New Confirmation that Climate Models Overstate Atmospheric Warming

by Ross McKitrick

Two new peer-reviewed papers from independent teams confirm that climate models overstate atmospheric warming and the problem has gotten worse over time, not better. The papers are Mitchell et al. (2020) “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability”  Environmental Research Letters, and McKitrick and Christy (2020) “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science. John and I didn’t know about the Mitchell team’s work until after their paper came out, and they likewise didn’t know about ours.

Mitchell et al. look at the surface, troposphere and stratosphere over the tropics (20N to 20S). John and I look at the tropical and global lower- and mid- troposphere.  Both papers test large samples of the latest generation (“Coupled Model Intercomparison Project version 6” or CMIP6) climate models, i.e. the ones being used for the next IPCC report, and compare model outputs to post-1979 observations. John and I were able to examine 38 models while Mitchell et al. looked at 48 models. The sheer number makes one wonder why so many are needed, if the science is settled. Both papers looked at “hindcasts,” which are reconstructions of recent historical temperatures in response to observed greenhouse gas emissions and other changes (e.g. aerosols and solar forcing). Across the two papers it emerges that the models overshoot historical warming from the near-surface through the upper troposphere, in the tropics and globally.

Mitchell et al. 2020

Mitchell et al. had, in an earlier study, examined whether the problem is that the models amplify surface warming too much as you go up in altitude, or whether they get the vertical amplification right but start with too much surface warming. The short answer is both.

In this Figure the box/whiskers are model-predicted warming trends in the tropics (20S to 20N) (horizontal axis) versus altitude (vertical axis). Where the trend magnitudes cross the zero line is about where the stratosphere begins. Red= models that internally simulate both ocean and atmosphere. Blue: models that take observed sea surface warming as given and only simulate the air temperature trends. Black lines: observed trends. The blue boxes are still high compared to the observations, especially in the 100-200hPa level (upper-mid troposphere).

Overall their findings are:

  • “we find considerable warming biases in the CMIP6 modeled trends, and we show that these biases are linked to biases in surface temperature (these models simulate an unrealistically large global warming).”
  • “we note here for the record that from 1998 to 2014, the CMIP5 models warm, on average 4 to 5 times faster than the observations, and in one model the warming is 10 times larger than the observations.”
  • “Throughout the depth of the troposphere, not a single model realization overlaps all the observational estimates. However, there is some overlap between the RICH observations and the lowermost modelled trend, which corresponds to the NorCPM1 model.”
  • “Focusing on the CMIP6 models, we have confirmed the original findings of Mitchell et al. (2013): first, the modeled tropospheric trends are biased warm throughout the troposphere (and notably in the upper troposphere, around 200 hPa) and, second, that these biases can be linked to biases in surface warming. As such, we see no improvement between the CMIP5 and the CMIP6 models.” (Mitchell et al. 2020)

A special prize goes to the Canadian model!  “We draw attention to the CanESM5 model: it simulates the greatest warming in the troposphere, roughly 7 times larger than the observed trends.” The Canadian government relies on the CanESM models “to provide science-based quantitative information to inform climate change adaptation and mitigation in Canada and internationally.” I would be very surprised if the modelers at UVic ever put warning labels on their briefings to policy makers. The sticker should read: “WARNING! This model predicts atmospheric warming roughly 7 times larger than observed trends. Use of this model for anything other than entertainment purposes is not recommended.”

Although the above diagram looks encouraging in the stratosphere, Mitchell et al. found the models get it wrong too. They predict too little cooling before 1998 and too much after, and the effects cancel in a linear trend. The vertical “fingerprint” of GHG in models is warming in the troposphere and cooling in the stratosphere. Models predict steady stratospheric cooling should have continued after late 1990s but observations show no such cooling this century. The authors suggest the problem is models are not handling ozone depletion effects correctly.

The above diagram focuses on the 1998-2014 span. Compare the red box/whiskers to the black lines. The red lines are climate model outputs after feeding in observed GHG and other forcings over this interval. The predicted trends don’t match the observed trend profile (black line) – there’s basically no overlap at all. They warm too much in the troposphere and cool too much in the stratosphere. Forcing models to use prescribed sea surface temperatures (blue), which in effect hands the “right” answer to the model for most of the surface area, mitigates the problem in the troposphere but not the stratosphere.

