ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-14661-2017Surface ozone and its precursors at Summit, Greenland: comparison between
observations and model simulationsHuangYaoxianyaoxianh@mtu.eduhttps://orcid.org/0000-0003-0976-0228WuShiliangslwu@mtu.eduKramerLouisa J.https://orcid.org/0000-0002-0823-6638HelmigDetlevHonrathRichard E.Department of Geological and Mining Engineering and Sciences, Michigan
Technological University, Houghton, Michigan, USAAtmospheric Sciences Program, Michigan Technological University,
Houghton, Michigan, USACollege of Environmental Science and Engineering, Ocean University of
China, Qingdao, ChinaInstitute of Arctic and Alpine Research, University of Colorado,
Boulder, Colorado, USAnow at: Department of Climate and Space Sciences and Engineering,
University of Michigan, Ann Arbor, Michigan, USAnow at: School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKdeceasedShiliang Wu (slwu@mtu.edu) and Yaoxian Huang (yaoxianh@mtu.edu)8December20171723146611467415May201712July201726October20172November2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/14661/2017/acp-17-14661-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/14661/2017/acp-17-14661-2017.pdf
Recent studies have shown significant challenges for atmospheric
models to simulate tropospheric ozone (O3) and its precursors in the
Arctic. In this study, ground-based data were combined with a global 3-D
chemical transport model (GEOS-Chem) to examine the abundance and seasonal
variations of O3 and its precursors at Summit, Greenland
(72.34∘ N, 38.29∘ W; 3212 m a.s.l.). Model simulations for
atmospheric nitrogen oxides (NOx), peroxyacetyl nitrate (PAN), ethane
(C2H6), propane (C3H8), carbon monoxide (CO), and O3
for the period July 2008–June 2010 were compared with observations. The model
performed well in simulating certain species (such as CO and C3H8),
but some significant discrepancies were identified for other species and
further investigated. The model generally underestimated NOx and PAN (by
∼ 50 and 30 %, respectively) for March–June. Likely
contributing factors to the low bias include missing NOx and PAN
emissions from snowpack chemistry in the model. At the same time, the model
overestimated NOx mixing ratios by more than a factor of 2 in
wintertime, with episodic NOx mixing ratios up to 15 times higher than
the typical NOx levels at Summit. Further investigation showed that
these simulated episodic NOx spikes were always associated with
transport events from Europe, but the exact cause remained unclear. The model
systematically overestimated C2H6 mixing ratios by approximately
20 % relative to observations. This discrepancy can be resolved by
decreasing anthropogenic C2H6 emissions over Asia and the US by
∼ 20 %, from 5.4 to 4.4 Tg year-1. GEOS-Chem was able to reproduce the
seasonal variability of O3 and its spring maximum. However, compared
with observations, it underestimated surface O3 by approximately 13 %
(6.5 ppbv) from April to July. This low bias appeared to be driven by several
factors including missing snowpack emissions of NOx and nitrous acid in
the model, the weak simulated stratosphere-to-troposphere exchange flux of
O3 over the summit, and the coarse model resolution.
Introduction
Tropospheric ozone (O3) and its precursors, including nitrogen oxides
(NOx= NO + NO2), carbon monoxide (CO), and volatile organic
compounds (VOCs, such as ethane and propane), are important atmospheric
species affecting both air quality and climate (e.g., Jacob et al., 1992;
Fiore et al., 2002; Unger et al., 2006; Hollaway et al., 2012). Tropospheric
O3 is mainly produced through the photochemical oxidation of CO and VOCs in the
presence of NOx, with an additional contribution by transport from the
stratosphere. Its major sinks include chemical reactions and dry deposition.
As a reservoir species for NOx, peroxyacetyl nitrate (PAN) also plays
an important role in atmospheric chemistry. PAN, O3, and some
of their precursors have relatively long lifetimes in the atmosphere,
enabling them to be transported long distances to remote regions such as the
Arctic.
Recent studies have shown some significant challenges for atmospheric
chemical transport models to simulate O3 and its precursors in the
Arctic (e.g., Shindell et al., 2008; Alvarado et al., 2010; Walker et al.,
2012; Wespes et al., 2012; Fischer et al., 2014; Monks et al., 2015), but
the causes remain unclear. In the multi-model assessment by Shindell et al. (2008),
more than a dozen models all showed systematic and persistent
underestimation of O3 at the GEOSummit station in Greenland (hereafter
referred to as Summit). Alvarado et al. (2010) used NOx and PAN
measurements from the ARCTAS (Arctic Research of the Composition of the
Troposphere from Aircraft and Satellites) mission in the summer to compare
with model simulations. They found that model-simulated NOx mixing
ratios were higher than observations, while PAN mixing ratios were lower
than the observations in fresh boreal fire plumes. In terms of global PAN
simulations, Fischer et al. (2014) directly partitioned 40 % of NOx
emissions from wildfires to PAN formation, which improved the agreement
between the model and observations. However, the model still underestimated PAN
surface mixing ratios during springtime in the Arctic. Walker et al. (2012)
reported that model-simulated O3 mixing ratios were biased low when
compared with balloon data during summertime from two high-latitude sites at
Eureka (80∘ N, 86∘ W) and Ny-Ålesund (79∘ N, 12∘ E).
Wespes et al. (2012) also revealed that model-simulated
O3 mixing ratios within the boundary layer were significantly
underestimated during spring–summer compared with ARCTAS measurements. More
recently, Monks et al. (2015) further demonstrated that model-simulated
O3 mixing ratios in the Arctic at the surface and in the upper
troposphere were generally lower than the observations. In addition, a
recent study by Christian et al. (2017) compared O3 observations from
the ARCTAS campaign to GEOS-Chem model simulations and found consistent low
biases with the model-simulated O3 at all altitudes except the surface.
Field measurements at Summit show that the snowpack emits gas-phase
NOx, PAN, nitrous acid (HONO), and hydrogen peroxide
(H2O2) during spring–summer when the polar sun rises (Ford et al.,
2002; Honrath et al., 2002). Although several 1-D models (Thomas et al.,
2011, 2012; Frey et al., 2013; Murray et al., 2015) have validated the
importance of snowpack emissions for surface NOx and O3
formation, current global chemical transport models (CTMs) usually do not
include these emission sources (Zatko et al., 2016).
In this study, we examine the abundance and seasonal variations of O3
and its precursors at Summit with a global chemical transport model,
GEOS-Chem CTM, in conjunction with 2 years of in situ measurement data for
2008–2010. We first evaluate the model performance in simulating surface
O3 and its precursors and then implement a series of model updates to
resolve the identified model biases. This paper is organized as follows:
Sect. 2 describes model methods and observations, followed by detailed
comparisons of model simulations against observations for O3 and
O3 precursors in Sect. 3; conclusions are summarized in Sect. 4.
Observational data and model simulations
In situ measurements of NOx, PAN, and non-methane hydrocarbons (NMHCs)
were performed at Summit from July 2008 to June 2010 (Helmig et al., 2014b;
Kramer et al., 2015). An automated chemiluminescence instrument was used to
measure NOx (Ridley and Grahek, 1990), and a commercial PAN gas
chromatography analyzer (PAN-GC; Metcon, Inc., Boulder, CO, USA) was employed for
the measurement of PAN. Measurements of NMHC relied on an automated gas
chromatography–flame ionization detection (GC-FID) system. Readers are
referred to Kramer et al. (2015) and Helmig et al. (2014b) for the details
of the measurement techniques and equipment setup. In situ surface
measurements of O3 at Summit using an ultraviolet light absorption
technique (Petropavlovskikh and Oltmans, 2012) and CO data from weekly
flask sampling with analysis by using a GC–HgO reduction detection instrument
(Novellie et al., 2003) and an analyzer based on CO fluorescence in the
ultraviolet vacuum (Gerbig et al., 1999) were conducted by the National
Oceanic and Atmospheric Administration (NOAA) and downloaded from the NOAA
Earth System Research Laboratory (ESRL) Global Monitoring Division (GMD)
website (http://www.esrl.noaa.gov/gmd/dv/data/) for the period
between July 2008 and June 2010. Vertical ozonesonde data profiles were also
downloaded from NOAA ESRL GMD (McClure-Begley et al., 2014).
Box plot comparison for seasonal variations
in (a) NOx, (b) PAN, (c) C2H6, (d) C3H8, (e) CO,
and (f) O3 between GEOS-Chem model simulations (red) and
measurements (blue) at Summit for the period July 2008–June 2010. Data
shown are monthly averages during this period. The thick (thin) bars
represent the 67 % (95 %) confidence intervals. Black and green dots
represent median and mean values, respectively. The statistics are based on
daily averages.
