We measured the global distribution of tropospheric N2O
mixing ratios during the NASA airborne Atmospheric Tomography (ATom)
mission. ATom measured concentrations of ∼ 300 gas species and
aerosol properties in 647 vertical profiles spanning the Pacific, Atlantic,
Arctic, and much of the Southern Ocean basins, nearly from pole to pole,
over four seasons (2016–2018). We measured N2O concentrations at 1 Hz
using a quantum cascade laser spectrometer (QCLS). We introduced a new spectral
retrieval method to account for the pressure and temperature sensitivity of
the instrument when deployed on aircraft. This retrieval strategy improved
the precision of our ATom QCLS N2O measurements by a factor of three (based
on the standard deviation of calibration measurements). Our measurements show that most
of the variance of N2O mixing ratios in the troposphere is driven by
the influence of N2O-depleted stratospheric air, especially at mid- and
high latitudes. We observe the downward propagation of lower N2O mixing
ratios (compared to surface stations) that tracks the influence of
stratosphere–troposphere exchange through the tropospheric column down to
the surface. The highest N2O mixing ratios occur close to the Equator,
extending through the boundary layer and free troposphere. We observed
influences from a complex and diverse mixture of N2O sources, with
emission source types identified using the rich suite of chemical species
measured on ATom and the geographical origin calculated using an
atmospheric transport model. Although ATom flights were mostly over the
oceans, the most prominent N2O enhancements were associated with
anthropogenic emissions, including from industry (e.g., oil and gas), urban sources, and biomass
burning, especially in the tropical Atlantic outflow from Africa. Enhanced
N2O mixing ratios are mostly associated with pollution-related tracers
arriving from the coastal area of Nigeria. Peaks of N2O are often
associated with indicators of photochemical processing, suggesting possible
unexpected source processes. In most cases, the results show how
difficult it is to separate the mixture of different sources in the atmosphere,
which may contribute to uncertainties in the N2O global budget. The
extensive data set from ATom will help improve the understanding of N2O
emission processes and their representation in global models.
Introduction
Nitrous oxide (N2O) is a powerful greenhouse gas and, due to its
oxidation to NOx, a major contributor to both stratospheric ozone loss
and to the passivation of stratospheric oxy-halogen radicals (Forster et al.,
2007; Ravishankara et al., 2009). The rate of increase
in atmospheric N2O since the Industrial Revolution, 0.93 ppb yr-1, implies a significant
(∼ 30 %) imbalance between emission rates and destruction in
the stratosphere. Seasonal cycles in tropospheric N2O are driven by
both stratosphere-to-troposphere exchange and surface emissions (Nevison
et al., 2011; Assonov et al., 2013; Thompson et al., 2014a). Most N2O
emissions are attributed to microbial nitrification and denitrification in
natural and cultivated soils, freshwaters, and oceans plus emissions related
to human activities, such as biomass burning and industrial emissions
(Butterbach-Bahl et al., 2013; Saikawa et al., 2014; Thompson et al., 2014a;
Upstill-Goddard et al., 2017; WMO, 2018).
Much effort has been made to reduce the uncertainties in the individual
components of the N2O global budget (e.g., Tian et al., 2012, 2020;
Xiang et al., 2013; Thompson et al., 2014a, b; Ganesan et al., 2020; Yang et
al., 2020). Recent estimates of global total N2O emissions to the
atmosphere from bottom-up and top-down methods average 17 Tg N yr-1
(12.2–23.5 from bottom-up analysis and 15.9–17.7 Tg N yr-1 from
top-down approaches, Tian et al., 2020). The most recent estimates of the
global ocean emissions of N2O range between 2.5 and 4.3 Tg N yr-1 (∼ 20 % of total emissions), with the tropics, upwelling coastal
areas, and subpolar regions the major contributors to these fluxes (Yang
et al., 2020; Tian et al., 2020). However, the magnitude of marine N2O
emissions is subject to large uncertainty due to spatial and temporal
heterogeneity (Nevison et al., 1995, 2005; Ganesan et al., 2020; Yang et
al., 2020). According to Tian et al. (2020), anthropogenic sources account
for ∼ 43 % of global N2O emissions (7.3 Tg N yr-1),
with industry and biomass burning emissions estimated to be 1.6–1.9 Tg N yr-1, respectively (Syakila and Kroeze, 2011; Tian et al., 2020) and the
rest originating from agriculture. N2O emissions from biogenic sources and fires in
Africa are estimated at 3.3 ± 1.3 Tg N2O yr-1 (Valentini et
al., 2014). Agricultural N2O emission estimates (up to ∼ 37 %)
range between 2.5 and 5.8 Tg N yr-1, and between 4.9 and 6.5 Tg N yr-1 in the case of natural soils (Kort et al., 2008, 2010; Syakila and
Kroeze, 2011; Tian et al., 2020). Recent estimates of N2O emissions
from fertilized tropical and subtropical agricultural systems are 3 ± 5 kg N ha-1 yr-1 (Albanito et al., 2017). Most of these estimates
are derived from short-term local-scale in-situ measurements and are
difficult to extrapolate with confidence to large regions or to the globe.
In the atmosphere, N2O is destroyed by oxidation (10 %, O(1D)
reaction) and photolysis (90 %, 190–230 nm photolysis) in the upper
stratosphere (> 20 km altitude; SPARC, 2013), which makes it a
good candidate for tracing the air exchange between the stratosphere and the
troposphere (Hintsa et al., 1998; Nevison et al., 2011; Assonov et al.,
2013; Krause et al., 2018). Atmospheric models tend to underestimate the
interhemispheric N2O gradient, which Thompson et al. (2014a) attribute
to an overestimation of N2O emissions in the Southern Ocean, an
underestimate of Northern Hemisphere emissions, and/or an overestimate
of stratosphere-to-troposphere exchange in the Northern Hemisphere. Overall,
the largest uncertainties in modeled N2O emissions are found in
tropical South America and South Asia (Thompson et al., 2014b).
We present atmospheric N2O altitude profiles at high temporal
resolution collected during the NASA Atmospheric Tomography (ATom) mission.
ATom was a global-scale airborne deployment conducted over a 3 year period
(2016–2018) using the NASA DC-8 aircraft. In ATom, the DC-8 flew vertical
profiles (0.2–13 km) nearly continuously almost from pole to pole while measuring mixing ratios of
∼ 300 trace gases and aerosol physical and chemical properties
over the Pacific and Atlantic basins and during
each of the four seasons. Each deployment (1–4) started and ended in Palmdale
(California, USA) and generally consisted of a loop
southward from the Arctic through the central Pacific, across the Southern
Ocean to South America, northward through the Atlantic, and across Greenland
and the Arctic Ocean. During ATom-3 and -4, two additional flights from
Punta Arenas (Chile) sampled the Antarctic troposphere and upper
troposphere/lower stratosphere (UT/LS) to 80∘ S.
In this work, we focus on the measurements taken during January–February 2017 (ATom-2), September–October 2017 (ATom-3), and April–May 2018
(ATom-4) (no quantum cascade laser spectrometer (QCLS) N2O data are
available for ATom-1 in August 2016). The motivation for this paper is
twofold. Firstly, we present a new retrieval strategy to account for the
pressure and temperature dependence of laser-based instruments, and specifically
for the use of quantum cascade laser spectrometers on aircraft. Secondly, we
report on the global distribution of N2O from the surface to 13 km and
examine the processes contributing to the variability of tropospheric
N2O based on the vertical profiles of N2O and a broad variety of
covariate chemical species and aerosol properties.