McKitrick and Christy 2020

John Christy and I had earlier compared models to observations in the tropical mid-troposphere, finding evidence of a warming bias in all models. This is one of several papers I’ve done on tropical tropospheric warm biases. The IPCC cites my work (and others’) and accepts the findings. Our new paper shows that, rather than the problem being diminished in the newest models, it is getting worse. The bias is observable in the lower- and mid-troposphere in the tropics but also globally.

We examined the first 38 models in the CMIP6 ensemble. Like Mitchell et al. we used the first archived run from each model. Here are the 1979-2014 warming trend coefficients (vertical axis, degrees per decade) and 95% error bars comparing models (red) to observations (blue). LT=lower troposphere, MT=mid-troposphere. Every model overshoots the observed trend (horizontal dashed blue line) in every sample.

Most of the differences are significant at <5%, and the model mean (thick red) versus observed mean difference is very significant, meaning it’s not just noise or randomness. The models as a group warm too much throughout the global atmosphere, even over an interval where modelers can observe both forcings and temperatures.

We used 1979-2014 (as did Mitchell et al. ) because that’s the maximum interval for which all models were run with historically-observed forcings and all observation systems are available. Our results would be the same if we use 1979-2018, which includes scenario forcings in final years. (Mitchell et al. report the same thing.)

John and I found that models with higher Equilibrium Climate Sensitivity (>3.4K) warm faster (not surprisingly), but even the low-ECS group (<3.4K) exhibits warming bias. In the low group the mean ECS is 2.7K, the combined LT/MT model warming trend average is 0.21K/decade and the observed counterpart is 0.15K/decade. This figure (green circle added; see below) shows a more detailed comparison.

The horizontal axis shows the model warming trend and the vertical axis shows the corresponding model ECS. The red squares are in the high ECS group and the blue circles are in the low ECS group. Filled shapes are from the LT layer and open shapes are from the MT layer. The crosses indicate the means of the four groups and the lines connect LT (solid) and MT (dashed) layers. The arrows point to the mean observed MT (open arrow, 0.09C/decade) and LT (closed arrow, 0.15 C/decade) trends.

While the models in the blue cluster (low ECS) do a better job, they still have warming rates in excess of observations. If we were to picture a third cluster of models with mean global tropospheric warming rates overlapping observations it would have to be positioned roughly in the area I’ve outlined in green. The associated ECS would be between 1.0 and 2.0K.

Concluding remarks

I get it that modeling the climate is incredibly difficult, and no one faults the scientific community for finding it a tough problem to solve. But we are all living with the consequences of climate modelers stubbornly using generation after generation of models that exhibit too much surface and tropospheric warming, in addition to running grossly exaggerated forcing scenarios (e.g. RCP8.5). Back in 2005 in the first report of the then-new US Climate Change Science Program, Karl et al. pointed to the exaggerated warming in the tropical troposphere as a “potentially serious inconsistency.” But rather than fixing it since then, modelers have made it worse. Mitchell et al. note that in addition to the wrong warming trends themselves, the biases have broader implications because “atmospheric circulation trends depend on latitudinal temperature gradients.” In other words when the models get the tropical troposphere wrong, it drives potential errors in many other features of the model atmosphere. Even if the original problem was confined to excess warming in the tropical mid-troposphere, it has now expanded into a more pervasive warm bias throughout the global troposphere.

If the discrepancies in the troposphere were evenly split across models between excess warming and cooling we could chalk it up to noise and uncertainty. But that is not the case: it’s all excess warming. CMIP5 models warmed too much over the sea surface and too much in the tropical troposphere. Now the CMIP6 models warm too much throughout the global lower- and mid-troposphere. That’s bias, not uncertainty, and until the modeling community finds a way to fix it, the economics and policy making communities are justified in assuming future warming projections are overstated, potentially by a great deal depending on the model.

References:

Karl, T. R., S. J. Hassol, C. D. Miller, and W. L. Murray (2006). Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences. Synthesis and Assessment Product. Climate Change Science Program and the Subcommittee on Global Change Research

McKitrick and Christy (2020) “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science.

Mitchell et al. (2020) “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability”  Environmental Research Letters.

108 responses to “New Confirmation that Climate Models Overstate Atmospheric Warming

  1. Can I take it you’ll be presenting these important findings in a keynote lecture at COP26?

  2. Thank you all for your excellent work Ross.

    Is it possible that the root cause is that tropical ocean surface temperatures haven’t warmed as much as expected? Is it possible that the driver of climate change is due to the tropical oceans upwelling more deeper colder water than expected, rather than the atmosphere warming first? Is it possible that equatorial tidal energy is mysteriously increasing unbeknown to modern science?

