ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-6047-2015Biomass burning influence on high-latitude tropospheric ozone and reactive nitrogen in summer 2008: a multi-model analysis based on POLMIP simulationsArnoldS. R.s.arnold@leeds.ac.ukEmmonsL. K.https://orcid.org/0000-0003-2325-6212MonksS. A.https://orcid.org/0000-0003-3474-027XLawK. S.RidleyD. A.https://orcid.org/0000-0003-3890-0197TurquetyS.TilmesS.https://orcid.org/0000-0002-6557-3569ThomasJ. L.BouararI.FlemmingJ.https://orcid.org/0000-0003-4880-5329HuijnenV.MaoJ.https://orcid.org/0000-0002-4774-9751DuncanB. N.SteenrodS.YoshidaY.LangnerJ.LongY.Institute for Climate and Atmospheric Science, School of Earth & Environment, University of Leeds, UKAtmospheric Chemistry Division, NCAR, Boulder, CO, USAUPMC Univ. Paris 06, Université Versailles St-Quentin; CNRS/INSU, UMR 8190, Paris, FranceDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USALaboratoire de Météorologie Dynamique, IPSL, CNRS, UMR8539, 91128 Palaiseau CEDEX, FranceECMWF, Reading, UKRoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsProgram in Atmospheric and Oceanic Sciences, Princeton University and Geophysical Fluid Dynamics Laboratory/National Oceanic and Atmospheric Administration, Princeton, NJ, USANASA Goddard Space Flight Center, Greenbelt, MD, USASwedish Meteorological and Hydrological Institute, 60176 Norrköping, SwedenS. R. Arnold (s.arnold@leeds.ac.uk)3June201515116047606817July201424September201429April20154May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-chem-phys.net/15/6047/2015/acp-15-6047-2015.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/15/6047/2015/acp-15-6047-2015.pdf
We have evaluated tropospheric ozone enhancement in air dominated by biomass
burning emissions at high latitudes (>50∘ N) in July 2008, using
10 global chemical transport model simulations from the POLMIP multi-model
comparison exercise. In model air masses dominated by fire emissions,
ΔO3/ΔCO values ranged between 0.039 and 0.196 ppbvppbv-1
(mean: 0.113 ppbvppbv-1) in freshly fire-influenced air, and between 0.140
and 0.261 ppbvppbv-1 (mean: 0.193 ppbv) in more aged fire-influenced air.
These values are in broad agreement with the range of observational
estimates from the literature. Model ΔPAN/ΔCO enhancement
ratios show distinct groupings according to the meteorological data used to
drive the models. ECMWF-forced models produce larger ΔPAN/ΔCO
values (4.47 to 7.00 pptvppbv-1) than GEOS5-forced models (1.87 to 3.28 pptvppbv-1),
which we show is likely linked to differences in efficiency of
vertical transport during poleward export from mid-latitude source regions.
Simulations of a large plume of biomass burning and anthropogenic emissions
exported from towards the Arctic using a Lagrangian chemical transport model
show that 4-day net ozone change in the plume is sensitive to differences in
plume chemical composition and plume vertical position among the POLMIP
models. In particular, Arctic ozone evolution in the plume is highly
sensitive to initial concentrations of PAN, as well as oxygenated VOCs
(acetone, acetaldehyde), due to their role in producing the peroxyacetyl
radical PAN precursor. Vertical displacement is also important due to its
effects on the stability of PAN, and subsequent effect on NOx abundance. In
plumes where net ozone production is limited, we find that the lifetime of
ozone in the plume is sensitive to hydrogen peroxide loading, due to the
production of HOx from peroxide photolysis, and the key role of
HO2 + O3 in controlling ozone loss. Overall, our results suggest
that emissions from biomass burning lead to large-scale photochemical
enhancement in high-latitude tropospheric ozone during summer.
Introduction
Vegetation fires play an important role in ecosystem function and regulation
(Bonan, 2008) and contribute substantially to atmospheric CO2, with
gross emissions from biomass burning estimated to be between 2 and 4 PgCa-1
globally, equivalent to 40 % of those from fossil fuel combustion
(Ciais et al., 2013). Biomass burning also impacts atmospheric chemistry,
releasing large quantities of aerosol and reactive gas-phase chemical
compounds, including CO, NOx (=NO+NO2) and volatile organic
compounds (VOCs) (Andreae et al., 1988; van der Werf et al., 2010). These
emissions result in perturbations to tropospheric oxidants, aerosol loading
and the atmospheric radiative balance. Studies have
demonstrated that wildfires in the boreal regions of North America and
Eurasia affect abundances of atmospheric trace gases and aerosol at high
latitudes (Bourgeois and Bey, 2011; Fisher et al., 2010; Hornbrook et
al., 2011; Jaffe and Wigder, 2012; Monks et al., 2012; Paris et al., 2009;
Real et al., 2007; Warneke et al., 2010; Wofsy et al., 1992). These
contributions peak during spring and summer, when large fires occur
naturally in the regions of Alaska and Canada and in central and eastern
Siberia (Monks et al., 2012; van der Werf et al., 2010). How
anthropogenic and natural sources of climatically relevant atmospheric
constituents will contribute to future high-latitude climate change is
highly uncertain
(Shindell et al., 2008).
In particular, our understanding of how boreal fires impact large-scale
Arctic and high-latitude budgets of climate-relevant atmospheric
constituents is limited, and is reliant on sparse observations, often in
specific events and isolated plumes. Short-lived climate pollutants (SLCPs)
such as tropospheric ozone, aerosol and methane may contribute to
accelerated rates of warming observed in the Arctic relative to the global
mean temperature increase (Quinn et al., 2008). Changes in tropospheric
ozone and aerosol may already have contributed 0.2–0.4 and
0.5–1.4 ∘C, respectively, to Arctic surface warming since 1890
(Shindell and Faluvegi, 2009). A better understanding of
boreal fire influence on high-latitude tropospheric ozone and aerosol is
essential for improving the reliability of our projections of future Arctic
and Northern Hemisphere climate change, especially considering proposed
climate–fire feedbacks which may enhance the intensity and extent of high-latitude wildfire under a warming climate (de Groot et al., 2013).
The role of boreal fires as a source of high-latitude tropospheric ozone is
particularly poorly constrained, and has been the subject of some
controversy, with different studies suggesting both minor and major roles
for fires as a source of Arctic ozone. A recent review by Jaffe
and Wigder (2012) showed that most studies have demonstrated net production
of tropospheric ozone from wildfire emissions, due to the propensity of
fires to emit large quantities of key ozone precursors (NOx, CO, VOCs). The
ΔO3/ΔCO enhancement ratio (defined as the excess ozone
mixing ratio above background ozone in an air mass normalized by an
enhancement in CO mixing ratio above background CO), is often used as a
measure of ozone production efficiency in fire plumes as they are processed
downwind from emission. Values of ΔO3/ΔCO in boreal
wildfire plumes from Siberia, Alaska and Canada vary between approximately
-0.1 and 0.6 ppbvppbv-1 (Alvarado et al.,
2010; Bertschi et al., 2004; Goode et al., 2000; Honrath et al., 2004;
Val Martin et al., 2006; Mauzerall et al., 1996; Paris et al., 2009; Parrington
et al., 2013; Pfister et al., 2006; Real et al., 2007; Singh et al., 2010;
Tanimoto et al., 2008; Wofsy et al., 1992). In addition, these values are
observed to generally increase with increasing plume age.
A robust estimate of the role of boreal fires in producing tropospheric
ozone is hampered by a large range in observational estimates of ozone
production efficiency, likely resulting from factors such as variability in
emission factors with combustion efficiency and vegetation type, differences
in plume age, different plume chemical processing, due to e.g. different
aerosol loadings, and mixing with anthropogenic emissions (Jaffe
and Wigder, 2012). Integrated analysis of data from multiple boreal fire
plumes sampled across Alaska and Canada during the ARCTAS-B campaign
concluded that boreal fire emissions had only negligible impact on
tropospheric ozone profiles in summer 2008 over Alaska and Canada
(Alvarado et al., 2010; Singh et al., 2010). However, plumes sampled were
mostly freshly emitted (<2 days), and box modelling based on the
same data suggests high in situ photochemical production rates, despite
little to no measured ozone enhancement in these plumes (Olson et al.,
2012). Other recent modelling studies have suggested greater ozone
sensitivity to boreal fire emissions in more aged air masses. Tropospheric
ozone in coastal Canada has been shown to be highly sensitive to NOx
emissions from central Canadian fires
(Parrington et al., 2012), and regional
modelling for the Arctic in summer 2008 suggests that ozone production
increases markedly in fire plumes downwind from emission as air masses
process chemically over time (Thomas et al., 2013).
Wespes et al. (2012), using a tagged NOx and ozone production scheme in the
MOZART-4 global CTM, showed that more than 20 % of ozone in the Arctic
lower troposphere is produced from NOx emitted from high-latitude fires in
North America and Asia. Boreal forest fires have also been shown to be an
important source of peroxyacetyl nitrate (PAN) in the Arctic during the
spring and summer months (Jacob et al., 1992; Singh et al., 2010, 1992). Transport of PAN from lower latitudes into the Arctic makes a
substantial contribution to local in situ ozone production, via NO2
released from PAN decomposition (Walker et al., 2012).
In light of uncertainties associated with these contributions, there is a
need to better evaluate how models simulate the influence of boreal fires on
high-latitude budgets of ozone and precursors, particularly in summer, when
local radiative processes play a major role in Arctic surface temperatures
(Shindell, 2007). While several model studies have investigated
simulated ozone production from boreal fires, there has been little attempt
to understand how differences in model treatments of chemistry and transport
affect estimates of ozone production in fire-influenced air masses.
