ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-11477-2015Transport pathways of peroxyacetyl nitrate in the upper troposphere and
lower stratosphere from different monsoon systems during the summer monsoon
seasonFadnavisS.suvarna@tropmet.res.inhttps://orcid.org/0000-0003-4442-0755SemeniukK.https://orcid.org/0000-0003-0853-9679SchultzM. G.https://orcid.org/0000-0003-3455-774XKieferM.MahajanA.https://orcid.org/0000-0002-2909-5432PozzoliL.https://orcid.org/0000-0003-0485-9624SonbawaneS.Indian Institute of Tropical Meteorology, Pune, IndiaDepartment of Earth and Space Sciences and Engineering, York
University, Toronto, CanadaInstitute for Energy and Climate Research-Troposphere (IEK-8),
Forschungszentrum Jülich, Jülich, GermanyKarlsruhe Institute of Technology, Institute for Meteorology and
Climate Research, Karlsruhe, GermanyEurasia Institute of Earth Sciences, Istanbul Technical University,
Istanbul, TurkeyS. Fadnavis (suvarna@tropmet.res.in)19October20151520114771149917April20151June20152October20152October2015This 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://acp.copernicus.org/articles/15/11477/2015/acp-15-11477-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/11477/2015/acp-15-11477-2015.pdf
The Asian summer monsoon involves complex transport patterns with large-scale
redistribution of trace gases in the upper troposphere and lower
stratosphere (UTLS). We employ the global chemistry–climate model
ECHAM5–HAMMOZ in order to evaluate the transport pathways and the
contributions of nitrogen oxide species peroxyacetyl nitrate (PAN), NOx and HNO3 from
various monsoon regions, to the UTLS over southern Asia and vice versa.
Simulated long-term seasonal mean mixing ratios are compared with trace gas
retrievals from the Michelson Interferometer for Passive Atmospheric
Sounding aboard ENVISAT(MIPAS-E) and aircraft campaigns during the monsoon
season (June–September) in order to evaluate the model's ability to
reproduce these transport patterns.
The model simulations show that there are three regions which contribute
substantial pollution to the South Asian UTLS: the Asian summer monsoon
(ASM), the North American monsoon (NAM) and the West African monsoon (WAM).
However, penetration due to ASM convection reaches deeper into the UTLS
compared to NAM and WAM outflow. The circulation in all three monsoon
regions distributes PAN into the tropical latitude belt in the upper
troposphere (UT). Remote transport also occurs in the extratropical UT where
westerly winds drive North American and European pollutants eastward where
they can become part of the ASM convection and lifted into the lower
stratosphere. In the lower stratosphere the injected pollutants are
transported westward by easterly winds. Sensitivity experiments with
ECHAM5–HAMMOZ for simultaneous NOx and non-methane volatile organic
compounds (NMVOCs) emission change (-10 %) over ASM, NAM and WAM confirm similar transport. Our analysis shows
that a 10 % change in Asian emissions transports ∼ 5–30 ppt of PAN
in the UTLS over Asia, ∼ 1–10 ppt of PAN in the UTLS of
northern subtropics and mid-latitudes, ∼ 7–10 ppt of HNO3
and ∼ 1–2 ppb of ozone in UT over Asia. Comparison of emission
change over Asia, North America and Africa shows that the highest transport of
HNO3 and ozone occurs in the UT over Asia and least over Africa.
The intense convective activity in the monsoon regions is associated with
lightning and thereby the formation of additional NOx. This also
affects the distribution of PAN in the UTLS. Simulations with and without
lightning show an increase in the concentrations of PAN (∼ 40 %), HNO3 (75%), NOx (70 %) and ozone (30 %) over the
regions of convective transport. Lightning-induced production of these
species is higher over equatorial Africa and America compared to the ASM
region. This indicates that the contribution of anthropogenic emissions to
PAN in the UTLS over the ASM is higher than that of lightning.
Introduction
Deep monsoon convection plays a key role in venting chemical constituents
from the boundary layer and their export from source regions (Dickerson et
al., 1987). The largest regional monsoon systems are the North American
monsoon (NAM), Asian summer monsoon (ASM), western North Pacific monsoon
(WNPM), South American monsoon (SAM), West African monsoon (WAM) and the
Australian monsoon (AUSM) (Chang et al., 2011). Recent observation and
modeling studies indicate that the Asian summer monsoon (Park et al., 2004, 2007;
Li et al., 2005; Randel and Park, 2006; Fu et al., 2006;
Xiong et al., 2009; Randel et al., 2010; Fadnavis et al., 2013), the North
American monsoon (Schmitz and Mullen 1996; Collier and Zhang, 2006;
Barth et al., 2012) and the West African monsoon (Bouarar et al., 2011) play
important roles in the transport of chemical constituents out of the
boundary layer into the Northern Hemisphere in the upper troposphere (UT). A
number of studies have documented that large amounts of pollution from Asia
are transported across the tropopause (Park et al., 2007; Fu et al., 2006); however, transport from other monsoon systems (WAM, NAM) and
their contribution to Asia have so far received less attention. Until now there
has been no attempt to assess the relative contributions from these source
regions and to analyze the transport patterns including possible
recirculation within one consistent model framework. Prior model simulations
suggest that pollutants transported from the Asian monsoon region can
contribute substantially to the budgets of stratospheric ozone, NOx and
water vapor (Randel et al., 2010). Ozone formation in the anticyclone is
also enhanced by transport of pollution plumes from the North American
monsoon which are rich in volatile organic compounds (VOCs) (Li et al., 2005;
Zhang et al., 2008; Choi et al., 2009; Barth et al., 2012). The deep monsoon
convection over West Africa transports central African emissions to the
upper troposphere and lower stratosphere (UTLS), leading to large ozone
changes in the lower stratosphere (Bouarar et al., 2011). A number of
studies have reported transport of chemical constituents into the UTLS due
to the Asian monsoon convection, while less attention has been paid to deep
convective transport from North/South America and West Africa to the lower
stratosphere and to their relative contributions to the UTLS composition
over the ASM region.
This study investigates the transport patterns and relative contributions to
the Asian monsoon anticyclone of three oxidized nitrogen species, namely
peroxyacetyl nitrate (PAN), NOx (the sum of NO and NO2) and
nitric acid (HNO3). PAN is a secondary pollutant that marks the
transport and conversion of surface NOx after it is emitted. The focus
of this study is placed on PAN as this species has a long lifetime (90–180 days) in the UT and can be favorably observed by satellite instruments. At
the same time, its short chemical lifetime in the lower troposphere (not
longer than 30 days) results in a much tighter association between the
emissions regions of its precursors and transport compared to species such
as carbon monoxide (CO). The much longer chemical lifetime of CO in the
lower troposphere allows it to reach the UTLS via circuitous pathways that
are not accessible to PAN. In contrast, PAN is a tracer that allows for a
clearer identification of NOx pollution transport pathways between the
surface and the UTLS. We perform NOx and NMVOCs (non-methane volatile organic
compounds) emission sensitivity simulations (where emissions of NOx and NMVOCs were simultaneously
reduced by 10 %) in order to investigate the relative contributions from
Asia, Africa and America to the PAN, HNO3 and O3 concentrations in
the UTLS.
PAN is formed through the oxidation of NMVOCs in the presence of NOx (Fischer et al., 2014). It is primarily
formed after oxidation of acetaldehyde (CH3CHO) or after photolysis of
acetone (CH3COCH3) and methyl glyoxal (CH3COCHO), all of
which are oxidation products of various NMVOCs. The actual formation of PAN
proceeds in the reaction of the peroxy acetyl radical (CH3CO3)
with NO2. This reaction is reversible and the thermal decomposition of
PAN back to CH3CO3 and NO2 is the main sink of PAN, although
in the UTLS, PAN photolysis becomes the dominant loss process. Two minor loss
processes of PAN are reactions with OH and dry deposition (Talukdar et al.,
1995; Fischer et al., 2014). As stated by Fischer et al. (2014) global,
biogenic VOCs like isoprene and terpenes, contribute most to PAN formation,
but in the context of our study it is important to note that the oxidation
of many alkanes and alkenes which are emitted from anthropogenic sources
lead to PAN formation as well. The major anthropogenic sources of NMVOCs are
the emissions from fossil fuel and biofuel combustion and from industrial
solvents (Tang et al., 2009). Biomass burning, biogenic and soil emissions
also contribute to NMVOC and NOx production. Anthropogenic sources are
dominant in the extratropical Northern Hemisphere outside the spring
season. In spring, when surface PAN peaks, biogenic and anthropogenic NMVOCs
species are responsible for ∼ 50 % of the PAN burden.
