Towards Closing the Hydroxyl Radical (OH) Budget: Assessing the Feasibility and Uncertainties in Constraining Primary OH Production From Space

Recent progress in constraining the atmosphere’s primary oxidant, the hydroxyl radical (OH), with machine learning (ML) and satellite data raises the intriguing possibility of also constraining individual OH chemical production and loss terms. Here, we present a methodology to constrain primary OH production (i.e., OH production from the reaction of water vapor with O1D) from 60°S – 60°N at 500m above ground level (magl) (POH_500) using a combination of ML, satellite observations, and meteorological data. The aim of this work is to establish methodological feasibility and to assess and quantify the uncertainties of that methodology. This methodology produces geophysically-credible distributions of POH_500 across all seasons, with seasonal variability being driven primarily by changes in water vapor and ozone photolysis rates. Regions with quantifiable 1? uncertainties of 25% or less comprise approximately 68% - 73% of global POH_500, suggesting the product is of sufficient quality to inform the relationship between POH and trends and variability in OH. The incorporation of additional satellite retrievals into the machine learning model as well as increased spatial and temporal averaging could reduce errors in regions with higher uncertainties, such as those areas with frequent clouds or biomass burning. Ultimately, the results presented here can provide a blueprint to observationally constrain other production and loss terms within the OH budget.