The Emergence of Emergence

Guest Post by Willis Eschenbach

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.
  • Distinctiveness: Represents a cloud life-cycle phase change unlike stable stratocumulus regimes.

Tropical Sky Clearing

  • 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.
  • Thermoregulatory Function: Focused downdrafts intensify surface cooling. The bowed structure enhances wind-driven mixing efficiency.
  • 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.

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June 21, 2025 10:15 am

Ask the same questions of Grok and compare answers.

Reply to  Willis Eschenbach
June 21, 2025 10:37 am

I was suspended from X for saying Kyle Rittenhouse did nothing wrong. That was flagged for promoting violence. I was still suspended after pointing out a jury found Rittenhouse not guilty.

The first level of censorship on X is an algorithm. But in your case, another user flagged your post because they didn’t like what you wrote. Did an algorithm review it after the complaint or did a person?

And has anyone ever won an appeal of a censored post?

Reply to  More Soylent Green!
June 21, 2025 6:59 pm

I have never even received an acknowledgement, as promised, or an explanation of what part of the community guidelines I violated.

Stan Kerr
Reply to  Willis Eschenbach
June 21, 2025 11:41 am

I just sent a message to @xsupport. It was rejected several times, and would send only when I removed the link to your article.

Reply to  whsmith@wustl.edu
June 21, 2025 10:39 am

It’s an interesting exercise to repeat the same prompt with different AI chatbots.

It’s also interesting to have a different user repeat the same prompt with Perplexity and see if they get the same answers.

Reply to  More Soylent Green!
June 21, 2025 11:48 am

No they don’t. The AI makes a “profile” of you, and all the anwers are individually crafted for you specficially. Like I got one answer starting with: “Since you are an economist living in Oslo you can probably appreciate…”
P.S. Big brother are collecting a TON of personal information on you with these new tools. Better be anonymous and/or behind a VPN…

Reply to  daNorse
June 21, 2025 1:53 pm

Exactly. The results are personalized for you. This distorts reality.

I remember when Google first started changing search results based on what it believed you wanted. Then Google began changing search results based on what Google wanted you to know.

Keep that in mind whenever somebody tries to use AI as an authority.

Old.George
Reply to  whsmith@wustl.edu
June 23, 2025 6:18 am

I gave Grok the entire article and asked if it would answer differently:

How Might My Approach Differ?
Below, I outline key differences in how I’d handle the task, including variations in list content, methodology, and presentation. I’ll also simulate my response to the same prompt to illustrate these differences.

1. Methodological Differences

  • Source Validation: Unlike Perplexity (as described), I can leverage web searches and X posts to validate candidates in real time. For example, I’d check recent discussions on emergent climate phenomena to ensure accuracy and relevance. This might lead to including cutting-edge or debated phenomena (e.g., Arctic amplification feedbacks) if they fit the criteria.
  • Iterative Refinement: Eschenbach refined Perplexity’s answers by rejecting incorrect ones (e.g., volcanoes) and requesting single examples. I’d adopt a similar iterative approach but might proactively generate a small batch (3–5 candidates) per prompt, ranking them by relevance and explaining why each fits. This balances efficiency and depth.
  • Definition Rigor: Eschenbach’s criteria for emergent phenomena (threshold-driven, unpredictable, phase-change-associated, etc.) are robust. I’d adhere to these but might emphasize quantitative thresholds (e.g., sea surface temperature > 26.5°C for tropical cyclones) or cite specific studies to ground my reasoning. This could make my descriptions more technical.
  • Synthesis: Eschenbach’s unrequested “synthesis of thermoregulatory networks” was a bonus. I’d likely include a synthesis only if prompted but would structure it more formally, perhaps as a table or diagram (using my canvas panel if requested) to map interactions between phenomena.