In this paper, we use results from POLMIP (POLARCAT model intercomparison
Project) (Emmons et al., 2014) and observations collected in the Arctic
troposphere as part of the ARCTAS-B mission
(Jacob et al., 2010), to evaluate
simulated summertime tropospheric ozone and its precursors in the northern
high latitudes and how it is influenced by boreal fire emissions in a series
of state-of-the-art global atmospheric chemical transport models. The POLMIP
model experiments and observations used to evaluate them are described in
Sect. 2. In Sect. 3, we use idealized model tracers to track fire
emissions, and compare ozone enhancement ratios (ΔO3/ΔCO)
in air dominated by fire emission influence across the range of models,
and investigate relationships with model NOy partitioning. Section 4
describes a case study of a large biomass burning plume exported from
Siberia in July 2008, which we use to investigate the sensitivities of
Arctic tropospheric ozone to model chemistry based on Lagrangian chemical
model simulations of the plume. Our findings and conclusions are summarized
in Sect. 5.
Model simulations and observations
The POLARCAT Model Intercomparison Project (POLMIP) was designed to evaluate
the performance of several global- and regional-scale chemical transport
models (CTMs) in the Arctic troposphere (Emmons et al., 2014). POLMIP
contributes to the POLARCAT project aim to better understand model
deficiencies identified in a previous evaluation of CTM simulations of
Arctic tropospheric ozone and its precursors, and aims to exploit the large
amount of observational data collected during the IPY aircraft experiments
in the Arctic troposphere during spring and summer 2008. Further details on
the POLARCAT project and the 2008 aircraft campaigns are given in Law et
al. (2014). All models used the same data for emissions, with the aim of
allowing an investigation of model differences due to atmospheric transport
and chemical processes only. The exception was the GEOS-Chem model, which
used different anthropogenic emissions (Emmons et al., 2014). POLMIP
anthropogenic emissions are those provided for the ARCTAS project by D.
Streets (Argonne National Lab) and University of Iowa (http://bio.cgrer.uiowa.edu/arctas/emission.html). Daily biomass burning
emissions are taken from the Fire Inventory of NCAR (FINN), based on MODIS
fire counts (Wiedinmyer et al., 2011). All POLMIP models injected biomass
burning emissions into the lowest boundary layer model level, in order to
remove any differences produced through treatments of fire emission
injection heights. Other emissions (biogenic, ocean, volcano) were derived
from the MACCity inventory
(Lamarque et al., 2010).
Table 1 summarizes details of the POLMIP model simulations used in this
study. Further details of the POLMIP model experiments, emissions data and
evaluation of the simulations can be found in Emmons et al. (2014).
In addition to using different anthropogenic emissions, the GEOS-Chem model includes a parametrization for transition
metal-catalyzed formation of H2O from aerosol uptake of HO2,
rather than formation of H2O2. This process is effectively an
irreversible loss for HOx, and is motivated by the suggestion from field
observations that HO2 uptake to aerosol may not produce H2O2.
This motivation and the implementation of this scheme are described by Mao
et al. (2013a). The same study showed that inclusion of this process
reduces the mass-weighted global mean OH concentration by 12 %, and
substantially increases CO concentrations at high latitudes due to an
increased CO lifetime. It was also shown to reduce surface ozone by 3–10 ppbv over North America and Eurasia.
To further aid in understanding inter-model differences in transport, POLMIP
models included fixed-lifetime tracers from anthropogenic and biomass
burning emission sources. A total of six tracers were simulated, each with a
prescribed fixed atmospheric lifetime of 25 days. A 25-day tracer lifetime
is sufficiently long relative to the transport timescale for long-range
transport from mid-latitudes to the Arctic (days to a week), while being
short enough to avoid the formation of a homogeneous well-mixed tracer
distribution. Two tracers were emitted from each of three mid-latitude
continental source regions (Europe, North America and Asia), one with the
same source as the anthropogenic CO emissions and one from the CO emissions
from biomass burning sources. Details on the exact definition of source
regions and emission magnitudes are given in Emmons et al. (2014). The
Asian biomass burning tracer is dominated by emissions from large Siberian
fires in July 2008 (see Emmons et al., 2014). Monks et al. (2012)
demonstrated that variability in emissions from boreal fires
dominates the inter-annual variability of the ozone precursor, CO in the
Arctic troposphere. Using the fixed-lifetime CO tracers from the POLMIP simulations,
in conjunction with observed and simulated CO, Monks et al. (2015) investigated the
contributions from differences in model transport and oxidants to inter-model
variability in simulated seasonal CO in the Arctic. They showed that emissions from Asian fires are
the dominant source of CO tracer in the lower and middle summertime Arctic troposphere,
and are approximately equal to the contribution from Asian anthropogenic
sources in the upper troposphere. Here, we exploit these tracers to identify
regions and periods in the POLMIP model simulations for which air is
strongly influenced by fire emissions.
POLMIP model ozone interpolated to selected
ARCTAS-B DC8 flight tracks north of 50∘ N, and between 3–9 km
altitude plotted as a function of the DC8-observed concentrations.
Blue and red colours show model points which are dominated by anthropogenic
and biomass burning emissions respectively, as diagnosed by 25-day fixed-lifetime CO
tracers simulated by the models (see text for details). Mean fractional model biases (%)
in anthropogenic- and fire-dominated air are shown in blue and red text respectively.
The GEOS-Chem model did not simulate 25-day fixed-lifetime tracers.
As Fig. 1, but for CO.
As Fig. 1, but for PAN.
As Fig. 1, but for HNO3.
Several aircraft flew missions into the Arctic troposphere during summer
2008 as part of the POLARCAT experiment (Law et al., 2014). The
POLARCAT-France and GRACE experiments, based in Southwest Greenland, sampled
aged fire plumes and anthropogenic air masses transported into the Arctic
from Siberia and North America. ARCTAS-B, based in central Canada, sampled
fresh and aged fire emissions over Canada and the Arctic. In this analysis,
we make use only of data from the ARCTAS-B mission, for which the NASA DC8
aircraft was equipped with an extensive suite of gas phase and aerosol
instrumentation, including ozone, CO, speciated oxides of nitrogen (NOy),
volatile organic compounds and peroxides
(Jacob et al., 2010). Monks et al. (2015)
present a detailed comparison of the POLMIP model simulations with CO
and ozone data from all POLARCAT experiments.
During ARCTAS-B, the DC8 aircraft made seven flights, based from Cold Lake,
Canada from 29 June to 10 July 2008. The vast majority of observations
were made in fresh Saskatchewan fire plumes, although some flights also
targeted aged plumes transported to Canada from Siberian and Californian
fires. All ARCTAS DC8 data are available in a publicly accessible archive
(http://www-air.larc.nasa.gov/cgi-bin/arcstat-c), and described
in Jacob et al. (2010).
Fire emission influence on ozone and NOy enhancement in POLMIP
modelsEvaluation of model ozone and ozone precursors in air dominated by
fire emissions
Using the fixed-lifetime tracers from the models, we evaluate simulations of
ozone and precursors against ARCTAS-B aircraft observations in air dominated
by fire emissions in the summertime Arctic troposphere. Figures 1–4
respectively show aircraft observations of ozone, CO, PAN and HNO3
plotted against hourly model output interpolated in time and space to the
aircraft position. For each model, points have been coloured according to
whether the simulated tracers from fire sources or from anthropogenic
sources contribute more than 50 % of the total (fire + anthropogenic)
tracer mixing ratio at the aircraft location. In model air dominated by fire
emissions, simulated ozone generally falls close to the observation–model
1:1 line, and model median biases vary between -22 and +5 %,
compared with -19 to -2 % in anthropogenic-dominated air. As discussed
in detail by Monks et al. (2015), all POLMIP models display a negative CO
bias, throughout the depth of the troposphere. Use of the POLMIP
fixed-lifetime tracers shows that this is the case in both anthropogenic and
fire-dominated air. Global models typically underestimate CO in the northern
extratropics. A recent multi-model study showed negative annual mean model
biases exceeding -45ppbv compared with surface CO observations at high
latitudes, and as large as -30ppbv compared with satellite-retrieved CO
concentrations at 500 hPa over the extra-tropical oceans (Naik et al., 2013).
The majority of ARCTAS-B observations were made in fresh biomass burning
plumes, leading to larger CO concentrations on average in fire-dominated air
masses. The models also simulate larger CO concentrations in these air
masses, but with a general underestimate. Monks et al. (2015) demonstrated
that POLMIP model-simulated global mean OH was generally biased slightly
high compared with observational constraints, possibly contributing to their
low CO bias.
Simulated distributions of NOy species show some of the largest diversity
between models and largest fractional biases against observations. Emmons et
al. (2014) showed that POLMIP models display large variability in their
budgets of NOy throughout the depth of the Arctic troposphere. The POLMIP
models fall into two distinct groups in terms of their simulation of
ARCTAS-B PAN concentrations. Models forced by GEOS5 meteorology tend to have
lower PAN than observed in fire-dominated air (median biases: -47 to
-28 %), while the ECMWF-forced models produce PAN concentrations close to
or larger than those observed in fire-dominated air (median biases: -2
to +24 %). This major difference appears to be related to differences in
the efficiency of vertical transport between models using the two different
sets of meteorological data (see Sect. 3.3). Models that transport PAN and
its precursors more rapidly to higher altitudes and lower temperatures will
likely promote enhanced PAN formation and stability (Singh and
Hanst, 1981). These effects on differences in NOy partitioning are explored
further in Sect. 3.3.
