Reposted from Dr. Judith Curry’s Climate Etc.
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.
To be fair, it’s not the models that overstate warming, it’s the warmists who push results by design-and-assumptions. It’s part of the statists’ business model.
‘Statists’? Who are these imaginary bad guys?
They are the people motivated by the results the models are predicting. ANYONE who produces a computer model should at very least provide the confidence intervals on which the results are provided. If they don’t then you should immediately smell a rat. That’s just good science.
I don’t recall ever seeing confidence intervals associated with any climate model. The reason being they’d be so poor that the results would be pretty much worthless. This is a statement of fact for any model given the number of variables and timescales over which climate models are run.
How these people have been able to get away with this is unbelievable. Yet I still don’t see any statisticians calling them out, which in itself is an appalling reflection on the climate “science” community. The only explanation is that these people have an agenda over and above proper science. At best this is that they have to pay the mortgage but at worst it suggests political interests.
Griff, the statists, I call them globalists, are the group of business people, bankers, lobbyists and rent seekers who found they could make lots of money by shipping American jobs to low wage countries. Especially China. There have been benefits to this but not all good. Many American men who once could find productive work in small fab shops and assembly plants were priced out of the job market by Chinese workers. If you were not aware of this then you need to open your eyes.
The first thing to ask is who is reaping the most profit from globalization and the New Green Deal. I assure you, it isn’t the general workers.
On the other hand, consumers are doing great.
The “consumers” won’t be doing great when they’re freezing to death and starving to death in the dark, which is where “climate policies” will lead them to be.
They also won’t be doing great when they can’t afford the “consumption” because they no longer have gainful employment and the government has raided their retirement plans and other savings via money printing.
The wealthy and politically powerful, on the other hand, with have consolidated more wealth and political power.
1) They are not American jobs. They are just jobs. Nobody is entitled to be employed.
2) I love it when people who declare there opposition to government intervention in the economy, turn around and demand government intervention in the economy. I guess all depends on who’s ox is getting gored.
3) If you want companies to stop looking for the least expensive way to make their products, the first thing you are going to have to do is find a way to get consumers to stop buying whatever is the cheapest.
MarkW: You note soild economic thinking, but there is more.
China’s economic success over us depends on a few things, but two specifically.
First is super-low wages. Second is weak environmental protections.
We can argue for “protectionist” policies based not just on the preference for protecting job markets while suffering the higher prices that involves by arguing for humane worker conditions and for responsible environmental protections.
This is palatable to both Republicans and what would count as “Democrats” 30 years ago (now, they are eagerly awaiting the fall of America to China, since America has so many faults and is so oppressive).
Instead of our typical international business / international politics of trade policies, with tariffs and “most favored nation” deals and such, we could build tariff levels based on the degree that foreign producers hold reasonable employment conditions, and protect the environment.
[By “protect the environment,” I don’t mean the “snail darter” type or “Man-made Gobal Warming” tyranny. I mean the type of stuff that allows us to swim in lakes, rivers, and beaches, and that cleaned up Pittsburgh’s air, and cleaned up the Ohio River so it no longer catches on fire.]
We in the U.S. set employment standards, and corresponding tariff levels. The initial default level is high tariff. This percent is lower if a manufacturing firm demonstrates, by audit, that they meet certain graded levels of employment standards and environmental protection standards.
If a Chinese firm allows regular bathroom breaks etc., they qualify for a lower level of tariff.
If a Chinese firm does not pollute the river, they qualify for a lower level of tariff. Lead exposure, etc.
All along the lines if what we have here.
This allows a Chinese firm to both remain in the position they are in now – being able to pollute and to work employees in the rough way they are – if they choose, but also to devote more money to meeting the more ambitious stages of tariff level, and reducing their tariff level.
This takes away the economic advantage China has over USA. Not by “protectionism,” but by promoting decent standards for labor, and good environmental protections.
In the long run, American production would have the advantage since an American-produced item does not have to be transported across the Pacific.
“Certification” programs are wide-spread in our economy. Arguably, they build quality and add value. Same same. We Americans form these tariff levels, and we audit the firms hoping to be so certified. So, we make money from that, as well.
Oh come on, “Statists” are people who support the aims, policies and apparatus of a State, and nothing more or less.
They are the people reaping profits from subsidies and a future increase in the number of renewable energy and EV’s. In essence we are seeing the creation of a Fascist state (not Communist) where businesses and the government collaborate to benefit each other.
