While we are collecting and storing more and more data, understanding and interpreting the underlying processes and relationships between different observations remains a challenging problem. The Future Earth Research School on “Data Science and Machine Learning for Climate Research” aims to provide a comprehensive overview of the mathematical foundations of machine learning, with a particular focus on methods for the analysis of complex dynamical systems. Topics of interest include model reduction, system identification, estimation of associated transfer operators such as the Koopman operator, control, uncertainty quantification, deep learning, but also applications in climate science and sustainability.

The sustainable management of water resources, land and forestry is a key challenge for humanity in the 21st century, that is tightly related to the UN sustainable development goals. The nexus between water and ecology is essential to sustainability, economic and environmental development and therefore well-being for the whole humanity. The growing desire for higher living standards and the concurrent environmental changes – in particular climate change – require a forward looking and transdisciplinary approach to manage water resources and environmental resources in general. In particular, an innovative approach to adaptation to climate change is required to close the gap between climate change scenarios, policy making and technical design of adaptation strategies.

The effects of climate change are becoming more and more tangible. Especially in recent years extreme events such as heat waves, heavy rains and floods are becoming more frequent. Based on the results of the IPCC, it is clear that in some regions of the world there would be fewer droughts, less heat waves, heavy rain events and floods if global warming could be limited to 1.5 °C instead of 2 °C by the end of this century. The goal of the course is to gain understanding how risks occur and how they can be assessed and managed focussing on interaction between the physical and human environment, and how risks are shaped by their interactions. This requires understanding of both physical and geographical processes, as well as economic and human development under climate change.