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Why Climate Modeling Cannot Work

Discussion in 'Political Discussion' started by patsfan13, Jun 14, 2011.

  1. patsfan13

    patsfan13 Hall of Fame Poster PatsFans.com Supporter

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    Very good essay on why Climate Models like those promoting MMGW cannot work, due to the math involved in the systems. Very clear explanation for the non mathematicians.

    Intelligence as a Service: The Chaos theoretic argument that undermines Climate Change modelling




    Very good article interesting read for the curious.

    On some of the climate discussions here I referenced the difficulty of modeling systems with many degrees of freedom and the problems of the boundary conditions (ie outer space), I didn't appreciate the importance of initial conditions in these models and the fact that the major input to the system is also a chaotic process (ie the Sun).
     
  2. Mrs.PatsFanInVa

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    #24 Jersey

    Most especially good for those who want to read things which are not intended to be scientific and tells you so, quite clearly, in the introductory sentence.

    This is not intended to be a scientific paper, but a discussion of the disruptive light Chaos Theory can cast on climate change, for non-specialist readers.
     
  3. patsfan13

    patsfan13 Hall of Fame Poster PatsFans.com Supporter

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    ie discussing a topic without getting too technical ie for the non technical, as I stated.
     
  4. PatsWSB47

    PatsWSB47 Veteran Starter w/Big Long Term Deal

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    #12 Jersey


    I read that to mean he was dumbing it down, so to speak, for lay people(non-specialists), no?
     
  5. Mrs.PatsFanInVa

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    Maybe - although I'm doubtful you can "dumb down" things like The Chaos Theory for anyone in a blog post.

    Besides which, did it read "dumbed down" to you? There was some fairly technical descriptions and formulas in there - as well as the mention of several different concepts and theories which would impress most people - because most people have never heard of them and therefore would be unable to judge if they were being used correctly or incorrectly.

    I guess we look at it differently - I assume if someone begins a scientific sounding explanation with the disclaimer that it is not "a scientific paper" he's warning other scientists not to take him seriously and not to try to argue with him. I mean, how can you scientifically discredit something which has already been declared "not scientific" by the author?
     
  6. patsfan13

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    If you are not interested fine Please don't hijack the thread by worrying about nonsense.
     
  7. PatsWSB47

    PatsWSB47 Veteran Starter w/Big Long Term Deal

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    LOL, Spiderman comics are too technical to me:p I really think he was forewarning specialists that this wasn't a scientific paper complete with all the back up data required to make it a publishable theory and at the same time telling non-specialists what to and what not to expect. I'd like to see him to take the next step though and do another complete report that can stand up to scrutiny from the scientific community.

    I disagree with the Title of the thread though. The article explains that such models are problematic and therefore shouldn't be taken as completely reliable. I didn't read that such modeling cannot work.

     
  8. patsfan13

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    I think he is saying that due to the nature of Chaotic systems, you cannot model them as a time series see this statement:


    Now due to the sensitivity of these systems to initial conditions and how they affect the later states of the model if you can't predict the time series in the short the uncertainty grows over time.
     
  9. wistahpatsfan

    wistahpatsfan Pro Bowl Player

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    Nice article. Very interesting but incomplete as he announced at the beginning. I appreciate the link because I'm having a slow morning waiting for something to happen (like in the Dr Seuss book).

    This is an interesting conclusion he makes:
    "They can never match up the model to shorter term events, like say six months away, because as we’ve seen, the weather six months away is completely and utterly unpredictable, except in very general terms.

    This has terrible implications for their ability to model...."

    And he closes with this:
    "It is my view that governmental policymakers should not act on the basis of these models. The likelihood seems to be that they have as much similarity to the real world as The Sims, or Half-life.

    On a final note, there is another school of weather prediction that holds that long term weather is largely determined by variations in solar output. Nothing here either confirms or denies that hypothesis, as long term sunspot records have shown that solar activity is chaotic too."


    Two things about these exerpts strike me: First, if weather is completely and utterly unpredictable, why isn't it ever 95 and sunny in January in Maine? Why doesn't it ever snow in Jamaica? Why are there never tornadoes in Ireland? Why is there never 100 inches of rain in the Sahara desert? By weather I assume he means events on a very small scale of predictability as he states himself He fails to make the distinction between "weather" and "climate" unless I missed something.

    The other thing that jumps out at me after quickly reading the article is this: If a model can be rendered meaningless for government decision because of a certain amount of chaos involved, shouldn't that conclusion be reached about every single model used in governmental decisions? The government has no problem using economic models analysis of behavior of humans and, by extension, governments, and any number of models currently being used by government agencies. Is there not an inherent degree of chaos in any system that incorporates the natural world? Why is "weather" the only domain that "should not be" analysed by our present-day modelling? People of all shades of the political spectrum have their predictors all lined up when arguing their positions about economic policy, and many of those predictors are economic models. What makes those models any less chaotic and more reliable? You talk about averaging out data and including more and more factors for input into a model!

    Also, isn't the variability of models reduced by our incremental improvement in computing capability? I'm not saying any model is ever going to be perfectly reliable. I'm just saying that calculating speed will continue to improvbe, and with that, so should the accuracy and reliability of modeling of any kind, regardless of the amount of chaos involved.
     
  10. patsfan13

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    Interesting observations wistah. The reason Brute computing power cannot solve the problem has to do with the underlying nature of the systems. The sensitivity to initial conditions and the propagation of small errors through the system make exact predictions impossible over a long time series as I read it.


    That does not mean that we can't say for example that it will tend to be colder in Jan that in July. What we can't say is that on Next Jan 23 in Boston there will be 10" on snow the wind will avg 15mpg and the high temp will be 23F.



    Think of a river, we can determine that the flow will go from a higher potential to a lower potential (ie water will flow downhill, but to try to predict the behavior of a group of mocules and the behavior of the eddies, and rapids is beyond our scope to pin down specifically.

    Many natural systems are chaotic those are the kinds of problems science has a hard time pinning down.

    If you area about 'attractors' in chaos theory, you will see a mechanism that shows up a lot in nature (and electronics).
     
    Last edited: Jun 14, 2011
  11. wistahpatsfan

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    I knowq a lot about watershed modeling. Very rarely is it necessary to include the characteristics of eddys and pebbles in potholes. As far as humans are concerned in many cases, what matters is knowing the big picture, and that is how high will the river be 10 miles downstream from a rain event and when it will happen. Chemical compositions and temperature are also important. Other factors are assessed and inserted into the model such as turbuduty, stream contours, gradient, and local surface conditions like bank vegetation, surface permeability, soil characteristics and bedrock countours. There are many dynamic and ever-evolving models that can be used for these assessments. Thing is, until about 30 years ago, watershed science and management was in its infancy as a discipline and models were crude to say the least. They aren't anymore. And while the small pictures are important to some on an event-to event basis, policy making is more dependent on on-going improvement of the assessment tools and should be made to the best of our ability until the science and modeling improves, at which point we can and should make adjustments.
     

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