Climate “Science” Based Upon Computer Modeling Is Magic, Not A Science

  • Date: 05/04/16
  • Al Fin blog

“Most of the greatest evils that man has inflicted upon man have come through people feeling quite certain about something which, in fact, is false.”

Currently, Computer Models are Ridiculous Failures — At Least When Used in Climate

The real world is muddy and messy and full of things that we do not yet understand. It is much easier for a scientist to sit in an air-conditioned building and run computer models, than to put on winter clothes and measure what is really happening outside in the swamps and the clouds. That is why the climate model experts end up believing their own models. —Freeman Dyson in Edge

Computer Models are Incredibly Useful For What They Can Do

Computer model output is not science, and cannot substitute for scientific experimentation. Computer models can only process data, using the assumptions and hypotheses that are force-fed into the model. The output of computer models — once validated — can lead to revolutionary new hypotheses which can be tested by more experiments in the messy, muddy, real world.

Computer models and their output are not evidence of anything. Computer models are extremely useful when we have hypotheses about complex, multi-variable systems. It may not be immediately obvious how to test these hypotheses, so computer models can take these hypothesized formulas and generate predicted values of measurable variables that can then be used to compare to actual physical observations. —Warren Meier

More details on underlying climate feedbacks

Upcoming Conference on Computer Modeling: Reproducibility, Sustainability, and Preservation

This week in Oxford, an important conference on improving the credibility and usefulness of computer modeling will be held at the Alan Turing Institute. If one of the outcomes of the conference is an improvement in data transparency and reproducibility — and an upgrading in model validation — we may begin to see computer modeling come into its own, instead of being used as a tool of political policy deception.

Modelling is used across scientific fields – ranging from astrophysics and climate prediction to bioinformatics and economics. But there is increasing debate about the fact that this science is difficult to validate through reproduction.

… Humans – even scientists – are after all fallible. Transforming any information into a program almost invariably introduces bugs along the way. —

Even if models are built and used with the best of intentions — a big “if” in modern climate policy-making — inadvertent mistakes are always built into the model, which can be almost impossible to discover without painstaking validation. Today’s climate scientists, activists, and policy-makers do not want to get their hands dirty with the hard work of testing their models. They would rather assume that if their models tell them what they mean to hear, that the model is virtually omnipotent.

People Cling to the Illusion of Certainty to the Bitter End

In his fascinating 1950 book “Unpopular Essays,” mathematician and philosopher Bertrand Russell wrote that

“Most of the greatest evils that man has inflicted upon man have come through people feeling quite certain about something which, in fact, is false.”

He explained how the nature of people made such inflictions possible.

The demand for certainty is one which is natural to man, but is nevertheless an intellectual vice. So long as men are not trained to withhold judgment in the absence of evidence, they will be led astray by cocksure prophets, and it is likely that their leaders will be either ignorant fanatics or dishonest charlatans. To endure uncertainty is difficult, but so are most of the other virtues. — Yes, You are certain it is true! But is it true?

Why IPCC Models Cannot Reliably Predict the Future

The climate is a complex, multivariate system which displays emergent behaviour. This makes a big difference when predicting future system behaviour.

“Reductionism argues that deterministic approaches to science and positivist views of causation are the appropriate methodologies for exploring complex, multivariate systems … where the behavior of a complex system can be deduced from the fundamental reductionist understanding. Rather, large, complex systems may be better understood, and perhaps only understood, in terms of observed, emergent behavior. The practical implication is that there exist system behaviors and structures that are not amenable to explanation or prediction by reductionist methodologies … the GCM is the numerical solution of a complex but purely deterministic set of nonlinear partial differential equations over a defined spatiotemporal grid, and no attempt is made to introduce any quantification of uncertainty into its construction … [T]he reductionist argument that large scale behaviour can be represented by the aggregative effects of smaller scale process has never been validated in the context of natural environmental systems .” — quoted by Dr. Norman Page in WUWT

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