Predictability of extreme events in a nonlinear stochastic-dynamical model
The objective of this work is to evaluate the potential of reduced order models to reproduce the extreme event and predictability characteristics of higher dimensional dynamical systems. A nonlinear toy model is used which contains key features of comprehensive climate models. First, we demonstrate that the systematic stochastic mode reduction strategy leads to a reduced order model with the same extreme value characteristics as the full dynamical models for a wide range of time-scale separations. Second, we find that extreme events in this model follow a generalized Pareto distribution with a negative shape parameter; thus extreme events are bounded in this model. Third, we show that a precursor approach has good forecast skill for extreme events. We then find that the reduced stochastic models capture the predictive skill of extreme events of the full dynamical models well. Consistent with previous studies we also find that the larger the extreme events, the better predictable they are. Our results suggest that systematically derived reduced order models have the potential to be used for the modeling and statistical prediction of weather-and climate-related extreme events and, possibly, in other areas of science and engineering too.