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.
Guest Post by: The Committee for a Constructive Tomorrow, The Heartland Institute, the Competitive Enterprise Institute, the Energy & Environmental Legal Institute, and the International Climate Science Coalition, and Truth in Energy and Climate.
Editor’s note: This compilation serves as a fact check on the most widespread false claims made about climate change by the media in July, 2026.
‘Climate change’ is NOT causing droughts, heatwaves, shrinking squid brains, wildfires, snow droughts & more tornadoes
A new study celebrates artificial intelligence for translating National Weather Service forecasts into multiple languages, claiming accuracy above 95 percent and dramatically faster turnaround times. Yet the history of weather communication shows that translation failures often stem from culture, context, and human interpretation—areas where AI still faces significant challenges.
A new study celebrates artificial intelligence for translating National Weather Service forecasts into multiple languages, claiming accuracy above 95 percent and dramatically faster turnaround times. Yet the history of weather communication shows that translation failures often stem from culture, context, and human interpretation—areas where AI still faces significant challenges.
CHAMPAIGN, Ill. — Nearly 69 million people in the United States speak a language other than English at home, yet weather warnings have long been issued almost exclusively in English. A new study documents how the National Weather Service is using artificial intelligence to change that, developing a comprehensive translation program that delivers life-saving forecasts and alerts in Spanish, Chinese, Vietnamese, Samoan, French and other languages.
The new multidisciplinary study was led by University of Illinois Urbana-Champaign climate, meteorology and atmospheric sciences professor Joseph Trujillo-Falcón and NWS scientist Monica Bozeman and is published in the journal Artificial Intelligence for the Earth Systems.
“Translating weather forecasts has always been a critical, time-consuming task, often added to the plates of bilingual forecasters managing full operational responsibilities, but it provides information for the 68.8 million people who do not speak English at home,” said Trujillo-Falcón, who also leads the ALERTAS lab in partnership with the department of communication at Illinois.
A new study led by Illinois professor Joseph Trujillo-Falcón documents how the National Weather Service is using artificial intelligence to develop a comprehensive translation program that delivers life-saving forecasts and alerts in Spanish, Chinese, Vietnamese, Samoan, French and other languages. Photo by Fred Zwicky
The urgency for accurate and culturally sensitive translation of weather forecasts in the U.S. intensified in the 1970s and ’80s. In 1987, an F4 tornado struck the town of Saragosa, Texas, and 151 of the town’s 183 residents suffered injuries or casualties. The town, which had many Spanish-only speakers, had only one Spanish-language radio station, which did provide the NWS warning; however, the literal translation of the English message failed because the word “warning” has no Spanish equivalent.
The new program partners the NWS with the AI translation platform LILT, whose patented training process enables large language models to adapt neural machine translation tools for weather terminology and messaging.
“Before this program, bilingual NWS forecasters spent up to an hour manually translating a single product. The AI system now completes the same translation in 5 to 7 minutes for some NWS products, with accuracy scores above 95%,” Trujillo-Falcón said. “More than 30 NWS offices from across the country are using it and, starting with the 2025 hurricane season, the National Hurricane Center officially began issuing AI-translated Spanish advisories for all hurricanes in the Pacific and Atlantic coasts, providing lifesaving information to the U.S. and even Latin America.”
This graphic details the workflow for translating NWS text products via the LILT application programming interface and posting them to the public NWS experimental website at weather.gov/translate. Photo courtesy Joseph Trujillo-Falcón
Beyond the operational aspect of the new program, the team is also very interested in how it will benefit the American people and economy.
“Tourists from across the world come back time and time again, benefiting local businesses, and we can provide them with life-saving information,” Trujillo-Falcón said. “And with the World Cup approaching, the NWS has made an agency-wide effort to provide decision-support services in the event of dangerous storms approaching the stadiums.”
Trujillo-Falcón emphasized the importance of the multidisciplinary focus the U. of I. brings to the program. For example, Illinois geography and geographic information science graduate student and study co-author Liam Llewellyn has overlaid geographic information systems, weather and census data to identify which language translations are needed for all 122 NWS offices across the country.
“We plan to expand these collaborations in the future here at Illinois,” Trujillo-Falcón said. “For example, I have been working with Spanish and Portuguese professor Salvatore Callesano, who is helping with the social science evaluation of how the public responds to AI tools.”
Researchers from the University of North Dakota, Pace University, Colorado State University and the University of Oklahoma also contributed to this study.
No historical tornado data was presented, and no climate scientists were interviewed. There are only the usual anecdotal claims and appeals to emotion.
It’s getting too easy to debunk the Guardian and their alarmist claims. The latest one is that recent tornadoes in Michigan are “undeniably” linked to climate change:
No historical tornado data was presented, and no climate scientists were interviewed. There are only the usual anecdotal claims and appeals to emotion. The only connection to climate change was through a reference toward the end of the article citing the increased frequency of billion-dollar disasters (BDD) in the US. Of course, the BDD data is severely flawed as it doesn’t take into account the fact that property values have risen 50% greater than the CPI index, they use and that there’s way more property & people in harm’s way today vs. 1980 (among other flaws like increases in insurance participation rates over time).
So are tornadoes becoming more frequent or intense in Michigan? The data says no, and it’s not even close!
“There have been several comments on social media about the lack of balloon launches on Thursday, along with the usual snide political remarks claiming that the DOGE cuts are impacting the accuracy of the forecast.”
As if there is shock that could have been prevented except for the cuts.
Heck, it’s like Casablanca, only with the weather. Shocking that gambling was going on in Rick’s place. I guess so are tornadoes.
As far as blaming the administration for cuts, round up the usual suspects
Before I go on, I want to say that some of the cuts that have been made are ones I am against. I believe much of this is collateral damage from the aggressive push of the climate change agenda. The new team came in with a blunt hammer, and as a result, some very valuable research has been cut. I had no input on any of this.
Again, if NOAA had been actively promoting its weather-related research and forecasting progress so the public was aware of it — rather than constantly yelling about climate change — the backlash we’re now seeing might not have happened.
It has hit me hard personally: the Physical Science Division Reanalysis is no longer being updated. Interesting, isn’t it? The biggest advocate for using the past to counter the hysteria about the future has been kicked in the gut by these cuts — along with many other old-school, analog-based forecasters.
I can only imagine what some of the other researchers (tornadoes, hurricanes, etc) must be thinking.
But since I did a case study on this tornado event for all to see, I feel I have the gravitas to speak on this matter,
The moaning and groaning about the lack of balloon launches for the Thursday outbreak is nonsense. By extension, so is bashing that aspect of cuts.
You could see this coming, and I showed this a week ago.
