Climate change Darwinism FYI-FTR General interest

From Darwinism to Global Warming and Back

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I was reading an exchange of emails that took place between noted physcist (and skeptical warmer) Freeman Dyson and his interlocutor, Steve Conner, of the Independent of London.

To my eye, Dyson is spot on in his critical thinking. But what most caught my eye was his analysis between the ‘experts’ and the general public that seems to have occurred. I think it serves as a good understanding of where Darwinism/neo-Darwinism now stands in academia.

When I was in high-school in England in the 1930s, we learned that continents had been drifting according to the evidence collected by Wegener. It was a great mystery to understand how this happened, but not much doubt that it happened. So it came as a surprise to me later to learn that there had been a consensus against Wegener. If there was a consensus, it was among a small group of experts rather than among the broader public. I think that the situation today with global warming is similar. Among my friends, I do not find much of a consensus. Most of us are sceptical and do not pretend to be experts. My impression is that the experts are deluded because they have been studying the details of climate models for 30 years and they come to believe the models are real. After 30 years they lose the ability to think outside the models. And it is normal for experts in a narrow area to think alike and develop a settled dogma. The dogma is sometimes right and sometimes wrong. In astronomy this happens all the time, and it is great fun to see new observations that prove the old dogmas wrong.

Unfortunately things are different in climate science because the arguments have become heavily politicised. To say that the dogmas are wrong has become politically incorrect. As a result, the media generally exaggerate the degree of consensus and also exaggerate the importance of the questions.

I am glad we are now talking about more general issues and not about technical details. I do not pretend to be an expert about the details.

Thought you might be interested.

22 Replies to “From Darwinism to Global Warming and Back

  1. 1
    Robert Byers says:

    AMEN. I see it as a upper class movement to make a greener, cleaner world for the upper class to enjoy their next decades in happiness.
    So they need to control the pollution as they anticipate
    Then its forceful people who press it home.
    I think also everybody has a instinct it must be man affects the planet and so signs of warming proves it is man.
    Its not warmer here in canada. !!
    Its all dumb and is motivated by more then honest investigation.
    Then also its just incompetent like investigation done by evolutionists.

  2. 2
    Starbuck says:

    “Amen” lol it is so obvious you all are religious and deny science for bronze age beliefs it is pathetic.

  3. 3
    Jerad says:

    Too bad Dr Dyson undercuts his own point somewhat.

    When I was in high-school in England in the 1930s, we learned that continents had been drifting according to the evidence collected by Wegener. It was a great mystery to understand how this happened, but not much doubt that it happened. So it came as a surprise to me later to learn that there had been a consensus against Wegener. If there was a consensus, it was among a small group of experts rather than among the broader public.

    So the consensus against Wegener was wrong but the experts who had studied the evidence got it right.

    I think that the situation today with global warming is similar. Among my friends, I do not find much of a consensus. Most of us are sceptical and do not pretend to be experts. My impression is that the experts are deluded because they have been studying the details of climate models for 30 years and they come to believe the models are real.

    So, being well versed in the data can lead one to become deluded?

    Which is it: the experts see things the generalists do not OR the experts get too far down the rabbit hole?

    After 30 years they lose the ability to think outside the models. And it is normal for experts in a narrow area to think alike and develop a settled dogma. The dogma is sometimes right and sometimes wrong. In astronomy this happens all the time, and it is great fun to see new observations that prove the old dogmas wrong.

    Unfortunately things are different in climate science because the arguments have become heavily politicised. To say that the dogmas are wrong has become politically incorrect. As a result, the media generally exaggerate the degree of consensus and also exaggerate the importance of the questions.

    Now he’s saying it’s partly down to politics instead of the science? The experts are deluded AND no one is allowed to disagree? And maybe there is no real consensus.

    I am glad we are now talking about more general issues and not about technical details. I do not pretend to be an expert about the details.

    Well, good thing ’cause otherwise you’d probably be deluded.

    Look, the ICCC has published reports where a large number of climate scientists have presented their data and are in general agreement. If anyone has a problem with the data or the conclusions then address those. Finding famous people whose opinion you agree with is NOT doing science.

  4. 4
    Mapou says:

    After 30 years they lose the ability to think outside the models.

    Yep, and these are the same models that failed to predict the great PAUSE in global warming. Now they’re blaming the pause on the oceans. Imagine that! Models that failed to account for the apparently huge dampening effect of the oceans.

