As the third most important greenhouse gas (GHG) after carbon
dioxide (CO2) and methane (CH4), tropospheric ozone (O3) is also
an air pollutant causing damage to human health and ecosystems. This study
brings together recent research on observations and modeling of tropospheric
O3 in the Arctic, a rapidly warming and sensitive environment. At
different locations in the Arctic, the observed surface O3 seasonal
cycles are quite different. Coastal Arctic locations, for example, have a
minimum in the springtime due to O3 depletion events resulting from
surface bromine chemistry. In contrast, other Arctic locations have a
maximum in the spring. The 12 state-of-the-art models used in this study
lack the surface halogen chemistry needed to simulate coastal Arctic surface
O3 depletion in the springtime; however, the multi-model median (MMM)
has accurate seasonal cycles at non-coastal Arctic locations. There is a
large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among
models or improved accuracy in simulating tropospheric O3 and its
precursor species. The MMM underestimates Arctic surface O3 by 5 % to
15 % depending on the location. The vertical distribution of tropospheric
O3 is studied from recent ozonesonde measurements and the models. The
models are highly variable, simulating free-tropospheric O3 within a
range of ±50 % depending on the model and the altitude. The MMM
performs best, within ±8 % for most locations and seasons. However,
nearly all models overestimate O3 near the tropopause (∼300 hPa or ∼8 km), likely due to ongoing issues with
underestimating the altitude of the tropopause and excessive downward
transport of stratospheric O3 at high latitudes. For example, the MMM
is biased high by about 20 % at Eureka. Observed and simulated O3
precursors (CO, NOx, and reservoir PAN) are evaluated throughout the
troposphere. Models underestimate wintertime CO everywhere, likely due to a
combination of underestimating CO emissions and possibly overestimating OH.
Throughout the vertical profile (compared to aircraft measurements), the MMM
underestimates both CO and NOx but overestimates PAN. Perhaps as a
result of competing deficiencies, the MMM O3 matches the observed
O3 reasonably well. Our findings suggest that despite model updates
over the last decade, model results are as highly variable as ever and have
not increased in accuracy for representing Arctic tropospheric O3.
Introduction
Tropospheric ozone (O3) is the third most important greenhouse gas
(GHG) after CO2 and methane (IPCC, 2021), and it is an air pollutant
causing damage to human health (World Health Organization (WHO), 2021). It also causes damage to
vegetation following dry deposition to the surface (U.S. EPA, 2013).
However, our knowledge about the sources and sinks of tropospheric O3
is still uncertain (AMAP, 2015, 2022; Gaudel et al., 2018), in particular in
regions where fewer observations exist and where our understanding of key
processes is still evolving. The Arctic is one such region where few
long-term measurements of O3 exist and measurements of compounds that
are important for producing and destroying O3 in the atmosphere are
scarce at the surface and even more so in the free troposphere. Progress has
been made recently in terms of our understanding of certain processes, and a
picture is emerging about the distribution of Arctic tropospheric O3 as
well as seasonal cycles and trends at different locations (e.g., Young et al., 2018; Tarasick et al., 2019b). In particular, the connection between
surface O3 depletion episodes and halogens is now well-established
(e.g., Simpson et al., 2007; Abbatt et al., 2012).
However, the role of natural cycles in the Arctic O3 budget relative to
O3 produced from anthropogenic emissions and how that relationship is
changing in response to rapid warming in the Arctic are still uncertain.
Arctic warming and associated development in the Arctic are also driving
changes in local anthropogenic emissions, which could already be leading to
changes in the relative contributions of O3 produced due to long-range
transport of midlatitude anthropogenic emissions and O3 produced from
within or near Arctic anthropogenic emissions. Increases in emissions, such
as from shipping (Gong et al., 2018) or boreal fires, can affect Arctic air
quality (Schmale et al., 2018).
Ozone radiative forcing resulting from changes in tropospheric O3 in
the Arctic is highly sensitive to altitude. The sensitivity of the Arctic
O3 vertical profile and resultant forcing from particular
anthropogenic emission sources and regions vary substantially with altitude
(Rap et al., 2015). Arctic surface O3 may be most sensitive to European
or local sources (Sand et al., 2015; AMAP, 2015, 2022), whereas emissions
from North American and Asian sources are more important in the middle and
upper troposphere (Monks et al., 2015; Wespes et al., 2012). Therefore, a
combination of varied source sensitivities in the vertical profile and the
increased efficacy of longwave O3 forcing with altitude in the
troposphere leads to a complex picture in terms of drivers of climate
forcing by Arctic O3. The presence of temperature inversions in the
Arctic lower troposphere may result in negative local forcing (Rap et al.,
2015; Flanner et al., 2018), in particular for local sources such as
shipping (Marelle et al., 2018). Hence, to improve the quantification of
O3 radiative effects in the Arctic there is a need first to assess
model performance in terms of seasonal cycles and vertical distributions.
The annual mean vertical distributions of O3 and CO were examined in
AMAP (2022) and Whaley et al. (2022) compared to the Tropospheric
Emission Spectrometer (TES) and Measurement of Pollution in the Troposphere
(MOPITT) satellite retrievals. Those studies showed good agreement between
models and satellite measurements for O3 in the free troposphere, where
it is a strong GHG.
This paper assesses the current state of knowledge about the dynamics of
Arctic tropospheric O3 and the ability of a suite of current
chemistry–transport and chemistry–climate models to simulate seasonal cycles
of O3 and selected precursors. We first review our current
understanding of sources and sinks of Arctic tropospheric O3 in Sect. 2. We summarize the models used in this study in Sect. 3 and the recent
findings from satellite observations in Sect. 4. We then examine the
extent to which our understanding of Arctic tropospheric O3 can explain
observed seasonal cycles at different surface sites in the Arctic and assess
the ability of models to simulate observed distributions (Sect. 5). We
also examine vertical distributions of O3 and its precursors and the
extent to which models are able to capture observed seasonal variations
(Sect. 6). Finally, conclusions are presented in Sect. 7. Trends in
Arctic tropospheric O3 over the last 20–30 years and possible changes
in seasonal cycles will be presented in a follow-on study and compared to
results from a subset of these models.
Arctic O3: sources and sinks
This section reviews tropospheric O3 sources and sinks that are
particularly relevant to the Arctic region, and many of these processes are
shown in the schematic in Fig. 1.
Schematic of Arctic tropospheric O3 sources, sinks, and
relevant processes.
Ozone sources
Tropospheric O3 is a secondary air pollutant, which is not directly
emitted but produced from the photochemical reactions of anthropogenic and
natural precursor emissions of volatile organic compounds (VOCs), CO, and CH4 in the presence of
NOx. Besides significant anthropogenic sources of these O3
precursors, there are also important natural sources for these species, such
as boreal fires, lightning, vegetation, and transport of O3 from the
stratosphere (Fig. 1), which show marked seasonal and interannual
variations. Away from the surface and in remote environments the
tropospheric O3 lifetime is around 20 d or more (Young et al.,
2013), which facilitates the long-range transport of O3 in the
troposphere. Production of O3 from lower-latitude emission sources and
its subsequent transport to the Arctic constitute a substantial source of Arctic
tropospheric O3 (Hirdman et al., 2010), where the dry Arctic conditions
and stably stratified atmosphere further prolong the O3 lifetime. In
addition, the stratosphere–troposphere exchange of O3 makes a
substantial contribution to the Arctic O3 budget. The weak in situ
O3 formation in the Arctic relative to lower latitudes increases the
relative importance of this exchange.
Downward transport of O3 from the stratosphere is an important source
of O3 in the Arctic troposphere and may be key in driving seasonality
in Arctic tropospheric O3 (Shapiro et al., 1987; Hess and Zbinden,
2013; Ancellet et al., 2016). Based on modeling, Liang et al. (2009) show
that in spring (March and April), most of the O3 in the Arctic upper
troposphere originates from stratospheric injection (78 %) and that
20 %–25 % of surface O3 originates from direct injection of O3 or
the injection of NOy and secondary O3 formation. Analysis of
observations by Tarasick et al. (2019a) is consistent with this picture.
Global model simulations conducted as part of the Coupled Model
Intercomparison Project Phase 6 suggest an increase in near-surface O3
over the Arctic during the 21st century, driven by increased stratospheric
O3 import into the troposphere, particularly in winter (Zanis et al.,
2022). In contrast, during summer, in situ production in the Arctic
contributes a significant fraction, with a model study estimating a
contribution of more than 50 % of O3 in the Arctic boundary layer and
30 %–40 % in the free troposphere for the month of July (Walker et al.,
2012). Methane (CH4) is a key precursor for tropospheric O3 via
its oxidation in the presence of sufficient NOx. Increases in
anthropogenic CH4 emissions are estimated to be responsible for
44 ± 12 % of the global tropospheric ozone radiative forcing from the
pre-industrial era to present day (Stevenson et al., 2013). Fiore et al. (2008)
estimated that anthropogenic CH4 emissions contribute 15 % to the
annual average total global O3 burden (including natural and
anthropogenic sources). Based on parameterized source–receptor sensitivities
for a range of CMIP6 SSP scenarios, Turnock et al. (2019) illustrated the
significant contribution of CH4 to future O3 concentration
reductions at high latitudes under future conditions with lower NOx
concentrations. Using a similar approach based on parameterized responses
to O3 precursor emission perturbations, it was found that CH4
accounts for approximately 40 % of the Arctic O3 response to
precursor emission perturbations (AMAP, 2015). Thawing permafrost and
release from organic deposits in shallow Arctic Ocean waters in a warmer
climate represent a new source of methane (Isaksen et al., 2014).
Import of O3 and its precursors from lower latitudes associated with
episodes of long-range transport of anthropogenic or biomass burning
pollution leads to enhancements in Arctic tropospheric O3 (Wespes et
al., 2012; Monks et al., 2015; Ancellet et al., 2016). Whilst very low
levels of NOx within the Arctic, away from local sources, often limit
local O3 production, the release of NOx from thermal decomposition
of peroxy-acetyl nitrate (PAN) (an important NOx reservoir) imported
from lower latitudes can lead to in situ production of O3,
particularly in the warmer Arctic summer lower troposphere (Wespes et al.,
2012; Walker et al., 2012; Arnold et al., 2015). Investigation of long-range
transport of O3 precursors has shown efficient export of PAN from East
Asia to the North Pacific, with relative contributions to long-range O3
transport of 35 % in spring and 25 % in summer (Jiang et al., 2016).
Ship observations over the Arctic Ocean and Bering Sea also identified
events of long-range pollution transport with enhancements in O3
(Kanaya et al., 2019).
Recently, there has been progress in improving knowledge of local O3
precursor sources. Surface O3 in summer is influenced by shipping
NOx emissions along the northern Norwegian coast (Marelle et al., 2016;
Marelle et al., 2018) and the Northwest Passage (Aliabadi et al., 2015).
Additionally, Tuccella et al. (2017) showed that background O3 is
influenced by emissions downwind of oil and gas extraction platforms in the
southern Norwegian Sea. Natural sources of Arctic tropospheric O3
precursors include lightning NOx, emissions of NOx and reactive
VOCs from the snowpack (Honrath et al., 1999; Guimbaud et al., 2002;
Hornbrook et al., 2016; Pernov et al., 2021), natural emissions of VOCs from
high-latitude vegetation (Holst et al., 2010; Ghirardo et al., 2020), and
the sea surface microlayer (Mungall et al., 2017). Evidence from both
observations and models suggests that boreal fires are also an important
source of O3 precursors and NOx reservoir species like PAN in
spring and summer, with impacts on Arctic O3 (Thomas et al., 2013;
Arnold et al., 2015; Viatte et al., 2015; Ancellet et al., 2016).
Ozone sinks
Photochemical loss of O3 is mainly via photolysis in the presence of
water vapor or direct reaction of O3 with hydroperoxyl (HO2) or
hydroxyl radicals (OH). Photochemical destruction involving HO2 may be
particularly important in the Arctic where water vapor abundances are low
(Arnold et al., 2015). Where local emission sources give rise to high
NOx concentrations in urban regions or regions of shipping activity,
O3 loss via titration with NO can be dominant (Thorp et al., 2021; Raut
et al., 2022). Dry deposition of O3 and its precursors to ice and ocean
surfaces is slower than to vegetated terrestrial surfaces (Fig. 1). Van Dam
et al. (2016) reported O3 dry deposition velocities that were 5 times
higher over Arctic snow-free tundra in the summer months at Toolik Lake
(northern Alaska) compared to the snow-covered ground. Dry deposition,
combined with possible chemical loss (e.g., involving biogenic volatile
organic compounds, BVOCs) producing lower O3 concentrations during
stable (lower light) night conditions may explain the different diurnal
cycle observed at this tundra site compared to Arctic coastal locations.
Interestingly, gradient studies at the NOAA Barrow Observatory near
Utqiaġvik and at Zeppelin showed a positive gradient with height during
O3 depletion events (ODEs) and atmospheric mercury depletion events
(AMDEs), suggesting that O3 was removed at the surface due to fast
photochemical reactions at or close to snow surfaces initiated by the
release of halogen species (Skov et al., 2006; Solberg et al., 1996; Berg et al., 2003; Eneroth et al., 2007). During ODEs at Arctic sites in the Canadian
archipelago (Alert, Resolute, and Eureka), vertical profiles show that ozone is
typically uniformly depleted in the boundary layer, whereas a positive
gradient is observed above the boundary layer (Tarasick and Bottenheim, 2002).
Ozone lidar measurements from Eureka in the spring of 2008 showing
effects of large-scale meteorology including low O3 in the lower
troposphere when air masses originate from the north over the Arctic Ocean
and enhanced O3 during downward transport into the Arctic boundary
layer when the airflow was from the south over mountains. From Fig. 3 in
Seabrook and Whiteway (2016).
During Arctic spring, photochemical cycling of halogens in so-called
“bromine explosion” events leads to rapid depletion of surface O3 to
low or near-zero concentrations (Barrie et al., 1988; Skov et al., 2004;
Helmig et al., 2007; Simpson et al., 2007). These phenomena are observed at
Arctic coastal locations and in the Arctic Ocean (Bottenheim et al., 2009;
Jacobi et al., 2010) in March–April and attributed to bromine (halogen)
sources linked to Arctic sea ice, coupled with stable surface temperature
inversions (e.g., Fig. 1; Hermann et al., 2019). Some model studies were able
to explain major depletion events in simulations by introducing the
wind-induced release of bromine from the snowpack and have shown that both
blowing snow and the snowpack are important sources of bromine during the
spring (e.g., Yang et al., 2010; Toyota et al., 2011; Yang et al., 2020; Huang
et al., 2020; Swanson et al., 2022). Figure 2 shows the vertical extent of
low O3 episodes observed by lidar at Eureka in northern Canada. On 7 May, low O3 concentrations were observed, and back trajectories showed
that air masses came in from the ice-covered Arctic Ocean and had been in
contact with the surface multiple times during the previous 6 d, whereas
the concentrations were high on 9 May, when air came down from the mountains
located to the south (Seabrook and Whiteway, 2016). Peterson et al. (2018)
showed that active halogen chemistry and related O3 depletion can also
occur up to 200 km inland over snow-covered tundra in Alaska. Simpson et al. (2018) reported high levels of bromine oxide (BrO) at Utqiaġvik
occurring earlier in February in air masses originating from the Arctic
Ocean polar night. Their findings suggest a dark wintertime source of
reactive bromine (halogens) that could feed halogen photochemistry at lower
latitudes as the sun returns. This dark mechanism was also observed over sea
ice in Antarctica by Nerentorp Mastromonaco et al. (2016).
