Regime-Dependent Modeling of Extremes in the Extratropical Atmospheric Circulation

The contribution discusses data-based statistical-dynamical modeling of vorticity and wind speed extremes in the extratropical atmospheric circulation. The extreme model is conditional on the large-scale flow, consisting of a collection of local generalised extreme value or Pareto distributions, each associated with a cluster or regime in the space of large-scale flow variables. The clusters and the parameters of the extreme models are estimated from data, either separately or simultaneously. The large-scale flow is represented by the leading empirical orthogonal functions (EOFs). Also temporal clustering of extremes is investigated using an inhomogeneous Poisson process model whose rate parameter is conditional on the large-scale flow regime. The study is performed in the framework of an intermediate complexity atmospheric model with realistic mean state, variability and teleconnection patterns. The regime-dependent extreme model clearly outperforms the standard extreme model with no flow dependence. The methodology can also be applied to data from general circulation model scenario simulations, predicting future extremes.