Causal inference for stratospheric chemistry: insights into tropical middle stratospheric ozone variability

This study investigates the coupling between chemical and dynamical processes driving tropical middle stratospheric ozone (O ₃ ) variability using a causal inference framework that combines causal discovery with causal effect estimation. This approach integrates qualitative physical knowledge through a causal graph applied to satellite observations and a chemistry-transport model (CTM) simulation. The analysis is split into two subperiods of monthly data: 2004–2011, characterized by an O ₃ decrease, and 2012–2018, when O ₃ increased in the tropical middle stratosphere. Causal inference identifies distinct processes governing O ₃ behaviour. During 2004–2011, a robust negative contemporaneous connection from N ₂ O to NO ₂ emerged, while in 2012–2018 this shifted to a one-month lag. This slower response reduced NO ₂ production from N ₂ O oxidation, limiting O ₃ loss via the NO _x catalytic cycle. Further analysis across Quasi-Biennial Oscillation (QBO) regimes reveals regime-dependent differences in the causal links. The N ₂ O to NO ₂ connection is weaker during westerly shear, associated with reduced upwelling, and stronger during easterly shear, reflecting enhanced upwelling. Our study highlights the pivotal role that causal inference can play in disentangling complex chemical-dynamical influences on O ₃ , complementing traditional statistical methods. This approach lays the foundation for broader applications in stratospheric chemistry, where many relations remain uncertain. By discovering and quantifying causal links, this methodology addresses open questions with environmental and societal relevance. Therefore, integrating causal reasoning into data-driven science enhances process understanding and strengthens the synergy between machine learning and statistical methods in Earth and environmental sciences.