Geoscientific systems are typically characterized by inherently nonlinear processes determining their structure and dynamics. Therefore, it is usually not possible to infer sufficiently holistic information from data when considering only classical linear concepts of statistics. As an alternative, a variety of complex systems based approaches have been developed, the foundations of which originate in the theory of nonlinear dynamical systems. In this short course, we will specifically introduce some prominent examples of contemporary analysis frameworks highlighting the methodological variety of complex systems based data analysis and modeling.

All techniques discussed in this short course will be illustrated by applications to real-world geoscientific data. Course materials will be made available to the participants upon request after the course, including example codes for the platform-independent open-source software Python (http://www.pik-potsdam.de/~donges/pyunicorn)