In addition, whilst earlier studies proposed indirect evidence that O3
and gaseous elemental mercury (Hg0) are removed by reaction with Br
atoms (e.g., Skov et al., 2004; Skov et al., 2020; Dastoor et al., 2008), Wang
et al. (2019) showed, for the first time, a direct connection between
O3 and Hg0 with atomic bromine (Br) during O3 and Hg0
depletion episodes at Utqiaġvik on the northern coast of Alaska (see Fig. 3)
where O3 and Hg0 are removed in competing reactions with Br. Here,
the Br/BrO ratio anti-correlates with O3 concentrations, and box
modeling confirms that O3 is removed by Br.
Time series at Utqiaġvik on 20 March 2012 of measured (a) atomic
bromine (Br) and bromine monoxide (BrO), (b)Br/BrO ratios, and O3.
Error bars represent propagated measurement uncertainties. From Fig. 2 in Wang et al. (2019)(EPS figure provided for the report).
This result is significant since the main source of halogens in the Arctic
is the release from refreezing sea ice, blowing snow over sea ice,
heterogeneous reactions of aerosol particles, and snowpack recycling
(Petersen et al., 2016; Peterson et al., 2017, Wang et al., 2017; Yang et al., 2020). Burd et al. (2017) found a strong relationship between the end of the
reactive bromine season and snowmelt timing. In the future, continued
decreases in Arctic sea ice extent or the relative distributions of
multi-year and seasonal sea ice cover (Peterson et al., 2019), coupled with
increases in the length of the snow-free season over land, could influence
the magnitude and seasonality of O3 sinks via changes in halogen fluxes
or dry deposition fluxes to tundra and ocean rather than snow and ice surfaces.
It has also recently been shown that substantial O3 depletion can occur due
to reactions with iodine (Benavent et al., 2022). That study, which was based
on ship measurements during the MOSAiC expedition in March to October 2020,
found that iodine contributed more to O3 loss than bromine, thus
highlighting how the dynamics of high Arctic O3 depletion are still not fully
elucidated.
AMAP models and simulations
To evaluate our process understanding of controls on the Arctic tropospheric
O3 budget and distribution, we evaluate a subset of the same model
simulations that were used in AMAP (2022) and by Whaley et al. (2022).
A total of 12 atmospheric models participated in this study: seven chemical transport
models (DEHM, EMEP MSC-W, GEOS-Chem, MATCH, MATCH-SALSA, OsloCTM, WRF-Chem)
and five chemistry–climate models (CESM, CMAM, GISS-E2.1, MRI-ESM2, and
UKESM1), with simulations of the years 2014–2015 for comparisons to
observations. All models used the same set of anthropogenic emissions called
ECLIPSEv6b (AMAP 2022), though they had different sources for fire, biogenic
emissions, and meteorology (see Table S1 in the Supplement). The years 2014–2015 were chosen
for model validation as it was the most recent time period that the ECLIPSE
v6b historical emissions were available when the model simulations were
being set up. All participating models prescribe CH4 concentrations
based on box model results, which are, in turn, based on the ECLIPSEv6b
anthropogenic CH4 emissions and various assumptions on natural
CH4 emissions (Olivié et al., 2021; Prather et al., 2012). Models
then allow CH4 to take part in photochemical processes. The
participating models have varying degrees of spatial resolution and chemical
complexity; air-quality-focused models, such as DEHM, EMEP MSC-W, GEOS-Chem,
MATCH, and WRF-Chem, have detailed HOx–NOx–hydrocarbon O3
chemistry, with speciated VOCs and secondary
aerosol formation, and they tend to run at higher resolution. The Earth system
models GISS-E2.1, MRI-ESM2, and UKESM1 also contain this level of
tropospheric chemistry, though they run globally at coarser resolution, whereas
climate-focused models like CMAM run at a coarse resolution and have
simplified tropospheric chemistry in order to be able to run for long
periods. For example, CMAM's tropospheric chemistry consists only of
CH4–NOx–O3 chemistry, with no VOCs.
As mentioned above, Arctic tropospheric O3 is heavily influenced by
imports from the stratosphere. The models vary, too, in their representation
of the stratosphere. Only a subset of participating models has a fully
simulated stratosphere. CMAM, MRI-ESM2, GISS-E2.1, OsloCTM, and UKESM1
contain relatively complete stratospheric O3 chemistry (NOx,
NOx, Clx, Brx chemistry that controls stratospheric O3).
Other models have a simplified stratosphere, such as GEOS-Chem which has a
linearized stratospheric chemistry scheme (LINOZ; McLinden et al., 2000)
and WRF-Chem which specifies stratospheric concentrations from
climatologies. Finally, several models have no stratosphere or stratospheric
chemistry at all (e.g., DEHM and EMEP MSC-W). Most atmospheric models,
including all of the models in this study, do not yet contain Arctic
tropospheric bromine chemistry and thus cannot simulate the surface-level
bromine-driven O3 depletion events that occur during spring. However,
there are research versions of some models which are starting to contain
this chemistry (e.g., Parrella et al., 2012; Falk and Sinnhuber, 2018; Badia
et al., 2021)
These same 12 model simulations were also evaluated against a different set
of measurements in AMAP (2022) and Whaley et al. (2022). Those studies
focused on many SLCF species over the Northern Hemisphere and generally
reported model biases for the annual mean concentrations. They found that
all models overestimated surface O3 concentrations at midlatitudes
but that there were both overestimation and underestimation in the Arctic.
Particularly, models overestimated surface O3 in the western Arctic
(e.g., Alaska), particularly in the summertime, but were better able to
simulate the surface O3 seasonal cycle in the eastern Arctic (e.g.,
northern Europe). They also found that model biases were small throughout
the free troposphere when compared to remote measurements from the TES
satellite instrument.
In the next sections, these models are compared with observations of O3
(at measurement sites located in Fig. 4) and its precursors either
individually or as the multi-model median (MMM) – whereby the median of all
12 atmospheric models at the measurement locations is shown unless otherwise
noted. The model output was selected from the model grid box that contains
the latitude and longitude of the observation location without any spatial
interpolation.
Map of the surface (red) and ozonesonde (blue) sites cited in the
present study, with coordinates and elevation. Eureka and Alert are both
surface and sounding sites. Utqiaġvik was formerly called Barrow. The
Arctic Circle at 66.55∘ N is also shown in the figure for
reference.
Arctic-wide tropospheric distributions from satellite data
Despite the potential limitations of some satellite data products at high
latitudes, several studies have exploited satellite observations to
investigate tropospheric O3 and precursor distributions as well as trends
relevant to the Arctic. Pommier et al. (2012) presented Infrared Atmospheric Sounding Interferometer (IASI) retrievals of
0–8 and 0-12 km sub-column O3 for the Arctic in spring and summer
2008. These showed widespread enhancements in the springtime (March–April)
tropospheric O3 column compared with summer (June–July), particularly
over northeastern Siberia, northern Canada, and the Arctic Ocean. Generally,
good agreement with in situ aircraft profiles was demonstrated, but low
thermal contrast between the Arctic surface and boundary layer was found to
produce bias in IASI retrievals compared with aircraft measurements in the
Arctic lower troposphere. Wespes et al. (2012) showed that IASI was able to
detect enhancements in midlatitude-sourced O3 enhancements during
summer at the edge of the Arctic, but also showed a lack of sensitivity over
snow and ice surfaces, potentially resulting in missing some O3
enhancements. Sodemann et al. (2011) analyzed the cross-polar transport of a
large pollution plume originating from Asia during the summer of 2008 using
IASI CO retrievals. IASI was able to detect features and structures of the
plume consistent with in situ aircraft data.
Satellite observations are also useful in evaluating the sources and export
of O3 precursors from midlatitude source regions and their subsequent
transport to the Arctic. Tropospheric NO2 columns measured from the
Ozone Monitoring Instrument (OMI) have been used to detect enhancements and
trends in NOx emissions due to gas flaring in high-latitude (up to
67∘ N) areas of Russia and North America (Li et al., 2016).
Assessment of a suite of chemical transport models using OMI tropospheric
NO2 columns for summer 2008 showed a potential overestimate in NO2
over biomass burning regions in eastern Siberia, with lower biases over
European and North American source regions and underestimates over China
(Emmons et al., 2015). A comparison of regional model-simulated tropospheric
NO2 columns with observations from OMI
suggests potential underestimates in anthropogenic NO2 emissions over
high-latitude Siberia and the Russian Arctic (Thorp et al., 2021). Monks et al. (2015) exploited limited profile information from MOPITT CO retrievals
to evaluate relationships between CO seasonal cycles in the lower and upper
troposphere over the Arctic and midlatitude source regions. Atmospheric
Infrared Sounder (AIRS) CO retrievals from 2007 to 2018 have been used to
characterize atmospheric circulation patterns coincident with pollution
enhancements during Arctic spring (Thomas et al., 2021), and IASI CO column
measurements have been used to analyze transport pathways for Asian
anthropogenic pollution to the Arctic (Ikeda et al., 2021). Osman et al. (2016) constructed three-dimensional (5∘×5∘,1 km)
gridded climatologies of CO via a domain-filling trajectory mapping
technique based on MOZAIC-IAGOS in situ measurements of commercial aircraft
flights. These climatologies agreed well using forward and backward
trajectories (<10 % difference for most cases) and against
vertical measurements from MOZAIC-IAGOS not included in the climatologies.
These climatologies were compared with CO retrievals from MOPITT; small
biases were found in the lower troposphere, while differences of
∼20 % were found between 500 and 300 hPa, which declined
throughout the study (2001-2012). Interannual variability in PAN retrieved
by TES over eastern Siberia for April 2006–2008 was documented by Zhu et al. (2015), and it was shown to be largely controlled by boreal fire emissions
at this time of year. More recently, PAN data from the TES instrument were
used to help characterize Asian influence on exported PAN and downwind
O3 production (Jiang et al., 2016). A temperature-dependent high bias
in TES PAN was found at cold temperatures over high latitudes.
In both Chapter 7 of the 2022 AMAP SLCF report (AMAP, 2022) and Whaley et al. (2022), data from satellite instruments, TES, the Atmosphere Chemistry Experiment (ACE) Fourier Transform
Spectrometer (FTS) (ACE-FTS), and MOPITT are
used to evaluate modeled O3, CH4, and CO in the Northern
Hemisphere. They showed that model biases for CH4 were small, though they
tended to be negative in the Arctic due to a lack of north–south gradient in
the prescribed global distribution. Model biases were also negative for
free-tropospheric O3; however, it was by approximately the same amount
that TES O3 retrievals have been shown to be biased high by Verstraeten
et al. (2013). The ACE-FTS comparison for O3 showed good agreement but
had higher model biases around 300–100 hPa in Whaley et al. (2022) and AMAP
(2022). The MOPITT CO comparisons in AMAP (2022) showed that all models' CO values
are biased low over land in the midlatitudes but biased high over the
oceans at lower latitudes. Monks et al. (2015) discussed the fact that models had
high biases in the outflow from Asia and low biases north of there due to
lack of transport. The Quennehen et al. (2016) study also suggested that
summertime CO transport out of Asia is zonal. This could explain some of the
underestimations in the Arctic CO in the mid-troposphere.
Arctic surface O3 and precursors: seasonal cycles
In the high Arctic (>70∘ N), there is very little diurnal
variation in surface O3 because the local and regional photochemistry
is of limited importance most of the time and due to the 24 h daylight
during Arctic spring, summer, and autumn as well as the polar night during
winter. The lack of diurnal cycle is also because there is inefficient O3 deposition to the ice, snow, and water surfaces in the Arctic and a sparsity of
vegetation. Therefore, with less deposition and limited photochemical
production, there is a very limited diurnal cycle. For high Arctic sites, the
seasonal dynamics of O3 can be explained mostly by long-range
transport, particularly in the winter and springtime, and intrusion from
aloft (Hirdman et al., 2010); see Figs. 1 and 5a. Moving southwards to the
Polar Circle a clearer diurnal pattern is evident caused by the
seasonal behavior of vertical mixing, deposition, transport, and local
chemistry (Andersson et al., 2017; Aas et al., 2021; AMAP 2022) like the
stations on the Scandinavian peninsula and Denali, central Alaska.
Surface ozone
Seasonal differences in the Arctic are important because of differences
between the local meteorological conditions, as well as atmospheric
transport, in the warm and the cold seasons and seasonal variations in
O3 sources and sinks. Surface O3 at remote midlatitude sites with
limited influence from local and regional anthropogenic O3 precursor
emissions has been found to frequently exhibit a characteristic seasonal
cycle with peak values during spring and a minimum in the summer, while
sites with high exposure to O3 from anthropogenic precursors have
summertime O3 maxima (Monks, 2000; Parrish et al., 2013, 2019; Gaudel et
al., 2018). The spring maxima have been explained by stratospheric intrusions
as well as enhanced photochemical formation during this period of the year.
The summer minima, e.g., observed at the Mace Head site (Derwent et al.,
1998, 2013, 2020), which is strongly influenced by marine air, appear to be
explained by photochemical destruction in the absence of anthropogenic
precursors. Seasonal cycles at Arctic stations have been discussed in the
literature, and it is evident that the halogen chemistry discussed above, which is
most frequently observed at high Arctic coastal stations, leads to a
significant reduction during the springtime (e.g., Oltman and Komhyr, 1986;
Tarasick et al., 1995; Monks et al., 2015, Helmig et al., 2007). Anderson et al. (2017) found that monthly mean observed near-surface O3
concentrations at background sites in Sweden from 1990 to 2013 had a maxima
in spring, with the most northerly stations experiencing their maximum in
April and the southerly (non-Arctic) ones in May.
In order to get an overview of the annual O3 cycles at different types
of Arctic surface measurement sites, we have calculated the monthly medians
and interquartile range for the period 2003–2019 for a series of sites. A
map of the stations as well as their coordinates and elevation can be seen
in Fig. 4. Figure 5 illustrates the range of seasonal cycle behavior
observed in the Arctic at different measurement sites and shows different
seasonal cycles depending on location.
Seasonal behavior of surface O3 at selected Arctic stations
that are representative of sites in the (a) coastal high Arctic, (b) near the Arctic Circle, and
(c) at high elevation. Monthly
medians are calculated for the period 2003 to 2018. Data were not available
from 2003 to 2006 for Villum or 2004 and 2013–2015 for Alert. Data from
Tiksi were available for the period 2013–2018, and at Karasjok the
measurements stopped in 2010. The error bars show upper (75 %) and lower
(25 %) quartiles.