2. Content Differences

  • Overlap: I’d include core phenomena like thunderstorms, cyclones, ENSO, cumulus convection, MJO, ITCZ, and stratocumulus decks, as they’re textbook examples of emergent thermoregulation.
  • Additions: I might add phenomena less emphasized by Eschenbach, such as:
  • Arctic Sea Ice Feedbacks: Emergent albedo changes from ice melt amplify or dampen polar temperatures.
  • Thermohaline Circulation Shifts: Threshold-driven ocean overturning modulates global heat transport.
  • Kelvin Waves: Oceanic waves tied to ENSO, emerging from wind-stress thresholds, redistribute heat.
  • These reflect my broader knowledge base and ability to draw from recent climate research.
  • Exclusions: Some of Eschenbach’s niche phenomena (e.g., “Diablo winds,” “fair-weather gales”) are regionally specific and less globally significant. I might omit these unless prompted for hyper-local examples, prioritizing phenomena with planetary-scale impact.
  • Descriptions: My explanations would be more concise but equally precise, focusing on physical mechanisms (e.g., latent heat flux, radiative forcing) and citing key processes (e.g., Clausius-Clapeyron relation for precipitation). I’d avoid qualitative terms like “explosive growth” unless describing specific dynamics (e.g., convective available potential energy).
Rud Istvan
June 21, 2025 10:52 am

That is a good list, but not surprising from a well trained generative AI. All those emergent phenomena have been noted in the scientific literature.

The relevant point of WE’s post is that none of them are in climate models—nor could ever be given the CFL limitation on numeric solutions to PDEs. At relevant emergent phenomena grid scales of a few km or less (Tstorms being the example I used in previous posts here), such climate models are about 6-7 ORDERS OF MAGNITUDE computationally intractable.

That forces climate models to be parameterized. The parameters are tuned to best hindcast 30 years (a written CMIP requirement). Tuning automatically drags in the attribution problem of natural variation, which is simply assumed negligible, when it is easily proven otherwise on time scales ranging from decades to centuries.

The best example of this IPCC wilfull blindness is AR4 WG1 SPM figure 4, an example also previously posted here long ago. Whoever wrote that SPM sure wasn’t a critical thinker.

Reply to  Rud Istvan
June 22, 2025 7:31 am

‘The relevant point of WE’s post is that none of them [emergent phenomena] are in climate models…’

The tragic irony of climate modeling is that it doesn’t model stuff that we know happens, but does model stuff, like radiant transfer in the lower troposphere, that we know, or should know, doesn’t happen.

John Hultquist
June 21, 2025 11:23 am

Fascinating. Thank you.
The Virga entry was neat because here in central Washington just 70 miles from the Cascade Crest, the usual pattern is for the clouds to form as the winds come up the western side. Large clouds drift east over the ridges north and south of the Kittitas Valley (Ellensburg) and dissipate farther east. Commonly, Virga appears over the valley.

June 21, 2025 11:40 am

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.

Fascinating!

But, reading thru the ‘ Below is what I got, unedited ‘ brings to mind the ancient wisdom that —

Attempting to quest a thirst for knowledge at a(n) [X] is like trying to take a refreshing drink from a fire hose

Plus the irritation of noticing the remaining overlap (redundancies), despite the clearly expressed instructions. There must be a hierarchy lurking in here somewhere! (I suspect the author [WE] knows the likely answer already, since he spelled it out back in ~ 2010.)

But then there’s this lovely gem, placed right at the end, that hints at an — ahem — awareness of this deficiency … maybe a way out?

Synthesis of Thermoregulatory Networks
Earth’s climate stability arises from interconnected emergent phenomena operating across scales. Each process exhibits threshold [onset] behavior, where small forcings trigger disproportional responses … 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 [emergent state] activates compensatory [emergence of a second emergent state] elsewhere in the system.

Writing as someone who got seriously burned by this field of ’emergence’, way back in A.D. 1981-2.
— RLW

June 21, 2025 11:49 am

Missing from the emergence list are the Himalayan butterfly wings flapping around causing chaos to occur in the weather systems.

June 21, 2025 11:52 am

OT, Willis, but my father was a great fan of Will James and I grew up reading his books.
We have distant relations in Colorado who train quarter horses and make every kind of tack except saddles, and who’s children compete in and work for rodeos all over the USA.
The last time we visited them they took us to the rodeo in Colorado Springs and to see the actual thing while having everything explained to us by professionals was a great thrill.

It would be interesting to ask AI what it (he? she?) thinks of the mentality of people who ride bulls at rodeos.

