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 identical ensemble-based approaches to weather modeling and prediction. In both cases, the foundation of the uncertainty terms in the forecast--that is, the grounding of locutions like “there is a 70% chance that it will rain in Manhattan tomorrow” or “there is a 90% chance that the global average temperature will increase by two or more degrees Celsius in the next 20 years”--is in an analysis of the ensemble output. The methods by which the output of different models (as well as different runs of the same model) are concatenated into a single number are worthy of investigation (as well as, perhaps, criticism), but are beyond the scope of this dissertation.

6.4 You Can’t Get Struck By Lightning In a Bottle: Why Trust Simulations?

How do we know that we can trust what these models tell us? After all, computational models are (at least at first glance) very different from standard scientific experiments in a number of different ways. Let us close this chapter with a discussion of the reliability of simulation and computational models in general.

6.4.1 Something Old, Something New

Oreskes (2000) points out that some critics of computational modeling echo a species of hard-line Popperian verificationism. That is, some critics argue that our skepticism about computational models should be grounded in the fact that, contra more standard models, computational models can’t be tested against the world in the right way. They can’t be falsified, as by the time evidence proves them inadequate, they’ll be rendered irrelevant in any case. The

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