High Arctic sites
Figure 5a shows that the seasonalities in O3 at Villum,
Barrow (Utqiaġvik), Alert, Tiksi, and Eureka are similar: they have a local
minimum in spring due to the occurrence of ODEs, a slight increase or recovery
in June, and a second minimum in July due to surface removal and
photochemical degradation of O3. These stations are located at high-latitude coastal sites close to sea level. During winter, O3 reaches a
maximum; due to an absence of photochemical degradation of O3, vertical
mixing is suppressed during polar night since the Arctic boundary layer is
often highly stratified, thus hampering removal by dry deposition (Esau and
Sorokina, 2016).
Sites near the Arctic Circle
The characteristic seasonal variations of surface O3 measured at
stations close to the Arctic Circle are shown in Fig. 5b. The stations are
Karasjok and Tustervatn in Norway (Aas et al., 2021), Esrange in Sweden,
Pallas in Finland, and Denali in Alaska (note that regular O3 monitoring at
Karasjok ended in February 2010). The sites in Fig. 5b, which are not
influenced by ODEs, exhibit a yearly cycle that is more similar to lower-latitude European stations at remote locations. Here, surface O3
exhibits a late spring maximum which is attributed to photochemical
production and transport of O3 from the stratosphere (Monks, 2000). The
largest differences between the stations are mainly found during the summer
months, most likely due to differences in the influence of local sources on
photochemical O3 production (e.g., shipping; Marelle et al., 2016) and
differences in the distance to pollution sources (Anderson et al., 2017).
Inland, high-elevation sites
Summit (located in the free troposphere on the Greenland Ice Sheet) is much
less affected by bromine chemistry originating from sea ice or other low-altitude processes than the coastal high Arctic sites (Huang et al., 2017).
Consequently, the seasonal variation is different with a clear maximum in
May and a minimum in September; the higher concentrations compared to other
surface stations can be explained by the high sensitivity to stratospheric
O3-enriched air (Monks et al., 2015) at this high-elevation (3211 m a.s.l.)
site. Short episodes of depletion have been reported (Brooks et al., 2011),
but they do not appear to substantially affect the monthly mean values as
shown in Fig. 5c.
Zeppelin, although it is a high Arctic site, is located on a mountain ridge at 474 m a.s.l. and thus experiences free-tropospheric air masses more often compared
to sea level sites. For this reason, it is less influenced by ODEs and
consequently does not have an O3 minimum in spring like the other high
Arctic coastal stations (Fig. 5c). That said, ODEs have been reported there
by Solberg et al (1996), Lehrer et al. (1997), Berg et al. (2003), Eneroth et al. (2007), and Steffen et al (2008), for example. ODEs have also been
observed at the foot of the mountain, at the coastal station Gruvebadet, Ny-Ålesund (40 m a.s.l.), by Ianniello et al. (2021).
We also note that surface O3 can be influenced by local anthropogenic
emissions such as shipping (e.g., Marelle et al., 2016; Aliabadi et al.,
2015; Eckhardt et al., 2013) or oil field emissions (McNamara et al., 2019).
McNamara et al. (2019) discussed potentially important interactions between
local anthropogenic NOx emissions from the Barrow (Utqiaġvik) settlement
or the Prudhoe Bay oil extraction facilities in northern Alaska and snowpack
(chlorine) chemistry leading to elevated concentrations of
nitrogen-containing compounds (e.g., N2O5, HO2NO2), with
implications for Arctic tropospheric O3. Therefore, while none of the
Arctic sites currently exhibit summertime surface maxima due to
photochemical production, as often observed in polluted locations further
south, this may change in the future with increasing local anthropogenic
emissions (e.g., Granier et al., 2006; Law et al., 2014; Marelle et al., 2018).
He et al. (2016) measured O3 and black carbon on a ship cruise to the
Arctic Ocean (31.1 to 87.7∘ N and 9.3–90∘ E to 168.4∘ W) from June to September 2012.
Comparing the observed O3 concentrations to those measured at
Barrow (Utqiaġvik) showed no statistically significant differences; the
authors suggest that coastal stations between July and September may be
representative of the entire Arctic, but this hypothesis requires further
investigation. Indeed, our results show significant differences in the
O3 seasonal cycles at different Arctic locations depending on whether
they were coastal, inland, or high-elevation.
Arctic surface O3 by month; seasonal cycle model comparisons.
Top row: coastal high Arctic sites; middle row: sites near the Arctic Circle;
bottom row: high-elevation sites. The solid black line is the observed
O3 monthly means, and the dashed black line is the multi-model median.
Bottom row: sub-panels show the MMM percent difference [(MMM - measurements)/measurements × 100].
Note that model results are from the 2014–2015 mean. When available, the same
years are used for the observations. However, Alert did not have data for
2014–2015, so its most recent years were used: 2010–2013. Summit had 2014 but
only 1 month in 2015, so its 2013–2015 data were used.
Surface O3 model evaluation
It has been found that halogen chemistry, stable boundary layers, and dry
deposition explained differences between measured and modeled O3
concentrations, as demonstrated by Kanaya et al. (2019), who performed
measurements of CO and O3 during several ship cruises in the Bering Sea
and the Arctic Ocean in September (2012 to 2017). None of the models in our
study contain surface halogen chemistry, but they also display highly
variable agreement in their surface O3 seasonal cycles. Figure 6 shows
the seasonal cycle from the models and observations averaged for 2014–2015 at
several Arctic observation locations. Since the models do not contain
surface-level bromine chemistry, at locations like Alert and
Barrow (Utqiaġvik), they do not capture the springtime minimum in O3.
Some models (e.g., UKESM1) greatly underestimate wintertime O3. This may
be related to deficiencies in boundary layer mixing or an overly shallow
boundary layer depth, resulting in the overly active titration of O3 by
NO near NOx emission sources and subsequent underestimation of Arctic
surface O3. However, other model deficiencies could also play a role,
including dry deposition and NOx lifetime. Indeed, Barten et al. (2021)
found that overestimation of oceanic O3 deposition can explain some
differences between modeled and measured surface O3 in the high Arctic.
Some models in Fig. 6 do not agree on the timing of the springtime peak,
with CMAM, DEHM, and GISS-E2.1 peaking in April and EMEP MSC-W and MRI-ESM2
peaking in May–June. The same groupings of models display different O3
behavior at the end of the year (October–December), with CMAM, DEHM, and
GISS-E2.1 all correctly simulating an increase in O3 and EMEP-MSC-W
and MRI-ESM2 having a decrease. All models agree better with observations
and each other on summertime surface O3 abundance at all locations and
on the full seasonal cycle at Summit, the high-elevation background
location. The large range of modeled surface O3 is similar to previous
model studies (Shindell et al., 2008; Monks et al., 2015; Gaudel et al.,
2018). Despite the large range in model performance, the overall average
negative O3 bias and the seasonality in model bias at Barrow (Utqiaġvik)
and Summit are consistent with these previous studies. The comparisons
highlight little change in the skill of models in simulating Arctic surface
O3 over the past decade.
These particular model simulations have been evaluated in Whaley et al. (2022), who grouped all western Arctic (defined as lat >60∘ N, and long <0∘) and eastern Arctic (lat
>60∘ N, long >0∘) O3
measurements together and showed the range in modeled and measured seasonal
cycles for those two regions. That analysis included additional locations at
lower latitudes, and thus their results emphasized that some models
overestimated summertime O3 in the western Arctic. Otherwise, the
results from that study are consistent with what we report here.
Ozone precursors
NOx monitors have been used at several Arctic sites, but in a study at
Zeppelin, it was shown that most of the NOx was in the form of the
reservoir species PAN (Beine et al., 1997; Beine and Krognes, 2000). We
evaluate and discuss PAN in Sect. 6.3 from aircraft measurements. There
are only limited sources for NOx in the Arctic and the lifetime of
NOx is on the order of a day. Whaley et al. (2022) evaluated surface
NOx volume mixing ratios and found that these models underestimated
surface NO2 by -59 % at low Arctic latitudes that were mostly around
60∘ N.
The dominant source for NOx is long-range transport of dominantly PAN
(Beine and Krognes; 2000) and particulate-bound HNO3, followed by
reactivation in the Arctic by thermal decomposition and photoreduction
processes, respectively. Kramer et al. (2015) determined at Summit from July
2008 to July 2010 that PAN accounted for 295 ppt and NOx for 88 ppt.
In a more recent study, Huang et al. (2017) found in the period July
2008–June 2010 that PAN and NOx were maximum in spring at about 250 ppt
and 25 ppt, respectively, and in summer 75 and 20 ppt, respectively.
Beine and Krognes (2000) measured PAN at Zeppelin Mountain between 1994 and
1996. They found that 3-month seasonal mean values were lowest in summer at 89.4 ppt and highest in spring at 222.6 ppt. HNO3 in the gas phase is in
general very low (Wespes et al., 2012). Particulate-bound nitrate –
potentially a significant source of NOx in the atmosphere and snowpack
– is close to the detection limit in summer and up to 124.7 ng N m-3
in winter at Villum (Nguyen et al., 2013).
In general, non-methane VOC (NMVOC) concentrations in the Arctic are low, and thus their
photo-oxidation has only a limited impact on O3. There is a long-term
measurement study by Gautrois et al. (2003): studies that focus on long-range
transport (Stohl, 2006; Harrigan et al., 2011), snowpack emissions (Boudries
et al., 2002; Dibb and Arsenault, 2002; Guimbaud et al., 2002; Barret et
al., 2011; Gao et al., 2012), and shipborne measurements (Sjostedt et al.,
2012 and Mungall et al., 2017). The Gautrois et al. (2003) study reported
long-term VOC concentrations for Alert, NU; they found that yearly levels of
ethane, propane, and toluene are 1.7, 0.6, and 26 pptv,
respectively. For comparison, mixing ratios of ethane, propane, and toluene
in China ranged from 3.7–17, 1.5–20.8, and 0.4–11.2 ppbv, respectively
(Barletta et al., 2005).
Pernov et al. (2021) measured organic O3 precursors online with a
proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) at Villum from April to October 2018. Sources were apportioned
with positive matrix factorization. During the late spring, the Arctic haze
factor was a source of oxygenated VOCs (OVOCs) arising from long-range
transport of anthropogenic emissions, whilst during summer, OVOCs, namely
organic acids, and dimethyl sulfide (DMS) originated from the marine cryosphere factor, with
source regions in the Greenland Sea. During autumn, the biomass burning
factor peaked in importance and was dominated by acetonitrile. The most
abundant compound during the campaign was acetone, with a mean mixing ratio
of 0.6 ppbv, as well as 0.027 ppbv for benzene and 0.046 ppbv for DMS. In the future, local
NMVOC emissions might increase from both natural and anthropogenic sources
due to retreating sea ice, with more biological activity, more
industrial activity, and shipping affecting future levels of O3. The
long-term VOC measurements at Zeppelin and Pallas (Platt et al., 2022;
Hellén et al., 2015) provide valuable datasets for better understanding
tropospheric O3 at those locations. However, in this study, models did not
provide much VOC output and, when they did so, only as monthly means of a few species
(e.g., ethane C2H6). Therefore, we did not evaluate modeled VOCs
in this study other than CO.
Arctic surface CO by month; seasonal cycle model comparisons. The
solid black line represents the observed CO monthly means, and the dashed black line
is the multi-model median (MMM). Bottom panels show the MMM percent difference
[(MMM - measurements)/measurements × 100].
Note that model results are from the 2014–2015 mean. When available, the same
years are used for the observations. However, for Zeppelin observations are
the mean of 2013–2014.
Figure 7 shows the observed and simulated seasonal cycle of CO at Zeppelin
and Barrow (Utqiaġvik). Simulated CO ranges about 50 ppbv across models, and
all models underestimate surface CO at these sites. The low model biases are
dominated by the winter and spring months. The 2014–2015 annual multi-model
median (MMM) bias is -11 % and -16 % at Zeppelin and Barrow (Utqiaġvik),
respectively. Figure 7 shows that for the first 6 months of the year, the
MMM is 20 %–30 % too low, but that in the summer, the MMM is much closer to
observations. These CO results are very similar to those found in previous
multi-model studies (Shindell et al., 2008; Monks et al., 2015; Whaley et
al., 2022). Similar to O3, these results imply little change in the
skill of models in simulating Arctic surface CO over the past decade. The
modeled CO underestimations are well-reported in the literature and
attributed to either a lack of CO from combustion sources in the emission
inventories (e.g., Kasibhatla et al., 2002; Pétron et al., 2002; Jiang
et al., 2015) or to errors in OH, which impact the lifetime of CO (e.g.,
Monks et al., 2015; Quennehen et al., 2016). Indeed, both may be at cause
here, as the anthropogenic CO emissions from ECLIPSEv6b are lower than those
in the CMIP6 emission inventory, neither of which have taken into account
the reported discrepancies from top-down emissions studies (Kasibhatla et
al., 2002; Pétron et al., 2002; Jiang et al., 2015, Miyazaki et al.,
2020). Monks et al. (2015) showed that models with lower global mean OH
concentrations produced smaller underestimates in Arctic surface CO and that
models with larger underestimates in CO over midlatitude source regions
also had larger underestimates in Arctic CO. Emmons et al. (2015) showed
that the models with larger tropospheric OH also had higher photolysis rates
of O3 to O(1D) and that there was also some relationship between
higher photolysis rates and lower cloud cover fraction in some models.
Previous multi-model results have also shown that variability in model water
vapor abundance in the Arctic appeared to be the leading driver of model
variability in OH, despite being much less important at lower latitudes
(Monks et al., 2015). Evaluating OH and water vapor is unfortunately beyond
the scope of our study.
The models of this study prescribed CH4 concentrations, including their
increasing trend, and they were found to have a small bias of
∼2 % in Whaley et al. (2022) compared to surface and
satellite measurements. Going forward, models are starting to simulate
CH4 explicitly from emissions, and this will be important for
simulating future changes in Arctic tropospheric chemistry.
Vertical distributions of O3 and precursors in the Arctic
Observations and models have both demonstrated extensive layering of
pollution signatures in the Arctic troposphere vertical profile, associated
with varying air mass origins with altitude (Zheng et al., 2021; Willis et
al., 2019). Large-scale isentropic transport pathways result in air masses
from warmer more southerly latitudes being imported into the Arctic upper
troposphere, while emissions from cooler northerly latitudes enter the
Arctic near the surface and in the lower troposphere (Stohl, 2006). The
presence of the Arctic dome during winter essentially shuts off access to
the Arctic surface to air mass import from southerly midlatitudes, while it
facilitates efficient low-level transport of emissions from northern Eurasia
and Russia to the Arctic surface, giving rise to the well-known Arctic haze
(Shaw, 1995). In practice, this large-scale dynamical control on long-range
transport to the Arctic gives rise to a well-characterized vertical
dependence of source region sensitivities for O3 and precursors through
the Arctic troposphere, where emissions from South and East Asia have the
most influence in the Arctic upper troposphere, emissions from North America
have the most influence in the Arctic mid-troposphere, and northern Eurasian
and Russian emissions dominate at the surface (in addition to local
influences) (Wespes et al., 2012; Monks et al., 2015). As mentioned in
Sect. 1, this vertical layering and changes in the efficacy of O3
radiative forcing with altitude have implications for the sensitivity of
Arctic tropospheric O3 forcing to regional emission perturbations.
