Summary: A new book by a Japanese climate scientist explains why the predictions of climate models are useful but insufficiently reliable for large scale public policy action. None of this is new information. But none of it is widely known.
Excerpts from Confessions of a climate scientist
(2019)
By Mototaka Nakamura.
From the English intro and summary in the Kindle version of his Japanese book.
This excerpt shows some of his findings and explanations.
Read the e-book to see his detailed and technical analysis, and his references.
Climate simulation models are fine tools to study the climate system, so long as the users are aware of the limitations of the models and exercise caution in designing experiments and interpreting their output. In this sense, experiments to study the response of simplified climate systems, such as those generated by the “state-of-the-art” climate simulation models, to major increases in atmospheric carbon dioxide or other greenhouse gases are also interesting and meaningful academic projects that are certainly worth pursuing. So long as the results of such projects are presented with disclaimers that unambiguously state the extent to which the results can be compared with the real world, I would not have any problem with such projects. The models just become useless pieces of junk or worse (worse, in a sense that they can produce gravely misleading output) only when they are used for climate forecasting.
All climate simulation models have many details that become fatal flaws when they are used as climate forecasting tools, especially for mid- to long-term (several years and longer) climate variations and changes. These models completely lack some of critically important climate processes and feedbacks, and represent some other critically important climate processes and feedbacks in grossly distorted manners to the extent that makes these models totally useless for any meaningful climate predictions. …
Measuring global temperature.
A quasi-global observation system has been operating only for 50 years or so, since the advent of artificial satellite observation. Temperature data before then were collected over extremely small (with respect to the earth’s entire surface area) areas and, thus, have severe spatial bias. We have an inadequate amount of data to calculate the global mean surface temperature trend for the pre-satellite period. …
The Sun.
It has been only several decades since we acquired an ability to accurately monitor the incoming solar energy. In these several decades only, it has varied by 1 to 2 Watts per square meters. Is it reasonable to assume that it will not vary any more than that in the next hundred years or longer for forecasting purposes?
Editor’s note: The Sun emits 1,361 watts per sq. meter. Over the 11-year solar cycle, it varies by roughly 0.1%. The IPCC’s AR5 WGI says that estimates of radiative forcing from 1750 to present have medium agreement, medium evidence, and medium confidence – although confidence is higher for the past 3 centuries. (8.4.1.2). As NASA says, “Even fluctuations at just a tenth of a percent can affect Earth.” There is as yet limited data about variation over longer time periods. From another NASA article …
“Over the past century, Earth’s average temperature has increased by approximately 0.6°C (1.1°F). Solar heating accounts for about 0.15°C, or 25%, of this change, according to computer modeling results published by NASA Goddard Institute for Space Studies researcher David Rind in 2004.”
{The} first of the two problematic details of climate simulation models mentioned earlier: erroneous representation of actions of oceanic motions …{I}t can be remedied only by increasing the resolution of climate simulation models from the typical 1˚ x 1˚ or lower, to 0.1˚ x 0.1˚ or higher in longitude and latitude. It is simply an issue of limited computer resources and is not an issue of our limited knowledge of the ocean dynamics and thermodynamics. …
Needless to say, it is absolutely vital for any meaningful climate prediction to be made with a reasonably accurate representation of the state and actions of the oceans. …
Water vapor.
Another major contributor to the predicted major global warming is water vapor, the most important greenhouse gas in the Earth’s atmosphere. …The enhanced warming effect of its changes predicted by the climate simulation models also dwarfs that of the projected carbon dioxide increase. So, predicting changes in the radiative forcing associated with the atmospheric water vapor accurately is essential for any meaningful prediction of climate changes. But the fact is this: all climate simulation models perform poorly in reproducing the atmospheric water vapor and its radiative forcing observed in the current climate. …
Clouds.
Clouds’ role in the global climate is extremely important and extremely complex, to say the least. Ad hoc representations of clouds in climate models may be the greatest source of uncertainty in climate prediction. A profound fact is that only a very small change, so small that it cannot be measured accurately with the currently available observational devices, in the global cloud characteristics can completely offset the warming effect of the doubled atmospheric carbon dioxide. …
Clouds are represented with parametric methods in climate models. Are those methods reasonably accurate? No. If one seriously studies properties of clouds and processes involved in cloud formation and dissipation, and compare them with the cloud treatment in climate models, one would most likely be flabbergasted by the perfunctory treatment of clouds in the models. The parametric representations of clouds are ad hoc and are tuned to produce the average cloud cover that somewhat resembles that seen in the current climate. Can we, or should we, expect them to simulate the cloud coverage and properties in the “doubled atmospheric carbon dioxide” scenario with reasonable accuracy? No. …
Parameterization.
