Increased warming in latest generation of climate models likely caused by clouds

New representations of clouds are making models more sensitive to carbon dioxide

National Center for Atmospheric Research/University Corporation for Atmospheric Research

As scientists work to determine why some of the latest climate models suggest the future could be warmer than previously thought, a new study indicates the reason is likely related to challenges simulating the formation and evolution of clouds.

The new research, published in Science Advances, gives an overview of 39 updated models that are part of a major international climate endeavor, the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models will also be analyzed for the upcoming sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC).

Compared with older models, a subset of these updated models has shown a higher sensitivity to carbon dioxide – that is, more warming for a given concentration of the greenhouse gas -though a few showed lower sensitivity as well. The end result is a greater range of model responses than any preceding generation of models, dating back to the early 1990s. If the models on the high end are correct and Earth is truly more sensitive to carbon dioxide than scientists had thought, the future could also be much warmer than previously projected. But it’s also possible that the updates made to the models between the last intercomparison project and this one are causing or exposing errors in their results.

In the new paper, the authors sought to systematically compare the CMIP6 models with previous generations and to catalog the likely reasons for the expanded range of sensitivity.

“Many research groups have already published papers analyzing possible reasons why the climate sensitivity of their models changed when they were updated,” said Gerald Meehl, a senior scientist at the National Center for Atmospheric Research (NCAR) and lead author of the new study. “Our goal was to look for any themes that were emerging, especially with the high-sensitivity models. The thing that came up again and again is that cloud feedbacks in general, and the interaction between clouds and tiny particles called aerosols in particular, seem to be contributing to higher sensitivity.”

The research was funded in part by the National Science Foundation, which is NCAR’s sponsor. Other supporters include the U.S. Department of Energy, the Helmholtz Society, and Deutsches Klima Rechen Zentrum (Germany’s climate computing center).

Evaluating model sensitivity

Researchers have traditionally evaluated climate model sensitivity using two different metrics. The first, which has been in use since the late 1970s, is called equilibrium climate sensitivity (ECS). It measures the temperature increase after atmospheric carbon dioxide is instantaneously doubled from preindustrial levels and the model is allowed to run until the climate stabilizes.

Through the decades, the range of ECS values has stayed remarkably consistent – somewhere around 1.5 to 4.5 degrees Celsius (2.7 to 8.1 degrees Fahrenheit) – even as models have become significantly more complex. For example, the models included in the previous phase of CMIP last decade, known as CMIP5, had ECS values ranging from 2.1 to 4.7?C (3.6 to 8.5?F).

The CMIP6 models, however, have a range from 1.8 to 5.6?C (3.2 to 10?F), widening the spread from CMIP5 on both the low and high ends. The NCAR-based Community Earth System Model, version 2 (CESM2) is one of the higher-sensitivity models, with an ECS value of 5.2?C.

Model developers have been busy picking their models apart during the last year to understand why ECS has changed. For many groups, the answers appear to come down to clouds and aerosols. Cloud processes unfold on very fine scales, which has made them challenging to accurately simulate in global-scale models in the past. In CMIP6, however, many modeling groups added more complex representations of these processes.

The new cloud capabilities in some models have produced better simulations in certain ways. The clouds in CESM2, for example, look more realistic when compared to observations. But clouds have a complicated relationship with climate warming – certain types of clouds in some locations reflect more sunlight, cooling the surface, while others can have the opposite effect, trapping heat.

Aerosols, which can be emitted naturally from volcanoes and other sources as well as by human activity, also reflect sunlight and have a cooling effect. But they interact with clouds too, changing their formation and brightness and, therefore, their ability to heat or cool the surface.

Many modeling groups have determined that adding this new complexity into the latest version of their models is having an impact on ECS. Meehl said this isn’t surprising.

“When you put more detail into the models, there are more degrees of freedom and more possible different outcomes,” he said. “Earth system models today are quite complex, with many components interacting in ways that are sometimes unanticipated. When you run these models, you’re going to get behaviors you wouldn’t see in more simplified models.”

An unmeasurable quantity

ECS is meant to tell scientists something about how Earth will respond to increasing atmospheric carbon dioxide. The result, however, cannot be checked against the real world.

“ECS is an unmeasurable quantity,” Meehl said. “It’s a rudimentary metric, created when models were much simpler. It’s still useful, but it isn’t the only way to understand how much rising greenhouse gases will affect the climate.”

One reason scientists continue to use ECS is because it allows them to compare current models to the earliest climate models. But researchers have come up with other metrics for looking at climate sensitivity along the way, including a model’s transient climate response (TCR). To measure that, modelers increase carbon dioxide by 1% a year, compounded, until carbon dioxide is doubled. While this measure is also idealized, it may give a more realistic view of temperature response, at least on the shorter-term horizon of the next several decades.

