Pielke Senior on Climate Science Misconceptions #3

Climate modelers forecast with digital crystal balls

Guest post by Dr. Roger Pielke Senior

Climate Science Myths And Misconceptions – Post #3 On Multi-Decadal Regional Climate Predictions Of Changes In Decadal And Longer Statistics Of Extreme Weather

This past Friday, Ben Herman commented in

Guest Post By Ben Herman Of The University Of Arizona

that

“Scientific predictions are just that, predictions, and until they have been verified, are just that, unverified predictions.”

This inability to validate predictions decades from now has not stopped  journals and funding agencies, such as the National Science Foundation, from reporting and funding such studies.

Misconception #3:  Regional climate predictions provide testable skillful predictions of changes in the decadal and longer statistics of extreme weather.

There is a new article which perpretuates this myth that multi-decadal global model preditions are skillful. This, unfortunately is just one example of many who are making unsubstantiated scientific claims, yet, whose research is being accepted in peer reviewed journals and being funded by the National Science Foundation and other agencies.

This article is

Ren, Diandong, Rong Fu, Lance M. Leslie, Robert E. Dickinson, 2011: Predicting Storm-triggered Landslides. Bulletin on the American Meterological Society. 129–139.

The abstract of the paper reads

“An advanced numerical modeling system projects rain-triggered landslides in a warming climate.”

Just a few excerpts from the article shows that the foundation of this paper is flawed. They write [bold face added]

What can we say about changes in storm-triggered landslides on 50-yr (or longer) time scales when we cannot predict rainfall next week? On one hand, the overall climate response of the precipitation to the increasing atmospheric concentrations of greenhouse gases may be proven predictable by current global coupled ocean–atmosphere climate models (CGCMs; Allen and Ingram 2002, and references therein). On the other hand, only very heavy or extreme precipitation triggers landslides (Iverson 2000). Although CGCMs are unable to project a specific storm’s location and timing, they can provide a statistically correct rainfall scenario for the region of interest.

“….for point accuracy in predicting a slope’s stability, very high-resolution (spatial and temporal) precipitation data from observations or more likely from numerical weather prediction models are required to drive the landslide model. In the following experiments, we linearly downscale the CGCM-provided meteorological forcing to a high resolution digital elevation map.”

All known physics considered, the CGCMs are well positioned to answer the question of whether increased temperatures cause increases in precipitation intensity and amount. Climate prediction is concerned with quantifying general trends and not with accurate prediction of specific storm events. This does not devalue the CGCM projections because, for many purposes (e.g., infrastructure construction), we are not interested in the exact timing of a mudslide and we only are required to know the time frame of its recurrence. The CGCMs, because of their limited horizontal resolution, are not expected to resolve individual precipitation events.”

That this paper passed peer review with statements that the CGCMs provide ”statistically correct rainfall scenario for the region of interest ” and  ”CGCMs, because of their limited horizontal resolution, are not expected to resolve individual precipitation events” is astounding.

 There is no way that realistic model simulations of extreme precipitation is possible by linearly downscaling from the CGCMs, as was discussed, for example, in my posts

Dynamic Downscaling From Multi-Decadal Global Model Projections Does Not Add Spatial and Temporal Accuracy Of Value To The Impacts Community

Statistical Downscaling From Multi-Decadal Global Model Projections Does Not Add Spatial and Temporal Accuracy Of Value To The Impacts Community

The Difference Between Prediction and Predictability – Recommendations For Research Funding Related to These Distinctly Different Concepts

Summary Of Why Dynamic And Statistical Downscaling Of Multi-Decadal IPCC-type Forecasts Are Misleading The Impacts Community And Policymakers

They also have not been shown to provide “statistically correct rainfall scenarios” as discussed, for example, by

Robert L. Wilby and Suraje Dessai, 2010: Robust adaptation to climate change,  first published online: 29 June 2010. Weather DOI: 10.1002/wea.543

where they wrote

“The scientific community is developing regional climate downscaling (RCD) techniques to reconcile the scale mismatch between coarse-resolution OA/GCMs and location-specific information needs of adaptation planners……It is becoming apparent, however, that downscaling also has serious practical limitations, especially where the meteorological data needed for model calibration may be of dubious quality or patchy, the links between regional and local climate are poorly understood or resolved, and where technical capacity is not in place. Another concern is that high-resolution downscaling can be misconstrued as accurate downscaling (Dessai et al., 2009). In other words, our ability to downscale to finer time and space scales does not imply that our confidence is any greater in the resulting scenarios.”