Instrument specifications, spectral analysis, and calibrationSpecifications of QCLS
We measured N2O mixing ratios with the Harvard/NCAR/Aerodyne Research
Inc. Quantum Cascade Laser Spectrometer (QCLS). This instrument was
previously deployed on the NCAR/NSF Gulfstream V for the HIAPER Pole-to-Pole
Observations mission (HIPPO, Wofsy et al., 2011;
https://www.eol.ucar.edu/field_projects/hippo, last access: 14 October 2020) and the
O2/ N2 Ratio and CO2 Southern Ocean Study (ORCAS, Stephens et
al., 2018; https://www.eol.ucar.edu/field_projects/orcas, last access: 14 October 2020).
Detailed information about the spectrometer configuration can be found in
Jiménez et al. (2005, 2006) and Santoni et al. (2014). A brief
description follows.
QCLS provides continuous (1 Hz) measurements of N2O, methane (CH4),
and carbon monoxide (CO) using two thermoelectrically cooled pulsed quantum
cascade lasers, a 76 m pathlength multiple-pass absorption cell
(∼ 0.5 L volume), and two liquid-nitrogen-cooled solid-state
HgCdTe detectors. All these components are mounted on a
temperature-stabilized, vibrationally isolated optical bench. The
temperature in QCLS is controlled by Peltier elements coupled with a
closed-circuit recirculating fluid kept at 288.0 ± 0.1 K. QCLS
measures CH4 and N2O by scanning the spectral interval of 1275.45 ± 0.15 cm-1. A second laser is used to scan CO at 2169.15 ± 0.15 cm-1. The supply currents to QCLS are ramped at a rate of 3.8 kHz
to scan the laser frequency for 200 channels (steps in frequency) in laser 1
and 50 channels in laser 2; an extra 10 channels are used to measure the laser
shut off (zero-light level). The spectra and fit residuals for CH4,
N2O, and CO are shown in Fig. S1 of the Supplement. Mixing ratios are
derived at a rate of 1 Hz by a least-squares spectral fit assuming a Voigt
line profile at the pressure and temperature measured inside the sample cell
and using molecular line parameters from the HIgh-resolution TRANsmission
molecular absorption database (HITRAN, Rothman et al., 2005). The
temperature and pressure inside the cell are monitored with a 30 kΩ
thermistor and a capacitance manometer (133 hPa full scale), respectively.
During sampling, the air passes through a 50-tube Nafion drier to remove the
bulk water vapor. A Teflon diaphragm pump downstream of the cell reduces the
air pressure to ∼ 60 hPa. Both ambient air and calibration
gases pass through a Teflon dry-ice trap to reduce the dew point to -70 ∘C. After ATom-1, we added a bypass between the inlet and the
instrument to increase the flushing rate of the inlet and inlet tubing. The
calibration sequence includes 2 min of ultra-high-purity zero air
followed by 1 min each of low- and high-mixing ratio gases every 30 min (see Fig. S2). We measured zero air every 15 min during ATom-1 and -2, and every 30 min during ATom-3 and -4. A data logger (CR10X,
Campbell Scientific) was used to automate the sampling sequence. The CR10X
controlled the pressure controller on the cell and managed the data
transfer.
We use gas cylinders traceable to the National Oceanic and Atmospheric
Administration World Meteorological Organization scales for calibration
(NOAA-WMO-X2004A scale for CH4, WMO-X2014A for CO, and NOAA-2006A for
N2O). These gas standards were recalibrated before, during and after
the deployments to maintain traceability. The low mixing ratio gas cylinder
contained 298.5 ± 0.3 ppb of N2O, 1692.4 ± 0.2 ppb of
CH4, and 119.1 ± 0.3 ppb of CO. The high mixing ratio gas
cylinder contained 399.1 ± 0.3 ppb of N2O, 2182.5 ± 0.3 ppb
of CH4, and 192.8 ± 0.5 ppb of CO. Detailed
information on calibrations of the gas cylinders used during ATom is given in
Table S1 of the Supplement.
QCLS also measures carbon dioxide (CO2) in a separate unit. Detailed
information about QCLS CO2 measurements can be found in Santoni et al. (2014).
Spectral analysis and calibration
QCLS was damaged during shipping to the deployment site before the start
of ATom-1, and the resulting alteration in the optical alignment modified
the sensitivity of the instrument to temperature and pressure changes during
aircraft maneuvers. This increased sensitivity was observed in all ATom
deployments. At constant altitude, instrumental precision was similar to
the precision measured during HIPPO (see the Allan–Werle variance analysis in
Fig. 2 in Santoni et al., 2014 for HIPPO and Fig. S3 for ATom), but drifts
were observed during altitude changes due to the effects of changes in cabin
pressure and temperature on the spectral location of interference fringes
that arise in the optical path outside the sample cell. In addition, flight
altitude changes mechanically stressed the optical elements surrounding
the cell, further modulating fringes or changing the shape of the detected
laser intensity profile. These spectral artifacts ultimately reduced the
accuracy of mixing ratios retrieved from spectral fitting. The spectral
artifacts most strongly affected the measurements of CH4 and N2O.
Several post-processing methods using the TDL-Wintel software were explored
to improve the precision and accuracy of ATom QCLS N2O data, most with
little success. Since the measured spectra were all saved, it is possible to
refit the data with different fit parameters. A limited number of
interference fringes may be included in the set of fitting functions.
However, none of the previously used full refitting strategies
significantly improved the data accuracy.
We have achieved significant improvements in the precision and accuracy of
the ATom QCLS N2O data using a new method dubbed the “Neptune
algorithm,” developed by Aerodyne Research, Inc., and that method has been further
developed and applied to the data sets described here. Using this algorithm,
the precision of the retrieved N2O data measured with the damaged QCLS
was similar to that reported in HIPPO. The Neptune algorithm generates
corrections to the mixing ratios retrieved from the original fits by
associating specific spectral features with anomalies in retrieved mixing
ratios observed during calibrations, i.e., during intervals when the mixing
ratios are held constant. The spectral baseline is defined as the spectral
channels outside the boundaries of the spectral lines of the target gas.
Fluctuations in the spectral baselines are quantified for the entire data
set by means of principal component analysis (PCA). PCA provides an
efficient description of the spectral fluctuations, naturally producing an
ordered set from the strongest to the weakest orthogonal vectors (spectral
forms), each with an amplitude history spanning the data set. The PCAs are
defined by an optimization procedure during calibrations, when mixing ratio
fluctuations are designed to be ∼ 0. The finite fluctuations
in retrieved mixing ratios during calibrations are fitted in the spectral space
of the baseline as linear combinations of the leading PCA vector amplitudes,
creating a linear combination of amplitudes of spectral fluctuations that
predict errors in the mixing ratios for each gas for an entire flight. The
error-producing linear combination of amplitudes of PCA spectral
fluctuations produces a full set of anomaly estimates that are subtracted
from the retrieved mixing ratios during the flight. The computational time
for a 10 h long data set is only seconds, so variations in the algorithm's
parameters (i.e., how many PCAs are retained) can be optimized rapidly.
The Neptune–PCA analysis improved the overall precision by a factor of four for
CH4 and a factor of three in the case of N2O with respect to the
precision of the original retrievals, as measured by the standard deviation
of the retrieved mixing ratios during calibrations. The repeatability of the
retrieved calibrations was 0.2 ppb for N2O and 1 ppb for CH4 (Fig. S4). The laser path of the CH4/ N2O laser was realigned between
ATom-1 and -2, and the Neptune retrieval was applied to CH4 and N2O
measurements corresponding to the ATom-2, -3, and -4 deployments. Mixing
ratios of CH4 and N2O could not be retrieved during ATom-1 because
light levels were too low for the CH4/ N2O laser due to the
damage-induced misalignment.
