Using machine-learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets

Abstract. Monitoring the atmospheric concentrations of greenhouse gases (GHGs) is crucial in order to improve our understanding of their climate impact. However, there are no long-term profile data sets of important GHGs that can be used to gain a better insight into the processes controlling their variations in the atmosphere. Here, we merge chemical transport model (CTM) output and profile measurements from two solar occultation instruments, the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS), to construct long-term (1991–2021), gap-free stratospheric profile data sets (hereafter, TCOM) for two important GHGs. The Extreme Gradient Boosting (XGBoost) regression model is used to estimate the corrections needed to apply to the CTM profiles. For methane (TCOM-CH4), we use both HALOE and ACE satellite profile measurements (1992–2018) to train the XGBoost model while profiles from three later years (2019–2021) are used as an independent evaluation data set. As there are no nitrous oxide (N₂O) profile measurements for earlier years, XGBoost-derived correction terms to construct TCOM-N2O profiles are derived using only ACEFTS profiles for the 2004–2018 time period, with profiles from 2019–2021 again being used for the independent evaluation. Overall, both TCOM-CH4 and TCOM-N2O profiles show excellent agreement with the available satellite measurement-based data sets. We find that compared to evaluation profiles, biases in TCOM-CH4 and TCOM-N2O are generally less than 10 % and 50 %, respectively, throughout the stratosphere. Daily zonal mean profile data sets on altitude (15–60 km) and pressure (300–0.1 hPa) levels are publicly available via https://doi.org/10.5281/zenodo.7293740 for TCOM-CH4 (Dhomse, 2022a) and https://doi.org/10.5281/zenodo.7386001 for TCOM-N2O (Dhomse, 2022b).