than good if the future electric system relies only on wind, solar, and energy storage because of reliability and affordability risks. Moreover, I take the heretical position that our understanding of the causes of climate change are not understood well enough to support the idea that reducing GHG emissions represents sound policy. I have been a practicing meteorologist for nearly 50 years, was a Certified Consulting Meteorologist, and have B.S. and M.S. degrees in meteorology. The opinions expressed in this post do not reflect the position of any of my previous employers or any other organization I have been associated with, these comments are mine alone.
Background
Weather and climate are often confused. According to the National Oceanic and Atmospheric Administration’s National Ocean Service “Weather reflects short-term conditions of the atmosphere while climate is the average daily weather for an extended period of time at a certain location.” They go on to say: “Climate is what you expect, weather is what you get.”
The standard climatological average is 30 years. It is important to understand that programs like the Climate Act’s GHG emission reduction targets are intended to reduce global warming over longer time scales than 30 years. Statements suggesting that even if aggressive mitigation reduces greenhouse gases that temperature will still increase for 20-30 years due to inertia in the climate system are based on the premise that CO2 is the control knob for the climate.
I often hear and have noticed myself that “winters aren’t what they used to be” and that leaves are turning color later than the past. The goal of this article is to show that there are climatic oscillations with time periods greater than 30 years that are likely causing these perceived examples of climate change. However, I will show there is no connection between those observations and the value of the Climate Act as a potential reason to reduce GHG emissions in hopes of changing those observations.
Climate Oscillation Analysis
Earlier this year Andy May published 14 articles about climate oscillations in the oceans and atmosphere. I think his analysis is notable because it is data driven. The basis of his analysis is articles describing observed oceanic and atmospheric changes, not modeled simulations. Given the complexity of the interactions between oceans and the atmosphere and the poor understanding of their relationships, assuming that modeled simulations are credible is not reasonable.
His articles provide compelling evidence that each of the 14 oscillations is natural. I believe his work provides sufficient evidence proving that “each oscillation is natural and has been around since the pre-industrial period, or even earlier, and thus is natural and not random variability.” This is important relative to claims that reducing the GHG emissions will affect global temperatures.
May’s work consists of a statistical regression analysis of observed features in the oceans and atmospheres that have occurred over many years. He uses the HadCRUT5 global average temperature data set used by the Intergovernmental Panel on Climate Change (IPCC) to track global warming in his analyses. May offers the following caveat about his work.
Finally, this is a regression analysis to predict HadCRUT5 with climate oscillations to try and detect the climate oscillations that best correlate to “global warming.” This is not a climate model, it is not an attempt to make a climate model, it is only a statistical exercise. Statistics and statistical analysis are not proof of anything, it isn’t even scientific analysis, they are just useful tools to sort through datasets. Just as AI is not intelligent, statistics is not science, both are useful tools.
In Climate Oscillations 1: The Regression May provides the following table that lists the oceanic and atmospheric oscillations considered in his series of articles. For each of these oscillations he did a statistical regression analysis. The first seven of the oscillations correlated with the GMST measured using HadCRUT5. May points out that “HadCRUT5 is not representative of global climate, it is just an average temperature”. Nonetheless, it is the primary climate change parameter. The rationale for the Climate Act uses climate change and global warming interchangeably.
May Table 1. A list of the climate oscillations discussed and analyzed in this series. The first eight oscillations are listed in order of importance in modeling HadCRUT5, the remaining six did not add to the model. The links in this table will not work, to see the list in a spreadsheet with working links, download it here.
I am not going to review each post in this article but will describe several of the oscillations. If you want to review the articles and are content with a summary using Perplexity AI I did get a review of his work. It notes:
The series begins with a foundational regression analysis that ranks fourteen major climate oscillations by their statistical correlation with HadCRUT5 global surface temperature. May’s analysis reveals that the top three oscillations—the Atlantic Multidecadal Oscillation (AMO), Western Hemisphere Warm Pool (WHWP) area, and Southern Annular Mode (SAM)—together explain 77% of HadCRUT5 variability since 1950. This finding directly contradicts the IPCC’s characterization of these oscillations as unpredictable “internal variability” with minimal influence beyond a few years.
