Causal inference for quantifying chemical-dynamical pathways controlling tropical middle stratospheric ozone variability

Kurzfassung

Our study highlights the pivotal role that causal inference can play in disentangling complex chemicaldynamical influences on O3, complementing traditional statistical methods. This approach lays the foundation for broader applications in stratospheric chemistry, where the understanding of various feedback pathways remains uncertain. By discovering and quantifying causal links, this methodology can be adapted to address open questions with environmental and societal relevance. Integrating causal reasoning into data-driven science can enhance process understanding and also strengthen the synergy between machine learning and statistical methods in Earth and environmental sciences.