Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
We have recently published a paper that shows how neural networks (NNs) can be used to quantify the extent to which a wide range of factors influence differences in the tropospheric burden of hydroxyl radical (OH) and the methane lifetime found by various chemical transport models (CTMs), using monthly mean output of a dozen variables [Nicely et al., JGR, 2017]. Across all CTMs, the largest mean differences in CH4 lifetime (ΔτCH4) result from variations in O3 (ΔτCH4 = 0.29 years), J(O3 --> O(1D)) (0.25 years), CO (0.22 years), and the chemical mechanisms (0.38 years). This work has since been expanded to study a larger group of chemistry-climate models (CCMs), which internally calculate free-running meteorological fields, that participated in the Chemistry-Climate Model Initiative (CCMI). We share the results of this analysis in addition to ongoing developments to the NN method to diagnose the fast radical cycling of OH and hydroperoxy radical, HO2, using high temporal frequency (30 minute) output from a focused group of global models. We then demonstrate how such insight into the diurnal variations and short timescale processes within a model can be used in conjunction with a global-scale observational data set, such as that of the Atmospheric Tomography Mission (ATom) aircraft campaign, to identify pathways forward to improve model representations of tropospheric OH and corresponding CH4 lifetime.
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