Text S1 Simulation Experiment for Background Influence
Here, we examine the sensitivity of ACT BL samples to background
influences, and evaluate a method to account for background conditions
empirically using data collected in the FT. Atmospheric BL signals in
North America contain a mixture of background air arriving from distant
oceanic and terrestrial sources outside of North America and more local-
to regional- sources originating within North America. These latter
surface influences (Figure 4) are a significant but incomplete component
of observed variability. Background air flowing into the WRF-Chem model
domain contains substantial seasonal variability that is synchronized
with upstream surface influences, and thus can amplify surface-driven
signals. Background air also contains emissions from oceanic sources,
which can offset biospheric uptake signals.
We examine the influence of background air on predicted signals by
sampling observationally constrained global atmospheric models using BL
end points from the 500-particle back trajectories. Here,
CO2 and CO fields are determined by running posterior
fluxes through GEOS-Chem and saving output every 3 hours at the native
horizontal grid (4° x 5° and 2° x 2.5°, respectively). Atmospheric COS
fields are determined by an independent 4DVar data assimilation system
of the TM5 chemistry transport model (TM5-4DVAR), which infers surface
COS flux from NOAA surface observations, and projects optimized fluxes
into the atmosphere over the period 2000-2019 (4° x 6°; Ma et al.,
2020). The back trajectory 500-particle ensembles contain a mixture of
particles that remain within the North America WRF domain over the 10
day period, as well as particles that reach the boundary and exit
(Figure S1). Most particles (> 90% on average) exit the
domain in winter time, while a larger percentage of particles remain
within the domain in the summer (25-50% on average) under a weaker and
less advective jet-stream. We then average all particle endpoints
together, and repeat for each ACT-America BL flask sample receptor.
Seasonal varations for each region and tracer show a consistent pattern
of peak concentration in spring, and gradual drawdown through later
summer (Figure S2). COS drawdown continues into fall, at which time
CO2 becomes enriched and secondary production of CO
apparently increases. We find similar seasonal and regional patterns
under fair weather condions as are found for cold and warm air masses
(top vs bottom row in Figure S2, respectively), with the exception of
amplified CO and COS enrichment in summer 2016 in the NE.
We then ask the question: How representative is FT air sampled by
ACT-America of these background influences? The main assumption here is
that FT air within the continental interior is influenced more by large
scale horizontal advection (from boundaries) than by vertical mixing of
underlying regional surface exchange. To address this question, we
compare seasonal variations of background air from BL particle end
points to samples from FT particle start points, using the same
atmospheric models. Seasonal variations of FT air (particle receptors)
are indicated by markers in Figure S2. The two approaches agree with
respect to the timing and magnitude of seasonal drawdown and enrichment,
including diverging patterns between CO2/CO and COS in
fall. Moreover, we find higher agreement between the approaches under
fair weather conditions than under cold and warm air masses, as
indicated by a near doubling of RMSE values driven by the Northeast
region. .
These results suggest that BL and FT samples are more representative of
the same air mass on fair weather days, and consequently, that removing
FT values provides a viable approach for estimating observed BL
enhancements in the ACT-America data with improved accuracy for fair
weather conditions. More precisely, the vertical difference, referred to
here as tracer “enhancements” and defined as the difference between BL
and FT concentrations, gives a robust measure of observed regional
surface flux influences, enabling direct comparison between observed and
predicted signals. We note that differences in transport between the
sampled fields (from GEOS-Chem and TM5) and particle back trajectories
(from WRF-Chem) are unlikely to have a significant influence on these
results due to (1) our focus on regional scale conditions, (2) sampling
of 500 particle end points, (3) the use of observationally constrained
transport fields.
References
Ma, J. et al. Inverse modelling of carbonyl sulfide: implementation,
evaluation and implications for the global budget. Atmos Chem Phys
Discuss 2020, 1–39 (2020).
https://doi.org/10.5194/acp-2020-603