This course introduces the most important physical components of the climate system and their interactions. The mechanisms of anthropogenic climate change are analysed against the background of climate history and variability. Those completing the course will be in a position to identify and explain simple problems in the area of climate systems.

Practical work using selected measurement techniques in meteorology and hydrology. The course consists of field work with different measuring systems to determine turbulence, radiation, soil moisture, evapotranspiration, discharge and the atmospheric state as well as of data analysis.

This is an interdisciplinary course at the intersection of computer science, peace and security, global health, and climate science. Students will implement practical projects that involve the application of their skills on public data to address global challenges. In addition, students will critically engage with the ethical, social, and political implications of using AI for global challenges.

This course offers a systematic introduction to statistical and machine learning methods with focus on applications in atmospheric and climate science. Focus is on the theoretical and mathematical basis of supervised statistical learning (advanced regression, nonparametric methods) and their application in practice with hands-on exercises.