The NASA Atmospheric Tomography (ATom) mission built a
photochemical climatology of air parcels based on in situ measurements with
the NASA DC-8 aircraft along objectively planned profiling transects through
the middle of the Pacific and Atlantic oceans. In this paper we present and
analyze a data set of 10 s (2 km) merged and gap-filled observations of the
key reactive species driving the chemical budgets of O3 and CH4
(O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH,
C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3,
HNO4, peroxyacetyl nitrate, other organic nitrates), consisting of
146 494 distinct air parcels from ATom deployments 1 through 4. Six models
calculated the O3 and CH4 photochemical tendencies from this
modeling data stream for ATom 1. We find that 80 %–90 % of the total
reactivity lies in the top 50 % of the parcels and 25 %–35 % in the top
10 %, supporting previous model-only studies that tropospheric chemistry
is driven by a fraction of all the air. In other words, accurate simulation
of the least reactive 50 % of the troposphere is unimportant for global
budgets. Surprisingly, the probability densities of species and reactivities
averaged on a model scale (100 km) differ only slightly from the 2 km ATom
data, indicating that much of the heterogeneity in tropospheric chemistry
can be captured with current global chemistry models. Comparing the ATom
reactivities over the tropical oceans with climatological statistics from
six global chemistry models, we find excellent agreement with the loss of
O3 and CH4 but sharp disagreement with production of O3. The
models sharply underestimate O3 production below 4 km in both Pacific
and Atlantic basins, and this can be traced to lower NOx levels than
observed. Attaching photochemical reactivities to measurements of chemical
species allows for a richer, yet more constrained-to-what-matters, set of
metrics for model evaluation.
Prologue
This paper is based on the methods and results of papers that
established an approach for analyzing aircraft measurements, specifically
the NASA Atmospheric Tomography Mission (ATom), with global chemistry
models. Here we present a brief overview of those papers to help the reader
understand the basis for this paper. The first ATom modeling paper (“Global atmospheric chemistry – which air matters”,
Prather et al., 2017, hence P2017) gathered six global models, both
chemistry–transport models (CTMs) and chemistry–climate models (CCMs). The models
reported a single-day snapshot for mid-August (the time of the first ATom
deployment, ATom-1), and these included all species relevant for
tropospheric chemistry and the 24 h reactivities. We limited our study to
three reactivities (Rs) controlling methane (CH4) and tropospheric
ozone (O3) using specific reaction rates to define the loss of CH4
and the production and loss of O3 in parts per billion (ppb) per day. The critical
photolysis rates (J values) are also reported as 24 h averages.
L-CH4:CH4+OH→CH3+H2O(1)2aP-O3:HO2+NO→NO2+RO2bRO2+NO→NO2+RO,2cwhereNO2+hν→NO+O and O+O2→O32dO2+hν→O+O(x2)3aL-O3:O3+OH→O2+HO23bO3+HO2→HO+O2+O23cO(1D)+H2O→OH+OH4J-O1D:O3+hν→O(1D)+O25J-NO2:NO2+hν→NO+O
Models also reported the change in O3 over 24 h, and these match
the P-O3 minus L-O3 values over the Pacific basin (a focus of this study).
The models showed a wide range in the three Rs average profiles across latitudes
over the Pacific basin, as well as 2D probability densities (PDs) for key
species such as NOx (NO + NO2) versus HOOH. A large part of the model
differences was attributed to the large differences found in chemical
composition. We found that single transects from a model through the
tropical Pacific at different longitudes produced nearly identical 2D PDs,
but these PDs were distinctly different across models. This result supported
the premise that the ATom PDs would provide a useful metric for global
chemistry models.
In P2017, we established a method for running the chemistry modules in the
CTMs and CCMs with an imposed chemical composition from aircraft data: the ATom
run, or “A run”. In the A run, the chemistry of each grid cell does not
interact with its neighbors or with externally imposed emission sources.
Effectively the CTM/CCM is initialized and run for 24 h without
transport, scavenging or emissions. Aerosol chemistry is also turned off
in the A runs. This method allows each parcel to evolve in response to the
daily cycle of photolysis in each model and be assigned a 24 h integrated
reactivity. The instantaneous reaction rates at the time an air parcel is
measured (e.g., near sunset at the end of a flight) do not reflect that
parcel's overall contribution to the CH4 or O3 budget; a full diel
cycle is needed. The A run assumption that parcels do not mix with
neighboring air masses is an approximation, and thus for each model we
compared the A runs using the model's restart data with a parallel standard
24 h simulation (including transport, scavenging, and emissions). Because
the standard grid-cell air moves and mixes, we compared averages over a
large region (e.g., tropical Pacific). We find some average biases of order
±10 % but general agreement. The largest systematic biases in the
A runs are caused by buildup of HOOH (no scavenging) and decay of NOx (no
sources). The A runs are relatively easy to code for most CTM/CCMs and allow
each model's chemistry module, including photolysis package, to run
normally. The A runs do not distinguish between CTMs and CCMs, except that
each model will generate/prescribe its own cloud fields and photolysis rates.
Our goal is to create a robust understanding of the chemical statistics
including the reactivities with which to test and evaluate the free-running
CCMs, and thus we do not try to model the specific period of the ATom
deployments. Others may use the ATom data with hindcast CTMs to test
forecast models, but here we want to build a chemical climatology.
The first hard test of the A runs came with the second ATom modeling paper
(“How well can global chemistry models calculate the reactivity of short-lived greenhouse gases in the remote troposphere, knowing the chemical composition”, Prather et al., 2018, hence P2018). The UCI CTM simulated an aircraft-like
data set of 14 880 air parcels along the International Date Line from a
separate high-resolution (0.5∘) model. Each parcel is defined by
the following core species: H2O, O3, NOx, HNO3, HNO4, PAN
(peroxyacetyl nitrate), CH3NO3, HOOH, CH3OOH, HCHO,
CH3CHO (acetaldehyde), C3H6O (acetone), CO, CH4,
C2H6, alkanes (C3H8 and higher), C2H4,
aromatics (benzene, toluene, xylene) and C5H8 (isoprene), plus
temperature. Short-lived radicals (e.g., OH, HO2, CH3OO) were
initialized at small concentrations and quickly reached daytime values
determined by the core species. The six CTM/CCMs overwrote the chemical
composition of a restart file, placing each pseudo-observation in a unique
grid cell according to its latitude, longitude, and pressure. If another
parcel is already in that cell, then it is shifted east–west or north–south
to a neighboring model cell. For coarse-resolution models, multiple restart
files and A runs were used to avoid large location shifts. CTM/CCMs usually
have a locked in 24 h integration step starting at 00:00 UTC that is
extremely difficult to modify in order to try to match the local solar time
of observation, especially as it changes along aircraft flights. We tested
the results with a recoded UCI CTM to start at 12:00 UTC but retain the same
clouds fields over the day and found only percentage-level differences between a
midnight or noon start.