GEOS-Chem underestimates DC8 PAN concentrations by the largest magnitude
overall (median bias -51 %), with lower-than-observed PAN at all locations
where observed PAN exceeds 250 pptv. Recent work has substantially improved
the simulation of PAN in the GEOS-Chem model
(Fischer et al., 2014), however these
model updates are not included here. The CIFS model shows very large PAN
overestimates (> factor of 4) in fire air masses sampled close to
the surface. Comparisons with aircraft observations (see Emmons et al.,
2014) show coincident overestimates in NO2 and acetaldehyde, suggesting that
these very large PAN concentrations may be partly produced by overestimates
in PAN precursors near to fire source regions. In general, the models
display substantially larger range in PAN biases in fire-dominated air
(median biases: -47 to +24 %) compared with anthropogenic-dominated
air (median biases: -34 to +5 %). Fresh biomass burning plumes
observed in ARCTAS-B displayed enhancements in peroxyacetyl precursors such
as acetaldehyde and acetone (Hornbrook et al., 2011). Simulated
oxygenated (o)VOC enhancements relative to CO (particularly for acetone) in
the POLMIP models show large variability close to Canadian fires (Emmons et
al., 2014), which may in turn lead to a large range in simulated PAN
production. With the exception of the GEOS-Chem and TM5 models, emissions of
acetone and acetaldehyde are the same for all models. The large diversity in
model concentrations of these species therefore mainly results from
different treatments of organic chemistry, differences in rates of
photochemical processing of their parent VOCs and differences in their
photolysis and OH loss.
Several models show a large positive bias in Arctic HNO3 concentrations
(up to a factor 32 in anthropogenically dominated air). In an earlier study,
Alvarado et al. (2010) used the GEOS-Chem model to study HNO3 in
fire-influenced air masses. This study concluded that the over-prediction of
HNO3 was due to under-prediction of NOx conversion to PAN in fire-influenced air masses. The POLMIP models do not generally support this
offsetting of positive biases in HNO3 with under-prediction of PAN.
July 2008 monthly mean O3 vs. CO
from POLMIP model simulations coloured by fire influence and relative
age of air since emission. Black: all points north of 50∘ N, with 850 hPa> pressure > 250 hPa.
Coloured points show model grid boxes where the fire-emitted fixed-lifetime CO
tracer contributes more than 66 % of the total (fire + anthropogenic) tracer mixing ratio.
Blue and red points denote younger than average and more aged than average of these
points respectively, as diagnosed by the ln([C3H8]/[C2H6])
concentration ratio. Models that did not simulate fixed-lifetime tracers, or do not carry
[C3H8] explicitly, do not have coloured points, but instead show slopes from
linear regressions of the coloured points from the other models (red and blue lines).
Blue and red text give ΔO3/ΔCO slope values from linear
regressions of the youngest and most aged populations respectively. Letters
in square brackets denote the meteorological analysis data used to drive
the models – E: ECMWF; G: GEOS-5. Panel (k) shows ARCTAS-B aircraft observations.
Model ozone production in fire-dominated Arctic air masses
Previous studies have directly determined the contribution from fire
emissions to model ozone by removing emissions from fires (Pfister et
al., 2006; Thomas et al., 2013) or by chemically tagging ozone produced by
NOx emitted from fires (Wespes et
al., 2012). To investigate this contribution in the POLMIP models, we use
the 25-day fixed-lifetime tracers to identify the dominant emission source
that influences high-latitude air in the models. We calculate enhancement in
tropospheric ozone as a ratio to CO enhancement (ΔO3/ΔCO) where the fixed-lifetime tracers indicate that the model domain is
dominated by fire emissions. Points are considered to be fire-dominated
where the fire-sourced fixed-lifetime tracer concentration is at least
66 % of the total fixed-lifetime tracer concentration, and where the
fire-sourced fixed-lifetime tracer mixing ratio is at least 10 ppbv. Using
this minimum tracer mixing ratio to define air enhanced in fire emissions,
we use the slope of CO vs. ozone in these air masses to calculate the ΔO3/ΔCO ratio directly. This avoids the definition of a CO
mixing ratio enhancement above background CO, which due to OH differences
between the models is highly model dependent (Monks et al., 2015).
Recent studies have highlighted the need for caution regarding the use of
O3/CO slopes to diagnose photochemical ozone production, particularly
in remote regions, due to slopes being artificially increased by chemical
loss of CO due to reaction with OH (e.g. Voulgarakis et al., 2011; Zhang et
al., 2014). Chemical rate output from the MOZART-4 model shows that in the
domain of our study (latitude 50–90∘ N, 850–250 hPa) the daily chemical loss
rate of CO is small (average 1.9 ppbvday-1), equivalent to 1.5–4.5 %.
This loss is partly offset by chemical production of CO from VOC oxidation
(average 0.9 ppbvday-1), and daily fractional rates of chemical ozone
production at the same locations are substantially larger (∼5–45 %). This analysis suggests that chemical CO loss is unlikely to have
a significant effect on our calculated O3/CO slopes.
Using changes in the ratio of concentrations of two co-emitted VOCs with
differing atmospheric lifetimes, it is also possible to estimate how model
ΔO3/ΔCO values change, as air dominated by fire
emissions is transported away from the source region and ages
photochemically. For primary-emitted VOCs that have losses dominated by
OH-oxidation, the concentration ratio of a more reactive to a less reactive
VOC is expected to reduce over time since emission (Calvert,
1976). Propane (C3H8) and ethane (C2H6) have respective
atmospheric e-folding lifetimes of approximately 5 and 24 days (for an
average OH concentration of 2×106moleccm-3
(Atkinson et al., 2006)). In the absence of
mixing with background concentrations, a decrease in the
ln([C3H8]/[C2H6]) ratio is directly proportional to the
time elapsed since emission.
We use the ln([C3H8]/[C2H6]) ratios from the POLMIP
models to create relationships between broad classifications of air mass age
and ΔO3/ΔCO. Based on model values of the
ln([C3H8]/[C2H6]) ratio, we separate the distribution of
high-latitude tropospheric model grid boxes into two populations of
“youngest” (points with ln([C3H8]/[C2H6]) values larger
than the mean) and “oldest” (points with
ln([C3H8]/[C2H6]) values smaller than the mean) air
masses, in terms of their estimated age since emission. Figure 5 shows
POLMIP model-simulated relationships between [O3] and [CO] in
fire-dominated air in the high-latitude free troposphere (latitude
>50∘ N; 850 hPa> pressure > 250 hPa), with
calculated ΔO3/ΔCO slopes in youngest and oldest air
mass groups as defined by the ln([C3H8]/[C2H6]) ratios.
The SMHI-MATCH and GEOS-Chem models respectively did not explicitly simulate
propane and the fixed-lifetime source tracers. Therefore, it is not possible
to calculate ΔO3/ΔCO slopes in fire-dominated air
according to these age classes.
ΔO3/ΔCO ratios in boreal biomass
burning pollution from previous studies and from the POLMIP model simulations
analysed in this study. Green: values from plumes of age > 5 days; yellow: values from
plumes of age < 1–5 days. POLMIP model values are classified by age since
emission (red: aged; blue: young), based on the ln([C3H8]/[C2H6])
concentration ratio (see text for details). Hatched bars indicate average values for
each category. Literature values for previous studies are based on an
updated version of the review of Jaffe and Wigder (2012).
July 2008 PAN/CO relationships for POLMIP models
coloured by fire influence. Black: all points north of 50∘ N, with 850 hPa> pressure > 250 hPa.
Red points show model grid boxes where the fire fixed-lifetime CO tracer contributes
more than 66 % of the total (fire + anthropogenic) tracer mixing ratio. Red text
gives ΔPAN/ΔCO slope values for linear regressions of the red points.
GEOS-Chem model did not supply fixed-lifetime tracers. Letters in square brackets denote
the meteorological analysis data used to drive the models – E: ECMWF; G: GEOS-5.
Panel (k) shows ARCTAS-B aircraft observations with slopes from ECMWF (blue) and GEOS-5 (green) models shown for comparison.
POLMIP model ΔO3/ΔCO slopes are positive in both the
younger and aged fire-dominated air in all models. Slopes in the aged air
masses (mean: 0.193, min: 0.140, max: 0.261 ppbvppbv-1) are larger on
average compared with slopes in the younger air masses (mean: 0.113 , min:
0.039, max: 0.196 ppbvppbv-1). This is indicative of photochemical ozone
production in fire emission-dominated air emitted into and advected to high
latitudes in the POLMIP models, with an increase in ozone enhancement
relative to CO enhancement in these air masses as they age photochemically.
Two models (TOMCAT and CAM5-Chem) show a slight decrease in ΔO3/ΔCO with air mass age defined by the
ln([C3H8]/[C2H6]) ratio. Supplementary Fig. S1 shows
that the ln([propane]/[ethane]) ratio for these models show less distinct
separation in their corresponding fire tracer concentrations between the
young and old age classes. This suggests that the
ln([C3H8]/[C2H6]) ratio may be a less robust proxy for
photochemical age since emission in these models. Figure 5k shows ozone and
CO observations from ARCTAS-B DC8 flights over-plotted with ΔO3/ΔCO slopes from the different POLMIP models. Although the
DC8 aircraft sampled only a small proportion of the fire-dominated domain
simulated by the models, the aircraft points lie close to the model ΔO3/ΔCO slopes. Observed ozone concentrations appear slightly
larger as a function of CO than those in the POLMIP simulations. There is
also evidence that observed air masses show a larger range in ozone
enhancements for a given range of CO enhancement than those simulated,
perhaps reflecting a diverse range of fresh plumes sampled by the aircraft
close to the fires on the model sub-grid scale.