Surface NO2 measurements over Europe during
1 December 2009–31 January 2010.
Simulations of O3 and related species (NOx, PAN, NMHCs) were
conducted using the GEOS-Chem model (Bey et al., 2001) with a coupled
O3–NOx–VOC–aerosol chemistry mechanism (i.e., these species
interact with each other in the model). The GEOS-Chem CTM is driven by
assimilated meteorological data from the Goddard Earth Observing System
version 5.2.0 (GEOS-5.2.0) of the NASA Global Modeling Assimilation Office.
The GEOS-Chem model has been extensively evaluated and applied in a wide
range of applications (Martin et al., 2002; Park et al., 2004; Wu et al.,
2007; Hudman et al., 2009; Johnson et al., 2010; Huang et al., 2013; Kumar
et al., 2013; Zhang et al., 2014; Hickman et al., 2017), including
studies in the Arctic (e.g., Alvarado et al., 2010; Monks et al., 2015;
Christian et al., 2017). GEOS-Chem v10-1, with a grid resolution of
4∘ latitude by 5∘ longitude and 47 vertical layers, was
used for the model control simulation. Following McLinden et al. (2000), the
Linoz stratospheric O3 chemistry scheme was used. The simulation was
run from June 2007 to June 2010, and the results from the last 2 years
were used in the final analysis. Time series data were archived with 3 h
temporal resolution at the Summit grid box for each model vertical level,
including the model bottom layer. For comparison with surface observations
at Summit, Greenland, we sampled the data for the model bottom layer. We
acknowledge that the topography in GEOS-Chem model is not well resolved at
such a coarse model resolution (4∘ latitude by 5∘
longitude), and we used the model bottom layer at the Summit grid cell for
O3 and its precursor concentrations to compare with surface
observations, which worked better than sampling O3 and its
precursor concentrations at the model vertical layer at about
3212 m a.s.l. (above the sea level; Summit's elevation).
Global anthropogenic emissions of NOx, SO2, NH3, and CO in
the model were based on the Emission Database for Global Atmospheric
Research (EDGAR) v4.2 inventory, which was overwritten by regional emission
inventories where applicable, such as the BRAVO inventory for Mexico (Kuhns
et al., 2005), the CAC over Canada, the EMEP emissions over Europe, the
Model Inter-comparison Study for Asia Phase III (MIX) emissions over Asia
(Li et al., 2017), and the US EPA NEI 2011 (NEI11) emission inventory (Simon
et al., 2010). The soil NOx emission scheme followed Hudman et al. (2012).
Lightning NOx emissions were calculated per flash rate based on
GEOS-5 computed cloud-top heights (Price and Rind, 1992), which were
determined by deep convection and constrained by satellite observations for
monthly average flash rates from the Lightning Imaging Sensor and Optical
Transient Detector (OTD/LIS; Sauvage et al., 2007; Murray et al., 2012).
Biomass burning emissions were from the Global Fire Emissions Database
version 4 (GFED4) inventory with monthly resolution (Giglio et al., 2013).
The RETRO (Reanalysis of the TROpospheric chemical composition) global
anthropogenic NMHC emission inventory (van het Bolscher et al., 2008) was
used except for ethane (C2H6) and propane (C3H8), which
followed Xiao et al. (2008, hereafter referred to as X08) for the year 2001.
In GEOS-Chem, RETRO served as the default global anthropogenic
emission inventory for C2H6, the annual budget of which
has been as shown too low compared with observations. The global biofuel emission
inventory followed Yevich and Logan (2003), which included emissions for
C2H6 and C3H8. For biogenic VOC emissions, the Model of
Emissions of Gases and Aerosols from Nature (MEGAN) scheme (Guenther et al.,
2006) was used. The dry deposition of species in GEOS-Chem used a standard
resistance-in-series scheme (Wesely, 1989), as implemented in
Wang et al. (1998). Wet scavenging followed Liu et al. (2001), including scavenging in
convective updraft, rainout (in-cloud), and washout (below-cloud) from
convective anvils and large-scale precipitation.
We first ran the standard GEOS-Chem model with a priori emissions and
compared the simulation results against observations for various species
(including NOx, PAN, C2H6, C3H8, CO, and O3,
as shown in Fig. 1). Then we focused on the model–observation discrepancies
and where applicable made revisions to the model simulations and further
evaluated the improvement in model performance, as discussed in detail
below.
Results and discussionNOx
We first combined the 2 years of data for July 2008–June 2010 and
analyzed their seasonal variations. As shown in Fig. 1a, the GEOS-Chem
model-simulated NOx agrees well with the observations for July–October.
However, compared to observations, the model results significantly
overestimate NOx mixing ratios for November–January by about 150 %
while underestimating the data in spring and early summer by approximately
60 %. Another challenge for the model simulation is that it does not
capture the decrease in NOx for May–November. We find that during the
2009–2010 winter season, model simulations show several high NOx spikes
with peak NOx mixing ratios reaching ∼ 0.15 ppbv or
higher, which is ∼ 15 times greater than typical background
levels (Fig. 2). These large peaks in NOx were not observed in the
data. Similar peaks were also seen in the model simulations during the
2008–2009 winter season; however, there are no measurements available for
this period to compare with.
Time series of surface NOx mixing ratios over Summit from
observations, GEOS-Chem model control simulations, EURO_ EDGAR, and EMEP50
during 1 December 2009–31 January 2010. EURO_EDGAR represents simulations
with anthropogenic NOx emissions over Europe following EDGAR v4.2, while
EMEP50 denotes simulations with anthropogenic NOx emissions from the
EMEP emission inventory over Europe reduced by 50 %; other model
configurations are identical to the control simulations.
Further analyses showed that the model-simulated high NOx spikes during
wintertime were all associated with transport events from Europe. We carried
out a sensitivity study to examine the impacts of European emissions on
Arctic NOx by manually reducing anthropogenic NOx emissions from
the EMEP emission inventory over Europe by 50 % (EMEP50). The results showed
that surface peak NOx mixing ratios over Summit during the spike events
(e.g., dates around 9 and 15 December 2009, 15 and 22 January 2010) from
EMEP50 declined almost proportionally by ∼ 50 % during
1 December 2009–31 January 2010 (Fig. 2), which confirmed that the modeled NOx
spikes at Summit during wintertime were associated with transport from
Europe. However, the model-simulated NOx was still significantly higher
than observations. Comparisons for surface NO2 mixing ratios between
model simulations and 11 in situ observational sites over Europe during this
period were conducted with data downloaded from http://ebas.nilu.no. For detailed site information and the NO2 measurement
technique and resolution, refer to Table 1. Measurement data over these 2 months
for each site were averaged to compare with the corresponding grid
cell in the model. As shown in Fig. 3a, GEOS-Chem overestimated surface
NO2 mixing ratios at these sites by over 66 % compared with
observations (slope = 1.07; correlation coefficient = 0.88).
Scatter plots of model simulations from (a) GEOS-Chem
control simulations and (b) EURO_EDGAR during
1 December 2009–31 January 2010 and measured monthly mean NO2 mixing
ratios at 11 observational sites over Europe; also shown are the
corresponding model-to-observation slopes (k) and correlation coefficients
(r) for each panel. The dashed line is the 1:1 ratio. Explanations of
site abbreviations are listed in Table 1. EURO_EDGAR represents simulations
with anthropogenic NOx emissions over Europe following EDGAR v4.2, with
other model configurations identical to the control simulations.
In addition to using EMEP, we carried out another sensitivity study to force
anthropogenic NOx emissions over Europe following EDGAR v4.2
(EURO_EDGAR), with other model configurations identical to
control simulations. As shown in Fig. 2, the NOx mixing ratios over
Summit during December 2009–January 2010 agreed much better with observations,
especially for January 2010 when the model captured the magnitudes of
observational peaks. This is because NOx emissions from EDGAR over
Europe (1.97 Tg NO) were 12 % lower than those from EMEP (2.24 Tg NO) for
the months of December 2009 and January 2010. Furthermore, the discrepancy for the
differences in surface NO2 mixing ratios over Europe between
EURO_EDGAR and observations was further reduced (by 50 %)
relative to the control runs, with a model-to-observation slope of 0.92 and
a correlation coefficient of 0.83 (Fig. 3b). Similarly, we also tested the
sensitivity of surface NOx mixing ratios over Summit in response to the
changes in the anthropogenic NOx emissions from NEI11 over the US and MIX
over Asia (including Siberia) during these 2 months and found that
surface NOx mixing ratios over Summit during these 2 months were
quite close to the control simulations (not shown), reflecting insensitivity
to emission perturbations from the US and Asia. Therefore, we conclude that
uncertainties in fossil fuel NOx emissions of EMEP associated with
transport events from Europe in the model are the most likely cause for the
wintertime NOx spikes over Summit.