In the new paper, Meehl and his colleagues also compared how TCR has changed over time since its first use in the 1990s. The CMIP5 models had a TCR range of 1.1 to 2.5?C, while the range of the CMIP6 models only increased slightly, from 1.3 to 3.0?C. Overall, the change in average TCR warming was nearly imperceptible, from 1.8 to 2.0?C (3.2 to 3.6?F).

The change in TCR range is more modest than with ECS, which could mean that the CMIP6 models may not perform that differently from CMIP5 models when simulating temperature over the next several decades.

But even with the larger range of ECS, the average value of that metric “did not increase a huge amount,” Meehl said, only rising from 3.2 to 3.7?C.

“The high end is higher but the low end is lower, so the average values haven’t shifted too significantly,” he said.

Meehl also noted that the increased range of ECS could have a positive effect on science by spurring more research into cloud processes and cloud-aerosol interactions, including field campaigns to collect better observations of how these interactions play out in the real world.

“Cloud-aerosol interactions are on the bleeding edge of our comprehension of how the climate system works, and it’s a challenge to model what we don’t understand,” Meehl said. “These modelers are pushing the boundaries of human understanding, and I am hopeful that this uncertainty will motivate new science.”


About the article

Title: “Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models”

Authors: Gerald A. Meehl, Catherine A. Senior, Veronika Eyring, Gregory Flato, Jean-Francois Lamarque, Ronald J. Stouffer, Karl E. Taylor, and Manuel Schlund

Journal: Science Advances

From EurekAlert!


75 thoughts on “Increased warming in latest generation of climate models likely caused by clouds

  1. “These modelers are pushing the boundaries of human understanding, and I am hopeful that this uncertainty will motivate new science.”

    I guess the science isn’t settled.

    • “especially with the high-sensitivity models”

      A high sensitivity model is almost the epitomy of unsettled science in fact it is almost an oxymoron. Models should not be ‘high sensitivity’. If the mathematics are particularly sensitive to certain paramaters, e.g. cloud ‘feedback’ and other effects on warming/cooling, then those parameters should be properly and objectively explored and measured so there is minimal uncertainty as to their values.

      I suppose they will get around to it one day when they accept that the independent direct measurements from satellite and ballon systems as objective and reasonably accurate and far better indication of trends.

      • CMIP5 “average” results warming about 2x what is shown in the climate record since 1990. That should lead to the correction or exclusion of the more sensitive models in the group.

        Instead the whole climatology industry is simply doubling down on ignoring the flagrant contradiction with observation and producing ever more erroneous models.

        The abandonment of the scientific method is now total. The cargo cult pseudo-science is now the basis for global energy policy.

        What could be wrong with that?

        • Spot on Greg. This area of what had been actual scientific study has been infested with politically motivated lightweights, often transferring from the humanities, who are also the fore runners of the selfie/influencer subculture now so prevalent in society.

          This is narcissism meets chrystal meth+heroin stuff with no integrity at all. These people are the inheritors of the sort of lunacy pioneered by Timothy Leary such as the Harvard Psilocybin Project where the nutjobs conducting the tests on the drug were taking it themselves! LOL Now THATS a positive feedback!

        • Meehl says, “When you put more detail into the models, there are more degrees of freedom and more possible different outcomes,”

          I guess they just want to wag the trunk of the elephant more strongly.

          Also, if the Climate Sensitivity is not measurable, does that mean it is parameterized? If so, then why don’t they try models using many values mostly lower to see if they’re any more accurate.

  2. Clouds are not the control valve, but are the property that the system adjusts to achieve the required steady state. Clouds are a free variable, not a constraining variable whose behavior is guided by chaotic self reorganization towards a system that minimizes changes in entropy as the system changes state.

    • Yes.
      A falsifiable statement. I love it and that’s why this not a formally trained scientist adores this website.
      I track comments here more than any other site. I follow Judith Curry’s site closely also, her most excellent ‘things of interest’ to her amazing mind are also a treasure.
      You brainaics amaze me and I love it.

  3. “…Earth system models today are quite complex, with many components interacting in ways that are sometimes unanticipated…”


    • “with many components interacting in ways that are sometimes unanticipated”

      and totally unrealistic !

  4. The fact that their model results are diverging after so many years of effort is a very clear indication that they are not looking in the right places. Instead of staying obscenely obsessed with their models, they should start looking at the real world. One of the first places would be the behaviour of clouds. Not the reaction of clouds to climate change, which is about as far as they get when parameterising clouds in their models, but the real independent behaviour of clouds. My paper shown on WUWT recently showed that clouds do indeed have their own behaviour.