  3. Mathematical modelling of any system is difficult. If the modellers of engineering systems get them wrong, equipment doesn’t work. When climate modellers get it wrong they just go on and on getting it wrong because they are never held to account. We all know they cannot predict the temperature next week, and yet we allow them to generate nonsense about the future.

    • The bad actors wheel these experts out when they need them. Just like Neil Ferguson and his team was wheeled out to scare the world about a virus that has amounted to a bad flu season.
      Modelling is just another scam, might as well look into a crystal ball.
      The world is on a cooling trend so that tells us the modellers are so invested in their ‘hobby’ and prestigious careers that they cannot stop themselves continuing to spend time creating these complex models.
      If they were honest with themselves they would walk away from modelling the impossible and find a career that is honest and productive.

    • If you model chemical processes wrong, the processes may not work, they may not produce what you are trying to produce, a plant may have to be shut down and the processes restudied and the equipment redesigned. If you design the control of chemical processes wrong, because you don’t understand the processes involved or because your control strategy is not appropriate, things can really get out of control. Fires, explosions, leaks of dangerous liquids and gases, and other large-scale disasters can result from control strategies that lead to instabilities. Usually, though, the disasters can be limited in scope. If you try to control global processes that are too complex for your modeling ability, like climate processes, and rely on untested assumptions that grossly contradict reality, when you attempt to implement a control strategy that is hopelessly simplistic and mismatched to the processes you are trying to control, who knows what disasters can result? If you can’t run controlled studies to test your models and your control strategies, and you know that mistakes can lead to widespread catastrophes that can affect millions of people’s lives, you should stop pretending that you know what you are doing.

    • Gerald Browning

      A Thorpe,

      Hear, hear.

      Jerry

  4. [ROSS] New Confirmation that Climate Models Overstate Atmospheric Warming

    [ALSO ROSS] Mitchell et al. look at the surface, troposphere and stratosphere over the tropics (20N to 20S).

    Where’s Kid when one needs him?

    • Do you mean the kid that pointed out the Emperor had no clothes? UN IPCC CliSciFi modelers are the weavers.

    • W

      What part of “ it’s all excess warming.” don’t you understand.

      It’s all unraveling faster than the debacle in Afghanistan.

      Go to the chalkboard and write 100 times “I won’t be scammed again”

      It only gets worse from here on out as the months and years of T come in.

  5. Danley B. Wolfe

    We need to keep reminding everyone about the propaganda going on that is not based on real science. Everyone remember that the models Cimp models are themselves modeled results and using modeled / massaged data not raw data.

  6. Danley B. Wolfe

    And don’t forget Thomas Karl 2015 exercise (right before the Paris mtg) to hide the decline by lowering historical data in the larger data set to magnify global warming.

  7. Considering that the the weather definition in the AMS – Glossary (American Meteorological Society) assumes that
    __ Popularly, weather is thought of in terms of temperature, humidity, precipitation, cloudiness, visibility, and wind, and
    __ the “present weather” table consists of 100 possible conditions, and
    __ the “past weather”; of 10 possible conditions;
    and according IPCC
    “Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. (cont.)”
    climate models will hardly ever get the matter pieced together. http://www.whatisclimate.com/

    The main problem is that the models lack sufficient ocean data. The “prime driver” of climate are the oceans as expressed in a letter to NATURE 1992: “Climate is the continuation of the oceans by other means”. Diuscussed in an open letter 2009:
    http://www.whatisclimate.com/b202-open-letter.html

  8. What are the error bars on the observed trends?

    • Gerald Browning

      David,

      Is it just a coincidence that the initial largest error in weather models
      is in the boundary layer and that grows vertically to destroy the interior solution (Sylvie Gravel et al.)? I suspect not.

      Jerry

      • David Appell

        Gerald, this doesn’t address my question at all. It’s completely off topic.

        Does anyone know why there are no error bars on the curves for the observed trends? Are they too small to plot?

      • Curious George

        David, we are not your research assistants.

      • David Appell

        George, everyone else here should be wondering the same thing.

      • Curious George

        Yes, Your Highness. Everybody should think the same thing.

      • Gerald Browning

        David,

        No it addresses the reason for the error at the surface and its growth upward. This problem can be observed in a short term forecast in one to two days. No surprise it shows up in climate models as Ross as shown.
        Way to go Ross.

        Jerry.

    • There are blue error bars in Figure 3. I was wondering about the first two figures however. Are the dashed, dotted, and solid black lines different datasets? If so that gives some idea of the uncertainty.