These A runs averaged over cloud conditions by simulating 5 d in August
at least 5 d apart. Assessment of the modeled photolysis rates and
comparison with the ATom-measured J values is presented in Hall et al. (2018, hence H2018). All models agreed that a small fraction of chemically hot
air parcels in the synthetic data set controlled most of the total
reactivity. Some models had difficulty in implementing the A runs because
they overwrote the specified water vapor with the modeled value, but this
problem is fixed here. In both P2017 and P2018, the GISS-E2 model stood out with the
most unusual chemistry patterns and sometimes illogical correlations.
Efforts by a co-author to clarify the GISS results or identify errors in the
implementation have not been successful. GISS results are included here for
completeness in the set of three papers but are not reconciled. Overall,
three models showed remarkable inter-model agreement in the three Rs with less
than half of the RMSD (root-mean-square difference) as compared with the
other models. UCI also tested the effect of different model years (1997 and
2015 versus reference year 2016), which varies the cloud cover and
photolysis rates, and found an inter-year RMSD about half of that of the
core model's RMSD. Thus, there is a fundamental uncertainty in this approach
due to the inability to specify the cloud/photolysis history seen by a
parcel over 24 h, but it is less than the inter-model differences among
the most similar models.
Introduction
The NASA Atmospheric Tomography (ATom) mission completed a four-season
deployment, each deployment flying from the Arctic to Antarctic and back,
traveling south through the middle of the Pacific Ocean, across the Southern
Ocean and then north through the Atlantic Ocean, with near-constant
profiling of the marine troposphere from 0.2 to 12 km altitude (see Fig. S1 in the Supplement). The DC8 was equipped with in situ instruments that documented the
chemical composition and conditions at time intervals ranging from < 1 to about 100 s (Wofsy et al., 2018). ATom measured hundreds of gases
and aerosols, providing information on the chemical patterns and reactivity
in the vast remote ocean basins, where most of the destruction of
tropospheric ozone (O3) and methane (CH4) occurs. Reactivity is
defined here as in P2017 to include the production and loss of O3 (P-O3 and
L-O3, ppb/d) and loss of CH4 (L-CH4, ppb/d). Here we report on this
model-derived product that was proposed for ATom, the daily averaged
reaction rates determining the production and loss of O3 and the loss
of CH4 for 10 s averaged air parcels. We calculate these rates with 3D
chemical models that include variations in clouds and photolysis and then
assemble the statistical patterns describing the heterogeneity (i.e., high
spatial variability) of these rates and the underlying patterns of reactive
gases.
Tropospheric O3 and CH4 contribute to climate warming and global
air pollution (Stocker et al., 2013). Their abundances in the troposphere
are controlled largely by tropospheric chemical reactions. Thus,
chemistry–climate assessments seeking to understand past global change and
make future projections for these greenhouse gases have focused on the
average tropospheric rates of production and loss and how these
reactivities are distributed in large semi-hemispheric zones throughout the
troposphere (Griffiths et al., 2021; Myhre et al., 2014; Naik et al., 2013;
Prather et al., 2001; Stevenson et al., 2006, 2013, 2020; Voulgarakis et al., 2013; Young et al., 2013). The
models used in these assessments disagree on these overall CH4 and
O3 reactivities (a.k.a. the budgets), and resolving the cause of such
differences is stymied because of the large number of processes involved and
the resulting highly heterogeneous distribution of chemical species that
drive the reactions. Simply put, the models use emissions, photochemistry
and meteorological data to generate the distribution of key species such as
nitrogen oxides (NOx=NO+ NO2) and hydrogen peroxide (HOOH)
(step 1) and then calculate the CH4 and O3 reactivities from these
species (step 2). There is no single average measurement that can test the
verisimilitude of the models. Stratospheric studies such as Douglass et al. (1999) have provided a quantitative basis for testing chemistry and
transport and defining model errors, but few of these studies have tackled
the problem of modeling the heterogeneity of tropospheric chemistry. The
major model differences lie in the first step because when we specify the
mix of key chemical species, most models agree on the CH4 and O3
chemical budgets (P2018). The intent of ATom was to collect an atmospheric
sampling of all the key species and the statistics defining their spatial
variability and thus that of the reactivities of CH4 and O3.
Many studies have explored the ability of chemistry–transport models (CTMs)
to resolve finer scales such as pollution layers (Eastham and Jacob, 2017;
Rastigejev et al., 2010; Tie et al., 2010; Young et al., 2018; Zhuang et
al., 2018), but these have not had the chemical observations (statistics) to
evaluate model performance. In a great use of chemical statistics, Yu et al. (2016) used 60 s data (∼ 12 km) from the SEAC4RS aircraft
mission to compare cumulative probability densities (PDs) of NOx, O3,
HCHO and isoprene over the Southeast US with the GEOS-Chem CTM run at
different resolutions. They identified clear biases at the high and low ends
of the distribution, providing a new test of models based on the statistics
rather than mean values. Heald et al. (2011) gathered high-resolution
profiling of organic and sulfate aerosols from 17 aircraft missions and
calculated statistics (mean, median, quartiles) but only compared with the
modeled means. The HIAPER Pole-to-Pole Observations (HIPPO) aircraft mission
(Wofsy, 2011) was a precursor to ATom with regular profiling of the
mid-Pacific including high-frequency 10 s sampling that identified the small
scales of variability throughout the troposphere. HIPPO measurements were
limited in species, lacking O3, NOx and many of the core species needed
for reactivity calculations. ATom, with a full suite of reactive species and
profiling through the Atlantic basin, provides a wealth of chemical
statistics that challenge the global chemistry models.
Our task here is the assembly of the modeling data stream (MDS), which
provides flight-wise continuous 10 s data (air parcels) for the key reactive
species. The MDS is based on direct observations and interpolation methods
to fill gaps as documented the Supplement. Using the MDS, we
have six chemical models calculating the 24 h reactivities, producing a
reactivity data stream (RDS) using protocols noted in the Prologue (P2017) and
described further in Sect. 2. There, we describe the updated modeling
protocol RDS* necessary to address measurement noise in key species that can
be very short-lived. In Sect. 4, we examine the statistics of
reactivity over the Atlantic and Pacific oceans, focusing on air parcels
with high reactivity; for example, 10 % of the parcels produce 25 %–35 %
of total reactivity over the oceans. We compare these ATom-1 statistics,
species and reactivities with August climatologies from six global
chemistry models. In one surprising result, ATom-1 shows a more reactive
tropical troposphere than found in most models' climatologies associated
with higher NOx levels than in the models. Section 5 concludes that the ATom PDs based on 10 s air parcels do
provide a valid chemistry metric for global models with 1∘
resolution. It also presents some examples where ATom measurements and
modeling can test the chemical relationships and may address the cause of
differences in the O3 and CH4 budgets currently seen across the
models. With this paper we release the full ATom MDS-2 from all four
deployments, along with the updated RDS* reactivities from the UCI model.
Models and dataThe modeling data stream (MDS)
The ATom mission was designed to collect a multi-species, detailed chemical
climatology that documents the spatial patterns of chemical heterogeneity
throughout the remote troposphere. Figure S1 in the Supplement maps the 48
research flights, and the Supplement has tables summarizing
each flight. We required a complete set of key species in each air parcel to
initialize the models that calculate the CH4 and O3 reactivities.