In the UT, lightning can add substantial amounts of NOx and thus lead
to additional PAN production if NMVOC precursors are present, e.g., from
convective uplifting from the boundary layer (Tie et al., 2001). The
estimated global NOx production by lightning is ∼ 3–5 Tg N year-1 (Schumann and Huntrieser, 2007; Martin et al., 2007; Murray et
al., 2012). Strong lightning activity during ASM, NAM and WAM (Shepon, et
al., 2007; Evett et al., 2008; Ranalkar and Chaudhari, 2009; Barret et al.,
2010; Penki and Kamra, 2013) hence contributes to PAN production in the
UTLS. The estimated increase in PAN is ∼ 20–30 % due to
NOx enhancement by lightning (Tie et al., 2001).
The thermal decomposition rate of PAN is highly temperature-dependent. In
the UTLS, temperatures are sufficiently low to prevent thermal decomposition
of PAN and therefore the chemical lifetime of PAN in this region is
∼ 90–180 days (Arnold and Hauck, 1985). The PAN lifetime in
our ECHAM5–HAMMOZ simulations varies between 80 days and 170 days in the
tropical UTLS. Several studies (Tereszchuk et al., 2013; Glatthor et al.,
2007; Singh et al., 1987) have reported that the lifetime of PAN varies between
2 and 4 months. PAN thus travels over long distances and affects the NOy
partitioning in areas that are far away from the precursor emission regions.
Upon descent into warmer regions of the troposphere, PAN releases NOx
which in turn increases ozone and OH production in remote regions (Singh et
al., 1986, 1998; Hudman et al., 2004). PAN mixing ratios vary
from less than 1 pptv in the remote marine atmosphere (as observed during
the NASA GTE PEM-Tropics B campaign in the South Pacific lower marine
boundary layer, data available at http://acd.ucar.edu/~emmons/DATACOMP/) to
several parts per billion by volume in the polluted urban environment and
biomass burning plumes (Ridley et al., 1992; Singh et al., 1998). In the
UTLS, mixing ratios are typically in the range 10–300 pptv (Emmons et al.,
2000; Keim et al., 2008).
To our knowledge, our study is the first study that analyzes the influence of
monsoon outflow from different world regions on the distribution of
peroxyacetyl nitrate (PAN) in the UTLS over the Asian monsoon region, and
its recirculation in the UTLS. We run decadal simulations with the
chemistry–climate model ECHAM5–HAMMOZ. In emission sensitivity experiments, NOx
and NMVOCs emissions were simultaneously reduced by 10 % over ASM, WAM and
NAM to understand regional contribution. We apply statistical comparisons
with satellite and aircraft data, thereby contributing to the objectives of
the Chemistry-Climate Model Initiative (CCMI, see
http://www.igacproject.org/CCMI). The model climatology is evaluated with
data from aircraft campaigns and the Michelson Interferometer for Passive
Atmospheric Sounding (MIPAS) instrument onboard the ENVIronmental SATellite
(ENVISAT) (referred to as MIPAS-E hereafter). The transport of HNO3 and
NOx due to monsoon convection from different monsoon regions and the
impacts of lightning on the UTLS distributions of nitrogen oxide are
also analyzed and compared to the results obtained for PAN. The paper is
organized as follows: Sect. 2 contains a short description of the data and
model including the simulation setup. Comparisons of model simulations with
observations are given in Sect. 3. In Sect. 4, we discuss the various
convective transport pathways of PAN into the UTLS, its redistribution in
the stratosphere and its recirculation across the various monsoon regions
as well as results of the emission sensitivity simulations depicting the
contributions from major monsoon systems. The analysis of percentage changes
in lightning-produced ozone, HNO3, PAN and NOx on total
concentrations over the convective zones is presented in Sect. 5.
Conclusions are given in Sect. 6.
MethodsSatellite measurements
MIPAS-E instrument was launched in March 2002 into a
polar orbit of 800 km altitude, with an orbital period of about 100 min
and an orbit repeat cycle of 35 days. MIPAS-E (Fischer and Oelhaf, 1996;
Fischer et al., 2008) was a Fourier transform spectrometer that provided
continual limb emission measurements in the mid-infrared over the range 685–2410 cm-1 (14.6–4.15 µm). From January 2005 through the end
of the mission in April 2012, MIPAS-E was operated with a spectral resolution
of 0.0875 cm-1, and a stepping of the tangent altitude of 1.5–2 km
in the UTLS region. As a mid-infrared sounder, MIPAS-E could not provide
spectral information from below the cloud top.
MIPAS-E monitored several atmospheric trace constituents affecting
atmospheric chemistry including PAN, NOx and O3. The details of
the general retrieval method and setup, error estimates and use of averaging
kernel and visibility flag are documented by von Clarmann et al. (2009). In
this study we analyze the MIPAS-E observed PAN data during the period 2005–2012, i.e., the data version V5R_PAN_220/V5R_PAN_221 (different naming 220/221
merely due to technical reasons). The data are available from http://share.lsdf.kit.edu/imk/asf/sat/kiefer/To_Richard/.
Details of the MIPAS-E PAN retrievals, error budget and vertical resolution are given by Glatthor et al. (2007) and by
Wiegele et al. (2012). Table 3 in Wiegele et al. (2012) indicates that for
the total error of single profiles of the V5R_PAN_220/221 product, the spectral noise and the uncertainty of
the instrument pointing are the main contributors. However, since noise is a
major contributor a reduction of the total error can be expected for
vertical profiles of binned data. For typical bins used in this work the
total errors are less than 10 % below 12 km, 30 % at 15 km, 50 % at
19 km and 80 % at 23 km.
The sensitivity of the PAN retrievals can be judged by the averaging
kernels. For the V5R_PAN_220/221 product an
example of the respective averaging kernel rows is shown in Fig. S1 in the Supplement for an
altitude range of 5 to 25 km at 28∘ N and 85∘ E for cloud-free
atmospheric conditions. The diamonds indicate the respective nominal
altitudes of the retrieval grid. The figure shows that the retrieval results
below 8–9 km are dominated by information from above the nominal altitude.
A similar, albeit less obvious, situation develops for altitudes above 22–23 km. There and above, the information has an increasing weight from lower
than nominal altitudes. This is the reason why the MIPAS-E PAN data are not
considered below 8 km and above 23 km. Another effect clearly visible in the
example is that the altitude region which influences the retrieved PAN value
at a given altitude increases with altitude, i.e., the vertical
resolution decreases with altitude. To account for the comparatively low,
and altitude-dependent, vertical resolution, the model data to be directly
compared to MIPAS-E measurements were convolved with the MIPAS-E PAN averaging
kernel.
The data are contoured and gridded at 4∘ latitude and 8∘
longitude resolution. In the process the data quality specifications as
documented at
http://share.lsdf.kit.edu/imk/asf/sat/mipas-export/Documentation/ were
employed, namely: only data with a visibility flag equal to 1 and a diagonal value
of averaging kernel greater than 0.03 were used.
ECHAM5–HAMMOZ model simulation and experimental setup
The ECHAM5–HAMMOZ aerosol-chemistry–climate model used in the present study
is comprised of the general circulation model ECHAM5 (Roeckner et al., 2003),
the tropospheric chemistry module, MOZ (Horowitz et al., 2003), and the
aerosol module, Hamburg Aerosol Model (HAM) (Stier et al., 2005). It
includes ozone, NOx, VOC and aerosol chemistry. The gas-phase chemistry
scheme is based on the MOZART-2 model (Horowitz et al., 2003), which
includes comprehensive Ox–NOx–hydrocarbons chemistry with 63
tracers and 168 reactions. The O(1D) quenching reaction rates were
updated according to Sander et al. (2003) and isoprene nitrates chemistry
according to Fiore et al. (2005). In the model simulations we included
emissions of acetone from anthropogenic sources and wild fires (primary
sources), while acetaldehyde and methylglyoxal are produced by oxidation of
other NMVOCs (secondary sources). In particular, oxidation of primary NMVOCs
like ethane (C2H6), propane (C3H8) and propene
(C3H6) forms acetaldehyde, while CH3COCHO is mainly formed
from the oxidation products of isoprene and terpenes. Higher acyl peroxy
nitrates (MPAN) have been included in the MOZART-2 chemical scheme, which
are also formed through oxidation of NMVOCs, but their production is small
compared to PAN. Thermal decomposition, and reaction with OH as well as the
absorption cross sections for PAN photolysis are all specified according to
Sander et al. (2003).
In ECHAM5–HAMMOZ dry deposition follows the scheme of Ganzeveld and
Lelieveld (1995). Soluble trace gases such as HNO3 and SO2 are
also subject to wet deposition. In-cloud and below-cloud scavenging follows
the scheme described by Stier et al. (2005). PAN is not water-soluble,
therefore dry and wet deposition are insignificant removal processes.