      • Well, in a sense you’re right, but the idea that divergence is caused by lack of top level constraints doesn’t seem quite right to me. If the divergence occurs when there aren’t any top level constraints, then something else that IS present must be causing it. To my way of thinking, this problem will continue for as long as models have bottom-up structure. Even if you put in top level constraints, the bottom levels of the models will keep making the same errors. The answer is to go top-down. That way you start with the top level, both constructs and constraints, and if you can eventually get from there to find out how the bottom levels work then that’s great, but in the meantime you could have a working climate model.

        • Divergence is not caused by the lack of top down constraints, but that the lack of top down validation allows the heuristics to diverge unchecked. Unless the simulation is perfect, it will diverge and there’s no way to construct an accurate enough bottom up model for the Earth’s climate system that wont.

        • If the answer has to be “dangerous global warming”, there are indeed constraints on how the questions may be asked.

          I wonder how many models gave been created based on real data, not “garbage in….”, that generated predictions of at most moderate, non-dangerous, warming, resulting in those models being scrapped before publication….

  5. It is amazing that research had to be done to determine that the change in cloud formation is responsible for the higher sensitivities. After all, the modelers should simply document the changes made in their models so that others don’t have to play hide and seek. The issue is whether they are right, but comparison to measurements lags far behind models. Often, the adjustments to cool historical data have been to cool the 1930-1940 to fit the model projects and to amplify the sensitivity estimates.

    NIST built a group of Class A reference stations in response to Anthony’s quality audit, but it doesn’t see that the accepted US data tracks well with the reference stations.

    • cloud feedbacks in general, and the interaction between clouds and tiny particles called aerosols in particular, seem to be contributing to higher sensitivity.

      Should this not read “our interpretation and representation of cloud feedbacks ……”

      Model outputs are not real data

  6. The formation of more clouds causes the Earth to warm? And here I thought heat was being transferred through convection from water at or near the surface to high up in the air… That would be a surface cooling effect I do believe. Otherwise cloud formation would be a runaway process and the entire Earth would be hidden by clouds.

    Oh well, its worse (again) than we thought (again). Ignore what’s going on in the real world – the average of the assembly of models predict with perfect accuracy. (Now that’s magic at work).

    • This is the hard part. Putting all the parts on the table at one time.

      Solar input goes to vaporize water and the warm humid air moves upward, undergoes adiabatic cooling, and condenses into clouds. The latent heat of condensation is lost to space. Then, there might be afternoon showers as the cool rain falls back to Earth. That is a normal summer day.

      But, clearly during an average day, when it is cloudy all day, it does not get as warm as it would on a sunny day. So, clouds cool the planet. HOWEVER, at the same time the clouds are slowing down heat loss to space, the upper white part of the cloud is reflecting/blocking a ton of solar input. Even though your cloudy day was cooler than if it was sunny, the planet’s solar input has indeed been short-stopped by clouds.

      Overall clouds cool the planet.

      • Stop ruining my model with your facts!

        Actually, that is a really concise and tidy explanation of the self-evident.

        But you’ll not get a penny for mentioning dihydrogen monoxide.

      • The high albedo of the upper side of the clouds HAS to be taken into account, particularly when they have daytime 24/7 in their models.. Overnight clouds indeed keep the night from being as cold as it could be but the loss of solar input during daytime is now part of the overall system and cannot be ignored.

        Many people are not aware of the self-evident details, such as why do clouds condense, Does the water simply decide to condense? Or is there something acting upon it. Warm humid air not only undergoes adiabatic cooling as it rises, but it also mixes with colder air at altitude and warms while the colder air gets a bit warmer. The upshot is more condensation and the release of water’s latent heat. The water eventually falls to ground, completing the water cycle, which accounts for up to 85% of the energy budget. Another self-evident cycle that people do not understand regarding its effects.

        That is the problem with AGW, it ignores a lot of basic science and then tries to say it’s too complicated for an average person to understand.

        CO2 and water vapor are not”greenhouse” gases—the term was invented. They are radiative gases that, during the day are saturated with IR radiation, absorbing and emitting constantly and having zero effect. However, at night, with no solar energy in put, these gases serve to convert heat energy in the air to IR which is either reflected by the surface, which is always warmer, or lost to space. This is why the air chills down so quickly after sunset and why little breezes pick up so rapidly in the shadows of clouds on a scudding cloud/sunny day. That’s how effective these gases are.

        Also, what should not be ignored, but is, is the work of Mizkolski who showed years ago that, when CO2 goes up, the absolute humidity decreases, such that more CO2 means less water vapor and lower radiative gas effects. In other words, they are fairly self-compensating, to create a rather stable cooling effect.

        • Also, when the self-evident is evidently not being properly considered in the models, it is important to remind them of this and indicate the elephant in the room, real science.