Ed. note: See Wikipedia for a description.
I hate to say this, because I know well how much of serious efforts have been put into improving these parametric representations (I spent hundreds of hours in vain myself), but all of these parametric representations, even the best of them, are Mickey Mouse mockeries when compared with the reality. …That is, the selection of the parameter values is an engineering process to “make the model work” rather than a scientific process. The models are “tuned” by tinkering around with values of various parameters until the best compromise is obtained. I used to do it myself. It is a necessary and unavoidable procedure and is not a problem so long as the user is aware of its ramifications and is honest about it. But it is a serious and fatal flaw if it is used for climate forecasting/prediction purposes. …
Even if the best compromise so obtained from the tuning looks very close to the observation, the models’ behaviors are guaranteed to be grotesquely unrealistic, since the tuning requires other aspects of the models to be extremely distorted in order to counterbalance the distortion associated with the Mickey Mouse representations described above. …
The models use various parametric representations that estimate the water vapor profiles from the large-scale atmospheric state that can be calculated by the models. All but one of these parametric representations are ad hoc and rely on major simplifying assumptions that are not justifiable when scrutinized against the reality. They have only a few parameters that can be used to “tune” the performance of the models and utterly unrealistic. …
With values of parameters that are supposed to represent many complex processes being held constant, many nonlinear processes in the real climate system are absent or grossly distorted in the models. It is delusion to believe that simulation models that lack important nonlinear processes in the real climate system can predict at least the sense or direction of the climate change correctly. …
Conclusions.
The real or realistically-simulated climate system is far more complex than an absurdly simple system simulated by the toys that have been used for climate predictions to date …{N}one of the climate simulation models used for predictions can reproduce the current climate accurately despite the heavy tuning and engineering efforts by climate researchers.
————— End of excerpt. —————
Buy your own copy
It is only $0.99, well-written, and well worth your time.
Editor’s note
Sadly, as Nakamura notes, all of this has been discussed by climate scientists for a decade or more. In an amazing display of power, this has been hidden from the general public. I still see people saying that models are built from first principles of physics – and are therefore as reliable as F=MA.
Those who get their information from journalists or activists will see Nakamura’s analysis as astonishing or impossible to believe. It is, however, roughly consistent with the findings of the WGI in the IPCC’s Fifth Assessment Report. Climate models are a pyramid of assumptions and estimates. AR5 describes many of these as having low or medium confidence. It creates a rickety structure.
So how reliable are long-term forecasts by climate models? Nobody knows. Climate science institutions have focused their efforts on back-testing, quite futile since models are tuned to match past observations. Scientists debate the question among themselves, producing no evidence sufficiently robust for taking drastic public policy action.
Yet there is a large body of expert knowledge about the validation of quantitative models. Climate science just ignores it. A simple first step would be for climate scientists to restart the climate change debate by testing the models. That is, run past models (i.e., those used in past Assessment Reports) with actual data from model creation until present – and compare their forecasts with observed weather. The result would tell us much. I wonder why they do not do so. Congress would cheerfully fund such tests.
Until then, some climate scientists will attempt to bust the “science is settled” myth. And the policy deadlock might continue, preventing us from even preparing for the repeat of past extreme weather.
About the author
Mototaka Nakamura has a Sc.D. in Meteorology from MIT (1995). He has worked at the Georgia Institute of Technology and the Goddard Space Flight Center. Now he is a Visiting Associate Researcher at the U Hawaii’s International Pacific Research Center at the School of Ocean and Earth Science and Technology. See his papers here.
For More Information
Ideas! See my recommended books and films at Amazon.
Hat tip on this to Tony Thomas’ article at Quadrant: “A Climate Modeller Spills the Beans.”
Eminent climate scientist Judith Curry has written much about this. I strongly recommend these!
- “Climate Science and the Uncertainty Monster” in BAMS, December 2011.
- “Climate Models for the Layman” (2017).
If you liked this post, like us on Facebook and follow us on Twitter. For more information about this vital issue see the keys to understanding climate change, all posts about computer models, and these posts about the climate wars …
- We must rely on forecasts by computer models. Are they reliable? (Many citations.)
- Milton Friedman’s advice about climate models, & how to win the policy debate.
- We can end the climate policy wars: demand a test of the models.