Predictability too low?
Speak for yourself. Actually, since they did not take meteo 434 at PSU 50 years ago, for whoever is putting this out, it was. But not if you used pattern recognition and had the relatively simple parameters to line this up.
The outside red line is a slight risk, the second area enhanced, the inside was moderate.
This is what happened btw
Without getting too deep into the details, the parameters I teach use simple surface wet-bulb temperatures and sea-level pressures in March, April, and May as quick-and-dirty ways to predict this.
Why? Because if you have those values at the surface, you’re very likely to have the necessary support throughout the entire atmospheric column. The method relies on understanding the typical relationships between average sea-level pressure (SLP) and wet-bulb temperatures at this time of year, and what it takes to produce them.
So when you see a strong trough trying to emerge into increasingly warm, moist air, and you understand what the overall pattern is doing, you can quickly sketch out the broad outlines of what’s coming. Simple, right?
Actually, yes — and that’s exactly the problem today. If it’s simple, anyone can do it, and that threatens the authority of those who want to keep forecasting mysterious and complicated. Some is, but not all.. I can teach this method to any high school freshman. Then he’ll start posting forecasts online and drive a lot of people crazy who don’t want some kid challenging their forecasts.
I say bring it on. Heck in wrestling, we have high school kids beating world champions. So why not in the weather?
In any case, a slight risk is where Sea Level Pressure is below 1008, and wet bulbs above 65. The lower the pressure and the higher the wet bulb, obviously, the higher the risk as the air is more buoyant.
So if you are looking at this pattern a week away ( like I showed in the blizzard case study), you set it up and hone in as you get closer
No balloon measurements in the area that got hit the hardest, right?
Well, why should that be an issue 6 hours away, it sure wasn’t if you were looking at the old school method. It was what I anticipated a week before would develop.
Look at the SLP forecast for 18z
Never mind the wet bulbs, the dew points were way up there ( meaning the wet bulbs were even higher)
So if you dissected the pattern a week out and suspected this setup was going to develop, you didn’t need balloon launches the morning of the event to tell you to watch out.
Obviously, our risk assessment a week in advance was higher than the NWS risk on the day of the event — we were already at moderate risk, while they only went to enhanced
But maybe if they used the criteria, simply having wet bulb and pressure, they would have had it. Since wet bulbs got into the 70s and pressures under 1000 mb, that is naturally a higher risk than the start of the risk area at 1008 and 65.
But you don’t need balloon launches on the day of the event to tell you what could break loose. The required parameters were already in place well before these outbreaks.
We’re in the heart of tornado season, and the overall pattern — with the MJO in Phase 1, which is historically linked to more extreme weather in the U.S. — was clearly in place. As I demonstrated for anyone who was paying attention, you could see it coming a week out, and on the day of the event, it was all there.
But this is part and parcel of what has happened in my field. Comments like “people care more about illegal immigration than cutting weather balloon launches” are perfect examples. Or the idea that launching more balloons on the day of an event — even when all the key parameters are already in place using methods developed 50 years ago — would have somehow made a real difference.
So, they circle back to politics. The notion that weather and climate are simply political tools, or that meteorologists are complaining about missing balloon data when a pattern has been screaming “look out” from a week in advance (and into next week), shows just how far we’ve sunk.
The weather and climate should never be dragged into the cesspool of politics. That happens largely because political operatives can’t keep their hands off anything they believe might advance their agenda. Look at what politics has done to our field. Think about the implications of people claiming that because a couple of balloon launches were cut, an otherwise obvious forecast was compromised. Do you think for one second that if weather and climate hadn’t been dragged into that cesspool, this event — like so many others — would have drawn any political comments at all?
Heh, they came into our house, I didn’t go into theirs.
Again, I am dead set against some of the cuts I’ve seen, and I’ve said so loudly. Perhaps that’s why I haven’t been asked to comment on them — even as the most experienced daily private-sector global forecaster. I don’t know.
But I do know this: the recently announced EPA advisory committee does not include a single global, everyday forecaster. My question is simple — how in the world are you going to make sound decisions about the environment without anyone who actually forecasts the weather, when weather is what happens in the environment every single day?
But as far as the lack of balloon launches is concerned, given the pattern and how clearly you could see this setup coming from far out, complaining about it and then linking it to political bashing is absurd. You did not need balloon launches to see this coming.
Yeah, shocking that on April 23rd, with the pattern so obvious, tornadoes occurred and weren’t predicted perfectly because of the lack of balloon launches.
I’m shocked, SHOCKED to find tornadoes going on around the US in April. If only we had launched more weather balloons.
According to NOAA’s National Centers for Environmental Information (NCEI) and Storm Prediction Center (SPC), the contiguous U.S. recorded 1,559 preliminary tornadoes in 2025. This was 127% of the 1991–2020 annual average (~1,225 tornadoes) and ranked as the fifth-highest annual total on record (behind 2004, 2024, 2008, and 2011). Some tallies list slight variations (e.g., ~1,429–1,591) due to preliminary vs. confirmed counts, but the year was clearly above average.
According to NOAA’s National Centers for Environmental Information (NCEI) and Storm Prediction Center (SPC), the contiguous U.S. recorded 1,559 preliminary tornadoes in 2025. This was 127% of the 1991–2020 annual average (~1,225 tornadoes) and ranked as the fifth-highest annual total on record (behind 2004, 2024, 2008, and 2011). Some tallies list slight variations (e.g., ~1,429–1,591) due to preliminary vs. confirmed counts, but the year was clearly above average.
Activity concentrated in the southern Plains, central Gulf Coast states, and Mid-Mississippi/Ohio valleys, with notable outbreaks from March through June. March set a record with ~300 tornadoes. November was the quietest month. North Dakota shattered its state record with 72 (or up to 82 in final tallies) tornadoes.
Strong/violent tornadoes: The year included the first EF5 since 2013—the Enderlin, North Dakota tornado on June 20 (upgraded in October 2025 after detailed surveys showing winds >210 mph, with extreme damage indicators like derailed loaded train cars and swept foundations). There were also multiple EF4s (at least five confirmed), including events in Arkansas, Louisiana, Illinois, and Kentucky during March and May outbreaks.
Fatalities: Approximately 67–68 direct deaths in the U.S. (higher than 2024’s 54 but well below 2011’s 553). Major contributors included the March 14–16 outbreak (multiple fatalities across states) and the May 16 outbreak (dozens of deaths, notably in Kentucky). Many fatalities occurred in mobile/manufactured homes. Worldwide, tornado fatalities totaled ~105.