We choose the key reactive species (H2O, O3, CO, CH4,
NOx, PSSNOx (photostationary state NOx), HNO3, HNO4, PAN, CH2O, H2O2,
CH3OOH, acetone, acetaldehyde, C2H6, C3H8,
i-C4H10, n-C4H10, alkanes, C2H4, alkenes,
C2H2, C5H8, benzene, toluene, xylene, CH3ONO2,
C2H5ONO2, RONO2, CH3OH) directly from the ATom
measurements and then add corollary species or other observational data
indicative of industrial or biomass burning pollution or atmospheric
processing (HCN, CH3CN, SF6, relative humidity, aerosol surface
area (four modes) and cloud indicator). We choose 10 s averages for our air
parcels as a compromise and because the 10 s merged data are a standard
product (Wofsy et al., 2018). A few instruments measure at 1 s intervals,
but the variability at this scale is not that different from 10 s averages
(Fig. S2). Most of the key species are reported as 10 s values, with some
being averaged or sampled at 30 s or longer such as ∼ 90 s for
some flask measurements.
Throughout ATom, gaps occur in individual species on a range of timescales
due to calibration cycles, sampling rates or instrument malfunction. The
generation of the MDS uses a range of methods to fill these gaps and assigns
a flag index to each species and data point to allow users to identify
primary measurements and methods used for gap-filling. Where two instruments
measure the same species, the MDS selects a primary measurement and
identifies which instrument was used with a flag. The methodology and
species-specific information on how the current MDS version 2 (MDS-2) is
constructed, plus statistics on the 48 research flights and the 146 494 10 s
air parcels in MDS-2, are given in the Supplement.
Over the course of this study, several MDS versions were developed and
tested, including model-derived RDSs from these versions, some of which are
used in this paper. In early ATom science team meetings, there was concern
about the accuracy of NO2 direct measurements when at very low
concentrations. A group prepared an estimate for NOx using the NO and
O3 measurements to calculate a photostationary value for NO2 and
thus NOx. This PSS-NOx became the primary NOx source in version 0 (i.e.,
MDS-0). The numbering of versions initially followed the notation of
revisions in the mission data archive (MDS_R0,
MDS_R1, …), but this was restrictive, and we
adopted the simpler notation here but still beginning with version 0. With
MDS-0, we chose to gap-fill using correlations with CO to estimate the
variability of the missing measurement over the gap. The science team then
rejected PSS-NOx as a proxy, and we reverted to the observed NO+ NO2
for MDS-1, resulting in increased NOx and reactivities (RDS-1). MDS-1 NOx
values are 25 % larger on average than MDS-0 values (unweighted mean of 66
vs. 52 ppt), and this affects P-O3 most and L-CH4 least. We then estimated
errors in the gap-filling and found that CO had little skill as a proxy for
most other species. With MDS-2, we optimized and tested the treatments of
gap-filling and lower limit of detection, along with other quality controls.
MDS-2 is fully documented in the Supplement.
The reactivity data stream (RDS)
The concept of using an MDS to initialize 3D global chemistry models and
calculate an RDS was developed in the pre-ATom methodology papers (P2017;
P2018). In this paper, we use the original six models for their August chemical
statistics, and we use five of them plus a box model to calculate the
reactivities; see Table 1. The RDS is really a protocol applied to the MDS.
It is introduced in the Prologue, and the details can be found in P2018. A model
grid cell is initialized with all the core reactive species needed for a
regular chemistry simulation. The model is then integrated over 24 h
without transport or mixing, without scavenging and without emissions. Each
global model uses its own varying cloud fields for the period to calculate
photolysis rates, but the F0AM box model simply takes the instant J values
as measured on the flight and applies a diurnal scaling. We can initialize
with the core species and let the radicals (OH, HO2, RO2) come
into photochemical balance. The 24 h integration is not overly sensitive
to the start time of the integration, and thus models do not have to
synchronize with the local time of observation (see P2018's Fig. S8 and Table S8).
Chemistry models.
Used forIDModel nameModel typeMeteorologyModel gridclimGFDLGFDL-AM3CCMNCEP (nudged)C180 × L48clim, MDS-0GISSGISS-E2.1CCMDaily SSTs, nudged to MERRA2∘× 2.5∘× 40Lclim, MDS-0/1GMIGMI-CTMCTMMERRA1∘× 1.25∘× 72Lclim, MDS-0GCGEOS-ChemCTMMERRA-22∘× 2.5∘× 72Lclim, MDS-0NCARCAM4-ChemCCMNudged to MERRA0.47∘× 0.625∘× 52Lclim, MDS-0/1/2UCIUCI-CTMCTMECMWF IFS Cy38r1T159N80 × L60MDS-0F0AMF0AMboxMDS + scaled ATom Jsn/a
The descriptions of models used in the paper. The first column denotes if
the model's August climatology is used (“clim”) and also the MDS versions
used. F0AM used chemical mechanism MCMv331 plus J-HNO4 plus
O1D)+CH4. For the global models, see P2017, P2017 and H2018. n/a – not applicable
The initial RDS came from MDS-0 and six of the models in Table 1. This paper
was nearly complete when we identified the problem with PSS-NOx. We had
gathered enough information on how models agree, or disagree, with RDS-0
and thus chose to assess MDS-1 with two of the models that closely agreed
(GMI and UCI). The two models were very close in RDS-0 and also in RDS-1. We
then found the problems with the CO proxy and chose to use only the UCI
model as a transfer standard for the change from MDS-1 to MDS-2 (i.e., RDS-1
to RDS-2). This path avoided much extra work by the modeling groups and
generated the same information on cross-model differences and a robust
estimate of changes from RDS-0 to RDS-2.
Statistics for the three reactivities for six models using MDS-0, 2
alternative UCI model years using MDS-0, the GMI model using MDS-1 and the
UCI model using MDS-2 are given in Tables 2 and S8 for three domains:
global (all points), Pacific (oceanic data from 54∘ S to
60∘ N) and Atlantic (same constraints as Pacific). UCI MDS-1 is
similar to UCI MDS-2 and is not shown. The statistics try to achieve equal
latitude-by-pressure sampling by weighting each ATom parcel inversely
according to the number of parcels in each 10∘ latitude by 100 hPa
bin. We calculate the means and medians plus the percent of total reactivity
in the top 10 % of the weighted parcels (Table 2) and also the mean
reactivity of the top 10 %, percent of total reactivity in the top 50 %,
10 % and 3 % plus the mean J values (Table S8).