- A climate science milestone: a successful 10-year forecast!
- About Hansen’s powerful demo that climate models work!
- Panicking about climate change? See the rest of the story. – By Judith Curry, about models.
- Important: Let’s prepare for the repeat of past extreme weather instead of bickering about predictions of climate change.
Activists don’t want you to read these books
Some unexpected good news about polar bears: The Polar Bear Catastrophe That Never Happened
To learn more about the state of climate change see The Rightful Place of Science: Disasters & Climate Change
Hopefully, his story will be published in an English language book one of these days, which I will purchase, as that medium is my favorite. I don’t kindle (that’s a verb these days, isn’t it?)
Thanks for the coverage.
Chad,
He says that he is tired and bored with writing about climate science. I heat a lot of that. The subject has become toxic.
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Good news! I’d say Trump is listening.
Ron,
To what is he listening?
Reports from people who have worked with him in the White House are clear that he doesn’t read much.
Larry,
FOX news. Let’s hope this story makes the big time. #tucker
Remember; To Big Don, it’s a hoax.
Larry,
Please assemble all of your best climate commentary into a paperback book (edited for publication, of course). I would put that on my shelf immediately.
I have to agree with Joseph Ratliff, Larry: it’s time to start seeing some books from you. You must have a dozen or so lurking within the pages of this site already.
Professor Forte,
Now that’s advice I’ll listen to!
But as a professional marketer, the key point is selling books – not writing them. My heterodox beliefs don’t fit well in our tribal society. As the FM website shows. These posts seldom get links from nodal websites. We get traffic from shifting audiences (depending on the themes I’m working) – and by the blessing of the Great God Google (we have, I believe, a high Authority Score). I’m skeptical that this audience is sufficient to sell enough books to make the project worthwhile.
What level of traffic would suggest an adequate audience? My guess is 4 or 5 million page views per year (we’re now running at 2 million, minus dips when I become demotivated).
https://skepticalscience.com/climate-models.htm
https://www.carbonbrief.org/analysis-how-well-have-climate-models-projected-global-warming
Gosh, that was hard. It must be easier to just assert that “climate models are wrong” or “climate models have not been tested” on ones vanity blog.
Luckily I consider about half of your political observations more astute than your scientific acumen or there would be no reason to even read this place.
And no, I’m not denying that ‘climate activists’ are often alarmist and full of crap. I’m denying climate science is. Why you seem to think that Climate Science is somehow different from the other types of science (ask any physicist how physics articles are often simplistically misleading or worse, even in some of the ‘non professional’ scientific press, let alone popular magazines and newspapers) and hence is reported accurately, is beyond me.
Clarence,
Skepticalscience.com isn’t worth the effort of a mouse click to get there. Cook of the 97% fallacy.
CLarence,
“It must be easier to just assert that “climate models are wrong” or “climate models have not been tested” on ones vanity blog.”
You don’t appear to have read the post. This is an excerpt from a book by a climate scientist.
Re: your cites from Skeptical of Science and Carbon Brief
How odd. One is an unsigned post on an adovacy website. The other is by a guy working for a PhD. This is from a book by an experienced climate scientist, who has worked for major climate agencies, and has a long list of publications. Also, his work is consistent with the work of the IPCC. Yet you find the former definitive, not the latter. That says much about you, unfortunately.
“of your political observations more astute than your scientific acumen”
Since I’m just reposting the work of a climate scientist, I don’t see how my background is relevant.
“Why you seem to think that Climate Science is somehow different from the other types of science”
Now you are just making stuff up.
Sigh:
https://www.climatedepot.com/2013/06/30/japanese-researcher-predicts-cooler-climate-in-northern-hemisphere-from-2015/
I notice he hasn’t published a paper in over ten years.
I notice he has a degree in METEOROLOGY (gosh, like someone else who is a notorious denier) not a CLIMATOLOGIST.
https://www.climatedepot.com/2013/06/30/japanese-researcher-predicts-cooler-climate-in-northern-hemisphere-from-2015/
I wonder what else I’d be able to pull up on this guy with even the tiniest bit of additional research?
It’s always nice to watch people who think they are smart(you) get played.
Clarence,
“I notice he has a degree in METEOROLOGY”
Degrees in climate science are relatively recent. Professor Michael Mann, one of the most famous living climate scientists, has a PhD Geology & Geophysics (Yale, 1996). See his CV.
Also, not all jobs in climate science produce publications. That is most common with those in academia.