The model is run at a spectral resolution of T42 corresponding to about 2.8 × 2.8∘ in the horizontal dimension and 31 vertical hybrid σ-p
levels from the surface up to 10 hPa. We note that the nominal grid
resolution of 2.8∘ is somewhat misleading, because the spectral
truncation of T42 only allows to resolve details on the order of 180/42= 4.28∘. This is the main reason why we compare our model results with
the MIPAS-E PAN retrievals on a 4 × 8∘ grid. The details of model
parameterizations, emissions and validation are described by Pozzoli et al. (2008a, b, 2011) and Fadnavis et al. (2013).
The model simulations were performed with varying monthly mean sea surface
temperature (SST) and sea ice cover (SIC) data over the period 2000–2010
(AMIP) referred to as the control simulation. The simulations did not aim to
exactly reproduce specific meteorological years, and we ran 11-year periods
in order to obtain reasonable statistics. We used the RETRO project data set
of the year 2000 available at http://eccad.sedoo.fr/ for the surface
CO, NOx and hydrocarbon emissions from anthropogenic sources and biomass
burning (Schultz et al., 2007, 2008). Anthropogenic total RETRO emissions of
the year 2000 are 476 Tg year-1 for CO, 90 Tg year-1 for
NOx, 5 Tg year-1 of ethane, 3.5 Tg year-1 of propane and
2.7 Tg year-1 of propene, which are the main anthropogenic VOC
precursors of PAN. Biomass burning RETRO emissions of year 2000 are
357 Tg year-1 for CO, 16 Tg year-1 for NOx,
2.5 Tg year-1 for ethane, 1.3 Tg year-1 for propane,
2.7 Tg year-1 for propene and 2.7 Tg year-1 for acetone. CO
biomass burning emissions in Southeast Asia account for 7 Gg month-1
in spring, while up to 15 Gg month-1 were reported from Carmichael et
al. (2003). The anthropogenic and biomass burning emissions of SO2
(total of 142 Tg year-1), BC (7.7 Tg year-1) and OC
(66.1 Tg year-1) are based on the AEROCOM emission inventory (Dentener
et al., 2006), also representative of the year 2000. The biogenic NMVOC
emissions are calculated online with the MEGAN module of Guenther et
al. (2006). The simulated global annual mean emission of biogenic NMVOCs
between 1995 and 2004 is 830 Tg(C) year-1; isoprene contributes
57 %, followed by terpenes (21 %), methanol (12 %) and other
NMVOCs such as acetaldehyde (2.5 %) and acetone (2.3 %). Other
natural emissions calculated online by the model are the dimethyl sulfide
(DMS) fluxes (Kettle and Andreae, 2000; Nightingale et al., 2000; Pham et
al., 1995), sea salt aerosols (Schulz et al., 2004) from the ocean and
mineral dust aerosols (Tegen et al., 2002; Cheng et al., 2008).
Our base year for aerosol and trace gas emissions is 2000, and emissions
were repeated annually throughout the simulation period. One point to note
is that there were substantial emission changes in Asia and Africa
(increasing trends) and Europe and North America (decreasing trends) during
the study period, which are not captured in our simulations. A consequence of
these emission changes for our study would be that we may underestimate the
impact of local pollution sources on PAN concentrations in the UTLS over
the ASM region in recent years and that we overestimate the contribution
from long-range transport of northern hemispheric pollution. We provide an
estimate of this error in the discussion of the results. Lightning NOx
emissions are parameterized following Grewe et al. (2001). They are
proportional to the calculated flash frequency with a production rate of 9 kg(N) per flash, and distributed vertically using a C-shaped profile. The
calculated flash frequency is resolution-dependent and scaled globally to
yield annual global emissions of 3.4 Tg(N) per year. To study the impact of
lightning on the distributions of PAN we compare two sets of experiments;
each were conducted for 11 years, 2000–2010: (1) the control experiment (CTRL) and
(2) the lightning-off experiment (light-off).
Model-simulated PAN, NOx, HNO3 and O3 mixing ratios are
evaluated with climatological data sets of airborne campaigns during the
monsoon season (June–September). The data were retrieved from http://acd.ucar.edu/~emmons/DATACOMP/CAMPAIGNS/ (see also
the paper by Emmons, 2001). The NOx and ozone volume mixing ratios
observed during Cloud Aerosol Interaction and Precipitation Enhancement
Experiment (CAIPEEX) (details available in Kulkarni et al., 2012), September
2010, are evaluated over the Indian region. The details of instruments and
measurement techniques are available at
http://www.tropmet.res.in/~caipeex/about-data.php. The list
of data sets and aircraft campaign used for comparison are presented in
Table 1. For the comparison, aircraft observations are averaged over 0–2, 2–6 and 6–8 km and horizontally over the coherent flight regions.
In order to understand the impact of NOx and NMVOCs emissions on the
distribution of PAN, we conducted a reference run and three emission sensitivity
simulations for the year 2003 driven by European Centre for Medium-Range
Weather Forecasts operational analyses (Integrated Forecast System (IFS)
cycle-32r2) meteorological fields (available every 6 h) (Uppala et al.,
2005). Model simulations were performed for the year 2003 since there was no
significant oceanic/meteorological perturbation event like, e.g., El
Niño–Southern Oscillation or the Indian Ocean Dipole
(http://www.marine.csiro.au/~mcintosh/Research_ENSO_IOD_years.htm). In experiments 1 to 3,
emissions of both NOx and MNVOCs were simultaneously reduced by 10 %
over (1) Asia (10∘ S–50∘ N, 60–130∘ E), (2) Africa
(30∘ S-30∘ N, 15∘ W–45∘ E) and (3) North America
(15–45∘ N, 120–75∘ W), referred to separately as
Asia -10 %, Africa -10 % and North America -10 %.
Model production of PAN
PAN is a secondary pollutant that has a short lifetime in the lower
troposphere. This reduces the number of source points that contribute to PAN
concentrations at any location in the UTLS, resulting in a clearer
identification of source-receptor pathways. Figure 1 shows the distribution
of PAN production at 14 and 16 km. A striking feature is the confinement
of PAN production to regions of deep convection. A maximum daily production
rate of PAN in the UTLS, in these convective zones, is > 24 ppt day-1
near 14 km and > 12 ppt day-1 near 16 km. Production of PAN
from background concentrations of ethane (C2H6) and other NMVOCs
outside of deep convection regions is distinctly secondary. NMVOCs are
subject to the same convective transport as NOx and PAN formation
occurs where both have the highest values. The lifetime of NOx is short
throughout the troposphere which implies that PAN production in the UT can
be associated with deep convection. There is also a contribution to PAN
production from stratospheric air penetrating into the troposphere (Liang et
al., 2011). Tropopause folding is a significant source of exchange between
the stratosphere and the troposphere (Gettelman et al., 2011). This is an
extratropical process that likely contributes to the PAN formation maxima
over North America, Europe and Asia (shown in Fig. 1a) via enhanced
conversion of ethane. In the model it is not possible to obscure the relationship
between PAN formation and NOx pollution source regions.
Global aircraft measurements used for model evaluation.