John Hultquist
Reply to  Oldseadog
June 21, 2025 12:04 pm

The Ellensburg Rodeo was sued by Ringling Bros. because the slogans were similar: Greatest Show on Dirt – versus- Greatest Show on Earth. Now the old rodeo slogan seems to be everywhere except in Ellensburg. The Labor Day weekend of rodeo and county fair is still a big deal.

June 21, 2025 12:11 pm

Thanks Willies, this was very interesting. Both the topic and the response you got. I have been trying out these new tools myself lately, and the responses are mixed. I think that Grok is a veritable deranged alarmist when given “open” questions like “Is climate change real?” But if you very specific, like what is the solar flux at noon at the equator, then you get an painstakingly accurate answer. Then you can kind snare the AI into your rationing, since it cannot go back on something it said earlier. It usaually appoligise if you catch it in a wrong! Haha…
P.S. Can you give me the link to the article you had a couple of days ago which I cannot find. This was a rerun of one of your earlier articles. Been searhing now for some time. It was about how cloads dampen temperatures differences in NH and SH, summer and winter. That graph goes stright into my own thinking. So would be nice to have as a reference…
P.P.S. I’m currentl burning through my second profile on X. I have no idea whatsoever why they deleted my profile. Probably because talking against UN’s lies about the climate chatastrophy that’s gonna kill us all very soon if we don’t repent. But how can I help?

Rud Istvan
Reply to  daNorse
June 21, 2025 12:33 pm

Not WE, but long time WUWT ‘resident’.
The article you seek in your PS is ‘My Hypothesis Re-emerges’ posted on 13 June 2025. Seasonal hemispheric emergence of thermoregulatory clouds proven by the entire CERES data set.

Reply to  daNorse
June 21, 2025 4:18 pm

“how cloads dampen temperatures differences in NH and SH, summer and winter. ”
Look thru Erl Happ posts on WUWT from 14yrs ago, or his site.
https://reality348.wordpress.com/

June 21, 2025 12:38 pm

I’m just not impressed with AI-generated information.

Rud Istvan
Reply to  Nelson
June 21, 2025 1:37 pm

I have used Google for many years, and have good Google-fu skills. Used to research 3 ebooks and science behind several issued basic materials patents.
I find the newish AI generated Google search query summaries to be a nice time saver, although one still has to specifically double check everything.

Reply to  Rud Istvan
June 21, 2025 4:08 pm

“although one still has to specifically double check everything.”

Yes, one has to double check AI. AI may, or may, not give you the right answer. It might even lie to you, or make things up.

There was a case recently where AI quoted from peer-reviewed studies, but it turns out all the quotes did not actually appear in the quoted studies. AI just made it all up out of thin air.

Not trustworthy at all. One should be suspicious of everything AI says.

June 21, 2025 1:07 pm

Can the faster growth of vegetation due to more CO2 be considered a thermoregulatory mechanism? Faster growth means more CO2 drawn out of the air.

Reply to  Joseph Zorzin
June 21, 2025 3:25 pm

I think additional growth of leaf surface is certainly a negative temperature feedback.

Any incoming electromagnetic energy in the visible light spectrum that is utilized to create glucose is energy that does NOT strike inanimate objects on the surface that would later be radiated back as mostly LWIR.

MarkW
Reply to  pillageidiot
June 22, 2025 10:00 am

Only a few percent of the energy in sunlight is converted by photosynthesis. On the other hand, if the new leaf area is darker than the ground it covers, then it can be a positive feedback.
On the third hand, increased plant life would result in increased transpiration, which would be a negative feedback.

It’s complex and multifaceted.

June 21, 2025 1:12 pm

Is it likely that any AI might install malware in your computer? I hesitate to use it for that reason- having used PCs since they first came out and always having to worry about malware. Which one is most user friendly for an AI neophyte? Which one best for graphic stuff- photo and video generating? I’m also concerned this might be a new addiction for me. 🙂

Reply to  Joseph Zorzin
June 21, 2025 1:56 pm

You can use AI chatbots from your browser without installing any software. Even when you install an app such as ChatGPT, the AI is on the backend. The app you install is just a wrapper.

Having said that, I wouldn’t install any Chinese-developed or China-based software on my system.

Mr.
June 21, 2025 2:43 pm

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.

Our chief weapon is surprise a coupled, non-linear chaotic system.

