| Abstrakt: |
The tropospheric hydroxyl radical (TOH) is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1 degrees. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside an interpretable machine learning module named ECCOH, which is integrated into NASA's GEOS global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers/proxies. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases over biomass burning activities in Africa and Eastern Europe, resulting in overestimation of up to 20 % in TOH, regionally. OMI HCHO primarily impacts oceans where TOH linearly correlates with this proxy. Five key parameters including TO3, H2O(v), NO2, HCHO, and stratospheric ozone can collectively explain 65 % of variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over oceans, TOH trends are primarily positive in the northern hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases. [ABSTRACT FROM AUTHOR] |
