Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane

Abstract

<p><strong>Abstract.</strong> The hydroxyl radical (OH) is the main tropospheric oxidant and the main sink for atmospheric methane. The global abundance of OH has been monitored for the past decades using atmospheric methyl chloroform (<span class="inline-formula">CH₃CCl₃</span>) as a proxy. This method is becoming ineffective as atmospheric <span class="inline-formula">CH₃CCl₃</span> concentrations decline. Here we propose that satellite observations of atmospheric methane in the short-wave infrared (SWIR) and thermal infrared (TIR) can provide an alternative method for monitoring global OH concentrations. The premise is that the atmospheric signature of the methane sink from oxidation by OH is distinct from that of methane emissions. We evaluate this method in an observing system simulation experiment (OSSE) framework using synthetic SWIR and TIR satellite observations representative of the TROPOMI and CrIS instruments, respectively. The synthetic observations are interpreted with a Bayesian inverse analysis, optimizing both gridded methane emissions and global OH concentrations. The optimization is done analytically to provide complete error accounting, including error correlations between posterior emissions and OH concentrations. The potential bias caused by prior errors in the 3-D seasonal OH distribution is examined using OH fields from 12 different models in the ACCMIP archive. We find that the satellite observations of methane have the potential to constrain the global tropospheric OH concentration with a precision better than 1<span class="thinspace"></span>% and an accuracy of about 3<span class="thinspace"></span>% for SWIR and 7<span class="thinspace"></span>% for TIR. The inversion can successfully separate the effects of perturbations to methane emissions and to OH concentrations. Interhemispheric differences in OH concentrations can also be successfully retrieved. Error estimates may be overoptimistic because we assume in this OSSE that errors are strictly random and have no systematic component. The availability of TROPOMI and CrIS data will soon provide an opportunity to test the method with actual observations.</p>