Why climate predictions are so difficult

From Climate Etc.

by Judith Curry

An insightful interview with Bjorn Stevens.

Frank Bosse provided this Google translation of an interview published in Der Spiegel  -Print-Issue 13/2019, p. 99-101.   March 22, 2019

Excerpts provided below, with some minor editing of the translation.

begin quote:

Global warming forecasts are still surprisingly inaccurate. Supercomputers and artificial intelligence should help. By Johann Grolle

It’s a simple number, but it will determine the fate of this planet. It’s easy to describe, but tricky to calculate. The researchers call them “climate sensitivity”.

It indicates how much the average temperature on Earth warms up when the concentration of greenhouse gases in the atmosphere doubles. Back in the 1970s, it was determined using primitive computer models. The researchers came to the conclusion that their value is likely somewhere between 1.5 and 4.5 degrees.

This result has not changed until today, about 40 years later. And that’s exactly the problem.

The computational power of computers has risen many millions of dollars, but the prediction of global warming is as imprecise as ever. “It is deeply frustrating,” says Bjorn Stevens of the Hamburg Max Planck Institute for Meteorology.

For more than 20 years he has been researching in the field of climate modeling. It is not easy to convey this failure to the public. Stevens wants to be honest, he does not want to cover up any problems. Nevertheless, he does not want people to think that the latest decades of climate research have been in vain.

“The accuracy of the predictions has not improved, but our confidence in them has grown,” he says. The researchers have examined everything that might counteract global warming. “Now we are sure: she is coming.”

As a decision-making aid in the construction of dykes and drainage channels the climate models are unsuitable. “Our computers do not even predict with certainty whether the glaciers in the Alps will increase or decrease,” explains Stevens.

The difficulties he and his fellow researchers face can be summed up in one word: clouds. The mountains of water vapor slowly moving across the sky are the bane of all climate researchers.

First of all, it is the enormous diversity of its manifestations that makes clouds so unpredictable. Each of these types of clouds has a different effect on the climate. And above all: they have a strong effect.

Simulating natural processes in the computer is always particularly sensitive when small causes produce great effects. For no other factor in the climatic events, this is as true as for the clouds. If the fractional coverage of low-level clouds  fell by only four percentage points, it would suddenly be two degrees warmer worldwide. The overall temperature effect, which was considered just acceptable in the Paris Agreement, is thus caused by four percentage points of clouds – no wonder that binding predictions are not easy to make.

In addition, the formation of clouds depends heavily on the local conditions. But even the most modern climate models, which indeed map the entire planet, are still blind to such small-scale processes.

Scientists’ model calculations have become more and more complex over the past 50 years, but the principle has remained the same. Researchers are programming the earth as faithfully as possible into their computers and specifying how much the sun shines in which region of the world. Then they look how the temperature on their model earth adjusts itself.

The large-scale climatic events are well represented by climate models.

However, problems are caused by the small-scale details: the air turbulence above the sea surface, for example, or the wake vortices that leave mountains in the passing fronts. Above all, the clouds: The researchers can not evaporate the water in their models, rise and condense, as it does in reality. You have to make do with more or less plausible rules of thumb.

“Parametrization” is the name of the procedure, but the researchers know that, in reality, this is the name of a chronic disease that has affected all of their climate models. Often, different parameterizations deliver drastically divergent results. Arctic temperatures, for example, are sometimes more than ten degrees apart in the various models. This makes any forecast of ice cover seem like mere reading of tea leaves.

“We need a new strategy,” says Stevens. He sees himself as obliged to give better decision support to a society threatened by climate change. “We need new ideas,” says Tapio Schneider from Caltech in Pasadena, California.

The Hamburg Max Planck researcher has therefore turned to another type of cloud, the cumulonimbus. These are mighty thunderclouds, which at times, dark and threatening, rise higher than any mountain range to the edge of the stratosphere.

Although this type of cloud has a comparatively small influence on the average temperature of the earth, Stevens explains. Because they reflect about as much solar radiation into space as they hold on the other hand from the earth radiated heat. But cumulonimbus clouds are also an important climatic factor. Because these clouds transport energy. If their number or their distribution changes, this can contribute to the displacement of large weather systems or entire climatic zones.

Above all, one feature makes Stevens’ powerfully spectacular cumulonimbus clouds interesting: They are dominated by powerful convection currents that swirl generously enough to be predictable for modern supercomputers. The researcher has high hopes for a new generation of climate models that are currently being launched.

While most of its predecessors put a grid with a resolution of about one hundred kilometers over the ground for calculations, these new models have reduced the mesh size to five or even fewer kilometers. To test their reliability, Stevens, together with colleagues in Japan and the US, carried out a first comparison simulation.

It turned out that these models represent the tropical storm systems quite well. It therefore seems that this critical part of the climate change process will be more predictable in the future. However, the simulated period was initially only 40 days. “Stevens knows that to portray climate change, he has to run the models for 40 years. Until then it is still a long way.

Stevens, meanwhile, rather fears that it is the cumulonimbus clouds that could unexpectedly cause surprises. Tropical storm systems are notorious for their unpredictability. “The monsoon, for example, could be prone to sudden changes,” he says.

It is possible that the calculations of the fine-mesh computer models allowed to predict such climate surprises early. “But it is also conceivable that there are basically unpredictable climatic phenomena,” says Stevens. “Then we can still simulate so exactly and still not come to any reliable predictions.”

That’s the worst of all possibilities. Because then mankind continues to steer into the unknown.

end quote.


127 thoughts on “Why climate predictions are so difficult

  1. “But it is also conceivable that there are basically unpredictable climatic phenomena”

    Random Climatic Mutation?

    • “Global warming forecasts are still surprisingly inaccurate. “

      Recall IPCC’s TAR chapter 14 about “coupled non-linear chaotic system” being impossible to know future states of ??

      Being mathematically impossible may be part of the explanation of climate predictions being wrong.

      • It’s not that complicated. The complications are generally only along the path to a steady state and have little to do with what that steady state will be. The are cited only to provide the wiggle room to make the impossible seem plausible.

        If ‘e’ is the ratio between the planets average emissions (240 W/m^2) and the average surface emissions (390 W/m^2) corresponding to an average temperature, T (288K) , the sensitivity is given exactly by, 1/(4*e*o*T^3), where o is the SB constant of about 5.67E-8 W/m^2 per K^4. There’s no wiggle room for it to be anything else unless either COE or the SB LAW are violated. The consensus breaks COE by misapplying feedback analysis which literally creates energy out of thin air.