Unfortunately, while investigating sensitivities and uncertainties in the
RDS for a future study, we found an inconsistency between the reported
concentrations of both pernitric acid (HNO4) and peroxyacetyl nitrate
(PAN) with respect to the chemical kinetics used in the models. High
concentrations (attributed to instrument noise) were reported under
conditions where the thermal decomposition frequency was > 0.4
per hour in the lower troposphere (> 253 K for HNO4 and
> 291 K for PAN). Thus, these species instantly become NOx. There
is no easy fix for this, and we left the species data in the MDS as they were
reported but developed a new protocol RDS* to deal with them. Both species
are allowed to decay for 24 h using their thermal decomposition rate
before being put into the model. This avoids most of the fast thermal
release of NOx in the 24 h of the RDS calculation but does not affect
the release of NOx from photolysis or OH reactions in the upper troposphere
where thermal decomposition in inconsequential. It is possible that some of
the high concentrations of HNO4 and PAN in the lower troposphere are
real and that we are missing this large source of NOx with the RDS*
protocol, but there are no obvious sources of these species in the remote
oceanic regions that would produce enough to match the thermal loss. Both
this problem and its solution do not affect the initial NOx. This revised
protocol (UCI2*) is shown in Tables 2 and S8 next to the standard protocol
(UCI2). The reactivities drop slightly (3 % for P-O3, 2 % for L-O3 and
0 % for L-CH4) as expected with less NOx, but the sensitivity of the
reactivities to these compounds (∂lnR/∂lnX) drops by a factor
of 2 or more. We use the UCI2* results as our best estimate of the
ATom reactivities for the figures in this paper.
Reactivity statistics for the three large domains (global, Pacific, Atlantic).
ValueRegionMDS-0 MDS-1MDS-2 F0AMGCGISSGMINCARUCIU15U97GMI1UCI2UCI2*P-O3, mean, ppb/d Global1.941.912.311.861.972.152.132.132.072.182.11Pacific1.911.951.941.921.922.132.082.102.062.332.26Atlantic1.881.993.292.072.282.322.322.342.222.082.02L-O3, mean, ppb/d Global1.631.451.751.501.511.561.551.551.501.571.54Pacific1.601.481.741.511.441.541.501.521.481.531.50Atlantic2.061.902.232.042.282.142.142.162.042.152.11L-CH4, mean, ppb/d Global0.720.660.380.650.620.680.680.680.670.680.68Pacific0.810.780.380.760.730.790.770.780.770.790.79Atlantic0.770.740.490.770.800.800.800.810.790.790.79P-O3, % of total R in top 10 % Global37 %34 %32 %34 %32 %36 %36 %36 %33 %34 %34 %Pacific34 %28 %28 %29 %29 %31 %30 %30 %28 %27 %27 %Atlantic25 %26 %24 %27 %25 %28 %28 %28 %25 %25 %25 %L-O3, % of total R in top 10 % Global37 %37 %35 %38 %38 %38 %39 %39 %39 %38 %38 %Pacific34 %32 %30 %32 %32 %32 %32 %32 %34 %31 %31 %Atlantic29 %31 %30 %31 %35 %31 %31 %31 %31 %31 %31 %L-CH4, % of total R in top 10 % Global36 %33 %29 %34 %34 %35 %35 %35 %35 %33 %33 %Pacific33 %29 %26 %30 %30 %30 %30 %30 %31 %27 %27 %Atlantic28 %26 %22 %27 %28 %28 %28 %28 %27 %28 %28 %
Global includes all ATom-1 parcels, Pacific considers all measurements over the Pacific Ocean from 54∘ S to 60∘ N and Atlantic uses parcels from 54∘ S to 60∘ N over the Atlantic basin. All parcels are weighted inversely by the number of parcels in each 10∘ latitude by 100 hPa bin. Results from the different MDS versions (0, 1, 2) are shown. UCI2* uses the revised RDS* protocol that preprocesses the MDS-2 initializations with a 24 h decay of HNO4 and PAN according to their local thermal decomposition frequencies; see text. See additional statistics in Table S8.
Inter-model differences
Variations in reactivities due to clouds are an irreducible source of
uncertainty: predicting the cloud-driven photolysis rates that a shearing
air parcel will experience over 24 h is not possible here. The protocol
uses 5 separated 24 h days to average over synoptically varying cloud
conditions. The standard deviation (σ) of the 5 d, as a percentage of the
5 d mean, is averaged over all parcels and shown in Table S9 for the five
global models. Three central models (GC, GMI, UCI) show 9 %–10 % σ(Js) values and similar σ(Rs) values as expected if the variation
in J values is driving the reactivities. Two models (GISS, NCAR) have
12 %–17 % σ(Js), which might be explained by more opaque clouds, but
the amplified σ(R) values (14 %–32 %) are inexplicable. This
discrepancy needs to be resolved before using these two models for ATom RDS
analysis.
Inter-model differences are shown in the parcel-by-parcel root-mean-square
(rms) differences for RDS-0 in Table 3. Even when models adopt standard
kinetic rates and cross sections (i.e., Burkholder et al., 2015), the number
of species and chemical mechanisms included, as well as the treatment of
families of similar species or intermediate short-lived reaction products,
varies across models. For example, UCI considers about 32 reactive gases,
whereas GC and GMI have over 100, and F0AM has more than 600. The other
major difference across models is photolysis, with models having different
cloud data and different methods for calculating photolysis rates in cloudy
atmospheres (H2018). The three central models (GC, GMI, UCI) in terms of their
5 d variability (Table S9) are also most closely alike in these statistics,
with rms = 20 %–30 % for L-CH4 up to 26 %–35 % for P-O3. These rms values appear
to be about as close as any two models can get. The intra-model rms for
different years (UCI 2016 versus 1997) is 10 %–13 % and shows that we are
seeing basic differences in the chemical models across GC, GMI and UCI.
F0AM is the closest to the central models, but it will inherently have a larger
rms because it is a 1 d calculation and not a 5 d average. NCAR's rms is
consistently higher and likely related to what is seen in the 5 d σ values in Table S9. GISS is clearly different from all the others (L-CH4
MS > 100 %, while L-O3 rms < 66 %).
Cross-model rms differences (RMSDs as a percentage of mean) for the three reactivities.
Matrices are symmetric. Calculated with the 31 376 MDS-0 unweighted parcels using the standard RDS protocol. F0AM lacks 5510 of these parcels because there are no reported J values. UCI shows RMSD between years 2016 (default) and 1997 as the value in parentheses on diagonal. The unweighted mean R values from three core models (GC, GMI, UCI) are P-O3 = 1.97, L-O3 = 1.50 and L-CH4 = 0.66; all are in units of ppb/d. The three core-model RMSDs are shown in bold.
Results
Our analysis of the reactivities uses the six-model RDS-0 results to examine
the consistency in calculating the Rs across models. Thereafter, we rely on
the similar results from the three central models (GC, GMI, UCI) to justify
use of UCI RDS*-2 as our best estimate for ATom reactivities. The
uncertainty in this estimate can be approximated by the inter-model spread
of the central models as discussed above. When evaluating the model's
climatology for chemical species, we use MDS-2. A summary of the key data
files used here, as well as their sources and contents, is given in Table 4.
ATom data files used here.