The steps involved in the Neptune correction process were as follows:
We paired the mixing ratio records with the corresponding spectra (1 s
resolution) for each species (CH4 and N2O).
We grouped the mixing ratios and spectra by type – into calibrations (zeros,
low span, and high span) and air samples – and by time. The spectral data were
thus arranged in an array with point number in the spectrum as x and
spectrum number as y. We calculated an average spectrum for each group type
and subtracted these from each individual spectrum within a group.
We zeroed out the spectral arrays at the positions of the absorption
lines to concentrate on the fluctuations observed in the baseline and to
prevent the PCA from finding line-depth fluctuations as relevant vectors
during the calibrations. Some degree of smoothing (in x) was applied to the
subtracted spectra so that high-frequency fluctuations, which have little
influence on the mixing ratio determination, are not represented. An example
of such a processed spectral array is shown in Fig. 1a.
We applied PCA to the whole line-zeroed spectral array to evaluate the
fluctuations. PCA was applied in two steps: multiply the spectral array by
its transpose to generate an autocovariance array and then perform
singular value decomposition on the autocovariance array. The PCA generated
an efficient description of how the baseline of the spectrum changed with
cabin pressure and temperature. The description of spectral fluctuations consisted of a set of products of vectors and amplitudes.
We fitted the spectra to the PCAs to express mixing ratio fluctuations
during the set of calibrations and zeros as a linear combination of PCA
vector histories. The number of vector histories included in the fit
is typically limited to less than 30 because the weaker PCA amplitudes tend
to just describe random noise.
The linear combination of amplitudes that links spectral fluctuations in the
baseline to mixing ratio fluctuations during calibrations was then applied
to the full data set. That generated the retrieval errors for uncalibrated
mixing ratios for the whole time series. We subtracted the errors from the
initial retrievals from the TDLWintel-QCLS software and computed calibrated
mixing ratios using the corrected retrievals for both calibrations and
samples. An example of the result of applying the Neptune algorithm to the N2O
samples and calibrations for the ATom-4 flight on 12 May 2018 is shown in
Fig. 1b. The approach used here to minimize the effect of changes in
pressure and temperature in optical instruments was based on the observation
of fluctuations of the baseline during calibrations. Hence, this methodology does not provide any
improvement in cases
where altitude changes occurred during sampling but not during any of the
calibrations for an individual flight. Due to frequent calibrations, we did not observe this rare
scenario in the whole mission. To evaluate the ultimate accuracy of our
measurements, we compared the QCLS N2O measurements with other onboard
N2O measurements as well as with the surface N2O measurements of
stations located along the flight tracks.
(a) A processed spectral array from the ATom-4 flight on 12 May 2018. “Channel” represents a point number in the spectra. Spectra have
been grouped by type (i.e., calibration or ambient), with averages subtracted,
absorption lines zeroed out (near channels 75, 140, and 225). This residual spectral array is then smoothed with a binomial filter where the filter width corresponds to the linewidth of the original spectra. Shifts in fringe phases during altitude changes
are apparent. (b) Time series of ambient air samples and high-span, low-span,
and zero calibrations for the same flight as (a). Green dots are the original N2O
data record. Black dots are the N2O data corrected with Neptune (no
calibration applied at this point).
(a) Comparisons between Neptune-corrected QCLS N2O and (1) UCATS N2O, (2) PANTHER N2O, and (3) PFP N2O for ATom-2
(orange circles), ATom-3 (green stars), and ATom-4 (blue squares). We used the
10 s averaged merged file to compare QCLS, UCATS, and PANTHER data. The PFP
flask samples had longer sampling times (30 s to a few minutes). The 1:1 line
is shown as a dashed line. (b) Comparisons between NOAA N2O surface
flask measurements and Neptune-corrected and airborne data from (1) QCLS
N2O, (2) UCATS N2O, (3) PANTHER N2O, (4) and PFP N2O for
ATom-2, -3, and -4, similar to (a1)–(a3). The solid line shows the 1:1
relationship + offset. For plots (b1)–(b4), the airborne data are the mean
N2O values within ± 5∘ of latitude of each surface station
and between 1 and 4 km.
Accuracy of N<inline-formula><mml:math id="M140" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O measurements from QCLS
We evaluated N2O mixing ratios measured by QCLS against three other
instruments that measured N2O on the NASA DC-8 aircraft during ATom. In
addition, we compared the set of four airborne measurements to data from the
flask sampling network at ground stations from the NOAA Global Monitoring
Laboratory (GML, https://gml.noaa.gov/, last access: 10 December 2020) to evaluate the differences
between the airborne data and the ground-based measurements in the NOAA
reference network.
Comparison between airborne N<inline-formula><mml:math id="M143" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O measurements
Measurements of N2O on the DC-8 during ATom were obtained by four
instruments: (i) the Unmanned Aircraft Systems Chromatograph for Atmospheric
Trace Species (UCATS, Hintsa et al., 2021), (ii) the PAN and other Trace
Hydrohalocarbon ExpeRiment (PANTHER; Moore et al., 2006; Wofsy et al.,
2011), (iii) the Programmable Flask Package Whole Air Sampler (PFP; Montzka
et al., 2019), and (iv) our 1 Hz QCLS.
We compared QCLS, PANTHER, and UCATS at 10 s intervals, as provided in the ATom
merged file MER10_DC8_ATom-1.nc available at
the Oak Ridge National Laboratory Distributed Active Archive Center
(ORNL-DAAC, Wofsy et al., 2018, 10.3334/ORNLDAAC/1581, last access: 28 February 2021). The
ATom file MER-PFP merged with the PFP sampling interval (also available in
the above repository) was used to compare QCLS and PFP data. A one-to-one
comparison between these instruments showed an approximately 1 ppb positive
bias in N2O mixing ratios from QCLS (see Fig. 2a1–a3). The 95 %
confidence interval of the mean difference for each pair (95 % C.I.) was
0.75 ± 0.04 ppb between QCLS and PANTHER, 1.13 ± 0.03 ppb
between QCLS and UCATS, and 1.18 ± 0.09 ppb between QCLS and PFP,
respectively, for the full data set (ATom-2, -3, and -4). Information about
the coefficients of the linear fit for each instrument comparison and the
95 % C.I. of the difference for each pair are shown in Table S2. The
offset that QCLS N2O shows against PFP N2O coincides with the
offset already reported by Santoni et al. (2014) during HIPPO in 2009–2011,
which may be attributed to our calibration procedure. PFP flasks are considered the reference measurement on board as the flasks are analyzed with excellent precision and accuracy.
Comparison between airborne and surface measurements of N<inline-formula><mml:math id="M151" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O
We evaluated the traceability of lower-troposphere N2O mixing ratios by
ATom by comparing the four airborne instruments with the surface
measurements of N2O from the NOAA flask sampling network. If a surface station was encountered within a latitude range of 5∘ north and south with respect to the flight track during a
flight, that surface station
was used in the study.
The mean value of N2O within that latitude grid of ± 5∘ and
at instrument altitudes of 1–4 km was compared with the mean N2O observed
at the surface station during the period ± 5 d relative to the flight
(due to the non-daily frequency of flask samples). We chose the altitude
range between 1 to 4 km to agree with the low free tropospheric conditions
that characterized most of the selected ground stations. Information about
the surface stations used here is shown in Table S3 of the Supplement.