Damage: Preliminary U.S. property damage estimates were at least $1.9 billion, with specific outbreaks contributing significantly (e.g., March 14–16: ~$11 billion in one billion-dollar disaster analysis; May 14–16: ~$6.3 billion). These fit within recent-year ranges rather than historical extremes when considering broader severe weather context. Billion-dollar tornado/severe storm events were part of 23 such disasters totaling ~$115 billion across all weather events in 2025.
Early 2026 (through mid-April) showed near- to above-average activity in some regions, with outbreaks like April 17 in the Upper Midwest, but full-year data is pending.
Reported tornado counts have risen since the mid-20th century, driven primarily by improved detection of weak (EF0–EF1) events via Doppler radar, storm spotters, and media. In contrast, strong/violent tornadoes (F/EF3+) show no increase—and a clear decline in consistent-quality data periods:
The 1975–1984 decade averaged ~49 F/EF3+ tornadoes per year.
The 2015–2024 decade averaged ~26 per year — a roughly 46% decline.
Overall tornado frequency remains dominated by natural variability (e.g., seasonal patterns, La Niña/El Niño influences) rather than any detectable long-term climatic uptrend in intensity or count of significant events.
Today, I share the latest data on normalized U.S. tornado losses since 1954 and a time series of the incidence of the strongest tornadoes since 1975. I doubt you’ll come across these data anywhere else.
Comparing tornado losses across decades is not straightforward. A tornado striking what was a rural county 70 years ago would cause far less damage than the same tornado striking that same heavily developed county today — because there is now more property and wealth exposed to loss.
To make meaningful historical comparisons of loss estimates, researchers “normalize” losses: they ask what each historical storm or event would cost if it occurred under today’s societal conditions, adjusting for factors such as inflation, wealth, building types and counts, population, and, in some cases, efforts to improve building quality.
This approach was first applied systematically to tornadoes by Simmons, Sutter & Pielke Jr. (2013), which analyzed NOAA SPC data from 1950 through 2011. Zhang et al. (2023) reproduced and extended the Simmons series through 2018, confirming the earlier results and updating the time series. They concluded: “[O]ur results suggest a downward trend in tornado losses for the U.S. as a nation.”
The figure below shows my replication of the Zhang et al. tornado normalization from primary data and extends it through 2025. The normalized losses are expressed in 2026 dollars.
The dominant loss years are 1954 ($36 billion),1965 ($44 billion), and 1974 ($29 billion). The largest recent loss year is 2011 at $16 billion — the largest post-1980 value and the only recent year approaching the scale of the 1960s–1970s peaks. Since 2012, annual normalized losses have largely remained below $5 billion.
The time series shows a significant decrease in annual normalized losses.The 1954–1963 decade averaged $4.8 billion per year; the 2015–2025 decade averaged $1.9 billion per year.
Our 2013 paper identified this trend:
“We can definitively state that there is no evidence of increasing normalized tornado damage or incidence on climatic time scales.”
At the time, we hypothesized that underlying the decrease may be an actual reduction in severe tornado incidence. However, because economic data should not be used to infer trends in related climate variables, we suggested that any such trend in tornadoes would depend upon analyses of climate data.
More than a decade later, tornado data is strongly suggestive of an overall decline in the incidence of the strongest tornadoes. The figure below shows the annual count of F3/EF3 and stronger tornadoes from 1975 through 2024, the period over which data quality is most consistent.
The time series shows a clear decrease in major tornado incidence. The 1975–1984 decade averaged 49 F/EF3+ tornadoes per year; the 2015–2024 decade averaged 26 per year — a decline of roughly 46 percent. Note that data available back to 1954 makes this decrease look much larger, but is accompanied by questions of data quality.
The interpretation of this declining trend requires caution for several reasons. First, the Enhanced Fujita (EF) scale introduced in 2007 changed rating methodology, creating a potential discontinuity in the series. Most analysts believe EF ratings are somewhat more conservative than legacy F ratings for comparable damage, which could contribute to the apparent post-2007 decline. Second, improved public warnings and storm-resistant construction may have contributed to changing the nature of observable damage markers that drive intensity ratings, which are often established based on damage patterns rather than direct measurements of tornado intensity.
Neither of these caveats undermines the the fact that there is no evidence of an increase in violent tornado incidence over the observational record. The IPCC Sixth Assessment Report concluded with low confidence in the detection of any trend in tornado frequency or intensity at the global or regional level, and with low confidence in attribution of any observed changes to anthropogenic forcing. The data reviewed here are consistent with that assessment.
Several hypotheses have been offered in the literature for how accumulating greenhouse gases in the atmosphere may influence tornado behavior. Some commonly cited examples include:
Reduced wind shear.Trapp & Hoogewind (2018) proposed that Arctic amplification could weaken lower-tropospheric wind shear, reducing the environmental favorability for supercell formation. If correct, this would represent a climate signal leading to fewer strong tornadoes.
Geographic shift eastward.Gensini & Brooks (2018) documented a westward decline and eastward increase in tornado activity — a shift toward “Dixie Alley” and away from the traditional “Tornado Alley.” An eastern shift moves tornado tracks closer to higher-density development. They conclude: “At this point, it is unclear whether the observed trends in tornado environment and report frequency are due to natural variability or being altered by anthropogenic forcing on the climate system.”
Increased variability.Brooks, Carbin & Marsh (2014) argued that over 1954 to 2013 tornado activity became more variable — with years of very high activity alternating with years of historically low counts — rather than showing a simple trend in frequency. They explain: “At this point, we cannot offer a physical hypothesis for the increased variability.”
The normalized loss series and the F/EF3+ incidence series both show the same pattern: no upward trend, and arguably, a significant downward trend that has contributed to lower normalized damage levels in recent decades.
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The Chicago Tribune recently ran a story claiming that climate change is making storms in Illinois “more severe,” particularly with regard to hail and tornadoes. This is false. There is no evidence that hail is becoming larger or tornadoes more powerful, or that either has become more common.
The Chicago Tribune recently ran a story claiming that climate change is making storms in Illinois “more severe,” particularly with regard to hail and tornadoes. This is false. There is no evidence that hail is becoming larger or tornadoes more powerful, or that either has become more common. The article relies entirely on computer model projections that it presents as fact, without reference to real world data. The article suggests that higher insurance claims are evidence that storms are more destructive, but this isn’t actually evidence of anything other than increased property values.
The study is a few years old, but it comes up almost every time the media wants to tie a notable hail event to climate change. It is not based on real world observations, but on projections from computer modelling that assumes that global warming will result in more storms with stronger updrafts, which would allow for larger hailstorm formation before they drop to earth. This same assumption was behind an NBC News report from July 2025, which was refuted in Climate Realism by meteorologist Anthony Watts.