          Without including real science, the models are bound to fail, which what they all do, with the exception of the Russian model that the other modelers specifically ignore. Hmmm, so what are the Russians doing? More likely they are including real science and not a selective, biased view of science. Science is a hard task master and we have to work within how it works and we cannot always do what we want. Real world. Making people use electric cars and destroying the environment with “renewable” and unsustainable energy does not change the fact that CO2 does not warm the climate.

          You can have your own opinion, but you cannot have your own facts. Because of facts, not all opinions are equal (valid). Science is not democratic.

          Your response was clearly critical of me and not of the science, which is typical of __________ (fill in the blank) biased thinking.

          • It could that, even as the Russians were vaccinated against the sickness of socialism after 70 years of USSR horror, so the politicized science of Lysenkoism may have vaccinated Russian scientists against the modern disease of postnormal science, and its prevalent subtype: climate alarmism.

        • Yes yes yes. Go stand on the moon and look at the Earth.
          The dark areas are absorbing sunlight and the bright areas are reflecting it. And don’t say “but what about the non-visible light?” because the spectrum of sunlight is overwhelmingly visible (not an accident: human eyes are attuned to the wavelengths where the most information is). All those white areas (either ice or cloud) are white because they are reflecting the sun’s energy before it ever becomes heat. Given that CO2 does not absorb any energy in the visible spectrum, all the climate models should 100% agree on one thing: in the daytime, more ice and clouds means less warming.
          What about at night? NASA says ( that “Clouds emit energy in proportion to their temperature. Low, warm clouds emit more thermal energy than high, cold clouds. This image illustrates that low clouds emit about the same amount of thermal energy as Earth’s surface does.”
          So adding clouds is a win-win: during the day, less light-energy is absorbed by the Earth and (day or night) clouds do not significantly reduce the emission of thermalized energy.

      • Mike Jonas had written an interesting paper based on satellite data (no models or assumptions) indicating that the feedback from clouds, if there is one is negative. If the IPCC models set this feedback as being positive, then they must set other parameters wrong so that their models won’t run astray… like fixing a broken leg using a rusty nail…

  7. Gerald Meehl (NCAR): “it’s a challenge to model what we don’t understand”

    Wrong! It’s impossible to model what you don’t understand.

    And I thought the science was settled?!

    • How do the models explain the 1895-1946 warming period? How would they explain the Medieval Warm Period? Or the Roman Warm Period? This obsession with CO2 being the fundamental driver of climate change makes no scientific sense.

  8. “The high end is higher but the low end is lower, so the average values haven’t shifted too significantly,”

    Accountant: So how did you go with the new computerised farming business model I recommended?
    Punter: Unfortunately you forgot to mention the increased variability with returns and I ran out of cash flow.
    Accountant: That’s the law of averages I suppose. Now about my outstanding bill…

  9. So, after ~ 40 years and $billions of taxpayers’ money, the “holy grail” of climate “science” – the ECS value, has not essentially changed from a range of 5C.

    What a gyp!

  10. When improving an engineering or physics-based model, the hoped for improvements are greater accuracy and a smaller uncertainty, as measured by the precision. There seems to not be a good way to verify the accuracy of climate model predictions, or what is called the “skill” in forecasting. Even general agreement between current models is not a good test when the models have similar assumptions and are based on similar parameterizations. It becomes circular reasoning. However, if the range of uncertainty is increasing for something like ECS, that should be a red flag that the models are not improving and may well be getting worse.

    • The Pause should have given them all time to reflect upon their settled science but it would seem cutting edge ‘pushing the boundaries of human understanding’ is much more fruitful with the flow of grants.

  11. Empirical evidence show all major greenhouse gases (H2O Vapor (30,000ppm)/16 doublings), CO2 (412ppm)/9.6 doublings and CH4 (1.8ppm/.8 doublings) warm the earth by a total of 30C..

    If CO2’s ECS were at the low-end of CMIP6 projection of 1.8C/doubling, then CO2’s total warming would be 17.28C (ECS=1.8C/doubling x 9.6 doublings) leaving H20 vapor’s total warming at just 12.72C (ECS=0.8C/doubling x 16 doublings)….

    Moreover, if CO2’s ECS is at the extreme projection of 5.6C/doubling, CO2’s total warming would be 53.76C, leaving MINUS 23.76C for water vapor, meaning water vapor has a net cooling effect, even though water vapor absorbs almost the entire LWIR spectrum, and CO2 only absorbs a tiny sliver of LWIR at 15 microns… That is complete insanity…

    Reality is that water vapor probably generates a total of 27C of global warming (ECS=1.69C x 16 doublings) leaving and CO2’s total global warming At around 3C (ECS=.31C x 9.6 doublings) and CH4 warming is negligible as it basically absorbs the same LWIR as water vapor…

    CAGW is an insane hypothesis which has already been disconfirmed and should have never been taken seriously.