        Yes, the Earth is not an ideal BB, but a non ideal BB can be exactly quantified as a gray body with non unit emissivity (e). There are no other laws of physics that can quantify how a radiating body like the Earth must behave in response to new energy input (forcing) and feedback is not new energy as the models seem to think.

    • We’ve known that the climate is chaotic since the first computer models.

      Edward Norton Lorenz (May 23, 1917 – April 16, 2008) was an American mathematician and meteorologist who established the theoretical basis of weather and climate predictability, as well as the basis for computer-aided atmospheric physics and meteorology.[1][2] He is best known as the founder of modern chaos theory, a branch of mathematics focusing on the behavior of dynamical systems that are highly sensitive to initial conditions.

      “highly sensitive to initial conditions”, that’s the butterfly effect. Lorenz discovered and described it. Basically, it is impossible to predict the climate with any precision.

      So, we know the climate can’t be modeled with any precision, no matter how powerful the computer. Still, they throw money at the problem. MOB’s irreducibly simple model solved on a slide rule is just as valid as the latest finite element model solved on a super computer.

      This isn’t a secret nor is it arcane, but it is the elephant in the room.

    • Just because we can’t solve a problem doesn’t mean it isn’t solvable. Our inability to predict process shouldn’t lead us to believe that the process isn’t predictable. Unless that is, it can be shown that the laws of cause and effect don’t apply in a particular case! Think I’d rather opt for our ignorance/inability as the explanation, because causelessness as an explanatory mechanism is the ultimate opt out – at any level, quantum included – and makes no sense at all to me. Climate is “simply” an exceedingly complex phenomenon and that complexity is beyond our current capacity to fully grasp and predict. Chaotic is how it seems, not how it is. We need to work harder and smarter is all.

  2. “The accuracy of the predictions has not improved, but our confidence in them has grown,” he says. The researchers have examined everything that might counteract global warming. “Now we are sure: she is coming.”

    Seriously??? The accuracy hasn’t improved, but you’re more confident in their predictions? Delusional.

    Everything? What about the consequences of utterly crap input data, especially the hopelessly inadequate historical data?

    • You beat me to it, Don. Did Bjorn Stevens even listen to himself?

      Well, unless he meant that he is more confident than ever that the models are crap for predicting the future state of Earth’s climate, in which case a model is only useful for studying the interactions of the variables based on the built-in assumptions of a model. I can go with that.

      • “The accuracy of the predictions has not improved, but …**…”

        ** =
        1.) …the climate conferences are much better.
        2.) …my paycheck has really grown.
        3.) …the girls studying climate today are prettier.
        4.) …you should see my office.

    • Does mean they more confident in their inability to predict the climate.

      1970, top of the line computer was an IBM System/360, now they spend $70 million on a computer and get the same result.

      • Similar to my stock market picking, or sports pools.

        I do just as well randomly selecting as I do spending weeks looking at all the data…

        • I do just as well randomly selecting as I do spending weeks looking at all the data…

          I once tried out a currency trading service. After my trial, with virtual money, they asked if I’d like to sign up.

          I told them that I’d found a really, really good way to make money at this. All they had to do was watch whatever I did, and do exactly the opposite! Basically every trade I made lost me money.

          Needless to say, I did not take up their offer.

          • While it is tempting to solely use the gambit of watching dumb people and doing the opposite, it can backfire. As they say, even a broken watch is correct twice a day.

            But it can be a useful start. I remember 20 years ago discussing buying some bonds for my portfolio.

            My friends laughed. “You get, what, 3%. Pffft…”

            They went on to talk about Bre-X and how it was going up…

      • “Does mean they more confident in their inability to predict the climate.”

        Certainly makes more sense when interpreted that way. 🙂

    • ” Nevertheless, he does not want people to think that the latest decades of climate research have been in vain.”

      I quit reading at that point. I am fully capable of thinking for myself completely outside what a climate modeler wants for me. All I need are facts/truth

      This is the beating heart of the problem with the CAGW science flim flam and what the world is being subjected to with this Concensus B.S. What we need to “Think,” “Feel” about climate change is WHAT THEY WANT US TO KNOW, truth be damned.

    • Of course you have more confidence in results that have not improved in accuracy with bigger, faster and more expensive computers. How else would you explain the money expended to polish GIGO?

    • Congratulations, Don, you’re getting right into the heart of the matter. To utilize supercomputers and Artificial Intelligence you have to train the computer to think, and this is done by presenting examples of successful calculations in the general theme of interest. So what examples of successful climate change due to increasing CO2 do they have to present? Nothing! So you cannot utilize AI until you have successful examples to present. Period. You computer geeks don’t bother to argue this, I managed a high-tech research group and we examined and rejected AI because of training examples issues.

      • An artificial intelligence is, at best, as good as the training set. Given what the carbon climatists have done torturing data, I doubt their training set would be very good. Similarly, their more conventional models are plagued by bad assumptions and avoidance of important details which are hard to simulate.

    • AI is not likely to help but make matters worse. Consider the following article:

      AAAS: Machine learning ‘causing science crisis’

      “Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.
      Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”.
      She warned scientists that if they didn’t improve their techniques they would be wasting both time and money. Her research was presented at the American Association for the Advancement of Science in Washington.
      A growing amount of scientific research involves using machine learning software to analyse data that has already been collected. This happens across many subject areas ranging from biomedical research to astronomy. The data sets are very large and expensive.
      But, according to Dr Allen, the answers they come up with are likely to be inaccurate or wrong because the software is identifying patterns that exist only in that data set and not the real world.#“Often these studies are not found out to be inaccurate until there’s another real big dataset that someone applies these techniques to and says ‘oh my goodness, the results of these two studies don’t overlap‘,” she said.
      “There is general recognition of a reproducibility crisis in science right now. I would venture to argue that a huge part of that does come from the use of machine learning techniques in science.”
      The “reproducibility crisis” in science refers to the alarming number of research results that are not repeated when another group of scientists tries the same experiment. It can mean that the initial results were wrong. One analysis suggested that up to 85% of all biomedical research carried out in the world is wasted effort….

      • “One analysis suggested that up to 85% of all biomedical research carried out in the world is wasted effort…”

        I wonder if this analysis relies on machine learning software ◔̯◔

    • Charitable reading suggests that we shouldn’t rush to judgement when interpreting someone’s words. They may have meant something slightly different from our interpretation, and in a case like this where it seems that the guy is bright and honest, and not using his first language (or it’s a translation), it seems unlikely that “delusional” is an appropriate response. It could be that we as readers are misjudging his intent. Might be worth getting an explanation for the apparent contradiction.