For April–July, model-simulated monthly mean NOx mixing ratios over
Summit were a factor of 2 lower than the observations (Fig. 4a).
Experiments at Summit by Honrath et al. (1999, 2000a, b, 2002) showed
upward fluxes of NOx (2.52 × 108 molecules cm-2 s-1) from the photolysis of nitrate in snowpack during the summertime,
leading to an enhancement of NOx levels in the surface layer by
approximately 20 pptv, which was comparable to surface NOx mixing
ratios in the Arctic from other sources. Similar results were found over the
East Antarctic Plateau snow and ice sheet (Frey et al., 2013; Legrand et al.,
2014). The standard GEOS-Chem model did not include the photolysis of
nitrate from snowpack, implying a missing source for NOx in the
Arctic–Antarctic boundary layer.
Monthly mean surface (a) NOx and (b) PAN
mixing ratios from observations (black circles), simulations with (green
triangles) and without (purple squares) snowpack emissions, and GEOS-Chem
simulations with a horizontal grid resolution of
2∘× 2.5∘ (orange diamonds) for April–July during
July 2008–June 2010. Vertical bars denote standard deviations over the
course of observations for each month.
In order to test the sensitivity of model-simulated surface NOx mixing
ratios to the snowpack emissions, we implemented in the model a constant
NOx flux of ∼ 2.52 × 108 molecules cm-2 s-1 during April–July
over Greenland (60–85∘ N,
20–60∘ W), following the measurements conducted at Summit during
summertime by Honrath et al. (2002). As a result, we found that on average
the model-simulated surface NOx mixing ratios for April to July over
Summit more than doubled compared to the control simulation, which improved
the agreement between the model and observations for April–June (Fig. 4a).
However, the assumed NOx flux from snowpack in the model led to
an overestimation of NOx mixing ratios in July, and the model was still not
able to reproduce the decreasing trend of NOx for May–October. This
decreasing trend of NOx may be driven by the decreasing NOx
production rate in snowpack resulting from a gradual depletion of the
snowpack NOx reservoir (Van Dam et al., 2015), which is not reflected
in the model since we implemented a simple constant NOx emission flux.
Dibb et al. (2007) reported that nitrate concentrations in the Summit
snowpack peaked in June and declined toward fall by ∼1/3. Van
Dam et al. (2015) further showed a decreasing trend for NOx mixing
ratios within the snowpack at Summit from June to October. This may
partially explain why we would see the declining trend of surface NOx
mixing ratios over Summit from June toward fall. The NOx emissions from
snowpack are affected by a number of factors, including nitrate
concentrations and the solar radiation available, and the responses can be very
nonlinear. Further investigations are needed to account for the seasonal
variations in snowpack NOx emissions from nitrate photolysis in the
model, i.e., constrained by seasonal snowpack NOx emission flux
measurements in the future.
PAN
We then examined the model performance for PAN, which serves as a reservoir
for NOx. Figure 1b shows the comparison of model-simulated monthly mean
PAN mixing ratios with the measurement data. The model captured the seasonal
variation of PAN well, although it significantly (by ∼ 30 %)
underestimated the PAN mixing ratios for April–June. By running the model
simulation with higher horizontal resolution at 2∘ latitude by
2.5∘ longitude (hereafter referred to as GEOS-Chem 2 × 2.5), we
found that the monthly mean PAN mixing ratios over Summit during April–July
increased by up to 23.3 pptv compared to the 4 × 5 simulation (Fig. 4b). This
can be explained by two factors. First, the coarse model resolution (e.g., 4 × 5
horizontal resolution) could artificially smear the intense emission sources
throughout the entire grid cell (e.g., over urban regions), leading to
underestimates of downwind concentrations for species like O3 and
O3 precursors (Jang et al., 1995; Yu et al., 2016). Second, ventilation
in the lower atmosphere could be better resolved by a finer model
resolution, leading to more efficient vertical advection (Wang et al., 2004;
Chen et al., 2009; Yu et al., 2016). However, on average, the monthly mean model-simulated PAN mixing ratios were still underestimated by 20 % during this
period compared with observations. This is consistent with the study by
Arnold et al. (2015), which reported that model-simulated PAN mixing ratios
in GEOS-Chem were lower than ARCTAS observations in the Arctic. Meanwhile,
this study also revealed that GEOS-Chem produced less PAN relative to CO in
Arctic air parcels that were influenced by fires compared with other
models.
Snowpack can emit not only NOx, but also PAN as indicated based on field studies at
Summit during summertime by Ford et al. (2002). GEOS-Chem did not contain
snowpack PAN emissions and chemistry. For a sensitivity study similar to
snowpack NOx emissions as discussed in Sect. 3.1, we considered a
24 h constant flux of 2.52 × 108 molecules cm-2 s-1 of PAN
over Greenland from April to July, following Ford et al. (2002). As a
result, model-simulated PAN mixing ratios agreed much better with
observations (Fig. 4b). Note that there are also other possible factors that
lead to model bias. For instance, a study by Fischer et al. (2014) showed
improved agreement between modeled and measured PAN in the high latitudes
when assigning a portion of the fire emissions in the model above the
boundary layer and also directly partitioning 40 % of NOx emissions
from fires into PAN. We carried out a sensitivity test with similar
treatments, but no significant improvements in the model-simulated surface
PAN were observed at the Summit site. Therefore, we did not include the PAN
updates from Fischer et al. (2014) in other model simulations in this study.
NMHC
Comparisons of observed surface C2H6 and C3H8 mixing
ratios with GEOS-Chem simulations at Summit are shown in Fig. 1c and d.
The model simulations agreed well with surface measurements of
C3H8 but systematically overestimated C2H6 (by
approximately 25 % annually), with the largest bias (0.48 ppbv) occurring
during summer. This is consistent with the study from Tzompa-Sosa et al. (2017),
which used the same model as our study and pointed out that using
X08 as a global anthropogenic C2H6 emission inventory
systematically overestimated surface C2H6 mixing ratios over the
Northern Hemisphere compared with ground-based observations. Anthropogenic
C2H6 emissions over the US from NEI11 were shown to geographically
match the distribution of active oil and natural wells (Tzompa-Sosa et al.,
2017), and the most recent MIX has been updated to synergize anthropogenic
C2H6 emissions from various countries in Asia (Li et al., 2017).
Therefore, instead of using global anthropogenic fossil fuel emissions of
C2H6 following X08, we first conducted sensitivity simulations by
overwriting global emission inventories by NEI11 over the US and MIX over
Asia (hereafter referred to as NEI11_MIX). Both NEI11 and MIX
contain emissions for the years from 2008 to 2010, which could realistically
represent the annual and seasonal variations in C2H6 emissions
over the US and Asia and thus be spatially and temporally more representative of
anthropogenic C2H6 emissions from the midlatitudes transported to the
Arctic regions. In general, model control simulations overestimated annual
mean surface C2H6 mixing ratios primarily in the Northern
Hemisphere, with large differences occurring over Asia and the US by up to 5 ppbv compared with NEI11_MIX during the period
July 2008–June 2010 (Fig. S1 in the Supplement). All the above changes were driven by the
substantial reductions of anthropogenic C2H6 emissions between
emission inventories, from 3.5 (X08) to 2.5 Tg year-1 (MIX) over Asia and from
1.9 Tg year-1 (X08) to 1.4 Tg year-1 (NEI11) over the US, reflecting the decreasing
trend of anthropogenic C2H6 emissions during 2001–2009 (Helmig
et al., 2014a) because the X08 emission inventory is based on the year
2001. Substantial changes in surface C2H6 mixing ratios over the
US between control simulations and NEI11_MIX reflected tempo-spatial changes in C2H6 emissions from oil and
gas production during the period 2001–2009. A similar pattern was also
found by Tzompa-Sosa et al. (2017). In contrast to the control simulations,
NEI11_MIX model simulations showed that monthly mean
C2H6 mixing ratios over Summit were systematically underestimated
by 24 % compared with observations (Fig. 5). Tzompa-Sosa et al. (2017)
reported that NEI11 C2H6 emissions were likely underestimated by
40 % compared with in situ and aircraft observations over the US. We
therefore ran a sensitivity simulation by increasing the NEI11
C2H6 emissions by 40 % and keeping other model configurations
identical to NEI11_MIX (hereafter referred to as
NEI11_40_MIX). We found that this update led
to an increase in the model-simulated annual mean surface C2H6
mixing ratios over Summit by only 6 % during the period July 2008–June 2010
(figure not shown), which still does not explain the high model bias.