The comparison of the whole data set (ATom-2, -3, -4) shows that, overall, QCLS and PANTHER
overestimated N2O mixing ratios with respect to
the surface data by 1.37 ± 0.35 and 0.44 ± 0.51 ppb (95 % C.I.), respectively. In contrast, UCATS and PFP showed low bias with respect to the surface
data: 0.27 ± 0.37 and
0.008 ± 0.34 ppb (95 % C.I.), respectively (Fig. 2b1–b4). Due to the excellent agreement between PFP
and the surface stations and the consistent offset that QCLS showed against
PFP and the stations, the QCLS N2O data
presented in the following sections of this publication were corrected by subtracting the offset with
respect to the PFP data onboard in each deployment: 1.03 ± 0.13 ppb
in AT-2, 1.49 ± 0.19 ppb in AT-3, and 1.18 ± 0.17 ppb in AT-4.
The final official archive data file includes a new column where these
corrections have been applied (N2O_QCLS_ad).
These results show the very close comparability of the ATom airborne N2O
instruments (differences were < 0.5 ppb for UCATS and PANTHER
instruments and 0 ppb in the case of PFP) relative to the surface stations and
demonstrate the feasibility of using ATom N2O measurements to evaluate
the impact of stratospheric air and meridional transport of N2O
emissions on N2O tropospheric column measurements over the ocean
basins. In the following section, we define the boundary conditions that were used to evaluate that impact, which were based on
the NOAA Greenhouse Gas Marine Boundary Layer Reference from the NOAA GML
Carbon Cycle Group (NOAA/ESRL GML CCGG,
https://gml.noaa.gov/, last access: 10 December 2020). The NOAA-MBL N2O product is a
synthetic latitude profile generated at 0.05 sine latitude and weekly
resolution from individual flask
measurements of marine boundary-layer surface stations distributed along the
two ocean basins, and provides the scenario needed to evaluate the traceability of
aircraft measurements relative to ground measurements at remote sites
(https://gml.noaa.gov/ccgg/arc/?id=13, last access: 10 December 2020).
Results and discussion
The vertical profiles of N2O from ATom provide a global overview of the
N2O distribution in the troposphere, with observations performed over the Pacific
and Atlantic basins. For this study, we do not include data collected over
and close to land. In ATom, N2O ranged between 280 and 335 ppb over the
oceans. In each season, the lowest N2O mixing ratios are observed at
high latitudes (HL, > 60∘) in the UT/LS (8–12.5 km),
in air transported downward from the stratosphere. The highest N2O
mixing ratios are found close to the Equator (30∘ S–30∘ N, 326 to 335 ppb), and extend along the tropospheric column up to 6 km. They are
influenced by convective activity over the tropical regions (Kort et al.,
2011; Santoni et al., 2014). At mid-latitudes (ML, 30–60∘ N), tropospheric N2O values range between 322
and 333 ppb. Tropospheric N2O tends to increase towards northern
latitudes as a result of higher anthropogenic emissions in the Northern
Hemisphere relative to the Southern Hemisphere. More details on the
variability of N2O mixing ratios along the tropospheric column are
described in Sect. S1.
We study the impact of N2O sources and stratospheric air on the
N2O column based on the anomalies (enhancements and depletions) we
observed in the airborne N2O mixing ratios relative to the N2O
“background,” defined here as the NOAA-MBL product. We use the NOAA-MBL
product to constrain a latitudinal gradient of N2O mixing ratios at the
surface for each deployment. These data have been widely used to estimate
the N2O background (Assonov et al., 2013; Nevison et al., 2011). More
information about the NOAA-MBL product and the latitudinal gradient of their
measurements is discussed in Sect. S2. This approach highlights the extra
information that aircraft profiles can provide. Cross-sections of N2O
anomalies are shown in Fig. 3. The data describe the overall homogeneity of
N2O in the troposphere (30 % of the anomalies ranged between ± 0.5 ppb). We suppose that the ± 0.5 ppb interval accounts for the
day-to-day and seasonal variability of N2O. Episodes of N2O
depletion (<-0.5 ppb) that relate to the influence of stratospheric air
are observed in 53.5 % of the aircraft samples during ATom-2 to -4,
whereas episodes of N2O enhancement (> 0.5 ppb) that relate to
the contribution of N2O sources account for 16.5 % of the calculated
anomalies.
Cross-sections of N2O anomalies (ppb) representing the
differences between the airborne N2O (10 s resolution) and the surface
N2O mixing ratios, interpolated to 0.25∘ latitude and 250 m
altitude for each deployment. Shown are the N2O anomalies over (a)–(c)
the Pacific and (d)–(f) the Atlantic, with each column representing a
deployment (ordered by season: ATom-2, -4, and -3). The color scale ranges
from -15 to 5 ppb. Values between -50 and -15 ppb, observed at the highest
altitudes (> 10 km), are shown in white to allow better visualization
of small changes in positive anomalies. Lilac dashed lines represent the
flight tracks. Black contours are areas of N2O anomalies.
Trajectories and associated surface influence functions were computed using
the Traj3D model (Bowman, 1993) and wind fields from the National Center for
Environmental Prediction Global Forecast System (NCEP GFS). Model
trajectories were initialized at receptors spaced 1 min apart along the
ATom flight tracks, followed backwards for 30 d, and reported at
3 h resolution. From these trajectories, we calculated the surface
influence for each receptor point (footprints in units of concentration mixing ratio per emission flux; ppt nmol-1 m2 s). The footprint can be convolved with a known flux inventory of a
nonreactive gas to calculate the expected enhancement/depletion of that gas
for each receptor point.
Impact of stratospheric air on tropospheric N<inline-formula><mml:math id="M218" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O mixing ratios
during ATom
We observe the strongest depletions (> 5 ppb) in N2O mixing
ratios at high latitudes and altitudes, consistent with stratospherically
influenced air (Fig. 3). Stratosphere–troposphere exchange processes allow
stratospheric-depleted N2O to be distributed throughout the
troposphere. The NOAA surface network shows a seasonal minimum of N2O
2–4 months later than the stratospheric polar vortex break-up season. This
seasonal minimum is observed at the surface around May in the Southern
Hemisphere and around July in the Northern Hemisphere (see Figs. S8 and S9)
(see Nevison et al., 2011 and references therein). The enhanced downwelling of
the Brewer–Dobson circulation (BDC) in late winter–spring reinforces the
downward transport of stratospheric air depleted in N2O throughout the
free troposphere (1–8 km), as observed in October in the Southern Hemisphere
(ATom-3, Fig. 3c and f) and in May in the North Atlantic (ATom-4, Fig. 3e).
The N2O depletion is likely the result of stratospheric air being moved
downwards by the BDC and trapped by the polar vortex, with a more pronounced
effect in the Southern Hemisphere, where the polar vortex is stronger. These
results support previous work suggesting that downward transport of
stratospheric air with low N2O exerts a strong influence on the
variance of tropospheric N2O mixing ratios (Nevison et al., 2011;
Assonov et al., 2013).
The impact of stratosphere-to-troposphere transport can be studied by
combining information on tracers of stratospheric air such as ozone (O3
from the NOAA – NOyO3; Bourgeois et al., 2020), sulfur
hexafluoride (SF6 from PANTHER), CFC12 (from PANTHER), and carbon
monoxide (CO from QCLS). These tracers are usually used either because they
are strongly produced in the stratosphere (e.g., O3) or because they are
tracers of anthropogenic emissions in the troposphere with a strong
stratospheric sink (e.g., CO, SF6, and CFC12). In addition,
meteorological parameters such as potential vorticity (PV), the product of
absolute vorticity and thermodynamic stability (PV was generated by GEOS5-FP
for ATom), can be used to trace the stratosphere-to-troposphere transport.