  3. Interestingly, I have seen several calculations of ECS based on observations, and they are all about 2 or below. Perhaps the real problem is that they dont like the fact that the calculations keep on coming on on the low end of the 1.5-4.5 spectrum

    • The entire 1.5-4.5 spectrum can be falsified in many ways. The sensitivity factor used to arrive at this is 0.8C +/- 0.4C per W/m^2. Doubling CO2 is claimed to be equivalent to 3.7 W/m^2 of incremental solar forcing where 3.7 * 0.8 = 2.96 degrees C and is the origin of the middle of the CO2 ‘sensitivity’ range of 3C +/- 1.5C.

      If the surface temperature increases by 0.4C per W/m^2 (the low end of the IPCC’s range), from 288K up to 288.4K, the NET emissions increase from 390.08 W/m^2 to 392.25 W/m^2 for an increase of 2.17 W/m^2 per W/m^2 of forcing. Currently, the 390.08 W/m^2 of average NET surface emissions are the result of 240 W/m^2 of average NET solar forcing where the LINEAR sensitivity metric is 390.08/240 = 1.625 W/m^2 of surface emissions per W/m^2 of forcing.

      Since the Earth can’t possibly distinguish the next Joule of solar forcing from all the others, the next W/m^2 of forcing must also increase surface emissions by 1.62 W/m^2 which is less than the 2.17 W/m^2 required to satisfy the low end of the IPCC’s sensitivity range, thus falsifying the entire range. A second path to falsification is to show that the maximum possible linear sensitivity metric is 2 W/m^2 of surface emissions per W/m^2 of forcing which is also less than the 2.17 W/m^2 of surface emissions per W/m^2 of forcing required to support the low end of the IPCC’s ECS.

      Expressing the sensitivity in terms of at temperature change per doubling CO2 is just an additional level of indirection, beyond expressing the ECS as a non linear temperature change per W/m^2 and that has no purpose other than to obfuscate the underlying linear sensitivity metric. The ONLY proper sensitivity metric is the undeniably linear metric of W/m^2 of surface emissions per W/m^2 of forcing. The IPCC can’t accept this as it undermines their reason to exist.

      The LINEAR sensitivity metric for an ideal black body is 1 W/m^2 of emissions per W/m^2 of forcing. The Earth is not an ideal BB and rather than 100%, only about 62% of the average NET surface emissions eventually leave the planet which can be expressed equivalently as a gray body with an emissivity of about 0.62 and leads to an EXACT quantification of the sensitivity factor of 0.3C per W/m^2 which when multiplied by 3.7 W/m^2 of EQUIVALENT forcing results in a sensitivity to doubling CO2 of about 1.1C and less than the 1.5C claimed as the lower limit.

      • They assume that a CO2 driven temperature increase will drive an increase in water vapor, a very potent IR active gas. What they then neglect is an increase in cloud formation or any other factor driven that might counter the heating.

        • On the one hand, the alarmists claim that emissions of water vapor from combustion (including H2 combustion) are insignificant relative to the effects of the emitted CO2 from burning fossil fuels, yet on the other, they claim that the effect of water vapor is so large that a tiny increase in temperature resulting in a tiny increase in water vapor will amplify themselves into oblivion causing the world to fry. How does this nonsense get past peer review?

          What they fail to understand is that the combined effect beyond the forcing from CO2, CH4, O3, water vapor, clouds and ice is only 620 mw per W/m^2 of forcing and that no amount of fabricated feedback or any other such nonsense will ramp this up to the 3.4 W/m^2 per W/m^2 of forcing required to support their insane ECS.

  4. “That’s the worst of all possibilities. Because then mankind continues to steer into the unknown.”

    As mankind has done forever. We are just along for the ride and we go where the planet takes us we can not and never will be steering.

    • Sometime in the 50’s Hollywood got the idea that somehow a group of politicians, informed by a group of greybearded scientists, and spearheaded by a hunk military scientist (aided by a young pretty female scientist and quirky sidekick engineer/technician/scientist) could save the World from alien invasion, radioactive monsters, or just some plain old nature disaster. This meme has only grown. When I was a kid I sat on the rug and watched these reruns, I didn’t have any idea of the vastness and complexity of the Earth but that meme, to some degree, was planted in my brain.

    • To paraphrase a quote attributed to LBJ: “Washington is like a turd covered with ants, floating down the Potomac — and every ant thinks it’s driving.” … No one is steering this climate turd and any claim to know what is really going on is just statistical hubris.

  5. Climate models fail because there is zero warming from greenhouse gasses.

    Earth’s temperatures are driven solely by changing levels of SO2 aerosols in the atmosphere, of both volcanic and anthropogenic origin, which affects the intensity of the sun’s radiation striking the Earth’s surface, and natural recovery from the Little Ice Age cooling (~ .05 deg. C/decade).

    • Your single controlling knob view of world climate as it is ridiculous as the climate being controlled by CO2.

      • Not =entirely= as ridiculous, SO2 aerosols at least have a demonstrated effect on global temperature.

  6. It indicates how much the average temperature on Earth warms up when the concentration of greenhouse gases in the atmosphere doubles.

    Not “gasses”. CO2 only. They leave out water, the most important greenhouse gas.

    I wonder what would happen if they created a model that left out CO2?

  7. Climate sensitivity may be difficult to determine, but this isn’t — that a modeler will always promote models, whether they are worth anything or not. Because that’s where his salary is coming from….

  8. Clouds are a part of the natural water cycle. They are powerfully intrinsic factors that affect land conditions and sea surface solar insolation. The effects of teensy tiny amounts of atmospheric anthropogenic CO2 are buried in the highly variable and wholly natural variations in clouds.

    Case. Closed.

  9. Any model will be useless when the premise upon which it is based is flawed, and when reliable validation data are non-existent.

  10. “That’s that’s the worst of all possibilities. Because mankind continues to steer into the unknown”

    Bullshit. Homo Sapiens has steered into the unknown since we looked around the East African savannah and wondered what dangers lurked over the horizon. Our adaptability and survival instinct have served us well thus far and there is absolutely no reason to think that they won’t enable us to cope with a slightly warmer world.

  11. The fallacy is in believing that humans can model an extremely complex planetary and astrophysical system of systems at all. We can’t. And probably never will. At least not accurately.

    Also the notion of CO2 sensitivity is fallacious too – it assumes that it is just one thing – CO2 – that somehow controls the planetary thermostat. It completely fails to recognize that earthian climate is an extremely complex system of systems.

    That kind of fallacious thinking is akin to thinking that the only thing that controls an automobile is the volumetric flow rate of gasoline to the engine … and that any two cars with equal gasoline flow rates will necessarily travel at the same speed down the road. Even that is a vast simplification, compared to the massively complex earthian system of systems that results in what we call climate. Yet is is obvious on its face that the flow of gasoline does not solely determine the speed of the vehicle.

