Attribution of tropical hydroxyl radical variability from amulti-species chemical data assimilation

The hydroxyl radical (OH) is the primary oxidizing agent in the atmosphere, making characterization of its variability essential for accurate forecasting and reanalysis of greenhouse gases and air pollutants such as methane. We seek to understand and quantify the processes governing OH anomalies, with a focus on the El Niño–Southern Oscillation (ENSO). In this presentation, we share results from the Tropospheric Chemical Reanalysis (TCR) data assimilation system, analyzed using a machine learning approach. Our results indicate that the spatio-temporal variability of monthly mean OH anomalies in the tropics from 2019-2024 can, to a large extent, be attributed to variations in nitrogen dioxide (38%), ozone (20%), carbon monoxide (15%), and specific humidity (21%). We find that anomalies in these species are linked to ENSO in both space and time. The spatial pattern of OH variability in the Tropics also shows the imprint of ENSO through these gases. When considering the time-mean OH anomaly, three variables are sufficient to explain the majority of the variance, namely nitrogen dioxide (46%), carbon monoxide (30%), and ozone (15%). These findings imply that questions regarding the contribution of the OH sink to the observed global growth rate of atmospheric methane may be addressed using only these three historically well-observed variables. Furthermore, our method delivers important new Information about regional variations in OH, required for a reliable process attribution in inverse modeling studies.