    And they wonder why we don’t trust them.

  5. 5
    Piotr says:

    #4 Mapou

    What makes you think the dampening effect of the oceans was ignored in climatic modelling? In another thread I cited a 1979 document (the Charney Report) which predicted both global warming and periodic pauses due to ocean dynamics. That was 26 years ago. The models used then were pretty crude, but the role of the oceans was appreciated. The report also contained a warning: temporarily suppressed surface temperatures do not mean that the extra heat inevitably trapped by greenhouse gases is not being stored in the global system.

  6. 6
    faded_Glory says:

    I am familiar with the method of using sophisticated computer models to predict complicated physical processes. I used to work in the oil sector where petroleum engineers use exactly such models to predict the behaviour and production over time of oil and gas reservoirs under deletion. These models are among the most complex ones in the world, directly comparable to the ones used in climate studies.

    These models are industry standard. Very significant investments are routinely made on the basis of the output of such models – investments of hundreds of millions of dollars and more.

    Models may be (and often are) incorrect. Stuff is complicated and it takes time to refine models through feedback so that the prediction errors get smaller and smaller. It is also possible that there are unknown factors influencing the predictions, factors that therefore are not built into the models, making them less accurate.

    All this is true. Models have their weaknesses. Still, on the whole they have proven to be better than any other method of predicting such complicated physical systems. They do not come cheap. If sufficiently accurate predictions could be made without them, they would be dropped in a heartbeat to cut costs.

    I would like to know what people would propose as a better alternative to understanding the climate and how it may change in the near future, than building and calibrating highly sophisticated computer models.

    fG

  7. 7
    hrun0815 says:

    I would like to know what people would propose as a better alternative to understanding the climate and how it may change in the near future, than building and calibrating highly sophisticated computer models.

    Listening to experts (self-proclaimed or real) that happen to agree with our intuitions while ignoring all others, of course!

  8. 8
    Zachriel says:

    How excess heat is distributed through the climate system (atmosphere, hydrosphere, cryosphere) is chaotic, and difficult to predict. However, the overall energy of the climate system is increasing consistently.

  9. 9
    velikovskys says:

    Mapou:
    Yep, and these are the same models that failed to predict the great PAUSE in global warming.

    It is a great pause now?

  10. 10
    kairosfocus says:

    Jerad:

    Popped by a moment and saw:

    So, being well versed in the data can lead one to become deluded?

    Which is it: the experts see things the generalists do not OR the experts get too far down the rabbit hole?

    yes, being versed and immersed in the school of thought of the day can in fact lead to being locked in ideationally. That’s a famous effect, and it is part of the debate over the ~ 26 year old genius effect that has repeatedly popped up in really big breakthroughs over several centuries — especially in Physics.

    A serious argument is that at that stage, such are sufficiently immersed to have adequate insight and independent creativity, but are not so locked into the paradigm that they are not going to lead breakthroughs. Newton, Maxwell and Einstein are three capital cases in point.

    So is the remark as to how a new paradigm progresses one funeral at a time.

    And, do I need to point out the insight that research programmes can be progressive or degenerative, and can reverse status? Or, that there can be an incommensurate-ness between paradigms such that there is no defined simple point or critical experiment that decides between them.

    If in that context a paradigm of a day is embedded in institutions or worldviews, and/or becomes politically important, that can lead to very dangerous closed-mindedness effects. The history of the past century is replete with sad cases in point.

    I find that there has been a failure to properly orient people that science is a social and often ideological process, with deep worldview issues involved, and the scientism myth of triumphalistic progress linked to the notion that Science has cornered the market on knowledge, has been substituted for sober insight.

    So, yes, please think afresh.

    KF

  11. 11
    kairosfocus says:

    fG:

    Yes, we would be well advised to appreciate that models are not observations (indeed they are strictly necessarily false so their ability to predict accurately is not a guarantee against limitations).

    And indeed theories are in fact models.

    Ex falso, quodlibet.

    That should be inscribed over the lintel of every Science Lab.

    Instead of confusing theories for truth, we should appreciate them critically as explanations that we may validate per reliability to a certain extent but which can and do go wrong, even spectacularly wrong. Economics models are notorious, but in physics there were some revolutions that happened on breakdown of apparently reliable models. And there are cases farther afield. Success and complexity or sophistication are not to be allowed to lull us into overconfidence.