Monthly mean surface C2H6 mixing ratios at Summit from
observations (black circles), GEOS-Chem model control simulations (purple
squares), NEI11_MIX (orange diamond), and NEI11_ONLY (green triangles)
simulations during 2008–2010; vertical bars denote the standard deviation
over the course of observations for each month. NEI11_MIX represents model
perturbations with global C2H6 emission inventories overwritten by
NEI11 over the US and by MIX over Asia, with other model configurations identical
to the control simulations. NEI11_ONLY denotes the simulation that is the
same as the control simulation, except that the C2H6 emission
inventory over the US is overwritten by NEI11. NEI11_MIX20 is the simulation
that is identical to NEI11_MIX except for the 20 % increased MIX
C2H6 emission inventory over Asia.
Similar to NEI11_MIX, we further conducted sensitivity
studies by only replacing the regional emission inventory for C2H6 over the
US, with other regions still following X08 (hereafter referred to
as NEI11_ONLY). Consequently, model-simulated surface
C2H6 mixing ratios over Summit agreed better with observations
during winter–spring (Fig. 5), decreasing the bias from +15 % (control
simulations) to +6 %. However, model-simulated C2H6 mixing
ratios during summer–fall were higher than the observations by over 30 %.
We then scaled up the MIX emissions for C2H6 by 20 % over Asia,
with other model configurations identical to NEI11_MIX
(hereafter referred to as NEI11_MIX20). By doing this, we
increased fossil fuel C2H6 emissions from 2.5 to 3 Tg year-1. We found
that the simulated annual mean surface C2H6 mixing ratios at
Summit from NEI11_MIX20 agreed quite well with observations
(within 1 %). Similarly, better agreement between the model and observations
were found for monthly average values for October–January. However, the
new simulation was not able to reproduce the seasonal cycle of
C2H6; the model significantly underestimated C2H6 in
February–April but overestimated it in June–September (Fig. 5). This
implies that further assessments of anthropogenic C2H6 emissions
from MIX over Asia are needed and a more accurate global anthropogenic
C2H6 emission inventory should be developed and validated to
replace X08 in the future. It should be noted that our modeling period
reflects a time when there was a reversal of the atmospheric C2H6
trend, most likely reflecting emission changes during that time. Atmospheric
C2H6 had a decreasing trend from 1980 to 2009 (Simpson et al.,
2012; Helmig et al., 2014a) but then began to increase around 2009 (Franco
et al., 2015, 2016; Hausmann et al., 2016; Helmig et al., 2016) in the
Northern Hemisphere at a rate of increase that is approximately 4–6 times
higher than its earlier rate of decline. It has been argued that the most
likely cause for this trend and emission reversal is increasing emissions
from oil and gas production, mostly from North America (Franco et al., 2015,
2016; Hausmann et al., 2016; Helmig et al., 2016). None of the considered
inventories considered these emission changes and their timing. Also note
that this standard version of GEOS-Chem does not account for the sink of
C2H6 from the reaction with chlorine, which could reduce the
global annual mean surface C2H6 mixing ratios by 0–30 % and the
global burden of C2H6 by about 20 % (Sherwen et al., 2016). This
omission likely introduces additional uncertainty into our measurement–model
comparison together with uncertainty in the seasonality of C2H6
chemistry.
CO
Figure 1e shows the comparison of model-simulated CO mixing ratios with
observations over Summit. Overall, the model generally captures the
abundance and seasonal variation of CO. Compared with observations, the
annual mean CO mixing ratio was slightly overestimated by about 3 ppbv in
the model.
O3
Surface O3 mixing ratios from model simulations and surface
observations are compared in Fig. 1f. The GEOS-Chem model captured the
seasonal variation of O3 including the spring peak. However, the model
shows a systematic low bias for most of the year, in particular for
April–July when the surface O3 mixing ratios were underestimated by
∼ 13 % (∼ 6.5 ppbv). Here we focus our
analysis on the possible causes that led to the model low bias during
April–July.
As discussed earlier, snowpack emissions due to the photolysis of nitrate in
the snow during late spring and summer could contribute to NOx and HONO
levels in the ambient air, which could enhance O3 production (Crawford
et al., 2001; Zhou et al., 2001; Dibb et al., 2002; Honrath et al., 2002;
Yang et al., 2002; Grannas et al., 2007; Helmig et al., 2008; Legrand et
al., 2014). We ran a sensitivity study to test the response of surface
O3 mixing ratios to the perturbations of NOx and HONO from
snowpack emissions. In addition to snowpack NOx emissions that are
described in Sect. 3.1, we implemented in the model a constant flux of
HONO (4.64 × 107 molecules cm-2 s-1) from April to July
(Honrath et al., 2002). As a result, monthly mean model-simulated surface
O3 mixing ratios increased by up to 3 ppbv during this period (Fig. 6).
The largest effect occurred in July due to relatively strong solar
radiation. O3 formation due to the snowpack emissions in our study was
slightly higher than that in Zatko et al. (2016) because HONO from snowpack
emissions was not considered in their study. However, for the months of
April and May, surface O3 mixing ratios only increased by ∼ 1 ppbv
compared with the control runs. That is, even after accounting for the
snowpack emissions, the model-simulated O3 mixing ratios were still
significantly lower than the observations.
Monthly mean surface O3 mixing ratios from observations (black
circles), GEOS-Chem control runs (purple squares), simulations with snowpack chemistry
(green triangles), and simulations with a horizonal grid resolution
of 2∘× 2.5∘ (orange diamonds) for April–July. Vertical
bars denote the variability over the course of observations for each month.
A comparison of the model simulations at different resolutions (4 × 5 vs. 2 × 2.5)
showed that the finer-resolution simulations substantially increased monthly
mean O3 mixing ratios over Summit by up to 6 ppbv for the months of
June and July (Fig. 6). As discussed in Sect. 3.2, a fine model resolution
can better resolve the emission strengths, which could significantly affect
downwind chemical reactions like O3 production efficiency (Liang and
Jacobson, 2000). Moreover, terrain elevations from fine model resolution are
better represented (thus more representative of Summit's elevation), and
more efficient vertical ventilation of O3 and O3 precursors can be
achieved (Wang et al., 2004). Together with the impact of snowpack
chemistry, this brought model-simulated surface O3 mixing ratios over
Summit into better agreement with observations for June–July. However, there
was still a low bias in the model for the months of April and May.
Another possible cause for the low O3 biases in model simulations is
the calculated stratosphere-to-troposphere exchange (STE) O3 flux in
the model. Liang et al. (2011) have pointed out that STE could be a
significant direct source of O3 in the Arctic during spring–summer.
We retrieved vertical profiles of O3 mixing ratios and specific
humidity from ozonesondes (0–5 km of elevation above the Summit surface)
launched at Summit for the months of June and July 2008 and compared
those data with model control runs. Ozonesondes were launched intensively
during these 2 months (a total of 19 times). As shown in Fig. 7,
compared with observations the model-simulated O3 mixing ratios averaged
over 0–5 km above ground level were underestimated by 3 and 9 % in
June and July 2008 (Fig. 7a). However, specific humidity in GEOS-5 was
overestimated by 50 and 81 % (Fig. 7b), respectively. Ozonesonde data
showed that Summit frequently encountered high O3 and low water vapor
events (e.g., 9–11 July 2008), which were likely of upper
tropospheric or stratospheric origin (Helmig et al., 2007), but these were not
captured by the model, which implied that GEOS-Chem possibly underestimated
STE for O3 over Summit. This is consistent with the study by
Choi et al. (2017), which found low bias with model-simulated O3 mixing ratios
for the upper troposphere of the high-latitude Northern Hemisphere compared
with ozonesonde data and attributed the low bias to an underestimated STE
in the model.
Comparisons of vertical profiles of (a) O3
and (b) specific humidity between GEOS-Chem simulations and
ozonesondes in June and July 2008, respectively, averaged over 1 km altitude
bins. Black and green solid circles represent observations and simulations in
June 2008, while purple and red triangles denote observations and simulations
for July 2008. Solid and dashed horizontal error bars represent observational
standard deviations for June and July, respectively.
