Predicting climate extremes - a complex network approach

Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods is moderate, and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for 8 regions in Europe to construct time evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to predict the low frequency dynamics of heat periods is similar to the one of the standard approach, with the potential of being even better in some regions.