    • “CAGW is an insane hypothesis which has already been disconfirmed and should have never been taken seriously.”

      And we damn sure shouldn’t be spending Trillions of taxpayer dollars on trying to fix something that doesn’t need fixing (the Earth’s atmosphere)..

      • We are in total agreement, Tom-san.

        Historians will have a very difficult time understanding how so many people could have been brainwashed into believing such an absurd and laughable Leftist hoax.

        • The Timothy Leary Harvard Psilocybin Experiment is perhaps the forerunner of this sort of brain fried lunacy. Leary literally invented scientific ‘kool ade’. Unfortunately history is part of the humanities and they are also utterly riddled with the types of loons that infest climate ‘science’.

    • You are assuming that water vapor content is 30,000 ppm….a ground level assumption…but check the equilibrium water content at top of troposphere, only 12 km up, where it is -55 C …..water content is less than 1/4 of the 400 ppm CO2 content up there….CO2 controls how much IR is radiated to outer space at that altitude, except for the 40% or so that goes directly from ground to outer space through the “atmospheric window”.
      But increased CO2 at high altitude actually causes more IR radiation to outer space and thus more COOLING of the upper troposphere and stratosphere.
      Down here where we live, the extra 100 or so ppm of CO2 since start of industrial age when compared to the H20 (say your 30,000) is nearly irrelevant.
      Models don’t seem to predict the cooler temperatures “up there” will eventually work their way back down to ground level……views on why are invited…..

  12. Can anyone shed any light on the ownership and maintenance of these models ? (Perhaps a topic for another article by Robert).

    Are they freely available in the public domain? (If not, why not given the research is seemingly funded by taxpayers via government grants?)

    Does each have an “English language description” of each part of the model, and a corresponding section in the computer code?

    Is the code (usually / ever) commented?

    Most importantly, are the computer models subject to rigorous Change Management; in which a change in the “English language” description (Change History) is tracked through the corresponding changes in the code, with different versions of the model preserved under different version numbers? ie so it is possible to decide that one of the changes introduced is rubbish and we can just back it out from the baseline without having to “un-code” it? ie each model maintained the way a professional computer application would be maintained. Or are they generally 15,000 lines of hacked undocumented code, as the Imperial College epidemic model is reputed to have been ?

    Are the (major releases) of the different versions peer reviewed? And are they peer reviewed not only for their “climate science” theory but also for their “best-practice IT” maintenance?

    I suspect I know the answers to these questions. I also suspect that the bast was of countering these people who are threatening civilization with their wild claims is not by disputing the “science theory” but by showing that they are inept at their modelling practice.

    • @OldFogey:
      “Most importantly, are the computer models subject to rigorous Change Management”

      I would venture that would be a BIG FAT NO on that one.

      “Or are they generally 15,000 lines of hacked undocumented code”

      According to the press, you forgot to add a 0 to that number to make it seem more impressive!

      My problem has always been the “models”. The word sounds impressive, mysterious to the layman. But in reality, they aren’t all that. Nor are they the soothsayers and oracles of modern science. They are tools that is all. But you tell that to any climate doomsayer and they are quick to point out how complex the models are—so what? The Tacoma Narrow’s bridge was vastly complex, except in building the model, they failed to take into consideration harmonic stresses. They built a bridge to sustain high winds, but didn’t model harmonics of lower speed winds. We all know how that turned out.

      I always point out the Biodome failure. See I remember when it was being built and how it was in the media. This huge complex dome built to simulate the Earth. It was touted as the be all end all to save us all from global warming. Right? They made a big show about the rainforest in the dome and how it would provide everything the human lab rats needed. So huge expenditure on the rainforest, little spend on the desert (forget adding plants to that desert BTW and it was filled with playground sand as I recall) and very, very little space devoted to the “ocean” which they couldn’t get plankton to survive in–but man, that rainforest! OH it was going to save everything! Yea…they killed the ocean and had to open the giant lung of the dome so people didn’t die of CO2 poisoning in 3 days. Guess what they “discovered’? That the engineers building the thing were smart enough to incorporate a giant lung of the machine so people didn’t die. THAT was the only system that worked. Because the huge complex model of the Earth they used didn’t take into account the role of the ocean….the ocean for cripes sake! Almost 3/4 of our planet is covered by WATER and yet the alarmists STILL have yet to even bother understanding how the ocean works in regard to keeping plankton alive enough to produce enough O2 (and as I recall they created a “tropical” ocean–pretty deserts as we call them–although theirs was almost sterile). No meiofauna, no microflora, and filled with aquarium salt water–clean looking and sterile.

      got on a rant there…..