Misrepresentation of boundary layer height is another factor that could lead
to model–data discrepancy in O3 mixing ratios. The mean springtime
afternoon (12:00–14:00 LT, local time) boundary layer height in the model at
Summit for the year 2009 was 160 m, which agreed reasonably well with
inferred boundary layer heights from vertical balloon soundings (Helmig et
al., 2002). Therefore, it is unlikely that model uncertainties in boundary
layer height representation in springtime cause the low bias of O3
mixing ratios between the model and observations.
Conclusions
We combined model simulations with 2-year (July 2008–June 2010) ground-based measurements at Summit, Greenland to investigate the abundance and
seasonal variations of surface O3 and related species in the Arctic. In
general, the GEOS-Chem model was capable of reproducing the seasonal cycles
of NOx, PAN, C2H6, C3H8, CO, and O3. However,
some major discrepancies between the model and observations, especially for
NOx, PAN, C2H6, and O3, were identified.
There were significant differences between model-simulated NOx mixing
ratios and observations for the spring and winter seasons. The model
underestimated NOx mixing ratios by approximately 50 % during late
spring to early summer, which was likely due to the missing NOx
emissions from nitrate photolysis in the snowpack. At the same time, the
model overestimated NOx mixing ratios by more than a factor of 2 in
wintertime. Model simulations indicated episodic but frequent transport
events from Europe in wintertime, leading to NOx spikes reaching 15
times typical NOx mixing ratios at Summit; these large NOx spikes
were not seen in the observations. We carried out multiple sensitivity
model studies but were still unable to fully reconcile this discrepancy.
The model successfully captured the seasonal cycles and the spring maximum
PAN mixing ratios, although it underestimated PAN by over 30 % during late
spring and early summer. Model sensitivity studies revealed that this
discrepancy could be largely resolved by accounting for PAN emissions from
snowpack.
For C3H8 and CO, model simulations agreed well overall with the
surface measurements. However, the model tended to systematically
overestimate surface C2H6 mixing ratios by ∼ 20 %
on annual average compared with observations. This may be explained by
the fact that annual emission budgets of C2H6 over the US and Asia from the X08
emission inventory were higher than those from NEI11 and MIX by over 40 %.
By replacing X08 over the US with NEI11 for C2H6 and scaling up
MIX by 20 %, the model–observation bias can be resolved, resulting in an
annual mean bias of less than 1 %. However, care must be taken in
interpreting this result because we did not take into account other factors
that might influence the discrepancy in surface C2H6 mixing ratios
at Summit between the model and observations, such as the C2H6
chemistry with chlorine.
GEOS-Chem was able to reproduce the seasonal variation in surface O3 at
Summit but persistently underestimated O3 mixing ratios by
∼ 13 % (∼ 6.5 ppbv) from April to July. This
low bias was likely caused by a combination of misrepresentations, including
the missing snowpack emissions of NOx and HONO, an inaccurate
representation of Summit's elevation with a too-coarse model resolution, and
the underestimated STE.
All the results presented above reveal the importance of local snowpack
emissions in regulating the atmospheric composition and chemistry over the
Arctic. Improvements in global CTMs could likely be achieved by coupling
snowpack emissions of reactive gases and photochemistry modules in order to
better simulate O3 precursors and O3 over snow and ice (Zatko et
al., 2016). Moreover, this study also demonstrates that anthropogenic
emissions from the midlatitudes play an important role in affecting the Arctic
atmosphere.
Data used in this study can be provided upon
request to the corresponding authors, Yaoxian Huang (yaoxianh@mtu.edu) and
Shiliang Wu (slwu@mtu.edu).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-14661-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was funded by US EPA grant 83518901. The findings are solely
the responsibility of the grantee and do not necessarily represent the
official views of the US EPA. Further, the US EPA does not endorse the
purchase of any commercial products or services mentioned in the publication.
Superior, a high-performance computing cluster at Michigan Technological
University, was used to obtain the results presented in this publication.
Louisa J. Kramer, Detlev Helmig, and on behalf of the late
Richard E. Honrath thank NASA (grant NNX07AR26G) for supporting the
measurements at Summit. Shiliang Wu acknowledges the sabbatical fellowship
from the Ocean University of China. Detlev Helmig acknowledges support from
the National Science Foundation under grant NSF AON 1108391. We also thank
NOAA ESRL for providing the observational dataset of O3 and CO.
Technical support from Melissa Sulprizio and Christoph Keller is also
acknowledged. Edited by: Jason
West Reviewed by: Hongyu Liu and one anonymous referee
ReferencesAlvarado, M. J., Logan, J. A., Mao, J., Apel, E., Riemer, D., Blake, D.,
Cohen, R. C., Min, K.-E., Perring, A. E., Browne, E. C., Wooldridge, P. J.,
Diskin, G. S., Sachse, G. W., Fuelberg, H., Sessions, W. R., Harrigan, D. L.,
Huey, G., Liao, J., Case-Hanks, A., Jimenez, J. L., Cubison, M. J., Vay, S.
A., Weinheimer, A. J., Knapp, D. J., Montzka, D. D., Flocke, F. M., Pollack,
I. B., Wennberg, P. O., Kurten, A., Crounse, J., Clair, J. M. St., Wisthaler,
A., Mikoviny, T., Yantosca, R. M., Carouge, C. C., and Le Sager, P.: Nitrogen
oxides and PAN in plumes from boreal fires during ARCTAS-B and their impact
on ozone: an integrated analysis of aircraft and satellite observations,
Atmos. Chem. Phys., 10, 9739–9760, 10.5194/acp-10-9739-2010,
2010.Arnold, S. R., Emmons, L. K., Monks, S. A., Law, K. S., Ridley, D. A.,
Turquety, S., Tilmes, S., Thomas, J. L., Bouarar, I., Flemming, J., Huijnen,
V., Mao, J., Duncan, B. N., Steenrod, S., Yoshida, Y., Langner, J., and Long,
Y.: Biomass burning influence on high-latitude tropospheric ozone and
reactive nitrogen in summer 2008: a multi-model analysis based on POLMIP
simulations, Atmos. Chem. Phys., 15, 6047–6068,
10.5194/acp-15-6047-2015, 2015.
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A.
M., Li, Q., Liu, H., Mickley L. J., and Schultz, M. G.: Global modeling of
tropospheric chemistry with assimilated meteorology: Model description and
evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, 2001.Chen, D., Wang, Y., McElroy, M. B., He, K., Yantosca, R. M., and Le Sager,
P.: Regional CO pollution and export in China simulated by the
high-resolution nested-grid GEOS-Chem model, Atmos. Chem. Phys., 9,
3825–3839, 10.5194/acp-9-3825-2009, 2009.Choi, H.-D., Liu, H., Crawford, J. H., Considine, D. B., Allen, D. J.,
Duncan, B. N., Horowitz, L. W., Rodriguez, J. M., Strahan, S. E., Zhang, L.,
Liu, X., Damon, M. R., and Steenrod, S. D.: Global O3–CO correlations in
a chemistry and transport model during July–August: evaluation with TES
satellite observations and sensitivity to input meteorological data and
emissions, Atmos. Chem. Phys., 17, 8429-8-452,
10.5194/acp-17-8429-2017, 2017.Christian, K. E., Brune, W. H., and Mao, J.: Global sensitivity analysis of
the GEOS-Chem chemical transport model: ozone and hydrogen oxides during
ARCTAS (2008), Atmos. Chem. Phys., 17, 3769–3784,
10.5194/acp-17-3769-2017, 2017.
Crawford, J. H., Davis, D. D., Chen, G., Buhr, M., Oltmans, S., Weller, R.,
Mauldin, L., Eisele, F., Shetter, R., Lefer, B., Ari-moto, R., and Hogan, A.:
Evidence for photochemical production of ozone at the South Pole surface,
Geophys. Res. Lett., 28, 3641–3644, 2001.
Dibb, J. E., Arsenault, M., Peterson, M. C., and Honrath, R. E.: Fast
nitrogen oxide photochemistry in Summit, Greenland snow, Atmos. Environ., 36, 2501–2511, 2002.Dibb, J. E., Whitlow, S. I., and Arsenault, M.: Seasonal variations in the
soluble ion content of snow
at Summit. Greenland: Constraints from three years of daily surface snow
samples, Atmos.
Environ., 41, 5007–5019, 10.1016/j.atmosenv.2006.12.010, 2007.Fiore, A. M., Jacob, D. J., Field, B. D., Streets, D. G., Fernandes, S. D.,
and Jang, C.: Linking
ozone pollution and climate change: The case for controlling methane,
Geophys. Res. Lett.,
29, 1919, 10.1029/2002GL015601, 2002.Fischer, E. V., Jacob, D. J., Yantosca, R. M., Sulprizio, M. P., Millet, D.
