The “Data-Driven Modelling and Predictions of the Earth System” course is designed to equip graduate students with the essential tools and methodologies for understanding and interpreting the complex relationships within Earth system data. As the volume of climate-related data continues to grow, the challenge lies not only in managing this data but in effectively utilizing it to uncover the underlying processes that drive climate dynamics. This course will offer a deep dive into the mathematical foundations of data-driven modeling, with a particular focus on techniques tailored for analyzing and predicting the behavior of complex dynamical systems.

Climate change is a major threat to human well-being, posing risks to social and legal normativity, as well as challenging the current configuration of institutional structures. In this sense, legal frameworks and socio-political arrangements have addressed the adverse effects of climate change through norms, financial mechanisms, and litigation. Against this background, this course will provide an overview of the legal instruments and financial mechanisms aimed at regulating climate change, its risks and future consequences in Europe.

Sea level rise is one of the most pressing challenges posed by climate change, with significant implications for coastal communities around the world. This comprehensive course on “Sea Level Rise and Coastal Adaptation” provides a deep understanding of the causes and impacts of rising sea levels, delving into the consequences on coastal regions including intensification of extreme events, increased coastal erosion, inundation of low-lying areas and heightened vulnerability to storm surges.

While we keep collecting and storing data over data, understanding and interpreting the underlying processes and relationships between different observations is still a challenge to tackle. The Future Earth Research School on “Data Science and Machine Learning for Climate Research” will provide participants with a comprehensive overview of the mathematical foundations of machine learning, with a particular focus on methods for analysing 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, and 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 the well-being of humanity. The growing desire for higher living standards and the concurrent environmental changes – particularly climate change – requires a forward-looking and transdisciplinary approach to managing water and environmental resources. 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.