Overall, the interhemispheric gradient of N2O is much smaller than those
of CO and SF6 (Fig. 4), but the difference for each species is driven
by larger anthropogenic emissions in the Northern Hemisphere. The
tracer–tracer correlations shown in Fig. 4 show different patterns. The
linear trend between N2O and O3 or CFC-12 highlights the role of
depletion (N2O and CFC-12) and production (O3) in the stratosphere
(Fig. 4a1, a4). When N2O is plotted against the anthropogenic tracers
CO and SF6, two distinct trends are observed. Tropospheric N2O can
be identified as the horizontal band containing high N2O (> 328 ppb) and variable CO and SF6, whereas the vertical band with
variable N2O and small changes in CO and SF6 is due to the mixing
between tropospheric air and stratospheric air depleted in N2O (Fig. 4a1–a3). The N2O versus CO plot shows an L-shaped (bimodal) curve
similar to those typically observed in O3–CO correlations during stratosphere-to-troposphere airmass mixing events (Fig. 4a2, Krause et al.,
2018). A quasi-vertical line in the N2O–CO plot (i.e., constant CO) is
indicative of a strong impact of stratospheric air, as the
stratospheric equilibrium mixing ratio of CO is observed (Krause et al., 2018). The lower the
CO background, the greater the influence of the stratospheric air during the
airmass mixing (North Atlantic high latitudes in Fig. 4a2) and vice versa. A
strong correlation is also indicative of rapid mixing between the two air
masses. During ATom, the strongest impact of stratospheric air was observed
in the Pacific mid- and high latitudes in February (ATom-2) and in the
Atlantic in May (ATom-4, Fig. S11). At the North Pacific mid- and high
latitudes (NMHL > 30∘ N), we find a consistent
linear relationship between N2O and O3, with a relatively constant
N2O / O3 slope (-0.05 to -0.04) during all seasons. Linear
correlations between N2O and CFC-12 highlight the dominant influence of
stratospheric air that was depleted in these two substances in the range of mixing
ratios observed at mid- and high latitudes (Fig. S11).
(a) Correlations between N2O and O3 (a1), CO (a2),
SF6 (a3), and CFC-12 (a4) at mid- and high latitudes
(30–85∘ N) during Northern Hemisphere spring
(ATom-4). The data are colored as a function of the ocean basin and
hemisphere: North Pacific mid–high latitudes (Pac-NH, > 30∘ N) in red, South Pacific mid–high latitudes (Pac-SH, < 30∘ S) in dark blue, South Atlantic mid–high latitudes (Atl-SH,
< 30∘ S) in light blue, and North Atlantic mid–high
latitudes (Atl-NH, > 30∘ N) in orange. Note that the
N2O and O3 axes are reversed. (b) Correlations between anomalies
in potential vorticity relative to its mean latitudinal distribution in the
free troposphere (2–8 km) and anomalies in N2O (b1, b3) and CFC-12
(b2, b4) as a function of latitude during spring (ATom-4) over the Pacific
and Atlantic basins. Mid-latitudes are shown in orange in the SH and in light
brown in the NH.
During spring, the mid-latitudes are strongly impacted by stratospheric air
due to the occurrence of tropopause folds and cutoff lows to the south of
the westerly subtropical jets (Hu et al., 2010 and references therein). The
stronger depletion of N2O mixing ratios observed over the Atlantic
relative to the Pacific during spring is due to a greater number of deep
stratosphere-to-troposphere transport events at mid-latitudes in the
region between May and July (Fig. 3e; Cuevas et al., 2013 and references
therein). Anomalies in PV relative to its mean latitudinal distribution in
the free troposphere (2–8 km) highlight events involving the strong downward transport
of stratospheric air. Negative PV, N2O, CO, and CFC-12 anomalies
(positive for O3) describe the transport of stratospheric air into the
troposphere in the SH, whereas positive PV and negative N2O, CO, and
SF6 anomalies (positive for O3) describe the downward transport of
stratospheric air in the NH (Fig. 4b1–b4). The correlations between
N2O and PV and the similarities with CFC-12 indicate that
stratosphere-to-troposphere exchange leads to variations in tropospheric
N2O of up to 10 ppb at the higher latitudes for the altitudes covered
during the flights. This influence is notably larger than the 2–4 ppb
enhancements associated with regional emissions (see below).
Impact of emissions on tropospheric N<inline-formula><mml:math id="M286" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O mixing ratios during ATom
During ATom, episodes of positive N2O anomalies relative to the surface
station MBL reference occurred close to the equator (Fig. 3a–c) and in a few
locations at mid-latitudes in both ocean basins across all seasons. We used
the information from the vertical profiles, including back trajectories and
correlated chemical tracers, to trace the origins of these enhancements.
We investigated data on CO, CH4, and CO2 measured by QCLS and NOAA Picarro
2401 m; hydrogen cyanide (HCN), sulfur dioxide (SO2), hydrogen peroxide
(H2O2), and peroxyacetic acid (PAA) measured with the California
Institute of Technology Chemical Ionization Mass Spectrometer (CIT-CIMS,
Crounse et al., 2006; St. Clair et al., 2010); ammonium (NH4+),
sulfate (SO42-), nitrate (NO3-), and organic aerosols
(OA) from the Colorado University Aircraft High-Resolution Time-of-Flight
Aerosol Mass Spectrometer (HR-AMS, DeCarlo et al., 2006; Canagaratna et al.,
2007; Jimenez et al., 2019; Guo et al., 2021; Hodzic et al., 2020); NOy
from the NOAA NOyO3 four-channel chemiluminescence instrument (CL, Ryerson et al.,
2019); CH2Br2, CH3CN, benzene, and propane from the NCAR Trace
Organic Gas Analyzer (TOGA, Apel et al., 2019); and atmospheric
potential oxygen (APO ≈ O2+1.1× CO2) from the NCAR
Airborne Oxygen Instrument (AO2, Stephens et al., 2020).
We calculated the correlations between N2O and the mentioned species in
three layers (0–2000, 2000–4000, and 4000–6000 m). Correlation
coefficients in each layer for a given profile were calculated using a
minimum threshold of 15 data points per layer. These profiles show that many
of the most prominent enhancements of N2O are closely associated with
pollutants such as HCN, CH3CN, H2O2,, and other pollutants
associated with combustion and photochemical air pollution. Some profiles
show peaks that are closely correlated with SO2 and enhanced PM1 particles, and
vertical gradients were sometimes correlated with gradients of APO and HCN.
Several N2O peaks are observed together with enhancements of
H2O2 and PAA, which are primarily formed in chemical processes that occur
in the atmosphere. For the altitude range 2–4 km, regressions produced
r2> 0.7 for 16 profiles of N2O vs. H2O2 and
15 profiles of N2O vs. HCN (a tracer for combustion of biomass), but
only three such profiles produced these strong associations for both
H2O2 and HCN in common. Some of these profiles also showed
correlated enhancements of SO2 and NO (nine profiles with r2> 0.6). This result raises the question of whether globally significant
production of N2O may be occurring in heterogeneous reactions involving
SO2, NO redox chemistry, and HONO near to strong sources of reactive
pollutants, which have been observed in heavily polluted atmospheres (Wang et
al., 2020) and have been theorized to occur in the plumes of refineries or power
plants (e.g., Pires and Rossi, 1997).
