By Eviatar Bach and Oliver Dunbar To understand this blog post, you will need some basic familiarity with probability (Bayes’ theorem, covariance) and multivariate calculus. In climate modeling, small-scale processes that cannot be resolved, such as convection and cloud physics, are represented using parameterizations (see two previous blog posts here and here). The parameterizations depend on uncertain parameters, which leads to uncertainty in simulations of future climates. At CliMA, we use observations of the current climate, as well as high-resolution simulations, to estimate these parameters. The learning problem is challenging, as the parameterized processes typically are not directly observable, and…
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