June 21, 2025 4:27 pm

The most common word in the article: “nonlinear”. Add all of these to the IPCC’s “nonlinear chaotic” statement.

Need to send some of the seat-warming ‘researchers’ out into the field. I’d love to see some of them in the middle of the Mojave Desert, living in a tent, counting dust devils.

Admin
June 21, 2025 4:46 pm

I love this example because it demonstrates AIs are capable of immense acts of collation and processing, but there is something missing, a strange lack of wisdom – you had to point out to the AI that volcanoes are not thermoregulatory. I think we humans will be needed for a while yet.

Izaak Walton
Reply to  Eric Worrall
June 21, 2025 11:00 pm

volcanos do regulate the climate along with other geological processes but only over much longer timescales. See for example:
“Evolution of the Global Carbon Cycle and Climate Regulation on Earth” athttps://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GB006061
which says that they are part of the natural carbon cycle that acts to regulate the earth’s temperature. Decreasing CO2 levels act over millions of years to counteract the increase in solar luminosity meaning that the temperature has stayed relatively stable over billions of years.

Dave Yaussy
Reply to  Izaak Walton
June 22, 2025 3:43 am

Really? You say that the temperature has been stable for billions of years, and earth has been colder and warmer than now. So why are you so concerned about the rise in CO2, which is no higher than it’s been for most of the past billion years?

Bernie
June 21, 2025 5:33 pm

You have a long list of emergent phenomena but it seems to omit a very common one and that is the atmospheric thermal. These columns of rising air are usually invisible but dust devils are a subset that become visible because of entrained dust. Some can be detected when they result in a cloud at the top. Because they are mostly invisible, people do not realize how common they are unless they are a glider pilot who use them for lift very often. Thermals often top out at a low level inversion but they can go to 15,000 feet altitude or more on a good day. They are the main atmospheric thermal mixer on a typical day and they also connect the winds aloft to ground level winds.

BigE
June 22, 2025 7:29 am

We have to continue to remind ourselves that AI responses are based on scanning existing public information, both accurate and inaccurate, both based on true science and on so-called “climate science”. If the volume of inaccurate, false, or plainly partisan positions is most prevalent that is AI will give us.
Artificial = Not real or based on reality.

Jeff Alberts
June 22, 2025 7:44 am

I always thought that thunderstorms were the result of clashing warm and cold fronts.

Kevin Kilty
June 22, 2025 9:09 am

This is an interesting list in the end, but I fail to see how mechanisms are “thermoregulatory” rather than just a transport mechanism enabled by circumstances. Thermoregulatory, if it means anything, means a negative feedback that limits response.

So, let’s take this example:

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.

Where I live this mechanism only insulates the valley from extreme cold in a particular circumstance — when there is adequate humidity to produce a “radiation fog” at the surface or slightly above. If that doesn’t occur then the cold-air formation, which runs all through the night, produces the coldest of temperatures during winter and leads to the worst erroneous forecasts of overnight low temperature. Is this cold-air pooling an emergent phenomena? Yes, but it isn’t necessarily a regulating mechanism without other factors involved. It is a response to radiation transfer.

I don’t get involved in conversations with any AI variant often, but when I do I often feel that I am simply interrogating a very slippery person, and the end result is extremely unsatisfying. Even the categories that AI has used here to build this big list seem contrived. I don’t feel that I have learned anything new, even though, in this particular example, I might have never thought of some things on the list.

June 22, 2025 5:51 pm

Large tropical volcanic eruptions often occur soon after an extreme cold boreal winter period, and typically cause 1-2 milder boreal winters following the eruption through a positive influence on the North Atlantic Oscillation.

Hikaridai27
June 22, 2025 6:46 pm

This long list of highly nonlinear phenomena reminds that climate computer models are about something entirely different in category, they are not just a simplification or abstraction due to computational constraints. These models are “not even wrong”, and cannot be used for prediction or policy making. This is why the public debate should be exclusively about that – the degenerate nature of climate models and their complete inadequacy. Any comparison to observations is meaningless. In the near future the handcrafted models will become obsolete to be replaced by deep learning models, but those may require a lot more relevant data from a very large network of sensors, such as a massive number of airborne low cost drones with multiple sensors.