      In any case, these results are exactly what every CFDer would expect given the dramatic under resolution of the models. There are some recent good signs of more honesty from climate modelers about that as well. But these facts will never displace the falsehood peddled in the media.

      I also don’t know Ross if it is even possible to fix these biases. Just like designing a perfect turbulence model, it is virtually impossible. The real absence of reliable data for example on tropical convection (a chaotic process) and cloud processes I suspect makes the task not feasible.

    • Seems like a good question.

    • Geoff Sherrington

      DA,

      Unless I am missing some point of your question, they are the boxes and whiskers that replace the customary dot points. Geoff S

    • I would think someone would want to know, even Judith, why there are no error bars on trends derived from measured numbers. Measured numbers come with error bars. Therefore trends do. Why aren’t they shown on the chart?

    • The problem with observing the tropical tropospheric hot spot was, for a long time — maybe still? — even in the 2010s, that the error bars were too big to distinguish observations from models.

      So I’m wondering if that’s also the case here.

  9. What about “the cloud-aerosol interactions whose influence on the climate system is about the same size as the human caused warming influence, see S. E. Koonin’s book “Unsettled” pg. 93 where he wonders if the AR6 report addresses this.

    “All models are wrong, but some are useful” George Box U of Wisconsin,1978, quoted in Koonin’s book too.

  10. Thank you.

  11. ‘Perhaps we can visualize the day when all of the relevant physical principles will be perfectly known. It may then still not be possible to express these principles as mathematical equations which can be solved by digital computers. We may believe, for example, that the motion of the unsaturated portion of the atmosphere is governed by the Navier–Stokes equations, but to use these equations properly we should have to describe each turbulent eddy—a task far beyond the capacity of the largest computer. We must therefore express the pertinent statistical properties of turbulent eddies as functions of the larger-scale motions. We do not yet know how to do this, nor have we proven that the desired functions exist.’ Lorenz, E. N. 1969 Three approaches to atmospheric predictability. Bull. Am. Met. Soc. 50,
    345–351.

    We are still at the stage where the relevant physical processes are imperfectly known, computers are still not powerful enough to model at cloud resolving scale and the functions to describe the statistics of turbulent eddies are still unknown. 🙄

  12. Empirically the models have consistent problems with the observed data. An engineer would thus add “fudge” factors to compensate these errors, such that the results match the observations. Most modelers hate to do this as the theoretical basis does not exist – just a cloud/eddy/fudge Factor for each vertical slice of the atmosphere.
    Be interesting to see what the adapted results would look like and what effect these would have on future predictions.
    Great paper Ross!
    BTW
    Recent surveys of the Eocene (Hutchinson et al 2021) and the Miocene (Steinthorsdottir et al 2021) state that no model can reproduce the observed warmth (especially at high latitudes) with the observed pCO2 values. They also find that derived pCO2 from marine and terrestrial sources are very different and have yet to be reconciled. We find that these periods were of higher patm, but apart from Chemke, Kapsi & Toon, little work has been done on models with a variable atmospheric mass. An interesting future field for the model community…
    Alan

  13. A special prize goes to the Canadian CanESM5 model:
    it simulates the greatest warming in the troposphere, roughly 7 times larger than the observed trends.”
    The Canadian government relies on the CanESM models “to provide science-based quantitative information to inform climate change adaptation and mitigation in Canada and internationally

    This model predicts atmospheric warming roughly 7 times larger than observed trends.

  14. David Appell said
    What are the error bars on the observed trends?

    Who cares?

    “This model predicts atmospheric warming roughly 7 times larger than observed trends.”

  15. Why would anyone bother with the delusional nonsense from the Appell donkey.
    He doesn’t bother about the science and always fall back on his religious true believer fantasies.

  16. Pingback: New Confirmation that Climate Models Overstate Atmospheric Warming |

  17. Geoff Sherrington

    It seems that these CMIP results deliver an outcome that allows the future likelihood of a 2 deg C global warming, a benchmark invented by Potsdam people to help policy makers control folk through projection of fear.
    The question arises of whether we will ever see modelling by this clan that is more plausible because they drop the self-imposed restriction of allowing for 2 deg C. Geoff S

  18. Here’s further confirmation of the IPCC consensus being highly biased:

    …..
    Study Finds Sun—not CO2—May Be Behind Global Warming

    New peer-reviewed paper finds evidence of systemic bias in UN IPCC’s data selection to support climate-change narrative

    The sun and not human emissions of carbon dioxide (CO2) may be the main cause of warmer temperatures in recent decades, according to a new study with findings that sharply contradict the conclusions of the United Nations (UN) Intergovernmental Panel on Climate Change (IPCC).