      To your point though, I would say that they have failed to model even the simplest of their complexities effectively and forget about Change Management–that would mean documenting where that model failed and when new variables were introduced. Can’t have that….because someone might come looking through and saying, “uh….wha..? is this?” No. No. Can’t have that, no questioning of the oracle is allowed.

      • In simple terms they are not ‘models’ they are virtual automatons. That is they are confected to deliver output of a predetermined range.

  13. I tried to model a cloud once with pen and paper. Many sheets with little grids and iteration numbers, vector arrows… but it kept skittering off one of the edges of the sheet onto another and finally I had to bring vectors in from the opposite side. It was a captive cloud. Then I tried a hexagonal grid and tilted grids 60 degrees each iteration because, I decided, Brownian Motion, and things kept looping and spiraling and jumping two cells then three, until I had all the sheets thumb-tacked in the center and spinning madly. Velocity increased and vector arrows grew bolder so I used marker then paint brushes and then rollers and hoses. Exhausted I fell back but there was so much kinetic energy modeled the apparatus was simulating itself with a roar and a blur. The roof lifted like a lunchbox and I a perfect funnel rose from the table toward another funnel descending from a real dark angry cloud above. I imagined when the funnels met it would merge into a great hellmouth.

    Fortunately I had been running the experiment clockwise and nature’s funnel was counter. They cancelled out and the roof slapped back on and the simulation ended. It even re-capped my pens.

  14. The models aren’t going to get things right until the modelers start considering multidecadal oscillations, which the modelers have generally been ignoring. Most models, especially in general the CMIP3, CMIP5 and CMIP6 ones, are selected/tuned for hindcasting the past, especially the last 30 years before their hindcast to forecast transition dates. Multidecadal oscillations were favoring extra warming from the mid 1970s to a few years after 2000, I figure about .2 degree C of extra warming, according to my efforts of using Fourier on HadCRUT3. So, these models (in general) were tuned to have positive feedbacks to increase of greenhouse gases accounting for about .2 degree C more warming than they actually caused (as I figure). So, these models are generally predicting more warming this century than they would if the modelers did not ignore multidecadal oscillations.

    • Yes, they are hindcasting the period 70’s onward. Call me when their models also can successfully hindcast 1919-1950 and then the 50’s through 70’s. Any model with a CO2 ‘control knob’ is unlike to get that right, you need big natural cycles.

      • There are models that hindcast these periods rather accurately, the russian climate models. They are a bit more sophisticated. They have two control knobs = aerosols and CO2.

        • I heard the Russian models get Arctic amplification wrong. I doubt any model in the CMIP has gotten right the temperature change globally, in the tropical upper troposphere and in the Arctic, and I see this is from hindcasting the past without considering multidecadal oscillations.

    • Donald
      One could slap on multidecadal oscillations as an exogenous influence. However, I suspect that the oscillations are a result of feedback loops of the primary forcings. If the GCMs aren’t developing similar oscillations, it is prima facie evidence that 1) the models have either left out something, 2) have functions with the wrong mappings between independent and dependent variables, 3) have the wrong parameterization for clouds, or 4) all of the above.

  15. They took models that were already running hot, and made them run hotter. Then declared this to be an improvement.

  16. Contriving a transient climate response (TCR) range is a ticklish business, the lower number cannot be so low that doesn’t elicit some concern and the higher number cannot be so high that it is absurdly unbelievable.

  17. Let me get this straight. CMIP5 models were running too “hot” so the solution is to come up with a new generation of CMIP6 models that run even hotter? What am I missing when modelers call this ‘science’?

    • “What am I missing when modelers call this ‘science’?”

      It’s not science, it’s politics.

      Current politics dictates that the Data Manipulators turn up the Human-caused climate change (CAGW) scaremongering to 11. So the too-hot computer models of the recent past are made even hotter. Double-down, it’s called. It’s a sign of desperation on the part of those trying to sell the CAGW narrative.

  18. It’s models all the way down! Why look at reality, the models are much neater, not messy. What’s important is, get the models to match each other. Who cares if they don’t match reality – if that had ever been a goal, they would have chucked them a long time ago.

    As long as they can get everyone scared and excited about models, who needs reality?

    • The NOAA trends for the last few years has been a bit flat. They will have to have another adjustment to bring it in line with the models

    • The NOAA trends for the last few years has been a bit flat. They will have to have another adjustment to bring it in line with the models

    • I seem to recall many years ago the IPCC saying quite clearly that the models were not programmed to reflect the actual climate. Somebody, I’m sure, will know the exact wording.

      My puzzlement has often been why that admission wasn’t enough to make everyone pack their bags and go home. If you aren’t modelling the actual climate then what’s the point?

  19. Google Seawifs measurement of oil and surfactant spills, then calculate the reduction of wave breaking and consequences to aerosol production.

    Research synthetic nitrate pollution of the ocean and its effect on plankton population:is there a reduction in DMS?