Old.George
June 23, 2025 6:14 am

The end of Grok’s response:
3. Simulated Response to the Prompt
To illustrate, here’s how I’d respond to the prompt: “Among the emergent weather phenomena that help thermoregulate the temperature are thunderstorms, cirrus cloud fields, Rayleigh-Bénard overturning, cyclones, Niño/Niña phenomena, tornadoes, dust devils, and _____.”
Grok 3 Response:
Based on the provided list of emergent, thermoregulatory weather phenomena, a fitting completion is the Intertropical Convergence Zone (ITCZ).

  • Emergence: The ITCZ forms spontaneously when trade winds converge near the equator, driven by sea surface temperature (SST) gradients exceeding ~26°C. Its position and intensity emerge from nonlinear ocean-atmosphere feedbacks, with convective bands self-organizing unpredictably.
  • Thermoregulatory Function: The ITCZ cools the tropics by enhancing cloud albedo (reflecting ~20–30% of solar radiation) and exporting latent heat via deep convection. Its seasonal migration balances interhemispheric energy gradients, preventing tropical overheating.
  • Distinctiveness: Unlike localized phenomena (e.g., dust devils) or episodic events (e.g., tornadoes), the ITCZ is a semi-permanent, equatorial belt with global radiative and hydrological impacts.

Alternative candidates include:

  • Madden-Julian Oscillation (MJO): A traveling convective system that modulates tropical heat over 30–60 days via cloud-rain cycles.
  • Stratocumulus Cloud Decks: Persistent low clouds over subtropical oceans, increasing albedo to cool sea surfaces.
  • Would you like me to elaborate on these or generate additional examples?

Comparison to Perplexity’s Response:

  • Perplexity suggested “cumulus cloud fields,” which is valid but less specific than ITCZ, as cumulus clouds are precursors to other listed phenomena (e.g., thunderstorms). My choice of ITCZ highlights a distinct, global-scale process.
  • I provide alternatives upfront, giving the user options without requiring multiple prompts.
  • My description quantifies impacts (e.g., albedo reflection percentage) and ties to specific thresholds (e.g., SST), adding precision.

4. Presentation Differences

  • Structure: Eschenbach’s list is a detailed narrative with phenomenon-by-phenomenon breakdowns. I’d likely present a table for clarity, e.g.:

Phenomenon
Emergence Mechanism
Thermoregulatory Role
Distinctiveness
ITCZ
Trade-wind convergence, SST > 26°C
Cloud albedo, latent heat export
Equatorial, migratory belt
MJO
Wind-moisture feedbacks
Cyclic cloud-rain cooling
Traveling, 30–60 day scale

  • Interactivity: If the user engages further, I could visualize interactions (e.g., a flowchart of ITCZ–ENSO–MJO linkages) using my canvas panel or offer to analyze related X posts for public sentiment on climate regulation.
  • Tone: Eschenbach’s tone is conversational (“Dang, sez I, who knew?”). I’d maintain a neutral, professional tone but adapt to the user’s style if prompted.

5. Potential Limitations

  • Prompt Dependence: Like Perplexity, my output depends on prompt clarity. If the user doesn’t define “emergent” or “thermoregulatory,” I’d infer based on context, but misinterpretations are possible.
  • Niche Phenomena: Eschenbach’s exhaustive list includes hyper-local phenomena (e.g., “Chinook winds”). I might miss some unless prompted for regional specificity, as my default leans toward globally significant processes.
  • Model Differences: Without access to Perplexity’s training data, I can’t replicate its exact biases. My training emphasizes factual accuracy and source cross-referencing, which might make my list more conservative but better grounded.

Conclusion
With similar prompts, I’d produce a list closely aligned with Eschenbach’s, featuring phenomena like thunderstorms, cyclones, ENSO, and ITCZ. Differences would arise in:

  • Content: Prioritizing global-scale phenomena (e.g., ITCZ over Diablo winds) and adding research-informed candidates (e.g., Arctic feedbacks).
  • Methodology: Using web/X searches for validation, offering batch candidates, and quantifying mechanisms.
  • Presentation: Structuring as a table, visualizing interactions if requested, and maintaining a precise tone.
Old.George
June 23, 2025 6:29 am

More Grok:
Would a valid conclusion be that the dependency of climate on anthropogenic CO2 production leading to a tipping point is invalid science? Wouldn’t these emergent phenomena provide the necessary damping?