POLMIP model ΔO3/ΔCO values are highly consistent
compared with the wide range of ΔO3/ΔCO values
determined from observational studies in boreal fire plumes. Figure 6
compares the ΔO3/ΔCO values from the POLMIP models with
ΔO3/ΔCO values from previous model and observational
studies on fire plumes at high latitudes. Average ΔO3/ΔCO values from a previous GEOS-Chem model study based on ARCTAS-B range
between -0.07 and 0.01 (Alvarado et al., 2010), substantially smaller
than values from the POLMIP models. However, these values were diagnosed
in freshly fire-influenced air masses. The POLMIP models agree well with
regional WRF-Chem model simulations for the ARCTAS-B campaign, which
produced mean ΔO3/ΔCO values in fresh and aged biomass
burning plumes of 0.08 and 0.49 ppbvppbv-1 respectively, and used the same
FINN fire emissions as the POLMIP models (Thomas et
al., 2013). Differences in simulated photolysis between the POLMIP models
are likely contributors to model spread in photochemical ozone enhancement
relative to CO. Such differences are presented and explored for the POLMIP
models by Emmons et al. (2014). Mao et al. (2013b), using the GFDL AM3
model with aerosol loss uptake of HO2, characterized a suppressed
large-scale ozone enhancement from fires (ΔO3/ΔCO=0.16) at high latitudes (>60∘ N) compared with the tropics. This
is also seen in comparisons of observational studies between different
latitudes – however, observed ΔO3/ΔCO at high latitudes
is often larger than this large-scale average value derived from their model
(Jaffe and Wigder, 2012). Both heterogeneous HO2 loss on
aerosol (Mao et al., 2013a, b) and bromine chemistry
(Parrella et al., 2012), implemented in
GEOS-Chem for POLMIP, may also play a role in reducing tropospheric ozone
abundance.
Overlaying O3/CO slopes from the other POLMIP models onto plots of
GEOS-Chem and SMHI-MATCH [O3] vs. [CO] allows some comparison of their
Arctic tropospheric O3 enhancement with other POLMIP
models. POLMIP model O3/CO slopes lie through the [O3] vs. [CO]
distribution from the SMHI-MATCH model, which at larger [CO], shows a slope
value consistent with the smaller slope values from other POLMIP models.
GEOS-Chem shows the lowest ozone enhancement as a function of CO among the
POLMIP models, outside of the range of the majority of other models and the
ARCTAS-B observations.
High-latitude PAN enhancement in POLMIP models
Enhancements in PAN relative to CO in the high-latitude troposphere in the
POLMIP models show grouping according to the source of meteorological data
used to drive the models. Analogous to the ozone enhancement ratio (ΔO3/ΔCO), ΔPAN/ΔCO can be used to evaluate the
efficiency of PAN formation and its transport to high latitudes in the
POLMIP models (Fig. 7). Observations show that PAN was the dominant NOy
component in the Arctic troposphere during summer 2008 (Alvarado et al.,
2010; Liang et al., 2011), and as a source of NOx, may be an important
driver of tropospheric ozone production at high latitudes (Walker et al.,
2012). Average ΔPAN/ΔCO values in GEOS5-forced models range
between 1.87–3.28 pptvppbv-1, and in ECMWF-forced models range between
4.47–7.00 pptvppbv-1. Along with the biases shown in Fig. 3, this further
suggests that major differences in summertime NOy partitioning may be driven
by differences in model vertical transport efficiency. While differences in
PAN abundances in the Arctic troposphere shown in Fig. 3 could be explained
by differences in efficiency of poleward pollution transport in the models
generally, differences in ΔPAN/ΔCO slopes reflect
inter-model variability in the efficiency of PAN production or transport
relative to CO. CO has a long atmospheric lifetime relative to the transport
timescales characteristic of poleward frontal export, and is dominated by
primary emissions. Therefore, ΔPAN/ΔCO variability likely
represents differences in the rate of PAN formation and its stability. This
may be driven by different efficiencies of air mass uplift during boundary
layer export, promoting PAN stability, or differences in organic chemistry,
controlling the abundance of the acetyl peroxy radical precursor.
Zonally averaged difference between simulated 25-day fixed-lifetime CO
tracer mixing ratios at 900 and 500 hPa in the POLMIP model simulations for (a) spring (MAM) and (b) summer (JJA) 2008.
The vertical distributions of the 25-day fixed-lifetime CO tracers in the
models indicate a more vertically well-mixed lower troposphere in the ECMWF
models compared with the GEOS-5 models in general. Figure 8 shows zonal mean
differences in tracers between 900 and 500 hPa at Northern Hemisphere
mid-latitudes in spring and summer. In spring, TOMCAT, TM5 and CIFS show a
weaker vertical tracer gradient than CAM4-Chem, CAM5-Chem, MOZART-4 and GMI,
suggesting less efficient vertical transport in the GEOS5-driven models over
mid-latitude source regions. This pattern is less clear in summer, however
between 45 and 55∘ N this general behaviour is evident among the same
models, with the exception of MOZART-4, which becomes more vertically
well mixed. Mid-latitude convection is likely more important for vertical
transport in summer. Increased convective vertical mixing in the models may
therefore mask some of the differences in vertical tracer structure produced
by differences in large-scale vertical transport.
Average values of ΔPAN/ΔCO from a range of fresh and aged
fire plumes sampled during ARCTAS-B varied between 2.8 and 0.35 pptvppbv-1
(Alvarado et al., 2010), in better agreement with values produced by the
GEOS5-driven models. Figure 7k shows PAN and CO from ARCTAS-B observations.
Observed PAN/CO slopes are broadly consistent with those simulated by the
POLMIP models. The majority of observations support larger slopes consistent
with the ECMWF-driven models. The largest PAN enhancements are produced by
the CIFS model, which also shows the largest overall positive bias
(+40 %) against high-latitude PAN observations (Fig. 3). Across all POLMIP models,
we see no robust relationship between increased Arctic PAN import efficiency
and increased ozone production efficiency (ΔO3/ΔCO).
Differences in photochemistry between the models likely determine the
efficiency with which NOy import is manifested in high-latitude ozone
enhancement. In addition, a reduction in NOx through more rapid PAN
formation in the ECMWF models, and consequent suppression of ozone
production in plumes transported poleward may also play a role (Jacob et
al., 1992; Mauzerall et al., 1996).
The NOy biases shown by GEOS-Chem are consistent with those shown in
Alvarado et al. (2010), who found that PAN and HNO3 in the GEOS-Chem
model were under- and overestimated respectively by almost a factor of 2. In
particular, the large negative bias in high-latitude PAN (Fig. 3) may
explain the lower ozone enhancement compared with other POLMIP models. This
bias is largest among the POLMIP models. The simulated low PAN abundances
are unlikely explained by the composition of emissions, since all POLMIP
models use the same fire emissions.
Arctic fire plume sensitivities to model chemistry
In order to further investigate the sensitivities of high-latitude
tropospheric ozone production to differences in POLMIP model NOy
partitioning and photochemistry in fire plumes, we analyse chemical
processing during the export of a large plume of Siberian biomass burning
and anthropogenic emissions from Asia to the Arctic. By carrying out
additional simulations using a Lagrangian chemical transport model, we
quantify how differences in chemical composition of this plume between the
POLMIP models following export from Asia and poleward transport, and
differences in subsequent transport in the Arctic, impact the evolution of
ozone in the plume.
Siberian biomass burning and Asian anthropogenic plume case study
Between 6 and 9 July 2008, a low-pressure system travelled from Siberia
across the Arctic Ocean towards the North Pole, carrying with it smoke
plumes from Siberian wildfires and emissions from anthropogenic sources in
East Asia. This extensive plume of polluted air was sampled both remotely
from satellite and by aircraft in situ measurements. The IASI (Infrared
Atmospheric Sounding Interferometer) satellite instrument observed the plume
as a large feature of enhanced CO that was exported from the Asian east
coast and advanced towards the North Pole
(Pommier et al., 2010). On 6 and 7 July
the plume was between 850 and 1600 km wide, large enough to be
represented on the grid-scale of the POLMIP global models. As part of
ARCTAS-B, the DC8 aircraft also sampled the plume on 9 July, between
80 and 85∘ N, to the north of Greenland. Despite excessive diffusion in
the polar region due to the singularity at the pole on the Eulerian global
grid, Sodemann et al. (2011) showed that the TOMCAT global model was able
to capture the large-scale export of the plume, its horizontal position, and
its poleward transport into the Arctic region. This event provides a good
case study for evaluation of differences in transport and chemistry of
fire-influenced pollution to the Arctic among the POLMIP models.
Total column CO from the POLMIP model simulations at 06:00 UT on 7 July 2008.
Total column concentrations of the 25 day fixed-lifetime Asian anthropogenic
tracer from the POLMIP model simulations at 06:00 UT on 7 July 2008.
Total column concentrations of the 25-day fixed-lifetime Asian fire tracer from the
POLMIP model simulations at 06:00 UT on 7 July 2008.
The general horizontal position, size and shape of the plume agree well
between the different POLMIP model simulations. This is likely due to the
use of the same emissions data in each model, and large-scale horizontal
flow associated with the low-pressure system being largely consistent
between different driving meteorological data. Figure 9 shows total column
CO from the POLMIP models at 06:00 UT on 7 July 2008, just as the
leading edge of the plume reaches 80∘ N, at ∼180∘ W.
The plume extent and position simulated by the POLMIP models is also
consistent with the observed IASI satellite CO columns (Sodemann et al.,
2011). The positions and relative enhancement of simulated column CO maxima
are controlled by simulated horizontal transport and diffusive processes at
the sub-plume scale, but also vertical transport processes which control the
export CO from the boundary layer and the extent to which exported pollution
layers remain distinct or become vertically diffusive.
There are large differences in the magnitude of CO simulated in the plume.