    Ditto dissolved silica from intensive farming and its effect on diatom/phytoplankton balance.

    Investigate phytoplankton use of surfactant to manipulate the light levels in the upper few metres of the ocean.

    Water droplets polluted by oil or surfactant will have different coalescent behaviour. Check.

    Current climate science has failed. Feynman says that if the data doesn’t match your theory then your theory is wrong. A range of 1.5 to 4.7 is not useful the theory is wrong. When that happens Feynman tells us what to do.

    It’s time to guess again.


  20. You cant even model laminar to turbulent boundary layer flow. WHat the hell chance is there of modelling an entire planets atmosphere!

    These are amusing toys, no more.

  21. The horse behind the cart. More clouds cause more warming? Warming causes more clouds? If both we have a perfect feedback loop. So why are we not walking around in a steaming mist? Because the more clouds more warming bit is nonsense.

  22. Yessir – Fenyman said “we guess – don’t laugh – we guess – that is our hypothesis”

    The alarmists guessed dead wrong but just simply won’t let it go. It has degenerated into scientific mass hysteria.

    When your hypothesis repeatedly fails when tested against reality then it is time go go back to square one.

    Square one requires you start by validating each and every single one of your assumptions….

    Starting with –

    The greenhouse effect
    CO2 causes warming
    Amplification by water vapour….etc. etc.

    I’m pretty sure that none of the assumptions underlying this preposterous theory are demonstrable or can be derived from first principals and known (as opposed to imaginary) physics.
    Whatever effects can survive such testing and logic are almost certainly going to require much smaller values than are currently postulated.

    I’m constantly shocked that supposedly intelligent people can believe such utter nonsense.

    “The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd; indeed in view of the silliness of the majority of mankind, a widespread belief is more likely to be foolish than sensible.” – Bertrand Russell

    Global Warming and its utterly nebulous progeny “Climate Change” is, to quote Michael Crichton “a faith without proof – global warming is undoubtedly a religion.”

    • “I’m constantly shocked that supposedly intelligent people can believe such utter nonsense.”

      This is a phenomenon that needs a lot more study.

      One thing it shows is even intelligent people can be misled under the right circumstances. Why? Why is it some intelligent people are misled while others can see through the misleading information? That’s the question.

      Then, once you have decided on your worldview, does emotion, rather than intelligence, take over as you defend your position? Seeing what you want to see, and what you want to see is that you have the right worldview.

      Human psychology in the Age of Mass Communication and Manufactured Mass Delusion..

  23. NASA clear that the modelling of clouds is FUBAR

    “NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds.
    Clouds have the capacity to dramatically influence climate changes in both radiative longwave (the “greenhouse effect”) and shortwave.

    Cloud cover domination in longwave radiation

    In the longwave, clouds thoroughly dwarf the CO2 climate influence. According to Wong and Minnett (2018):

    • The signal in incoming longwave is 200 W/m² for clouds over the course of hours. The signal amounts to 3.7 W/m² for doubled CO2 (560 ppm) after hundreds of years.

    • At the ocean surface, clouds generate a radiative signal 8 times greater than tripled CO2 (1120 ppm).

    • The absorbed surface radiation for clouds is ~9 W/m². It’s only 0.5 W/m² for tripled CO2 (1120 ppm).

    • CO2 can only have an effect on the first 0.01 mm of the ocean. Cloud longwave forcing penetrates 9 times deeper, about 0.09 mm.”

  24. ECS is OK as a concept, for comparing models between themselves or with observations. It is not a real thing in any other way. It most certainly cannot be used to predict temperature changes given a change in CO2. It cannot be expected to be the only thing going on over millenia and multiple doubling. It can’t be used to explain the difference between some ridiculous global average temperature and some equally ridiculous claim of what that average would be given no atmosphere. The whole debate here is based on a false premise, or a number of false premises.

  25. “We have introduced yet more uncertainty necessitating further study. Send more money”

  26. “These modelers are pushing the boundaries of human understanding, and I am hopeful that this uncertainty will motivate new science.”
    Heh. “new science”. They can’t even get the “old science” right. It’s like having a science based on the idea that heavier objects always fall faster than lighter ones. No matter how many models, and no matter how much tweaking and fiddling with them they do, they will never work right.

  27. I stopped reading after this:

    “The first, which has been in use since the late 1970s, is called equilibrium climate sensitivity (ECS). It measures the temperature increase after atmospheric carbon dioxide is instantaneously doubled from preindustrial levels and the model is allowed to run until the climate stabilizes”

    That says it all folks.