    The peer-reviewed paper, produced by a team of almost two dozen scientists from around the world, concluded that previous studies did not adequately consider the role of solar energy in explaining increased temperatures.
    …..
    https://m.theepochtimes.com/challenging-un-study-finds-sun-not-co2-may-be-behind-global-warming_3950089.html

  19. Here’s further confirmation of the IPCC consensus being highly biased:

    ….
    Study Finds Sun—not CO2—May Be Behind Global Warming

    New peer-reviewed paper finds evidence of systemic bias in UN IPCC’s data selection to support climate-change narrative

    The sun and not human emissions of carbon dioxide (CO2) may be the main cause of warmer temperatures in recent decades, according to a new study with findings that sharply contradict the conclusions of the United Nations (UN) Intergovernmental Panel on Climate Change (IPCC).

    The peer-reviewed paper, produced by a team of almost two dozen scientists from around the world, concluded that previous studies did not adequately consider the role of solar energy in explaining increased temperatures.
    ….

    • Source: The Epoch Times

    • Scientific papers which report that all the planets of the solar system are mysteriously heating, have also been omitted from the IPCC AR6.

    • No one in their right mind should get scientific information from the conspiracy theorists at the Epoch Times. A link to the paper authored by many prominent skeptic can be found below. The problem is that these authors focus warming and estimated TSI changes over more than the past century and go back to the Maunder minimum. They don’t directly analyze warming and TSI changes during the satellite era – when we have the best information about warming and real measurements of the change in TSI. That data says that changes in TSI have not been a major factor in the roughly 0.2 degC/decade global warming we have experienced over the past half-century.

      https://arxiv.org/pdf/2105.12126

      Changes in TSI could have played an important role in EARLIER climate change: in the modest warming from 1860-1970, in the LIA, and in the end of the LIA around two centuries ago. These periods are important for our understanding of naturally-forced variability in climate, but natural variability in TSI hasn’t been an important factor the last half-century of warming.

      (Climate variability can be can be broken down to anthropogenically-forced climate change (mostly GHGs and aerosols), naturally-forced climate change (volcanos and solar), and unforced or internal variability (mostly due to chaotic changes in ocean and air currents). ENSO is a classic example of internal variability. Naturally-forced climate change has been insignificant and dwarfed by anthropogenic forcing in the past half-century.)

      • stevenreincarnated

        Frank, remind me again how long it takes for the Earth to come to equilibrium with a change in forcing.

  20. Pingback: New Confirmation that Climate Models Overstate Atmospheric Warming – Watts Up With That?

  21. Models do not provide deterministic solution trajectories but they are far from the main climate science game.

    ‘As climate scientists, we are rightfully proud of, and eager to talk about, our contribution to settling important and long-standing scientific questions of great societal relevance. What we find more difficult to talk about is our deep dissatisfaction with the ability of our models to inform society about the pace of warming, how this warming plays out regionally, and what it implies for the likelihood of surprises. In our view, the political situation, whereby some influential people and institutions misrepresent doubt about anything to insinuate doubt about everything, certainly contributes to a reluctance to be too openly critical of our models. Unfortunately, circling the wagons leads to false impressions about the source of our confidence and about our ability to meet the scientific challenges posed by a world that we know is warming globally.’

    • You don’t understand subtlety.

      • I understand enough to recognize when weak subtlety is used to hide hard facts.

        It is disingenuous for them to claim that CliSciFi models are “… far from the main climate science game.” They are highlighted in all UN IPCC CliSciFi publications and pronouncements of impending doom. They are used (and exaggerated) by activists and politicians to incite fear in the populace. The rest of the statement is the in-crowd patting themselves on the back for being so intelligent.

      • I can’t argue that the uses models – that intrinsically cannot predict the future – are put to are not propaganda. I was referring to the physical science.