Differences in model OH have been shown to have a strong influence on
inter-model variability in Arctic CO in the POLMIP models (Monks et al.,
2015). These same differences are evident in Fig. 9, particularly in CO
column differences in Arctic background air surrounding the plume
enhancements. Figures 10 and 11 show column distributions of 25-day lifetime
tracers emitted from Asian anthropogenic and Asian fire sources
respectively. The lower-resolution models tend to simulate more diffuse and
poleward penetration of anthropogenic-emitted tracer into the Arctic
compared with the CIFS model.
Positions and chemical composition of fire plume maxima
in the POLMIP model simulations at 06:00 UT on 7 July 2008. Plume maxima positions
are defined based on abundance of simulated 25 day fixed-lifetime Asian biomass burning tracer. See text for details.
Although the plume appears as a largely coherent single feature in total
column CO, the fixed-lifetime tracers reveal large-scale separation of
anthropogenic and fire contributions. The leading edge of the plume in all
models is dominated by fire emissions, with the main part of the
anthropogenically sourced air mass further to the south (Fig. 10). Backward
modelling simulations with the FLEXPART Lagrangian particle dispersion model
have also demonstrated that when this plume was sampled by the DC8 aircraft
on 9 July 2008, CO contributions from anthropogenic and fire sources
showed large-scale separation, with Asian fossil fuel sourced CO dominating
above 6–7 km altitude (Sodemann et al., 2011). Enhanced CO from the
anthropogenic part of this plume was transported into the lowermost
stratosphere, and was sampled by the DLR Falcon aircraft during the
POLARCAT-GRACE campaign on 10 July 2008 (Roiger
et al., 2011). The separation between anthropogenic and fire influence
within the plume is highly consistent across the POLMIP models, suggesting
good agreement in the locations of export and large-scale horizontal
transport of emissions from these two sources from the Asian boundary layer
to the Arctic. The GMI and MOZART-4 model plume maxima are situated at lower
altitudes compared to the other models (Table 2), again consistent with less
efficient vertical export in GEOS5-driven models (Fig. 8).
Lagrangian chemical model simulations
From each of the POLMIP global model simulations, the position of the plume maximum is determined from the plume distributions shown in Fig. 9. Maxima
locations are determined by locating the model grid box that contains the
maximum Asian fire tracer mixing ratio in the horizontal and vertical in the
region of the simulated plume. Table 2 shows the longitude, latitude and
pressure of plume maxima in the POLMIP models at 06:00 UT on 7 July 2008,
following export from the Asian continental boundary layer and import
into the Arctic. Table 2 also shows POLMIP model concentrations of key
species for ozone photochemistry at these maxima locations. From these
maxima locations in each POLMIP model, Lagrangian forward air mass
trajectories are calculated using the ROTRAJ (Reading Offline Trajectory)
Lagrangian transport model (Methven et al., 2003). Kinematic
forward-trajectories from the plume maxima locations are calculated by
integration of velocity fields taken from operational analyses of the
European Centre for Medium-range Weather Forecasts (ECMWF). The fields at
the Lagrangian particle positions are obtained from the 1.0125∘
horizontal resolution analyses by cubic Lagrange interpolation in the
vertical followed by bilinear interpolation in the horizontal and linear
interpolation in time. Five-day forward-trajectories were calculated with
position output every 6 h. These trajectories account for large-scale
advection by the resolved model winds, and neglect convective and turbulent
transport.
CiTTyCAT Lagrangian box model simulations of fire plume ozone
evolution in the Arctic. Coloured lines show simulations initialized with chemical composition
from each of the POLMIP global models at the fire plume maxima locations at 06:00 UT on 7 July 2008. (a) Net
ozone change over the 4-day simulations. (b) Integrated ozone production (solid)
and loss (dashed) rates. (c) Rate of HO2+NO ozone production;
(d) Rate of O(1D)+H2O ozone loss; (e) Rate of HO2+O3
ozone loss; (f) Pressure of forward trajectories from each POLMIP model plume maximum position
used for the Lagrangian simulations. See text for details of the Lagrangian model simulations.
Using initial chemical conditions from Table 2, and following the forward
trajectories calculated from the plume maxima locations for each POLMIP
model, we carry out Lagrangian chemical box model simulations using the
CiTTyCAT (Cambridge Tropospheric Trajectory model of Chemistry and
Transport) Lagrangian CTM (Pugh et al., 2012).
The aim of these simulations is to test the sensitivity of ozone in the
plume to differences in the chemical composition and the vertical position
of the plume following import into the Arctic. Using the same Lagrangian
model, Real et al. (2007) simulated photochemistry in an Alaskan biomass
burning plume advected over the North Atlantic Ocean and sampled
sequentially by several research aircraft. The model was able to reproduce
the observed ozone change in the plume observed between aircraft
interceptions. We use the CiTTyCAT model in single box mode, with a chemical
time step of 5 min and physical conditions taken from the ECMWF
trajectory data (position, temperature, specific humidity) updated every 30 min.
Photochemical kinetic data are updated with information where
available from the JPL recommendation (Sander et al., 2011), with further
data from IUPAC (Atkinson et al., 2004, 2006; Crowley et al., 2010) and the
Leeds Master Chemical Mechanism (http://mcm.leeds.ac.uk). In the absence of
adequate observations of aerosol size distribution in the plume, we specify
fixed aerosol surface area based on observed aerosol size distributions in
the boreal fire plume analysed by Real et al. (2007). This surface area
(3.0×10-6cm2cm-3) is used to calculate rates of
heterogeneous chemistry in the CiTTyCAT plume simulations. In order to
isolate sensitivities to chemical composition of the plume, we use a single
chemistry scheme in the CiTTyCAT Lagrangian simulations
(Pugh et al., 2012), and a single set of
meteorological data (ECMWF operational analyses data) to calculate transport
of the plume forward from 06:00 UT on 7 July 2008.
Time evolution of fire plume ozone (a) and PAN (b)
from CiTTyCAT Lagrangian model simulations in which the initial plume concentrations from
each of the POLMIP models have been used in combination with each of the forward trajectories
from the different POLMIP model plume positions (shown in Fig. 12f). Different
colours correspond to the POLMIP model initial chemical conditions and line styles correspond to POLMIP model forward trajectories.
CiTTyCAT Lagrangian box model simulations showing sensitivity of fire
plume ozone and NOx evolution in the Arctic to initial concentrations of
key species. Dotted and dashed lines show simulations initialized with 200 and 50 %, respectively,
of PAN (a–b), acetone and acetaldehyde (c–d), H2O2 and CH3OOH (e–f).
Ozone change from simulations with unperturbed initial concentrations are shown in Fig. 12a.
Simulated plume ozone change and sensitivities to transport and
chemistry
We investigate the range of plume ozone production and loss rates produced
by the diversity in chemical initial conditions and forward transport from
the plume maxima positions in the POLMIP models. Figure 12a shows simulated
4-day ozone change from the CiTTyCAT model in the plume when initialized by
different POLMIP model chemical conditions and when following individual
forward trajectories from plume maxima locations. The 4-day evolution of
pressure along each of these trajectories is shown in Fig. 12f. The 4-day
ozone change differs by ∼2.5ppbv across the range of POLMIP
model initial concentrations and forward trajectories, with some models
showing near-zero net ozone change (TM5, LMDZ-INCA), while others show net
ozone loss of more than 2 ppbv (CAM4-Chem, CAM5-Chem). These differences are
produced both by differences in the chemical composition of the plume and
different transport pathways forwards over the 4-day period. In particular,
differences in plume altitude and subsequent vertical displacement over the
4 days affects the formation and stability of PAN, as well as the balance
between ozone production and loss via the reactions of O(1D) with water
vapour and of O3+HO2 (Fig. 12d, e).
All plume simulations result in net ozone loss over 4 days, with ozone
destruction dominated by the reaction of O3+HO2. The
TM5-initialized plume shows very little net ozone change, likely due to its
relatively high altitude, suppressing both ozone production due to PAN
stability and ozone loss due to the dry upper tropospheric conditions of the
Arctic. The GMI-initialized plume shows a large ozone production tendency of
2 ppbvday-1 on average, which is balanced by large ozone loss rates of
slightly larger but similar magnitude (Fig. 12b). Larger ozone production tendency is
driven by the larger NOx concentrations in this plume compared to those
initialized from the other POLMIP models. Similar strong NOx limitation of
ozone production in Arctic biomass burning plumes was first noted during
flights made in western Alaska during the ABLE3A mission
(Jacob et al., 1992). The cycle of
ozone production and loss rates in this simulation also suggests that the
pathway followed by this plume from its initial position favours more
efficient photochemistry, due to exposure to relatively more hours of peak
solar radiation.
The simulated ozone change shows strong sensitivity to the physical position
and displacement of the air mass forward trajectories. Figure 13 shows net
changes in ozone and PAN from analogous CiTTyCAT simulations in which
forward trajectories from each of the seven model plume locations have been used
in conjunction with the seven sets of chemical initial conditions from the
POLMIP models. This produces an ensemble of 49 Lagrangian model simulations,
with varying combinations of chemical composition and physical displacement.
This diversity produces a much larger range in ozone change over 4 days
(approx. -5 to +4ppbv). Several simulations initialized by chemical
conditions from the TOMCAT and GMI models result in net ozone production (Fig. 13a).
These models have plume compositions enhanced in NOx and PAN compared with
the other POLMIP models (Table 2). In particular, the conversion of enhanced
PAN from the TOMCAT initial state to NOx (Fig. 13b) results in enhanced
ozone production in forward trajectories that descend (LMDZ-INCA, MOZART-4)
or begin at lower altitudes (GMI) (Fig. 12f).