ExperimentTime frameSpeciesLocationPOLINAT-2 (Falcon), Ziereis et al. (2000)19 Sep–25 Oct 1997O3, NOxCanary Islands: LAT =25, 35∘ N, LONG =160, 170∘ W,East Atlantic: LAT =35, 45∘ N, LONG =150, 160∘ W,Europe: LAT =45, 55∘ N, LONG =5, 15∘ E,Ireland: LAT =50, 60∘ N, LONG =165, 175∘ WPEM-Tropics A (DC8), Talbot et al. (2000)24 Aug–15 Oct 1996O3, NOx, HNO3, PANChristmas Island: LAT =0, 10∘ N, LONG =20, 40∘ W,Easter Island: LAT =-40∘ N, 20∘ S, LONG =60, 80∘ W,Fiji: LAT = 0∘, 10∘ S, LONG =170∘ E, 10∘ W,Hawaii: LAT =10, 30∘ N, LONG =10, 30∘ W, Tahiti: LAT =20∘ S, 0∘, LONG =20, 50∘ WPEM-Tropics A (P3), O'Sullivan et al. (1999)15 Aug–26 Sep 1996O3, HNO3Christmas Island: LAT = 0∘, 10∘ N, LONG =20, 40∘ W, Easter Island: LAT =40, 20∘ S, LONG =60, 80∘ W, Hawaii: LAT =10, 30∘ N, LONG =10, 30∘ W, Tahiti: LAT =20∘ S, 0∘, LONG =20, 50∘ WABLE-3B (Electra), Harriss et al. (1994)6 Jul–15 Aug 1990O3, NOx, HNO3, PANLabrador: LAT =50, 55∘ N, LONG =120, 135∘ W,Ontario: LAT =45, 60∘ N, LONG =90, 100∘ W, US east coast: LAT =35, 45∘ N, LONG =100, 110∘ WCITE-3 (Electra), Hoell et al. (1993)22 Aug–29 Sep 1989O3, NOxNatal: LAT =15∘ S, 5∘ N, LONG =145, 155∘ W,Wallops: LAT =30, 40∘ N, LONG =100, 110∘ WELCHEM (Sabreliner), Ridley et al. (1994)27 Jul–22 Aug 1989O3, NOxNew Mexico: LAT =30, 35∘ N, LONG =70, 75∘ WABLE-3A (Electra), Harriss et al. (1992)7 Jul–17 Aug 1988O3, NOx, PANAlaska: LAT =55, 75∘ N, LONG =10, 25∘ WABLE-2A (Electra), Harriss et al. (1988)12 Jul–13 Aug 1985O3East Brazil: LAT =10∘ S, 0∘, LONG =120, 135∘ W,West Brazil: LAT =5∘ S, 0∘, LONG =110, 120∘ WSTRATOZ-3 (Caravelle 116), Drummond et al. (1988)4–26 Jun 1984O3Brazil: LAT =20∘ S, 0∘, LONG =135, 155∘ W,Canary Islands: LAT =20, 35∘ N, LONG =160, 155∘ W,E tropical North Atlantic: LAT =0∘, 20∘ N, LONG =150, 165∘ W,England: LAT =45, 60∘ N, LONG =10∘ E, 5∘ W,Goose Bay: LAT =45, 60∘ N, LONG =110, 125∘ W,Greenland: LAT =60, 70∘ N, LONG =110, 150∘ W,Iceland: LAT =60, 70∘ N, LONG =150, 155∘ W,NW South America: LAT =-5, 10∘ N, LONG =95, 115∘ W,Puerto Rico: LAT =10, 25∘ N, LONG =110, 120∘ W,S South America: LAT =65, 45∘ S, LONG =95, 120∘ W,SE South America: LAT =45, 20∘ S, LONG =115, 140∘ W,SW South America: LAT =-45, 25∘ S, LONG =105, 112∘ W,Spain: LAT =35, 45∘ N, LONG = 15∘ W, 0∘,W Africa: LAT = 0∘, 15∘ N, LONG = 15∘ W, 0∘,W South America: LAT =25, 5∘ S, LONG =95, 110∘ W,Western North Atlantic: LAT =25, 45∘ N, LONG =110, 120∘ WCITE-2 (Electra), Hoell et al. (1990)11 Aug–5 Sep 1986O3, NOx, HNO3, PANCalifornia: LAT =35, 45∘ N, LONG =55, 70∘ W, Pacific: LAT =30, 45∘ N, LONG =45, 55∘ WINTEX-A, Singh et al. (2006)Jul–Aug 2004O3, PAN, NOxEastern North America: LAT =29, 51∘ N, LONG: 44–120∘ WCAIPEEX, Prabha et al. (2011)Sep–Oct 2010O3, NOxLAT = 12, 22∘ N, LONG =74, 78∘ E
PAN production rates at (a) 14 km and (b) 16 km. Key regions of
biomass burning and anthropogenic emissions of pollutants are evident and
correspond to maxima in PAN production. Weaker dispersed background
formation is evident as well.
Scatter plot between model simulation (averaged for for 1995–2004)
and aircraft observations of PAN (ppt), ozone (ppb), HNO3 (ppt) and
NOx (ppt) (averaged for the monsoon season (June–September) ). The
model simulations and aircraft observations are averaged for altitude ranges
over the coherent regions. The Pearson's correlation coefficient (R) and
corresponding p-value is given in each subplot.
Comparison of model simulations with observationsComparison with aircraft measurements
Figure 2 shows scatter plots between aircraft observations and model
simulations at the coherent locations. Both aircraft observations and model
simulations are averaged for the monsoon season and altitude ranges. It
indicates that model-simulated PAN,O3 and NOx show good agreement
with aircraft measurements; correlation coefficient > 0.7 and
significance (P value) varies between 0.00 and 0.3, indicating that correlation is
significant at 95 % confidence level; however, simulated HNO3, between
2 and 6 km, and 6 and 10 km, does not agree well with aircraft observations.
A point-to-point comparison of (latitude–longitude transects at various
altitudes) simulated PAN, NOx, O3 and HNO3 (for the period
1995–2005) with aircraft observations are presented by Fadnavis et al. (2014). These plots show good agreement between model simulations and
aircraft observations. Vertical variation of simulated ozone also shows good
agreement with ozonesonde measurements over India (see Fig. S3 in the Supplement in Fadnavis 2014). It should be noted that current model simulations
(2000–2010) show better agreement with aircraft observations than Fadnavis
et al. (2014). Figures showing the difference between these simulations and
the aircraft observations are provided in the Supplement as Fig. S2. The
model bias varies with species and altitude. In general, the bias in PAN
ranges from -20 ppt to 80 ppt, for ozone from -2 to 40 ppb and for
HNO3 from -20 to 75 ppt, while NOx mixing ratios show a good
agreement with CAIPEEX measurements over the Indian region. Unfortunately,
there were no measurements of PAN or HNO3 made during CAIPEEX.
Comparison with MIPAS-E retrievals
In order to study the influence of monsoon circulation on the distribution
of PAN in the UTLS region, multi-year averages (2005–2011) of seasonal mean
(June–September) PAN retrievals from MIPAS-E are analyzed. Figure 3a
presents these data for the altitude range 14–16 km, and Fig. 3b shows
the corresponding ECHAM5–HAMMOZ results for comparison. MIPAS-E observations
show maximum PAN mixing ratios (∼ 200–230 ppt) over (1) the
Asian monsoon anticyclone region (12–40∘ N, 20–120∘ E), and (2) over parts of North America, the Gulf Stream, (3)
southern Atlantic Ocean and the west coast of tropical Africa. ECHAM5–HAMMOZ
CTRL simulations also show high PAN concentration at these locations;
however, PAN concentrations are lower than MIPAS-E observations and appear
somewhat more localized. MIPAS-E exhibits a PAN maximum originating from
African sources over the South Atlantic, whereas the model shows this
maximum over the African continent. This may be the outflow of biomass
burning over central and southern Africa during summer monsoon, which might
be underestimated in the model. The biomass burning region of Africa during
the ASM season is ∼ 30∘ S–20∘ N; 20∘ W–30∘ E (Galanter et al., 2000). The longitude–altitude and
latitude–altitude cross sections of MIPAS-E observed and simulated PAN over
the biomass burning region are plotted in Fig. S3. The model simulation shows
that the biomass plume rising from Africa moves westward and northward over
the Atlantic Ocean and merges with South American plume. From satellites,
aircraft observations and model simulations, Real et al. (2010) and Barret
et al. (2008) reported a plume in the middle and upper troposphere (UT) over
the southern Atlantic which originates from central African biomass burning
fires.
Distribution of seasonal mean PAN concentration (ppt) averaged for
14–16 km (a) observed by MIPAS-E (climatology for the period 2002–2011)
(b) ECHAM5–HAMMOZ CTRL simulations. Wind vectors at 16 km are indicated by black
arrows in (b).
Longitude–altitude cross section of PAN (ppt) averaged for the monsoon
season and 0–30∘ N; (a) MIPAS-E climatology (b) ECHAM5–HAMMOZ
CTRL simulations. (c) Difference in PAN (ppt) (MIPAS-E–ECHAM5–HAMMOZ). PAN
(ppt) averaged for the monsoon season and 0–25∘ S (d) MIPAS-E climatology
(e) ECHAM5–HAMMOZ CTRL simulations (f) difference in PAN (ppt)
(MIPAS-E–ECHAM5–HAMMOZ). ECHAM5–HAMMOZ simulations are smoothed with averaging kernel
of MIPAS-E. Wind vectors are indicated by black arrows in (b) and
(e). The vertical velocity field has been scaled by 300. The black line in
(b) and (e) indicates the tropopause.
The difference between ECHAM5–HAMMOZ simulation and MIPAS-E observations are
shown in Fig. S3c and f. These figures show that the model
underestimates biomass burning PAN by 20–60 ppt. These differences may
also be related to issues in the vertical transport of PAN, or to a possible
underestimation of the emission sources of NMVOCs. Uncertainties in the rate
coefficients and absorption cross sections of PAN may also play a role.
Furthermore, anthropogenic NOx emissions are mostly underestimated in
the emission inventories (Miyazaki et al., 2012). As discussed in Fadnavis
et al. (2014), UTLS PAN over the ASM is sensitive to NOx emission
changes in India or China. In their study, also performed with
ECHAM5–HAMMOZ, a 73 % NOx emission change in India lead to a PAN
increase of 10–18 %, while a 73 % NOx emission change in China
changed PAN over the ASM by 18–30 %. The cross-section plots of (see
Fig. S4) differences in MIPAS-E PAN with model-simulated PAN indicate that
in the UTLS, MIPAS-E PAN is higher than model-simulated PAN by
∼ 20–60 ppt (except at 20 km). PAN is lower by 20–40 ppt
over the eastern part of ASM anticyclone (southern India and Southeast Asia)
and also over Indonesia and northern Australia. In general, in the ASM
region, during the monsoon season, MIPAS-E PAN is higher than the model by 30–60 ppt between 8 and 16 km and the difference between MIPAS-E and model PAN
varies between +40 ppt and -40 ppt between 17 and 20 km.