It is clear that considerable research funding is being provided to support what is not following the scientific method. As was presented in the post

Hypothesis Testing – A Failure In The 2007 IPCC Reports

I wrote

There has been a development over the last 10-15 years or so in the scientific peer reviewed literature that is short circuiting the scientific method.

The scientific method involves developing a hypothesis and then seeking to refute it. If all attempts to discredit the hypothesis fails, we start to accept the proposed theory as being an accurate description of how the real world works.

A useful summary of the scientific method is given on the website sciencebuddies.org.where they list six steps

  • Ask a Question
  • Do Background Research
  • Construct a Hypothesis
  • Test Your Hypothesis by Doing an Experiment
  • Analyze Your Data and Draw a Conclusion
  • Communicate Your Results

Unfortunately, in recent years papers have been published in the peer reviewed literature that fail to follow these proper steps of scientific investigation. These papers are short circuiting the scientific method.

As written in the IAC Review of the IPCC report (which I reproduced in the above weblog post)

“…the guidance was often applied to statements that are so vague they cannot be falsified.”

This is a correct conclusion and applies to the Ren et al Bulletin of the American Meteorological Society paper, as well as all such studies whose findings cannot be falsified.

The acceptance of hypotheses as facts in the publication process including this Ren et al 2011 paper,  is one main reason that the policy community is being significantly misinformed about the actual status of our understanding of the climate system and the role of humans within it.

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32 thoughts on “Pielke Senior on Climate Science Misconceptions #3

  1. So what you mean is that too much reliance has been put upon these models. Most of us knew that but did not know the full background. Thanks for your views which are of great value. Keep on digging.

  2. Well, this is just a reiteration of what has been stated for some time now. Peer-review is not a euphemism for validity. Far from it. I’m glad to see more and more speak out against this tripe.

  3. None of the basic sciences would make such an outrageous assumptions, btw.

    Can you imagine Astronomers saying, “well, we understand all there is to know about this solar system, therefore we know what all possible solar systems should be like.”??

    What about physicists saying, “We have now gathered all available data on subatomic particles and can now predict what the next layer down looks like.” ??

    It is truly beyond the pale for this one area of science to act as if they can simply use “all available physics” and use their prediction for local areas and apply it to a system N^N times more complex.

    What if NASA said, “The shuttle works most of the time, therefore we can build a warp ship.” ??

    It’s about the same.

  4. Got it, change will happen. Regardless of good, bad or indifferent, we’re the cause. I’d like to thank Al Gore’s Warming and the MSM for the hype. Without it, I would never be enlightened. /sarc

  5. “Another concern is that high-resolution downscaling can be misconstrued as accurate downscaling”.

    I see this as analogous to upscaling DVD players that take a standard definition disk and upscale to 1080p – these researches would seem to expect to be able to zoom in on the 1080p image, and so get more information than actually exists in the SD image.

  6. Das ist nicht nur nicht richtig, es ist nicht einmal falsch!

    “Not only is it not right, it’s not even wrong!”

    Wolfgang Pauli

  7. There is an amazing concept in some branches of science that they have the ability to predict the outcome of complex processes that they don’t really understand if they can only get more computer power. This provided a wonderful customer base for high-performance computers, for which I am personally grateful, but it is inherently stupid. You still have to do basic science. Inconvenient as that may be, you really do have to understand. Please, Dr. Pielke, keep pointing that out.

  8. “It is clear that considerable research funding is being provided to support what is not following the scientific method” ………………….

    ……but is purely political posturing….

    would be an accurate ending in my opinion.

    Political funding has skewed the science of Climate ‘science’ such that it has lost much credibility. Lets hope that the efforts of Professors Pielke, Lindzen, Carter, Christy and others are enough to rescue it from the brink.

