The research group on Climate Informatics develops innovative data science methods to advance our understanding of the climate system and address climate change topics of critical importance for the society.
The Earth system is one of the best-observed complex dynamical systems with satellite observations and weather stations providing almost global coverage for the past decades to centuries. These datasets are complemented by the output of global climate models that simulate the basic physical laws underlying the atmosphere and oceans and can give us an intuition of how future climate looks like - given different scenarios of anthropogenic influence.
Yet, our tools to analyze and understand Earth system data are still in their infancy due to several challenges: (1) Climate data is big data on the Petabyte scale, (2) processes interact on vastly different spatio-temporal scales, (3) highly nonlinear interactions.
The goal and mission of the Climate Informatics group is to develop tools that handle these challenges based on graphical models and causal discovery, nonlinear time series analysis, and deep learning.