Transport of PAN during monsoon seasonTransport from the northern tropical land mass
Figure 3a shows high concentrations of MIPAS-E PAN at 14–16 km over Asia,
North America and tropical Africa. ECHAM5–HAMMOZ simulations (Fig. 3b)
also show similar distribution. This may be due to transport from the boundary
layer into the UTLS by the monsoon convection from respective regions.
ECHAM5–HAMMOZ-simulated outgoing long-wave radiation (OLR) and 850 hpa winds averaged for the monsoon season
are shown in Fig. S5a. They indicate the extent of deep convection near
the surface. NCEP reanalysis OLR and 850 hPa winds averaged for the monsoon
season (2000–2010) are plotted in Fig. S5b for comparison. These
figures indicate that the model can reproduce deep convection as well as the
large-scale circulation. The cross section of distribution of simulated cloud
droplet number concentration (CDNC) and ice crystal number concentration
(ICNC) over Asia, North America and tropical Africa confirms strong
convective transport from these regions (Fig. S5c–e). It should be
noted that vertical velocities in a large-scale model also indicate rapid
uplift in deep convective regions. From satellite observations and model
simulations Park et al. (2009) reported transport of fraction of boundary-layer carbon monoxide (CO) into the UTLS by the Asian monsoon convection.
To illustrate vertical transport, longitude–altitude cross sections of PAN
mixing ratios averaged over the region 0–30∘ N for
June–September as obtained from MIPAS-E and ECHAM5–HAMMOZ are shown in
Fig. 4a and b, respectively. Both MIPAS-E observations and
ECHAM5–HAMMOZ simulations show elevated levels of PAN (200–250 ppt) near
80–100∘ E (ASM), 30∘ W–30∘ E (WAM) and
80–100∘ W (NAM) region. The simulated PAN distribution
along with winds plotted in Fig. 4b show cross-tropopause transport from
these regions. It reveals that transport of boundary-layer PAN to the UTLS
mainly occurs from strong convective regions, i.e., Bay of Bengal
(∼ 80–90∘ E), South China Sea
(∼ 100–120∘ E), western Atlantic Ocean
(Gulf Stream region) and Gulf of Mexico (80–100∘ W).
MIPAS-E observations and model simulations show that the transport due to
ASM is strongest and reaches deepest into the lower stratosphere. This is
due to the more intense deep convection activity over the ASM region
compared to the NAM region (see Fig. S5c–e). Figure 4c presents the
differences between MIPAS-E and model-simulated PAN. It appears that the model
PAN is overestimated over the ASM (20–30 ppt) and underestimated over the
NAM (50–70 ppt) and WAM (20–50 ppt) regions between 8 and 18 km.
However, the overestimation in the UT in the ASM is difficult to explain on
physical grounds and is more likely to be a MIPAS-E sampling issue as
discussed later.
Transport from the southern tropical land mass
In order to understand transport of PAN due to southern WAM, SAM and AUSM,
we show longitude–pressure sections of MIPAS-E observations and model-simulated PAN concentrations averaged over 0–25∘ S in Fig. 4d, e, respectively. The model has plumes near 20, 100∘ E
and 80∘ W. These three regions of convective transport are (1)
tropical southern Africa 10–40∘ E, referred to as southern Africa, (2)
Indonesia and northern parts of Australia ∼ 100–110∘ E and (3) South America ∼ 70–80∘ W. Outflow from Indonesia and from northern parts of Australia
(∼ 100∘ E) penetrates deep into the UTLS. Tropical
Rainfall Measuring Mission (TRMM) satellite observations show high frequency
of intense overshooting convection over these areas (during the monsoon
season) with highest density in the belt 0–10∘ S over the
Caribbean, Amazon, Congo and southern maritime continent (Liu and Zipser,
2005). The analyses of vertical winds show strong transport from 10–40∘ E, 100–110∘ E, 70–80∘ W (in the
belt 0–10∘ S) (figure not shown). The amount of high-level
cloud fraction is also high over these regions. Distribution of CDNC and
ICNC show deep convection over these regions (figure not shown). The model
simulations show high PAN concentrations reaching the UTLS. Thus transport
due to deep convection is reasonably well captured by the model. However,
the MIPAS-E retrievals only show a plume rising over southern Africa and no
enhancement over the AUSM (Indonesia–Australia) and SAM regions. Figure 4e
shows that the plumes from the three outflow regions are mixed in the UT (8–14 km) by the prevailing westerly winds. The reasons for a single plume
seen in MIPAS-E may be that lower concentrations of PAN reach these
altitudes (above 8 km) from SAM and AUSM and mix with the plume over
southern Africa. There are indications of elevated PAN concentrations at the lower
boundary in Fig. 4d. Simulations show lower PAN mixing ratios over the
longitudes of SAM and AUSM (see Fig. 4e). The differences between MIPAS-E
observations and simulations (Fig. 4f) show that model PAN is
overestimated in the AUSM (10–30 ppt) and is underestimated over the southern
WAM (20–70 ppt) and SAM (20–50 ppt) between 10 and 18 km. It is likely
that the three-plume structure in the UT seen in the model is being obscured in
the observations due to sampling issues since periods of deep convection
that reach significantly above 8 km are associated with significant cloud
cover.
Latitude–altitude cross section of PAN (ppt) (a) MIPAS-E
climatology, averaged for the monsoon season and for 60–120∘ E, (b) PAN
from ECHAM5–HAMMOZ CTRL simulations, averaged for the monsoon season and
60–120∘ E, (c) difference in PAN (ppt) (MIPAS-ECHAM5–HAMMOZ),
(d) longitude–altitude section averaged over 10–30∘ N obtained
from reference–Asia -10 % simulations (e) same as (d) but
latitude–altitude section averaged over 60–120∘ E,
(f)–(i)
latitude–longitude sections of reference–Asia -10 % simulations at 14,
16, 18 and 21 km, respectively. Wind vectors are indicated by black
arrows. The vertical velocity field has been scaled by 300.
Figure 4 shows that simulated transport of PAN due to ASM, NAM and WAM
convection is stronger and penetrates deeper into the UT compared to SAM and
AUSM. This is consistent with the distribution of deep convection noted by
Gettelman et al. (2002). In general, the PAN amounts in the UTLS in the
model are less than those observed by MIPAS-E. This may be due to an
underestimation of the chemical PAN source from VOC precursors or too little
vertical transport in the model or a combination of both. Earlier model
studies with ECHAM also exhibited concentrations of CO in the upper
tropospheric outflow that were too low (M. Schultz, unpublished data from the NASA Global
Tropospheric Experiment TRACE-P mission).
Transport from the Asian summer monsoon region
The ASM anticyclone extends from 60 to 120∘ E and 10 to
40∘ N (see Fig. 3b). Latitude-altitude cross sections over the ASM
anticyclone (60–120∘ E) of MIPAS-E observed PAN (plotted
in the altitude range 8–20 km) and ECHAM5–HAMMOZ CTRL simulations are
shown in Fig. 5a and b, respectively. ECHAM5–HAMMOZ simulations are
similar to MIPAS-E retrievals of PAN. There is indication of plume ascent
into the lower stratosphere. The ECHAM5–HAMMOZ simulations also show
transport of subtropical boundary-layer PAN into the UTLS due to deep
convection. This is not visible in the MIPAS-E data because of the lack of
data below 8 km. Figure 5b shows that there is transport from 40–50∘ N
reaching up to 10 km (∼ 200 hPa). Park et al. (2004, 2007, 2009) and Randel and Park (2006) noted that trace species are
introduced into the monsoon anticyclone at its eastern end around 200 hPa.
The uplift over Southeast Asia and the base of the Himalayas in India pumps
tracers into the upper tropical troposphere where they get horizontally
redistributed by the anticyclonic circulation and form the region of high
PAN values between 40∘ N and high latitudes. Figure 10c shows that
the mid-latitude maximum seen in Fig. 5c is due to pollution transport
from Europe. The Chinese emissions are feeding into this large plume over
Russia and are transported partly and diluted over the extratropical Pacific
Ocean. The latitude–altitude section of differences between MIPAS-E and
simulated PAN indicates that ASM plume is underestimated in the model (see
Fig. 5c). It is interesting to compare Fig. 4c (longitude–altitude section) and Fig. 5c (latitude–altitude section). The reason
for underestimation of the ASM plume in the latitude–altitude section may be
due to a lower contribution from the eastern part of anticyclone in the
model. Figure S4 shows model PAN is underestimated over southern India and
Southeast Asia in the UT, and overestimated in the lower stratosphere.