In most cases, because we were sampling in the middle of each ocean and not
over the source regions, it was not possible to distinguish between the different sources that
contributed to the observed N2O enhancements. We also
observed that the impacts of the different sources on N2O mixing ratios
were region dependent. Here, we describe, with some examples, the sources
that contribute to the major N2O enhancements observed during ATom by
oceanic region, although we cannot precisely pinpoint the source processes.
N<inline-formula><mml:math id="M331" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O enhancements over the Pacific
Episodes of N2O enhancement were frequently observed at mid-latitudes in the southern
Pacific Ocean, and these were linked by the associated footprints to
emissions over the continents. In this region, N2O enhancements are
predominantly associated with air masses with enhanced H2O2, PAA,
and CO. For example, consider Fig. 5, which shows data from profile 12, obtained at 49.5–50∘ S and near the Date Line on 3 May 2018. A distinct peak in
N2O of amplitude 1 ppb at 1700 m altitude is significantly correlated
with enhancements in CH3CN. These associations and the footprints
suggest a regional contribution from fuel types from the industrial zone of
Australia (Fig. 5c), which is also supported by the aerosol characterization
from PALMS (not shown for brevity). In this profile, close to the surface,
the lowest QCLS N2O mixing ratios agree with the NOAA MBL N2O
(dashed line in Fig. 5b). At higher altitudes (2.5–6 km), strong
correlations between N2O, H2O2, PAA, CO, and HCN but not
SO2 suggest the influence of biomass burning from central Australia
(3–5 km) and South America (6 km) (Fig. 5b, middle and right-hand
panels in Fig. 5c, and Fig. S11f). The relatively low mixing ratios of short-lived
trace gases (PAA, H2O2, and PM1 aerosols with lifetimes ranging
from hours to a few days) and the surface influence based on the back
trajectories (Fig. S13a) indicate that most of these profiles sampled
significantly aged air masses that were transported for extended periods over the
South Pacific.
(a) Vertical profiles of potential temperature (PT), relative
humidity (RH), N2O, APO, CH4, CO2, CO, HCN, CH3CN,
NO3-, NH4+, SO42+, H2O2, PAA
(CH3C(O)OOH), SO2, NOy, benzene, toluene, and propane from
profile 12, obtained on 3 May 2018. The dotted blue line in the plot of APO and N2O represents the
NOAA-MBL reference (N2O-MBL) at the latitude of the flight. (b) Correlations between N2O and HCN and PAA for altitudes between 2.5 and
6 km, and between N2O and SO2 for altitudes between 0 and 2.5 km,
indicate an admixture of marine, biomass-burning, urban, and oil and
gas industry contributions to N2O mixing ratios (s represents the slope
of the linear fit). (c) Footprint maps tracing surface regions that influence
mixing ratios measured at the altitude ranges of 1–2, 2.5–5, and 5–7 km,
respectively. Blue squares show sampling locations. Values below 3 ppt nmol-1 m2 s are not included. Note that the APO axes are
reversed.
In the equatorial Pacific, episodes of N2O enhancement were frequently
associated with a mixture of potential marine, industrial, and biomass
burning emissions. Atmospheric potential oxygen (APO) is primarily a tracer
of oxygen exchange with the oceans, defined as deviations in the
oxygen-to-nitrogen ratio (δ(O2/ N2)) corrected for changes
in O2 due to terrestrial photosynthesis and respiration and for influences from combustion (Stephens et al., 1998),
δAPO=δ(O2/N2)+1.1/XO2(XCO2-350).
Here, δ(O2/ N2) is the deviation in the O2/ N2 ratio
(per meg), 1.1 is an approximation to the O2/ CO2 ratio for
photosynthesis and respiration, XO2 is the mole fraction of O2 in
dry air, and XCO2 is the mole fraction of CO2 in the air sample
(dry, µmol mol-1). Since APO primarily tracks oxygen exchange
between the ocean and the atmosphere, APO depletions can indicate marine
N2O emissions from areas with strong upwelling (Lueker et al., 2003;
Ganesan et al., 2020). However, APO is also sensitive to pollution such as
biomass burning and fossil fuel combustion (Lueker et al., 2001) and, because
both N2O and APO have meridional gradients resulting from many
influences, correlations can result simply from sampling air transported
from different latitudes. In ATom, nine profiles showed significant
correlations (r2> 0.7) between N2O and APO (or δ(O2/ N2), which has lower measurement noise) for altitude bins
0–2 (8) and 2–4 km (1), with back trajectories indicating that they originated close
to the west coast of North America and the Mauritanian coast as well as in the
equatorial Pacific. The median slope of regressions of APO vs. N2O for
these profiles in ATom is -0.04 ppb per meg, and the mean is -0.05 (± 0.04, 1σ) ppb per meg – very similar to the range found by Ganesan et al. (2020) and
Lueker et al. (2003) in coastal areas.
An example is shown in Fig. 6 for 1 May 2018. We observe a high correlation
between N2O and APO (r2=0.66) between 0 and 4 km altitude. At
these altitudes we also see enhancements in dibromomethane
(CH2Br2), a tracer of phytoplankton biomass (Liu et al., 2013 and
references therein), consistent with a marine biological flux of halogenated
VOCs (Asher et al., 2019), dimethyl sulfide (DMS), and methanesulfonic acid
(MSA), the main particulate product of DMS oxidation in the MBL. However, on
this flight, the footprints and the influence of the surface ocean (Fig. S12b)
indicate that this N2O gradient represents a difference between sampling a
near-surface marine air mass from the south and a more continental air
mass from the east at 4 km (Fig. 6a–c). Close to the surface, the lowest
QCLS N2O mixing ratios agree with the NOAA MBL N2O at the origin
of the air masses suggested by the footprints (25∘ S, dashed
red line in Fig. 6b), whereas the lowest QCLS N2O mixing
ratios at 4 km agree with the NOAA MBL N2O (dotted blue line in Fig. 6b). Thus,
the N2O to APO correlation most likely represents the latitudinal and
ocean–land gradients established for a combination of reasons, with higher
APO and lower N2O originating from higher southern latitudes away from continents.
During this flight, there were particularly noticeable N2O variations between
4 and 6 km height that appear to be related to biomass-burning plumes from fires
occurring in Venezuela and the Caribbean, in agreement with simultaneous
enhancements in CO and HCN mixing ratios (Fig. 6a, c and Fig. S12), and there was increasing SO2 in the first 2 km, which was linked to oil and gas pollution sources near coastlines. The nature of these emissions was also confirmed by aerosol
characterization using the PALMS instrument (figure not shown).
(a) Vertical profiles of PT, RH, and the tracers N2O, APO,
MSA, CH2Br2, CH4, CO2, CO, HCN, CH3CN,
NO3-, NH4+, SO42-, H2O2, PAA
(CH3C(O)OOH), SO2, NOy, benzene, toluene, and propane from
profile 9 on 1 May 2018. The dotted blue line in the plot of APO and N2O
represents the NOAA-MBL reference (N2O-MBL) at the latitude of the
flight; the dashed red line shows the N2O-MBL at the origin of the
air masses suggested by the footprints (25∘ S). (b) N2O–APO
correlations between 0 and 4 km that possibly describe the latitudinal gradient
of N2O (s represents the slope of the linear fit). (c) Footprint maps
tracing surface regions that influence mixing ratios measured in the altitude
ranges 0–2, 2–4, 3–5, 5–7, and 9–11 km, respectively. Blue squares
show sampling locations. Values below 3 ppt nmol-1 m2 s are not included. Note that the APO axes are reversed to illustrate
the negative correlation with N2O.
