Key research questions 

How can techniques from Complexity Science and Machine Learning complement physical process-based approaches

  • quantify the likelihood of abrupt transitions and extreme events in a warming Earth system?
  • improve predictions of extreme weather events on time scales of days to weeks?
  • assess the ecological impacts of a warming climate and changing extreme event characteristics?


We mainly employ methods from Complexity Science and Machine Learning such as

  • <li>Complex Networks / Graphs for exploring dependencies in large datasets of climatic and ecosystem observables, to develop first hypotheses on underlying coupling mechanisms, and as a tool to coarse grain the data to extract the most relevant information
  • Bayesian inference for systematic calibration of physics-based low-order models that capture the key dynamics of the natural systems under study
  • Artificial Neural Networks to model (emergent) processes that are challenging to tackle with more traditional, primitive differential-equation-based approaches


We currently focus on the following areas of applications within the Earth system:

  • Abrupt climate transitions that have occurred in the Earth's long-term past, as evidenced in paleoclimate proxy records
  • Extreme events such as heat waves, droughts, and floods
  • Impacts of a warming climate and changing extreme-event characteristics on ecosystems, currently with a focus on boreal forests