Module 1 METRICS

Adressed key deliverable: To develop and apply novel statistical methods to consistently describe multi-factorial extreme events in physical and biological records. The primary research data are collated by Module 2 (biological PROXIES) as well as from long-term historical climate station data series. Different future climate change scenarios and subsequent downscaling techniques will be screened for their regional multiple climatic extreme event patterns to facilitate impact model simulations and adaptive management procedures.


Modeling changes in variability in climatic variables

Like a climate mantra, it is stated in almost all publications that anthropogenic climate change has already led and will also in the future lead to more frequent and more severe climatic extremes due to shifts in mean values and due to increasing variability. Whereas there is plenty of evidence for the first fact, the latter one is rarely checked. Thus, we develop appropriate statistics to thoroughly model variability based on robust methods and a homogenized long-term European climate station data sets.


Modeling joint extremes in climatic variables

Addressing the need to improve current models for statistical extremes and their impacts on ecosystems, we focus on the development of novel methodologies to model, understand and predict climate extremes. Since the occurrence of extremes is simultaneously influenced by more than one climatic variable, we expect that the dependency patterns will characterize extreme conditions and thus form the basis for the development of novel methodologies.