CausalEarth is an interdisciplinary ERC Starting grant, aiming to improve our understanding of the causal interdependencies between major drivers (modes) of climate variability by developing novel machine learning-based causal inference methods for both observations and model data. The modes' interdependencies are characterized by common drivers, indirect effects, nonlinearities, nonstationarity, and all these between highly complex spatio-temporal phenomena. CausalEarth will develop causal inference methods that account for such complex characteristics and apply them to observational and climate model data to improve our understanding of the climate system and climate change.
Start/End:
2021/2026
Project Leader:
Jakob Runge