  9. I really do not believe the lengths that the academic “climate scientific society” are prepared to go to to give credence to erroneous theories. More astounding is that the political numbskulls are falling for it.
    I see some hope in the USA, Australia, France, China and a few others that this nonsense has been uncovered. Not such joy in the UK as yet, but I keep on telling people about the “fraud” that is being played upon them. keep at it and thanks for all your work here.

  10. the thing about numerical modeling and simulation is that they are a bit like masturbation. If you do it for long enough you forget its not the real thing

  11. There is great interest in downscaling GCM results in the municipal region where I work. The output is being analyzed to infer future patterns in water supply and stormwater collection. I attend regular meetings to discuss this.

    What strikes me is that extreme scenarios and conclusions are presented as food for thought and consideration – always with the appropriate warning that these are ‘extreme’ and not certain – while ‘no change’ scenarios are ignored. It’s not that there is deception going on. It’s that the unproven is presented as such, but by virtue of its shocking nature it influences policy development.

    In the discussions of which I am a part the bias seems to me to be subtle, but very powerful. It is not permitted to say, ‘Perhaps we should do nothing, no problem here.’ Well, it is permitted, and we may get to that, but it won’t be welcomed!!

    I am certain that in other arenas, the bias is more powerful, naked, and brutally applied.

  12. What I find to be incredible is that it is considered necessary to print this sort of paper. I learned this over 60 years ago when I studied Physics 101 at Cavendish Labs, Cambridge, under my mentor, a gentleman who went on the be Prof. Sir Gordon Sutherland, Head of the NPL.

  13. Was this paper one of those test cases to see if any peer review was actually being done, or do they take themselves seriously? Perhaps it was intended as a spoof? Who was stupid enough to pay them to write this garbage? Was there a random text generator involved?
    Just asking.

  14. There should be a name for the type of scientist who believes a man-made machine can prove a hypothesis that attempts to solve a complex problem. How about “conjecturalist?”

  15. It just slays me that reliance on models is so ‘in-your-face” and that that whole community seems completely immune to the obvious pointlessness of it. Models are simply NOT reality, nor can they predict it, or alter it. Yet that is what is going on, and we are paying for it with tax dollars. Insidious.
    The number of assertions that begin with “models indicate that” has gone haywire.

  16. Can someone explain what the following means in a scientific context (or even in plain English), particularly ‘may be proven predictable’?

    “the overall climate response of the precipitation to the increasing atmospheric concentrations of greenhouse gases may be proven predictable by current global coupled ocean–atmosphere climate models”

    Surely either the models have demonstrated a skill at predicting changes in precipitation, in which case just say that, otherwise they have not demonstrated any skill in prediction and so all that follows is nothing beyond speculative nonsense.

  17. ‘The CGCMs, because of their limited horizontal resolution, are not expected to resolve individual precipitation events.”’

    So they cannot get the weather right, as is painfully obvious.
    They cannot even get seasons close enough to call.
    Even the decadal climate they have missed (It’s a cooling travesty they cannot find the missing heat).
    That’s 3 strikes, you’re out.
    Strip funding to NSF, they have failed every measure.
    With a negative rating handed down by S&P on the US debt, every billion counts.

  18. many who are making unsubstantiated scientific claims, yet, whose research is being accepted in peer reviewed
    ==============================================
    Being accepted by peers who’s own unsubstantiated scientific claims in their own papers were peer reviewed by peers who’s own unsubstantiated scientific claims in their own papers were peer reviewed by peers who’s own unsubstantiated scientific claims in their own papers………………………………..

    Tell me again exactly what you think “peer” means?

  19. I am not a scientist, but in my view the climate models may be excellent talking points among theorticians, but their relationaship with reality is zip; if the climate cannot be forecast accurately one month into the future, the models’ predictive value is also zip.

  20. Another illustration that “climate science” is most unscientific, or in other words, pseudoscience.

    In high school physics, I learned that the essence of science is successful predictions based on empirical observation. Over and over we heard the theory from “reason,” always Aristotle, and then we tested to see what the rolling ball would really do. Aristotle was wrong every time in that class, though he must have been more successful in some of his other ideas.

    Predictions based on “models” should never be used to advise politicians nor used in any other way than at climate science meetings and journals, with the understanding that we are trying to create a science. Until they have a model that successfully hindcasts, and then successfully forecasts events, they are less scientific than the Farmer’s Almananc.