They are people like you griff, who believe that the state should run everything and make all decisions.
I only wish they were imaginary.
Everyone who benefits financially from the product of climate models. There is no doubt that using models alone, which rely solely on manipulated raw data, have no scientific value. They predict nothing, confirm nothing, nor do they provide any useful information … considering they rely on, at minimum, a 400% spread.
I think there is an unfortunate side effect to how this research is likely going to be perceived. Should it prove that the bias actually exists, the one thing the authors say is that they actually do not know why. Which means we do not actually know what is causing the error.
So, although there would be no basis to say one way or the other whether this confirms or denies climate change, the way this is presents seems like they are concluding that climate change is a myth-which is actually not what they said, but it is implied.
However, regardless of that issue, it seems logical this could be used to springboard further research relevant to weather prediction in general. The type of measurements they do seem to be the sort of thing that would drive hurricane predictions, speed and strength. Tornado predictions. Stuff like that.
I’d imagine that the reason there is a warming bias error is because the climate system is poorly understood and consequence they cannot model it.
Climate change is not denied, the causes are not understood nearly as well as the catastrophists would like us to believe, and their models run hot because they don’t have a developed understanding of the climate system.
To say that it is mostly or largely due to ‘human created’ CO2 emissions is hubris on their part.
There is a “warming bias error” because the models are designed to show precisely that.
“Which means we do not actually know what is causing the error.”
As we do not know a plethora of other things.
And we will never know if we cancel all scientific attempts to investigate the thing.
That is why “science is settled”…
Follow the money! Do you honestly think these modelers and so-called climate scientists are going to say, “Oops, we were wrong so we’ll give up future grants to study climate”?
Good luck predicting the location and time of formation, intensity, and direction of a tornado 24 hours in advance. The best we can do is give a rather vague chance of a tornado being formed somewhere along a storm front sometime during the predicted storm (that also might or might not occur as predicted).
Now you settle for a 15 to 30 minute warning and we can get pretty good – still a lot of random error but not useless information. This points out the biggest problem with weather prediction – it’s so chaotic that over enough time the current information is just useless.
Climate change is the same way…we might get good at predicting overall “climate” a year out, and maybe even 2 or 3 years out – but 40 years? Forget it, it’s meaningless. Too much chaos is introduced – that is assuming we had any valid understanding of climate behavior in the first place.
Global climate change is measured in sediment deposition patterns or sequences of deposition. Talking about global climate change in time-slices any shorter than about 250 years is quite delusional.
It may be the modern meme when garbage must be written by the always lying media, to sell copy, and scare the chickens, but it’s pure bunk.
There’s plenty of “basis” to say this denies “climate change” as the Eco-Fascists mean it. It’s called no empirical evidence supports the notion that atmospheric CO2 drives temperature, while a good deal of empirical evidence says it does not.
The basic assumption built into the “models” is “CO2 drives temperature,” and this is not empirically shown in the Earth’s climate history. When the implicit assumption of the “models” is wrong, their output is garbage.
Your “concern” about the “perception” of this research is laughable – this research SHOULD lead people to realize that “climate change” as they postulate it IS a “myth.”
As Ross McKitrick pointed out recently, the attribution of warming to CO2 in models is based on bad and incomplete statistics.
Agree, that is what it implies, a vital physical process is missing from the models.
The fact is AGW (“climate change science” has little value, today or into the future. Anyone who has spent more than a few weeks researching the AGW fraud understands how preposterous the IPCC position is. It has grown increasingly unscientific since its inception in 1988.
Running hot is a feature, not a bug. In fact, it is precisely what makes them “fit for use”.
The IPCC is getting exactly what it is paying for.
Gordon’s first rule for any superfluous bureaucracy: Establish an alarming need for its continued existence.
“…what it is payed for”.
???
The IPCC pays to get some 30+ supercomputer models run every five or so years (leading to CMIP-x predictions) to have a “scientific basis” for their alarmist, obviously-far-too-hot predictions of future global warming.
The IPCC “scientists” intentionally
front tomislead the world into believing supercomputer model outputs are equivalent to unbiased data, where in fact nothing could be further from the truth.But “better models” (improved predictions) are always just around the corner, hence the UN must continue funding the IPCC and its noble mission. (Hah!)
Thanks, Joao, for this opportunity to expand upon my previous post . . . and I believe its original sentence structure stands correct.
Thank you for the explanation. My interpretation was wrong, thank you for kindly having come back to my comment.