    Put another way, scientific work is provisional, and scientific knowledge (especially explanatory aspects) is weak form.

    And, where we cannot directly observe, such as the remote past of origins or the future of the weather systems, we should have due caution on that inescapable provisionality.

    Models simply are not reality, and simulations are not experimental observations.

    (In the case of climate models, I suggest the coarseness of observations in the face of a sensitively dependent nonlinear system, the gaps between what our models often say and the weather over the next couple of weeks (or days, in hurricane tracks) and more, should serve as pointers to caution. Don’t forget, one of the roots of chaos theory was Lorentz’s weather models. And, the difference between the structure of the warming of the atmosphere per models and the actual observations, multiplied by the plateau for over 15 years that was not expected and was viewed with skepticism for year after year, should add to that.)

    Anyway, I have but little interest in getting into long hot disputes on climate issues, I only want to point out the basic points on limits of science and of modelling.

    A glance at the tone of several remarks above, shows why some fresh thinking would be helpful.

    KF

  12. 12
    Piotr says:

    KF That’s a famous effect, and it is part of the debate over the ~ 26 year old genius effect that has repeatedly popped up in really big breakthroughs over several centuries — especially in Physics.

    Good. Charles Keeling was ~26 years old when he started measuring CO2 concentrations and drawing conclusions from his measurements.

  13. 13
    kairosfocus says:

    F/N: Just got around to Drudge. Looks like the models failed on the snow storm:

    http://www.myfoxny.com/story/27950449/missed-call

    As I was saying . . . KF

  14. 14
    Zachriel says:

    kairosfocus: Looks like the models failed on the snow storm

    Snow? In Boston? What snow?
    http://i.imgur.com/1Cn1awR.gifv

  15. 15
    kairosfocus says:

    F/N: The linked article:

    __________________________

    >> My deepest apologies to many key decision makers and so many members of the general public.
    — Gary Szatkowski (@GarySzatkowski) January 27, 2015

    National Weather Service to evaluate work after missed call

    Posted: Jan 27, 2015 10:39 AM Updated: Jan 27, 2015 11:08 AM

    MYFOXNY.COM/AP – A National Weather Service official says the agency will evaluate its storm modeling after a storm that was predicted to dump a foot or more of snow on many parts of New Jersey and the Philadelphia region delivered far less than that.

    “You made a lot of tough decisions expecting us to get it right, and we didn’t. Once again, I’m sorry,” said meteorologist Gary Szatkowski of the NWS.

    Jim Bunker, who leads the weather service’s observing program in the Mount Holly office, says the storm tracked a bit to the east of what forecasting models predicted.

    Parts of Long Island and New England are getting slammed. But many parts of New Jersey received less than 4 inches.

    Bunker says the agency will evaluate what happened to see how it can do better in the future. >>
    ________________________

    KF

    PS: From Ari in Metaphysics 1011b, truth says of what is that it is, and of what is not that it is not. Accuracy to reality in what is affirmed and in what is denied. I forget who said it is better to be roughly right than precisely wrong. Which is where distributions and averages come in, and probabilities, which are inherently indices of ignorance or uncertainty save when they are 0 or 1.

  16. 16
  17. 17
    velikovskys says:

    Kf:
    MYFOXNY.COM/AP – A National Weather Service official says the agency will evaluate its storm modeling after a storm that was predicted to dump a foot or more of snow on many parts of New Jersey and the Philadelphia region delivered far less than that.

    I don’t believe they use the same models for short term local as long term global

  18. 18
    kairosfocus says:

    VS, the chaotic nature of atmospheric dynamics joined to coarseness of observation data relative to actual scales of relevant interactions already poses problems in the short-term, so we should be very cautious in the longer term of decades and centuries. Remember, too, it is weather data (often, as filtered and calibrated) that feed the global circulation models etc,; so if they are inadequate to feed short term models and lend them high enough accuracy to soundly guide decision makers [note the cluster of cities involved], that should give us pause on long term models for which our global grid is coarse and in a context where things like the projected structure of atmospheric warning and observations diverged, and the 15+ y plateau seems to have been by and large unanticipated and often not recognised until it stretched into the better part of two decades, and more. On the whole, we also need to bring to bear for instance the much more robust models on economic impacts of suggested policies . . . as a man-made disaster can have at least as much impact; cf the blunders that worsened the 1930’s depression. On balance, we need to address ranges of possibilities and options with broad-spectrum consequences as problematiques have feedback loops and lags cutting across whole domains; i.e. the biophysical, socio-cultural and economic domains are inextricably intertwined and interactive. As a consequence sustainable livelihoods — one of the pivotal issues in all this — is a massively non-trivial problem. KF

  19. 19
    faded_Glory says:

    KF in #11, I don’t disagree with most of what you say and, like you, I am also right now not interested in hot (or cold!) discussions about climate change.