      • ‘How can we can reconcile our dissatisfaction with the comprehensive models that we use to predict and project global climate with our confidence in the big picture? The answer to this question is actually not so complicated. All one needs to remember is that confidence in the big picture is not primarily derived from the fidelity of comprehensive climate models of the type used to inform national and international assessments of climate change. Rather, it stems from our ability to link observed changes in climate to changes derived from the application of physical reasoning, often as encoded in much simpler models or in the case of the water cycle, through a rather simple application of the laws of thermodynamics. Comprehensive climate models have been effective and essential to address the concern that such a basic understanding could be overly simplistic (i.e., missing something important, such as the existence of a mode of internal variability, which could, if it were to exist, explain trends in global mean temperature). The enterprise of making models more and more comprehensive through the incorporation of computationally expensive* but poorly understood additional processes has not so much sharpened our ability to anticipate climate change as left the blurry picture established by physical reasoning and much simpler models intact (1). When it comes to global climate change, it is what the present generation of comprehensive climate models do not show—namely, a sensitivity of global changes to either the vagaries of unpredictable regional or global circulations or effects of processes neglected in simpler models—which makes them such a powerful confirmation of inferences from basic physics and simple models.’ op. cit.

  22. Great summary:

    Quote:
    CMIP5 models warmed too much over the sea surface and too much in the tropical troposphere. Now the CMIP6 models warm too much throughout the global lower- and mid-troposphere. That’s bias, not uncertainty, and until the modeling community finds a way to fix it, the economics and policy making communities are justified in assuming future warming projections are overstated, potentially by a great deal depending on the model.”
    End Quote

    And this:
    Quote
    A special prize goes to the Canadian model! “We draw attention to the CanESM5 model: it simulates the greatest warming in the troposphere, roughly 7 times larger than the observed trends.” The Canadian government relies on the CanESM models “to provide science-based quantitative information to inform climate change adaptation and mitigation in Canada and internationally.” I would be very surprised if the modelers at UVic ever put warning labels on their briefings to policy makers. The sticker should read: “WARNING! This model predicts atmospheric warming roughly 7 times larger than observed trends. Use of this model for anything other than entertainment purposes is not recommended.”
    End quote

  23. If the IPCC follows the scientific method, it has now to admit that its “attribution” hypotheses is invalidated and must be revised or replaced.

  24. Christos Vournas

    Please also visit Ron Clutz’s blog:

    https://rclutz.com/2021/07/21/how-to-calculate-planetary-temperatures/

  25. Even some modelers think it is getting bad!

    https://www.cfact.org/2021/08/15/climate-modeling-civil-war/

  26. Pingback: New Confirmation that Climate Models Overstate Atmospheric Warming – Climate- Science.press

  27. This post is an inadvertent re-post of one that first appeared here in August 2020. However, the content is still just as relevant so I am glad people have enjoyed discussing it. The post that was supposed to appear yesterday has now been uploaded.

  28. This is 40 year old news.

    It was always obvious that computer models will “predict” whatever the owners and programmers want predicted.

    The predictions ./ projections / simulations are for rapid warming because that’s what governments want predicted.

    There is no obvious attempt to make correct predictions.

    If correct predictions were a goal, the one model that least over predicts global warming, the Russian INM model, would get a large majority of attention.

    It does not.

    The results of the Russian INM model are binned with dozens of other models, that all over predict warming by a larger amount, to create an average.

    Can you imagine meteorologists having one decent weather prediction model, out of dozens, and not focusing on that most accurate model ?

    Models are computer games that deliberately make wrong predictions used for climate scaremongering propaganda. They are not real science.

  29. “Keep calm, and give Denizens citations!” I don’t care if you keep calm or not, Willard, but give Denizens your real name and qualifications. Otherwise, you are a nonentity.

  30. Dr. Curry, I support Gerald Browning’s suggestion. If someone can convince you, as site curator, that they have valid reasons for protecting their identity, they should reveal themselves when commenting on scientific matters.

    • Anonymity is allowed here. CE is about the arguments, not the person. Please behave, everyone. I have deleted a ton of comments over the past day.

      • joe - the non climate scientist

        JC comment – “Anonymity is allowed here. CE is about the arguments, not the person. Please behave, everyone. I have deleted a ton of comments over the past day.”

        Thanks – As a layperson, I have strong preference for coherent & logical arguments about the science. I greatly appreciate your efforts. Its a shame that you have to waste time moderating comments. Adults shouldnt need adult supervision. (my apologies since I am sometimes guilty).

      • I did put in a comment in support of Willard being allowed to comment along the lines that you have mentioned.
        Is it possible to restore it if deleted?
        Thanks.

      • Thank you for your excellent blog, Dr. Curry. Your blog, your rules. However, I’ll still discount comments from keyboard cowboys and Trolling Devices.

      • Some card carrying atmospheric scientists have commented here under pseudonyms since they don’t want to take ‘heat’ from the consensus enforcers and denial policy.