As for the Canadian Model, only a 700% exaggeration in warming? The Canadians must have lacked ambition!
For the Canadians it’s likely based on hope.
If you live on the Canada/USA border, who do you believe?
We Canadians are too polite to tell our climate scientists that their parents wasted their money on a science education.
Lorenz pointed out that the climate is a chaotic system. In other words, it is exquisitely sensitive to initial conditions.
You could justify many models and runs in an attempt to find possible attractors. That’s not what they seem to be doing though.
It seems to me that the modeling community completely ignores Lorenz without once justifying why they’re ignoring him. It’s like if they ignore the problem it will go away.
The IPCC did say in the third assessment report that “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible”. They get around this problem by calling their model outputs “projections” instead of predictions, so they didn’t ignore the problem, they just changed names in an attempt to hide it.
… so that they could ignore the problem. (fixed that 😉 )
“The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible”.
Even from a sea of lies and propaganda, an occasional kernel of truth emerges.
It all boils down to how you present the data.
Create the data ?
Boils down to…
So… Cooking the books, then?
Yup that is the IPPC that I know and love.
Fixed that for you.
“…the economic and policy making communities are justified in assuming future warming projections are overstated…”
But that is what they want, what they need to justify their economy and standard of living destroying policies that provide guaranteed profits for crony capitalist investing in “renewable energy” and other “climate mitigation strategies” that would without government subsidies be guaranteed losers.
The models are deliberately designed to run hotter than the weather records, as that is the propaganda that the Warmists want. They run even hotter than the reality of weather as the weather records are ‘adjusted’ to be hotter than reality using homogenisation and taking full advantage of urban compression generated UHI.
My reading of this is that the situation has actually got better rather than worse. It is the predictions which have got worse, but who believes them?
Mike Lowe
The BBC?
The Government?
Princess Nut-Nut?
97% of “Scientists” (especially all those who would like more of your money…)
Princess Nut Nut is an incredibly juvenile bit of name calling which does nothing to help our cause. If you can’t use real names just forget it.
Maybe it is not whether they believe them rather than what mileage they can make out of presenting them as credible.
😉
There’s a good chance that almost all of warming is due to the oceans. That’s why using the warming oceans as input gives them better results. If they completely ignored GHGs the models might actually be reasonable.
The oceans are warming too, though. At the surface and at all reliably measured depths. By what metod are they warming?
They may be warming, the claimed warming of a few hundredths of a degree is more than an order of magnitude less than the error bars on the measuring system.
And that is just the errors on the thermometers, only a total fool would declare that a few hundred sensors are capable of measuring the temperature of the oceans with any degree of accuracy.
I’ll give you one hint about what is NOT warming the oceans. Infrared radiation (i.e., what CO2 supposedly will “increase” the amount of), which can’t penetrate beyond a few microns of the ocean surface, where at best it might cause some evaporation, a cooling effect.
Nobody has claimed that it is infrared radiation that is warming the ocean.
By which you imply relative humidity of the #1 GHG in the atmosphere is the dominent control of warming and cooling weather cycles.
In which case, I’d agree.
The Big Lie being perpetrated very deliberately is evidenced by the tireless efforts, BEFORE observations in the new millennium, to adjust individual weather station data in the first instance and to add an algorithm that adjusts the entire surface stations network continuously to increase the warming trend in tiny amounts -thermageddon demise by a thousand ‘cuts. One recent commenter counted over 300 station temp adjustments in the US network in one day by GISS!
They knew the models were running too hot. Even Tom Karl remarked on this in 2005 (noted in this current article). But they opted to fix it by this felonious method. Surely this obvious premeditation, in light of the enormous costs to the world economy and the expiry of countless elderly and impoverished persons who had to choose between food, medication, home heating, air conditioning and lighting. Add the huge cost of climate terror induced mental health agonies of hundreds of millions of children and adults that will rend society. Shame shame shame on you all!
This must have a reckoning and named felons
The GCMs are doing exactly what their creators intended them to do. They bring home grant bacon and pork from politicians who need scare stories to sell their socialist destruction of Western capitalism.
That is: GCM moddlers sell a fake story. i.e. they are committing fraud.
I call them Climate Dowsers, since dowsing by definition is:
“Dowsing is a type of pseudoscientific divination employed in attempts to locate ground water, buried metals or ores, gemstones, oil, grave sites, malign ‘earth vibrations’ and many other objects and materials without the use of a scientific apparatus.”