    I would still be interested in hearing your answer, as a physicist, to my question:

    I would like to know what people would propose as a better alternative to understanding the climate and how it may change in the near future, than building and calibrating highly sophisticated computer models.

    Thanks,

    fG

  20. 20
    kairosfocus says:

    fG: The issue is not whether we should construct and run sims, but how we should view them — with a due measure of caution in light of inherent limitations. Years ago, there was a phrase, “Pascal washes whiter,” that aptly captured the linked GIGO problem. No computer model is better than its data base, its assumptions and structures, its algorithms. And in the case of climate modelling sims were in my view prematurely graduated for use as policy drivers without due cautions, in a context where the very data . . . especially long term proxies but also measurements . . . are to be treated with caution. Putting on another hat, I am fairly confident that energy is deeply connected to economic activities and opportunities for development, and so we would be well advised to reckon with the implications for economic activity and livelihoods. Crashing economies to postpone warming projections by a few years does not seem a particularly sound solution, especially where every potentially serious alternative energy base is brought under fire in turn. That makes me very uncomfortable when I see global centralisation on the table as a policy option, and when I see talk of 1 billion as comfortable population . . . the entire world pop in families could be put in plots of about 1 acre in the land area of Australia. Ill-considered policy action or inaction (business as usual) can do serious damage. There are no easy or obvious solutions, and we should be careful of getting caught in GIGO traps. KF

  21. 21
    kairosfocus says:

    F/N: On GIGO

    https://www.princeton.edu/~achaney/tmve/wiki100k/docs/Garbage_In,_Garbage_Out.html

    >> Garbage In, Garbage Out (abbreviated to GIGO, coined as a pun on the phrase First-In, First-Out) is a phrase in the field of computer science or information and communication technology. It is used primarily to call attention to the fact that computers will unquestioningly process the most nonsensical of input data (garbage in) and produce nonsensical output (garbage out). It was most popular in the early days of computing, but applies even more today, when powerful computers can spew out mountains of erroneous information in a short time. The actual term “Garbage in, garbage out”, coined as a teaching mantra by George Fuechsel, an IBM 305 RAMAC technician/instructor in New York, was soon contracted to the acronym “GIGO”.[citation needed] Early programmers were required to test virtually each program step and cautioned not to expect that the resulting program would “do the right thing” when given imperfect input. The underlying principle was noted by the inventor of the first programmable computing device design:

    [On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” … I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
    —Charles Babbage, Passages from the Life of a Philosopher[3] ]

    It is also commonly used to describe failures in human decision making due to faulty, incomplete, or imprecise data.

    The term can also be used as an explanation for the poor quality of a digitized audio or video file. Although digitizing can be the first step in cleaning up a signal, it does not, by itself, improve the quality. Defects in the original analog signal will be faithfully recorded, but may be identified and removed by a subsequent step. (See Digital signal processing.)

    Garbage In, Gospel Out is a more recent expansion of the acronym. It is a sardonic comment on the tendency to put excessive trust in “computerized” data, and on the propensity for individuals to blindly accept what the computer says. Because the data goes through the computer, people tend to believe it. >>

    As they say, a due caution.

    KF

  22. 22
    faded_Glory says:

    KF,

    If I understand you correctly, you agree that computer modelling is actually the best way to understand the climate and how it may change in the future, but we should use the output of these models with caution because of potentially significant error bars.

    In other words, you don’t so much take exception to the models themselves, but to the way the politicians are using the results.

    I have, again, no real problem with that position. Everybody should weigh for themselves how they want to see the balance between waiting too long with action in case warming is truly happening, and curtailing emissions too sharply which may result in economic hardships, in case warming is not happening.

    Those are imo valid considerations based on accepting the models but with due appreciation of their error bars.

    Which is very different from constantly accusing climate scientists of fraud, bias, corruption, incompetence and what not – the tune sung by a lot of the ID people on this site.

    fG

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