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

The critical importance of stratospheric ozone (O3) lies in its function of absorbing harmful ultraviolet solar radiation as well as affecting the thermal structure, and thus the dynamics, of the stratosphere. Understanding stratospheric O3 variability requires addressing complex interactions among climate change, ozonedepleting substances (ODSs) regulated by the Montreal Protocol, and gases like nitrous oxide (N2O) concerning non-linear transport and chemistry. In the tropical (10°S-10°N) middle (∼10 hPa) stratosphere, a region of strong chemical O3 production and loss, satellite measurements revealed an unexpected O3 decline in the early 2000s. However, since then, O3 has shown a positive trend in this region, yet a comprehensive explanation for this distinct behaviour is still lacking. To quantify the chemical-dynamical factors influencing tropical middle stratospheric O3 variability, we apply a novel causal inference approach comprising causal discovery and causal effect estimation. This approach integrates qualitative physical knowledge through a causal graph applied to satellite observations and chemistry-transport model (CTM) simulations. By leveraging the causal effect estimation framework, we provide robust insights into the drivers of O3 fluctuations and showcase the method’s potential for uncovering causal relationships in stratospheric chemical-dynamical interactions. By splitting the analysis into two subperiods (2004–2011 when O3 decreased and 2012–2018 when O3 increased), causal inference identifies distinct processes governing O3 behaviour. During 2004–2011, causal discovery detected a strong negative contemporaneous connection between N2O and NO2, whereas, in 2012–2018, this connection shifted to a one-month lag. This slower response reduced NO2 production from N2O oxidation, leading to lower O3 loss via the NOx catalytic cycle, directly explaining the differences in trends. Further process-oriented analysis of the Quasi-Biennial Oscillation (QBO) regimes indicates that the slower residual vertical velocity (w*)-N2O connection was consistently detected during the westerly phase of the QBO, associated with weaker vertical upwelling. Our study showcases causal inference’s pivotal role in disentangling complex chemical-dynamical influences on O3, 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, it addresses open questions with environmental, economic, and societal implications. 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.