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 (H2 O) 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.