    Elsewise a 1974 Chevrolet Caprice would necessarily be a faster vehicle than a 2019 Ferarri 812 because it consumes more gas per mile.

    • Yes, accuracy is the key. Anything can be modeled.

      Models might even be based on a complete physical understanding, mathematical and otherwise and still be unable to predict the near term future because of some random nature of the system. Of course climate modeling is close to an infantile endeavor at this point.

  12. It’s like building a bicycle with square wheels and then wondering why it’s so difficult to ride, blaming the rider’s ability, the terrain, air temperature, air pressure in the tires, and everything but the design itself.

  13. Faster, more powerful supercomputers just let them make failing predictions faster. Until they understand WHY their predictions are failing they will never be able to improve them.


    • And even then it likely won’t matter, as they’re modeling a non-linear chaotic system, and therefore highly likely to be quite sensitive to initial conditions… and when the available input dataset is rife with errors and inprecise, inadequately sampled datapoints, there’s no way we can realistically predict what the temps will be two to four decades from now.

  14. I just wish PhD’s had learned how to write English in grade school particularly to edit translations:
    “For no other factor in the climatic events, this is as true as for the clouds.” An aimless sentence with no subject.

    “This result has not changed until today, about 40 years later.” What has the climate sensitivity change to?

    “The computational power of computers has risen many millions of dollars, but the prediction of global warming is as imprecise as ever.” A user of computers would know that the cost of computing power has decreased something like 10 orders of magnitude over the last century.

    “Simulating natural processes in the computer is always particularly sensitive when small causes produce great effects.” The misunderstood problem is that computers cannot calculate very small, but significant effects. The problem is machine epsilon- the smallest difference between two numbers that can be calculated. Many ways to minimize the effects of machine epsilon have been discovered, but for climate models the bottom line is that many calculations fall below that level and can’t be reliably calculated.

    • This was stated to be a Google translation – in other words, a computer did it. Not PhDs.

      • It is Google. “Now we are sure: she is coming.”

        Most likely, the German was sie, and should have been translated as “it”, not “she”. But idiom is hard.

        Of course PhDs can emit bafflegab in any language. And German has a noted tendency toward that.

        I recall the story of a scholar who came to the US as a refugee from the Nazis. He never again wrote or published in German. When asked why, he said that he had discovered that it is easy to write high flown nonsense in German, but that he found that very difficult to do in English.

        Of course, the contemporary American academy has thousands of “scholars” who can write dreadful nonsense in English. But, it is easy to spot as nonsense.

  15. Ignore the variability in main driver (almost of everything) and readjust the data to suit the large changes in, but insignificant in influence gas, namely the solar activity and carbon dioxide.

    Solar activity picked up a bit in March. The ‘classic’ sunspot count (Wolf SSN) is just under 7 points while the new SIDC reconstructed number is at 9.5
    Composite graph is here 
    SC24 has entered what might be the start of a prolong minimum (possible late start of SC25 too) but even a ‘dead cat bounce’ from these levels appear to be out of question.
    What about SC25?
    If my calculations are any good (the last time gave ‘incredibly’ accurate result 🙂 , see the link above), we have to estimate peak time of the next cycle. Assuming it occurs some time in 2025/26 the SC25 annual smoothed max will be in the low 50s in the old (Wolf) numbers while Dr. Svalgaard predicts much higher peak possibly around 100, or in the new corrected numbers somewhere in 140s.

    • As I said above, with the true believers of climate modeling, it’s “garbage in, _gospel_ out” ^_^

  16. Can Judith (or someone) confirm this : the global climate models that the AGW’ers use are basically the exact same computer models that our weather forecasters use, except they run for much longer on much bigger computers. And while the weather forecast for 24-48 hours is extremely accurate, once you get out to the weekly forecast, it’s no batter than 30% accurate (approx). If this is the case – then how can these people claim any accuracy in their 100 year models ?

    • I wouldn’t think so, daily and weekly forecasts do not take into account CO2 rise or fall over such short periods, while in the climate models it is the main ingredient.

    • “Now we are sure she is coming” in one paragraph and many other paragraphs about how unsure the models are. Sounds like someone I know who made a horse racing program to predict the winner of a given race…..

    • No they aren’t the same, most weather forecast models are based on expected similarities to previous weather patterns, similar to a police program that searches for fingerprints in the database looking for matches to today’s crime scene. Climate predictions are based on equations of physics, with fudge factor multipliers (paramaterization) to dampen out pesky variations that would cause either snowball Earth or hothouse Earth. One would expect the weather forecast to have a higher possibility of being correct because the likely extremum are known already from recent past averages.

    • The modelers will tell you that climate models are not like weather forecasting models. Climate models are modeling physical processes regardless of the initial conditions, while forecasting models are fundamentally dependant on initial conditions. Time is a vital component of weather models and an extremely important variable in the calculation of all the other variables for any given location. In climate models, time only impacts the CO2 variable, and is not a direct factor for the other variables. In other words, it is the change in CO2 that determines the other variables and the outcome, not the change in time.

      While this is true, it is largely irrelevant. Climate models still using calculus to derive the other variables, and calculus is inherently flawed for use with non-linear, chaotic systems. As I said below… computer models are just not the right tool for climate prediction, although they might be better if we understood the science of climate change first, but we don’t!

      So yes, the models are different. The climate models are worse than the weather models, which have proven their usefulness on a daily basis. The climate models have no observable skill, and are only useful as propaganda. Indeed, that is the only reason they are funded by politicians. The models must predict a climate crisis, or they will cease to be funded. They will have no use.

    • Can Judith (or someone) confirm this : the global climate models that the AGW’ers use are basically the exact same computer models that our weather forecasters use, except they run for much longer on much bigger computers.

      For the UK they have a unified model that does both climate and weather. Don’t know if that is what they are using for the IPCC.

  17. When people talk about “forecasting the climate” they generally mean forecasting the average temperature. But there are other dimensions to the climate than just the average temperature. Why should anyone expect that you can forecast only one dimension without simultaneously forecasting all the other important dimensions?

  18. Garbage in, garbage out. This is all that can and should be said about numerical “climate models.” Having more powerful supercomputers won’t help if the fundamental physics, chemistry, and biology in the models is junk. The parametrizations used are just ugly kludges[1], in plain language: cheating.

    Here is what physics tells us: the in-depth analysis of vibration-rotation radiative transitions in atmospheric CO2 molecules yields “climate sensitivity” of 0.4K/doubling only at the present level of concentration[2]. As the concentration rises, the sensitivity drops, due to saturation. All the warming due to the anthropogenic CO2 injection into the atmosphere since 1880 has been a mere 0.02K–puny and practically undetectable against the natural centennial global temperature variability which is (0.98+/-0.27)K/century[3].