Despite evidence for extensive vertical layering in the Arctic troposphere
and the potential for highly varying source contributions with altitude,
aside from a limited set of regular O3 sonde profiles, there is a
severe lack of observations available on the vertical distribution of
O3, and particularly its precursors, in the Arctic troposphere. There
is an especially poor constraint on seasonal and interannual variability in
O3 precursor profiles. In this section, we make use of available
vertical profile measurements of O3 and its precursors to document our
understanding of Arctic tropospheric O3 profiles and to evaluate
model-simulated vertical profiles of O3 and precursors.
Ozonesondes
Ozone soundings provide a long-term record of Arctic O3 through the
depth of the troposphere. Since 1966, weekly soundings have been available
at Resolute, and since the 1980s regular soundings, typically once a week,
have been available from six stations north of 60∘ N (Fig. 4,
Table S2). All of these stations are located in the Canadian and European
sectors, meaning that regular soundings are lacking in a large sector of the
Arctic (e.g., Russia and Alaska). The measurements are conducted using the
balloon-borne electrochemical concentration cell (ECC) ozonesondes,
typically reaching an altitude of about 30 km. Random uncertainties in
tropospheric measurements are about 5 %, and biases reported from field
and laboratory comparisons to UV reference photometers are 1.0 ± 4.4 % in the lower troposphere and 5.3 ± 4.4 % in the upper
troposphere (Tarasick et al., 2019b). Mean observed concentrations have a
minimum close to the surface, gradually increase throughout the
troposphere by about 50 %, and then increase sharply going into the upper
troposphere and lower stratosphere (Figs. 8 and S1–2 in the Supplement). Observed seasonal
cycles in the Arctic troposphere generally show a maximum in spring and
summer and a minimum in fall and winter. For example, Christiansen et al. (2017) examined long-term ozonesonde records at nine Arctic stations reporting
consistent seasonal cycles as a function of altitude between sites with
later maxima in the mid-troposphere compared to the surface layers and upper
troposphere.
Comparison between observed (thick black line in the left panels) and
AMAP models' (colored lines) O3 seasonal averages for 2014–2015 at
Eureka, NV, Canada. These use monthly mean model output. In each right
panel, the dotted black line is the MMM, and the dashed black line shows
zero bias for reference. See Fig. S1 for the rest of the
ozonesonde locations and a sample comparison done with 3-hourly model
output (Fig. S2).
Model evaluation against ozonesondes
Figure 8 shows a comparison of the ozonesonde measurements at Eureka to the
simulations from the 12 participating models for the annual and seasonal
averages for the years 2014–2015. In the Supplement (Fig. S2),
model–measurement comparisons at other Arctic locations are shown.
Generally, the models are highly variable, ranging ±50 % of the
measured O3 profiles for most seasons and locations. The MMM
performs well and is within ±8 % throughout most of the
troposphere. However, all models, except UKESM1, have a bulge with a high
model bias around 300–400 hPa, which is at or near the tropopause, implying
that most models simulate the tropopause height too low (having larger
stratospheric O3 concentrations appearing too low in altitude). This
results in a positive bias of about 20 % for the MMM around the
tropopause. This feature in models was also reported in AMAP (2015), where
model biases were particularly large at Ny-Ålesund and Summit. They
associated those with differences in the transport of air masses from the
stratosphere. This issue will have an impact on estimating the tropospheric
O3 burden, which is a common climate diagnostic (Griffiths et al.,
2021).
At Alert, there are both surface and ozonesonde measurements, and we find
that the results in the lowest levels of the Alert ozonesonde comparisons
(Fig. S1) are consistent with the model biases found in Fig. 8 in that both
show the models underestimating winter and fall O3, overestimating
spring, and matching observations well in the summer at this location.
Note that the models' monthly average O3 values were used in this
comparison, which does not match the time of day and day of the week of the
ozonesonde measurements. However, when a careful time matching to 3-hourly
model output is carried out, the general features of the model biases remain
the same (Fig. S2), likely because of the lack of a strong diurnal cycle in
Arctic O3 and its relatively long lifetime in the free troposphere.
Mean vertical profiles of O3, CO, PAN, and NO2 (left)
measured in Alaska and Greenland from the NASA ATom missions during summer
2016, winter 2017, autumn 2017, and spring 2018 (horizontal lines indicate 1
standard deviation spread around mean values at each altitude). The (right)
MMM for the years 2014–2015 (with the MMM standard deviation as horizontal
lines). The observations appear as dashed lines in the right panels for
ease of comparing to the MMM.
The results of this model evaluation of the Arctic O3 vertical profiles
are consistent with Whaley et al. (2022), who compared the same model
simulations to TES O3 retrievals throughout the troposphere at lower
Arctic locations (∼60–70∘ N). They found models to
be biased low (around -10 %), though the TES measurements have been shown
to be biased high by about the same amount (+13 % bias in TES
measurements reported in Verstraeten et al., 2013). They also saw a small
positive shift in the model bias profile around 300 hPa. Finally,
the Whaley et al. (2022) study included O3, NOx, CH4, and CO
comparisons to the Atmosphere Chemistry Experiment (ACE) Fourier Transform
Spectrometer (FTS) satellite instrument, and those results also independently implied
that the modeled tropopause heights are too low.
Vertical distribution of O3 precursors
Intensive field measurement campaigns using aircraft provide the most
detailed observational constraint on vertical profiles of tropospheric
O3 precursors in the Arctic. While these datasets tend to provide
excellent spatial and temporal resolution measurements on a wide range of
species, they are episodic in nature and often cover only a period of a few
days to several weeks, flying in specific regions of the Arctic and often
targeting specific layers or plumes. For example, Ancellet et al. (2016)
examined aircraft, lidar, and ozonesonde data over Canada and Greenland
during the summer of 2008 POLARCAT campaigns (Law et al., 2014). This study
showed clear latitudinal and longitudinal variations in the origins of
sampled air masses based on back trajectories and O3 potential
vorticity (PV) correlations. While downward transport of O3 was
important over Greenland, air masses with higher O3 were attributed to
North American boreal fires over Canada. Transport of polluted air masses
from midlatitudes also contributed, for example from Asia north of 80∘ N.
The airborne NASA ATom (Atmospheric Tomography) mission (Wofsy et al., 2018;
Thompson et al., 2022) has undertaken extensive surveying of the global
troposphere. This includes repeated vertical profile measurements between 60 and 90∘ N providing useful insights into the
variation of O3 (Bourgeois et al., 2020) and its precursors through the
depth of the Arctic troposphere at different times of the year. Figure 9
shows these mean results and their standard deviation in the left-side
panels, while the equivalent MMM results are in the right-side panels. The
models' monthly mean results went into the MMM calculation, and the standard
deviation from the models is shown.
The results show that near-surface NO2 is greatly enhanced during
winter, associated with a longer NO2 lifetime and accumulation of
pollution in the Arctic haze. The MMM simulates the surface NO2
increase and the seasonality of the NO2 profiles reasonably well.
However, generally, the modeled NO2 is biased low in the tropospheric
profile, having average values of about 15 pptv in the 2–6 km range, whereas
the measurements are about 25 pptv on average. This underestimate is
consistent with that found at the surface by Whaley et al. (2022). PAN is
also enhanced at the surface in the winter and can thermally decompose in
the spring and summer to release NOx. The MMM generally overestimates
PAN (Fig. 9c–d) and does not simulate the same shape in vertical profiles.
For example, models are not able to simulate the wintertime surface-level
increase in PAN, and they have the inverse shape of the observed profile in
April–May. The best agreement is in summertime PAN (July–August), when the MMM
vertical profile better matches that of the observations. The underestimate
of NOx and the lack of winter surface increases in PAN by the models
may be a reason why the wintertime surface O3 concentrations in Sect. 5.2 and Fig. 5 were underestimated.
In line with ozonesonde data and previous airborne campaigns (AMAP, 2015),
ATom profiles also demonstrate a springtime enhancement in O3 extending
through the troposphere, with evidence of stratospheric influence in the
upper troposphere and lower O3 in the summertime lower troposphere. The
models capture that springtime O3 enhancement as well. Summer
enhancements in O3 precursors, such as CO and PAN in the
mid-troposphere, were also observed associated with the import of forest
fire and anthropogenic emissions from lower latitudes, as also seen during
POLARCAT in 2008. The models capture this feature for PAN, but less so for
CO. Indeed, most models underestimate CO. The annual mean MMM bias for
surface CO in the Northern Hemisphere has been reported to be -30 %
(Whaley et al., 2022). Figure 9 shows that below the tropopause, modeled
O3 is actually close to observed O3, despite the significant MMM
biases for CO, NOx, and PAN. Around the tropopause, the aircraft data
show the same issue that the ozonesonde data did – that models significantly overestimate
O3 near the tropopause.
Conclusions
Recent research on Arctic tropospheric O3 has resulted in improvements
to our understanding of this pollutant and GHG in the rapidly changing and
sensitive Arctic environment. We have shown in this study that Arctic
surface O3 seasonal cycles are different depending on whether sites are
near the coast, inland, or at high elevation. Coastal sites have springtime
minima due to halogen chemistry causing ODEs and show a maximum during the
winter. The inland locations near the Arctic Circle have quite consistent
seasonal cycles, with maxima in April and minima in August. While the
high-elevation sites that are less influenced by halogen chemistry than coastal
locations are more variable, Summit has a later maximum (May) and minimum
(September); Zeppelin has an earlier maximum (March) and minimum
(July).
Despite model development that has occurred since the 2015 AMAP assessment
report on ozone (AMAP, 2015) to add processes, improve parameterizations, and
increase resolution, among others, the resulting performance of the models remains
more or less the same in terms of model variability and biases compared to
measured O3 and O3 precursor species in the Arctic. Model results
for CO would improve if CO emissions from combustion were increased, as
suggested in the literature. It would also be useful to compare modeled OH
and VOCs in the Arctic, but that was beyond the scope of this study.
However, as Arctic O3 is limited by NOx availability, improvements
to CO and VOCs may not have a large effect on O3. Improvements to
modeled PAN and NOx are needed; however, sensitivity studies to
determine the cause of the model biases will be required to improve model
performance for those species. For surface O3 distributions in the
Arctic, models simulate background levels reasonably well (e.g., at the
high-elevation location of Summit), but surface bromine–halogen chemistry
needs to be included to simulate springtime surface O3 properly in
the Arctic. Except near the tropopause, models simulate O3 throughout
the vertical profile well, with the MMM performing best at ±8 %
depending on the location and altitude in the troposphere. Attention to
improving the height of the modeled tropopause and/or the
stratosphere–tropospheric exchange is still required since downward
transport of high stratospheric O3 concentrations is causing model
biases around 6 to 8 km (400 to 300 hPa) to be significantly large
(>20 %).
While they are logistically challenging, additional O3 measurements in
the Arctic, such as O3 deposition measurements, observations of
stratospheric–tropospheric exchange, and O3 concentrations in the
Siberian Arctic, together with long-term measurements of O3 precursors
(such as those performed at Zeppelin and Pallas), would be particularly
helpful to improve our understanding and modeling capabilities. This is
particularly important as climate change alters the chemistry and dynamics
of tropospheric O3 in the future.
Code and data availability
The surface monitoring datasets are available online. WDCGG for CH4:
https://gaw.kishou.go.jp/login/user (last access: 14 April 2022, Global Atmosphere Watch, 2022). EBAS
for European (EMEP) and several Arctic locations: http://ebas.nilu.no/
(last access: 14 April 2022, Norwegian Institute for Air Research, 2022). NAPS:
https://open.canada.ca/data/en/dataset/1b36a356-defd-4813-acea-47bc3abd859b
(last access: 14 April 2022, Environment and Climate Change Canada, 2022). The ozonesonde data were
obtained from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC)
at https://woudc.org and from the Network for Detection of Atmospheric Composition
Change (NDACC) at https://www.ndacc.org (last access: 4 January 2023, NDACC, 2023).
The model output files in NetCDF from the simulations used in this
project can be found here:
https://open.canada.ca/data/en/dataset/c9a333ea-b81c-4df3-9880-ea7c3daeb76f (last access: 4 January 2023, ECCC, 2023).
Some of the model codes are available online at the following locations.
CESM2: https://www.cesm.ucar.edu/models/cesm2/ (last access: 14 April 2022, UCAR, 2022). GEOS-Chem:
http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_12{#}12.3.2 (last access: 14 April 2022, Harvard University, 2022). GISS-E2.1:
https://www.giss.nasa.gov/tools/modelE/ (last access: 14 April 2022, NASA, 2022). Oslo CTM:
https://github.com/NordicESMhub/OsloCTM3 (last access: 14 April 2022, Section for Meteorology and
Oceanography, 2022). The other model codes may be available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-637-2023-supplement.
Author contributions
CHW, KSL, JLH, HS, SRA, JL, and JBP wrote the paper and created Figs. 3–9. RYC, JF, and XD provided the GEOS-Chem model output. JF, ST, and DT
edited and provided comments on the paper. JHC provided the DEHM model
output. GF, UI, and KT provided the GISS-E2.1 model output. MG and ST
provided the EMEP MSC-W model output. KSL, JCR, TO, and LM provided the
WRF-Chem model output. MD and NO provided the MRI-ESM2 model output. DAP
provided the CMAM model output. LP provided the CESM model output. RS
provided the OsloCTM model output. MAT provided the MATCH-SALSA model
output. SRA and STT provided the UKESM1 model output. DT provided the
Canadian ozonesonde measurements, and RK provided Finnish ozonesonde
measurements. MF and KvS provided the model strategy for this project. PE
and IP provided the Summit and Barrow datasets, and SS provided the
Karasjok and Tustervatn datasets. GB, GH, IB, TR, JP, and CT provided the ATom
datasets.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We wish to acknowledge Siyuan Wang and Kerri A. Pratt for their figure originally published
in PNAS, as well as Jeffrey Seabrook and James Whiteway for their figure originally
published in the Journal of Geophysical Research. We thank the ATom team for the original aircraft
measurements. The technicians and logistical support staff at the different
stations are gratefully acknowledged for their work, particularly Doug
Worthy for Alert data, Karin Sjöberg for the Esrange data, and Karri
Saarnio for the Pallas data.