The Atlantic Multidecadal Oscillation (AMO) has the most significant relationship with global mean surface temperature (GMST). There are several definitions based on different measurements. For example, Gray, et al. use detrended raw tree-ring measurements to demonstrate “a strong and regular 60-100 year variability in basin-wide (0-70°N) North Atlantic sea surface temperatures (SSTs) that has been persistent for the past five centuries.”
The general approach used by May is simple. Figure 4 plots GMST using the HadCRUT 5 data and the AMO parameter using the HadSST 4.1 data. It is obvious that the two parameters track well. May used regression analysis to show the strength of the relationship. Note the variation in global temperature since 1850 shown in this graph. The first challenge for proponents of the idea that CO2 is the driver of climate change is that it is acknowledged that it is only since 1950 that CO2 has affected global warming. So, what happened in the past to cause the observed variations? I do not think it is reasonable to claim that all the natural drivers that caused variations before 1950 stopped and global warming became entirely dependent upon CO2 since, but that is the argument used by Climate Act proponents.
May Figure 4. HadSST and HadCRUT detrended temperature anomalies plotted together. Both anomalies are from 1961-1990 originally but are from their respective linear least squares trends. This is updated from figure 2 in (May & Crok, 2024).
The reason for the AMO SST 60-70-year pattern is unknown, but according to Gray et al. it extends back to 1567AD, so it is a natural oscillation of some kind. Some have speculated that it is a result of the thermohaline circulation in the North Atlantic or a “combination of natural and anthropogenic forcing during the historical era.” (Mann, Steinman, & Miller, 2020). But while interesting these ideas are speculative. Further if the oscillation has existed since 1567, it seems unlikely that it is caused by human CO2 and aerosol emissions.
The AMO has the best correlation with GMST in all the statistical analyses. Combined with two other oscillations – Western Hemisphere Warm Pool (WHWP) area, and Southern Annular Mode (SAM) these three explain 77% of HadCRUT5 variability since 1950.
The Western Hemisphere Warm Pool Area (WHWP) is an area of abnormally warm ocean that extends from the eastern North Pacific (west of Mexico, Central America, and Columbia) to the Gulf of Mexico, the Caribbean, and well into the Atlantic during the WHWP peak in August and September. Because this area is important to hurricane formation, the strength and extent of the warm pool is important. May points out that the WHWP combined with the Antarctic Oscillation or Southern Annular Mode and the AMO predict GMST well. He concludes that “This suggests that The North Atlantic and the Southern Hemisphere circulation patterns correlate very well with global climate trends, CO2 may fit in there somewhere, but it must share the spotlight with these natural oscillations.”
The Southern Annular Mode/Antarctic Oscillation (AAO) is defined as the difference between the zonal (meaning east-west or circumpolar) sea level air pressure between 40°S and 65°S. This parameter has a powerful influence on global climate and can affect weather in the Northern Hemisphere (Lin, Yu, & Hall, 2025), in particular the Warm Arctic-Cold Eurasian weather pattern that causes a lot of extreme winter weather. The AAO also affects the Indian summer monsoon and other eastern Asia weather phenomena.
Synthesis
The final article in the series, Climate Oscillations 12: The Causes & Significance, addressed the claim by proponents of the Climate Act that “ocean and atmospheric oscillations are random internal variability, except for volcanic eruptions and human emissions, at climatic time scales.” May explains:
This is a claim made by the IPCC when they renamed the Atlantic Multidecadal Oscillation (AMO) to the Atlantic Multidecadal Variability (AMV) and the PDO to PDV, and so on. AR6 (IPCC, 2021) explicitly states that the AMO (or AMV) and PDO (or PDV) are “unpredictable on time scales longer than a few years” (IPCC, 2021, p. 197). Their main reason for stating this and concluding that these oscillations are not influenced by external “forcings,” other than a small influence from humans and volcanic eruptions, is that they cannot model these oscillations, with the possible exceptions of the NAM and SAM (IPCC, 2021, pp. 113-115). This is, of course, a circular argument since the IPCC models have never been validated by predicting future climate accurately, and they also make some fundamental assumptions that simply aren’t true.