Latitude–altitude cross section of PAN (ppt) (a) MIPAS-E
climatology, averaged for the monsoon season and for 70–120∘ W, (b) PAN
from ECHAM5–HAMMOZ CTRL simulations, averaged for the monsoon season and
70–120∘ W, (c) difference in PAN (ppt) (MIPAS-ECHAM5–HAMMOZ),
(d)
longitude–altitude section averaged over 0–30∘ N obtained
from reference–North America -10 % simulations (e) same as (d) but
latitude–altitude section averaged over 70–120∘ W,
(f)–(i)
latitude–longitude sections of reference–North America -10 % simulations
at 10, 12, 14, and 16 km, respectively. Wind vectors are indicated by black
arrows. The vertical velocity field has been scaled by 300.
Latitude–altitude cross section of PAN (ppt) (a) MIPAS-E
climatology, averaged for the monsoon season and for 0–45∘ E, (b) PAN
from ECHAM5–HAMMOZ CTRL simulations, averaged for the monsoon season and
0–45∘ E, (c) difference in PAN (ppt) (MIPAS-ECHAM5–HAMMOZ),
(d)
longitude–altitude section averaged over 30∘ S–30∘ N
obtained from reference–Africa -10 % simulations (e) same as (d) but
averaged over 0–30∘ N, (f) same as (d) but averaged over 0–30∘ S. Wind
vectors are indicated by black arrows. The vertical velocity field has been
scaled by 300. Longitude–latitude section of PAN obtained from reference–Africa -10 % simulations at (g) 6 km, (h) 8 km, (i) 10 km and (j) 12 km.
(a) Latitude–altitude cross section of seasonal mean
ECHAM5–HAMMOZ NOx (ppt) averaged for (a) 60–120∘ E
and (b) 70–120∘ W; (c) longitude–altitude cross section
averaged over 10–40∘ N, (d) latitude–altitude cross section
averaged over 0–45∘ E and (e) longitude–altitude cross section
averaged over 0–25∘ S, (f)–(i) same as (a)–(e) but for HNO3.
Latitude–altitude variation of (a) HNO3 (reference–Asia -10 %), averaged over 60–120∘ E (b) HNO3 (difference
of reference–North America -10 %), averaged over 70–120∘ W
(c) HNO3 (reference–Africa -10 %), averaged over 0–45∘ E
(d)
O3 (difference of reference–Asia -10 %) averaged over 60–120∘ E
(e)
O3 (reference–North America -10 %) over North America averaged over
70–120∘ W (f) O3 (reference–Africa -10 %) over Africa
averaged over 0–45(reference–Africa -10 %). HNO3 is expressed in
ppt and ozone in ppb.
In order to understand the impact of transport from ASM region on the rest
of the world, we analyze differences between reference and Asia -10 %
simulations (reference–Asia -10 %). The latitude–altitude and
longitude–altitude cross sections over the ASM region (Fig. 5d and e) show transport of ∼ 5–20 ppt of PAN into the lower
stratosphere. The horizontal cross sections at 14 to 21 km (Fig. 5f–i) show that Asian PAN is transported to northern Atlantic by
subtropical westerly winds. These figures show that a 10 % change in Asian
emissions (NOx and NMVOCs) transports ∼ 5–30 ppt into the
UTLS over Asia and 1–7 ppt of PAN in the UTLS of northern subtropics and
mid-latitudes.
Transport from the North American monsoon region
Figure 6a and b exhibit latitude–altitude sections of PAN from MIPAS-E
retrievals and ECHAM5–HAMMOZ simulations (seasonal mean for July–September)
over the North American monsoon region between 70 and 120∘ W.
MIPAS-E observations and the model indicate transport of PAN into the UTLS.
The distribution of ECHAM5–HAMMOZ-simulated PAN from the boundary layer to
UTLS shows the source region is at around 40∘ N. There is convective
uplift of PAN over the northern Gulf of Mexico region and over the Gulf
Stream. High amount of pollutants emitted from northeast America
from a number of power plants are located in Atlanta, Washington, Chicago,
Boston and Jacksonville (CEC report, 2011). The tropospheric NO2 columns
retrieved from the SCIAMACHY and OMI satellite instruments show high amounts
of anthropogenic NO2 emissions over this region (Lamsal et al., 2011;
Miyazaki et al., 2012). The model simulations show a high amount of PAN
concentrations over this region (see Fig. 10a–d). The monsoon
convection lifts these pollutants to the UT. The outflow of these pollutants
is over the Atlantic (see Fig. 3a). TRMM precipitation radar
observations show significant overshooting convective activity over this
region during the monsoon season (Liu and Zipser, 2005).
The vertical distribution of differences in MIPAS-E and simulated PAN
shows that PAN is underestimated in the model (see Fig. 6c) over North
and South America (10–60 ppt) between 10 and 18 km, however PAN is overestimated
in the model between 8 and 10 km in the region near 30∘ N.
As discussed above this may be associated with European emissions and
transport.
Figure 6d–e show impact of North American emission (reference–North America -10 %) on the transport of PAN. The figure shows
cross-tropopause transport of PAN by North American monsoon convection. The amount
of PAN transported (∼ 1–5 ppt) into the lower stratosphere is
less than for the ASM (∼ 10–20 ppt). The latitude–longitude
distribution of PAN (Fig. 6f–i) shows that the upper
tropospheric westerly winds transport ∼ 1–10 ppt of PAN to the Atlantic, Europe and North China.
Transport from the West African region
Figure 7a–b show vertical distributions of PAN over the African
region (averaged over 0–45∘ E). MIPAS-E observations and
model simulations indicate a plume that crosses the tropopause and enters
the lower stratosphere. The model surface fields (see Fig. 7b) show that
this plume arises from latitudes 5–20∘ S over Africa and that it moves
equatorward. It subsequently merges with the ASM plume. A prominent tongue
of high PAN values between 30 and 60∘ N is captured in model
simulations. This feature appears to be related to emissions from Europe
being transported towards the equator in the upper subtropical troposphere.
However, in the model, emissions from Europe are transported poleward
instead of equatorward (Fig. 7b). There is a region of strong descent in
the model between 30 and 40∘ N (see Fig. 7b) which
deforms the PAN isopleths around 12 km around 30∘ N. This feature is
not seen in the MIPAS-E retrievals and indicates a disagreement of the model
with the transport pattern of the atmosphere in this region. The transport
of PAN in the 10–20∘ S latitude band over the Congo, Angola and
Tanzania regions of southern and tropical Africa is not pronounced in the
model compared to MIPAS-E observations. This behavior indicates that deep
tropical convection is underestimated in the model in this latitude band.
The vertical distribution of differences in MIPAS-E and simulated PAN (Fig. 7c) shows that simulated PAN is
underestimated over these regions (5–20∘ S and 20–40∘ N) between 10 and 18 km. The
reason may be related to underestimation of deep tropical convection in the
model in this latitude band. Simulated PAN is overestimated between 8 and
12 km near the equator.
The reference–Africa -10 % simulation (Fig. 7d–e) shows that
African PAN is transported up to the tropopause. The cross sections over
North and southern Africa show penetration of the North African plume into the
lower stratosphere (∼ 19km). However, PAN transport into the
lower stratosphere (∼ 0.2–0.6 ppt) is comparatively less than
Asia or North America. Figure 7g–j show transport of
∼ 5–50 ppt of PAN in the UT (6–12 km) of tropical Africa. There
is transport from equatorial Africa to the Atlantic and Mexico between 6 and 8 km
(Fig. 7g–h) which is then transported to North China by upper
tropospheric (12 km) westerly winds (see Fig. 7j).
The model-simulated latitude–altitude and longitude–altitude cross sections of
NOx and HNO3 over the ASM (10–40∘ N, 60–120∘ E),
NAM (10–40∘ N, 70–120∘ W) and
WAM (0–25∘ S, 0–45∘ E) are shown in
Fig. 8a–j, respectively. Figure 8a–e show transport
features of NOx. These are similar to those seen in the distribution of
PAN, but with sharper signatures due to the shorter lifetime of NOx.