      • I have no argument with that, Dr. Curry. Thank you for your blog. It is pretty obvious, though, who the “card carrying atmospheric scientists” are vs the Trolling Devices and keyboard cowboys.

        I’ll keep trying to not violate your standards because I appreciate what you are doing. However, I am but a weak human. Just ask my not-so-silent-suffering wife.

      • Gerald Browning

        Judy,

        And some IPCC supporters too so it is a toss up who you are responding to.

        Jerry

      • You’re still playing the ref, Jerry.

      • Gerald Browning

        Judith,

        Did you delete my Zwack comment? That is pertinent to my response to frankclimate?

        Jerry

  31. Gerald Browning

    Willard,

    I responded to Dave’s comment. Is that trolling or is your accusation that everyone that does not agree with you is a troll in fact trolling. I will now provide scientific refernces or pertinent facts that cannot be disputed in my comments. Hope you will do the same.

    Jerry

    • Jerry,

      I really could not care less about your opinion.

      You’re repeating yesterday’s experiment with full knowledge that it is against the owner’s policy. That’s more than dishonest and antisocial: it invalidates whatever point you think you had about real names.

      Please try to focus on what you know best. If you don’t know PaulS, you’re about to find out that your silly “but volcanoes” won’t work with him.

      Thanks for the citation, btw. Progress!

      Cheers.

      • Gerald Browning

        Willard,

        > Please try to focus on what you know best. If you don’t know PaulS, you’re about to find out that your silly “but volcanoes” won’t work with him.

        Bring it on. I can hardly wait to have him elaborate on how the model knows apriori when a volcano is going to erupt when no geologist knows that. Only knowing from historical data can a programmer add that to the climate model program as a forcing (tuning) trick.

        Jerry

      • Jerry,

        If they were Volcanoes Circulation Models, you might have a point.

        They’re not.

      • Gerald Browning

        Willard,

        >angech | August 19, 2021 at 8:13 pm |
        Thanks,
        Gerald Browning | August 19, 2021 at 4:39 pm
        “paulskio, >Also not true, there is no such assumption in climate models. ”
        Paul S needs to be called out on all these misleading comments.
        I am not totally surprised that he has made them given his strong viewpoints but there was a time a few years ago where he was very diligent on making accurate and pertinent comments and calling on others on both sides to use correct protocol and science.

        My argument has already been accepted. There were times in history when volcnic eruptions affected the climate. The only way that effect could be included in a model is by tuning.

        Jerry

      • > My argument has already been accepted.

        Let’s grant the following premises:

        P1. We can’t predict volcanic eruptions.
        P2. Stoopid modulz need to be tuned accordingly.

        The conclusion is still missing.

        It might help to complete an implicit premise of the form: since stoopid modulz need to be tuned for volcanic eruptions, etc.

      • Sensitive dependence on initial conditions means that tuning of non unique parameters is used to reproduce the temperature signal within broad limits.

        ‘Fig. 1, which is taken from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (13), illustrates this situation well. It shows that all of the climate models can adequately reproduce the observed change in temperature—part of what we call the blurry outline of climate change. This is something that the Assessment Report draws attention to in its summary for policy makers. What is not discussed in the summary is what is shown by the thin horizontal lines on the edge of Fig. 1. Even after being tuned to match observed irradiance at the top of the atmosphere,† models differ among themselves in their estimates of surface temperature by an amount that is 2 to 3 times as large as the observed warming and larger yet than the estimated 0.5 °C uncertainty in the observations. The deemphasis of this type of information, while helpful for focusing the reader on the settled science, contributes to the impression that, while climate models can never be perfect, they are largely fit for purpose. However, for many key applications that require regional climate model output or for assessing large-scale changes from small-scale processes, we believe that the current generation of models is not fit for purpose.’ https://www.pnas.org/content/116/49/24390#F1

        https://www.pnas.org/content/pnas/116/49/24390/F1.large.jpg

        For the future there are 1000’s of exponentially divergent solution trajectories starting from plausible differences in parameter estimation – and of course no signal to tune to.

        https://watertechbyrie.files.wordpress.com/2014/06/rowlands-fig-1-e1612040479369.png

        And it goes without saying that poor wee willie is a clueless zealot who has been ill served by team IPCC.

      • > ill served by team IPCC.