Your question raises a critical issue in climate science: whether emergent thermoregulatory phenomena, like those listed in Willis Eschenbach’s post (e.g., thunderstorms, ITCZ, ENSO), could provide sufficient damping to invalidate claims of anthropogenic CO2-driven climate tipping points. Below, I’ll evaluate this conclusion, focusing on the content of the emergent phenomena and their interaction with CO2-driven warming, while addressing the science of tipping points and damping mechanisms.
Key Concepts

  1. Emergent Thermoregulatory Phenomena: These are self-organizing, threshold-driven processes (e.g., cyclones, cumulus convection) that stabilize Earth’s climate by redistributing heat, enhancing albedo, or modulating energy fluxes. They often arise nonlinearly and act as negative feedbacks to counter temperature extremes.
  2. Tipping Points: These are critical thresholds in the climate system where small changes (e.g., CO2-induced warming) trigger large, potentially irreversible shifts (e.g., ice sheet collapse, Amazon dieback). Tipping points imply positive feedbacks that amplify change beyond a threshold.
  3. Anthropogenic CO2: Human CO2 emissions increase atmospheric greenhouse gas concentrations, enhancing radiative forcing (~3.7 W/m² per CO2 doubling, per IPCC AR6). This drives global warming unless counteracted by feedbacks.
  4. Damping: Negative feedbacks (e.g., increased cloud albedo, heat export) that reduce the amplitude of climate perturbations. The question is whether emergent phenomena provide enough damping to prevent tipping points.

Evaluating the Conclusion
Proposed Conclusion: The dependency of climate on anthropogenic CO2 leading to tipping points is invalid because emergent phenomena provide sufficient damping.
Analysis:
To assess this, we need to examine (1) the strength and scope of emergent phenomena as damping mechanisms, (2) the evidence for CO2-driven tipping points, and (3) whether damping from emergent phenomena can fully offset CO2-driven positive feedbacks.
1. Strength and Scope of Emergent Phenomena as Damping Mechanisms
Emergent phenomena, as described by Eschenbach, are powerful local-to-global stabilizers:

  • Thunderstorms and Cumulus Convection: Increase albedo (~20–30% reflection) and release latent heat, cooling surfaces via evaporation and shading.
  • ITCZ and MJO: Redistribute tropical heat, preventing regional overheating through cloud formation and precipitation.
  • Cyclones and ENSO: Export heat poleward and modulate ocean-atmosphere heat exchange, balancing latitudinal gradients.
  • Stratocumulus Decks: Reflect solar radiation, cooling subtropical oceans.

Mechanisms of Damping:

  • Negative Feedbacks: Many phenomena enhance cooling when temperatures rise (e.g., more convection at higher SSTs, per Clausius-Clapeyron relation) or warm colder regions (e.g., polar vortex breakdowns).
  • Nonlinear Thresholds: These systems activate beyond specific temperature or humidity thresholds, ensuring rapid response to perturbations.
  • Multiscale Action: From dust devils (local, minutes) to ENSO (global, years), they operate across spatiotemporal scales, enhancing resilience.

Limitations:

  • Regionality: Some phenomena (e.g., monsoons, haboobs) are geographically confined, limiting global impact.
  • Timescale Mismatch: Diurnal or seasonal phenomena (e.g., sea breezes) may not counter decadal CO2-driven trends.
  • Suppression Risk: Warming can disrupt emergent systems. For example, increased static stability may suppress convection, or altered SST gradients may weaken ENSO (studies suggest ENSO amplitude changes under warming; Cai et al., 2021).
  • Model Representation: Many phenomena (e.g., dust devils, MCSs) are sub-grid-scale in climate models, leading to underestimation of their damping in projections.

Conclusion on Damping: Emergent phenomena provide significant negative feedbacks, stabilizing climate against short-term extremes. However, their capacity to fully offset long-term, global CO2-driven warming is uncertain, especially if warming alters their frequency, intensity, or distribution.