Primary aircraft dataFormatting and contentComments(a) Mor.all.at1234.2020-05-27.tbl (b) Mor.WAS.all.at1234.2020-05-27.tbl (c) Mor.TOGA.all.at1234.2020-05-27.tbl All from Wofsy et al. (2018).(a) 149 133 records × 675 csv columns, 10 s merges of flight data plus chemistry & environmental measurements (b) 6991 records × 729 csv columns, 30–120 s intervals to fill flasks (c) 12 168 records × 727 csv columns, 35 s intervals of instrumentCore source of ATom measurements. irregular and difficult formatting; extremely long asci records; large negative integers or “NA” for some non-data.Modeling data stream (MDS-2)(a) MDS_DC8_20160729_R3.ict (b) MDS_DC8_ 20170126_ R4.ict (c) MDS_DC8_20170928_R4.ict (d) MDS_DC8_ 20180424_R4.ict (e) ATom_MDS.nc Derived here.(a) ATom-1: 32 383 records × 87 csv columns, 10 s intervals of chemical & other data, plus flags to indicate gap-filling (b) ATom-2: 33 424 records × 87 csv columns (c) ATom-3: 40 176 records × 87 csv columns (d) ATom-4: 40 511 records × 87 csv columns (e) ATom MDS-2: all data in netcdfRegular formatting; all data gap-filled; NaNs only for flight 46; for use in modeling of the chemistry and related statistics from the ATom 10 s data.Reactivity data stream (RDS*-2)(a) RDS_DC8_20160729_R1.ict (b) RDS_DC8_ 20170126_ R1.ict (c) RDS_DC8_20170928_R1.ict (d) RDS_DC8_ 20180424_R1.ict (e) ATom_RDS.nc Derived here.(a) ATom-1: 32 383 records × 16 csv columns, 10 s intervals of flight data, modeled reactivities & J values plus 5 d SD (b) ATom-2: 33 424 records × 16 csv columns (c) ATom-3: 40 176 records × 16 csv columns (d) ATom-4: 40 511 records × 16 csv columns (e) ATom RDS*-2: all data in netcdfResults from UCI CTM only, using RDS* protocol and MDS-2; NaNs only for flight 46; for use analyzing the reactivities from the ATom 10 s data.Probability densities of the reactivities
The reactivities for three large domains (global, Pacific, Atlantic) from the
six-model RDS-0 are summarized in Tables 2 and S8. Sorted PDs for the three Rs and
Pacific and Atlantic Ocean basins are plotted in Fig. 1 and show the
importance of the most reactive “hot” parcels with deeply convex curves and
the sharp upturn in R values above 0.9 cumulative weight (top 10 %). Both
basins show a similar emphasis on the most reactive hot parcels: 80 %–90 %
of total R is in the top 50 % of the parcels, 25 %–35 % is in the top
10 % and about 10 %–14 % is in the top 3 %. The corollary is that the
bottom 50 % parcels control only 10 %–20 % of the total reactivity, which
is why the median is less than mean (except for P-O3 in the Atlantic). Each
R value and each ocean has a unique shape; for example L-O3 in the Atlantic
is almost two straight lines breaking at the 50th percentile. In Fig. 1 the agreement across all models (except GISS) is clear, indicating that
the conclusion in P2018 (i.e., that most global chemistry models agree on the
O3 and CH4 budgets if given the chemical composition) also holds
for the ATom-measured chemical composition. Comparing the dashed brown (UCI, RDS-0)
and black (UCIP, RDS*-2) lines, we find that the shift to observed
NOx and new HNO4+PAN protocol has introduced noticeable changes only
for P-O3: increasing reactivities overall in the Pacific while
decreasing them slightly in the Atlantic. From Table 2, these changes
primarily affected mean P-O3 and were due primarily to the shift from MDS-0
to MDS-2 and secondarily to the RDS* protocol, which reduced both P-O3 and
L-O3 in both basins. We conclude that accurate modeling of chemical
composition of the 80th and greater percentiles is important but that
modest errors in the lowest 50th percentile are inconsequential;
effectively, some parcels matter more than others (P2017).
Sorted reactivities (P-O3, L-O3, L-CH4, ppb/d) for the Pacific
and Atlantic domains of ATom-1. Each parcel is weighted; see text. The six
modeled reactivities for MDS-0 using the standard RDS protocol are shown
with colored lines, and the UCI calculation for MDS-2 using the new RDS*
protocol (HNO4 and PAN damping, denoted UCIP) is shown as a
dashed black line. The mean value for each model is shown with an open circle
plotted at the 50th percentile. (Flipped about the axes, this is a
cumulative probability density function.)
How well does this ATom analysis work as a model intercomparison project?
Overall, we find that most models give similar results when presented with
the ATom-1 MDS. The broad agreement of the cumulative reactive PDs across a
range of model formulations using differing levels of chemical complexity
shows this approach is robust. The different protocols for calculating
reactivities as well as the uncertainty in cloud fields appear to have
a small impact on the shape of the cumulative PDs but are informative
regarding the minimum structural uncertainty in estimating the 24 h
reactivity of a well-measured air parcel.
Spatial heterogeneity of tropospheric chemistry
A critical unknown for tropospheric chemistry modeling is what resolution is
needed to correctly calculate the budgets of key gases. A similar question
was addressed in Yu et al. (2016) for the isoprene oxidation pathways using
a model with variable resolution (500, 250 and 30 km) compared to
aircraft measurements; see also ship plume chemistry in Charlton-Perez et
al. (2009). ATom's 10 s air parcels measure 2 km (horizontal) by 80 m
(vertical) during most profiles. There are obviously some chemical
structures below the 10 s air parcels we use here. Only some ATom
measurements are archived at 1 Hz, and we examine a test case using 1 s data
for O3 and H2O for a mid-ocean descent between Anchorage and Kona
in Fig. S2a in the Supplement. Some of the 1 s (200 m by 8 m) variability is
clearly lost with 10 s averaging, but 10 s averaging preserves most of the
variability. Lines in Fig. S2 demark 400 m in altitude, and most of the
variability appears to occur on this larger, model-resolved scale. Figure S2b shows the 10 s reactivities during that descent and also indicates that
much of the variability occurs at 400 m scales. A more quantitative example
using all the tropical ATom reactivities is shown in comparisons with
probability densities below (Fig. 5).
How important is it for the models to represent the extremes of reactivity?
While the sorted reactivity curves (Fig. 1, Tables 2 and S8) continue to
steepen from the 90th to 97th percentile, the slope does not
change that much. Thus we can estimate the 99th + percentile
contributes < 5 % of the total reactivity. Thus, if our model
misses the top 1 % of reactive air parcels (e.g., due to the inability to
simulate intensely reactive thin pollution layers), then we miss at most
5 % of the total reactivity. This finding is new and encouraging, and it
needs to be verified with the ATom-2, 3 and 4 data.
The spatial structures and variability of reactivity as sampled by the ATom
tropic transects (central Pacific, eastern Pacific and Atlantic) are
presented as nine panels in Fig. 2. Here, the UCI RDS*-2 reactivities are
averaged and plotted in 1∘ latitude by 200 m thick cells,
comparable to some global models (e.g., GMI, NCAR, UCI). We separate the
eastern Pacific (121∘ W, research flight (RF) 1) from the central
Pacific (RFs 3, 4 and 5) because we are looking for contiguous
latitude-by-pressure structures.
Curtain plots for P-O3 (0–6 ppb/d), L-O3 (0–6 ppb/d) and L-CH4
(0–3 ppb/d) showing the profiling of ATom-1 flights in the central Pacific
(RF 3, 4 and 5), eastern Pacific (RF 1) and Atlantic (RF 7, 8 and 9).