This is a good point to remind readers that little fluctuations in incoming radiation have big impacts on the climate. The Milankovitch theory is the most widely accepted cause of glaciation. It states that variations in earth’s orbit and tilt cause changes in the amount of sunlight that cause climate fluctuations strong enough to trigger continental glaciers.
May’s analysis finds relationships between similarly small external variations that correlate with global surface temperatures. Note however that proponents of CO2 as the control knob disregard all climate drivers but the greenhouse effect. May explains:
Finally, oscillations are inconsistent with anthropogenic greenhouse gas emissions as a dominant forcing of climate change. Greenhouse gas emissions do not oscillate; recently they have only increased with time. So, we will examine the relationship between solar and orbital cycles and the climate oscillations. As Scafetta and Bianchini (2022) have noted, there are some very interesting correlations between solar activity and planetary orbits, and climate changes on Earth.
May’s final article describes multiple observed oscillations including a period of about ~64 years, ±5 years (Wyatt, et al., 2012), Nathan Mantua and colleagues (Mantua, et al., 1997) identified 20th century “climate shifts” which results in a major multidecadal climate oscillation of 22 to 30 years and there are shorter 2-, 5-, 5-, and 9-year observed oscillations. Note that there also are other cycles that are longer than these.
The ~64 year oscillation is of particular interest. Marcia Wyatt’s “stadium wave” hypothesis shows that a suite of global and regional climate indicators vary over roughly the same 64-year period. Wyatt explains:
“Stadium wave” is an allusive term for a hypothesis of multidecadal climate variability. Sequential propagation of an “audience wave” from one section of sports fans to another in a sports arena – i.e. a “stadium wave” – is analogous to the premise of the climate stadium-wave hypothesis. It, too, involves sequential propagation of a signal. In the case of the climate stadium wave, propagation proceeds sequentially through ocean, ice, and atmospheric systems. Key to signal propagation is network, or collective behavior – a feature ubiquitous throughout natural and man-made systems, a product of time and self-organization.
I think of climate as a product primarily of the climate stadium wave cycle plus contributions from other oscillations. May explains:
If we define “global climate change” as the observed changes in HadCRUT5 or BEST global mean surface temperature (GMST) as the IPCC does, then the oscillations that correlate best are the AMO and the global mean sea surface temperature (SST) as shown in figure 2. None of the other oscillations correlate well with GMST.
In figure 2, the gray curve is a 64-year cosine function. It fits the 20th century data but departs significantly around 2005 and before 1878. The early departure could be due to poor data, the 19th century temperature data is very bad, see figure 11 in (Kennedy, et al., 2011b & 2011). Data quality problems still exist today, but are much less of a factor and the departure after 2005 is likely real and could be caused by any combination of the of the two following factors:
Human-emitted greenhouse gases.
The full AMO/world SST/GMST period is longer and/or more complex than we can see with only 170 years of data.
It is probably a combination of the two. As discussed by Scafetta and Stefani, climate, orbital, and solar cycles are known to exist that are longer than 170 years. The fact that I had to detrend all the records shown in figure 2 testifies to that. It is also noteworthy that the ENSO ONI trend since 2005 is trending down; as shown in the last post. So is the current PDO trend. All the notable oscillations are not synchronized, teleconnections or not, climate change is not simple. The trends in figure 2 result from complex combinations of gravitational forces and teleconnections (Scafetta, 2010), (Ghil, et al., 2002), and (Stefani, et al., 2021).
Discussion
May gives a concise summary of the potential human influence that has never been considered by the State of New York:
Whether global warming is a problem or not is in dispute, but it is a fact that the world is warming, and some are concerned about it. What is the cause of the warming? Is it natural warming after the cold winters of the Little Ice Age? Is it caused by human emissions of CO2? Most of the natural ocean and atmospheric circulation oscillations examined in this post are not modeled properly (some say not modeled at all) in current global climate models (Eade, et al., 2022). The IPCC AR6 report admits that the AMO (they call it the “AMV”) signal in the CMIP6 climate models is very weak, specifically on page 506:
“However, there is low confidence in the estimated magnitude of the human influence. The limited level of confidence is primarily explained by difficulties in accurately evaluating model performance in simulating AMV.” (IPCC, 2021, p. 504)
In other words, the models that predict gloom and doom that are used as the rationale that we must reduce New York GHG emissions don’t accurately predict the oscillation that correlates best with global temperatures. If you cannot model this relationship, then the likelihood that future temperature projections are accurate is zero.
