1. Introduction
The environmental damage caused by chemically reactive greenhouse gases and air pollutants is controlled by a balance between sources and sinks, with the major sink usually being atmospheric photochemistry. The net chemical loss is a key budget term, and it comprises a highly heterogeneous mixture of air parcels, each with its own mix of species, and each with its own chemical production and/or loss rates designated here as the reactivities: P-O3, L-O3, and L-CH4 (see Prather et al., 2017; 2018, hence P2017 and P2018). Reactivity here is simply the 24-hour integration of a reaction rate, or the sum of several reaction rates, that describe budgets of species in units of ppb per day. In this paper we continue our efforts to understand how tropospheric chemistry is constructed by examining reactivities starting at the finest spatial scales.
A recent observation-based study of the chemical reactivity of individual air parcels used the 10 s in situ aircraft measurements from the NASA Atmospheric Tomography (ATom) mission’s first deployment (ATom-1; Guo et al., 2021, hence G2021). Recent publications have identified new scientific opportunities coming from ATom with its intensive, chemically comprehensive measure of composition combined with an extensive, semi-global profiling through the remote troposphere (Wofsy et al., 2018; Thompson et al., 2021). Topics include scales of variability (Schill et al., 2020; G2021), global CO forecasting (Strode et al., 2018), and OH oxidative capacity (Wolfe et al., 2019; Brune et al., 2020; Travis et al., 2020; Anderson et al., 2021; G2021), as well as areas unrelated to this work, such as aerosol distribution, formation, and precursors (Brock et al., 2021; Williamson et al., 2021; Veres et al., 2020).
Here we extend G2021 to report reactivities for all four seasonal deployments (ATom-1234, see Figure S1) and examine how their statistical patterns change with each deployment. Second, we perform sensitivity analyses to identify which of the ATom-measured species drives the reactivities, and thus which are critical for the chemistry-climate models (CCMs) to simulate accurately. Third, probability densities for these critical species from ATom are presented as possible performance metrics for CCMs.
Our interests in the reactivity of air parcels or model grid cells began with P2017, continuing with P2018 and G2021. We focused on the budgets of O3 and CH4, and reactivities were defined by some key reaction rates:
loss of CH4 (L-CH4),
CH4 + OH CH3 + H2O (1)
production of O3 (P-O3),
HO2 + NO NO2 + RO (2a)
RO2 + NO NO2 + RO (2b)
O2 + O + O (x 2) (2c)
and loss of O3 (L-O3),
O3 + OH O2 + HO2 (3a)
O3 + HO2 HO + O2+O2 (3b)
O(1D) + H2O OH + OH (3c)
These rates are readily calculated in most CCMs, and we found that the net P-O3 minus L-O3 accurately described the 24 h O3tendencies over the ocean basins but not in highly polluted boundary layers. Reaction 2c is important in the upper tropical troposphere (Prather, 2009), but only above ATom flight levels. The calculation of reactivities here use the UCI model and the RDS*-2 protocol described in G2021.
We mainly consider the Pacific and Atlantic oceanic flights of ATom, which we constrain to be 54°S to 60°N (see the included flights in Figure S2), but also include a focus on the tropical flights (30°S to 30°N), splitting the Pacific flights into Central and Eastern Pacific (0°-30°N only). Reactivities at latitudes poleward of 60° are smaller than over the oceans, and we present a less extensive analysis for the Arctic and Antarctic.
Latitude-by-altitude curtain plots of the reactivities along flight tracks are presented in Section 2, along with altitude profiles of the mean reactivities and probability densities over the ocean basins. In Section 3 we present our analysis of the sensitivity of the reactivities to ATom MDS species, identifying those critical species where a model bias will introduce large errors in the O3 and CH4 budgets. Critical species probability densities from ATom are introduced in Section 4 as a possible model metric, and Section 5 concludes this analysis.