Follow-up note –
I was looking at people with the highest H-indexes in climate science. Their most common PhD’s were in meterology, geology, and physics. Chemistry and geography were also common. Some were combined (eg, atmospheric chemistry or atmospheric physics). None mention climate. That will soon change, of course.
Looking through PhD programs, many of these are adapting to the interest in climate: geophysics and climate, geography and climate (eg, Cambridge), etc.
This is an excellent article.
Part of the work that I do in engineering mobile communication systems requires that I use computational analysis to predict the strength of electric fields on and around military vehicles. This is to ensure that personnel are not subjected to unhealthy levels of electromagnetic energy. In plain terms – I put radios into tanks and model the signal strength so nobody gets overexposed.
Given all this, still, when we check the predictions of the models against the field strengths that we actually measure, we expect to be within an error factor of five and are ecstatic if the models are within an error factor of two. The models are never ever ever ever bang on.
There are no terms that express the vast difference in complexity between the Earth’s climate and a tank with a radio – maybe the term “a billion orders of magnitude” comes close. And yet the world is convinced that a computer model can accurately predict a 2 degree increase in global temperature over the span of decades. And we’re talking of betting trillions of dollars and millions of lives on this???
This is an extreme case of the mass abdication of rational thought to self-proclaimed authority. Short of dismantling this global farce it will not end well.
Kris,
Thanks for sharing that real-world experience! Food for thought.
I stumbled across this while looking for the subject book, and just had to chime in. Kris, you could not be more right. I have spent a substantial fraction of my engineering career doing Computational Fluid Dynamics and Finite Element Analysis, and even worked alongside RF engineers using the software you described (which is insanely expensive compared to the stuff I used). My experience was precisely parallel to yours.
There are a million ways to screw up a computer model, and a handful of ways to get it right. With FEA, my very best work came within 2% of the field testing results, but that was an outlier. Typically we were satisfied being within 5% of stress or deflection predictions. And for CFD, 10% or even higher was not unheard of. In comparison, the climate modelers are giving results within +/-.1 degree C for a global temperature on the order of 10C, or 283K. They’re claiming to be within 0.035%!! An error in their calculations of even 2% (which would be unbelievably amazing for any multiphysics model, and never achieved even with tightly controlled systems) would give error bars of +/-5.6C. In truth they must be far larger.
Reading Nakamura, I am livid at the hoax that they are perpetrating. Their models have a lat/long grid size of 1×1 degree?! That’s huge! To put that in perspective, each grid box represents and area 69×69 miles! You can’t model massive heat transfer events (hurricanes, tornadoes, microbursts, etc) with a grid size like that. And if you can’t model obvious, massive heat transfer effects like that, how can you possibly model much smaller, more obscure ones that are necessary to offer sub-degree accuracy?
And the parameterization! They are basically dumbing down complex physics to simple lookup tables! Chaotic, nonlinear systems cannot be simplified that way. There’s a reason that the solvers Kris and I use are internally using well proven and trusted physical equations taken straight from textbooks, and that’s because you don’t have any choice. The model must be tied back to known physics or it’s garbage. To hear that these so-called experts are using parameterization in these models is infuriating to anyone with expertise in the field.
I apologize for resurrecting this old thread, but I wanted to put this here for anyone else who comes across it. We’re being lied to. Hope this helps open some eyes.
Tom,
FYI – the CMIP3 global coupled ocean-atmosphere general circulation models were prepared in 2006-7 for the IPCC’s AR4 report. This ensemble made a successful ten-year prediction through 2017. See details here.
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“A simple first step would be for climate scientists to restart the climate change debate by testing the models. That is, run past models (i.e., those used in past Assessment Reports) with actual data from model creation until present – and compare their forecasts with observed weather. The result would tell us much. I wonder why they do not do so”
These folks attempted to do just that: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL085378
“Retrospectively comparing future model projections to observations provides a robust and independent test of model skill. Here we analyze the performance of climate models published between 1970 and 2007 in projecting future global mean surface temperature”
Thor,
No, they didn’t. I proposed something simple – re-run the models with actual numbers for forcings (not scenarios) up to the present, then compare predicted temperatures with actuals.
They made lots of assumptions and made many calculations (see the paper here). See more about this here.
None of that is necessary to test the models. Perhaps the earlier models no longer have the necessary documentation (the dog ate my files), but it is unlikely to be true for the early CMIP models, let alone CMIP3 (used in the IPCC’s AR4, 2007).
That nobody has re-run the models as proposed is evidence, in the sense that actions speak louder than words.
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