Investigating the global OH radical distribution using steady-state approximations and satellite data

We present a novel approach to derive indirect global information on the hydroxyl radical (OH), one of the most important atmospheric oxidants, using state-of-the-art satellite trace gas observations (key sinks and sources of OH) and a steady-state approximation (SSA). This is a timely study as OH observations are predominantly from spatially sparse field and infrequent aircraft campaigns, so there is a requirement for further approaches to infer spatial and temporal information on OH and its interactions with important climate (e.g. methane, CH₄) and air quality (e.g. nitrogen dioxide, NO₂) trace gases. Due to the short lifetime of OH (∼1 s), SSAs of varying complexities can be used to model its concentration and offer a tool to examine the OH budget in different regions of the atmosphere. Here, we use the well-evaluated TOMCAT three-dimensional chemistry transport model to identify atmospheric regions where different complexities of the SSAs are representative of OH. In the case of a simplified SSA (S-SSA), where we have observations of ozone (O₃), carbon monoxide (CO), CH₄ and water vapour (H₂O) from the Infrared Atmospheric Sounding Interferometer (IASI) on board ESA's MetOp-A satellite, it is most representative of OH between 600 and 700 hPa (though suitable between 400–800 hPa) within ∼20 %–30 % of TOMCAT modelled OH. The same S-SSA is applied to aircraft measurements from the Atmospheric Tomography Mission (ATom) and compares well with the observed OH concentrations within ∼26 %, yielding a correlation of 0.78. We apply the S-SSA to IASI data spanning 2008–2017 to explore the global long-term inter-annual variability of OH. Relative to the 10-year mean, we find that global annual mean OH anomalies ranged from −3.1 % to +4.7 %, with the largest spread in the tropics between −6.9 % and +7.7 %. Investigation of the individual terms in the S-SSA over this time period suggests that O₃ and CO were the key drivers of variability in the production and loss of OH. For example, large enhancement in the OH sink during the positive 2015/2016 El Niño–Southern Oscillation (ENSO) event was due to large-scale CO emissions from drought-induced wildfires in South East Asia. The methodology described here could be further developed as a constraint on the tropospheric OH distribution as additional satellite data become available in the future.