TCOM-CFC11 and TCOM-CFC12: A Gap-Free, Observationally Constrained Global Dataset of Stratospheric CFC-11 and CFC-12 Profiles (v2.0)

Abstract. Understanding the long-term trends of ozone-depleting substances (ODSs), particularly CFC-11 (CFCl₃) and CFC-12 (CF₂Cl₂), is essential for evaluating the effectiveness of the Montreal Protocol. However, reliably estimating these trends is complicated by the inherent sparse spatial and temporal coverage of high-quality stratospheric observations, such as those from the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS). To address this limitation, we have developed an innovative machine learning methodology to combine the strengths of sparse ACE-FTS observations with the continuous output of the TOMCAT global Chemical Transport Model (CTM).

We use XGBoost regression to constrain the TOMCAT tracers against co-located ACE-FTS measurements, thereby creating the TCOM (TOMCAT CTM and occultation-measurement-based) stratospheric profile datasets for CFC-11 and CFC-12. The resulting TCOM datasets described here (version 2.0) provide continuous, gap-free, global daily vertical profiles from 2000 to 2024. A comprehensive evaluation confirms the method’s effectiveness, showing the corrected TCOM data clustering significantly closer to the observations than the CTM and successfully removing a systematic low bias present in TOMCAT-simulated CFC concentrations. Furthermore, interpretable machine learning analysis reveals that the XGBoost model primarily functions as a "transport corrector", with dynamical features (like Age-of-Air, temperature, long-lived-tracers) being highly influential. This suggests that the dominant source of bias in the baseline TOMCAT simulation relates to its simulation of stratospheric circulation. These TCOM datasets are publicly available at https://doi.org/10.5281/zenodo.18145730 (Dhomse, 2026a) and https://doi.org/10.5281/zenodo.18147392 (Dhomse, 2026b), and will provide a valuable, observationally-constrained benchmark for refining chemical models and reducing uncertainties in ODS trend analyses.