    Introduce 1 modern AI and the model will go blue screen of death. This is ridiculous. I want to know 2 things: 1. Is this the same model Mann created? (which would explain a LOT about him) and 2. Why is CO2 instantly doubled(?) how does that even equate to observations? And doubled from what reference point exactly? Nobody cared about CO2 until relevantly recently..and the one person that had was debunked wayyyyy before CO2 became the “bady” in this new fear mongering scam.

    Scientific methodology anyone? No? Well then it’s not worth the 1’s and 0’s IMO.

    • Just Jenn,

      The one thing common to all fossil fuel combustion is the release of CO2.
      The eco-warriors don’t really give a damn about CO2 (per se) but they hate modern civilisation which relies for its very existence on coal and oil and gas.
      There is *absolutely no way* they will persuade us to give up our modern lifestyle — indeed they can’t even persuade their own followers who turn up at protest camps in polluting old camper vans and leave behind tons of detritus, 90% of which is usually plastic!
      HOWEVER … if they can persuade people — and especially governments — that there is global warming, that that global warming will irrevocably damage the planet and that CO2 in the atmosphere is the cause of that warming which can be stopped by putting an end to our use of hydrocarbons, then they get to have what they (think they) want and when the rest of us wake up what it really means in terms of lifestyles it will be too late!
      That, in a nutshell, is what this farce is all about. “Scientists” make a lot of easy money, as do the people who get into “renewable energy”; control-freaks on the political hard left start to dream about a utopian one-world government; the rest of us are too busy trying to earn a living to concern ourselves with matters we don’t fully understand, and anyway, it’s about saving the environment, innit? So it must be “good”. Stands to reason.
      And now at least you know why “nobody cared about CO2 until recently”! Because at any concentration below about 1,000 ppm it’s a complete irrelevance!

  28. “Many research groups have already published papers analyzing possible reasons why the climate sensitivity of their models changed when they were updated …”

    Are we to take it, then, that post-modern pseudo-science not only consists of playing computer “games” and pretending that the outcome is “evidence” of anything except the way the “games” were written and writing learned (?) papers to that effect but also writing equally learned (?) papers on why the results are different from the last “game” they invented.

    And all this — natch — on the taxpayer’s dime. Only it isn’t dimes any more; it’s mega-dollars!

  29. “But even with the larger range of ECS, the average value of that metric “did not increase a huge amount,” Meehl said, only rising from 3.2 to 3.7?C.”

    Taking an average of wrong predictions will invariably give you a wrong answer. It is also a”chickens” way out of choosing the right one. Scientists need to be pushed to take a stand and choose one to advocate for. I suspect you would see a quick rush to change them to meet observational evidence.

    The increase in ECS range is a tacit admission that either past models or the new ones are WRONG. There is simply no other conclusion you can reach.

  30. It is not surprising that the scientists are struggling; as the problem goes back to an error of logic in the early days.
    Initially as Radiative FORCING was defined which had units (in old money) of Lbs force. This was the magically? converted to an energy flux with units of foot Lbs/sec *ft^2 or Watts/m^2.**
    This could only have been done if a sensitivity value had already been assumed based on the perceived climate behaviour at the time.
    Thus, thereafter use of this flux value in subsequent models carried with it an intrinsic value of this sensitivity as a given; ignoring the fact that the climate must have inevitably changed since that flux was calculated.
    As for the statement that “ECS is an immeasurable quantity” by Meehl : Is he not aware that at the evaporative phase change of water the Planck sensitivity coefficient is ZERO since the process occurs at constant temperature? Easily measured I think.
    As this process is continuous and ubiquitous within the climate surely it deserves to be considered. Someone needs to start thinking outside the current Groupthink obsession with CO2. Perhaps they could start with looking at the basic thermodynamics of the Hydro Cycle.

    ** The electrical analogy here is where a Potential is defined but you can only convert this into a flux if the resistance of the circuit is known to enable a current and hence the flux to be calculated.

  31. More Climate Hyperreality – the inability to tell the difference between reality and climate model simulations.
    Perhaps they should build a Climate Change Disneyland or Potemkin Village.

  32. We have had three warm days so far this year. I look forward to the non-stop heat wave we are supposed to experience.

  33. Increased warming in latest generation of climate models likely caused by clouds

    — The headline baffles me.

    new research, published in Science Advances, gives an overview of 39 updated models that are part of a major international climate endeavor, the sixth phase of the Coupled Model Intercomparison Project (CMIP6)

    — As does that extract.

    1. Clouds do not cause warming in the climate system. They cause cooling. One empirical study after another shows that.
    2. How are new models “new research“? Climate models have nothing to do with research. They are studies, but not research. Research implies someone investigated the world to discover something, or they invented something. Models are neither inventions nor discoveries. Models are more ‘computer games‘, or propaganda, or both. Not “research”.
    3. “endeavor” indeed, but not climate endeavor.

    This degeneration and misuse of language really annoys me.

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