In that post, Watts points out:
Hail formation requires a very specific cocktail of atmospheric conditions, including strong updrafts and a freezing layer deep enough to support hail growth. Warmer temperatures tend to reduce the vertical depth of this freezing level, making it more difficult—not easier—for large hailstones to form and survive to ground level.
He also points to studies that actually did look at measured severe hail occurrence, which found a decrease in significant hail events in the United States in recent decades.
Looking at National Weather Service data for Illinois alone, from 1995 to 2025, there is no trend towards more hail.
Figure 1: Illinois hail reports, from 1995 to 2025, data from NWS Storm Prediction Center.
Computer model outputs are only as good as the assumptions and formulas built into them. In this case, the models are seriously flawed, as the long-term data demonstrate. There is no trend towards worse or more frequent hail.
While the article title also implies that tornadoes are also worsening due to climate change, tornadoes are hardly mentioned in the body of the article. That’s good for the Chicago Tribune, because the reality is that tornadoes are not becoming more extreme or common in Illinois. It is shameful to imply that correlation. Even though the story itself doesn’t get into it, it’s still worth presenting the data that debunk what is implied in the article’s title.
Looking at the overall tornado counts, there appears to be a slight upward trend, however this is an artifact of the introduction of better radar systems that had wider coverage introduced throughout the mid-1990s, capturing smaller, briefer tornados in locations the earlier system missed. The number of weak and short-lived tornados observed has increased greatly because they are now more easily spotted. Severe tornadoes, however, have not increased.
Most tornados occur in central and southeast Illinois, and most studies looking at Illinois storms focus there. But since this article talks about the Chicagoland area as well, Climate Realism presents data for all of Illinois.
Figure 2: Tornado count from 1995 to 2025 in Illinois, data from NWS Storm Prediction Center.
Figure 3: Strong (EF2+) tornadoes in Illinois from 1995-2025.
Data show no consistent trend towards an increase in the number of tornadoes, powerful or otherwise, despite 2023-2025 being high count years.
What should be among the most embarrassing parts of the article for the Chicago Tribune, aside from than their lack of fact-checking data on hail and tornadoes, is when paper tries to tie rising insurance claims from hail damage as proof that climate change is making hail worse. What the Chicago Tribune fails to note, as covered by multiple Climate Realism posts on the subject of insurance (here, here, here, for example), is that this is a terrible metric for measuring weather severity. That’s because while weather has not become more extreme, property values have become higher and inflation, affecting both labor and materials, has resulted in dramatically more expensive replacement costs. That means the cost of insurance claims rises too. This is basic economic logic, reinforced by the weather data shared above.
The Chicago Tribune failed in its journalistic duty of producing a story grounded in facts. Instead of merely reporting on the danger and human interest aspects of the recent severe storms, they attempted to fearmonger people into believing extreme weather is getting worse due to climate change. It’s not, as historical observational weather data show that there is no sustained trend of worsening storms. The computer model projections the Chicago Tribune cited as showing climate change-worsened weather are not much better than the simulations produced by fantasy computer games.
Boasting that Texas “has built more wind power than any state and is a top contender for the most solar power,” a Texas Tribune article bemoans a decline in federal subsidies for such energy sources and a potential loss of “billions in investments and thousands of jobs.”
Boasting that Texas “has built more wind power than any state and is a top contender for the most solar power,” a Texas Tribune article bemoans a decline in federal subsidies for such energy sources and a potential loss of “billions in investments and thousands of jobs.”
Interestingly, the writers focus on business interests of the climate industrial complex and ignore the stated reason for subsidies – to avoid supposed catastrophic global warming. Planetary health – purported to be threatened by industrial emissions of carbon dioxide (CO2) – was not even an afterthought in the handwringing over wind and solar financial fortunes.
Regardless, Texans face no such peril and the billions already spent on “green” obsessions in the Lone Star State are for naught. “There is no evidence of a climate crisis in Texas, and none can be reasonably expected,” says a report, “Texas and Climate Change,” recently published by the CO2 Coalition, Fairfax, Virginia.
Both the Fifth National Climate Assessment (NCA5) and a Texas A&M University report predict harm to Texans from human-induced warming. Climate change is “putting us at risk from climate hazards that degrade our lands and waters, quality of life, health and well-being, and cultural interconnectedness,” according to NCA5.
In contradicting those findings, the CO2 Coalition analyzed data from the National Oceanic and Atmospheric Administration (NOAA), U.S. Environmental Protection Agency (EPA), NASA, U.S. Department of Agriculture, reports published in peer-reviewed journals and others.
“The temperature in Texas has shown no unprecedented or unusual warming, despite increasing atmospheric carbon dioxide,” says the CO2 Coalition report. “Recent temperatures in Texas are similar to those found more than 100 years ago.”
In fact, the annual number of 100-degree days in Texas has an overall decreasing trend.
While some have claimed a connection between climate change and July’s tragic flooding in central Texas, no scientific basis for such a link exists. Though extreme, the flooding was not a first.
According to Harris County meteorologist Jeff Lindner, the July 4th flood of the Guadalupe River at Kerrville peaked at 34.29 feet, making it the third-highest flood on record for the city. The 2025 flood crest trails the 39.0-foot flood crest from 1932 and the 37.72-foot flood crest from in 1987.
“Over the last 28 years, flash floods, while varying greatly from year to year, have actually been in slight decline,” the CO2 Coalition report found.
Precipitation data from the U.S. Historical Climatology Network indicate that Texas has experienced a very slight increase (1 to 2 inches annually) in precipitation since 1895, which is contrary to the predictions of significant increases in rainfall from climate alarmists. If anything, the modest increase in Texas precipitation should have beneficial effects on the state’s agricultural yields.
As for drought – the primary scourge of crops throughout the world – government data show no discernable trend in the severity of arid spells in Texas, which is a direct contradiction to claims of increasing drought by both the Texas A&M report and NCA5.
Similarly rebutting the fearmongering of alarmists, the CO2 Coalition report found no increasing trends for wildfires, hurricanes and tornadoes.
With respect to tornadoes, the U.S., including Texas, has seen a decades-long decline in the most violent of twisters. The likely reason is a warming Earth – a natural phenomenon following the end of the Little Ice Age – reduces the temperature differentials between regions inside and outside equatorial regions that drive storms.
Like the rest of the world, Texas has experienced record-breaking growth in crop production over the last several decades. This is no coincidence, as research shows every increase of 1 part per million (ppm) in CO2 concentration boosts yields of corn and wheat by 0.4% and 1%, respectively. Based on these metrics, the 140-ppm increase in CO2 since the beginning of the Industrial Revolution has led to increases of 56%, 84% and 140% in corn, soybeans and wheat, respectively.
