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?
Methods
We mainly employ methods from Complexity Science and Machine Learning such as
- 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
Applications
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