Financial support
Makoto Deushi and Naga Oshima were supported by the Japan Society for the
Promotion of Science KAKENHI (grant numbers: JP18H03363, JP18H05292,
JP19K12312, JP20K04070 and JP21H03582), the Environment Research and
Technology Development Fund (JPMEERF20202003 and JPMEERF20205001) of the
Environmental Restoration and Conservation Agency of Japan, the Arctic
Challenge for Sustainability II (ArCS II) under program grant number
JPMXD1420318865, and a grant for the Global Environmental Research
Coordination System from the Ministry of the Environment, Japan (MLIT1753
and MLIT2253). Joakim Langner and Manu A. Thomas were supported by the
Swedish Environmental Protection Agency through contract NV-03174-20 and
the Swedish Clean Air and Climate research program. Svetlana Tsyro and
Michael Gauss have received support from the AMAP Secretariat and the EMEP
Trust Fund. Ulas Im received support from the Aarhus University
Interdisciplinary Centre for Climate Change (iClimate) OH fund (no.
2020-0162731), the FREYA project funded by the Nordic Council of Ministers
(grant agreement nos. MST-227-00036 and MFVM-2019-13476), and the EVAM-SLCF
funded by the Danish Environmental Agency (grant agreement no. MST-112-00298). Henrik Skov received funding from the Danish Ministry for Energy,
Climate and Utilities (grant agreement no. 2018-3767), the Danish
Environmental Agency (grant agreement no. MST-113-00140), and AMA. Kostas
Tsigaridis and Gregory Faluvegi received support from the NASA Modeling,
Analysis and Prediction Program (MAP). Steven T. Turnock received financial support from the Arctic Monitoring and
Assessment Programme. Kathy S. Law, Jean-Christophe Raut, Louis Marelle, and
Tatsuo Onishi (LATMOS) received support from the EU iCUPE (Integrating and
Comprehensive Understanding on Polar Environments) project (grant agreement
no. 689443) under the European Network for Observing our Changing Planet
(ERA-Planet) and from access to IDRIS HPC resources (GENCI allocation
A009017141) as well as the IPSL mesoscale computing center (CICLAD: Calcul Intensif
pour le CLimat, l'Atmosphère et la Dynamique) for model simulations.
Jesper Christensen (DEHM model) received support from the Danish Environmental Protection
Agency and Danish Energy Agency (DANCEA funds for Environmental Support to
the Arctic Region project: grant no. 2019-7975, grant no. MST-112- 00298,
grant no. TAS 4005-0153). Stephen R. Arnold and Steven T. Turnock both
received financial support from the Arctic Monitoring and
Assessment Programme. Stephen R. Arnold also received support from the
UK Natural Environment Research Council and Belmont Forum via the ACRoBEAR
project (grant NE/T013672/1). Joshua Fu received funding from the Oak Ridge
Leadership Computing Facility at the Oak Ridge National Laboratory, which is
supported by the Office of Science of the U.S. Department of Energy under
contract no. DE-AC05-00OR22725. The research by Irina Petropavlovskikh and
Peter Effertz was supported by NOAA cooperative agreements NA17OAR4320101
and NA22OAR4320151.
Review statement
This paper was edited by Radovan Krejci and reviewed by two anonymous referees.
References
Aas, W., Eckhardt, S., Fiebig, M., Platt, S. M., Solberg, S., Yttri, K. E.,
and Zwaaftink, C. G.: Monitoring of long-range transported air pollutants in
Norway. Annual Report 2020 (Norwegian Environment Agency, M-2072/2021),
(NILU report, 13/2021). Kjeller: NILU, 2021.Abbatt, J. P. D., Thomas, J. L., Abrahamsson, K., Boxe, C., Granfors, A., Jones, A. E., King, M. D., Saiz-Lopez, A., Shepson, P. B., Sodeau, J., Toohey, D. W., Toubin, C., von Glasow, R., Wren, S. N., and Yang, X.: Halogen activation via interactions with environmental ice and snow in the polar lower troposphere and other regions, Atmos. Chem. Phys., 12, 6237–6271, 10.5194/acp-12-6237-2012, 2012.Aliabadi, A. A., Staebler, R. M., and Sharma, S.: Air quality monitoring in communities of the Canadian Arctic during the high shipping season with a focus on local and marine pollution, Atmos. Chem. Phys., 15, 2651–2673, 10.5194/acp-15-2651-2015, 2015.Aliabadi, A. A., Thomas, J. L., Herber, A. B., Staebler, R. M., Leaitch, W. R., Schulz, H., Law, K. S., Marelle, L., Burkart, J., Willis, M. D., Bozem, H., Hoor, P. M., Köllner, F., Schneider, J., Levasseur, M., and Abbatt, J. P. D.: Ship emissions measurement in the Arctic by plume intercepts of the Canadian Coast Guard icebreaker Amundsen from the Polar 6 aircraft platform, Atmos. Chem. Phys., 16, 7899–7916, 10.5194/acp-16-7899-2016, 2016.
AMAP: Arctic Monitoring and Assessment Programme, Assessment 2015: Black
carbon and ozone as Arctic climate forcers, Technical report, AMAP, Oslo,
Norway, vii C 116 pp., 2015.AMAP: Arctic Monitoring and Assessment Programme, Assessment 2022:
short-lived climate forcers, Technical report, AMAP, Oslo, Norway,
https://www.amap.no/documents/doc/amap-assessment-2021-impacts-of-short-lived-climate-forcers-on-arctic-climate-air-quality-and-human-health/3614 (last access: 14 April 2022), in press, 2022.Ancellet, G., Daskalakis, N., Raut, J. C., Tarasick, D., Hair, J., Quennehen, B., Ravetta, F., Schlager, H., Weinheimer, A. J., Thompson, A. M., Johnson, B., Thomas, J. L., and Law, K. S.: Analysis of the latitudinal variability of tropospheric ozone in the Arctic using the large number of aircraft and ozonesonde observations in early summer 2008, Atmos. Chem. Phys., 16, 13341–13358, 10.5194/acp-16-13341-2016, 2016.Andersson, C., Alpfjord, H., Robertson, L., Karlsson, P. E., and Engardt, M.: Reanalysis of and attribution to near-surface ozone concentrations in Sweden during 1990–2013, Atmos. Chem. Phys., 17, 13869–13890, 10.5194/acp-17-13869-2017, 2017.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.Badia, A., Iglesias-Suarez, F., Fernandez, R. P., Cuevas, C.
A., Kinnison, D. E., Lamarque, J.-F., Griffiths, P. T., Tarasick, D. W., Liu, J., and Saiz-Lopez, A.: The role of natural halogens in global
tropospheric ozone chemistry and budget under different 21st century climate
scenarios, J. Geophys. Res.-Atmos., 126, e2021JD034859. 10.1029/2021JD034859, 2021.Bahramvash Shams, S., Walden, V. P., Petropavlovskikh, I., Tarasick, D., Kivi, R., Oltmans, S., Johnson, B., Cullis, P., Sterling, C. W., Thölix, L., and Errera, Q.: Variations in the vertical profile of ozone at four high-latitude Arctic sites from 2005 to 2017, Atmos. Chem. Phys., 19, 9733–9751, 10.5194/acp-19-9733-2019, 2019.Barrie, L., Bottenheim, J., Schnell, R. Crutzen, P. J., and Rasmussen, R. A.:
Ozone destruction and photochemical reactions at polar sunrise in the lower
Arctic atmosphere, Nature, 334, 138–141, 10.1038/334138a0,
1988.Barletta, B., Meinardi, S., Sherwood Rowland, F., Chan, C.-Y., Wang, X.,
Zou, S., Yin Chan, L., and Blake, D. R.: Volatile organic compounds in 43
Chinese cities, Atmos. Environ., 39, 5979–5990,
10.1016/j.atmosenv.2005.06.029, 2005.Barten, J. G. M., Ganzeveld, L. N., Steeneveld, G.-J., and Krol, M. C.: Role of oceanic ozone deposition in explaining temporal variability in surface ozone at High Arctic sites, Atmos. Chem. Phys., 21, 10229–10248, 10.5194/acp-21-10229-2021, 2021.Beine, H. J., Jaffe, D. a., Herring, J. a., Kelley, J. a., Krognes, T., and
Stordal, F.: High-Latitude Springtime Photochemistry. Part I: NOx, PAN
and Ozone Relationships, J. Atmos. Chem., 27, 127–153,
10.1023/A:1005869900567, 1997.
Beine, H. J. and Krognes, T.: The seasonal cycle of peroxyacetyl nitrate
(PAN) in the European Arctic, Atmos. Environ., 34, 933–940, 2000.Benavent, N., Mahajan, A.S., Li, Q., Cuevas, C. A., Schmale, J., Angot, H.,
Jokinen, T., Quéléver, L. L. J., Blechschmidt, A.-M., Zilker, B.,
Richter, A., Serna, J. A., Garcia-Nieto, D., Fernandez, R. P., Skov, H.,
Demitrascu, A., Pereira, P. S., Abrahamsson, L., Bucci, S., Duetsch, M.,
Stohl, A., Beck, I., Laurila, T., Blomquist, B., and Saiz-Lopez, A.: Substantial
contribution of iodine to Arctic ozone destruction, Nat. Geosci., 15,
770–773, 10.1038/s41561-022-01018-w, 2022.Berg, T., Sekkesaeter, S., Steinnes, E., Valdal, A. K., Wibetoe, G.:
Springtime depletion of mercury in the European Arctic as observed at
Svalbard, Sci. Total Environ., 304, 43–51, 10.1016/S0048-9697(02)00555-7, 2003.Bottenheim, J. W., Netcheva, S., Morin, S., and Nghiem, S. V.: Ozone in the boundary layer air over the Arctic Ocean: measurements during the TARA transpolar drift 2006–2008, Atmos. Chem. Phys., 9, 4545–4557, 10.5194/acp-9-4545-2009, 2009.Bourgeois, I., Peischl, J., Thompson, C. R., Aikin, K. C., Campos, T., Clark, H., Commane, R., Daube, B., Diskin, G. W., Elkins, J. W., Gao, R.-S., Gaudel, A., Hintsa, E. J., Johnson, B. J., Kivi, R., McKain, K., Moore, F. L., Parrish, D. D., Querel, R., Ray, E., Sánchez, R., Sweeney, C., Tarasick, D. W., Thompson, A. M., Thouret, V., Witte, J. C., Wofsy, S. C., and Ryerson, T. B.: Global-scale distribution of ozone in the remote troposphere from the ATom and HIPPO airborne field missions, Atmos. Chem. Phys., 20, 10611–10635, 10.5194/acp-20-10611-2020, 2020.Brooks, S., Moore, C., Lew, D., Lefer, B., Huey, G., and Tanner, D.: Temperature and sunlight controls of mercury oxidation and deposition atop the Greenland ice sheet, Atmos. Chem. Phys., 11, 8295–8306, 10.5194/acp-11-8295-2011, 2011.Burd, J. A., Peterson, P. K., Nghiem, S. V., Perovich, D. K., and Simpson, W.
R.: Snowmelt onset hinders bromine monoxide heterogeneous recycling in the
Arctic, J. Geophys. Res. Atmos., 122, 8297–8309, 10.1002/2017JD026906,
2017.Christiansen, B., Jepsen, N., Kivi, R., Hansen, G., Larsen, N., and Korsholm, U. S.: Trends and annual cycles in soundings of Arctic tropospheric ozone, Atmos. Chem. Phys., 17, 9347–9364, 10.5194/acp-17-9347-2017, 2017.
Dastoor, A. P., Davignon, D., Theys, N., Van Roozendael, M., Steffen, A.,
and Ariya, P. A.: Modeling Dynamic Exchange of Gaseous Elemental Mercury at
Polar Sunrise, Environ. Sci. Technol., 42, 5183–5188, 2008.Emmons, L. K., Arnold, S. R., Monks, S. A., Huijnen, V., Tilmes, S., Law, K. S., Thomas, J. L., Raut, J.-C., Bouarar, I., Turquety, S., Long, Y., Duncan, B., Steenrod, S., Strode, S., Flemming, J., Mao, J., Langner, J., Thompson, A. M., Tarasick, D., Apel, E. C., Blake, D. R., Cohen, R. C., Dibb, J., Diskin, G. S., Fried, A., Hall, S. R., Huey, L. G., Weinheimer, A. J., Wisthaler, A., Mikoviny, T., Nowak, J., Peischl, J., Roberts, J. M., Ryerson, T., Warneke, C., and Helmig, D.: The POLARCAT Model Intercomparison Project (POLMIP): overview and evaluation with observations, Atmos. Chem. Phys., 15, 6721–6744, 10.5194/acp-15-6721-2015, 2015.ECCC (Environment and Climate Change Canada) AMAP SLCF model datasets [data set], https://open.canada.ca/data/en/dataset/c9a333ea-b81c-4df3-9880-ea7c3daeb76f, last access: 4 January 2023.Eckhardt, S., Hermansen, O., Grythe, H., Fiebig, M., Stebel, K., Cassiani, M., Baecklund, A., and Stohl, A.: The influence of cruise ship emissions on air pollution in Svalbard – a harbinger of a more polluted Arctic?, Atmos. Chem. Phys., 13, 8401–8409, 10.5194/acp-13-8401-2013, 2013.Eneroth, K., Holmén, K., Berg, T., Schmidbauer, N., and Solberg, S.:
Springtime depletion of tropospheric ozone, gaseous elemental mercury and
non-methane hydrocarbons in the European Arctic, and its relation to
atmospheric transport, Atmos. Environ., 41, 8511–8526,
10.1016/j.atmosenv.2007.07.008, 2007.Environment and Climate Change Canada (ECCC): NAPS dataset, ECCC [data set], https://open.canada.ca/data/en/dataset/1b36a356-defd-4813-acea-47bc3abd859b, last access: 14 April 2022.
Esau, I. and Sorokina, S.: Climatology of the Arctic Planetary Boundary
Layer, Chapter 1 in Atmospheric Turbulence, Meteorological Modeling, ISBN
978-1-60741-091-1, Eds: Peter R. Lang and Frank S. Lombargo, 2016.Falk, S. and Sinnhuber, B.-M.: Polar boundary layer bromine explosion and ozone depletion events in the chemistry–climate model EMAC v2.52: implementation and evaluation of AirSnow algorithm, Geosci. Model Dev., 11, 1115–1131, 10.5194/gmd-11-1115-2018, 2018.Fiore, A. M., West, J. J., Horowitz, L. W., Naik, V., and Schwarzkopf, M. D.:
Characterizing the tropospheric ozone response to methane emission controls
and the benefits to climate and air quality, J. Geophys. Res., 113, D08307,
10.1029/2007JD009162, 2008.Flanner, M. G., Huang, X., Chen, X., and Krinner, G.: Climate Response to
Negative Greenhouse Gas Radiative Forcing in Polar Winter, Geophys.