        From the horse’s mouth:

        For the historical period, AR5 assessed with very high confidence that CMIP5 models reproduced observed large-scale mean surface temperature patterns, although errors of several degrees appear in elevated regions, like the Himalayas and Antarctica, near the edge of the sea ice in the North Atlantic, and in upwelling regions. This assessment is updated here for the CMIP6 simulations. Figure 3.3 shows the annual-mean surface air temperature at 2 m for the CMIP5 and CMIP6 multi-model means, both compared to the ERA5 reanalysis (see Section 1.5.2) for the period 1995-2014. The distribution of biases is similar in CMIP5 and CMIP6 models, as already noted by several studies (Crueger et al., 2018; Găinuşă-Bogdan et al., 2018; Kuhlbrodt et al., 2018; Lauer et al., 2018). Arctic temperature biases seem more widespread in both ensembles than assessed at the time of the AR5. The fundamental causes of temperature biases remain elusive, with errors in clouds (Lauer et al., 2018), oceanic circulation (Kuhlbrodt et al., 2018), winds (Lauer 26 et al., 2018), and surface energy budget (Hourdin et al., 2015; Séférian et al., 2016; Găinuşă-Bogdan et al., 27 2018) being frequently cited candidates. Increasing horizontal resolution shows promise of decreasing long-standing biases in surface temperature over large regions (Bock et al., 2020). Panels e and f of Figure 3.3 show that biases in the mean High-Resolution Model Intercomparison Project (HighResMIP, Haarsma et al., 2016) models (see also Table AII.6) are smaller than those in the mean of the corresponding lower-resolution versions of the same models simulating the same period (see also Section 3.8.2.2). However, the bias reduction is modest (Palmer and Stevens, 2019). In addition, the biases of the limited number of models participating in HighResMIP are not entirely representative of overall CMIP6 biases, especially in the Southern Ocean, as indicated by comparing panels b and f of Figure 3.3.

        I’ll let you find the chapter, Chief.

      • ‘Global storm and ocean-eddy resolving [O(1 km)] models make it possible to directly simulate deep convection, ocean mesoscale eddies, and important land–atmosphere interactions. Prototypes of such models are already being developed (21), 3 examples of which are compared with a satellite image. By avoiding the need to represent essential processes by semiempirical parameterizations, the simulated climate of such a model is more constrained by the laws of physics. This can be expected to lead to the reduction or even elimination of many systematic biases that plague the present generations of models (24⇓⇓⇓⇓⇓⇓–31).

        Commensurate with this focus on high resolution, uncertainties in the parameterization of remaining subgrid (or nonfluid-dynamical) processes should be represented explicitly through some application of stochastic modeling (32). Among other advantages, this will ensure that such parameterizations are not unjustifiably complex—or overfit to past changes—and that the numerical precision of both parameterizations and dynamical cores is commensurate with their information content (33). Data-driven methods could also play an important role in reducing computational costs and in improving the representation of processes that cannot be constrained by first principles (34).

        We can expect that such models will have substantially reduced biases against observations and a better characterization of uncertainty.’ Palmer and Stevens 2019

        Models are nowhere near that resolution – quantum computers might do it – so I expect that citing an informed opinion piece about the limitations of current models and future directions as validation of something else entirely is more like the horses arse.

      • ‘Atmospheric and oceanic computational simulation models often successfully depict chaotic space–time patterns, flow phenomena, dynamical balances, and equilibrium distributions that mimic nature. This success is accomplished through necessary but nonunique choices for discrete algorithms, parameterizations, and coupled contributing processes that introduce structural instability into the model. Therefore, we should expect a degree of irreducible imprecision in quantitative correspondences with nature, even with plausibly formulated models and careful calibration (tuning) to several empirical measures. Where precision is an issue (e.g., in a climate forecast), only simulation ensembles made across systematically designed model families allow an estimate of the level of relevant irreducible imprecision.’ https://www.pnas.org/content/104/21/8709

  32. Jerry, please show more respect for Dr. Curry. Her stated reasoning is valid for allowing anonymity. You just need to apply reasoned judgement in identifying Trolling Devices and keyboard cowboys.

    • Gerald Browning

      Dave,

      I knew that is how she would respond and the reasons for her response.I repect Judy and am aware how the climate modelers worked her over and anyone else that goes against “consensus”. We have disagreed in the past over some things but she is fully aware of my repect for her scientific abilities.

      Jerry

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  36. Apparently nobody noticed that this exact same article was published on this blog on August 25, 2020, exactly one year ago today! https://judithcurry.com/2020/08/25/new-confirmation-that-climate-models-overstate-atmospheric-warming/

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  40. Interesting commentary by Mallen Baker on relative humidity going in the opposite direction to that predicted.

    https://youtu.be/diiqsmpZEeY

    Miskolczi anyone?

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