In addition, NYSERDA presentations at meetings consistently attribute the latest extreme weather events to climate change. Maybe someday I will explain why I think that is completely divorced from reality and only serves to support the narrative that there is an existential threat. In the meantime Roger Pielke, Jr. recently eviscerated this line of reasoning and those that continually use it. He points out that this approach is “counter to the terminology, frameworks, and assessments of the IPCC and the broad base of research on which the work of the IPCC is based upon.” I strongly recommend his article as definitive proof that the Hochul Administration picks and chooses the “science” to fit their narrative.
Conclusion
The intent of this article was to explain why anecdotal “evidence” of climate change is no more than recognition that there are weather pattern cycles that currently show warming. It does not mean that there is conclusive evidence that continued GHG emissions will inevitably increase global temperatures. There is overwhelming evidence that the current warming cycle will eventually reverse. This does not mean that GHG emissions are not a factor but does mean they are a tweak not the primary driver. This combined with the fact that New York GHG emissions are so small relative to global emissions that we cannot meaningfully affect global emissions means that GHG emission reductions for the sake of the climate is a useless endeavor.
This article on No Tricks Zone, referencing a new analysis of daily temperature data from 1,600 weather stations. It reports a warming rate of 0.022°C per year (or 0.22°C per decade) from 1899–1940, compared to 0.017°C per year (0.17°C per decade) from 1983–2024.
This article on No Tricks Zone, referencing a new analysis of daily temperature data from 1,600 weather stations. It reports a warming rate of 0.022°C per year (or 0.22°C per decade) from 1899–1940, compared to 0.017°C per year (0.17°C per decade) from 1983–2024.
Global warming was significantly more pronounced in the early 20th century and prior to 1940, or back when CO2 emissions rates were 8.6 times lower than they were from 1983-2024.
A new study using 60 million daily maximum/minimum temperatures from 1600 global weather stations across 29 countries indicates the globe warmed at a rate of 0.022°C/yr from 1899-1940, a 42-year period when cumulative CO2 emissions only amounted to 139.6 GtC.
Then, over the next 42 years (1941-1982), global temperatures cooled at a rate of -0.011°C/yr even though cumulative CO2 emissions rose to 460.0 GtC. In other words, CO2 emission rates more than tripled from the 1940s to 1980s as global temperatures declined.
Global warming returned by the 1980s. However, over the next 42 years (1983-2024) the warming rate, 0.017°C/yr, was not as strong as it was in the early 20th century. This slower rate of warming is significant because cumulative CO2 emissions were 1,209.8 GtC during this most recent period, an amount 8.6 times higher than they were from 1899-1940.
So over the last 125 years of “global warming,” at no time has there been a decadal period of warming (or cooling) that could be shown to have closely aligned with the varying rates of anthropogenic CO2 emissions.
“These findings challenge the conventional assumption that human-induced CO2 is the primary driver of global warming.”
The EU has adopted fleet-wide carbon emission targets that effectively impose a quota on electric vehicle (EV) sales for automakers. In Germany, these quotas require that a growing proportion of newly registered vehicles be battery-electric (BEVs) or plug-in hybrid (PHEVs), progressively increasing toward 2030. This regulatory pressure significantly affects production planning, market incentives, and infrastructure development.
The EU has adopted fleet-wide carbon emission targets that effectively impose a quota on electric vehicle (EV) sales for automakers.
In Germany, these quotas require that a growing proportion of newly registered vehicles be battery-electric (BEVs) or plug-in hybrid (PHEVs), progressively increasing toward 2030.
This regulatory pressure significantly affects production planning, market incentives, and infrastructure development.