B., Mao, J., Paulot, F., Singh, H. B., Roiger, A., Ries, L., Talbot, R. W.,
Dzepina, K., and Pandey Deolal, S.: Atmospheric peroxyacetyl nitrate (PAN): a
global budget and source attribution, Atmos. Chem. Phys., 14, 2679–2698,
10.5194/acp-14-2679-2014, 2014.Ford, K. M., Shepson, P. B., Bertman, S. B., Honrath, R. E., Peterson, M.,
Dibb, J. E., and Bottenheim, J. W.: Studies of peroxyacetyl nitrate (PAN) and
its interaction with the snowpack at Summit, Greenland, J. Geophys. Res.,
107, 4102, 10.1029/2001JD000547, 2002.Franco, B., Bader, W., Toon, G., Bray, C., Perrin, A., Fischer, E., Sudo, K.,
Boone, C., Bovy, B., Lejeune, B., Servais, C., and Mahieu, E.: Retrieval of
ethane from ground-based FTIR solar spectra using improved spectroscopy:
Recent burden increase above Jungfraujoch, J. Quant. Spectrosc. Ra., 160,
36–49, 10.1016/j.jqsrt.2015.03.017, 2015.Franco, B., Mahieu, E., Emmons, L. K., Tzompa-Sosa, Z. A., Fischer, E. V.,
Sudo, K., Bovy, B., Conway, S., Griffin, D., Hannigan, J. W., Strong, K., and
Walker, K. A.: Evaluating ethane and methane emissions associated with the
development of oil and natural gas extraction in North America, Environ. Res.
Lett., 11, 044010, 10.1088/1748-9326/11/4/044010, 2016.Frey, M. M., Brough, N., France, J. L., Anderson, P. S., Traulle, O., King,
M. D., Jones, A. E., Wolff, E. W., and Savarino, J.: The diurnal variability
of atmospheric nitrogen oxides (NO and NO2) above the Antarctic Plateau
driven by atmospheric stability and snow emissions, Atmos. Chem. Phys., 13,
3045–3062, 10.5194/acp-13-3045-2013, 2013.
Gerbig, C., Schmitgen, S., Kley, D., Volz-Thomas, A., Dewey, K., and Haaks,
D.: An improved fast-response vacuum-UV resonance fluorescence CO instrument,
J. Geophys. Res., 104, 1699–1704, 1999.Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328,
10.1002/jgrg.20042, 2013.Grannas, A. M., Jones, A. E., Dibb, J., Ammann, M., Anastasio, C., Beine, H.
J., Bergin, M., Bottenheim, J., Boxe, C. S., Carver, G., Chen, G., Crawford,
J. H., Dominé, F., Frey, M. M., Guzmàn, M. I., Heard, D. E., Helmig, D.,
Hoffmann, M. R., Honrath, R. E., Huey, L. G., Hutterli, M., Jacobi, H. W.,
Klàn, P., Lefer, B., McConnell, J., Plane, J., Sander, R., Savarino, J.,
Shepson, P. B., Simpson, W. R., Sodeau, J. R., von Glasow, R., Weller, R.,
Wolff, E. W., and Zhu, T.: An overview of snow photochemistry: evidence,
mechanisms and impacts, Atmos. Chem. Phys., 7, 4329–4373,
10.5194/acp-7-4329-2007, 2007.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron,
C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of
Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6,
3181–3210, 10.5194/acp-6-3181-2006, 2006.Hausmann, P., Sussmann, R., and Smale, D.: Contribution of oil and natural
gas production to renewed increase in atmospheric methane (2007–2014):
top–down estimate from ethane and methane column observations, Atmos. Chem.
Phys., 16, 3227–3244, 10.5194/acp-16-3227-2016, 2016.Helmig, D., Boulter, J., David, D., Birks, J. W., Cullen, N. J., Steffen, K.,
Johnson, B. J., and Oltmans, S. J.: Ozone and meteorological boundary-layer
conditions at Summit, Greenland, during 3–21 June 2000, Atmos. Environ., 36,
2595–2608, 10.1016/S1352-2310(02)00129-2, 2002.Helmig, D., Oltmans, S. J., Morse, T. O., and Dibb, J. E.: What is causing
high ozone at Summit, Greenland?, Atmos. Environ., 41, 5031–5043,
10.1016/j.atmosenv.2006.05.084, 2007.
Helmig, D., Johnson, B., Oltmans, S. J., Neff, W., Eisele, F., and Davis, D.
D.: Elevated ozone in the boundary-layer at South Pole, Atmos. Environ., 42,
2788–2803, 2008.Helmig, D., Petrenko, V., Martinerie, P., Witrant, E., Röckmann, T.,
Zuiderweg, A., Holzinger, R., Hueber, J., Thompson, C., White, J. W. C.,
Sturges, W., Baker, A., Blunier, T., Etheridge, D., Rubino, M., and Tans, P.:
Reconstruction of Northern Hemisphere 1950–2010 atmospheric non-methane
hydrocarbons, Atmos. Chem. Phys., 14, 1463–1483,
10.5194/acp-14-1463-2014, 2014a.Helmig, D., Stephens, C., Caramore, J., and Hueber, J.: Seasonal behavior of
non-methane hydrocarbons in the firn air at Summit, Greenland, Atmos.
Environ., 85, 234–246, 10.1016/j.atmosenv.2013.11.021, 2014b.Helmig, D., Rossabi, S., Hueber, J., Tans, P., Montzka, S. A., Masarie, K.,
Thoning, K., Plass-Duelmer, C., Claude, A., Car- penter, L. J., Lewis, A. C.,
Punjabi, S., Reimann, S., Vollmer, M. K., Steinbrecher, R., Hannigan, J. W.,
Emmons, L. K., Mahieu, E., Franco, B., Smale, D., and Pozzer, A.: Reversal of
global atmospheric ethane and propane trends largely due to US oil and
natural gas production, Nat. Geosci., 9, 490–495, 10.1038/ngeo2721,
2016.Hickman, J. E., Huang, Y., Wu, S., Diru, W., Groffman, P. M., Tully, K. L.,
and Palm, C. A.: Nonlinear response of nitric oxide fluxes to fertilizer
inputs and the impacts of agricultural intensification on tropospheric ozone
pollution in Kenya, Glob. Change Biol., 23, 3193–3204,
10.1111/gcb.13644, 2017.Hollaway, M. J., Arnold, S. R., Challinor, A. J., and Emberson, L. D.:
Intercontinental trans-boundary contributions to ozone-induced crop yield
losses in the Northern Hemisphere, Biogeosciences, 9, 271–292,
10.5194/bg-9-271-2012, 2012.Honrath, R. E., Peterson, M. C., Guo, S., Dibb, J. E., Shepson, P. B., and
Campbell, B.: Evidence of NOx production within or upon ice particles in
the Greenland snowpack, Geophys. Res. Lett., 26, 695–698, 1999.Honrath, R. E., Guo, S., Peterson, M. C., Dziobak, M. P., Dibb, J. E., and
Arsenault, M. A.: Photochemical production of gas phase NOx from ice
crystal NO3-, J. Geophys. Res., 105, 24183–24190, 2000a.Honrath, R. E., Peterson, M. C., Dziobak, M. P., Dibb, J. E., Arsenault, M.
A., and Green, S. A.: Release of NOx from Sunlight-irradiated
Midlatitude Snow, Geophys. Res. Lett., 27, 2237–2240, 2000b.Honrath, R. E., Lu, Y., Peterson, M. C., Dibb, J. E., Arsenault, M. A.,
Cullen, N. J., and Steffen, K.: Vertical fluxes of NOx, HONO, and
HNO3 above the snowpack at Summit, Greenland, Atmos. Environ., 36,
2629–2640, 10.1016/S1352-2310(02)00132-2, 2002.Huang, Y., Wu, S., Dubey, M. K., and French, N. H. F.: Impact of aging
mechanism on model simulated carbonaceous aerosols, Atmos. Chem. Phys., 13,
6329–6343, 10.5194/acp-13-6329-2013, 2013.Hudman, R. C., Murray, L. T., Jacob, D. J., Turquety, S., Wu, S., Millet, D.
