Simulation, Calibration, And Chaotic Systems

     With the recent conclusion of the Paris Conference has arisen a fresh set of cries from the warmistas: this time, to the effect that “there’s an international agreement now, so it’s time to cease your blasphemous dissents.” As usual, they point to their simulations, chant that “the science is settled,” and expect everyone to fall in line. And as usual, they’re furious that we’re not doing as they ask. One fellow with whom I recently crossed swords gave me this for a valediction:

     You dipsticks are terminal failures to mankind. Let's hope your wilful ignorance of science extends to medicine and you die a long, slow, painful death of dick bleeding disease.

     Charming, eh? But that’s what you get from persons who claim to march under the banner of “science” without having the faintest idea what science is.

     Among other things, simulation is not science – and as I’m an expert on real-time simulation, I believe I can speak on this subject with some authority.

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     Simulation techniques can be useful to scientists, but in and of themselves, they are not science. The essence of science, without which the word means nothing, is scientific method:

1. Collect a substantial amount of real-world data centered on a phenomenon of interest.
2. Note patterns in the data, specifically with regard to sequences of events in time.
3. Formulate a hypothesis about what causal mechanism might lie behind those patterns. (Steps 2 and 3 constitute the inductive phase of the scientific method.)
4. Use the hypothesis to predict specific outcomes from related contexts and stimuli applied to them.
5. Design experiments that can test the above predictions. (Steps 4 and 5 constitute the deductive phase of the scientific method.)
6. Perform the experiments:
   • If the predictions come true, the hypothesis has been confirmed, and testing should continue. (Note: Confirmation is not proof; no scientific hypothesis is ever regarded as “proven.”
   • If the predictions don’t come true, the hypothesis has been falsified and must be discarded. From here, one must return to steps 2 and 3 to try again.

     The applicability of simulation to the scientific method is at step 3. Once a causal mechanism has been hypothesized, a simulation of that mechanism can be used to explore it further: in particular, to make the predictions which subsequent experiments will test. However, the simulation cannot and does not confirm the hypothesis. How could it? It’s an artificial environment under the complete control of the simulation writer. He has dictated its starting conditions and its rules, which don’t necessarily match reality...and reality is notably indifferent to anyone’s opinions about it.

     Only an experiment in the real world, with strictly specified and enforced starting conditions and a strictly specified and reproducible stimulus, can confirm or falsify a scientific hypothesis. The warmistas cannot produce such an experiment – no one can – because the Earth is an open system. So they fall back on simulations which:

• Omit consideration of admittedly mysterious atmospheric, oceanic, and solar phenomena;
• Employ disputed, heterogeneous data sets that they refuse to disclose;
• Require repeated adjustments to that data, hewing to no widely agreed-upon standard;
• Have yet to produce a result markedly similar to real-world conditions;
• Cannot even reproduce previous eras’ climatic developments.

     As one of my physics professors once said of a bit of theorizing, this is so bad it’s not even wrong.

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     A few words about calibration and chaotic systems and I’ll close for today.

     The atmosphere and oceans are empirically regarded as chaotic. Most laymen don’t understand the term, which is an arcane one that pertains to the equally arcane realm of perturbation theory. To be as brief as possible while remaining clear, in a chaotic system, there is no way to predict the extent of the system’s response to a proposed stimulus, regardless of the nature, magnitude, and duration of the stimulus. The response’s magnitude, or its scope, or its duration, or some combination thereof, can (and sometimes will) exceed any proposed upper bound.

     This is of central importance to the problem of calibrated measurement. In a chaotic system, if one’s measurements are not absolutely exact, then there is a possibility that either the starting conditions or the magnitude and duration of the stimulus will differ sufficiently from the experimental proposal to produce completely irrelevant results.

     There has never been a measurement technique for any measurable item or event that provides absolute accuracy. In the nature of things, there never will be one. Thus, experiments proposed to probe the laws that govern a chaotic system are inherently problematic. We can observe the behavior of such systems at a macroscopic order of magnitude. We can hope that the patterns we see in them provide some basis for prediction. But no matter how often we’ve used the pattern successfully, those predictions will sometimes be wrong, and there is nothing we can do about it.

     Ask your local weatherman.

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     The idea of a law against bad weather was once a commonplace joke about the limits of governments and legislation. (The Soviets would have loved to pass such a law, but then, they’d have loved to make a lot of counterfactual things true.) Weather – the instantaneous condition of the atmosphere in a particular region – is merely one of the aspects of a chaotic system. Climate – our large-scale, long-term expectations about weather patterns in a given region – can never be anything but chaotic. No simulation can capture all its oddities. It’s not merely a matter of “not knowing enough yet.” Chaos is inherent in an open system of fluids whose dynamics are influenced by Brownian motion and turbulence.

     Max Born once said that should the opportunity be afforded him in the next life, he would ask God two questions: “Why relativity?” and “Why turbulence?” Frankly, I’d like to know the answers myself. But I think I already know what He would say:

“They make the game interesting.”

     Hard to argue with that, isn’t it?