The GCMs in the hands of climate modelers certainly are not scientific instruments, anymore than a pair wires in a water dowsers hands are a scientific apparatus.
They may be not scientific instruments, but they are very good at dowsing where money is!
You’re speaking of the climate models, of course…
What else could be?…
I’d actually give a pair of wires in a “water dowsers” hands more credit than “climate models” in terms of their being “scientific tools.”
I sometimes wonder what goes on in the minds of “climate scientists”. Do they know that their models exaggerate past warming? Do they know that their paleoclimate reconstructions cherry-pick among proxies to show that the MWP or the Holocene Thermal Optimum didn’t happen? Do they know that they cherry-pick sea level histories to show that SLR is accelerating?
How can they not know? Even in today’s dumbed-down academia, no one could be that thick and maintain a semblance of a research-oriented career.
So, assuming they know that they are producing biased results using data selected for the purpose, and models that deliberately don’t reflect reality, the question is – do they care?
I suspect that the virulent hostility shown towards almost anyone daring to question the orthodoxy is enough to subdue any stirrings of conscience among all but the most courageous and the most principled. It takes a lot of backbone to rock the boat when the consequences will be ostracization within the academic community, future research grants drying up, possibly even job loss (either getting fired or finding the environment so hostile that they quit). Better just to keep your head down and carry on and be comfortable within the in-group.
Some, we can assume, know they are corrupting science, but don’t care because they really believe that global heating (the newly fashionable term) is serious enough to worry about, and that the politicians need scary stories to prod them into one-upmanship at the next COP – “100 percent reduction in emissions by next Tuesday!!!” and other impossibilities.
It’s ultimately the politicians who get the message about impending doom who turn on the taps so that the stream of grant money becomes a raging torrent, in a feedback loop without end. How else to explain why the ECS keeps getting bigger?
The other field where vast sums of money lead to corrupt science is pharmacology. The monstrous profits to be derived from a new drug pay for corrupt scientists to write papers saying that the new drug is x percent more effective than the other company’s old drug, even if x is zero or even slightly negative. See Actonel vs Fosamax, where it actually came to light, courtesy of a brave whistle blower.
Fact is: science in the service of politics, or science in the service of money, is no longer real science. It may use the methods of science and it may look like science, but science it is not. The money is paying for the conclusions, not the research. What a bloody mess. I despair for the future of humanity.
The likely ECS of between 1.0 and 2.0 as indicated in the article, Fig. 4, would be considerably lower if any solar multipliers, cosmic rays etc., were properly accounted for.
And if climate feedbacks were even considered.
Very well put.
There are two answers commonly used
“The data is no good (or the data is wrong)”
“The data doesn’t matter. Only the models matter.”
And there is a third: “The data doesn’t matter. Only my salary matters”.
To paraphrase the corrupt French policeman in Taken, “The data is x, the desired result is y, and I don’t care how I make up the difference.”
Is this a repeat of the 25-Aug-2020 post?
https://wattsupwiththat.com/2020/08/27/new-confirmation-that-climate-models-overstate-atmospheric-warming/
Not a repeat, but the latest evidence that the issue is perpetual.
From my 2-minute review the two articles are word-for-word identical. Still good information, but it seems strange that nowhere is it mentioned that this is a repeat.
The thng that has always baffled me: why does the IPCC use so many models? 48 in the present instance.
Not, why are there so many? That is completely understandable, many groups are working on a knotty problem, of course people come out with lots of different approaches to it.
What is incomprehensible, and I think has no comparison in any other area of science or engineering guided policy, is that apparently no effort is being made to reject failing ones, arrive at the best one, and use that.
Imagine there were a global pandemic. Imagine that we had 48 different models of infection rates, hospitalization and death rates, vaccination paramenters.
Imagine too that they all showed far more infections and deaths than were actually occurring. In one case they show seven times as many. Perhaps for the UK, one of them might suggest in June that if lockdown were eased in July, there would be 200,000 infections a day and daily death rates in the thousands, with hospital occupancy of 40,000.
The real numbers from observation in August are roughly 30,000 cases a day, death rates in the low hundreds, and roughly 6,000 in hospital.
Imagine the reaction of the scientific community to this situation. The parallel would be that they take all 48 of their models including the one just cited, and average the outputs to get a prediction they offer to government as the basis for policy.
This prediction is, unsurprisingly, far ahead of observations. It predicts for instance infection, death and hospitalization rates in a few years time so high that economic activity will be brought to a total halt and we will have to go back to subsistence gardening for our vegetables and a back yard pig and chickens for meat.