    Amongst countless factors that the modellers ignore, often intentionally, but most commonly because they just don’t know enough themselves–for most, they’re second-rate physicists, chemists, biologists–is this interesting fact: boreal pine forests exude organic compounds into the atmosphere that help clouds form[4,5]. Which climate model takes this important driver into account? Which climate model takes Svensmark’s Effect[6] into account?

    To make things worse, many of the kludges they use to mask their ignorance introduce unphysical effects into the solution, for example, the models end up violating the second principle of thermodynamics[7].

    The simple truth about the numerical “climate models” is that they are no better than Mickey Mouse cartoons, and that their only function is to be used for warm-monger propaganda. They are tools of deception.

    [1] https://doi.org/10.1175/BAMS-D-15-00135.1
    [2] https://doi.org/10.1088/1361-6463/aabac6
    [3] https://doi.org/10.1260/0958-305X.26.3.417
    [4] https://doi.org/10.1038/nature13032
    [5] https://doi.org/10.1038/NGEO1800
    [6] https://doi.org/10.1038/s41467-017-02082-2
    [7] https://doi.org/10.1002/qj.2404

  19. There is a wise expression about getting things done: “Any job is easy if you have the right tool!”

    It is very clear that numerical prediction models are simply not the right tool to predict climate change. Using them to predict climate change is like trying to use a saw to drive in a nail. No amount of fiddling with the saw will turn it into a good hammer.

    Since the advent of the computer, we have been mesmerized by the versatility of the device; marveling at all the many ways the computer has enriched our lives. It has certainly been a wonderful tool for short-range weather forecasting, leading to the belief that it should also be useful for climate forecasting. But the equations for short-term atmospheric weather phenomena existed before the computer came on the scene. In other words, the science was previously understood and was later used to build the computer model. There are no equations for climate forecasting. Those equations do not exist. So the models are not built on understood science, but on unsupported assumptions. Until the science is developed, and the equations that represent that science are created, the computer models of climate will be worthless. They are simply the wrong tool for the job.

    For now, pattern recognition is a far better tool. (What has happened in the past will tend to (generally) happen again in the future.) Pattern recognition, however, does not support a pending climate crisis. On the contrary, pattern recognition indicates that there is not enough CO2 in the air to support a truly healthy Earth biosphere. Pattern recognition reveals that the burning of fossil fuels and the restoration of CO2 to the atmosphere is one of the greatest gifts modern man could possibly leave to future generations of all species!

    While the reality of increasing CO2 is a phenomenon that deserves an annual holiday of global celebration, it is not something that can be used to frighten people out of their money and freedom, or manipulate and control the population. Therefore, the reality of the science, the futility of the climate models and history of the Earth are all being hidden from the general population.

  20. Last century the lunatic fringe often predicted that the “end is nigh”!
    Sensible people ignored them.
    This century the lunatics have finally taken over the asylum.
    They are now represented in mainstream science and some have been elected to high office.

    “Nos morituri te salutant!”

  21. “Now we are sure she is coming” in one paragraph and many other paragraphs about how unsure the models are. Sounds like someone I know who made a horse racing program to predict the winner of a given race…..

    • “Now we are sure she is coming”
      I was kinda wondering where the Metoo movement are on this. Like tropical cyclones nowadays don’t they take turn and turn about with the naming of these definite doomsday tipping points? Whatever happened to Zena and Zac I ask?

  22. Google “IPCC non linear system”. One of the results will be this link :

    IPCC – Intergovernmental Panel on Climate Change
    “Given the complexity of the climate system and the inherent multi-decadal … The climate system is a coupled non-linear chaotic system, and therefore the …”

    or this link :

    The Scientific Basis – IPCC
    In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction …

    The well known end of the above sentences is : “… (long-term prediction) of future climate states is not possible.”

    Click on the links above and the result is :

    Welcome to the IPCC, the Monty Pythonian and tragicomical farce of a total scientific failure.

  23. Climate worriers (often with Dr. before their name) seem to have three postulates.
    (1) Crisis carbon dioxide papers must be correct because they attract a lot of press and citations.
    (2) The IPCC must be correct because they got a Nobel Prize.
    (3) The necessary message cannot get out because of (conspiracy mass of) fossil fuel industry misinformation.

    From https://www.pnas.org/content/115/33/8252 (Trajectories of the Earth System in the Anthropocene) “The Stabilized Earth trajectory requires deliberate management of humanity’s relationship with the rest of the Earth System if the world is to avoid crossing a planetary threshold…….Our initial analysis here needs to be underpinned by more in-depth, quantitative Earth System analysis and modeling studies to address three critical questions.” The first one kills the paper–“(i) Is humanity at risk for pushing the system across a planetary threshold and irreversibly down a Hothouse Earth pathway?” They already concluded that. “The impacts of a Hothouse Earth pathway on human societies would likely be massive, sometimes abrupt, and undoubtedly disruptive.”

    I thought they had the answers. You don’t have to understand zilch about climate science to wonder how papers like this get published in supposed (pick your superlative) science journals. 16 authors makes it a committee, approved in a little over a month, so urgent, I suppose. As soon as I read the 88 citations, I will get back to you. So get scratched as a reviewer. Earth survival not that important.

  24. “Why climate predictions are so difficult”

    I’m surprised nobody has this yet. As usual, Yogi Berra said it first:

    “It’s tough to make predictions, especially about the future.”

    • It’s even worse than that :

      How can models which are unable to reconstruct basic past climate fluctuations, which are fed with garbage data, have any predicting ability about the future ?

      It would be truely amazing if they could.

      • Well, we live in a world where the MSM can get so much wrong, be infested with so many experts who can, like economists, correctly predict 11 of the last 3 recessions, and still people will spoon up the soma.

        BTW, that’s a reference to “Brave New World”, a novel I maintain is far more accurate than “1984”…

        Oh, and Uri Geller is back in the news, so not only can frauds find work, they can maintain their fame for literally decades.

  25. I don’t know – a complex system with hundreds of variables of which we don’t even know what they all are? Combined with wide-spread bias – and no little amount of intentional manipulation?

  26. In my experience with models, I’ve observed that when you have as few as 3 variables none of which are independent of each other (i.e. feedbacks exist), prediction of outcomes is nearly impossible unless 1 of the 3 variables utterly dominates the other 2 variables in amplitude or the period of prediction is very short (relative to the inverse of the oscillation frequency of the lowest frequency variable).