EU policy relies on binding targets for the market share of electric cars in company fleets. The core instrument is a quota that significantly increases the share of electric cars and other low-emission vehicles by 2030.
With a target value of around 77 percent, Germany is significantly higher than countries such as Romania with around 28 percent, Poland with 34 percent, Latvia with about 33 percent or Cyprus with around 52 percent.
This target is intended to reduce transport emissions, but drives costs, infrastructure requirements and competition issues. For many companies, this makes it clear that this rate is noticeably changing procurement, planning and mobility strategy.
Quota directly affects the economy and everyday life
Germany serves as a lead market for EU policy. The high target values have an impact on an area that affects around 70 percent of all new registrations.
This is exactly why the quota was set in this way. Companies have to restructure their vehicle fleet. Investment costs, charging availability and operational reliability are all important here. The company fleets are thus becoming the steering lever of European climate policy. At the same time, fleet managers are calling for resilient framework conditions. They need energy prices, infrastructure and reliable regulation.
EU policy is deliberately focusing on electromobility because it reduces emissions during driving. But this course requires practical solutions. Without sufficient charging points, stable grids and economically viable models, the pressure is growing. The strategy from Brussels must therefore ensure climate targets and the functioning of the economy at the same time.
Quota instead of open system strategy
The quota creates clear targets. However, it does not replace a system strategy. Electromobility only strengthens climate protection if energy supply and value creation follow suit. Germany is currently paying high electricity prices.
Fast charging often costs between 50 and 90 cents per kilowatt hour.
At the same time, company fleets need reliable operational readiness. This is exactly where it is decided whether climate policy works or triggers undesirable developments.
In addition, the price influences economic power relations. A large part of the battery value chain is outside Europe. Those who rely exclusively on the electric drive increase dependencies.
This increases the risk of creeping deindustrialization, because investments are moving to places where energy is cheaper and framework conditions are more stable.
Electromobility needs balance and a sense of reality
Electromobility can reduce emissions and advance new technologies. But it does not replace all other solutions in one fell swoop.
Many companies are therefore relying on transitional concepts, mixed fleets and efficient technologies. Company fleets need selection instead of coercion, pragmatism instead of symbolism. This is the only way to ensure that mobility remains reliable and economical.
If politicians only rely on standard figures and statistics, there is a risk of industrial loss instead of sustainable transformation. Then value creation migrates, while the burdens remain. A smart EU policy must enable progress and at the same time strengthen locations.
Europe needs clarity of purpose – without tunnel vision
Europe needs resilient climate strategies. This includes electromobility, but also economic stability. Current EU policy places high expectations on company fleets. It uses the quota as an instrument of pressure. This direction can work if infrastructure and economic power keep pace. If they don’t, there is a risk of side effects.
Europe is thus not only deciding on drives, but also on competitiveness. A sustainable transformation requires technology diversity, investment security and realistic schedules. Only then will progress be achieved without deindustrialisation – and Europe will remain an industrial location with a future. (KOB)
A recent Nature Climate Change paper titled “Peak glacier extinction in the mid-twenty-first century” presents itself as a careful, policy-relevant analysis of global glacier loss under a range of future warming levels. Rather than framing its projections around emissions pathways, the authors organize their results around four temperature outcomes by 2100: +1.5 °C, +2.0 °C, +2.7 °C, and +4.0 °C. This choice lends the paper a contemporary appearance, suggesting a move beyond the controversies that surrounded earlier scenario-based impact studies.
A recent Nature Climate Change paper titled “Peak glacier extinction in the mid-twenty-first century” presents itself as a careful, policy-relevant analysis of global glacier loss under a range of future warming levels. Rather than framing its projections around emissions pathways, the authors organize their results around four temperature outcomes by 2100: +1.5 °C, +2.0 °C, +2.7 °C, and +4.0 °C. This choice lends the paper a contemporary appearance, suggesting a move beyond the controversies that surrounded earlier scenario-based impact studies.
It is important to state clearly at the outset: the paper does not explicitly claim to avoid or correct for RCP 8.5. It does not present itself as a methodological advance over earlier work on those grounds, nor does it acknowledge the debates surrounding the plausibility of that scenario. RCP 8.5 simply does not appear by name.
