But the system of academic science is very good at improving models and, importantly, exposing their limits. None of this is to say that climate models are perfect. Modelling is often used at this level also. It's the reverse: reputation is earned by finding problems, not by hiding them.Ī large part of the challenge of climate modelling is the leveraging of increasing computer power to resolve progressively smaller scales of motion, and this uncovers the need to understand those scales in isolation. Note that there is no commercial element to any of this, which means there is no incentive to hide problems. This means that the models are open-source, so everyone can see what everyone else is doing. Models stem from the academic environment, not the business environment. Models must rely on a fairly large set of parameterizations of processes that cannot be resolved directly, and tests are done to see which parameterizations make the most sense. Simulations are done with a range of alternative models, or individual models with different conditions are applied, and the differences between the simulations are studied in detail. Test suites are developed along with models. All manner of tests are done in fluid-dynamical modelling.
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