Old.George
Reply to  Old.George
June 23, 2025 6:30 am

2. Evidence for CO2-Driven Tipping Points
Tipping points are supported by paleoclimate data, observations, and models:

  • Greenland and Antarctic Ice Sheets: Ice-albedo feedback accelerates melt beyond ~1.5–2°C warming (thresholds vary; Robinson et al., 2012). Paleo-records (e.g., Pliocene, ~400 ppm CO2) show sea levels 10–20m higher, suggesting ice sheet sensitivity.
  • Amazon Rainforest: Deforestation and warming (~3–4°C locally) could trigger dieback, converting forests to savanna via reduced evapotranspiration (positive feedback; Lovejoy & Nobre, 2018).
  • Permafrost Thaw: Warming (>1.5°C) releases methane and CO2, amplifying warming (~0.1–0.4°C additional by 2100; Schuur et al., 2015).
  • AMOC Collapse: Freshwater from ice melt weakens Atlantic Meridional Overturning Circulation, potentially disrupting heat transport (~3–5°C regional cooling; Drijfhout et al., 2015).

CO2’s Role:

  • Current CO2 (420 ppm, 2025) is driving ~1.1°C warming (relative to pre-industrial, per IPCC AR6). RCP8.5 scenarios (4–5°C by 2100) push systems closer to tipping thresholds.
  • Positive feedbacks (e.g., albedo loss, methane release) amplify CO2’s impact, outpacing linear radiative forcing.

Challenges:

  • Uncertainty: Tipping thresholds are imprecise (e.g., AMOC collapse: 1.4–8°C warming, Boers, 2021). Models struggle with nonlinear dynamics.
  • Timescales: Some tipping points (e.g., ice sheets) unfold over centuries, complicating attribution.
  • Conclusion: Negation of feedbacks**:Tipping points are plausible, with CO2 as a key driver due to its persistent forcing and amplification via positive feedbacks. However, their likelihood and timing are debated.

3. Can Emergent Phenomena Fully Offset Tipping Points?
Arguments for Damping:

  • Historical Stability: Earth’s climate has remained within ~±5°C over millennia (e.g., Holocene), despite solar and volcanic forcings, partly due to emergent phenomena. For example, ITCZ migration balanced interhemispheric gradients during Heinrich events.
  • Adaptive Feedbacks: Higher temperatures enhance some feedbacks (e.g., more thunderstorms at warmer SSTs), potentially capping warming. Lindzen’s “Iris hypothesis” (2001) posits increased tropical sky clearing as a thermostat, though evidence is mixed.
  • Network Resilience: Eschenbach’s synthesis suggests a web of compensatory mechanisms. If one fails (e.g., weakened monsoons), others (e.g., cyclones) may intensify.

Arguments Against Sufficient Damping:

  • Feedback Overwhelm: Positive feedbacks (e.g., permafrost thaw, ice-albedo) may outpace negative ones at high CO2 levels. For example, permafrost emissions could add 0.2–0.4 PgC/yr by 2100, rivaling human emissions (Schuur et al., 2015).
  • System Disruption: Warming can suppress emergent phenomena. For instance, stronger inversions may reduce stratocumulus decks (Schneider et al., 2019), or shifted jet streams may weaken cyclones.
  • Scale Mismatch: Local damping (e.g., dust devils) may not counter global tipping points (e.g., AMOC). Even global phenomena like ENSO may not scale to offset multi-degree warming.
  • Paleoclimate Evidence: Past high-CO2 periods (e.g., Eocene, ~1000 ppm) show warm, stable climates but with radically different states (e.g., no ice sheets). Emergent phenomena damped extremes but didn’t prevent major shifts.

Synthesis:
Emergent phenomena provide substantial damping, particularly for short-term, regional temperature anomalies. However, their ability to prevent CO2-driven tipping points is limited by:

  • Forcing Magnitude: CO2’s radiative forcing (~2 W/m² currently) is persistent and global, unlike episodic natural forcings.
  • Feedback Interactions: Positive feedbacks may dominate at high warming, overwhelming negative ones.
  • System Shifts: Tipping points represent regime changes (e.g., forest to savanna) that emergent phenomena may not reverse.