N<inline-formula><mml:math id="M443" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O enhancements over the Atlantic
Much more of a continental influence was observed during the Atlantic Basin ATom flights than during the
Pacific flights. In the North Atlantic at around 30∘ N during winter, we
observe small enhancements of N2O that contrast with the overall
influence of stratospheric air on the tropospheric column (AT-2, Fig. 3d).
The contribution is much higher during the fall season (AT-3, Fig. 3f).
Several episodes of N2O enhancement are associated with enhancements
of CH4, CO, and HCN. We also observe some episodes where N2O
increases while CO2 decreases (figure not shown), which could reflect
the accumulation of agricultural emissions over the summer or just greater
sampling of Northern Hemisphere summer air masses, whereas increases of
N2O with CO are indicators of urban pollution and are, together with HCN,
associated with a few episodes of biomass burning.
The influences of different regions on the N2O mixing ratios over the
Atlantic on 14 May 2018 are shown in Fig. 7. This profile shows the
contributions to tropospheric N2O from pollution
transported down over the Mauritanian coast from Western Europe, biomass-burning
emissions, urban and industrial emissions from southern Africa and the
Middle East (between 1.5 and 3 km), and polluted air masses from South
America and the west coast of Africa, which are mixed with the oceanic contribution to
N2O (∼ 10 km, Fig. 7a–c and Fig. S13). The aerosol
characterization (from PALMS, not shown) indicates that mineral dust and biomass-burning emissions influence the atmospheric layer between 1 and 6 km in altitude, while
oil combustion influences the layer below 4 km. At high altitudes, N2O
enhancements are caused by the interception of polluted air masses from South
America and the west coast of Africa mixed with the oceanic contribution to
N2O (∼ 10 km). The N2O:APO correlations for the
feature between 1.5 and 3 km most likely represent APO depletion through
industrial combustion, which is stoichiometrically consistent with the observed
increases in CO2 and CH4 for this feature.
(a) Vertical profiles of PT, RH, the tracers N2O, APO,
CH4, CO2, CO, HCN, CH3CN, NO3-, NH4+,
SO42-, H2O2, PAA, SO2, NOy, benzene, and propane,
and the volumes of coarse and fine particles from profile
8 on 14 May 2018. The dotted blue line in the plot of APO and N2O represents the NOAA-MBL
reference (N2O-MBL) at the latitude of the flight. (b) Correlations
between N2O and APO, HCN, SO2, and propane at altitudes of 1–3 km show
possible contributions from marine upwelling, biomass burning, and the oil and
gas industry, as supported by the footprints (s represents the slope of the
linear fit). (c) Footprint maps tracing surface regions that influence mixing
ratios measured in the altitude ranges 0–1, 2–4, 4–5, 5–7, and 7–10 km, respectively. The blue square shows the sampling location. Values below 3 ppt nmol-1 m2 s are not included. Note that the APO axes are
reversed.
During ATom, we observed large contributions to the tropospheric N2O over
the Atlantic Ocean from Africa, along with some contributions from Europe and South
America. During AT-2, we found strong correlations in the subtropical and tropical regions over the Atlantic between N2O, H2O2,
PAA, HCN, CO, CO2, SO2, OA, NH4+, and SO42-
at altitudes between 0 and 2.5 km, representing the combined influence of photochemistry
(rN2OvsPAA2=0.94), biomass-burning events from the Congo region
(rN2OvsHCN2=0.95), and the industrial production of N2O from
oil and gas emissions from the Niger River Delta in Africa
(rN2OvsSO22=0.84). An example is shown in Fig. 8 for 15 February 2017 (see also Fig. S12 and the land contribution in Fig. S13).
(a) Vertical profiles of PT, RH, and the tracers N2O, APO,
organic aerosols (OA), black carbon (BC), CH4, CO2, CO, HCN,
CH3CN, NO3-, NH4+, SO42+, H2O2,
PAA, SO2, NOy, benzene, toluene, and propane from
profile 2 on 15 February 2017. The dotted blue line in the plot of APO and N2O represents the
NOAA-MBL reference (N2O-MBL) at the latitude of the flight, and the red
dashed line shows the NOAA-MBL at the origin of the southern air masses shown
by the footprints below 2 km (20∘ S). (b) Correlations between
N2O and APO, HCN, and SO2 for data observed below 2.5 km indicate
an admixture of marine, biomass-burning, urban, and oil and gas
industry contributions to N2O mixing ratios (s represents the slope of
the linear fit). (c) Footprint maps tracing surface regions that influence
mixing ratios measured in the altitude ranges 0–2, 2–3, 3–4, and 4–7
km, respectively. Blue squares show sampling locations. Values below 3 ppt nmol-1 m2 s are not included in the footprint plot. Note that the APO
axes are reversed.
To understand the origin of the enhancements in N2O, we calculated the
enhancement expected in the atmosphere based on monthly mean estimates of
anthropogenic emissions from the Emissions Database for Global Atmospheric
Research (EDGAR, http://edgar.jrc.ec.europa.eu/, last access: 5 February 2021). We convolved the
calculated surface influence (footprint) with the inventory to calculate the
N2O enhancement expected for each receptor. We also calculated the
contribution of each region and source sector to the overall enhancement.
This allowed us to quantify the dominant sources for various layers within
each profile. Each of the calculated enhancements was then compared to the
enhancement in N2O observed for the profiles. The observed N2O
enhancements were calculated relative to the NOAA MBL reference (Fig. 8a,
dashed red line) for each 10 s observation, with background concentrations selected
from locations close to the origin of the air mass as indicated by the
surface influence (shown as dashed and dotted lines in the N2O altitude
profiles in Figs. 5–8). We also included 0.4 ppb of uncertainty for the
observed enhancements based on our measurement precision.
N2O enhancements estimated by EDGAR for the entire globe (gray
polygons) and for the African region (blue polygons), and the observed
QCLS-N2O enhancement relative to the NOAA-MBL N2O reference at the
origin of the southern air masses shown by the footprints below 2 km for the
profile 2017/02/15-P2 (20∘ S, 329 ppb, shown in Fig. 8).
QCLS-N2O was observed every 10 s and receptors were calculated
every 60 s.
The largest N2O
enhancement (peaking at 2 ppb at 2 km) observed over the Atlantic during ATom-2 (February 2017; Fig. 9) can be attributed to African agriculture,
along with smaller but significant influences from Asia and Europe (0.5 ppb each at
2–4 km, Fig. S14). The observed and modeled N2O enhancements agree
within an order of magnitude for the profile, but the model underestimates
the high-altitude (4–7 km) N2O enhancement by < 1 ppb and
overestimates the lower-altitude enhancement (2–4 km) by ∼ 1 ppb. This difference in N2O enhancement could be due to a strong
latitudinal gradient in N2O across this profile or the timing of
N2O emissions sampled along this single profile compared to a monthly
mean estimate from the inventory. Strong correlations between N2O and
HCN (r2=0.95), CO, and CH3CN suggest that N2O from
burning emissions also contributes to the N2O enhancement (Figs. 8 and S12). However, when we convolved the monthly mean fire contributions
from the Global Fire Emissions Database (GFED,
https://www.globalfiredata.org, last access: 5 February 2021) with the surface influence footprints (as
described above), we found that the wildfire-produced N2O is minimal for
this profile (∼ 0.2 ppb), suggesting that fires of anthropogenic or
urban origin might be the source of that contribution (Figs. 8a–c, 9, S12,
and S13).