This shows that monsoon convection lifts boundary-layer pollutants including
NOy species to the UTLS. The distribution of HNO3 (see Fig. 8f–j) shows a complex pattern. Comparing Fig.4b, the region around
100∘ E with intense convective uplift corresponds to HNO3
depletion from the surface to above 10 km. In fact, the upper tropospheric
region of the ASM anticyclone exhibits much lower values of HNO3
compared to all the other longitudes in the 10–40∘ N band
(Fig. 8h). This suggests that in the model, the convective transport in
the ASM region is associated with efficient removal by wet scavenging. In
contrast, the North American monsoon region has HNO3 ascending to the
UT with significantly less loss. This is likely due to the fact that
convection involved in vertical transport during the NAM is not as intense
and not as deep as in the case of the ASM and there are differences in wet
scavenging. Figure 8g shows that the plume rising from South America moves
towards the equator but does not have the extension into the UT as the North
American plume. These are June–September averages, and the intertropical convergence zone is on the
Northern Hemisphere side during this period. Thus, weaker convective
transport is to be expected on the Southern Hemisphere side of the equator
during this period. Figure 8i shows significant transport of African
emissions around ∼ 0–15∘ S and a plume rising
from Europe (∼ 35–60∘ N) as well.
Figure 9a–f show vertical distribution of HNO3 and O3
over Asia, North America and Africa as obtained from differences between the
reference and Asia -10 %, reference and North America -10 % as well as
reference and Africa -10 % simulation. It is evident that transport of
HNO3 for Asia -10 % simulation is deeper in the UT (∼ 16 km) than North America -10 % and Africa -10 % simulations. It can be
seen that Asia -10 %, North America -10 % and Africa -10 % simulations
transport ∼ 7–10 ppt, ∼ 5–7 ppt and
∼ 3–5 ppt of HNO3 in the UT of their respective regions.
In the UT, between 6 and 10 km, Asia -10 % simulation shows transport of
∼ 10–15 ppt of HNO3 over the western Pacific and
∼ 3–10 ppt over tropical America by the subtropical westerly
winds (figure not included). North America -10 % simulation shows transport
of ∼ 5–7 ppt of HNO3 over the Atlantic, North Africa, Saudi
Arabia and North China by the subtropical westerly winds and ∼ 3–5 ppt of HNO3 over the equatorial Pacific, Indonesia, China and India by
the tropical easterly winds. Africa -10 % simulation shows transport of
∼ 3–5 ppt HNO3 from North Africa to North America and the equatorial Pacific; there is also transport of ∼ 4 ppt of
HNO3 from southern Africa to the Atlantic, South America, Indonesia,
China and India by the tropical easterly winds (figure not included).
North America -10 % simulation shows transport of boundary-layer ozone
extending up to the tropopause, which is higher than for the Asia -10 %
and Africa -10 % simulations (Fig. 9d–f). Asia -10 %,
North America -10 % and Africa -10 % simulations show transport
∼ 1–2, ∼ 0.8–1.5 and ∼ 0.4–0.6 ppb of ozone in the UT of their respective regions.
In the UT, between 6 and 10 km, Asia -10 % simulation shows transport of
ozone ∼ 1.5 ppb to the western Pacific and 0.8 ppb to Mexico and
United States by the subtropical westerly winds (figure not included).
North America -10 % simulation shows transport of 0.4–1.5 ppb of O3
to the equatorial Pacific extending up to Indonesia by the tropical easterly winds.
There is some outflow (∼ 0.6 ppb) over the Atlantic by the
subtropical westerly winds as well (figure not included). Africa -10 %
simulation shows transport of ∼ 0.4–0.8 ppb of ozone to
equatorial Atlantic and Mexico (figure not included).
Latitude–longitude cross section of PAN (ppt) averaged for the monsoon season (a) ECHAM5–HAMMOZ simulations at 2 km (b) 4 km (c) 6 km (d)
8 km (e) 12 km and (f) 8 km. MIPAS-E climatology at (g) 12 km and (h) 18 km.
It can be seen that similar emission change over Asia, North America and
Africa causes highest change in HNO3 and ozone in the UT over Asia and
least over Africa. In the UT, between 6 and 10 km, transport of HNO3
by Asia -10 % (∼ 3–10 ppt of HNO3 to tropical America)
is higher than North America -10 % (∼ 3–7 ppt of HNO3 to
China and India) and Africa -10 % (∼ 3–5 ppt of HNO3 to
tropical America, China and India). Similarly ozone transport is higher for
Asia -10 % than North America -10 % and Africa -10 % simulations.
Horizontal transport
PAN concentrations from MIPAS-E and ECHAM5–HAMMOZ at different altitudes are
analyzed to investigate horizontal transport. Figure 10a shows the
distribution of PAN from ECHAM5–HAMMOZ simulations near the surface (2 km).
Sources of PAN are apparent over South America, southern Africa, North
America, Europe, Russia and northern China/Mongolia. The PAN distribution at
4 km (see Fig. 10b) shows high concentrations above these regions
indicating vertical transport. Figure 10c and d show the
distribution at 6 and 8 km. The upper level anticyclonic circulation
between 10∘ N and 30∘ S over the Atlantic transports PAN from
central Africa towards America and from Brazil towards southern Africa. The
large-scale Biosphere–Atmosphere Regional Experiment in Amazonia
(LBA-CLAIRE-98) campaign observations (Andreae et al., 2001) and African
Monsoon Multidisciplinary Analysis (AMMA) project (Real et al., 2010) show
that the biomass burning plume originating from Brazil is lifted to
altitudes around 10 km. This plume is entrained into deep convection over
the northern Amazon, transported out over the Atlantic and then returned to
South America by the circulation around a large upper-level anticyclone.
This transport is well captured by the model.
Zonal averaged seasonal mean changes (percentage) produced from
lightning in (a) ozone (b) HNO3(c) PAN and (d) NOx; distribution of
seasonal mean changes (percentage) produced from lightning in (e)
ozone (f) HNO3(g) PAN and (h) NOx at 12 km.
North American pollution also gets transported by the westerly winds
over Eurasia, forming an organized belt. This transport pattern persists up
to 12 km (Fig. 10e and g). MIPAS-E observations at 12 km also show
this transport pattern. The source region for the PAN from southern Africa
is the region of active biomass burning. Since this region is located in the
tropics, the outflow is over the Atlantic due to the prevailing easterly
winds. ECHAM5–HAMMOZ simulations show similar transport (see Fig. 10e).
But there are differences; in particular the transport over tropical Africa
does not get displaced over the Atlantic Ocean. As noted above, there are
significant transport differences between the model and observations in this
longitude band. Another difference is that PAN is not transported westward
over Central America and towards the Pacific Ocean.
Figure 10f–h show the distribution of PAN from ECHAM5–HAMMOZ
simulations and MIPAS-E retrievals, in the lower stratosphere (18 km). In
both data sets PAN is transported westwards from ASM, NAM and WAM by
prevailing easterly winds and maximizes in the region of the ASM
anticyclone.
As can be seen from the above discussions, the ASM, NAM and WAM outflow and
convection over the Gulf Stream play an important role in the transport of
boundary-layer pollution into the UTLS. Previous studies (e.g., Fadnavis et
al., 2013) indicated that over the Asian monsoon region, transport into the
lower stratosphere occurs and there is significant vertical transport over
the southern slopes of the Himalayas (Fu et al., 2006; Fadnavis et al.,
2013) and also over the region spanned by the Bay of Bengal and the South
China Sea (Park et al., 2009). Pollutant transport due to North American
convection and tropical African outflow does not penetrate as deep into the
stratosphere as the ASM; however, there is a clear indication that in the UT,
middle-latitude westerly winds connect the North American pollution to the
ASM.
Figures 3–7 and Fig. 10 show that in the UT, westerly winds drive North
American and European pollutants eastward to at least partly merge with the
ASM plume. Strong ASM convection transports these remote and regional
pollutants into the stratosphere. The Caribbean is a secondary source of
pollutant transport into the stratosphere. In the stratosphere the injected
pollutants are transported westward by easterly winds and into the southern
subtropics by the Brewer–Dobson circulation.
Impact of lightning on tropospheric PAN, NOx, HNO3 and ozone
In the ASM region and during the monsoon season, the NOx released from
intense lightning activity enhances the formation of PAN, HNO3 and
ozone in the middle and upper troposphere which is already relatively strong
due to the intense solar radiation along with high background concentrations
of NOx, HOx and NMVOCs (Tie et al., 2001). PAN, HNO3 and
O3 produced from lightning may get transported in the lower
stratosphere by deep monsoon convection and contribute to anthropogenic
emission transport of these species. In order to understand contribution of
lightning and the dominating lightning production regions, we analyze the
difference between control and light-off simulations. Figure 11a–d show
the percentage changes in model-simulated ozone, HNO3, PAN and NOx
due to lightning as zonally averaged spatial distribution of seasonal mean
(June–September) mixing ratios. The analysis indicates that the impact of
lightning on these species is largest in the tropical UT between 40∘ N and 40∘ S and between 8 and 14 km. In the tropical
mid-troposphere, lightning-produced maximum ozone is ∼ 15–25 % (12–24 ppb), HNO3∼ 40–60 % (50–90 ppt)
∼ PAN ∼ 15–25 % (70–140 ppt) and
NOx∼ 20–40 % (10–35 ppt), while in the UT ozone
is ∼ 20–30 % (20–28 ppb), HNO3∼ 60–75% (80–110 ppt), PAN ∼ 28–35 % (120–170 ppt),
and NOx∼ 50–75 % (20–65 ppt). Our results are
consistent with model simulations by Tie et al. (2001) and Labrador et al. (2005). The spatial distributions of NOx, ozone, PAN and HNO3
produced from lightning (see Fig. 11e–h) indicate that in the UT
(12 km) increases in O3∼ 20–25 % (11–17 ppbv),
HNO3∼ 40–70 %, PAN ∼ 25–35 %
and NOx∼ 55–75 %, over North America are in
agreement with previous studies (e.g Labrador et al., 2005; Hudman et al.