Reactivities are calculated with the UCI model using MDS-2 and the new RDS*
protocol (UCI RDS*-2). The 10 s air parcels are averaged into 1∘
latitude and 200 m altitude bins.
In the central Pacific (row 1), highly reactive (hot) P-O3 parcels
(> 6 ppb/d) occur in larger, connected air masses at latitudes
20–22∘ N and pressure altitudes 2–3 km and in more scattered
parcels (> 3 ppb/d) below 5 km down to 20∘ S. High
L-O3 and L-CH4 coincide with this 20–22∘ N air mass and also with
some high P-O3 at lower latitudes. This pattern of overlapping extremes in
all three Rs is surprising because the models' mid-Pacific climatologies show a
separation between regions of high L-O3 (lower-middle troposphere) and high
P-O3 (upper troposphere, as seen in P2017's Fig. 3). The obvious explanation is
that the models leave most of the lightning-produced NOx in the upper
troposphere. The ATom profiling seems to catch reactive regions in adjacent
profiles separate by a few hundred kilometers, scales easily resolvable with 3D
models.
Mean profiles of reactivity (rows: P-O3, L-O3, L-CH4 in ppb/d) in three domains (columns: C. Pacific, 30∘ S–30∘ N by 180–210∘ E; E. Pacific, 0–30∘ N by 230–250∘ E; Atlantic, 30∘ S–30∘ N by 326–343∘ E). Air parcels are cosine(latitude)-weighted. ATom-1 (gray) results are from Fig. 2, while model results are taken from the August climatologies in Prather et al. (2017).
In the eastern Pacific (row 2), the overlap of outbound and return profiles
enhances the spatial sampling over the 10 h flight. The region of very large
L-O3 (> 5 ppb/d) is extensive, beginning at 5–6 km at
10∘ N and broadening to 2–8 km at 28∘ N. The region of
L-CH4 is similar, but loss at the upper altitudes of this air mass is
attenuated because of the temperature dependence of L-CH4 and possibly
because of differing OH:HO2 ratios with altitude. Large P-O3
(> 6 ppb/d) occurs in some but not all of these highly reactive
L-O3 regions, suggesting that NOx is not as evenly distributed as HOx is.
P-O3 also show regions of high reactivity above 8 km that are not in the
high L-O3 and L-CH4 regions, probably evidence of convective sources of HOx
and NOx but too cold and dry for the L-O3 and L-CH4 reactions. ATom-1 RF1
(29 July 2016) occurred during the North American Monsoon when there was
easterly flow off Mexico; thus the high reactivity of this large air mass
indicates that continental deep convection is a source of high reactivity
for both O3 and CH4.
In the Atlantic (row 3), we also see similar air masses through successive
profiles, particularly in the northern tropics. The Atlantic P-O3 shows
high-altitude reactivity similar to the eastern Pacific. Likewise, the large
values of L-O3 and L-CH4 match the eastern Pacific and not central Pacific.
Unlike either Pacific transect, the Atlantic L-O3 and L-CH4 show some high
reactivity below 1 km altitude. Overall, the ATom-1 profiling clearly
identifies extended air masses of high L-O3 and L-CH4 extending over 2–5 km
in altitude and 10∘ of latitude. The high P-O3 regions tend to be
much more heterogeneous with greatly reduced spatial extent, likely of
recent convective origin as for the eastern Pacific.
Overall, the extensive ATom profiling identifies a heterogeneous mix of
chemical composition in the tropical Atlantic and Pacific, with a large range
of reactivities. What is important for those trying to model tropospheric
chemistry is that the spatial scales of variability seen in Fig. 2 are
within the capability of modern global models.
Testing model climatologies
The ATom data set provides a unique opportunity to test CTMs and CCMs in a
climatological sense. In this section, we compare ATom-1 data and the six
models' chemical statistics for mid-August used in P2017. The ATom profiles
cannot be easily compared point by point with CCMs, and we use statistical
measures of the three reactivities in the three tropical basins: mean profiles in
Fig. 3 and PDs in Fig. 5.
Profiles
For P-O3 profiles (top row, Fig. 3), the discrepancy between models and
measurements is stark. The models (less GISS) present a consistent picture
of one world, while the ATom profiles describe an entirely different world.
In the central Pacific at 8–12 km, the ATom-1 results tend to agree with
models, showing ozone production of about 1 ppb/d. Below 8 km, ATom's P-O3
increases to a peak of 4 ppb/d at 2 km, while the models' P-O3 stays
constant down to 4 km and then decrease to about 0.5 ppb/d below 2 km.
This pattern indicates that in the middle of the Pacific, the NOx+ HOx
combination that produces ozone is suppressed throughout the lower
troposphere in all the models. In the eastern Pacific and Atlantic, both
models' and ATom reactivities indicate that P-O3 is greatly enhanced above 6 km as compared to the central Pacific, but below 6 km ATom P-O3 is much
larger than that of the models', by a factor of 2. In the upper troposphere,
the agreement indicates that both models and ATom find the influence of deep
continental convection bringing reactive NOx+ HOx air masses to the nearby
oceanic regions but not to the central Pacific. The difference below 5 km
in all three regions implies a consistent bias across the models in some
combination of HOx sources and/or the vertical redistribution of lightning
NOx. This difference is unlikely to be a sampling bias in ATom-1, given it
occurs in all three regions.
For L-O3 (middle row), the agreement in the central Pacific is very good
throughout the 0–12 km range; i.e., ATom looks just like one of the models
(except GISS). Moving to the eastern Pacific and Atlantic, both models and
ATom show increased reactivity, consistent with continental convective
outflow. The large ATom reactivity in the eastern Pacific (3–8 km) is clear
in Fig. 2 and likely due to easterly mid-tropospheric flow from convection
over Mexico at that specific time (29 July 2016). Similarly, the ATom
reactivity at a low level (1–3 km) in the Atlantic is associated with biomass
burning in Africa and was measured in other trace species. Thus, in terms of
L-O3, the ATom–model differences may be due to specific meteorological
conditions, and this could be tested with CTMs using 2016 meteorology and
wildfires.
For L-CH4 (bottom row), the ATom–model pattern is similar to L-O3, but
higher ATom reactivity occurs at lower altitudes. Overall, the ATom L-CH4 is
slightly greater than the modeled L-CH4. L-O3 is dominated by O(1D) and
HO2 loss, while L-CH4 is limited to OH loss. Overall, there is clear
evidence that the Atlantic and Pacific have very different chemical mixtures
controlling the reactivities and that convection over land (monsoon or
biomass burning) creates air masses that are still highly reactive a day or
so later.