Conclusions
N2O mixing ratios at 1 Hz were obtained during the NASA ATom airborne
program by applying a new spectral retrieval method to account for the
pressure and temperature sensitivity of quantum cascade laser spectrometers
deployed on aircraft. This method improved the precision of our QCLS
N2O measurements by a
factor of three (based on the standard deviation of calibration measurements), allowing us to provide N2O measurements to the level of
precision shown in previous aircraft missions.
The N2O altitude profiles observed during ATom show that tropospheric
N2O variability is strongly driven by the influence of stratospheric
air depleted in N2O, especially at mid- and high latitudes. At high
latitudes, our profiles showed a strongly depleted N2O signal around the
time of the vortex break-up season, persisting for several months. Combining
the information from N2O profiles and other chemical tracers such as
CO, SF6, O3, and CFC-12, we traced the propagation of stratospheric
air along the tropospheric column down to the surface. This transport
dominates the N2O seasonal cycle and creates the seasonal surface
minima 2–3 months after the peak stratosphere–troposphere exchange in
spring.
The high resolution of this data set (10 s) allowed us to study the factors
influencing the enhancements in the N2O tropospheric mixing ratios, which are
associated with biomass burning and human activities such as urban and
industrial emissions. The highest N2O mixing ratios occur close to the
Equator, where they extend throughout the tropospheric column. The
strongest N2O enhancements were observed close to the Equator and at
a number of mid-latitude locations. We used the information given by
the vertical profiles of N2O and a variety of chemical tracers together
with footprints computed every 60 s along the flight track to identify and
trace the sources of these N2O enhancements. N2O enhancement
events were more frequent in the Atlantic than in the Pacific.
Over the Atlantic, the co-occurrence of excess N2O together with other
pollutants suggested that industrial and urban N2O emissions originating
from distant locations such as western and southern Africa, the Middle East,
Europe, and South America may be significantly greater than the emissions
from biomass burning in Africa. This view is supported by our observations
of a strong contribution to N2O from oil and gas emissions from the
Niger River Delta in Africa. The correlations observed between
N2O and SO2 (r2=0.90) could possibly be used to estimate N2O
emissions from oil and gas.
Over the southern Pacific Ocean and the tropical Atlantic Ocean, we observed
a significant number (> 12) of profiles where enhancements in
N2O were associated with increased H2O2 and PAA and notably
less well correlated with HCN or CO. Since H2O2 and PAA are
products of photochemical pollution, this observation raised the question of
whether significant N2O may be produced by heterogeneous processes
involving HONO or NOx reactions in acidic aerosols close to sources or
in very heavily polluted areas. It is hard to draw a definite conclusion
based on measurements obtained so far from the most active regions. Studies performed to address this question would have to be carried out directly in the
polluted areas. Because agricultural activities do not have unique tracer
signatures, we were not able to distinguish contributions from cultivated
and natural soils to N2O emissions from the ATom data. Previous
airborne studies have observed these inputs using flights in agricultural
areas (Kort et al., 2008) and at towers in these regions (e.g., Nevison et
al., 2017; Miller et al., 2008).
Our study shows that airborne campaigns such as ATom can help trace the
origins of biomass-burning and industrial emissions and investigate their
impact on the variability of tropospheric N2O, providing unique
signatures in vertical profiles and with covariate tracers. We hope that the
information provided by the global tropospheric N2O profiles from the
ATom mission will help better diagnose and reduce uncertainties in
atmospheric chemical transport models for constraining the N2O global
budget.
List of frequently used symbols and acronyms
DescriptionAcronymAtmospheric Potential OxygenAPOAtmospheric TomographyATomCalifornia Institute of Technology – Chemical Ionization Mass SpectrometerCIT-CIMSCU Aircraft High-Resolution Time-of-Flight Aerosol Mass SpectrometerHR-AMSGlobal Monitoring LaboratoryGMLHIAPER Pole-to-Pole ObservationsHIPPOHigh LatitudesHLHIgh-resolution TRANsmission molecular absorption databaseHITRANMarine Boundary LayerMBLMiddle LatitudesMLModern-Era Retrospective analysis for Research and Applications 2 modelMERRA2National Center for Environmental Prediction Global Forecast System modelNCEP GFSNational Oceanic and Atmospheric AdministrationNOAANCAR Airborne Oxygen InstrumentAO2NOAA Halocarbons and other Atmospheric Trace Species Flask Sampling ProgramNOAA-HATSNOAA NOyO3 4-channel chemiluminescenceCLNorthern HemisphereNHPAN and other Trace Hydrohalo-carbon ExpeRimentPANTHERParticle Analysis by Laser Mass Spectrometry instrumentPALMSPotential VorticityPVPrincipal Component AnalysisPCAProgrammable Flask Package Whole Air SamplerPFPQuantum Cascade Laser SpectrometerQCLSSouthern HemisphereSHStochastic Time-Inverted Lagrangian Transport ModelSTILTTrace Organic Gas AnalyzerTOGAUnmanned Aircraft Systems Chromatograph for Atmospheric Trace SpeciesUCATSUpper Troposphere/Lower StratosphereUT/LSWorld Meteorological OrganizationWMO
Data availability
Data from the ATom mission can be found in the NASA ESPO archive (https://espoarchive.nasa.gov/archive/browse/atom, last access: 10 February 2021), and in the ATom data repository at the NASA/ORNL DAAC (10.3334/ORNLDAAC/1581, Wofsy et al., 2018). The QCLS N2O data is available at 10.3334/ORNLDAAC/1747 (Commane et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-11113-2021-supplement.
Author contributions
YG did the data analysis and wrote and revised the paper. SCW and RC actively contributed to the design of the study and
data analysis. JBM designed the Neptune software for spectral
re-analysis and contributed to the writing. RC and BCD
performed and analyzed QCLS measurements of CH4, N2O, and CO and
contributed to the discussions. EM and LDS contributed
to the data analysis. KM performed and analyzed NOAA Picarro
measurements of CH4, CO, and CO2. JWE, EJH, and FM
performed and analyzed N2O, SF6, and CFC-12 measurements from
PANTHER and UCATS instruments. FM, SM, and CS
performed and analyzed N2O measurements with the Programmable Flask
Package Whole Air Sampler (PFP). POW, JC, MK, and HMA performed and analyzed the CIT-CIMS measurements of HCN and SO2
shown here. KF performed and analyzed PALMS measurements. JLJ, PCJ, and BAN performed and analyzed HR-AMS
measurements for a variety of aerosols. ER provided back trajectories for
each minute during the flight tracks, and PN provided the GEOS5 FP
meteorological products. TBR, IB, JP, and CRT performed and analyzed NOyO3 measurements of NOy and O3.
BBS and EJM performed and analyzed AO2 and Medusa
Whole Air Sampler measurements of O2/ N2 and CO2 and assisted
with the interpretation. ECA and RSH performed and
analyzed TOGA measurements of volatile organic compounds. All coauthors
provided comments on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank the ATom leadership team, the science team, and the NASA DC-8 pilot, technicians, and mechanics for their contribution and support during the mission. We thank Karl Froyd for the aerosol products during ATom that support this study. We also thank the National Aeronautics and Space Administration and the National Science Foundation for providing the financial support that made possible this study.
Financial support
This research has been supported by the National Aeronautics and Space Administration (grant nos. NNX15AJ23G, NNX17AF54G, NNX15AG58A, NNX15AH33A, and 80NSSC19K0124) and the National Science Foundation (grant nos. 1852977, AGS-1547626, and AGS-1623745).
Review statement
This paper was edited by Andreas Engel and reviewed by three anonymous referees.
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