That silence, however, is precisely what makes the paper instructive. While the scenario label has been removed, the high-end assumptions once associated with RCP 8.5 quietly re-enter the analysis under a different framework. The paper’s most dramatic conclusions—those concerning peak glacier extinction rates approaching 4,000 glaciers per year and near-complete loss by century’s end—are driven largely by a +4.0 °C warming case constructed from SSP5-8.5 and SSP3-7.0 simulations. The result is familiar: the scenario is gone, but the signal remains.
This approach will be instantly recognizable to anyone who followed the paleoclimate proxy reconstruction debates of the past two decades. Steve McIntyre, writing at ClimateAudit.org, documented a recurring procedural pattern. When a particular proxy series was shown to be flawed—often because it was inverted, truncated, obsolete, or otherwise methodologically indefensible—it would be removed. The authors would then announce that the reconstruction was “robust,” because the overall result did not change. What was rarely emphasized was that another proxy, carrying essentially the same statistical signal, had been quietly introduced to take its place.
The pea had not been removed. It had been moved.
The glacier paper follows this same structural logic, translated from proxy networks to scenario construction.
RCP 8.5 has become politically and rhetorically inconvenient. Its assumptions about long-term coal use, population growth, and emissions intensity no longer track well with observed energy trends, and its continued use has attracted criticism even within mainstream climate research. Rather than engaging those criticisms directly, the paper sidesteps them. The RCP framework disappears. SSPs take its place. The analysis is reframed around temperature end-states, severing the connection between projected impacts and the socioeconomic assumptions required to produce them.
The effect is subtle but consequential. By focusing on warming levels rather than pathways, the paper treats a +4.0 °C world as a policy-relevant comparator rather than as an extreme conditional outcome. Nowhere does it ask whether such a trajectory remains consistent with observed electricity generation trends, fuel substitution rates, or historical declines in energy intensity. The scenario exists because the model ensemble allows it to exist, not because the real world is demonstrably moving in that direction.
At this point, a reminder is useful: the issue is not what the authors claim, but what the results depend on.
The paper’s most emotionally potent comparisons—such as equating peak extinction rates to “losing the entire glacier population of the European Alps in just one year”—derive their force almost entirely from the high-end warming case. Under +1.5 °C, the projected peak loss rate is roughly half that value; under +2.7 °C it is intermediate. The wide spread between these outcomes should invite skepticism about policy inference, yet the paper treats the upper bound as a meaningful guide to decision-making.
This is especially striking given the authors’ own admissions about metric fragility. Glacier “extinction” is defined not by physical disappearance in any hydrological sense, but by an area threshold of 0.01 km² or a volume drop below 1 percent of the initial value. The paper acknowledges that glacier number is highly sensitive to inventory resolution, classification choices, and the treatment of small ice bodies, and that it should be interpreted with more caution than mass or area. These caveats are technically correct—and then largely set aside.
What follows is a pivot from conditional modeling to normative language. The authors conclude that their results “underscore the urgency of ambitious climate policy” and that the difference between losing 2,000 versus 4,000 glaciers per year by mid-century is “determined by near-term policies and societal decisions taken today.” This is not merely descriptive. It is prescriptive, and it rests squarely on the same high-end assumptions that have been relabeled rather than interrogated.
This illustrates a methodological culture that treats contested assumptions as interchangeable components so long as the preferred outcome survives. The glacier models are internally consistent. The statistics are competently executed. But the stability of the headline result under substitution is treated as validation, when it should instead prompt the same question McIntyre asked repeatedly in a different context: robust with respect to what, exactly?
In the proxy debates, robustness often meant that removing one criticized series changed nothing because another, functionally similar series had taken its place. In this case, robustness means that removing a discredited scenario label changes nothing because its high-end assumptions reappear under a new framework. The logic is the same. Only the objects have changed.
The pea, once again, has not disappeared. It has simply been moved.
And as before, the audience is invited to admire the steadiness of the result rather than to examine how carefully the cups have been arranged.
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