Additional plume simulations using the CiTTyCAT model and the model-specific
forward trajectories, reveal strong sensitivity of ozone in the plume to
chemical composition simulated by the POLMIP models. We have investigated
separately sensitivity to (a) PAN, (b) oVOCs (acetaldehyde, acetone) and
(c) peroxides (H2O2, CH3OOH), using simulations where the
initial concentrations of each of these three sets of species from each
POLMIP model are decreased and increased by a factor of 2. A factor 2
perturbation is consistent with inter-model differences and biases against
observations for these species along the ARCTAS DC8 flight tracks (Emmons et
al., 2014). We apply the same fractional perturbation to each species to
directly compare sensitivities of Arctic ozone photochemistry to
uncertainties in their abundances.
Ozone sensitivities to initial PAN concentration in the plume demonstrate
the potential importance of model biases in Arctic NOy for Arctic
tropospheric ozone. Figure 14 shows changes in simulated ozone and NOx
evolution in the plume produced by simulations with perturbations to initial
PAN. Increasing and decreasing initial PAN abundance in the plume leads to a
reduction in NOx and an increase in NOx, respectively (Fig. 14b). The
consequent impacts on ozone change in the plume largely depend on the
absolute NOx concentration, and the magnitude of the NOx perturbation
brought about by the fractional change in initial PAN. In the TOMCAT,
LMDZ-INCA and GMI-initialized plumes, an increase in initial PAN leads to a
shift from slight net ozone loss to net ozone production of between 0.5 and
1 ppbv over 4 days (Fig. 14b). Model plumes that descend over the 4 days
have increased NOx sensitivity to the PAN perturbation. Such altitude
changes promote reduced PAN stability and release of NO2. This
illustrates the potential sensitivity of in situ Arctic ozone production in
the simulated fire plume to model NOy partitioning errors. Increasing and
decreasing initial oVOC abundances leads to enhancement and suppression of
ozone loss in the plume respectively over the following 4 days (Fig. 14c and
d), due to the role of acetaldehyde and acetone as a source of the
peroxyacetyl radical during their photo-oxidation. This promotes the
formation of PAN, reducing NOx concentrations in the plume. Consequently,
model plumes in which NOx concentrations are large enough to promote ozone
production show larger ozone sensitivity to this perturbation. These results
suggest that after having undergone export from the continental boundary
layer and long-range transport into the Arctic, PAN formation and loss may
still play an important role in ozone photochemistry in such plumes. In
plumes with very low NOx abundances, and dominated by ozone loss,
perturbation to initial peroxide concentrations produces a larger effect on
ozone (approx. ±0.5ppbv over 4 days in the CAM5-Chem plume) (Fig. 14e).
Increased and decreased peroxide leads to increases and decreases in
HOx production from peroxide photolysis, resulting in changes to the
rate of ozone loss via O3+HO2. Increased initial peroxide
concentrations also lead to enhanced removal of NOx in the plume, due to
increased HOx production (Fig. 14f).
Summary and conclusions
We have evaluated tropospheric ozone enhancement in air dominated by biomass
burning emissions at high latitudes (>50∘ N) in the summer, using
simulations from the POLMIP multi-model comparison exercise for July 2008.
Using 25-day fixed-lifetime CO tracers emitted from fires and anthropogenic
sources in the models, we calculated ΔO3/ΔCO
enhancement ratios in air dominated by fire emissions. All POLMIP models
that simulated fixed-lifetime tracers demonstrate positive ozone enhancement
in fire-dominated air, with ozone enhancement increasing with air mass age
on average in the models, suggesting net tropospheric ozone production in
biomass burning air masses transported to the Arctic. ΔO3/ΔCO values ranged between 0.039 and 0.196 ppbvppbv-1 (mean:
0.113 ppbvppbv-1) in the younger air, and between 0.140 and 0.261 ppbvppbv-1
(mean: 0.193 ppbv) in the more aged air, with age since emission defined by
the ratio of propane to ethane mixing ratios. These values are in broad
agreement with the range of observational estimates from the literature, and
larger than those in some previous modelling studies. Model NOy partitioning
may play an important role in determining lower model-diagnosed ozone
production efficiencies.
Model ΔPAN/ΔCO enhancement ratios at high latitudes show
distinct groupings according to the meteorological data used to drive the
models. ECMWF-forced models produce larger ΔPAN/ΔCO values
(4.44–6.28 pptvppbv-1) than GEOS5-forced models (2.02–3.02 pptvppbv-1), which
we show is likely linked to differences in the efficiency of vertical
transport during poleward export from mid-latitude source regions.
Comparison with limited observations from the ARCTAS-B aircraft campaign
suggests that the larger PAN enhancement ratios simulated by the
ECMWF-forced models are consistent with the majority of these observations.
We find little relationship between the efficiency of Arctic PAN import in
fire-dominated air and Arctic ozone enhancement across the diverse range of
POLMIP models.
All POLMIP models are capable of resolving a large plume of mixed Asian
anthropogenic and Siberian fire pollution, which is imported to the Arctic
on 7 July 2008, with close similarities in simulated horizontal
structure. These features are in good agreement with CO observations from
the IASI satellite instrument and the FLEXPART Lagrangian particle
dispersion model, shown in a previous study
(Sodemann et al., 2011). Fixed-lifetime tracers
simulated by the models show that the leading edge of this plume is
dominated by fire emissions in all POLMIP models. Simulations using a
Lagrangian chemical transport model show that 4-day net ozone change in the
plume is sensitive to differences in plume chemical composition and plume
vertical position among the POLMIP models. In particular, Arctic ozone
evolution in the plume is highly sensitive to initial concentrations of PAN,
as well as oxygenated VOCs (acetone, acetaldehyde), due to their role in
producing the peroxyacetyl radical PAN precursor. Vertical displacement is
also important due to its effects on the stability of PAN, and subsequent
effect on NOx abundance. In plumes where net ozone production is limited, we
find that the lifetime of ozone in the plume is sensitive to hydrogen
peroxide loading, due to the production of HOx from peroxide
photolysis, and the key role of HO2+O3 in controlling ozone
loss.
Overall, our results suggest that emissions from biomass burning lead to
large-scale enhancement in high-latitude NOy and tropospheric ozone during
summer, with increasing production of ozone as air masses age, and that this
is consistent across a wide range of chemical transport models using the
same emissions data. In addition, model deficiencies and inter-model
differences in simulating species that are less commonly observed in the
Arctic (PANs, oxygenated VOCs, and peroxides) are important to understand
due to their substantial roles in governing in situ ozone production and
loss in plumes imported to the summertime Arctic troposphere.
The Supplement related to this article is available online at doi:10.5194/acp-15-6047-2015-supplement.
Acknowledgements
S. R. Arnold acknowledges support from the NCAR Advanced Study Program via a Faculty
Fellowship award, and the NCAR Atmospheric Chemistry Division. S. R. Arnold and S. A. Monks
were supported by the EurEX project, funded by the UK Natural Environment
Research Council (ref: NE/H020241/1). L. K. Emmons and S. Tilmes acknowledge the
National Center for Atmospheric Research, which is sponsored by the US
National Science Foundation. Author L. K. Emmons acknowledges support from the
National Aeronautics and Space Administration under Award No. NNX08AD22G
issued through the Science Mission Directorate, Tropospheric Composition
Program. Authors K. S. Law, J. L. Thomas, S. Turquety and Y. Long acknowledge support
from projects Agence National de Recherche (ANR) Climate Impact of
Short-lived Climate Forcers and Methane in the Arctic (CLIMSLIP) Blanc SIMI
5-6 021 01 and CLIMSLIP-LEFE (CNRS-INSU). V. Huijnen acknowledges funding from the
European Union's Seventh Framework Programme (FP7) under Grant Agreement no. 283576. Contributions from the Swedish Meteorological and Hydrological
Institute were funded by the Swedish Environmental Protection Agency under
contract NV-09414-12 and through the Swedish Climate and Clean Air research
programme, SCAC.
Edited by: T. Butler
ReferencesAlvarado, M. J., Logan, J. A., Mao, J., Apel, E., Riemer, D., Blake, D., Cohen, R. C.,
Min, K.-E., Perring, A. E., Browne, E. C., Wooldridge, P. J., Diskin, G. S., Sachse, G. W.,
Fuelberg, H., Sessions, W. R., Harrigan, D. L., Huey, G., Liao, J., Case-Hanks, A.,
Jimenez, J. L., Cubison, M. J., Vay, S. A., Weinheimer, A. J., Knapp, D. J., Montzka, D. D.,
Flocke, F. M., Pollack, I. B., Wennberg, P. O., Kurten, A., Crounse, J., Clair, J. M. St.,
Wisthaler, A., Mikoviny, T., Yantosca, R. M., Carouge, C. C., and Le Sager, P.:
Nitrogen oxides and PAN in plumes from boreal fires during ARCTAS-B and their impact
on ozone: an integrated analysis of aircraft and satellite observations, Atmos. Chem. Phys., 10, 9739–9760, 10.5194/acp-10-9739-2010, 2010.
Andreae, M. O., Browell, E. V., Garstang, M., Gregory, G. L., Harriss, R.