    And this is for “ideal” variable behaviors with no uncertainty (no errors). Add even very low levels of uncertainty to the behaviors and the outcomes of “runs” becomes wildly inconsistent and unpredictable.

    Cloud behavior is utter chaos compared to any “controlled 3 variable model”. And the basic science of the drivers of cloud formation and cloud behavior is not well understood yet. You cannot model what you don’t know about…though modeling exercises can certainly be used to help you learn (gain knowledge).

    Then, even if clouds should ever be “tamed” within simulations, the entire climate is way more complex! We’ll need about 10 orders of magnitude more data (to characterize geographical regions and various altitudes) and probably dozens of orders of magnitude more computing power.

    It’s really fun running models and working with them. Coupling them to sophisticated graphics generators can produce fascinating visual representations.

    unning climate modeling projects has value in assisting us in gaining knowledge in lots of ways but it is disturbing that climate models are being used as political tools for asserting knowledge and certainty that absolutely isn’t there.

    I scoffed out loud when first I learned that modelers were making confident predictions about climate using models.

    I hope I’m wrong about this someday…but I’m pretty certain someday won’t be any day soon. I’m thinking maybe 20 to 30 years. At least 15 years will be needed to test the models.

  27. RE cumulonimbus clouds:
    “If their number or their distribution changes, this can contribute to the displacement of large weather systems or entire climatic zones.”
    …um, I would have thought that cumulonimbus clouds were the ‘weather system’ or at least a significant manifestation of it. They don’t cause it they are an effect of it.

  28. “The accuracy of the predictions has not improved, but our confidence in them has grown,” … Isn’t that the opposite of science? Oberservations, collect facts, organize and draws conclusion based on what you found regardless of initial hypothesis .. well, at least what I was taught in HS.

    • Yes. Climate Change science is not as robust as a poorly done, grade school science fair project. At least the kids try to follow the scientific method. Current climate change science is an assault on the scientific method.

  29. Two possibilities. One, incompletely, or insufficiently, characterized and unwieldy. Two, incorrectly characterized, which leads to not only wrong but divergent conclusions. Either way it’s chaos. The models are hypotheses that have shown no skill to forecast or hindcast without significant heuristic overrides.

  30. CAGW advovates’ get-out-of-jail-free card will be their feigned ignorance of clouds’ effects on global climate.

    When CAGW finally crashes and burns, CAGW advocates will simply blame their misunderstanding of clouds to explain why their stupid climate models were so devoid from reality and why they wasted so many $trillions for no reason whatsoever.

    I don’t know how many people will buy this BS excuse, but the CAGW advocates will never admit to orchestrating one of the biggest and most expensive hoaxes in human history…

    I’ve looked at clouds from both sides now,
    From up and down and still somehow,
    It’s clouds’ illusions I recall,
    I really don’t know clouds at all..

  31. “That’s the worst of all possibilities. Because then mankind continues to steer into the unknown.”

    Well you could go to church on Sundays like some do and show a bit of humility.

  32. Of course it’s the clouds. And clouds have almost nothing to do with CO2. Must be the sun spots and cosmic rays.

  33. Some comments by one of Gavin Schmidt’s former grad students. Real Climatologists

    When I (Duane Thresher) was at NASA GISS I pointed this out to Dr. Gavin Schmidt, current head of NASA GISS (anointed by former head Dr. James Hansen, the father of global warming) and leading climate change spokesperson. His response was, “We just have to hope they are on the same attractor”, literally using the word “hope”. They are almost certainly not so a climate model can’t predict nature’s climate.

    Similarly, some climate modelers study whether climate systems have multiple equilibria — different possible steady-states. If there are multiple equilibria then you can’t predict which equilibrium will occur and thus you can’t predict climate.

    There’s a third issue that I have: boundary conditions. For example, ENSO is due to the Pacific trade winds blowing the warm equatorial waters to pile up in the archipelagos of East Asia. When they stop, the water flows east and warms the atmosphere. It’s a big effect that seems to happen with a rough period of about five years and has nothing to do with CO2, it’s been going on for 100s of years. This doesn’t happen in the Atlantic because there’s no place on the coast of South America where the warm water driven by the trade winds can pile up. No Atlantic ENSO because of this. The only real difference here is geography. There must be a bunch of other such effects, though not so obvious, that are also due to geography and strongly affect the temperature of the atmosphere.

  34. Global warming forecasts are still surprisingly inaccurate. Supercomputers and artificial intelligence should help
    There are some problems that appear relatively innocuous, which cannot be solved by a super computer, now or ever.
    Example – calculate the energy levels of the molecule FeS .
    Climate Science has at least 2 other problems.
    1) Oversimplified models.
    2) Insufficient data. (Including historical data).
    Even if these 2 difficulties are solved, we will still have computational problems that may be intractable on a classical computer, even a super computer.

    • Actually, the only thing AI could possibly bring to this discussion, (beyond a Terminator scenario…) would be knowing what it doesn’t know, then knowing how – if it could – find out what it doesn’t know.

      I won’t hold my breath.

  35. Lack of accuracy in climate change forecasts makes it clear we do not understand the climate mechanisms as well as we think we do.

    On a side note… I think the 10 day weather forecast in my area is being produce by a man who doesn’t want his wife planning his weekends. Every weekend for the past three weeks he predicted significant rain, and we got but a drop.

  36. “Global warming forecasts are still surprisingly inaccurate. Supercomputers and artificial intelligence should help. By Johann Grolle” No never, the equalizer is time, no matter how smart you are or your computer program is cannot defeat time, by the time you consider and calculate all the variables in climate you might have and accurate prediction the only problem is by the time you get done with the computer run no mater how powerful, fast or smart your computer is, you will complete the predication millions of years after it happen.

    • I am not sure why people believe that computer models know something that humans don’t. The model is a mathematical representation of human understanding. It doesn’t know anything.

      Computer models are useful tools for many reasons, including the testing of a hypothesis and identifying our ignorance quicker, but that only works if the humans are willing to admit that they are ignorant.

      Indeed, the climate models have been very good at showing us that the hypothesis of a CO2 driven climate is wrong, but they are not coming up with a better hypothesis on their own. The model only does what it is told to do. It knows nothing!

  37. ‘Because then mankind continues to steer into the unknown’

    Seems to me that whether the average temperature of the Earth is 287K or 289K is not really a matter of much consequence unless one is foolish enough to live within a foot of High Water.

    If you do, my advice is to move.

    Climate change or not, its a dumb place to build a house.

    And yes, I’m looking at you Miami and New Orleans.

    • Indeed.

      Here in Toronto, one of our local radio alarmists loved the idea of a local councillor who wants to sue the oil companies because of CAWG. Worrier-dude went on about “50 year floods becoming 10 year floods” on the Don River, etc.