B., Avery, M., Goldstein, A. H., and Holloway, J.: North American influence
on tropospheric ozone and the effects of recent emission reductions:
Constraints from ICARTT observations, J. Geophys. Res., 114, D07302,
10.1029/2008JD010126, 2009.Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R.,
Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global
soil nitric oxide emissions: implementation and space based-constraints,
Atmos. Chem. Phys., 12, 7779–7795, 10.5194/acp-12-7779-2012,
2012.Jacob, D. J., Wofsy, S. C., Bakwin, P. S., Fan, S.-M., Harriss, R. C.,
Talbot, R. W., Bradshaw, J. D., Sandholm, S. T., Singh, H. B., Browell, E.
V., Gregory, G. L., Sachse, G. W., Shipham, M. C., Blake, D. R., and
Fitzjarrald, D. R.: Summertime photochemistry of the troposphere at high
northern latitudes, J. Geophys. Res., 97, 16421–16431,
10.1029/91JD01968, 1992.Jang, J.-C., Jeffries, H., Byun, D., and Pleim, J.: Sensitivity of ozone to
model grid resolution – I. Application of high resolution regional acid
deposition model, Atmos. Environ., 29, 3085–3100,
10.1016/1352-2310(95)00118-I, 1995.Johnson, M.S., Meskhidze, N., Solmon, F., Gasso, S., Chuang, P. Y., Gaiero,
D. M., Yantosca, R. M., Wu, S., Wang, X., and Carouge, C.: Modeling Dust and
Soluble Iron Deposition to the South Atlantic Ocean, J. Geophys. Res., 115,
D15202, 10.1029/2009JD013311, 2010.Kramer, L. J., Helmig, D., Burkhart, J. F., Stohl, A., Oltmans, S., and
Honrath, R. E.: Seasonal variability of atmospheric nitrogen oxides and
non-methane hydrocarbons at the GEOSummit station, Greenland, Atmos. Chem.
Phys., 15, 6827–6849, 10.5194/acp-15-6827-2015, 2015.
Kuhns, H., Knipping, E. M., and Vukovich, J. M.: Development of a United
States-Mexico emissions inventory for the Big Bend Regional Aerosol and
Visibility Observational (BRAVO) Study, J. Air Waste Manage., 55, 677–692,
2005.Kumar, A., Wu, S., Weise, M. F., Honrath, R., Owen, R. C., Helmig, D.,
Kramer, L., Val Martin, M., and Li, Q.: Free-troposphere ozone and carbon
monoxide over the North Atlantic for 2001–2011, Atmos. Chem. Phys., 13,
12537–12547, 10.5194/acp-13-12537-2013, 2013.Legrand, M., Preunkert, S., Frey, M., Bartels-Rausch, Th., Kukui, A., King,
M. D., Savarino, J., Kerbrat, M., and Jourdain, B.: Large mixing ratios of
atmospheric nitrous acid (HONO) at Concordia (East Antarctic Plateau) in
summer: a strong source from surface snow?, Atmos. Chem. Phys., 14,
9963–9976, 10.5194/acp-14-9963-2014, 2014.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T.,
Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H.,
Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian
anthropogenic emission inventory under the international collaboration
framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963,
10.5194/acp-17-935-2017, 2017.Liang, J. and Jacobson, M. Z.: Effects of subgrid segregation on ozone
production efficiency in a chemical model, Atmos. Environ., 34, 2975–2982,
10.1016/S1352-2310(99)00520-8, 2000.Liang, Q., Rodriguez, J. M., Douglass, A. R., Crawford, J. H., Olson, J. R.,
Apel, E., Bian, H., Blake, D. R., Brune, W., Chin, M., Colarco, P. R., da
Silva, A., Diskin, G. S., Duncan, B. N., Huey, L. G., Knapp, D. J., Montzka,
D. D., Nielsen, J. E., Pawson, S., Riemer, D. D., Weinheimer, A. J., and
Wisthaler, A.: Reactive nitrogen, ozone and ozone production in the Arctic
troposphere and the impact of stratosphere-troposphere exchange, Atmos. Chem.
Phys., 11, 13181–13199, 10.5194/acp-11-13181-2011, 2011.Liu, H. Y., Jacob, D. J., Bey, I., and Yantosca, R. M.: Constraints from
pb-210 and Be-7 on wet deposition and transport in a global three-dimensional
chemical tracer model driven by assimilated meteorological fields, J.
Geophys. Res.-Atmos., 106, 12109–12128, 10.1029/2000JD900839, 2001.Martin, R. V., Jacob, D. J., Logan, J. A., Bey, I., Yantosca, R. M., Staudt,
A. C., Li, Q., Fiore, A. M., Duncan, B. N., and Liu, H.: Interpretation of
TOMS observations of tropical tropospheric ozone with a global model and in
situ observations, J. Geophys. Res., 107, ACH4-1–ACH4-27,
10.1029/2001JD001480, 2002.McClure-Begley, A., Petropavlovskikh, I., and Oltmans, S.: NOAA Global
Monitoring Surface Ozone Network. 1973–2014. National Oceanic and
Atmospheric Administration, Earth Systems Research Laboratory Global
Monitoring Division, Boulder, CO, available at:
10.7289/V57P8WBF (last access: 23 April 2017), 2014.McLinden, C. A., Olsen, S. C., Hannegan, B., Wild, O., Prather, M. J., and
Sundet, J.: Stratospheric ozone in 3-D models: A simple chemistry and the
cross-tropopause flux, J. Geophys. Res., 105, 14653–14665,
10.1029/2000JD900124, 2000.Monks, S. A., Arnold, S. R., Emmons, L. K., Law, K. S., Turquety, S., Duncan,
B. N., Flemming, J., Huijnen, V., Tilmes, S., Langner, J., Mao, J., Long, Y.,
Thomas, J. L., Steenrod, S. D., Raut, J. C., Wilson, C., Chipperfield, M. P.,
Diskin, G. S., Weinheimer, A., Schlager, H., and Ancellet, G.: Multi-model
study of chemical and physical controls on transport of anthropogenic and
biomass burning pollution to the Arctic, Atmos. Chem. Phys., 15, 3575–3603,
10.5194/acp-15-3575-2015, 2015.Murray, K. A., Kramer, L. J., Doskey, P. V., Ganzeveld, L., Seok, B., Van
Dam, B., and Helmig, D.: Dynamics of ozone and nitrogen oxides at Summit,
Greenland. II. Simulating snowpack chemistry during a spring high ozone event
with a 1-D process-scale model, Atmos. Environ., 117, 110–123,
10.1016/j.atmosenv.2015.07.004, 2015.Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.:
Optimized regional and interannual variability of lightning in a global
chemical transport constrained by LIS/OTD satellite data, J. Geophys. Res.,
117, D20307, 10.1029/2012JD017934, 2012.Novelli, P. C., Masarie, K. A., Lang, P. M., Hall, B. D., Myers, R. C., and
Elkins, J. W.: Re-analysis of tropospheric CO trends: Effects of the
1997–1998 wild fires, J. Geophys. Res., 108, 4464, 10.1029/2002JD003031,
2003.Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M.:
Natural and transboundary pollution influences on sulfate-nitrate-ammonium
aerosols in the United States: Implications for policy, J. Geophys.
Res.-Atmos., 109, D15204, 10.1029/2003JD004473, 2004.Petropavlovskikh, I. and Oltmans, S. J.: Tropospheric Ozone Measurements,
1973–2011, Version: 2012-07-10, NOAA, available at:
ftp://aftp.cmdl.noaa.gov/data/ozwv/SurfaceOzone/ (last access:
15 February 2016), 2012.Price, C. and Rind, D.: A simple lightning parameterization for calculating
global lightning distributions, J. Geophys. Res., 97, 9919–9933,
10.1029/92JD00719,1992.
Ridley, B. A. and Grahek, F.: A small, low flow, high sensitivity reaction
vessel for NO chemiluminescence detectors, Am. Meteorol. Soc., 7, 307–311,
1990.Sauvage, B., Martin, R. V., van Donkelaar, A., Liu, X., Chance, K., Jaeglé,
L., Palmer, P. I., Wu, S., and Fu, T.-M.: Remote sensed and in situ
constraints on processes affecting tropical tropospheric ozone, Atmos. Chem.