C., Hill, G. F., Jacob, D. J., Pereira, M. C., Sachse, G. W., Setzer, A. W.,
Dias, P. L. S., Talbot, R. W., Torres, A. L., and Wofsy, S. C.:
Biomass-burning emissions and associated haze layers over Amazonia, J. Geophys. Res.-Atmos., 93, 1509–1527, 1988.Atkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F., Hynes, R. G.,
Jenkin, M. E., Rossi, M. J., and Troe, J.: Evaluated kinetic and photochemical data for
atmospheric chemistry: Volume I – gas phase reactions of Ox, HOx, NOx and SOx species, Atmos. Chem. Phys., 4, 1461-1738, 10.5194/acp-4-1461-2004, 2004.Atkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F., Hynes, R. G.,
Jenkin, M. E., Rossi, M. J., Troe, J., and IUPAC Subcommittee: Evaluated
kinetic and photochemical data for atmospheric chemistry: Volume II – gas phase
reactions of organic species, Atmos. Chem. Phys., 6, 3625–4055, 10.5194/acp-6-3625-2006, 2006.Bertschi, I. T., Jaffe, D. A. Jaeglé, L., Price, H. U., and Dennison, J. B.:
PHOBEA/ITCT 2002 airborne observations of transpacific transport of ozone, CO,
volatile organic compounds, and aerosols to the northeast Pacific: Impacts
of Asian anthropogenic and Siberian boreal fire emissions, J. Geophys. Res., 109, D23S12, 10.1029/2003JD004328, 2004.Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444, 10.1126/science.1155121, 2008.Bourgeois, Q. and Bey, I.: Pollution transport efficiency toward the Arctic:
Sensitivity to aerosol scavenging and source regions, J. Geophys. Res., 116, D08213, 10.1029/2010JD015096, 2011.
Calvert, J. G.: Hydrocarbon involvement in photochemical smog formation in Los Angeles atmosphere, Environ. Sci. Technol., 10, 256–262, 1976.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A.,
DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao, S.,
and Thornton, P.: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013: The Physical
Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K.,
Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013.Crowley, J. N., Ammann, M., Cox, R. A., Hynes, R. G., Jenkin, M. E., Mellouki, A.,
Rossi, M. J., Troe, J., and Wallington, T. J.: Evaluated kinetic and photochemical
data for atmospheric chemistry: Volume V – heterogeneous reactions on solid
substrates, Atmos. Chem. Phys., 10, 9059–9223, 10.5194/acp-10-9059-2010, 2010.
de Groot, W. J., Flannigan, M. D., and Cantin, A. S.: Climate change impacts
on future boreal fire regimes, For. Ecol. Manage., 294, 35–44, 2013.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. Discuss., 14, 29331–29393, 10.5194/acpd-14-29331-2014, 2014.Fischer, E. V., Jacob, D. J., Yantosca, R. M., Sulprizio, M. P., Millet, D. B.,
Mao, J., Paulot, F., Singh, H. B., Roiger, A., Ries, L., Talbot, R. W.,
Dzepina, K., and Pandey Deolal, S.: Atmospheric peroxyacetyl nitrate (PAN):
a global budget and source attribution, Atmos. Chem. Phys., 14, 2679–2698, 10.5194/acp-14-2679-2014, 2014.Fisher, J. A., Jacob, D. J., Purdy, M. T., Kopacz, M., Le Sager, P., Carouge, C.,
Holmes, C. D., Yantosca, R. M., Batchelor, R. L., Strong, K., Diskin, G. S.,
Fuelberg, H. E., Holloway, J. S., Hyer, E. J., McMillan, W. W., Warner, J.,
Streets, D. G., Zhang, Q., Wang, Y., and Wu, S.: Source attribution and interannual
variability of Arctic pollution in spring constrained by aircraft (ARCTAS, ARCPAC)
and satellite (AIRS) observations of carbon monoxide, Atmos. Chem. Phys., 10, 977–996, 10.5194/acp-10-977-2010, 2010.Goode, J. G., Yokelson, R. J., Ward, D. E., Susott, R. A., Babbitt, R. E.,
Davies, M. A., and Hao, W. M.: Measurements of excess O3, CO2, CO, CH4,
C2H4, C2H2, HCN, NO, NH3, HCOOH, CH3COOH, HCHO, and CH3OH in 1997 Alaskan
biomass burning plumes by airborne fourier transform infrared spectroscopy
(AFTIR), J. Geophys. Res.-Atmos., 105, 22147–22166,
2000.Honrath, R. E., Owen, R. C., ValMartin, M., Reid, J. S., Lapina, K., Fialho, P.,
Dziobak, M. P., Kleissl, J., and Westphal, D. L.: Regional and hemispheric impacts
of anthropogenic and biomass burning emissions on summertime CO and O3
in the North Atlantic lower free troposphere, J. Geophys. Res., 109, D24310, 10.1029/2004JD005147, 2004.Hornbrook, R. S., Blake, D. R., Diskin, G. S., Fried, A., Fuelberg, H. E.,
Meinardi, S., Mikoviny, T., Richter, D., Sachse, G. W., Vay, S. A., Walega, J.,
Weibring, P., Weinheimer, A. J., Wiedinmyer, C., Wisthaler, A., Hills, A.,
Riemer, D. D., and Apel, E. C.: Observations of nonmethane organic compounds
during ARCTAS – Part 1: Biomass burning emissions and plume enhancements, Atmos. Chem. Phys., 11, 11103–11130, 10.5194/acp-11-11103-2011, 2011.
Jacob, D. J., Wofsy, S. C., Bakwin, P. S., Fan, S. M., Harriss, R. C.,
Talbot, R. W., Bradshaw, J. D., Sandholm, S. T., Singh, H. B., Browell, E.
V., Gregory, G. L., Sachse, G. W., Shipham, M. C., Blake, D. R., and
Fitzjarrald, D. R.: Summertime photochemistry of the troposphere at high
northern latitudes, J. Geophys. Res.-Atmos., 97,
16421–16431, 1992.Jacob, D. J., Crawford, J. H., Maring, H., Clarke, A. D., Dibb, J. E., Emmons, L. K.,
Ferrare, R. A., Hostetler, C. A., Russell, P. B., Singh, H. B., Thompson, A. M.,
Shaw, G. E., McCauley, E., Pederson, J. R., and Fisher, J. A.: The Arctic Research of
the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission:
design, execution, and first results, Atmos. Chem. Phys., 10, 5191–5212, 10.5194/acp-10-5191-2010, 2010.
Jaffe, D. A. and Wigder, N. L.: Ozone production from wildfires: A critical
review, Atmos. Environ., 51, 1–10, 2012.Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z.,
Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J.,
Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R.,
Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded
anthropogenic and biomass burning emissions of reactive gases and aerosols:
methodology and application, Atmos. Chem. Phys., 10, 7017–7039, 10.5194/acp-10-7017-2010, 2010.Law, K. S., Stohl, A., Quinn, P. K., Brock, C., Burkhart, J., Paris, J.-D., Ancellet, G.,
Singh, H. B., Roiger, A., Schlager, H., Dibb, J., Jacob, D. J., Arnold, S. R., Pelon, J.,
and Thomas, J. L., Arctic Air Pollution: New Insights from POLARCAT-IPY, B. Am. Meteorol. Soc., 95, 1873–1895, 10.1175/BAMS-D-13-00017.1, 2014.Liang, Q., Rodriguez, J. M., Douglass, A. R., Crawford, J. H., Olson, J. R.,
Apel, E., Bian, H., Blake, D. R., Brune, W., Chin, M., Colarco, P. R., da Silva, A.,
Diskin, G. S., Duncan, B. N., Huey, L. G., Knapp, D. J., Montzka, D. D., Nielsen, J. E.,
Pawson, S., Riemer, D. D., Weinheimer, A. J., and Wisthaler, A.: Reactive nitrogen,
ozone and ozone production in the Arctic troposphere and the impact of
stratosphere–troposphere exchange, Atmos. Chem. Phys., 11, 13181–13199, 10.5194/acp-11-13181-2011, 2011.Mao, J., Fan, S., Jacob, D. J., and Travis, K. R.: Radical loss in the atmosphere
from Cu–Fe redox coupling in aerosols, Atmos. Chem. Phys., 13, 509–519, 10.5194/acp-13-509-2013, 2013a.
Mao, J. Q., Horowitz, L. W., Naik, V., Fan, S. M., Liu, J. F., and Fiore, A.
M.: Sensitivity of tropospheric oxidants to biomass burning emissions:
implications for radiative forcing, Geophys. Res. Lett., 40,
1241–1246, 2013b.