CO2 is necessary for life on Earth, and reducing emissions of the gas would be harmful to vegetation, including forests, grasslands and agricultural crops.
Even if Texas could stop emitting CO2, the amount of atmospheric warming averted would be only 0.0093 degrees and 0.0237 degrees by 2050 and 2100, respectively. These changes are negligible and cannot be felt or measured.
If the reason for spending on Texas climate policy were to enrich wind and solar developers, then, yes, lamentations over the demise of subsidies are understandable. However, there is no basis for spending a cent on a fake crisis – and certainly not on technologies that offer no benefit.
This commentary was first published by Lone Star Standard on October 17, 2025.
So that’s the idea. Of course, I wasn’t interested in camping, but in climate. In particular, I wanted to find out more about my theory that emergent thermoregulatory climate phenomena are central to the surprising stability of global temperatures.
I’ve been exploring the abilities of Artificial Intelligence, which I described in a previous post as “The World’s Dumbest Genius Librarian“. I read a curious suggestion as to how to use AI, which was what they called the “blank line” technique. This is where you give the AI (in my case perplexity.ai) a list of things with a blank line “______” at the end, and it fills in the blank. The example they gave in the suggestion I read was something like “I’m going camping, and I’m taking a tent, a sleeping bag, a hatchet, and ___”. So, I tried it. Here, let me try it again right now so you can see what I mean. Hang on … OK, here’s what I got:
Me:
“I’m going camping, and I’m taking a tent, a sleeping bag, a hatchet, and ___”
Perplexity:
“Based on your camping list—tent, sleeping bag, hatchet—the next logical and essential item to bring would be a flashlight. A flashlight is crucial for safety and convenience in the outdoors, especially after dark, making it a natural addition to your camping essentials.”
So that’s the idea. Of course, I wasn’t interested in camping, but in climate. In particular, I wanted to find out more about my theory that emergent thermoregulatory climate phenomena are central to the surprising stability of global temperatures.
And what is an emergent phenomenon when it is at home? Here are the characteristics that differentiate emergent climate phenomena from other kinds of critters:
They are flow systems far from equilibrium that arise spontaneously, often upon crossing a critical threshold that is temperature-based.
Their properties are not predictable from the properties of the condition they emerge from. There’s nothing in the nature of atoms of water and air that would predict that they could spontaneously create lightning.
They move and act unpredictably.
They are often associated with phase changes.
They often exhibit “overshoot” (hysteresis).
They have a lifespan from their initiation to their dissolution.
Their patterns arise from many small interactions of their component parts.
So I used that blank line technique to find out more about emergent climate phenomena.
Me:
Among the emergent weather phenomena that help thermoregulate the temperature are thunderstorms, cirrus cloud fields, the Rayleigh-Benard overturning of the lower atmosphere and the upper ocean, cyclones, the Nino/Nina phenomena, tornadoes, dust devils, and _____
The first answer I got was:
Perplexity:
Among the emergent weather phenomena that help thermoregulate the temperature—such as thunderstorms, cirrus cloud fields, Rayleigh-Bénard overturning in the lower atmosphere and upper ocean, cyclones, the Niño/Niña phenomena, tornadoes, and dust devils—another significant process is volcanic eruptions.
BZZZT! Wrong, sez I! So I followed that with:
Nope. Volcanoes are 1) not an emergent phenomenon, and 2) not thermoregulatory in nature in that they act regardless of the temperature. Try again.
Perplexity:
Based on the context of emergent, temperature-triggered weather phenomena that actively help thermoregulate Earth’s climate, the most fitting completion for your list is cumulus cloud fields or cumulus convection.
Cumulus clouds, like thunderstorms, form when surface heating reaches a critical threshold, leading to vigorous upward convection. This process increases cloud cover and enhances the reflection of incoming solar radiation, thereby cooling the surface and stabilizing temperatures. This mechanism is described as a classic emergent thermoregulatory process—appearing spontaneously when local temperatures rise above a certain point, and acting to limit further warming
That showed me that perplexity had gotten the underlying idea. So I said that was a good answer, find me another example. It gave me another. I only asked perplexity for one at a time, because AI does better with single questions.
I repeated that a whole bunch of times, getting answers I’d thought of before, and answers it had given me upthread in a different form, and answers that were a total surprise to me.
I then asked perplexity to read through the whole thread, select the unique, distinct answers, and say why they are emergent phenomena rather than some other kind of phenomena, what their thermoregulatory action is, and how they are distinct from the other phenomena on the list. Below is what I got, unedited. And while I might quibble about some parts of the descriptions, overall it was a fascinating list.
Comprehensive Synopsis of Emergent Thermoregulatory Weather Phenomena
Thunderstorms
Emergence: These form spontaneously when surface heating creates convective instability that crosses critical temperature and humidity thresholds. The process is nonlinear, with small changes in conditions triggering explosive growth beyond predictable models.
Thermoregulatory Function: They vertically transport heat through updrafts and downdrafts while increasing planetary albedo via anvil clouds. Additionally, rainfall induces evaporative cooling at the surface, disrupting localized heat buildup.
Distinctiveness: Unlike planetary-scale oscillations, thunderstorms are short-lived, highly localized convective events driven by surface heating rather than large-scale wind patterns.
Cirrus Cloud Fields
Emergence: Self-organizing ice crystal structures arise from wave-induced humidity fluctuations in the upper troposphere. Their formation depends on nonlinear ice-nucleation dynamics that defy simple prediction.
Thermoregulatory Function: These clouds trap outgoing longwave radiation (warming) while reflecting sunlight (cooling), creating a context-dependent net effect. They modulate radiative balance across regional scales without producing precipitation.
Distinctiveness: Their wispy, high-altitude structure contrasts sharply with low marine stratocumulus decks, functioning more as radiative modulators than convective triggers.
Rayleigh-Bénard Overturning
Emergence: Convection cells emerge spontaneously when surface heating exceeds critical buoyancy thresholds in fluid layers. This self-organization stems from nonlinear feedback between temperature gradients and fluid viscosity.
Thermoregulatory Function: Cells vertically mix heat in atmospheric/oceanic boundary layers, preventing extreme surface temperatures. They maintain stability by continuously redistributing energy from heated surfaces to cooler layers.
Distinctiveness: As a universal fluid dynamic process, it operates in both oceans and atmosphere, unlike event-driven phenomena like haboobs or derechos.
Cyclones
Emergence: These large-scale vortices self-organize when wind shear, humidity, and sea-surface temperatures cross instability thresholds. Their structure emerges from nonlinear ocean-atmosphere coupling.