Res. Lett., 45, 1997–2004, 10.1002/2017GL076668, 2018.Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows,
J. P., Clerbaux, C., Coheur, P.-F., Cuesta, J., Cuevas, E., Doniki, S.,
Dufour, G., Ebojie, F., Foret, G., Garcia, O., Granados-Muñoz, M. J.,
Hannigan, J. W., Hase, F., Hassler, B., Huang, G., Hurtmans, D., Jaffe, D.,
Jones, N., Kalabokas, P., Kerridge, B., Kulawik, S., Latter, B., Leblanc,
T., Le Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E.,
McClure-Begley, A., Neu, J. L., Osman, M., Palm, M., Petetin, H.,
Petropavlovskikh, I., Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G.,
Schwab, J., Siddans, R., Smale, D., Steinbacher, M., Tanimoto, H., Tarasick,
D. W., Thouret, V., Thompson, A. M., Trickl, T., Weatherhead, E., Wespes,
C., Worden, H. M., Vigouroux, C., Xu, X., Zeng, G., and Ziemke, J.: Tropospheric
Ozone Assessment Report: Present-day distribution and trends of tropospheric
ozone relevant to climate and global atmospheric chemistry model evaluation.
Elem. Sci. Anth., 6, 39, 10.1525/elementa.291, 2018.Gautrois, M., Brauers, T., Koppmann, R., Rohrer, F., Stein, O., and Rudolph,
J.: Seasonal variability and trends of volatile organic compounds in the
lower polar troposphere, J. Geophys. Res., 108, 4393, 10.1029/2002JD002765, D13, 2003.Ghirardo A, Lindstein F, Koch K, Buegger, F., Schloter, M., Albert, A.,
Michelsen, A., Barbro Winkler, J., Schnitzler, J.-P., and Rinnan, R.: Origin of
volatile organic compound emissions from subarctic tundra under global
warming, Glob Change Biol., 26, 1908–1925, 10.1111/gcb.14935, 2020.Global Atmosphere Watch (GAW): WDCGG database for CH4 dataset, GAW [data set], https://gaw.kishou.go.jp/login/user, last access: 14 April 2022.Gong, W., Beagley, S. R., Cousineau, S., Sassi, M., Munoz-Alpizar, R., Ménard, S., Racine, J., Zhang, J., Chen, J., Morrison, H., Sharma, S., Huang, L., Bellavance, P., Ly, J., Izdebski, P., Lyons, L., and Holt, R.: Assessing the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions: current and future modelled scenarios, Atmos. Chem. Phys., 18, 16653–16687, 10.5194/acp-18-16653-2018, 2018.Guimbaud, C., Grannas, A. M., Shepson, P. B., Fuentes, J. D., Boudries, H.,
Bottenheim, J. W., Dominé, F., Houdier, S., Perrier, S., Biesenthal, T.
B., and Splawn, B. G.: Snowpack processing of acetaldehyde and acetone in the
Arctic atmospheric boundary layer, Atmos. Environ., 36, 2743–2752,
10.1016/S1352-2310(02)00107-3, 2002.Granier, C., Niemeier, U., Jungclaus, J. H., Emmons, L., Hess, P., Lamarque,
J.-F., Walters, S., and Brasseur, G. P.: Ozone pollution from future ship
traffic in the Arctic northern passages, Geophys. Res. Lett., 33, L13807,
10.1029/2006GL026180, 2006.Griffiths, P. T., Murray, L. T., Zeng, G., Shin, Y. M., Abraham, N. L., Archibald, A. T., Deushi, M., Emmons, L. K., Galbally, I. E., Hassler, B., Horowitz, L. W., Keeble, J., Liu, J., Moeini, O., Naik, V., O'Connor, F. M., Oshima, N., Tarasick, D., Tilmes, S., Turnock, S. T., Wild, O., Young, P. J., and Zanis, P.: Tropospheric ozone in CMIP6 simulations, Atmos. Chem. Phys., 21, 4187–4218, 10.5194/acp-21-4187-2021, 2021.Harrigan, D. L., Fuelberg, H. E., Simpson, I. J., Blake, D. R., Carmichael, G. R., and Diskin, G. S.: Anthropogenic emissions during Arctas-A: mean transport characteristics and regional case studies, Atmos. Chem. Phys., 11, 8677–8701, 10.5194/acp-11-8677-2011, 2011.Harvard University: GEOS-Chem model code, Harvard University [code], http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_12#12.3.2, last access: 14 April 2022.He, P., Bian, L., Zheng, X., Yu, J., Sun, C., Ye, P., and Xie, Z.:
Observation of surface ozone in the marine boundary layer along a cruise
through the Arctic Ocean: From offshore to remote, Atmos. Res.,
169, 191–198, 10.1016/j.atmosres.2015.10.009, 2016.
Hellén, H., Kouznetsov, R., Anttila, P., Hakola, H.: Increasing
influence of easterly air masses on NMHC concentrations at the
Pallas-Sodankyla GAW station, Boreal Environ. Res., 20, 542–552, 2015.Helmig, D., Oltmans, S. J., Carlson, D., Lamarque, J.-F., Jones, A.,
Labuschagne, C., Anlauf, K., and Hayden, K.: A review of surface ozone in the
polar regions, Atmos. Environ., 41, 5138–5161,
10.1016/j.atmosenv.2006.09.053, 2007.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, 2014.Herrmann, M., Cao, L., Sihler, H., Platt, U., and Gutheil, E.: On the contribution of chemical oscillations to ozone depletion events in the polar spring, Atmos. Chem. Phys., 19, 10161–10190, 10.5194/acp-19-10161-2019, 2019.Hess, P. G. and Zbinden, R.: Stratospheric impact on tropospheric ozone variability and trends: 1990–2009, Atmos. Chem. Phys., 13, 649–674, 10.5194/acp-13-649-2013, 2013.Hirdman, D., Sodemann, H., Eckhardt, S., Burkhart, J. F., Jefferson, A., Mefford, T., Quinn, P. K., Sharma, S., Ström, J., and Stohl, A.: Source identification of short-lived air pollutants in the Arctic using statistical analysis of measurement data and particle dispersion model output, Atmos. Chem. Phys., 10, 669–693, 10.5194/acp-10-669-2010, 2010.Holst, T., Arneth, A., Hayward, S., Ekberg, A., Mastepanov, M., Jackowicz-Korczynski, M., Friborg, T., Crill, P. M., and Bäckstrand, K.: BVOC ecosystem flux measurements at a high latitude wetland site, Atmos. Chem. Phys., 10, 1617–1634, 10.5194/acp-10-1617-2010, 2010.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,
10.1029/1999GL900077, 1999.Hornbrook, R. S., Hills, A. J., Riemer, D. D., Abdelhamid, A., Flocke, F.
M., Hall, S. S., Huey, L. G., Knapp, D. J., Liao, J., Mauldin III, R. L.,
Montzka, D., D., Orlando, J. J., Shepson, P. B., Dive, B., Staibler, R. M.,
Tanner, D., J., Thompson, C. R., Turnipseed, A., Ullmann, K., Weinheimer, A.
J., and Apel, E. C.: Arctic springtime observations of volatile organic
compounds during the OASIS-2009 campaign, J. Geophys. Res.-Atmos., 121,
9789–9813, 10.1002/2015JD024360, 2016.Huang, Y., Wu, S., Kramer, L. J., Helmig, D., and Honrath, R. E.: Surface ozone and its precursors at Summit, Greenland: comparison between observations and model simulations, Atmos. Chem. Phys., 17, 14661–14674, 10.5194/acp-17-14661-2017, 2017.Huang, J., Jaeglé, L., Chen, Q., Alexander, B., Sherwen, T., Evans, M. J., Theys, N., and Choi, S.: Evaluating the impact of blowing-snow sea salt aerosol on springtime BrO and O3 in the Arctic, Atmos. Chem. Phys., 20, 7335–7358, 10.5194/acp-20-7335-2020, 2020.Ianniello, A., Salzano, R., Salvatori, R., Esposito, G., Spataro, F.,
Montagnoli, M., Mabilia, R., and Pasini, A.: Nitrogen Oxides (NOx) in
the Arctic Troposphere at Ny-Ålesund (Svalbard Islands): Effects of
Anthropogenic Pollution Sources, Atmosphere, 12, 901,
10.3390/atmos12070901, 2021.Ikeda, K., Tanimoto, H., Sugita, T., Akiyoshi, H., Clerbaux, C., and Coheur,
P.-F.: Model and satellite analysis of transport of Asian anthropogenic
pollution to the Arctic: Siberian and Pacific pathways and their
meteorological controls, J. Geophys. Res.-Atmos., 126,
e2020JD033459, 10.1029/2020JD033459, 2021.IPCC: Climate Change 2021: The Physical Science Basis, Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani,
A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb,
L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Tech.
rep., Cambridge University Press,
https://www.ipcc.ch/report/ar6/wg1/{#}FullReport (last access: 14 April
2022), 2021.
Isaksen, I. S. A., Berntsen, T. K., Dalsoren, S. B., Eleftheratos, K.,
Orsolini, Y., Rognerud, B., Stordal, F., Sovde, O. A., Zerefos, C., and Holmes,
C. D.: Atmospheric Ozone and Methane in a Changing Climate, Atmosphere,
518–535, 2014.Jacobi, H.-W., Morin, S., and Bottenheim, J. W.: Observation of widespread
depletion of ozone in the springtime boundary layer of the central Arctic
linked to mesoscale synoptic conditions, J. Geophys. Res, 115, D17302,
10.1029/2010JD013940, 2010.Kanaya, Y., Miyazaki, K., Taketani, F., Miyakawa, T., Takashima, H., Komazaki, Y., Pan, X., Kato, S., Sudo, K., Sekiya, T., Inoue, J., Sato, K., and Oshima, K.: Ozone and carbon monoxide observations over open oceans on R/V Mirai from 67∘ S to 75∘ N during 2012 to 2017: testing global chemical reanalysis in terms of Arctic processes, low ozone levels at low latitudes, and pollution transport, Atmos. Chem. Phys., 19, 7233–7254, 10.5194/acp-19-7233-2019, 2019.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.Jiang, Z., Jones, D. B. A., Worden, J., Worden, H. M., Henze, D. K., and Wang, Y. X.: Regional data assimilation of multi-spectral MOPITT observations of CO over North America, Atmos. Chem. Phys., 15, 6801–6814, 10.5194/acp-15-6801-2015, 2015.Jiang, Z., Worden, J. R., Payne, V. H., Zhu, L., Fischer, E., Walker, T.,
and Jones, D. B. A.: Ozone export from East Asia: The role of PAN, J. Geophys.
Res.-Atmos., 121, 6555–6563, 10.1002/2016JD024952, 2016.Kasibhatla, P., Arellano, A., Logan, J. A., Palmer, P. I., and Novelli, P.:
Top-down estimate of a large source of atmospheric carbon monoxide
associated with fuel combustion in Asia, Geophys. Res. Lett., 29, 1900,
10.1029/2002GL015581, 2002.Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P., Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of particulate matter including black carbon, Atmos. Chem. Phys., 17, 8681–8723, 10.5194/acp-17-8681-2017, 2017.Law, K. S., Roiger, A., Thomas, J., L., Marelle, L., Raut, J.-C., Dalsoren,
S., Fuglestvedt, J., Tuccella, P., Weinzierl, B., and Schlager, H.: Local Arctic
air pollution: Sources and impacts, Ambio, 46, 453–463,
10.1007/s13280-017-0962-2, 2017.Lawrence, C. and Mao, H.: Anthropogenic and Natural Factors Affecting Trends
in Atmospheric Methane in Barrow, Alaska, Atmosphere, 10, 187,
10.3390/atmos10040187, 2019.Lehrer, E., Wagenbach, D., and Platt, U.: Aerosol chemical composition
during tropospheric ozone depletion at Ny Ålesund/Svalbard, Tellus B, 49, 486–495, 10.3402/tellusb.v49i5.15987, 1997.Li, C., Hsu, N., Sayer, A., Krotkov, N., Fu, J., Lamsal, L. N., Lee, J.,
and Tsay, S.-C.: Satellite observation of pollutant emissions from gas flaring
activities near the Arctic, Atmos. Environ., 133, 1–11,
10.1016/j.atmosenv.2016.03.019, 2016.Liang, Q., Douglass, A. R., Duncan, B. N., Stolarski, R. S., and Witte, J. C.: The governing processes and timescales of stratosphere-to-troposphere transport and its contribution to ozone in the Arctic troposphere, Atmos. Chem. Phys., 9, 3011–3025, 10.5194/acp-9-3011-2009, 2009.
Lorenzen-Schmidt, H., Wessel, S., Unold, W., Solberg, S., Gernandt, H.,
Stordal, F., and Platt, U.: Ozone measurements in the European Arctic during
the ARCTOC 1995 campaign, Tellus B,
50, 416–429, 1998.Mackie, A. R., Palmer, P.I., Barlow, J. M., Finch, D. P., Novelli, P., and
Jaeglé, L.: Reduced Arctic air pollution due to decreasing European and
North American emissions, J. Geophys. Res.-Atmos., 121, 8692–8700,
10.1002/2016JD024923, 2016.Marelle, L., Thomas, J. L., Raut, J.-C., Law, K. S., Jalkanen, J.-P., Johansson, L., Roiger, A., Schlager, H., Kim, J., Reiter, A., and Weinzierl, B.: Air quality and radiative impacts of Arctic shipping emissions in the summertime in northern Norway: from the local to the regional scale, Atmos. Chem. Phys., 16, 2359–2379, 10.5194/acp-16-2359-2016, 2016.Marelle, L., Raut, J.-C., Law, K., and Duclaux, O.: Current and future
arctic aerosols and ozone from remote emissions and emerging local
sources—Modeled source contributions and radiative effects, J. Geophys. Res.-Atmos., 123, 12942–12963.
10.1029/2018JD028863, 2018.Marelle, L., Thomas, J. L., Ahmed, S., Tuite, K., Stutz, J., Dommergue, A.,
Simpson, W. R., Frey, M. M., and Baladima, F.: Implementation and impacts of
surface and blowing snow sources of Arctic bromine activation within
WRF-Chem 4.1.1, J. Adv. Model. Earth Sy., 13, e2020MS002391,
10.1029/2020MS002391, 2021.
McNamara, S. M., Raso, A. R. W., Wang, S. Y., Thanekar, S., Boone, E. J.,
Kolesar, K. R., Peterson, P. K., Simpson, W. R., Fuentes, J. D., Shepson, P.
B., and Pratt, K. A.: Springtime Nitrogen Oxide-Influenced Chlorine
Chemistry in the Coastal Arctic, Environ. Sci. Technol.,
53, 8057–8067, 2019.
