Analysing complex and dynamic environmental systems often reaches technical and theoretical limits of statistical standard tools. This is the case as most clas- sical statistical approaches fundamentally require assumptions about statistical independence and stationarity, which are especially problematic as soon as we deal with non-linearity and coevolution. Many promising approaches founded in scientific fields ranging from theoretical physics to information theory have been developed, still their application to applied climate and environmental sciences remains challenging.
This 4-day block course will introduce a set of modern tools for rigorous analy- ses of dynamic systems. Starting off with examples from geophysics and fluid mechanics the course will guide you during an excursion through stochastic physics, information theory, phase-state analyses and applications with big data and scaling. We especially invite you to bring own data and application questi- ons to provide hands-on utilisation of the concepts and tools within your field of research.