Machine Learning For Atmospheric Chemistry: Creating Global, Gap-Free Stratospheric Datasets for Montreal Protocol Assessments

Understanding long-term trends in stratospheric species is vital for evaluating the success of the Montreal Protocol and its amendments. However, reliable trend estimation remains challenging due to the sparse spatial and temporal coverage of high-quality observations, such as those from the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS).To overcome this limitation, we present an innovative machine learning framework that fuses ACE-FTS observations with the continuous output of the TOMCAT global Chemical Transport Model (CTM). Using XGBoost regression, we constrain TOMCAT tracers against co-located ACE-FTS measurements, generating the TCOM (TOMCAT CTM and occultation-measurement-based) stratospheric profile datasets for key species: CFC-11, CFC-12, HCl, HF, HNO3, O3, CH4, N2O, and H2O.The latest TCOM release (version 2.0) provides gap-free, global daily vertical profiles from 2000 to 2024. Validation demonstrates substantial improvements over TOMCAT, including the removal of systematic low biases in simulated CFC concentrations. Interpretable machine learning analysis reveals that XGBoost primarily acts as a “transport corrector,” with dynamical features such as Age-of-Air, temperature, and long-lived tracers exerting the greatest influence. This finding highlights that circulation biases dominate TOMCAT’s baseline errors.TCOM datasets are publicly available and offer an observationally constrained benchmark for refining chemical models, improving stratospheric transport representation, and reducing uncertainties in ozone-depleting substance (ODS) trend analyses.Dataset links:CFC-11 v2: https://doi.org/10.5281/zenodo.18145730CFC-12 v2: https://doi.org/10.5281/zenodo.18147392CH4 v2: https://doi.org/10.5281/zenodo.18197333N2O v2: https://doi.org/10.5281/zenodo.18197444HCl v2: https://doi.org/10.5281/zenodo.18184430HF v2: https://doi.org/10.5281/zenodo.18184779HNO3 v2: https://doi.org/10.5281/zenodo.18199002O3 v2: https://doi.org/10.5281/zenodo.18199586H2O v2: https://doi.org/10.5281/zenodo.18199962COF2 v2: https://doi.org/10.5281/zenodo.18201786