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Quantifying the uncertainty of wind energy potential from climate models is avery time-consuming task and requires a considerable amount of computationalresources. A statistical model trained on a small set of runs can act as astochastic approximation of the original climate model, and be used to assessthe uncertainty considerably faster than by resorting to the original climatemodel for additional runs. While Gaussian models have been widely employed asmeans to approximate climate simulations, the Gaussianity assumption is notsuitable for winds at policy-relevant time scales, i.e., sub-annual. We proposea trans-Gaussian model for monthly wind speed that relies on an autoregressivestructure with Tukey $g$-and-$h$ transformation, a flexible new class that canseparately model skewness and tail behavior. This temporal structure isintegrated into a multi-step spectral framework that is able to account forglobal nonstationarities across land/ocean boundaries, as well as acrossmountain ranges. Inference can be achieved by balancing memory storage anddistributed computation for a data set of 220 million points. Once fitted withas few as five runs, the statistical model can generate surrogates fast andefficiently on a simple laptop, and provide uncertainty assessments very closeto those obtained from all the available climate simulations (forty) on amonthly scale.

A Stochastic Generator of Global Monthly Wind Energy with Tukey $g$-and-$h$ Autoregressive Processes