Thermoregulatory Function: Cyclones redistribute equatorial heat poleward via winds and induce evaporative cooling through heavy rainfall. They act as planetary-scale heat engines, balancing latitudinal temperature gradients.
Distinctiveness: Their synoptic scale and multi-day lifespan differ from mesoscale phenomena like bow echoes or gust fronts.
El Niño-Southern Oscillation (ENSO)
Emergence: This pattern arises nonlinearly from coupled ocean-atmosphere feedbacks in the tropical Pacific. Threshold-crossing events trigger phase transitions between warm and cold states.
Thermoregulatory Function: ENSO modulates global heat distribution by altering ocean currents, upwelling, and atmospheric circulation. It regulates equatorial Pacific temperatures over multi-year timescales.
Distinctiveness: Its interannual pacing contrasts sharply with diurnal processes like sea breezes or nocturnal boundary layer mixing.
Tornadoes
Emergence: These form spontaneously when supercell thunderstorms interact with wind shear and convective instability. Their rotation emerges from nonlinear vorticity dynamics.
Thermoregulatory Function: Tornadoes rapidly mix warm surface air with cooler upper-level air, disrupting heat buildup. Their intense updrafts/downdrafts enhance local heat redistribution.
Distinctiveness: Their small-scale, violently rotating structure differs from non-rotational density currents like gust fronts.
Dust Devils
Emergence: Self-organizing vortices arise when intense surface heating creates localized thermal instability. Their formation depends on nonlinear interactions between hot surface layers and cooler air.
Thermoregulatory Function: They lift hot surface air upward, replacing it with cooler air and enhancing vertical mixing. This process disrupts near-surface heat accumulation in arid regions.
Distinctiveness: As dry, warm-core vortices, they lack the moisture-driven dynamics of thunderstorms or monsoons.
Cumulus Convection
Emergence: Fluffy clouds self-assemble when surface heating exceeds convective initiation thresholds. Their growth is a nonlinear response to boundary layer instability.
Thermoregulatory Function: Cumulus clouds increase albedo to limit surface heating and promote cooling through shade and latent heat release. They act as precursors to deeper convective systems.
Distinctiveness: This represents an early convective phase distinct from mature mesoscale complexes or anvil-topped thunderstorms.
Madden-Julian Oscillation (MJO)
Emergence: Spontaneous convection clusters propagate eastward via wind-moisture feedbacks in the tropics. Its emergence stems from collective atmospheric interactions crossing organizational thresholds.
Thermoregulatory Function: The MJO modulates tropical heat buildup through cyclic cloud/rain development. It redistributes moisture and energy across ocean basins over 30–60 day periods.
Distinctiveness: As a planetary-scale traveling system, it differs from stationary monsoons or localized convective events.
Monsoon Circulation
Emergence: This system self-organizes when land-sea thermal contrasts exceed seasonal thresholds. Its onset involves nonlinear feedback between precipitation and atmospheric heating.
Thermoregulatory Function: Monsoons cool land surfaces via cloud cover and rainfall while exporting heat to the upper troposphere. They reset regional energy imbalances annually.
Distinctiveness: Their seasonally reversing winds contrast with diurnal coastal breezes or persistent trade winds.
Sea Breeze Circulation
Emergence: Forms spontaneously when land-sea temperature gradients overcome atmospheric resistance. The circulation emerges from nonlinear pressure adjustments.
Thermoregulatory Function: It transports cool marine air inland to moderate coastal temperatures and triggers convective clouds for additional cooling. The nightly reversal prevents excessive cooling.
Distinctiveness: This diurnal, coastal-scale process differs from continental-scale cyclones or monsoons.
Stratocumulus Cloud Decks
Emergence: Self-organizing cloud blankets form under stable marine boundary layers with strong inversions. Their persistence arises from turbulence-radiation feedback loops.
Thermoregulatory Function: These decks dramatically increase albedo to cool subtropical oceans. They suppress surface warming through persistent solar reflection.
Distinctiveness: Their uniform, low-altitude structure contrasts with transient cumulus or deep convective clouds.
Reflective Tropical Cloud Shields
Emergence: Spontaneously form when sea-surface temperatures exceed critical convection thresholds. Their development is a nonlinear response to oceanic heating.
Thermoregulatory Function: These shields limit solar heating via high albedo and often precede thunderstorm development. They act as a tropical thermostat constraining maximum temperatures.
Distinctiveness: Their thermodynamic triggering differs from dynamically driven systems like the ITCZ.
Intertropical Convergence Zone (ITCZ)
Emergence: Self-organizing convection bands arise from trade-wind convergence and SST gradients. Position emerges from hemisphere-scale energy imbalances.
Thermoregulatory Function: The ITCZ cools tropics through cloud albedo and latent heat export. Its seasonal migration balances interhemispheric energy.
Distinctiveness: As an equatorial migratory belt, it differs from off-equatorial systems like the MJO.
Mesoscale Convective Systems (MCSs)
Emergence: Thunderstorms self-aggregate into complexes via cold-pool interactions. Their structure emerges from nonlinear storm-scale feedbacks.
Thermoregulatory Function: MCSs increase albedo with expansive cloud shields and induce evaporative cooling via rainfall. They redistribute heat over regions spanning hundreds of kilometers.
Distinctiveness: Their organized cluster structure differs from isolated thunderstorms or synoptic-scale cyclones.
Hadley Cell Expansion
Emergence: Intensifies nonlinearly from tropical heating and eddy feedbacks. Poleward expansion emerges when thermal gradients cross critical thresholds.
Thermoregulatory Function: The cell exports heat poleward and modulates subtropical aridity. Its widening redistributes energy across hemispheres.
Distinctiveness: As a zonal-mean circulation, it contrasts with longitudinal phenomena like the MJO.
Polar Vortex Dynamics
Emergence: Self-amplifies from temperature/pressure gradients at high latitudes. Disruptions emerge from stratosphere-troposphere coupling.
Thermoregulatory Function: Contains frigid air; breakdowns export cold to mid-latitudes. This stabilizes polar temperatures while balancing hemispheric extremes.
Distinctiveness: Its stratospheric coupling differs from surface-driven phenomena like dust devils.
Cumulus-to-Cumulonimbus Transition
Emergence: Explosive vertical growth triggers when convective instability thresholds are crossed. This phase shift is a nonlinear atmospheric response.
Thermoregulatory Function: Rapid downdrafts and rain evaporation cool surfaces immediately. The process resets local instability by exporting heat vertically.
Emergence: Spontaneous cloud suppression occurs when temperatures fall below convection thresholds. Emergence depends on humidity-temperature hysteresis.