Phys., 7, 815–838, 10.5194/acp-7-815-2007, 2007.Sherwen, T., Schmidt, J. A., Evans, M. J., Carpenter, L. J., Großmann,
K., Eastham, S. D., Jacob, D. J., Dix, B., Koenig, T. K., Sinreich, R.,
Ortega, I., Volkamer, R., Saiz-Lopez, A., Prados-Roman, C., Mahajan, A. S.,
and Ordóñez, C.: Global impacts of tropospheric halogens (Cl, Br, I) on
oxidants and composition in GEOS-Chem, Atmos. Chem. Phys., 16, 12239–12271,
10.5194/acp-16-12239-2016, 2016.Shindell, D. T., Chin, M., Dentener, F., Doherty, R. M., Faluvegi, G., Fiore,
A. M., Hess, P., Koch, D. M., MacKenzie, I. A., Sanderson, M. G., Schultz, M.
G., Schulz, M., Stevenson, D. S., Teich, H., Textor, C., Wild, O., Bergmann,
D. J., Bey, I., Bian, H., Cuvelier, C., Duncan, B. N., Folberth, G.,
Horowitz, L. W., Jonson, J., Kaminski, J. W., Marmer, E., Park, R., Pringle,
K. J., Schroeder, S., Szopa, S., Takemura, T., Zeng, G., Keating, T. J., and
Zuber, A.: A multi-model assessment of pollution transport to the Arctic,
Atmos. Chem. Phys., 8, 5353–5372, 10.5194/acp-8-5353-2008,
2008.Simon, H., Beck, L., Bhave, P. V., Divita, F., Hsu, Y., Luecken, D., Mobley,
J. D., Pouliot, G. A., Reff, A., Sarwar, G., and Strum, M.: The development
and uses of EPA's SPECIATE database, Atmos. Pollut. Res., 1, 196–206,
10.5094/apr.2010.026, 2010.Simpson, I. J., Sulbaek Andersen, M. P., Meinardi, S., Bruhwiler, L., Blake,
N. J., Helmig, D., Rowland, F. S. and Blake, D. R.: Long-term decline of
global atmospheric ethane concentrations and implications for methane,
Nature, 488, 490–494, 10.1038/nature11342, 2012.Thomas, J. L., Stutz, J., Lefer, B., Huey, L. G., Toyota, K., Dibb, J. E.,
and von Glasow, R.: Modeling chemistry in and above snow at Summit, Greenland
– Part 1: Model description and results, Atmos. Chem. Phys., 11, 4899–4914,
10.5194/acp-11-4899-2011, 2011.Thomas, J. L., Dibb, J. E., Huey, L. G., Liao, J., Tanner, D., Lefer, B., von
Glasow, R., and Stutz, J.: Modeling chemistry in and above snow at Summit,
Greenland – Part 2: Impact of snowpack chemistry on the oxidation capacity
of the boundary layer, Atmos. Chem. Phys., 12, 6537–6554,
10.5194/acp-12-6537-2012, 2012.Tzompa-Sosa, Z. A., Mahieu, E., Franco, B., Keller, C. A., Turner, A. J.,
Helmig, D., Fried, A., Richter, D., Weibring, P., Walega, J., Yacovitch, T.
I., Herndon, S. C., Blake, D. R., Hase, F., Hannigan, J. W., Conway, S.,
Strong, K., Schneider, M., and Fischer, E. V.: Revisiting global fossil fuel
and biofuel emissions of ethane, J. Geophys. Res.-Atmos., 122, 2493–2512,
10.1002/2016JD025767, 2017.Unger, N., Shindell, D. T., Koch, D. M., and Streets, D. G.: Cross influences
of ozone and sulfate precursor emissions changes on air quality and climate,
P. Natl. Acad. Sci. USA, 103, 4377–4380, 10.1073/pnas.0508769103, 2006.
van het Bolscher, M., Pereira, J., Spesso, A., Dalsoren, S., van Noije, T.,
and Szopa, S.: REanalysis of the TROpospheric chemical composition over the
past 40 years: A long-term global modeling study of tropospheric chemistry,
Max Plank Inst. For Meteorology, Hamburg, Germany, 77, 2008.Van Dam, B., Helmig, D., Toro, C., Doskey, P., Kramer, L., Murray, K.,
Ganzeveld, L., and Seok, B.: Dynamics of ozone and nitrogen oxides at Summit,
Greenland: I. Multi-year observations in the snowpack, Atmos. Environ., 123,
268–284, 10.1016/j.atmosenv.2015.09.060, 2015.Walker, T. W., Jones, D. B. A., Parrington, M., Henze, D. K., Murray, L. T.,
Bottenheim, J. W., Anlauf, K., Worden, J. R., Bowman, K. W., Shim, C., Singh,
K., Kopacz, M., Tarasick, D. W., Davies, J., von der Gathen, P., Thompson, A.
M., and Carouge, C. C.: Impacts of midlatitude precursor emissions and local
photochemistry on ozone abundances in the Arctic, J. Geophys. Res., 117,
D01305, 10.1029/2011JD016370, 2012.Wang, Y. H., Jacob, D. J., and Logan, J. A.: Global simulation of
tropospheric O3-NOx-hydrocarbon chemistry 1. Model formulation, J.
Geophys. Res.-Atmos., 103, 10713–10725, 10.1029/98JD00158, 1998.Wang, Y. X., McElroy, M. B., Jacob, D. J., and Yantosca, R. M.: A nested grid
formulation for chemical transport over Asia: Applications to CO, J. Geophys.
Res., 109, D22307, 10.1029/2004JD005237, 2004.Wesely, M. L.: Parameterization of surface resistances to gaseous dry
deposition in regional-scale numerical-models, Atmos. Environ., 23,
1293–1304, 10.1016/0004-6981(89)90153-4, 1989.Wespes, C., Emmons, L., Edwards, D. P., Hannigan, J., Hurtmans, D., Saunois,
M., Coheur, P.-F., Clerbaux, C., Coffey, M. T., Batchelor, R. L.,
Lindenmaier, R., Strong, K., Weinheimer, A. J., Nowak, J. B., Ryerson, T. B.,
Crounse, J. D., and Wennberg, P. O.: Analysis of ozone and nitric acid in
spring and summer Arctic pollution using aircraft, ground-based, satellite
observations and MOZART-4 model: source attribution and partitioning, Atmos.
Chem. Phys., 12, 237–259, 10.5194/acp-12-237-2012, 2012.Wu, S., Mickley, L. J., Jacob, D. J., Logan, J. A., Yantosca, R. M., and
Rind, D.: Why are there large differences between models in global budgets of
tropospheric ozone?, J. Geophys. Res., 112, D05302,
10.1029/2006JD007801, 2007.Xiao, Y., Logan, J. A., Jacob, D. J., Hudman, R. C., Yantosca, R., and Blake,
D. R.: The global budget of ethane and regional constrainsts on U.S. sources,
J. Geophys. Res., 113, D21306, 10.1029/2007JD009415, 2008.Yang, J., Honrath, R. E., Peterson, M. C., Dibb, J. E., Sumner, A. L.,
Shepson, P. B., Frey, M., Jacobi, H.-W., Swanson, A., and Blake, N.: Impacts
of snowpack emissions on deduced levels of OH and peroxy radicals at Summit,
Greenland, Atmos. Environ., 36, 2523–2534,
10.1016/S1352-2310(02)00128-0, 2002.Yevich, R. and Logan, J. A.: An assesment of biofuel use and burning of
agricultural waste in the developing world, Global Biogeochem. Cy., 17, 1095,
10.1029/2002GB001952, 2003.Yu, K., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Miller, C.
C., Travis, K. R., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Cohen, R. C.,
Dibb, J. E., Fried, A., Mikoviny, T., Ryerson, T. B., Wennberg, P. O., and
Wisthaler, A.: Sensitivity to grid resolution in the ability of a chemical
transport model to simulate observed oxidant chemistry under high-isoprene
conditions, Atmos. Chem. Phys., 16, 4369–4378,
10.5194/acp-16-4369-2016, 2016.Zatko, M., Geng, L., Alexander, B., Sofen, E., and Klein, K.: The impact of
snow nitrate photolysis on boundary layer chemistry and the recycling and
redistribution of reactive nitrogen across Antarctica and Greenland in a
global chemical transport model, Atmos. Chem. Phys., 16, 2819–2842,
10.5194/acp-16-2819-2016, 2016.
Zhang, H., Wu, S., Huang, Y., and Wang, Y.: Effects of stratospheric ozone
recovery on photochemistry and ozone air quality in the troposphere, Atmos.
Chem. Phys., 14, 4079–4086, 10.5194/acp-14-4079-2014, 2014.
Zhou, X., Beine, H. J., Honrath, R. E., Fuentes, J., Simpson, W., Shepson, P.
B., and Bottenheim, J. W.: snowpack photochemical production of HONO: a major
source of OH in the Arctic boundary layer in springtime, Geophys. Res. Lett.,
28, 4087–4090, 2001.