Mauzerall, D. L., Jacob, D. J., Fan, S. M., Bradshaw, J. D., Gregory, G. L.,
Sachse, G. W., and Blake, D. R.: Origin of tropospheric ozone at remote high
northern latitudes in summer, J. Geophys. Res.-Atmos.,
101, 4175–4188, 1996.Methven, J., Arnold, S. R., O'Connor, F. M., Barjat, H., Dewey, K., Kent, J., and Brough, N.:
Estimating photochemically produced ozone throughout a domain using flight
data and a Lagrangian model, J. Geophys. Res., 108, 4271, 10.1029/2002JD002955, 2003.Monks, S. A., Arnold, S. R., and Chipperfield, M. P.: Evidence for El Niño –
Southern Oscillation (ENSO) influence on Arctic CO interannual variability through
biomass burning emissions, Geophys. Res. Lett., 39, L14804, 10.1029/2012GL052512, 2012.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.Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J.-F.,
Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V.,
Faluvegi, G., Folberth, G. A., Josse, B., Lee, Y. H., MacKenzie, I. A.,
Nagashima, T., van Noije, T. P. C., Plummer, D. A., Righi, M., Rumbold, S. T.,
Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S.,
and Zeng, G.: Preindustrial to present-day changes in tropospheric hydroxyl
radical and methane lifetime from the Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 5277–5298, 10.5194/acp-13-5277-2013, 2013.Olson, J. R., Crawford, J. H., Brune, W., Mao, J., Ren, X., Fried, A., Anderson, B.,
Apel, E., Beaver, M., Blake, D., Chen, G., Crounse, J., Dibb, J., Diskin, G.,
Hall, S. R., Huey, L. G., Knapp, D., Richter, D., Riemer, D., Clair, J. St.,
Ullmann, K., Walega, J., Weibring, P., Weinheimer, A., Wennberg, P., and Wisthaler, A.:
An analysis of fast photochemistry over high northern latitudes during spring
and summer using in-situ observations from ARCTAS and TOPSE, Atmos. Chem. Phys., 12, 6799–6825, 10.5194/acp-12-6799-2012, 2012.Paris, J.-D., Stohl, A., Nédélec, P., Arshinov, M. Yu., Panchenko, M. V., Shmargunov, V. P.,
Law, K. S., Belan, B. D., and Ciais, P.: Wildfire smoke in the Siberian Arctic in
summer: source characterization and plume evolution from airborne measurements, Atmos. Chem. Phys., 9, 9315–9327, 10.5194/acp-9-9315-2009, 2009.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.Parrington, M., Palmer, P. I., Henze, D. K., Tarasick, D. W., Hyer, E. J., Owen, R. C.,
Helmig, D., Clerbaux, C., Bowman, K. W., Deeter, M. N., Barratt, E. M., Coheur, P.-F.,
Hurtmans, D., Jiang, Z., George, M., and Worden, J. R.: The influence of boreal biomass
burning emissions on the distribution of tropospheric ozone over North America and the
North Atlantic during 2010, Atmos. Chem. Phys., 12, 2077–2098, 10.5194/acp-12-2077-2012, 2012.Parrington, M., Palmer, P. I., Lewis, A. C., Lee, J. D., Rickard, A. R., Di Carlo, P., Taylor, J. W.,
Hopkins, J. R., Punjabi, S., Oram, D. E., Forster, G., Aruffo, E., Moller, S. J., Bauguitte, S. J.-B.,
Allan, J. D., Coe, H., and Leigh, R. J.: Ozone photochemistry in boreal biomass
burning plumes, Atmos. Chem. Phys., 13, 7321–7341, 10.5194/acp-13-7321-2013, 2013.Pfister, G. G., Emmons, L. K., Hess, P. G., Honrath, R., Lamarque, J. F.,
Martin, M. V., Owen, R. C., Avery, M. A., Browell, E. V., Holloway, J. S.,
Nedelec, P., Purvis, R., Ryerson, T. B., Sachse, G. W., and Schlager, H.:
Ozone production from the 2004 North American boreal fires, J. Geophys. Res., 111, D24S07, 10.1029/2006JD007695, 2006.Pommier, M., Law, K. S., Clerbaux, C., Turquety, S., Hurtmans, D., Hadji-Lazaro, J., Coheur, P.-F.,
Schlager, H., Ancellet, G., Paris, J.-D., Nédélec, P., Diskin, G. S., Podolske, J. R.,
Holloway, J. S., and Bernath, P.: IASI carbon monoxide validation over the Arctic
during POLARCAT spring and summer campaigns, Atmos. Chem. Phys., 10, 10655–10678, 10.5194/acp-10-10655-2010, 2010.Pugh, T. A. M., Cain, M., Methven, J., Wild, O., Arnold, S. R., Real, E., Law, K. S.,
Emmerson, K. M., Owen, S. M., Pyle, J. A., Hewitt, C. N., and MacKenzie, A. R.:
A Lagrangian model of air-mass photochemistry and mixing using a trajectory
ensemble: the Cambridge Tropospheric Trajectory model of Chemistry And
Transport (CiTTyCAT) version 4.2, Geosci. Model Dev., 5, 193–221, 10.5194/gmd-5-193-2012, 2012.Quinn, P. K., Bates, T. S., Baum, E., Doubleday, N., Fiore, A. M., Flanner, M.,
Fridlind, A., Garrett, T. J., Koch, D., Menon, S., Shindell, D., Stohl, A.,
and Warren, S. G.: Short-lived pollutants in the Arctic: their climate impact
and possible mitigation strategies, Atmos. Chem. Phys., 8, 1723–1735, 10.5194/acp-8-1723-2008, 2008.Real, E., Law, K. S., Wienzierl, B., Fiebig, M., Petzold, A., Wild, O., Methven, J.,
Arnold, S. R., Stohl, A., Huntrieser, H., Roiger, A., Schlager, H., Stewart, D., Avery, M.,
Sachse, G., Browell, E., Ferrare, R., and Blake, D.: Processes influencing ozone levels
in Alaskan forest fire plumes during long-range transport over
the North Atlantic, J. Geophys. Res., 112, D10S41, 10.1029/2006JD007576, 2007.Roiger, A., Schlager, H., Schäfler, A., Huntrieser, H., Scheibe, M., Aufmhoff, H.,
Cooper, O. R., Sodemann, H., Stohl, A., Burkhart, J., Lazzara, M., Schiller, C.,
Law, K. S., and Arnold, F.: In-situ observation of Asian pollution transported
into the Arctic lowermost stratosphere, Atmos. Chem. Phys., 11, 10975–10994, 10.5194/acp-11-10975-2011, 2011.Sander, S. P., Abbatt, J., Barker, J. R., Burkholder, J. B., Friedl, R. R.,
Golden, D. M., Huie, R. E., Kolb, C. E., Kurylo, M. J., Moortgat, G. K., Orkin, V. L.,
and Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric,
Studies Evaluation Number 17, JPL Publication 10-6, available at: http://jpldataeval.jpl.nasa.gov (last access: 2 June 2015), Jet Propulsion Laboratory, Pasadena, 2011.Shindell, D.: Local and remote contributions to Arctic warming, Geophys. Res. Lett., 34, L14704, 10.1029/2007GL030221, 2007.
Shindell, D. and Faluvegi, G.: Climate response to regional radiative
forcing during the twentieth century, Nat. Geosci., 2, 294–300, 2009.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.
Singh, H. B. and Hanst, P. L.: Peroxyacetyl nitrate (PAN) in the unpolluted atmosphere – an important reservoir for nitrogen-oxides, Geophys.
Res. Lett., 8, 941–944, 1981.
Singh, H. B., O'Hara, D., Herlth, D., Bradshaw, J. D., Sandholm, S. T.,
Gregory, G. L., Sachse, G. W., Blake, D. R., Crutzen, P. J., and Kanakidou,
M. A.: Atmospheric measurements of peroxyacetyl nitrate and other organic
nitrates at high latitudes: Possible sources and sinks, J.
Geophys. Res.-Atmos., 97, 16511–16522, 1992.
Singh, H. B., Anderson, B. E., Brune, W. H., Cai, C., Cohen, R. C.,
Crawford, J. H., Cubison, M. J., Czech, E. P., Emmons, L., Fuelberg, H. E.,
Huey, G., Jacob, D. J., Jimenez, J. L., Kaduwela, A., Kondo, Y., Mao, J.,
Olson, J. R., Sachse, G. W., Vay, S. A., Weinheimer, A., Wennberg, P. O.,
Wisthaler, A., and Team, A. S.: Pollution influences on atmospheric
composition and chemistry at high northern latitudes: Boreal and California
forest fire emissions, Atmos. Environ., 44, 4553–4564, 2010.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.
Tanimoto, H., Matsumoto, K., and Uematsu, M.: Ozone-CO Correlations in
Siberian Wildfire Plumes Observed at Rishiri Island, Sola, 4, 65–68, 2008.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.Val Martin, M. R., Honrath, R. E., Owen, R. C., Pfister, G., Fialho, P., and Barata, F.:
Significant enhancements of nitrogen oxides, ozone and aerosol black carbon in the
North Atlantic lower free troposphere resulting from North American boreal wildfires, J. Geophys. Res., 111, D23S60, 10.1029/2006JD007530,
2006.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S.,
Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions
and the contribution of deforestation, savanna, forest, agricultural, and peat
fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, 10.5194/acp-10-11707-2010, 2010.Voulgarakis, A., Telford, P. J., Aghedo, A. M., Braesicke, P., Faluvegi, G.,
Abraham, N. L., Bowman, K. W., Pyle, J. A., and Shindell, D. T.: Global multi-year
O3–CO correlation patterns from models and TES satellite
observations, Atmos. Chem. Phys., 11, 5819–5838, 10.5194/acp-11-5819-2011, 2011.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.Warneke, C., Froyd, K. D., Brioude, J., Bahreini, R., Brock, C. A., Cozic,
J., de Gouw, J. A., Fahey, D. W., Ferrare, R., Holloway, J. S., Middlebrook,
A. M., Miller, L., Montzka, S., Schwarz, J. P., Sodemann, H., Spackman, J.
R., and Stohl, A.: An important contribution to springtime Arctic aerosol
from biomass burning in Russia, Geophys. Res. Lett., 37, L01801, 10.1029/2009GL041816, 2010.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.Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J.,
and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to
estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, 10.5194/gmd-4-625-2011, 2011.Wofsy, S. C., Sachse, G. W., Gregory, G. L., Blake, D. R., Bradshaw, J. D.,
Sandholm, S. T., Singh, H. B., Barrick, J. A., Harriss, R. C., Talbot, R.
W., Shipham, M. A., Browell, E. V., Jacob, D. J., and Logan, J. A.:
Atmospheric chemistry in the Arctic and sub-Arctic – influence of natural fires, industrial emissions and stratospheric inputs, J.
Geophys. Res.-Atmo., 97, 16731–16746, 1992.
Zhang, B., Owen, R. C., Perlinger, J. A., Kumar, A., Wu, S., Val Martin, M., Kramer, L.,
Helmig, D., and Honrath, R. E.: A semi-Lagrangian view of ozone production tendency
in North American outflow in the summers of 2009 and 2010, Atmos. Chem. Phys., 14, 2267–2287, 10.5194/acp-14-2267-2014, 2014.