Climate Networks and Extreme Events

We analyse some climate dynamics from a complex network approach. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? For this we propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system. This approach enables us to uncover relations to global circulation patterns in oceans and atmosphere. The global scale view on climate networks offers promising new perspectives for detecting dynamical structures based on nonlinear physical processes in the climate system. Moreover, we evaluate different regional climate models from this aspect.

This concept is also applied to Monsoon data in order to characterize the regional occurrence of extreme rain events and its impact on predictability. Changing climatic conditions have led to a significant increase in magnitude and frequency of spatially extensive extreme rainfall events in the eastern Central Andes of South America. These events impose substantial natural hazards for population, economy, and ecology by floods and landslides. For example, heavy floods in Bolivia in early 2007 affected more than 133.000 households and produced estimated costs of 443 Mio. USD.

Here, we develop a general framework to predict extreme events by combining a non-linear synchronization technique with complex networks. We apply our method to real-time satellite-derived rainfall data and are able to predict a large amount of extreme rainfall events. Our study reveals a linkage between polar and subtropical regimes as responsible mechanism: Extreme rainfall in the eastern Central Andes is caused by the interplay of northward migrating frontal systems and a low-level wind channel from the western Amazon to the subtropics, providing additional moisture. Frontal systems from the Antarctic thus play a key role for sub-seasonal variability of the South American Monsoon System.