A central challenge in environmental sciences is the high complexity of interacting systems (e.g. atmosphere and ecosystems), which are captured by very different variables and sensors, e.g. with weather stations, ecosystem monitoring, level measurements in rivers and satellite remote sensing. AI and machine learning open up the possibility to address the complex questions in an efficient and evidence-based way, e.g., to reveal interrelationships in the Earth system or to achieve more accurate (weather and climate) models and predictions of climate impacts. Furthermore, the questions arising from the complex interrelationships in the Earth system can provide new impulses for AI research.
The new CZS Junior Research Group "Knowledge Integration for Spatio-Temporal Environmental Modeling" at the Max-Planck-Institute for Biogeochemistry is part of the European network ELLIS (European Laboratory for Learning and Intelligent Systems) for excellent research in AI and will be located at the interface between AI and environmental research. The aim is to integrate expertise into AI approaches and to draw new insights from this, e.g. for climate extremes and their effects ecosystem functions or services.