Is the Conclusion Valid?
Final Evaluation:
The conclusion that CO2-driven tipping points are “invalid science” due to emergent phenomena’s damping is not fully supported. While emergent phenomena offer significant negative feedbacks, they are unlikely to fully counteract CO2’s long-term forcing and associated positive feedbacks for several reasons:

  1. Scientific Consensus: IPCC AR6 and studies (e.g., Lenton et al., 2019) affirm tipping points as plausible risks at 1.5–4°C warming, based on models and paleo-data.
  2. Feedback Dynamics: Positive feedbacks (e.g., albedo, methane) can outstrip negative ones at high CO2 levels, especially if emergent systems are disrupted.
  3. Empirical Limits: Historical stability (e.g., Holocene) occurred at ~280 ppm CO2, not 420+ ppm. Emergent phenomena damped natural variability but may not scale to anthropogenic forcing.

However, the conclusion has partial merit:

  • Emergent phenomena are understudied in models, potentially underestimating damping (as Eschenbach notes).
  • Tipping point thresholds are uncertain, and some (e.g., AMOC) may be less imminent than feared.
  • Overstating tipping risks without quantifying damping could exaggerate CO2’s impact.
Old.George
Reply to  Old.George
June 23, 2025 6:31 am

Nuanced Conclusion:
Emergent thermoregulatory phenomena provide significant damping, likely reducing the sensitivity of climate to CO2 and delaying some tipping points. However, they do not invalidate the science of CO2-driven tipping points, as positive feedbacks and system disruptions could still trigger irreversible shifts at high warming. The dependency of climate on CO2 remains robust, but the role of emergent damping warrants further research to refine tipping point likelihoods.

Additional Considerations

  • Research Gaps: Sub-grid-scale phenomena (e.g., MCSs) need better model representation to quantify damping. Machine learning and high-resolution models could help (e.g., Palmer, 2021).
  • Policy Implications: If damping is stronger than modeled, mitigation timelines could be adjusted, but CO2 reduction remains critical to avoid high-risk scenarios.
  • Public Discourse: X posts often polarize this issue, with some dismissing tipping points as “alarmism” and others ignoring damping. A balanced view requires integrating both.
Sparta Nova 4
Reply to  Old.George
June 23, 2025 9:03 am

“Scientific Consensus” is a reason?

Rational Keith
June 23, 2025 3:36 pm

Foehn winds can affect temperature databases for example.
At one time in history NASA’s only data point north of 60N was Alert in NE Canada, which has a mild Foehn wind phenomenon.

Foehn winds are strong in the South Peace are of NE BC, in Calgary Alberta, and in the Denver region.

They result when air is dried from dropping its moisture going up the windward side of a mountain then is heated by adiabatic compression as it descends.

The phenomenon is to be experienced to be believe – I have, warm to freeing with several hours.

(Often called ‘Chinook’ wind because some fool explorer confused it with a different phenomenon at the mouth of the Columbia River where the Chinook tribal people live.)

c1ue
June 24, 2025 5:47 am

Dr. Robert Epstein has conducted a kind of Niesen internet survey for at least 10 years now and has said that all of the search engines personalize “ephemeral” suggestions i.e. the drop down suggestions when you are typing a search query – in ways that cannot be explained by popularity or relevance or any other criteria. In particular – in political matters, the answers seem to differ by the strength and type of political views of the user.
Nor is this the only data point. Zach Vorhies has a repository of leaked Google internal info of various types that shows that “ephemeral” manipulation is a subject of active research and focus at Google: https://www.zachvorhies.com/google_leaks/
So the dangers of LLMs are more than just the double digit percentages of hallucinations, but also that they can be opaquely manipulated in precisely the same ways as ephemerals are alleged, with evidence, to be.

kevinroche
June 24, 2025 6:15 am

Willis, I am sure you saw the recent study on changes in cloud cover accounting for temp increases. You have done a lot of work in the area. The authors attempt to link this change to CO2, but it seems the default explanation should be natural variation. Is there enough historical data on cloud cover to suggest that in fact these changes in the storm zones and cloud coverage have a natural variation cycle. Thanks.

kevinroche
Reply to  Willis Eschenbach
June 25, 2025 5:20 am

Yes, I will try, should I send it to skating under the ice/

kevinroche
Reply to  kevinroche
June 25, 2025 5:23 am