      I don’t call in to these shows, but if I did, I’d remind him that the oil companies would hire experts and ask the city:

      1) why are you building anything on a flood plane; and,
      2) why does the Don River take a 90 degree turn when it hits Lake Ontario, and do you think that MIGHT be an issue?

      Don River
      Toronto, ON
      43.650672, -79.347188

      • My life, health, well being, freedom, enjoyment of life and so on, are completely dependant on fossil fuels. Can I sue the people who are sueing the oil companies for doing me irreparable harm?

        I think I have a much stronger case than they do.

        • That’s about it.

          To paraphrase the late, great Julian Simon, it must be great to be able to have solved all the world’s problems so that we can argue about who can use the women’s washroom…

          Seriously, every “green” person I know lives in a modern city where they would be living a “Walking Dead” episode if the power was out for more than Earth Hour. I mean, look at the panic over the loss of avocado toast if Trump shuts the Mexican border…

          The modern world owes its leisure to conspicuous consumption. At least some of us are able to admit it without shame.

  38. Predictions are particularly difficult if the hypothesis generating the predictions is predicted to be proved wrong.

  39. I tend to try to reduce things down to simplistic terms. Many multiple star systems are ternary; one star orbiting around a binary. We can determine the masses and distances, and have a firm grasp of gravitational forces. Nothing is unknown, per se, but try determining the orbit of a planet in that system. It is affected by only three, moving gravitational forces, yet the problem of plotting its orbit becomes intrinsically difficult. Predict where that planet will be in 40 years? Good luck.

    The climate has more than three variables, we are uncertain of the magnitude of each force, do not know the feed-back from each or how the variables respond to each other, and aren’t even sure we know all the potential variables and forcings. Predict where the climate will be in 40 years? Good luck.

  40. So we know, weather and climate systems are chaotic and not linear, therefore they are impossible to predict more than few days ahead. To predict or make projections of global climate many decades ahead is utterly nonsense…

    • Truly accurate weather forecasting (not just temperature) is only somewhat accurately predictable a few hours in advance. As an example, two weeks ago in North Texas it was predicted on Friday that there was an 80% of significant rain on Saturday. The vast majority of North Texas received almost zero rain.

      When N. Texas received over 12 inches of snow in 2010, the forecast that morning was for a light accumulation of 1 to 2 inches.

      Even with current technology we cannot ACCURATELY predict weather more than 24 hours in advance.

  41. “But it is also conceivable that there are basically unpredictable climatic phenomena,” says Stevens.

    I have some experience with complex financial models involving many variables. It only takes one bad assumption about a variable: for example, GDP growth, tax rates, interest rates, market share, or how quickly inventory turns to blow up the financial forecast.

    With climate, you not only have to be confident you KNOW all of the variables, but that you know how all the variables will behave, correlate and change under various states of nature in what is a chaotic system. I’m not at all confident all the relevant variables have been identified, let alone fullu understand how they all behave and interact with each other.

    It has always struck me as arrogance and hubris on steroids.

  42. I was looking for fun on an AR5 ‘state of the art’ climate model.

    A comment from there “Occasionally (every 15-20 model years), the model will produce very fast velocities in the lower stratosphere near the pole (levels 7 or 8 for the standard layering). This will produce a number of warnings from the advection (such as limitq warning: abs(a)>1) and then finally a crash (limitq error: new sn < 0")". Translation: the 'state of the art' climate model is pure crap.

    In the code, I found 'Celsius' spelled wrong, systematically. They spell it 'Celcius'. The code is plagued with conversions back and forth between Kelvin and Celsius, instead of working with Kelvin only. They describe very complex processes on Earth in a very simplistic manner, the chance of correctly simulating reality like that is practically zero.

    Not the first and not the last attempt to look into climate models (one example, here at the end: https://compphys.go.ro/chaos/ ). It's always fun to look into them, it's actually much worse than one exposed to computer models, numerical methods and so on, can imagine. It's way much worse than thought 🙂

  43. I read about the difficulties of using numerical analysis and super computers and wonder, is there a better way? I may be one of the last students who had to learn how to use an analog computer to solve difficult problems requiring integration and differentiation. They do take a lot of planning but they do work to model real world analog systems.

    Here is a link to a short article about designing one. https://www.clear.rice.edu/elec301/Projects99/anlgcomp/

    The thing about analog computers is that they require one to characterize the signals with a mathematical equation. Wouldn’t that be nice to see. Anything that is “fudged” would be apparent, but it would also be a flashing signal to identify what the modelers don’t know. Instead of playing games with data and coding, scientists would be forced to put their ideas and hypothesis down on paper in the form of mathematic equations. Just imagine having to define in equation form the relationships between radiation, temperature, humidity, convection currents, clouds, atmospheric compositions, and all the characteristics affecting our earthly climate. Just imagine, there would be one input, radiation, from which all things would flow until temperature (or more accurately, heat) could be seen.

    When you think about the millions (or billions) being spent on supercomputers, I wonder what kind of analog computer could be made that would quickly, as the article says, let you see what the output would be. As new equations were formulated for different items, aerosols perhaps, we would be getting closer and closer to an accurate science based concept of climate.

    Would it be so terrible to have “scientists” actually do science rather than game programming?

  44. “But it is also conceivable that there are basically unpredictable climatic phenomena,” says Stevens
    It is well known outside of climate science that you cannot reliably predict the future of a complex time series.

    And the reason gas nothing to do with climate science. No matter how well you understand climate science.

    The problem is that we have no practical mathematical solution to the problem.

    Computationally the problem sizes blows up to overwhelm any computer no matter how fast. Doubling the speed of the computer does not double your forecast horizon. Rather it is like CO2. The more you add, the less effective it becomes.

    The problem for climate science is that they have gone down a computational dead end.

  45. The GCM (general circulation models) climate models have more than 100 parameters that requiring ‘tuning’. The GCMs do not agree with reality and do not agree with each other.

    As noted, the GCM cannot be falsified due to political reasons.

    We need to have some cooling to change the paradigm.


    The standard model of physics, for example, is subject to falsification. If it fails to make correct predictions in controlled experiments, it is false. Projections are not good enough there. Even in astrophysics, models explain phenomena that are normally subject to falsification through broad questions asked about multiple occurrences of similar physical circumstances, even in highly data-starved contexts. What makes climate models fundamentally different is that they are presented as being unfalsifiable. Even when they deviate from actual observations, they are not superseded by a better competing model. Deviations simply invite some retuning. Moreover instead of replacement by better models retuning leads to all models becoming more alike.”

