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'Have the climate models been successful in predicting anything? They, of course, predict substantial global warming. This is not surprising given the expressed belief of some of the model builders in the global warming hypothesis and the many parameters in the model that need to be introduced. However, the models also predict unambiguously that the atmosphere is warming faster than the surface of the earth; but all the available observational data unambiguously shows the opposite! Truth in science is always determined from observational facts. One finds the truth by making a hypothesis and comparing observations with the hypothesis. It is absolutely essential that one should be neutral and not fall in love with the hypothesis. If the facts are contrary to any predictions, then the hypothesis is wrong no matter how appealing. 'Truth by Assertion' is not science.' [1]

David Douglass#1

ClimateDataScienceScientific method

About this quote

What does it mean?
Douglass argues that climate models should match observational data, and if they don't, the hypothesis must be rejected.
In plain terms
Science requires matching predictions to facts, not ignoring differences.
What can you take from it?
Hypotheses must be discarded if observations contradict them.

Where it applies

  • scientific debate
  • modeling
  • research methodology

Putting it to work

  • check model accuracy
  • compare data to predictions
  • prioritize facts

Questions to consider

  • How do you validate a complex model?
  • When should science reject a hypothesis?

Another view

Complex models may have valid reasons for discrepancies beyond simple falsification.

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