Monks, P.: A review of the observations and origins of the spring ozone
maximum, Atmos. Environ., 34, 3545–3561, 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.Mungall, E., Abbatt, J., Wentzell, J., Lee, A., Thomas, J., Blais, M.,
Gosselin, M., Miller, L., Papakyriakou, T., Willis, M., and Liggio, J.:
Microlayer source of oxygenated volatile organic compounds in the summertime
marine Arctic boundary layer. P. Natl. Acad. Sci. USA, 114, 201620571, 10.1073/pnas.1620571114, 2017.Miyazaki, K., Bowman, K. W., Yumimoto, K., Walker, T., and Sudo, K.: Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis, Atmos. Chem. Phys., 20, 931–967, 10.5194/acp-20-931-2020, 2020.NASA: GISS-E2.1 model code, NASA [code], https://www.giss.nasa.gov/tools/modelE/ last access: 14 April 2022.NDACC (Network for Detection of Atmospheric Composition Change) sonde data, [data set], https://ndacc.larc.nasa.gov/, last access: 4 January 2023.Nerentorp Mastromonaco, M., Gardfeldt, K., Jourdain, B., Abrahamsson, K.,
Granfors, A., Ahnoff, M., Dommergue, A., Méjean, G., and Jacobi, H.-W.:
Antarctic winter mercury and ozone depletion events over sea ice, Atmos.
Environ., 129, 125–132, 10.1016/j.atmosenv.2016.01.023, 2016.Norwegian Institute for Air Research (NILU): EBAS database, http://ebas.nilu.no/, last access: 14 April 2022.Olivié, D., Höglund-Isaksson, L., Klimont, Z., and von Salzen, K.:
Boxmodel for calculation of global atmospheric methane concentration,
Zenodo, 10.5281/zenodo.5293940, 2021.Oltmans, S. J. and Komhyr, W. D.: Surface ozone distributions and
variations from 1973–1984: Measurements at the NOAA Geophysical Monitoring
for Climatic Change Baseline Observatories, J. Geophys. Res., 91, 5229–5236, 10.1029/JD091iD04p05229, 1986.Osman, M. K., Tarasick, D. W., Liu, J., Moeini, O., Thouret, V., Fioletov, V. E., Parrington, M., and Nédélec, P.: Carbon monoxide climatology derived from the trajectory mapping of global MOZAIC-IAGOS data, Atmos. Chem. Phys., 16, 10263–10282, 10.5194/acp-16-10263-2016, 2016.Parrella, J. P., Jacob, D. J., Liang, Q., Zhang, Y., Mickley, L. J., Miller, B., Evans, M. J., Yang, X., Pyle, J. A., Theys, N., and Van Roozendael, M.: Tropospheric bromine chemistry: implications for present and pre-industrial ozone and mercury, Atmos. Chem. Phys., 12, 6723–6740, 10.5194/acp-12-6723-2012, 2012.Pernov, J. B., Bossi, R., Lebourgeois, T., Nøjgaard, J. K., Holzinger, R., Hjorth, J. L., and Skov, H.: Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment, Atmos. Chem. Phys., 21, 2895–2916, 10.5194/acp-21-2895-2021, 2021.Peterson, P. K., Pratt, K. A., Simpson, W. R., Nghiem, S. V., Pérez
Pérez, L. X., Boone, E. J., Pöhler, D., Zielcke, J., General, S.,
Shepson, P. B., Frieß, U., Platt, U., and Stirm, B. H.: The role of open
lead interactions in atmospheric ozone variability between Arctic coastal
and inland sites, Elem. Sci. Anth., 4, 000109, 10.12952/journal.elementa.000109, 2016.Peterson, P. K., Pöhler, D., Sihler, H., Zielcke, J., General, S., Frieß, U., Platt, U., Simpson, W. R., Nghiem, S. V., Shepson, P. B., Stirm, B. H., Dhaniyala, S., Wagner, T., Caulton, D. R., Fuentes, J. D., and Pratt, K. A.: Observations of bromine monoxide transport in the Arctic sustained on aerosol particles, Atmos. Chem. Phys., 17, 7567–7579, 10.5194/acp-17-7567-2017, 2017.Peterson, P. K., Pöhler, D., Zielcke, J., General, S.,
Frieß, U., Platt, U., Simpson, W. R., Nghiem, S. V., Shepson, P. B.,
Stirm, B. H., and Pratt, K. A.: Springtime Bromine Activation over Coastal
and Inland Arctic Snowpacks ACS, Earth Space Chem., 2, 1075–1086, 10.1021/acsearthspacechem.8b00083, 2018.Peterson, P. K., Hartwig, M., May, N. W., Schwartz, E., Rigor, I., Ermold,
W., Steele, M., Morison, J. H., Nghiem, S. V., and Pratt, K. A..: Snowpack
measurements suggest role for multi-year sea ice regions in Arctic
atmospheric bromine and chlorine chemistry, Elem Sci. Anth., 7, 14,
10.1525/elementa.352, 2019.Pétron, G., Granier, C., Khattatov, B., Lamarque, J.-F., Yudin, V.,
Muller, J.-F., and Gille, J.: Inverse modeling of carbon monoxide surface
emissions using Climate Monitoring and Diagnostics Laboratory network
observations, J. Geophys. Res.-Atmos., 107, D24,
10.1029/2001JD001305, 2002.Pittman, J. V., Pan, L. L., Wei, J. C., Irion, F. W., Liu, X., Maddy, E.,
S., Barnet, C., D., Chance, K., and Gao, R.-S.: Evaluation of AIRS, IASI,
and OMI ozone profile retrievals in the extratropical tropopause region
using in situ aircraft measurements, J. Geophys. Res., 114, D24109,
10.1029/2009JD012493, 2009.Platt, S. M., Hov, Ø., Berg, T., Breivik, K., Eckhardt, S., Eleftheriadis, K., Evangeliou, N., Fiebig, M., Fisher, R., Hansen, G., Hansson, H.-C., Heintzenberg, J., Hermansen, O., Heslin-Rees, D., Holmén, K., Hudson, S., Kallenborn, R., Krejci, R., Krognes, T., Larssen, S., Lowry, D., Lund Myhre, C., Lunder, C., Nisbet, E., Nizzetto, P. B., Park, K.-T., Pedersen, C. A., Aspmo Pfaffhuber, K., Röckmann, T., Schmidbauer, N., Solberg, S., Stohl, A., Ström, J., Svendby, T., Tunved, P., Tørnkvist, K., van der Veen, C., Vratolis, S., Yoon, Y. J., Yttri, K. E., Zieger, P., Aas, W., and Tørseth, K.: Atmospheric composition in the European Arctic and 30 years of the Zeppelin Observatory, Ny-Ålesund, Atmos. Chem. Phys., 22, 3321–3369, 10.5194/acp-22-3321-2022, 2022.Pommier, M., Clerbaux, C., Law, K. S., Ancellet, G., Bernath, P., Coheur, P.-F., Hadji-Lazaro, J., Hurtmans, D., Nédélec, P., Paris, J.-D., Ravetta, F., Ryerson, T. B., Schlager, H., and Weinheimer, A. J.: Analysis of IASI tropospheric O3 data over the Arctic during POLARCAT campaigns in 2008, Atmos. Chem. Phys., 12, 7371–7389, 10.5194/acp-12-7371-2012, 2012.Pope, R. J., Richards, N. A. D., Chipperfield, M. P., Moore, D. P., Monks, S. A., Arnold, S. R., Glatthor, N., Kiefer, M., Breider, T. J., Harrison, J. J., Remedios, J. J., Warneke, C., Roberts, J. M., Diskin, G. S., Huey, L. G., Wisthaler, A., Apel, E. C., Bernath, P. F., and Feng, W.: Intercomparison and evaluation of satellite peroxyacetyl nitrate observations in the upper troposphere–lower stratosphere, Atmos. Chem. Phys., 16, 13541–13559, 10.5194/acp-16-13541-2016, 2016.Prather, M. J., Holmes, C. D., and Hsu, J.: Reactive greenhouse gas
scenarios: Systematic exploration of uncertainties and the role of
atmospheric chemistry, Geophys. Res. Lett., 39, L09803,
10.1029/2012GL051440, 2012.
Salmi, T., Määttä, A., Anttila, P., Ruoho-Airola, T., and
Amnell, T.: Detecting trends of annual values of atmospheric pollutants by
the Mann-Kendall test and Sen's slope estimates – the Excel template
application Makesens, Finnish Meteorological Institute, Helsinki, Finland,
ISBN: 9516975631, 9789516975637, 2002.Rap, A., Richards, N. A. D., Forster, P. M., Monks, S. A., Arnold, S. R.,
and Chipperfield, M. P.: Satellite constraint on the tropospheric ozone
radiative effect, Geophys. Res. Lett., 42, 5074–5081,
10.1002/2015GL064037, 2015.Raut, J.-C., Law, K. S., Onishi, T., Daskalakis, N., and Marelle, L.: Impact of
shipping emissions on air pollution and pollutant deposition over the
Barents Sea, Environ. Pollut., 298, 118832,
10.1016/j.envpol.2022.118832, 2022.Sand, M., Berntsen, T. K., von Salzen, K., Flanner, M. G., Langner, J., and
Victor, D. G.: Response of arctic temperature to changes in emissions of
short-lived climate forcers, Nat. Clim. Change, 6, 286–289, 10.1038/nclimate2880, 2015.Seabrook, J. and Whiteway, J.: Influence of mountains on Arctic
tropospheric ozone, J. Geophys. Res.-Atmos., 121, 1935–1942,
10.1002/2015JD024114, 2016.Section for Meteorology and Oceanography (MetOs): OsloCTM model code, Github [code], https://github.com/NordicESMhub/OsloCTM3, last access: 14 April 2022.
Sharma, S., Barrie, L. A., Magnusson, E., Brattstrom, G., Leaitch, W. R.,
Steffen, A., and Landsberger, S.: A Factor and Trends Analysis of
Multidecadal Lower Tropospheric Observations of Arctic Aerosol Composition,
Black Carbon, Ozone, and Mercury at Alert, Canada, J. Geophys. Res.-Atmos., 124, 14133–14161, 2019.Shapiro, M. A., Hampel, T., and Krueger, A. J.: The Arctic tropopause fold,
Mon. Wea. Rev., 115, 444–454, 10.1175/1520-0493, 1987.
Shaw, G. E.: The Arctic Haze Phenomenon, B. Am. Meteor. Soc., 76, 2403–2414, 1995.Shindell, D.: Local and remote contributions to Arctic warming, Geophys.
Res. Lett., 34, L14704, 10.1029/2007GL030221, 2007.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.Simpson, W. R., von Glasow, R., Riedel, K., Anderson, P., Ariya, P., Bottenheim, J., Burrows, J., Carpenter, L. J., Frieß, U., Goodsite, M. E., Heard, D., Hutterli, M., Jacobi, H.-W., Kaleschke, L., Neff, B., Plane, J., Platt, U., Richter, A., Roscoe, H., Sander, R., Shepson, P., Sodeau, J., Steffen, A., Wagner, T., and Wolff, E.: Halogens and their role in polar boundary-layer ozone depletion, Atmos. Chem. Phys., 7, 4375–4418, 10.5194/acp-7-4375-2007, 2007.Simpson, W. R., Frieß, U., Thomas, J. L., Lampel, J., and Platt, U.:
Polar nighttime chemistry produces intense reactive bromine events.
Geophysical Research Letters, 45, 9987–9994. 10.1029/2018GL079444, 2018.
Skov, H., Christensen, J., Goodsite, M. E., Heidam, N. Z., Jensen, B., Wåhlin,
P., and Geernaert, G.: The fate of elemental mercury in Arctic during
atmospheric mercury depletion episodes and the load of atmospheric mercury
to Arctic, Environ. Sci. Technol., 38, 2373–2382, 2004.
Skov, H., Brooks, S., Goodsite, M. E., Lindberg, S. E., Meyers, T. P., Landis, M. S., Larsen, M. R. B., Jensen, B., McConville, G., and Christensen, J.: Measuring reactive
gaseous mercury flux by relaxed eddy accumulation, Atmos. Environ, 40,
5452–5463, 2006.Skov, H., Hjorth, J., Nordstrøm, C., Jensen, B., Christoffersen, C., Bech Poulsen, M., Baldtzer Liisberg, J., Beddows, D., Dall'Osto, M., and Christensen, J. H.: Variability in gaseous elemental mercury at Villum Research Station, Station Nord, in North Greenland from 1999 to 2017, Atmos. Chem. Phys., 20, 13253–13265, 10.5194/acp-20-13253-2020, 2020.Sodemann, H., Pommier, M., Arnold, S. R., Monks, S. A., Stebel, K., Burkhart, J. F., Hair, J. W., Diskin, G. S., Clerbaux, C., Coheur, P.-F., Hurtmans, D., Schlager, H., Blechschmidt, A.-M., Kristjánsson, J. E., and Stohl, A.: Episodes of cross-polar transport in the Arctic troposphere during July 2008 as seen from models, satellite, and aircraft observations, Atmos. Chem. Phys., 11, 3631–3651, 10.5194/acp-11-3631-2011, 2011.Solberg, S., Schmidbauer, N., Semb, A., Stordal, F., and Hov, Ø.:
Boundary-layer ozone depletion as seen in the Norwegian Arctic in spring, J.
Atmos. Chem., 23, 301-332, 10.1007/BF00055158, 1996.Steffen, A., Douglas, T., Amyot, M., Ariya, P., Aspmo, K., Berg, T., Bottenheim, J., Brooks, S., Cobbett, F., Dastoor, A., Dommergue, A., Ebinghaus, R., Ferrari, C., Gardfeldt, K., Goodsite, M. E., Lean, D., Poulain, A. J., Scherz, C., Skov, H., Sommar, J., and Temme, C.: A synthesis of atmospheric mercury depletion event chemistry in the atmosphere and snow, Atmos. Chem. Phys., 8, 1445–1482, 10.5194/acp-8-1445-2008, 2008.Stevenson, D. S., Young, P. J., Naik, V., Lamarque, J.-F., Shindell, D. T., Voulgarakis, A., Skeie, R. B., Dalsoren, S. B., Myhre, G., Berntsen, T. K., Folberth, G. A., Rumbold, S. T., Collins, W. J., MacKenzie, I. A., Doherty, R. M., Zeng, G., van Noije, T. P. C., Strunk, A., Bergmann, D., Cameron-Smith, P., Plummer, D. A., Strode, S. A., Horowitz, L., Lee, Y. H., Szopa, S., Sudo, K., Nagashima, T., Josse, B., Cionni, I., Righi, M., Eyring, V., Conley, A., Bowman, K. W., Wild, O., and Archibald, A.: Tropospheric ozone changes, radiative forcing and attribution to emissions in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 3063–3085, 10.5194/acp-13-3063-2013, 2013.Stohl, A.: Characteristics of atmospheric transport into the Arctic
troposphere, J. Geophys. Res., 111, D11306, 10.1029/2005JD006888, 2006.Swanson, W. F., Holmes, C. D., Simpson, W. R., Confer, K., Marelle, L., Thomas, J. L., Jaeglé, L., Alexander, B., Zhai, S., Chen, Q., Wang, X., and Sherwen, T.: Comparison of model and ground observations finds snowpack and blowing snow aerosols both contribute to Arctic tropospheric reactive bromine, Atmos. Chem. Phys., 22, 14467–14488, 10.5194/acp-22-14467-2022, 2022.Tarasick, D. W. and Bottenheim, J. W.: Surface ozone depletion episodes in the Arctic and Antarctic from historical ozonesonde records, Atmos. Chem. Phys., 2, 197–205, 10.5194/acp-2-197-2002, 2002.