    Climate Models Don’t Agree With Reality
    Problematically, even when they are re-tuned, climate models still yield widely divergent outputs both from one another and compared to observational evidence.
    Many new scientific papers have been published in recent months that document the failure of climate models to simulate the Earth’s climate. A sampling of 10 peer-reviewed papers from 2018 are highlighted below.
    In several cases, scientists have reported that none of the modern-day climate model results are consistent with real-world observations. In some cases the models yield opposite results (i.e., warming instead of cooling, rising instead of falling, etc.).
    It is increasingly being recognized that climate models “not only don’t agree with each other when it comes to dynamics, they also don’t agree with reality” (Essex and Tsonis, 2018).

    Unfalsifiable Models

    ….The refusal to discard climate models that conflict with observations is apparently rooted in politics. Kundzewicz et al. (2018) point out that the “hard” science standard that says results should be quantitatively validated with a measured degree of certainty before formulating policy initiatives is deemed “unrealistic and counterproductive” today. That’s why climate modeling thrives in the modern “soft” political world – a realm where the rigors of observation and falsification — the scientific method — need not apply.

  46. “But cumulonimbus clouds are also an important climatic factor.”

    So, the “scientists” are starting to catch up with Willis “it’s the intertropical convergence zone” Eschenbach.

    I guess it was bound to happen sometime.

  47. It is only predictions of weather and climate in the future, that are difficult.
    Stop trying to do that, and the problem evaporates.
    It is the future, and has not happened yet!

  48. Oh no! Please do some decent translation!

    “The computational power of computers has risen many millions of dollars,”

    “Now we are sure: she is coming.”

  49. No doubt some good science here, but this made me stop reading:

    “It’s a simple number, but it will determine the fate of this planet. ”

    So, he climate sensibility will decide the “fate” of the planet ?What does that mean
    ? Will it self-destruct at +4,5?

  50. Climate predictions are not difficult they are impossible and all climate models are worthless shit. Climate is not global, it is regional, and vast regions of the world have no data at all. Keep in mind the issue is not global temperature. The issue is global mean science. Can we pick on thing from the planet to represent everything else of that type? For example, is the rock in my front yard I picked up the global mean rock to define the geology of the world? Are my opinions global mean opinions so if you want to know anything, just ask me? Can we put all science of the world, one global mean thing for everything in a room and just study that? Do we have a global mean mammal? I think my dog fits that, and for a small fee you can pet him and make observations as he represents all mammals of the world. So why don’t we have this type of global mean science? Would it not make our science more efficient? Well, the problem with all global mean things is that we end up making a construct that is not real or that is so generalized it does not have the ability to answer any interesting questions. So until such time as the UN recognizes my opinions as global mean opinions where the opinions of all other humans on earth is recognized as noise, then I am not into global mean climate things like temperature.

  51. “Global warming forecasts are still surprisingly inaccurate. Supercomputers and artificial intelligence should help. By Johann Grolle”

    Read: Global warming forecasts that ever end up being competent would be so amazing and so startling, we should declare a national holiday to recognize its existence when it occurs. Global mean things are not real things and as such we cannot forecast them. We cannot even forecast real things.

  52. “…Nevertheless, he does not want people to think that the latest decades of climate research have been in vain…”

    He can WANT that but seems pretty sullen about the reality of it.

  53. I think part of the problem is that they are trying to model and make predictions (or projections) for the entire globe, which consists of all possible climates and all possible variables. Why not start by trying to model the climate of a small area with fewer variables like the city of Los Angeles, with it’s limited historic temperature range and limited weather possibilities. If you can successfully model the future of a small area, then you can model more areas, and maybe, eventually model the whole planet. I don’t think one mathematical formula will work for every place on Earth. Right now, nobody can tell me if Los Angeles is going to get wetter/drier/windier/calmer/colder/hotter/or anything that would actually be useful to know. And after all, if you can’t predict little things in the near future, I certainly don’t think you can predict huge things in the far distant future. Climate is regional, not global.

  54. IIt should be apparent to anyone paying attention that the existing GCMs (except possibly for the Russian one) are hopelessly faulty as demonstrated by their failure to predict average global temperature.

    Some things I know:
    (1) Assuming that CO2 is the ‘control knob’ on climate is wrong.
    (2) Multiple compelling evidence from paleo to present has demonstrated that CO2, in spite of being a ghg, has little, if any, effect on climate.
    (3) There are many factors contributing to climate but nearly all of them are insignificant and can be ignored.
    (4) Considering only three factors; an approximation of the net effect of ocean cycles, the time integral of SSN anomalies, and TPW (which is the absolute water vapor content of the atmosphere) has resulted in a 98+% match to measured average global temperature 1895 thru 2018.
    (5) Natural turbulence/roiling of the ocean and atmosphere produces random fluctuation of reported temperatures and this needs to be smoothed out.
    (6) The ‘notches’ in TOA and intermediate graphs of radiation flux vs wavenumber demonstrate that much (about 40%) of the radiation energy absorbed by ghg other than water vapor is made available to WV molecules by thermalization and emitted to space by WV.

    Some things I suspect:
    (1) Water vapor in the atmosphere is calculated somehow using the Clausius-/Clapeyron equation. (CC only applies at saturation.)
    (2) Indirect solar influence on average global temperature as quantified by SSN is ignored.
    (3) Cloud effects are simulated using human input parameters.
    (4) The models assume that CO2 drives temperature.

    What do the Russians do differently which results in a good match with measured?

  55. I am not sure why people believe that computer models know something that humans don’t. The model is a mathematical representation of human understanding. It doesn’t know anything.

    Computer models are useful tools for many reasons, including the testing of a hypothesis and identifying our ignorance quicker, but that only works if the humans are willing to admit that they are ignorant.

    Indeed, the climate models have been very good at showing us that the hypothesis of a CO2 driven climate is wrong, but they are not coming up with a better hypothesis on their own. The model only does what it is told to do. It knows nothing!

  56. AI: Since AI unites are starting to “think” for themselves one has to wonder what they would say about CO2 induces global warming?

  57. “It is very hard to predict, especially the future.” – Niels Bohr

    “The future, like everything else, is no longer quite what it used to be.” – Paul Valery

  58. “If the fractional coverage of low-level clouds fell by only four percentage points, it would suddenly be two degrees warmer worldwide.”


    After “more than 20 years [ you have ] been researching in the field of climate modeling”

    can you report a day / hour / minute / second / when “suddenly [ it was ] two degrees warmer WORLDWIDE?”

    And report the catastrophic event that heated the atmosphere 2 degrees warmer WORLDWIDE without loosing energy at TOA.

    During that ongoing forcing of energy into the atmosphere WORLDWIDE.

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