Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.

In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.

Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.