2007; Zhao et al., 2009; Cooper et al., 2009); over equatorial Africa (PAN
30–45 %, HNO3∼ 70–80 %, O3∼ 25 %, NOx∼ 70 %) they agree well with
Barret et al. (2010) and Bouarar et al. (2011) and over the ASM region (PAN
∼ 25 %, HNO3∼ 65–70 %, O3∼ 20 %, NOx∼ 60–70 %) they agree
with Tie et al. (2001). These regions coincide with regions of convective
vertical transport of PAN (as seen in Figs. 4 and 5). Lightning-produced
PAN will be lifted into the lower stratosphere by the monsoon convection
along with anthropogenic emissions and will redistribute in the tropical
lower stratosphere. Latitude–longitude cross sections of lightning-induced
PAN, NOx, ozone and HNO3 formation at altitudes between 8 and 14 km
show that the production of PAN, NOx, ozone and HNO3 is less over
the ASM region than over the equatorial Americas and Africa (also seen in Fig. 11). The high amounts of PAN over the ASM are therefore primarily due
to anthropogenic emission transport into the UTLS from the source regions in
southern and eastern Asia. As discussed in Fadnavis et al., 2014, NOx
emissions are estimated to have changed by 38 % in India and 76 % in
China, respectively, during the 2002–2011 period. From sensitivity simulations
they deduced that corresponding changes in upper tropospheric PAN are
> 40 %, O3 by > 25 % and HNO3 by
> 70 % over the Asian monsoon region. These effects are larger
than the impact of lightning NOx emissions over this region (Fig. 11e–h).
Conclusions
In this study statistical analysis of simulated and satellite-retrieved
mixing ratios of PAN, NOx, and HNO3 is presented in order to
determine the transport patterns of pollution into the Asian monsoon region
and the impact of pollution flowing out of the ASM into other regions of the
global atmosphere. The analysis focused on the upper troposphere and lower
stratosphere and covered the period 2002–2011. In ECHAM5–HAMMOZ
simulations both NOx and NMVOCs emission were simultaneously reduced by
10 % over ASM, NAM and WAM to understand transport pathways and their
relative contribution the UTLS. As discussed in Fadnavis et al. (2014),
NOx emissions are estimated to have changed by 38 % in India and 76 % in China, respectively, during this period. From sensitivity simulations
they deduced corresponding changes in upper tropospheric PAN > 40 %, O3 by > 2 5% and HNO3 by > 70 %
over the ASM region. These effects are larger than the impact of lightning
NOx emissions over this region, discussed in Sect. 3 of this study.
Interestingly, the ECHAM5–HAMMOZ reference simulation reveals that in the
UT, westerly winds drive North American and northward-propagating southern
African pollutants eastward where they mix with the ASM plume. Deep
convection and strong diabatic upwelling in the ASM convectively transports
a part of these plumes into the lower stratosphere. The Caribbean region is
another source of pollution transport into the stratosphere. Some cross-tropopause transport occurs due to convection over North America and
southern Africa as well. In the lower stratosphere, the injected pollutants
from ASM, WAM and NAM are transported westward by easterly winds and into
the Southern Hemisphere subtropics by the Brewer–Dobson circulation. The
emission sensitivity simulations Asia -10 %, North America -10 % and
Africa -10 % confirm these transport pathways. In the Southern Hemisphere,
plumes rising from convective zones of southern Africa, South America and
Indonesia–Australia are evident in the model simulations, but are not seen
in the MIPAS-E retrievals. PAN concentrations are higher in the plume rising
from southern Africa than SAM and AUSM. In the UT, they merge by the prevailing
westerly winds. MIPAS-E observations in the UTLS show a single plume over
southern Africa and no enhancement over SAM or AUSM. The reasons for the single
plume seen in MIPAS-E may be that although there is uplifting by each of the
three monsoon systems lower concentrations of PAN reach these altitudes
(above 8 km) from SAM and AUSM until they merge with the southern African plume. It
is also possible that the three-plume structure in the UT seen in the model
is being obscured in the observations due to sampling issues. Convective
cloud cover is strongly associated with deep convection in the ASM region.
The MIPAS-E data have a PAN minimum in the UT right in the longitude band of
the deep convection over the southern flanks of the Himalayas (Fig. 4a).
This feature is unphysical and clearly identifies a sampling bias; however,
the model does not also fully reproduce the latitudinal structure of the PAN
in the ASM region UTLS, which indicates that there are differences in both
the distribution of convection and the large-scale circulation.
The horizontal transport of PAN analyzed from ECHAM5–HAMMOZ simulations
show that the PAN from southern Africa and Brazil is transported towards
America by the circulation around a large upper-level anticyclone and then
lifted to the UTLS in the NAM region. This is also evident in the
Africa -10 % simulation.
The vertical distribution of simulated HNO3 over the monsoon regimes
shows low concentrations above 10 km at the foothills of the Himalayas. In
contrast, the results show strong uplifting of HNO3 into the UT with
NAM convection. This may be due to the fact that NAM convection is not as
intense as the ASM and there may be more wet removal of nitrogen oxides in
the ASM convection. The model simulations indicate a higher efficiency of
NOx conversion to HNO3 over the Indian region compared to NAM.
The change in emission (both NOX and NMVOCs emissions were
simultaneously reduced by 10 %) over each of the ASM, WAM and NAM regions
shows that Asia -10 % transports ∼ 5–30 ppt of PAN in the UTLS over
Asia and ∼ 1–10 ppt in the UTLS northern subtropics and mid-latitudes. North America -10 % simulation shows transport of ∼ 1–5 ppt of PAN over the Atlantic, Europe and North China (between 12 and 14 km) and
0.4–3 ppt over Asia (nearly 16 km). Africa -10 % simulation shows transport
from equatorial Africa to the Atlantic and North America between 6 and 8 km,
which is then transported to Asia by upper tropospheric westerly winds (near
12 km).
Transport of HNO3 is deeper in the UT (∼ 16 km) in
Asia -10 % simulation than North America -10 % and Africa -10 %
simulations. Asia -10 %, North America -10 % and Africa -10 % simulations
show transport of ozone ∼ 1–2 ppt, 0.8–1.5 ppt and 0.4–0.6 ppt
in the UT over respective regions.
In the UT between 6 and 10 km, transport of HNO3 by Asia -10 %
(∼ 3–10 ppt of HNO3 to tropical America) is higher than
North America -10 % (transport of 3–7 ppt of HNO3 to China and India)
and Africa -10 % (∼ 3–5 ppt of HNO3 to tropical America,
China and India) simulations. Similarly transport of ozone is higher for
Asia -10 % than North America -10 % and Africa -10 % simulations.
Comparison of emission change over Asia, North America and Africa shows
highest transport of HNO3 and ozone in the UT over Asia and least over
Africa.
Lightning production of NOx may enhance PAN concentrations in the UT
and affect its transport into the lower stratosphere. The percentage change
in lightning-produced ozone, HNO3, PAN and NOx has been evaluated
with a sensitivity simulation. In the UT, lightning causes significant
increases in these species over equatorial America, equatorial Africa and
the ASM region. These regions coincide with intense convective zones with
significant vertical transport. Lighting production is higher over
equatorial Africa and America compared to the ASM. However, the vertical
distribution shows that higher amounts of PAN are transported into the UT in
the ASM region. This indicates that the contribution of anthropogenic
emissions to PAN in the UTLS over the ASM is higher than that of lightning.
This is consistent with the fact that anthropogenic emissions in the ASM
region are higher than in the NAM and WAM (Lamsal et al., 2011; Miyazak et
al., 2012).
The Supplement related to this article is available online at doi:10.5194/acp-15-11477-2015-supplement.
Acknowledgements
The authors thank the MIPAS-E teams for providing data and the High Power
Computing Centre (HPC) in IITM, Pune, India, for providing computer
resources. Authors are also thankful to anonymous reviewers and the Editor
for their valuable suggestions.
Edited by: F. Khosrawi
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