In addition, lets imagine a parallel to the models’ use of RCP 8.5. This might be that they assume a vaccine effectiveness percentage of 50%. Whereas we know from observation that it is close to 90%. This is pointed out to them, but they continue to use the 50% figure.
And the Guardian, lets imagine, to continue the parallel, gets together will the rest of the mass media and adopts a vocabulary where we no longer speak of the ‘pandemic’, but invariably use the expression ‘lung death plague’. The phenomenon
is invariably referred to as the ‘global plague crisis’. The Guardian publishes almost daily pieces in the runup to a global conference to address it to the effect that this is civilization ending, a disease crisis, the most or only important problem facing our politicians.
And it demands that the UK Government, to avert this crisis, go back into lockdown and stay there indefinitely, regardless of what anyone else does.
And now imagine for the piece de resistance that the numbers on the pandemic show that 75% of cases are occurring in China, India etc, and that these governments refuse either to lock down or to vaccinate. The UK in this scenario has about 1% of the cases in the world…
You would say, would you not, that this is a political and scientific establishment which has taken leave of its senses.
But that is exactly what we are doing with the global warming hysteria. I read that the current Conservative government, as an example, based on the latest absurd projections of the IPCC, proposes to ban gas boilers . In new build housing in 2025, and totally, anywhere, in something like 2030. And they propose bringing these dates forward.
They are to be replaced either by hydrogen or heat pumps. Never mind that there is no source of hydrogen, and if there were, there is no pipeline to transport it, and there are no boilers which will burn it. And that retrofitting heat pumps to British housing will cost a fortune where it is possible at all. Air heat pumps being notoriously expensive and inefficient to run. And ground based ones even more expensive and impossible to install in cities or high ground water areas.
Don’t bother us with evidence, they say, we are saving the planet.
They have taken leave of their senses. I used to think it was due to the pervasive influence of post-modernism. But I now realize that pervasive influence is just a symptom of the prevailing insanity.
“why does the IPCC use so many models? 48 in the present instance.”
Because they love confusion.
The bigger problem is there’s zero potential to ever measure the results and incrementally improve climate models over the course of glacial to interglacial cycles. Which is actual unambiguous global climate-change.
Weather model ‘skill’ can be measured every day, and constantly made more accurate and more useful to people, over longer periods forward of now to provide good weather indications even 1 week out from the present.
But climate-model predictions can never be tested and verified like that. They can never be proved, nor how far they will depart from the climate-change reality, in 5,000 years. It can never be measured and assessed that way, like a weather model can be tested and verified continuously. So they can never be developed and evolved by trial and error, nor from first principles, on the basis of meteorology, or real global climate-change, as opposed to the dismally insufficient periods of mere decadal weather cycles.
So GCMs can never provide useful results to people over longer periods forward of now than even 1,000 years.
Even over a pathetically short 40 years they are hopelessly inaccurate. Inaccurate enough to be dangerous and harmful to sanity, as we see in legislative chambers everywhere right now.
So what good are they ever going to be, for anything scientific? They can’t make predictions that anyone can ever reliably use to actually forecast climate-changes!
That’s what they are supposed to do, what they claim to be able to do, but it’s a hollow and deceptive claim, as every adult alive will be dead in 100 years, let alone in 1,000 years, let alone in 5,000 years.
The whole field should be abandoned, there’s never been a more hopeless derailment of scientific efforts, disciplines and funding flow. GCMs are the sludge at the bottom of a 40 year old ‘climate-research’ septic-tank. It’s worthless.
There’s not even a point in adopting one (1) model alone to be used, that is closest to observations after 30 or 40 years, as even that one model would be completely unable to forecast a real global climate-change usefully for anyone. Let alone for testing that prediction which it made, with collected data, in 1,000 years time, or in 5,000 years time.
As I pointed out yesterday, in 1991 the UN IPCC were trying to make forecasts of sea-level change ranges in 2100, and they’ve gotten that hopelessly wrong too, a much simpler problem to solve, over a much shorter period of time. They’re unable to get that much right!
It’s like some utterly ludicrous Monty Python skit has escaped a BBC archive and taken over the UN. Pointless rancid stupidity of just that sort has taken hold here.