Key species
The deficit in modeled P-O3 points to a NOx deficiency in the models, and
this becomes obvious in the comparison of the PD histograms for NOx shown in
Fig. 4. In the central Pacific over 0–12 km (first row), ATom has a
reduced frequency of parcels with 2–20 ppt and corresponding increase in
parcels with 20–80 ppt. This discrepancy is amplified in the lower
troposphere, 0–4 km (second row). In the middle of the Pacific, our
chemistry models are missing a large source of lower tropospheric NOx. The
obvious source of oceanic NOx is lightning since oceanic sources of
organonitrates or other nitrate species measured on ATom could not supply
this amount. The ATom statistics indicate a lightning source must be
vertically mixed. In the eastern Pacific, the ATom 0–4 km troposphere
appears again to have large amounts of air with 20–50 ppt, while the full
troposphere more closely matches the models, except for the large occurrence
of air with 100–300 ppt NOx. These high-NOx upper troposphere regions are
probably direct outflow from very deep convection with lightning in the
monsoon regions over Mexico at this time. In the Atlantic, the models' NOx
shows too frequent occurrence of low NOx (< 10 ppt) and thus
underestimates the 10–100 ppt levels at all altitudes. ATom has a strong
peak occurrence about 80–120 ppt in the upper troposphere, and, like the eastern
Pacific, this is probably due to lightning NOx from deep convection over
land (Africa or South America). Overall, the models appear to be missing
significant NOx sources throughout the tropics, especially below 4 km.
Histograms of probability densities (PDs) of NOx (0–12 km,
row 1), NOx (0–4 km, row 2), HOOH (0–12 km, row 3) and HCHO (0–12 km,
row 4) for the three tropical regions (central Pacific, eastern Pacific,
Atlantic). The ATom-1 data are plotted on top of the six global chemistry
models' results for a day in mid-August and sampled as described in Fig. 3.
In Fig. 4, we also look at the histograms for the key HOx-related species
HOOH (third row) and HCHO (fourth row). For these species, the
ATom–model agreement is generally good. If anything, the models tend to have
too much HOOH. ATom shows systematically large occurrences of low HOOH
(50–200 ppt, especially central Pacific), indicating, perhaps, that
convective or cloud scavenging of HOOH is more effective than is modeled.
HCHO shows reasonable agreement in the Atlantic, but in both central and
eastern Pacific, the modeled low end (< 40 ppt) is simply not seen
in the ATom data. Also, the models are missing a strong HCHO peak at 300 ppt
in the eastern Pacific, probably convection-related. Thus, in terms of these
HOx precursors, the model climatologies appear to be at least as reactive as
the ATom data.
While the ATom-1 data in Fig. 4 are limited to single transects, the model
NOx discrepancies apply across the three tropical regions, and the simple
chemical statistics for these flights alone are probably enough to identify
measurement-model discrepancies. For the HOx-related species, the models
match the first-order statistics from ATom. In terms of using ATom
statistics as a model metric, it is encouraging that where individual models
tend to deviate from their peers, they also deviate from the ATom-1 PDs.
Probability densities
Mean profiles do not reflect the heterogeneity seen in Fig. 2, and so we
also examine the PDs of the tropical reactivities (Fig. 5). The model PDs
(colored lines connecting open circles at the center of each bin) are
calculated from the 1 d statistics for mid-August (P2017) using the model
blocks shown in Fig. S1. The model grid cells are weighted by air mass and
cosine(latitude) and limited to pressures greater than 200 HPa. The ATom PDs
(black lines connecting black open circles) are calculated from the 10 s
data weighted by (but not averaged over) the number of points in each
10∘ latitude by 200 hPa pressure bin and then also by cosine
(latitude) to compare with the models. In addition, a PD was calculated from
the 1∘ by 200 m average grid-cell values in Fig. 2 (black Xs),
and this is also cosine(latitude)-weighted. To check if the high
reactivities in the eastern Pacific affected the whole Pacific PD, a separate PD
using only central Pacific 10 s data was calculated (gray lines connecting
gray open circles). The mean reactivities (ppb/d) from the models and ATom are
given in the legend; note that these values disagree with some table data
that are not cosine-weighted. The PD binning is shown by the open circles,
and occurrences of off-scale reactivities are included in the last point.
Probability densities (PDs, frequency of occurrence) for the ATom-1
three reactivities (rows: P-O3, L-O3, L-CH4 in ppb/d) and for the Pacific
and Atlantic from 54∘ S to 60∘ N (columns left and
right). Each air parcel is weighted as described in the text for equal
frequency in large latitude–pressure bins and also by cosine(latitude). The
ATom statistics are from the UCI model, using MDS-2 and revised RDS*
protocol (HNO4 and PAN damping). The full Pacific results (solid black)
also show just the central Pacific (dashed gray). The six models' values for a
day in mid-August are averaged over longitude for the domains shown in
Fig. S1 and then cosine(latitude)-weighted. Mean values (ppb/d) are
shown in the legend but are different from some tables where the cosine
weighting is not applied. The PD derived from the ATom 10 s parcels binned at
1∘ latitude and 200 m altitude (shown for the tropics only in
Fig. 2) is typical of a high-resolution global model and denoted by black
Xs.
The obvious discrepancy is with P-O3 in both Pacific and Atlantic basins.
ATom data have very low occurrence of P-O3 < 1 ppb/d and a broad,
almost uniform frequency (∼ 0.1) extending out to 4 ppb/d.
This result is consistent with the mean profile errors (Fig. 3). The match
for L-CH4 is very good in both basins, although the models have a greater
occurrence in the middle 0.5–1.5 ppb/d range and reduced occurrence in the
higher 1.5–2.5 ppb/d range. For L-O3, the match is very good and similar to
L-CH4, although the Atlantic has a high frequency of L-O3 > 6 ppb/d that is not seen in the models (except GISS). The extreme eastern Pacific reactivities are seen in the mean values displayed in the legend
(e.g., CPac with 1.29 ppb/d L-O3 versus ATom (i.e., CPac + EPac) with 1.54 ppb/d), but the PDs (gray circles versus black circles) resemble each other
more closely than any of the models.
The ability to test a model's reactivity statistics with the ATom 10 s data
is not obvious, but the PDs based on 1∘ latitude by 200 m altitude
cells (the black Xs) is remarkably close to the PDs based on 2 km
(horizontal) by 80 m (vertical) 10 s parcels. With the coarser resolution, we
see a slight shift of points from the ends of the PD to the middle as
expected, but we find once again, that the loss in high-frequency, below-model grid-cell resolution is not great. Both ATom-derived PDs more closely
resemble each other than any model PD. Thus, current global chemistry models
with resolutions of about 100 km by 400 m should be able to capture much of
the wide range of chemical heterogeneity in the atmosphere, which for the
oceanic transects is, we believe, adequately resolved by the 10 s ATom
measurements. Perhaps more surprising, given the different mean profiles in
Fig. 3, is that the five model PDs in Fig. 5 look very much alike. This
points to some significant underlying difference between our current global
chemistry models and the ATom observations.
Scatterplot of L-CH4 (ppb/d) versus HCHO (ppt) for ATom 1 in the three
tropical regions shown in Fig. 3. The air parcels are split into the lower
troposphere (0–4 km pressure altitude, red dots), where most of the
reactivity lies, and the middle and upper troposphere (4–12 km, blue). A simple linear
fit to all data is shown (thin black line), and the slope is given in units
of 1 per day.