Thermoregulatory Function: Clearing allows solar heating to counterbalance cool conditions. This negative feedback prevents prolonged cooling periods.
Distinctiveness: Functions as the inverse of cloud-forming systems like MCSs or ITCZ convection.
Atmospheric Blocking
Emergence: Self-sustaining highs form from Rossby wave resonance. Persistence emerges from nonlinear energy trapping.
Thermoregulatory Function: Blocks redistribute heat/cold via meridional flow and prolong temperature extremes regionally. They modulate zonal heat transport efficiency.
Distinctiveness: Their quasi-stationary synoptic scale differs from traveling cyclones.
Open/Closed-Cell Stratocumulus
Emergence: Cellular patterns form spontaneously from turbulence-radiation feedbacks. Transitions exhibit hysteresis and critical thresholds.
Thermoregulatory Function: Open cells enhance infrared cooling; closed cells increase solar reflection. This dynamic optimizes local energy balance.
Distinctiveness: Pattern transitions differ from uniform decks or cumulus evolution.
Synoptic-Scale Air Advection
Emergence: Warm/cold surges arise from jet stream meanders. Their onset depends on pressure-anomaly interactions crossing instability thresholds.
Thermoregulatory Function: Advection rapidly redistributes air masses across continents. It prevents persistent regional temperature anomalies.
Distinctiveness: Driven by continental-scale dynamics unlike local convection.
Heat Bursts
Emergence: Downdrafts compress and warm adiabatically during thunderstorm collapse. Emergence requires specific vertical humidity/temperature profiles.
Thermoregulatory Function: Bursts mix elevated warm air downward, disrupting cold pools. They abruptly reset nocturnal surface temperatures.
Distinctiveness: Nocturnal, hyper-localized spikes differ from diurnal sea breezes.
Haboobs
Emergence: Thunderstorm outflow self-organizes into dust-laden density currents. Dust integration amplifies through particle-fluid feedbacks.
Thermoregulatory Function: Dust clouds increase albedo while cold pools enhance surface cooling. They rapidly export heat via dry convection.
Distinctiveness: Dust-integrated structure differs from precipitation-driven outflows.
Chinook/Foehn Winds
Emergence: Self-amplifying downslope winds form from mountain-wave dynamics. Warming emerges via nonlinear compressional heating.
Thermoregulatory Function: Winds rapidly warm leeward slopes, reducing cold extremes and melting snow. They ventilate valleys by exporting stagnant air.
Distinctiveness: Orographic triggering differs from flat-terrain wind systems.
Quasi-Resonant Amplification (QRA)
Emergence: Rossby waves resonate from jet stream instabilities and topography. Persistence emerges from atmospheric wave trapping.
Thermoregulatory Function: QRA locks heat/cold into semi-permanent regional patterns. This prolongs temperature redistribution during extremes.
Distinctiveness: Resonance-driven persistence differs from transient easterly waves.
Tropical Easterly Waves
Emergence: Self-generate from trade-wind shear and convective feedbacks. Wave organization crosses critical moisture convergence thresholds.
Thermoregulatory Function: Waves organize cloud clusters to modulate tropical heating. They propagate moisture imbalances westward.
Distinctiveness: Westward propagation contrasts with eastward-moving MJO systems.
Atmospheric Rivers
Emergence: Filamentary moisture plumes self-focus from wind convergence. Narrowing emerges via vapor-condensation feedbacks.
Thermoregulatory Function: Rivers redistribute latent heat globally and induce cooling via orographic precipitation. They prevent continental overheating during droughts.
Distinctiveness: Filamentary structure differs from broad monsoonal flows.
Derechos
Emergence: Squall lines self-organize into bows via cold pool-wind shear interactions. Bow echoes emerge from nonlinear storm-scale dynamics.
Thermoregulatory Function: Straight-line winds drive large-scale mixing and surface cooling. Their cold pools disrupt regional heat domes.
Distinctiveness: Non-rotational windstorms differ from tornadoes or cyclones.
Virga
Emergence: Precipitation evaporates spontaneously in dry subcloud layers. Phase-change thresholds govern fallstreak formation.
Thermoregulatory Function: Evaporative cooling lowers surface temperatures without rainfall. This provides “dry” heat redistribution in arid zones.
Distinctiveness: Evaporation-dominated process differs from rain-cooling in thunderstorms.
Cold-Air Pool Formation
Emergence: Cold air self-traps in topography when radiative cooling exceeds mixing thresholds. Pooling emerges from nonlinear density stratification.
Thermoregulatory Function: Pools insulate valleys from extreme cold and delay daytime warming. Their eventual mixing exports cold air regionally.
Distinctiveness: Topographic confinement differs from open-terrain radiative cooling.
Bow Echoes
Emergence: Spontaneous bowing develops from rear-inflow jet dynamics. Acceleration emerges via pressure-perturbation feedbacks.
Distinctiveness: Mesoscale bow geometry differs from linear squall lines.
Gust Fronts
Emergence: Density currents self-propagate from thunderstorm downdraft spreading. Frontogenesis depends on outflow-environment density contrasts.
Thermoregulatory Function: Gust fronts induce rapid cold-air advection to disrupt heat buildups. They often trigger secondary convection for additional cooling.
Distinctiveness: Leading-edge dynamics differ from parent storm mechanisms.
Fair-Weather Gale (California)
Emergence: Intense inland heating draws marine air through coastal gaps nonlinearly. Gale strength emerges from terrain-channeled acceleration.
Thermoregulatory Function: Persistently cools interior valleys via onshore flow. Prevents extreme heat buildup in Mediterranean climates.
Distinctiveness: Non-storm coastal wind differs from synoptic systems.
Diablo Winds
Emergence: Downslope warming self-accelerates from pressure-topography feedbacks. Wind intensity emerges via dry adiabatic compression.
Thermoregulatory Function: Rapidly warms coastal zones by breaking cold pools and fog. Exports interior heat seaward during autumn.
Distinctiveness: Northern California variant differs from Southern California’s Santa Anas.
Oceanic Bénard Cells
Emergence: Hexagonal convection forms spontaneously during surface cooling events. Cell geometry emerges from salt-fingering instabilities.
Thermoregulatory Function: Upwells warmer water to limit sea-ice formation. Enhances ocean-atmosphere heat exchange during cold outbreaks.
Distinctiveness: Salinity-driven convection differs from atmospheric overturning.
Synthesis of Thermoregulatory Networks
Earth’s climate stability arises from interconnected emergent phenomena operating across scales. Each process exhibits nonlinear threshold behavior, where small forcings trigger disproportional responses that redistribute heat. This network—from microscale dust devils to planetary-scale ENSO—forms a self-regulating system where local interactions constrain global temperature extremes through complementary cooling/warming feedbacks. The diversity of mechanisms (radiative, convective, advective) ensures resilience, as the suppression of one process activates compensatory phenomena elsewhere in the system.