Because they wrongly believe; the more strings of modeled output they average together the more accurate it becomes. They confuse that with data. Of course such a thing is absurd in the extreme. Averaging discrete information is pointless anyway, but then averaging the results of he previous averages is just a comedy act. Then remember that much, if not all, the input “data” is the result of selectively interpolating measured temperatures.
It’s saying something that the models run warmer than the temperature datasets which themselves have been made warmer with all the data tampering.
Yes. It says “The underlying assumption of the models, that being “CO2 drives temperature,” is absolute nonsense.”
But observed, actual, measured, recorded year on year recorded temperatures show warming and do not overstate it!
They do overstate it due to adjustments and the UHI effect.
Only satellites are reliable and we are currently barely above the average of the past 30 years and may soon drop below it again.
So speaks a man who doesn’t understand what a Statist is.
griff only understands what he is paid to understand.
0.5C over the last 70 years.
That’s nothing. Especially considering the fact that temperatures have fallen between 3C and 5C in the last 5000 years.
No, in fact it does nothing of he sort. But even if it did, that would mean nothing beyond the continuation of the post Little Ice Age warming, an indisputably natural event. No one has ever shown a human signature to that warming.
The sad fact is, Griffy; you really don’t know very much about the subject., but you do seem to be well schooled in the BS.
“Evidence is emerging from multiple directions that the models which show the greatest warming in the CMIP6 ensemble are likely too warm,” explains Dr. Gavin Schmidt, director of NASA’s Goddard Institute for Space Studies.”
It appears to be a sub-set of models that are to warm.
?
” … 30% of models showed a significant increase in their sensitivity to a doubling of atmospheric CO2.”
NOT all of them…..
Meanwhile ….
Climate models are running too hot.
Climate models still running too hot.
Climate models again still running too hot.
I seem to remember plots of predictions from models and real temperatures, where the Russian models were closest to reality, with all the other models predicting temperature rises much higher than reality. The Russian models seem to have the lowest ECS, about 1.8 to 2.0 C per doubling of CO2. .
Why can’t the other modelers get the hint and adjust their ECS downward, so that the models (if started in 1980 or so) track more closely with observations? Or do they have a ve$$$ted intere$t in $cary prediction$ to get more funding, and who cares about accuracy?
If anything, Russia would likely benefit from a warming of the climate, from a lengthening of the growing season in vast sparsely populated land areas, and increased navigability of the Arctic Ocean. Yet their models predict less future warming than those from other countries, and their predictions seem to be more accurate. Interesting…
I’m not sure the Russian model has any sensitivity to CO2. Perhaps someone who is in the know can elaborate.
I do seem to recall that “turning off” the CO2 sensitivity in the models resulted in much more realistic results.
The simple fact is the models are garbage. They do not accurately simulate anything, and are “tuned” to achieve known past results but quickly fall apart if the same tuning parameters are applied to a different time frame.
Without putting too fine a point on it; if Gavin Schmidt knows anything about climate, he’s keeping it a well hidden secret. He’s a pointless bag of hot air exactly like his predecessor. Averaging computer output is utterly meaningless. It does not and cannot cause the aggregate to show any greater precision. When averaging discrete information the result is almost always utter nonsense. What do you get when you average telephone numbers?
Any process that provides a > 400% spread has no useful benefit. It is neither accurate nor precise and it has no predictive value.
I sometimes wonder what really does go on inside A Computer Climate Model.
Just for starters they claim climate is ‘chaotic’ and also ‘non-linear’
I call BS on both counts
1) Over a short term, any short term (this is cherry picking in action), weather appears to be chaotic.
OK yes, in some parts of the world more than others maybe certainly and there-in is the real cause of Climate – WHY are some places different from others?
CO2 can not be the cause of regional variation.
2) They patently don’t even understand what ‘non-linear’ means.
A non-linear system is NOT one that has any variation of a squiggly line for its input/output function
A non-linear system contains any number of singularities – although just one is quite sufficient.
A Singularity defined as ‘division by zero‘
So, has Ma Nature worked out how to divide by zero?
Really. If so where?
Show me pictures or it didn’t and doesn’t happen
Oh you say, the temperature/energy graph of water has non-linearities where it freezes and boils.
Yes it does but temperature is a dimensionless quantity – it can do anything it likes or what you want it to.
As far as ma Nature is concerned, she is bothered about the energy content of the freezing and boiling processes and there is no ‘non-linearity’ there.
In the trivial sense, energy has mass and me you no-one can not do anything to anything possessing mass in zero time.