Discussion and path forwardMajor findings
This paper opens a door for what the community can do with the ATom
measurements and the derived products. ATom's mix of key species allows us
to calculate the reactivity of the air parcels and hopefully may become
standard for tropospheric chemistry campaigns. We find that the reactivity
of the troposphere with respect to O3 and CH4 is dominated by a
fraction of the air parcels but not by so small and infrequent a fraction
as to challenge the ability of current CTMs to simulate these observations
and thus be used to study the oxidation budgets. In comparing ATom results
with modeled climatologies, we find a clear model error – missing O3
production over the tropical oceans' lower troposphere – and traced it to the
lack of NOx below 4 km. The occurrence of the same error over the central
and eastern Pacific as well as the Atlantic Ocean makes this a robust
model–measurement discrepancy.
Building our chemical statistics (PDs) from the ATom 10 s air parcels on a
scale of 2 km by 80 m, we can identify the fundamental scales of spatial
heterogeneity in tropospheric chemistry. Although heterogeneity occurs at
the finest scales (such as seen in some 1 s observations), the majority of
variability in terms of the O3 and CH4 budgets occurs across
scales larger than neighboring 2 km parcels. The PDs measured in ATom can be
largely captured by global models' 100 km by 200 m grid cells in the lower
troposphere. This surprising result is evident by comparing the ATom 1D PDs
– both species and reactivities – with those from the models'
climatologies (Fig. 5). These comparisons show that the modeled PDs are
consistent with the innate chemical heterogeneity of the troposphere as
measured by the 10 s parcels in ATom. A related conclusion for biomass
burning smoke particles is found by Schill et al. (2020), where most of
the smoke appears in the background rather than in pollution plumes, and
therefore much of the variability occurs on synoptic scales resolved by
global models (see their Fig. 1 compared with Fig. 2 here).
2D frequency of occurrence (PDs in log ppt mole fraction) of HOOH vs. NOx for the tropical central Pacific for all four ATom deployments. The cross marks the mean (in log space), and the ellipse is fitted to the rotated PD having the smallest semi-minor axis. The semi-minor and semi-major axes are 2 standard deviations of PD in that direction. The ellipses from ATom-2 (red), ATom-3 (blue) and ATom-4 (dark green) are also plotted in the ATom-1 quadrant.
Opportunities and lessons learned
As a quick look at the opportunities provided by the ATom data, we present
an example based on the Wolfe et al. (2019) study, which used the F0AM model
and semi-analytical arguments to show that troposphere HCHO columns
(measurable by satellite and ATom) are related to OH columns (measured by
ATom) and thus to CH4 loss. Figure 6 extends the Wolfe et al. study
using the individual air parcels and plotting L-CH4 (ppb/d) versus HCHO
(ppt) for the three tropical regions where most of the CH4 loss occurs. The
relationship is linear, with slopes ranging from 4 to 6 per day, but the largest
reactivities (0–4 km, 1–3 ppb/d) are not so well correlated with HCHO.
As is usual with new model intercomparison projects, we have an opportunity
to identify model “features” and identify errors. In the UCI model, an error
in the lumped alkane formulation (averaging alkanes C3H8 and
higher) did not show up in P2018, where UCI supplied all the species, but when
the ATom data were used, the UCI model became an outlier. Once found, this
problem was readily fixed. The divergence of the NCAR RDS results is likely
due to the implementation of the RDS protocol where CCM values overwrite the
MDS values. We identified this problem in P2018 and thought it was solved, but
perhaps it is not. Inclusion of the F0AM model with its extensive
hydrocarbon oxidation mechanism provided an interesting contrast with the
simpler chemistry in the global CCM/CTMs. For a better comparison of the
chemical mechanisms, we should have F0AM use 5 d of photolysis fields
from one of the CTMs. The anomalous GISS results have been examined by a
co-author, but no clear causes have been identified as of this publication.
The problem goes beyond just the implementation of the RDS protocol, as it
shows up in the model climatology (Figs. 4 and 5, also in P2017).
Decadal-scale shifts in the budgets of O3 and CH4 are likely to be
evident through the statistical patterns of the key species, rather than
simply via average profiles. The underlying design of ATom was to collect
enough data to develop such a multivariate chemical climatology. As a quick
look across the four deployments, we show the joint 2D PDs on a logarithmic
scale as in P2017 for HOOH versus NOx in Fig. 7. The patterns for the tropical
central Pacific are quite similar for the four seasons of ATom deployments, and
the fitted ellipses are almost identical for ATom 2, 3 and 4. Thus, for
these species in the central Pacific, we believe that ATom provides a
benchmark of the 2016–2018 chemical state, one that can be revisited with an
aircraft mission in a decade to detect changes in not only chemical
composition, but also reactivity.
ATom identifies which “highly reactive” spatial or chemical environments
could be targeted in future campaigns for process studies or to provide a
better link between satellite observations and photochemical reactivity
(e.g., E. Pacific mid-troposphere in August, Fig. 2). The many corollary
species measured by ATom (not directly involved in CH4 and O3
chemistry) can provide clues to the origin or chemical processing of these
environments. We hope to engage a wider modeling community beyond the ATom
science team, as in H2018, in the calculation of photochemical processes,
budgets, and feedbacks based on all four ATom deployments.
Data availability
The MDS-2 and RDS*-2 data for ATom 1, 2, 3 and 4 are
presented here as core ATom deliverables and are now posted on the NASA
ESPO ATom website (https://espo.nasa.gov/atom/content/ATom, Science team of the NASA Atmospheric Tomography Mission, 2021). This
publication marks the public release of the reactivity calculations for ATom
2, 3 and 4, but we have not yet analyzed these data, and thus users should
be aware and report any anomalous features to the lead authors via
haog2@uci.edu and mprather@uci.edu. Details of the ATom
mission and data sets are found on the NASA mission website
(https://espo.nasa.gov/atom/content/ATom, last access: 13 September 2021) and in the final archive at Oak
Ridge National Laboratory (ORNL; https://daac.ornl.gov/ATOM/guides/ATom_merge.html, last access: 13 September 2021). The
MATLAB codes and data sets used in the analysis here are posted on Dryad
(10.7280/D1Q699, Guo, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-13729-2021-supplement.
Author contributions
HG, CMF, SCW and MJP designed the research and performed the data
analysis. SAS, SDS, LE, FL, JL, AMF, GC, LTM and GW contributed original atmospheric chemistry model results. GW, MK, JC, GD, JD, BCD, RC, KM, JP, TBR, CT, TFH, DB, NJB, ECA, RSH, JE, EH and FM
contributed original atmospheric observations. HG, CMF and MJP wrote
the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors are indebted to the entire ATom Science Team including the
managers, pilots and crew, who made this mission possible. Many other
scientists not on the author list enabled the measurements and model results
used here. Primary funding of the preparation of this paper at UC
Irvine was through NASA grants NNX15AG57A and 80NSSC21K1454.
Financial support
The Atmospheric Tomography Mission (ATom) was supported by the National Aeronautics and Space Administration's Earth System Science Pathfinder Venture-Class Science Investigations: Earth Venture Suborbital-2. Primary funding of the preparation of this paper at UC Irvine was through NASA (grant nos. NNX15AG57A and 80NSSC21K1454).
Review statement
This paper was edited by Neil Harris and reviewed by two anonymous referees.
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