  21. The real problem is the spigot of money from the NSF has not been curtailed nor even re-directed to improve the quality of science being done. The journals are interested in sensationalist claims and not falsification, and academics are engaged in a star system whose winners are decided by patronage and patronage by funding received.

    There’s no money or prestige in proving Michael Mann wrong and plenty in extending his “results”.

    It’s little wonder that academics are reluctant to rock the boat, even though in theory, that’s what we expect them to do.

  22. Latitude says: “Tell me again exactly what you think “peer” means?”

    peer (pī’-ər): n.; one who or that which pees

    theduke says: “There should be a name for the type of scientist who believes a man-made machine can prove a hypothesis…How about “conjecturalist?”

    Based on Phyllograptus’s comment, above, how about “conjacturalist?”

  23. The authors state:

    “In the following experiments, we linearly downscale the CGCM-provided meteorological forcing to a high resolution digital elevation map.”

    Don’t these guys know the experiments are only carried out in the real world not the virtual world? By using such terminology, they are able to fool a whole lot of people most of the time. But they don’t fool us. In not too distant future, they won’t be fooling anybody.

  24. A computer model is nothing more than a machine to do long winded mathematical calculations quickly. Like any other equation, the correct applied mathematical answer can only be obtained if all of the symbols are included and correctly indicate the relative factor. In the global climate, if there is such a thing, some of the relative factors could only be represented by question marks. The completely unknown factors that have never occurred to us might be represented by a question mark squared. A CGCM with its inevitably poorly weighted symbols and its missing symbols is IMO about as useful as a weather predictor, as a crystal ball that has had a torch battery and a diode attached to it. Nicely done it might look impressive but to expect useful results from it is just plain absurd. You can’t do good sums with bad numbers.

  25. Since humans are part of nature, by that very fact, they can never see the entire picture of reality (except as how we define it). Collectively, we have done a great job in describing how things work but can never see beyond a certain limit because our experience is inherently limited (we are inside the box or better yet, part of it). Experiment and observe and the conclusions are only the tip of the truth.

  26. I have the same reaction to the use of computerized climate models as I do to the ridiculous demand of PETA that they stop animal testing for drugs and use computer simulations. Given how bad / simplistic the climate models are and how unskillful they are at actually modeling our complex climate, can you imagine what would happen if you tried to model a drug’s interaction in a complex, higher order living organism? Sorry, I don’t want any of those drugs.

  27. Charles Babbage said everything about politicians and computers back 19th century, when he was outlining to Members of Parliament plans for his difference engine (computer) and they asked if the machine would still produce the right answers even if the wrong figures were entered: “I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.”

  28. Dr. Roger Pielke Senior,

    What does this say about the researchers and the people involved in allowing garbage to actually reach the print stage in the various journals. These are humans with all the failing that go with that fact but it would seem that the value of degrees, PHds etc has reached an all time low or the people involved have no moral value in their totally deluded lives!

    What I really would like to see or hear is a plan for both sides to get science and the once trusted journals back into the hands of more trustworthy people!

    I am not holding my breath!

  29. Nothing here surprises me. In EVERY FIELD of interest in which I have engaged I have been appalled at the dominance of manipulators of evidence and lack of objective rigidity. It is a sad reflection on society that so many people who ought to be more critical just go along with ideas and theories that they should challenge.

    Peter

  30. Well said, and it needs to be said more often. Hypothesis testing these days, in the popular press and amongst too many politicians, is irrefutable evidence of climate change denialism. It might be a good idea as well to emphasise that broad assertions about climate behaviours, past and present, quite often don’t amount to hypotheses at all. How do you falsify a climate model? Especially if you’re not told which bits of which data sets were used as input, or what the precision and accuracy of measurements might be. Or you’re not even allowed to see the model in the first place. Science has nothing to apologise for. The scientific method is pretty straightforward, and Pielke’s short summary isn’t beyond Joe and Jane Average. It says a lot about schools and university that so few ‘educated’ citizens just don’t get what science is, and how it works. Fixable, but it’s a great worry that the problem exists at all.

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