Thus there is no non-linearity. Climate is linear. Squiggly lines all over the place yes, but still linear and, in theory, predictable
You get where I’m going….
viz: Climate Models should have ‘energy‘ or ‘energy content‘ as their outputs.
Not temperature. Temperature is what The Emperor is wearing today
Telling peeps about temperature means everything & nothing to everybody & nobody.
It means perfectly Sweet FA unless you say what it is you are recording the temperature of.
And in the Climate System, dependant on water as it is, it is impossible to make a bigger gaffe.
Because water has such massive energy storage/release properties – it is quite unique in this Universe and must rank as the most misunderstood and ‘taken-for-granted’ substance of all time.
And this where Climate Science is such perfect junk – the very people who should understand water, patently don’t.
You, Peta, don’t understand even the basics. The primary dimensions are: mass, length, time, temperature, electric current, amount of light, and amount of matter. Temperature is not dimensionless. Your post is a random assortment of things – some true, some not, some understood, some not.
Energy content is directly related to temperature through Cp, the heat capacity, of the substance you’re measuring the temperature of. Cp has units of J kg^-1 K^-1; J is for Joule (energy) kg is for kilogram (mass), and K is for Kelvin, the unit of temperature.
You’ve reached the correct conclusion that Climate Science is perfect junk, but for several wrong and clearly not understood reasons. This is actually important because stuff like your post is used by the Alarmists to show that skeptics (climate rationalists) don’t know what they’re talking about when many do know perfectly well what they’re talking about.
You need to revise your idea of temperature. Heat is not a good indicator of temperature. Temperature measures the kinetic energy of a mass. Latent heat is not measured. Think of the constant temperature during a phase change.
Does water vapor add/increase temperature to the atmosphere as it rises? Not likely.
Does water vapor increase the temperature of CO2 as it rises?
The concentration of CO2 vs water vapor is such a small value, CO2 just can’t do much.
Any model that includes CO2 as a warming agent will always be wrong. It is a TRACE gas, and it cannot possibly be a control knob of climate. It has been thousands of times higher in the past and nothing happened.
At 400 PPM, it is one CO2 molecule for every 2500 ‘air’ molecules. When a photon is absorbed, a photon is almost instantaneously radiated. There IS NO TRAP.
It can’t be the control knob not because it is a “trace gas,” but because they very notion of it affecting temperature is purely hypothetical – it has never been demonstrated to have such an effect in the real world, where the foundational assumption of said hypothetical effect, all other things held equal, is simply not true.
As for it being a “trap,” I think the best description I’ve heard is “Attempting to ‘trap heat’ with CO2 is like attempting to trap mice with a chain-link fence.”
These models are created specifically to overstate warming and hide reality. Don’t get the tax dollar funding if you tell the truth.
The question arises of how accurate these modelled estimates are.
Accuracy of CMIP modelling can be seen by comparisons. In 2007, David Douglass et al published in Int. J. Climatol. (2007), “A comparison of tropical temperature trends with model prediction”.
http://www.blc.arizona.edu/courses/schaffer/182h/Climate/climatemodel.pdf
Table 2 from it:
http://www.geoffstuff.com/cmip_accuracy.jpg
From the Abstract,
“We examine tropospheric temperature trends of 67 runs from 22 ‘Climate of the 20th Century’ model simulations and try to reconcile them with the best available updated observations (in the tropics during the satellite era). Model results and observed temperature trends are in disagreement in most of the tropical troposphere, being separated by more than twice the uncertainty of the model mean. In layers near 5 km, the modelled trend is 100 to 300% higher than observed, and, above 8 km, modelled and observed trends have opposite signs.”
Here is the modelled trend of temperatures at various altitudes above the Earth surface, expressed in milli⁰C per decade.
Pressure Results #15 Average of all 67
hPa milli⁰C/decade milli⁰C/decade
1000 163 156
925 213 198
850 174 166
700 181 177
600 199 191
500 204 203
400 226 227
300 271 272
250 307 314
200 299 320
Model # 15 is Australia’s CSIRO MK3.0.
Attention is drawn to the exceptionally good model results at mid-altitude, which I have bolded. They agree with the average of the other runs/models to one millidegree C per decade. Accurate estimates to one thousandth of a degree per decade?
…………………….
My first science graduate job was with CSIRO. If I had shown my boss a comparison like this, he might well have said “Geoffrey, now sit down here, take your time, explain in full detail how you achieved this result that is so much better than anyone else in the world has shown here. Geoffrey, first, there is a critical question: Did you know the other results before you published, YES OR NO?”
