Exploring Biases and Long-term Trends in Tropospheric OH: A Synergistic Approach with Model Simulations, Interpretable Machine Learning, and Satellite Observations

The accurate representation of tropospheric hydroxyl radical (TOH) is crucial for reasonably modeling methane concentrations — a potent greenhouse gas. We use an improved parameterization of TOH using an interpretable and agile machine learning module named ECCOH (pronounced "echo") in NASA's GEOS global model to unravel the intricacies of TOH to its key inputs. However, the accuracy of this model is hampered by the accurate representation of its critical inputs. Fortunately, retrieving trace gases like nitrogen dioxide (NO2) and formaldehyde (HCHO) from space-borne sensors, like the Aura Ozone Monitoring Instrument (OMI), has seen remarkable progress. Consequently, we leverage these observations to assess how they can effectively alleviate some biases in TOH and can help better reproduce its long-term trends. In contrast to the earlier investigations, the refined representation of TOH archives a finer spatial resolution (1x1 degrees), and it is more up to date (2005-2019), allowing for elucidating the impact of recent emission regulations, such as those imposed in China, on TOH. OMI NO2 yields valuable insights over biomass-burning areas in Eastern Europe and central Africa, where our prior emission estimates possess significant biases, mitigating regional TOH biases up to 20%. Oceanic HCHO concentrations, serving as a proxy for TOH due to the predominant chemical pathway of VOC oxidation through OH, are only moderately altered by OMI HCHO, attributed to low signal-to-noise ratios and satisfactory representation of HCHO in the a priori simulations. Ultimately, we disentangle the convoluted map of TOH linear trends by isolating five pivotal inputs to the TOH parameterization, including stratospheric ozone, tropospheric ozone, water vapor, HCHO, and NO2. Our results demonstrate that these five parameters can collectively explain 65% of the variability in TOH trends alone. With the deployment of new satellites with enhanced sensor configurations and better temporal resolutions, our mission at NASA is to exploit those observations to improve the representation of many variables highly linked to TOH.