Tarasick, D. W., Wardle, D. I., Kerr, J. B., Bellfleur, J. J., and Davies, J.:
Tropospheric ozone trends over Canada: 1980-1993, Geophys. Res. Lett., 22,
4, 409–412, 1995.Tarasick, D. W., Carey-Smith, T. K., Hocking, W. K., Moeini, O., He, H.,
Liu, J., Osman, M., Thompson, A. M., Johnson, B., Oltmans, S. J., and
Merrill, J. T.: Quantifying stratosphere-troposphere transport of ozone
using balloon-borne ozonesondes, radar windprofilers and trajectory models,
Atmos. Environ., 198, 496–509,
10.1016/j.atmosenv.2018.10.040, 2019a.Tarasick, D., Galbally, I. E., Cooper, O. R., Schultz, M. G., Ancellet, G.,
Leblanc, T., Wallington, T. J., Ziemke, J., Liu, X., Steinbacher, M.,
Staehelin, J., Vigouroux, C., Hannigan, J.W., García, O., Foret, G.,
Zanis, P., Weatherhead, E., Petropavlovskikh, I., Worden, H., Osman, M.,
Liu, J., Chang, K.-L., Gaudel, A., Lin, M., Granados-Muñoz, M.,
Thompson, A. M., Oltmans, S. J., Cuesta, J., Dufour, G., Thouret, V.,
Hassler, B., Trickl, T., and Neu, J. L.: Tropospheric Ozone Assessment Report:
Tropospheric ozone from 1877 to 2016, observed levels, trends and
uncertainties, Elem. Sci. Anth., 7, 39,
10.1525/elementa.376, 2019b.Thomas, J. L., Raut, J.-C., Law, K. S., Marelle, L., Ancellet, G., Ravetta, F., Fast, J. D., Pfister, G., Emmons, L. K., Diskin, G. S., Weinheimer, A., Roiger, A., and Schlager, H.: Pollution transport from North America to Greenland during summer 2008, Atmos. Chem. Phys., 13, 3825–3848, 10.5194/acp-13-3825-2013, 2013.Thomas, M. A., Devasthale, A., and Nygård, T.: Influence of springtime atmospheric circulation types on the distribution of air pollutants in the Arctic, Atmos. Chem. Phys., 21, 16593–16608, 10.5194/acp-21-16593-2021, 2021.Thompson, C. R., Wofsy, S. C., Prather, M. J., Newman, P. A., Hanisco, T. F., Ryerson, T. B., Fahey, D. W., Apel, E. C., Brock, C. A., Brune, W. H., Froyd, K., Katich, J. M., Nicely, J. M., Peischl, J., Ray, E., Veres, P. R., Wang, S., Allen, H. M., Asher, E., Bian, H., Blake, D., Bourgeois, I., Budney, J., Bui, T., P., Butler, A., Campuzano-Jost, P., Chang, C., Chin, M., Commane, R., Correa, G., Crounse, J. D., Daube, B., Dibb, J. E., DiGangi, J. P., Diskin, G. S., Dollner, M., Elkins, J. W., Fiore, A. M., Flynn, C. M., Guo, H., Hall, S. R., Hannun, R. A., Hills, A., Hintsa, E. J., Hodzic, A., Hornbrook, R. S., Huey, L. G., Jimenez, J. L., Keeling, R. F., Kim, M. J., Kupc, A., Lacey, F., Lait, L. R., Lamarque, J.-F., Liu, J., McKain, K., Meinardi, S., Miller, D. O., Montzka, S. A., Moore, F. L., Morgan, E. J., Murphy, D. M., Murray, L. T., Nault, B. A., Neuman, J. A., Nguyen, L., Gonzalez, Y., Rollins, A., Rosenlof, K., Sargent, M., Schill, G., Schwarz, J. P., St. Clair, J. M., Steenrod, S. D., Stephens, B. B., Strahan, S. E., Strode, S. A., Sweeney, C., Thames, A. B., Ullmann, K., Wagner, N., Weber, N., Weinzierl, B., Wennberg, P. O., Williamson, C. J., Wolfe, G. M., and Zeng, L.: The NASA Atmospheric Tomography (ATom)
Mission: Imaging the Chemistry of the Global Atmosphere, Am.
Meteorol. Soc., 103, 3, 761–790,
10.1175/BAMS-D-20-0315.1, 2022.Thorp, T., Arnold, S. R., Pope, R. J., Spracklen, D. V., Conibear, L., Knote, C., Arshinov, M., Belan, B., Asmi, E., Laurila, T., Skorokhod, A. I., Nieminen, T., and Petäjä, T.: Late-spring and summertime tropospheric ozone and NO2 in western Siberia and the Russian Arctic: regional model evaluation and sensitivities, Atmos. Chem. Phys., 21, 4677–4697, 10.5194/acp-21-4677-2021, 2021.Toyota, K., McConnell, J. C., Lupu, A., Neary, L., McLinden, C. A., Richter, A., Kwok, R., Semeniuk, K., Kaminski, J. W., Gong, S.-L., Jarosz, J., Chipperfield, M. P., and Sioris, C. E.: Analysis of reactive bromine production and ozone depletion in the Arctic boundary layer using 3-D simulations with GEM-AQ: inference from synoptic-scale patterns, Atmos. Chem. Phys., 11, 3949–3979, 10.5194/acp-11-3949-2011, 2011.Tuccella, P., Thomas, J. L., Law, K. S., Raut, J.-C., Marelle, L., Roiger,
A., Weinzierl, B., Denier van der Gon, H. A. C., Schlager, H., Onishi, T.:
Air pollution impacts due to petroleum extraction in the Norwegian Sea
during the ACCESS aircraft campaign, Elementa, 5, 25, 10.1525/elementa.124, 2017.Turnock, S. T., Wild, O., Sellar, A., and O'Connor, F. M.: 300 years of
tropospheric ozone changes using CMIP6 scenarios with a parameterised
approach, Atmos. Environ., 213, 686–698, 10.1016/j.atmosenv.2019.07.001,
2019.UCAR: CESM2 model code, UCAR [code], https://www.cesm.ucar.edu/models/cesm2/, last access: 14 April 2022.
U.S. EPA: (Environmental Protection Agency): Integrated Science Assessment
for Ozone and Related Photochemical Oxidants. EPA/600/R-10/076F, Office of
Research and Development, Research Triangle Park, NC (February), 2013.Van Dam, B., Helmig, D., Doskey, P. V., and Oltmans, S. J.: Summertime
surface O3 behavior and deposition to tundra in the Alaskan Arctic, J.
Geophys. Res. Atmos., 121, 8055–8066, 10.1002/2015JD023914, 2016.Verstraeten, W. W., Boersma, K. F., Zörner, J., Allaart, M. A. F., Bowman, K. W., and Worden, J. R.: Validation of six years of TES tropospheric ozone retrievals with ozonesonde measurements: implications for spatial patterns and temporal stability in the bias, Atmos. Meas. Tech., 6, 1413–1423, 10.5194/amt-6-1413-2013, 2013.Viatte, C., Strong, K., Hannigan, J., Nussbaumer, E., Emmons, L. K., Conway, S., Paton-Walsh, C., Hartley, J., Benmergui, J., and Lin, J.: Identifying fire plumes in the Arctic with tropospheric FTIR measurements and transport models, Atmos. Chem. Phys., 15, 2227–2246, 10.5194/acp-15-2227-2015, 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, S. and Pratt, K. A.: Molecular halogens above the Arctic snowpack:
Emissions, diurnal variations, and recycling mechanisms, J. Geophys. Res.-Atmos., 122, 11991–12007.
10.1002/2017JD027175, 2017.
Wang, S. Y., McNamara, S. M., Moore, C. W., Obrist, D., Steffen, A.,
Shepson, P. B., Staebler, R. M., Raso, A. R. W., and Pratt, K. A.: Direct
detection of atmospheric atomic bromine leading to mercury and ozone
depletion, P. Natl. Acad. Sci. USA, 116, 14479–14484, 2019.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.Whaley, C. H., Mahmood, R., von Salzen, K., Winter, B., Eckhardt, S., Arnold, S., Beagley, S., Becagli, S., Chien, R.-Y., Christensen, J., Damani, S. M., Dong, X., Eleftheriadis, K., Evangeliou, N., Faluvegi, G., Flanner, M., Fu, J. S., Gauss, M., Giardi, F., Gong, W., Hjorth, J. L., Huang, L., Im, U., Kanaya, Y., Krishnan, S., Klimont, Z., Kühn, T., Langner, J., Law, K. S., Marelle, L., Massling, A., Olivié, D., Onishi, T., Oshima, N., Peng, Y., Plummer, D. A., Popovicheva, O., Pozzoli, L., Raut, J.-C., Sand, M., Saunders, L. N., Schmale, J., Sharma, S., Skeie, R. B., Skov, H., Taketani, F., Thomas, M. A., Traversi, R., Tsigaridis, K., Tsyro, S., Turnock, S., Vitale, V., Walker, K. A., Wang, M., Watson-Parris, D., and Weiss-Gibbons, T.: Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study, Atmos. Chem. Phys., 22, 5775–5828, 10.5194/acp-22-5775-2022, 2022.World Health Organization (WHO): Regional Office for Europe: Review of evidence on health aspects of air pollution: REVIHAAP project: technical report, World Health Organization, Regional Office for Europe, https://apps.who.int/iris/handle/10665/341712 (last access: 13 January 2023), 2021.Willis, M. D., Bozem, H., Kunkel, D., Lee, A. K. Y., Schulz, H., Burkart, J., Aliabadi, A. A., Herber, A. B., Leaitch, W. R., and Abbatt, J. P. D.: Aircraft-based measurements of High Arctic springtime aerosol show evidence for vertically varying sources, transport and composition, Atmos. Chem. Phys., 19, 57–76, 10.5194/acp-19-57-2019, 2019.Wofsy, S. C., Afshar, S., Allen, H. M., Apel, E. C., Asher, E. C., Barletta,
B., Bent, J., Bian, H., Biggs, B. C., Blake, D. R., Blake, N., Bourgeois,
I., Brock, C. A., Brune, W. H., Budney, J. W., Bui, T. P., Butler, A.,
Campuzano-Jost, P., Chang, C. S., Chin, M., Commane, R., Correa, G.,
Crounse, J. D., Cullis, P. D., Daube, B. C., Day, D. A., Dean-Day, J. M.,
Dibb, J. E., DiGangi, J. P., Diskin, G. S., Dollner, M., Elkins, J. W.,
Erdesz, F., Fiore, A. M., Flynn, C. M., Froyd, K. D., Gesler, D.W., Hall, S.
R., Hanisco, T. F., Hannun, R. A., Hills, A. J., Hintsa, E. J., Hoffman, A.,
Hornbrook, R. S., Huey, L. G., Hughes, S., Jimenez, J. L., Johnson, B. J.,
Katich, J. M., Keeling, R. F., Kim, M. J., Kupc, A., Lait, L. R., Lamarque,
J.-F., Liu, J., McKain, K., Mclaughlin, R. J., Meinardi, S., Miller, D. O.,
Montzka, S. A., Moore, F. L., Morgan, E. J., Murphy, D. M., Murray, L. T.,
Nault, B. A., Neuman, J. A., Newman, P. A., Nicely, J. M., Pan, X.,
Paplawsky, W., Peischl, J., Prather, M. J., Price, D. J., Ray, E. A.,
Reeves, J. M., Richardson, M., Rollins, A. W., Rosenlof, K. H., Ryerson, T.
B., Scheuer, E., Schill, G. P., Schroder, J., C., Schwarz, J. P., St.Clair,
J., M., Steenrod, S. D., Stephens, B. B., Strode, S. A., Sweeney, C.,
Tanner, D., Teng, A. P., Thames, A. B., Thompson, C. R., Ullmann, K., Veres,
P. R., Vieznor, N., Wagner, N. L., Watt, A., Weber, R., Weinzierl, B.,
Wennberg, P. O., Williamson, C. J., Wilson, J. C., Wolfe, G. M., Woods, C.
T., and Zeng, L., H..: ATom: Merged Atmospheric Chemistry, Trace Gases, and
Aerosols, Ornl Daac, Oak Ridge, Tennessee, USA.
10.3334/ORNLDAAC/1581, 2018.Yang, X., Pyle, J. A., Cox, R. A., Theys, N., and Van Roozendael, M.: Snow-sourced bromine and its implications for polar tropospheric ozone, Atmos. Chem. Phys., 10, 7763–7773, 10.5194/acp-10-7763-2010, 2010.Yang, X., Blechschmidt, A.-M., Bognar, K., McClure-Begley, A., Morris, S., Petropavlovskikh, I., Richter, A., Skov, H., Strong, K., Tarasick, D. W., Uttal, T., Vestenius, M., and Zhao, X.: Pan-Arctic surface ozone: modelling vs. measurements, Atmos. Chem. Phys., 20, 15937–15967, 10.5194/acp-20-15937-2020, 2020.Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M. Y., Neu,
J. L., Parrish, D. D., Rieder, H. E., Schnell, J. L., Tilmes, S., Wild, O.,
Zhang, L., Ziemke, J. R., Brandt, J., Delcloo, A., Doherty, R. M., Geels,
C., Hegglin, M. I., Hu, L., Im, U., Kumar, R., Luhar, A., Murray, L.,
Plummer, D., Rodriguez, J., Saiz-Lopez, A., Schultz, M. G., Woodhouse, M.
T., and Zeng, G.: Tropospheric Ozone Assessment Report: Assessment of
global-scale model performance for global and regional ozone distributions,
variability, and trends, Elem. Sci Anth., 6, 10,
10.1525/elementa.265, 2018.
Zanis, P., Akritidis, D., Turnock, S., Naik, V., Szopa, S., Georgoulias, A.
K., Bauer, S. E., Deushi, M., Horowitz, L. W., and Keeble, J.: Climate change
penalty and benefit on surface ozone: a global perspective based on CMIP6
earth system models, Environ. Res. Lett., 17, 024014,
10.1088/1748-9326/ac4a34, 2022.
Zheng, C., Wu, Y., Ting, M., Orbe, C., Wang, X., and Tilmes, S.: Summertime
transport pathways from different northern hemisphere regions into the
Arctic. J. Geophys. Res.-Atmos., 126, e2020JD033811.
https://doi. org/10.1029/2020JD033811, 2021.Zhu, L., Fischer, E. V., Payne, V. H., Worden, J. R., and Jiang, Z.: TES
observations of the interannual variability of PAN over Northern Eurasia and
the relationship to springtime fires, Geophys. Res. Lett., 42, 7230–7237,
10.1002/2015GL065328, 2015.