This paper was published in late 2017, and we didn’t notice it then. Today thanks to a tip from Dr. Willie Soon, via Willis Eschenbach, we notice it now. The paper is open access. See PDF link below.
Seeding Chaos: The Dire Consequences of Numerical Noise in NWP Perturbation Experiments
Abstract
Studying changes made to initial conditions or other model aspects can yield valuable insights into dynamics and predictability, but are associated with an unrealistic phenomenon called chaos seeding that can cause misinterpretations of results.
Perturbation experiments are a common technique used to study how differences between model simulations evolve within chaotic systems. Such perturbation experiments include modifications to initial conditions (including those involved with data assimilation), boundary conditions, and model parameterizations. We have discovered, however, that any difference between model simulations produces a rapid propagation of very small changes throughout all prognostic model variables at a rate many times…
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