I suppose that if one were going to try to make a case that every nook and cranny had been searched in an effort to make an envelop of possible futures, then 30 or 40 independent versions might help make a convincing argument. However, I doubt this is the case. I suspect these models are not independent, but rather as packages of software are descended from a few common ancestors of earlier software versions, and have similar if not identical parameterizations and who knows what flaws.
I see a cause of the warming bias, especially that in the tropical upper troposphere in the CMIP5 models: These models are tuned to hindcast the past, especially the 30 years before their hindcast-forecast transition times. This tuning to give better hindcasting results is done by adjusting the effects of several parameters, not including multidecadal oscillations. During the 30 years before the hindcast-forecast transition times of the CMIP3, CMIP5 and CMIP6 models, multidecadal oscillations were mostly in phases of contributing to increasing global temperature. As a result of ignoring multidecadal oscillations, these models had their positive feedbacks increased to account for the warming that happened during the 30 years before their hindcast-forecast transition times. One of these feedbacks is the water vapor feedback. If the water vapor feedback is modeled as greater than it actually is, then the model overpredicts warming that has a “hotspot” in the tropical upper troposphere.
Several commenters here have wondered about the source of the error/bias here. Arguing about these models from the stand point of a global mean temperature seems futile because it is such a narrow view of the performance of these models. However, I might point out an observation.
The performance of the models doesn’t appear too bad up to year 2000 or so, then the performance of the actual atmosphere drifts toward the edge of the envelop until 2016. Afterward it performs well for a time. To see this graphically Refer back to this comment.
Now one may ask, “What do these time periods of better performance have which the segment of poorer performance doesn’t?” Well, it appears that when there is an exceptionally powerful El Nino the observations are pulled back into better agreement with models. Thus, the bias in the models is approximately the same magnitude as the difference between average and maximum states of an internal variation in the real climate system — one involving clouds and the oceans. One could suppose that the models tend to maintain this internal cycle too far above its average conditions. El Nino is a good candidate, but perhaps there are others.
Instead of plotting mean earth temperature, what would a person find by plotting a number of the ENSO indices derived from the models against the observed index value? Do they handle it well, including its variations in strength, or do they persistently over state it?
“no one faults the scientific community for finding it a tough problem to solve”
I blame them! They are not even trying to solve the modeling problems. They need to start from scratch, re-examine all their assumptions, and begin building small models they can experiment and test more easily with. When they get some of the details sorted out, then they can move on to world simulations.
Clouds for example – one would think they could work out some details about clouds before moving on to world simulations – but no…they just parameterize them and hope they don’t matter.
They skipped all the really hard work and moved into world modeling thinking that their models must be representing something.
They skip the hard work because the models are a fancy graphic for a false premise – the notion that atmospheric CO2 drives the Earth’s climate.
When you begin with your conclusion and work your way backward trying to find support for the preconceived conclusion, you’re not doing science at all. That’s why the “models” are fancy expensive garbage.
Don’t worry. If 48 models can’t get it right, we will just double the number of models in CIMP7, average all the results and claim those are consistent with observations that were adjusted.
Diverse models that self-identify with 48 shades of reality, then injected with brown matter to conform with the consensus.
They’ll have the perfect back-pedaling set up, of course – “What we really meant when we (again and again) said “It’s worse than we thought” was how far out of touch with reality our models were.”
Tends to suggest they’re missing something very fundamental about how the Troposphere and Stratosphere operate, and interact with each other. They can’t resolve it because they don’t know what fundamental factor the models and meteorology are missing.
But at least they’ve comprehensively proven their models don’t work and can’t be reconciled with reality, or even a UHI degraded plus continuously fiddled met data.
Even systematic corruption couldn’t bridge the chasm between models and reality.
No Sheet Sherlock (-:
Profound: Smoking Guns!! Proof with accurate 2 decade long measurement of the actual amount of radiative forcing caused by CO2 of 1 irrefutable reason for WHY global climate models continue to be too warm. Climate emergency is really about social justice and brainwashing people. Even MORE confirmation that climate models overstate atmospheric warming. Models clearly too warm yet incredibly programmed to get even HOTTER! Now, even more confirmation why the models are too warm. August 2020 https://www.marketforum.com/forum/topic/57636/
New confirmation or just more? The modeling can go on ad infinitum and as long as the MSM fails to report it properly no one cares. It’s like the worse kept secret.