Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations

Abstract

The tropospheric hydroxyl (TOH) radical 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°. 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 a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the 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 the 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.