Dang, sez I, who knew? Certainly not me … most fascinating. I hadn’t realized that a number of those were emergent thermoregulatory phenomena, but yes, they are. And while I had not asked for a synthesis of thermoregulatory networks at the end, that’s a pretty good one.
I’m gonna leave it at that for people to ponder, with just one last thought.
Many of those important thermoregulatory phenomena either do not emerge spontaneously in climate models or are not even considered because they are sub-grid-scale in size.
My best regards to all,
w.
As usual … when you comment QUOTE THE EXACT WORDS YOU ARE DISCUSSING. It avoids heaps of problems.
PS—I won’t be around for about six hours, starting about half an hour after this goes live. I’m going to the Russian River Rodeo with the kids and grandkids. Hey, I grew up on a cattle ranch, the local rodeo was the highlight of the year in my youth, what can I say? So play nice, no eye gouging, don’t go all Tyson on your opponent’s ear, and I’ll get back to you when I return.
A recent story by ABC News, “Climate and environment updates: Tornado activity doubled March average: NOAA,” classifies recent severe tornado outbreaks under their “climate crisis” category, implying that these storms were caused by global warming. This is false. Climate change is not causing an increase in the number or severity of tornados, nor can it be connected to such a limited event.
A recent story by ABC News, “Climate and environment updates: Tornado activity doubled March average: NOAA,” classifies recent severe tornado outbreaks under their “climate crisis” category, implying that these storms were caused by global warming. This is false. Climate change is not causing an increase in the number or severity of tornados, nor can it be connected to such a limited event.
ABC warns:
The climate crisis is not a distant threat; it’s happening right now and affecting what matters most to us.
According to a new report from the National Oceanic and Atmospheric Administration, U.S. tornado activity in March was more than twice the monthly average, with over 200 tornadoes recorded.
Although ABC doesn’t explicitly state that this tornado season has been caused by climate change, the story strongly implies it, and as such it cannot be left unchallenged, because it is wrong.
The United States did experience a destructive and violent tornado season in March 2025, but it was not a record breaker in terms of numbers or strength, nor does it reflect a long-term trend that would be necessary to prove the climate connection.
The National Centers for Environmental Information (NCEI) disagree strongly with ABC’s claims. They report that tornado records in the past are spotty, because they are very short lived and aren’t always reported, especially as one moves further back into the past. NCEI reports “[m]any significant tornadoes may not make it into the historical record since Tornado Alley was very sparsely populated during the 20th century.”
Doppler weather radar did not become widespread until the 1990s. Because of the low coverage of weather radar before then, and much higher coverage in recent years, NCEI explains that “observation practices have led to an increase in the number of reported weaker tornadoes, and in recent years EF-0 tornadoes have become more prevalent in the total number of reported tornadoes.”
NCEI continues:
With increased National Doppler radar coverage, increasing population, and greater attention to tornado reporting, there has been an increase in the number of tornado reports over the past several decades. This can create a misleading appearance of an increasing trend in tornado frequency. To better understand the variability and trend in tornado frequency in the United States, the total number of EF-1 and stronger, as well as strong to violent tornadoes (EF-3 to EF-5 category on the Enhanced Fujita scale) can be analyzed. These tornadoes would have likely been reported even during the decades before Doppler radar use became widespread and practices resulted in increasing tornado reports. The bar charts below indicate there has been little trend in the frequency of the stronger tornadoes over the past 55 years.
Data from the National Oceanic and Atmospheric Administration (NOAA) tornado count show no trend in the number of tornadoes since the introduction of widespread Doppler radar. (See figures below)
Figure 1: Trend of all tornadoes counted since 1950. Note that the trend stops increasing once the widespread coverage of Doppler weather radar is achieved in the 1990s. Red are preliminary estimates for this year.
When it comes to the strongest tornadoes, EF-3 and above, there appears to be a downwards trend. (see figure below)
Climate at a Glance: Tornadoes points out that as recently as 2017 through 2018, the United States set records for the longest period in history without a tornado death and the longest period in history without an F3 or stronger tornado. In fact, the two record-low years for number of tornadoes both occurred this past dozen years, in 2014 and 2018.
Moreover, the United Nations Intergovernmental Panel on Climate Change finds that “[t]here is low confidence in observed trends in small spatial-scale phenomena such as tornadoes.”
ABC’s alarming coverage of the 2025 tornado season is at odds with the data. There is no dangerous climate signal in the tornado data, there may even be evidence that the modest warming of the past century is related to the decline in severe tornado outbreaks in the United States. A single severe tornado season, or concentrated outbreak, is not evidence of catastrophic climate change, only a long-term sustained trend would suggest climate change might be a factor, and there is no such trend.
For all the talk about the weather getting worse, if we look at floods and tornadoes, which, of course, are always incorporated into the narrative when they show up, overall, it certainly is not.
For all the talk about the weather getting worse, if we look at floods and tornadoes, which, of course, are always incorporated into the narrative when they show up, overall, it certainly is not.
Nor are hurricanes, wildfires, etc. getting worse. I will deal with tornadoes here, using charts that anyone can understand.
In the case of tornadoes, I am going to borrow from some of Joe D Aleos’s (et al.) work on this matter with simple graphics.
Overall, strong tornadoes are down:
Moderate tornadoes (EF 2). have trended down:
The annual count of tornadoes was far higher when it was colder and CO2 lower.
Very strong tornadoes (EF4) are down:
The super tornadoes overall are less frequent:
As mentioned above, apparently, more CO2 means fewer tornadoes. CO2 has nothing to do with the tornado count either way. The problem is you have to counter people who say it does. The fact that the Left will simply lie is a problem.
But you could see this week coming from mid-March. Look at the temperatures the past week, it’s the perfect setup for a monster clash for heavy rain and tornadoes.
So, what do you think is going to happen?
156 tornadoes, and this rain:
I will handle the flooding getting worse in another blog. One of the big problems there is that we have put a lot of concrete in areas that used to just absorb water. But again, another blog, another time.
And now, with the pattern flipping, the severe weather ( tornadoes, excessive rains) will stop.
One thing I was not right on was Denver snow; IT WOUND UP IN AMARILLO.
In any case, here comes the cold into the east and the shutdown of the severe weather season.
But fear not. There will be plenty to gripe about with all the cold and snow in the air, if not on the ground, in many places at times this week as spring takes a step back where it has been front and center.
But look at the good side. Severe weather the next 10 days will be well below normal.
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