Abstract

A faithful modeling of real-world dynamical systems necessitates model evaluation. A re-cent promising methodological approach to this problem has been based on complex net-works, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially ex-plicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively an-alyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchroniza-tions of extreme rain events. An evaluation against respective networks built from daily sat-ellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate