Introduction
Ammonium nitrate (NH4NO3) aerosols are produced by the
reaction of nitric acid (HNO3), a photochemical product of
NO oxidation, and ammonia (NH3). Emissions of
NH3 and NO are primarily from anthropogenic origin:
fossil fuel combustion for NO and agriculture for
NH3 (i.e., ). The
formation of NH4NO3 is favored by cold temperatures and
high relative humidity . NH4NO3
production competes with that of ammonium sulfate, which is generally
more thermodynamically stable , and that of
coarse-mode nitrate via heterogeneous uptake of HNO3 on dust
and sea salt (i.e., ).
NH4NO3 is an important component of surface particulate
matter in the USA (i.e., ), Europe (i.e., ), and Asia
(i.e., ), especially in
winter. As NH4NO3 rapidly volatilizes away from sources
of NO and NH3 and with warmer temperature, it is
only predicted to make an important contribution to aerosol optical
depth (AOD) over polluted regions , with global
annual estimates of nitrate optical depth ranging from 0.0023 to
0.025 . However,
recent modeling studies have shown that NH4NO3 may become
the largest contributor to anthropogenic AOD by the end of the
twenty-first century , following the
projected increase of NH3 emissions and decrease of
SO2 emissions. Such an increase of NH4NO3 would
offset some of the decline in anthropogenic aerosol radiative
forcing over the twenty-first century
.
In this study, we aim to characterize the mechanisms controlling the
response of NO3- optical depth to changes in
anthropogenic emissions from 2010 to 2050. We focus in particular on
how this response is modulated by the temporal and spatial
variations in NH3 emissions, the heterogeneous chemistry
of HNO3, and the surface removal of nitrate aerosols. In
Sect. , we first describe a new configuration (AM3N)
of the global chemistry–climate atmospheric model (AM3) from the
Geophysical Fluid Dynamics Laboratory (GFDL), with revised
treatments of sulfate and nitrate chemistry and aerosol deposition.
We emphasize significant differences in the simulated budgets of
SO42-, NO3-, and
NHx≡NH3+NH4+ between AM3N and the
version of AM3 used for the Coupled Model Intercomparison Project
(CMIP) 5. In Sect. , we evaluate the simulated
distribution of AOD, as well as SO42-, NO3-,
and NH3 concentrations at the surface and in precipitated
water. In particular, we evaluate AM3 and AM3N against the extensive
set of aerosol composition and optical properties routinely measured
at Bondville (40.1∘ N, 88.4∘ W). In
Sect. , we examine the response of
NO3- optical depth to projected changes in anthropogenic
emissions in 2050 and its sensitivity to different treatments of
removal and chemistry.
Method
Model description
We use the GFDL-AM3 chemistry–climate model to simulate gas and
aerosol chemistry. In its standard form, AM3 uses a finite volume
dynamical core on a cubed sphere grid with 200 km (c48)
horizontal resolution and 48 hybrid sigma pressure vertical layers
. AM3 simulations were conducted for the
Atmospheric Chemistry and Climate Model Intercomparison Project
(ACCMIP) and as the atmospheric component of the GFDL
coupled climate model CM3 for CMIP5 in support of the IPCC AR5.
The chemistry of AM3 has been described by
with updates to the gas-phase and heterogeneous chemistry . Briefly, AM3 includes SO42- formation
from gas-phase oxidation and the in-cloud reaction of SO2
with O3 and H2O2 .
In-cloud production of SO42- is sensitive to cloud pH,
which is calculated as a function of the concentration of
SO42- (assumed to be entirely in-cloud water),
NH3, SO2, HNO3, and CO2.
NH4NO3 formation is calculated following
, but is assumed irreversible. Dry
deposition and wet scavenging by large-scale and convective
precipitation are described by .
Aerosol optical properties (i.e., extinction efficiency,
single-scattering albedo, and asymmetry parameter) are described by
and . Sulfate is assumed
to be fully neutralized by ammonium. Its size distribution is taken as
log-normal following with hygroscopic growth
based on pure ammonium sulfate and capped at
95 % relative humidity. Aerosol activation into cloud droplets
follows the parameterization of . For radiative
calculations, aerosols are assumed to be externally mixed except for
sulfate and hydrophilic black carbon, which are assumed internally
mixed . Nitrate is not considered for radiative
calculations in AM3.
A new configuration of AM3 is introduced (referred to as AM3N
hereafter) with the following changes aimed at improving the
simulation of nitrate aerosols (see Sect. ).
Aerosol chemistry – we use ISORROPIA to simulate the
sulfate–nitrate–ammonia thermodynamic equilibrium
. Equilibrium between gas and aerosol is
assumed to be reached at each model time step (30 min),
which is generally justified for PM2.5
. In-cloud oxidation of SO2 is
restricted to liquid clouds and we revise the calculation of cloud
pH to account for the partitioning of HNO3/NO3- and
NH3/NH4+ between the gas phase and cloud
water.
Heterogeneous chemistry – we include the heterogeneous
uptake of HNO3, NO3-, N2O5,
SO2, and H2SO4 on dust particles
(Table S1 in the Supplement). The uptake of HNO3,
NO3-, and N2O5 is assumed to be limited by
alkalinity . Following
, dust alkalinity is comprised of calcium
and magnesium carbonates, with calcium and magnesium constituting 3
and 0.6 % (by mass) of coarse dust emissions
(radius > 1µm), respectively. Observations
suggest alkalinity is primarily found in the coarse mode
; we assume that fine dust carries half as
much alkalinity per kilogram as coarse dust. We also reduce the
reaction probabilities (γ) of N2O5, NO2,
and NO3 on aerosols relative to AM3
(see Table S1 in the Supplement and Sect. ). The
implications of these changes for the budget of HNO3 and
aerosol NO3- are described in Sect. .
Nitrate optical depth – the optical properties and the
mixing with black carbon of ammonium nitrate are assumed to be
identical to
those of ammonium sulfate. This approximation introduces an error in
mass extinction at 550 nm of less than 20 % for
relative humidity (RH) <90% and by less than 10 %
between 90 and 95 % (Fig. S1 in the Supplement). The optical depth
of NO3- associated with dust is expected to be small
relative to fine-mode NO3- (e.g.,
) and it is not considered here.
Dry deposition – similar to AM3, the dry deposition fluxes of
gases and fine aerosols are calculated based on a monthly climatology of
deposition velocities. We update this climatology to account for
recent observations of rapid deposition of H2O2 and some
oxygenated volatile organic compounds, using the deposition
velocities calculated in the GEOS-Chem chemical transport model as
described by .
Wet deposition – in AM3, aerosol removal by snow is
treated
like that by rain. In AM3N, water-soluble aerosols are not removed
by snow, when the snow is formed via the
Wegener–Bergeron–Findeisen mechanism (referred to as Bergeron
mechanism hereafter), i.e., when water evaporates from liquid cloud
droplets and condenses onto growing ice crystals. This treatment is
consistent with observations and similar to
that used in other global models . Scavenging by snow formed via
riming and homogeneous freezing is treated like that by rain. Gases
are not scavenged by snow except HNO3
. Convective plumes are discretized on
a vertical grid that has finer vertical resolution than AM3
. The improved discretization of the
convective plume has little impact on precipitation at the surface but increases the convective wet removal of tracers as
we will show in Sect. .
Emissions
We use anthropogenic emissions from the Hemispheric Transport of Air
Pollution v2 (HTAP_v2) task force regridded to
0.5∘×0.5∘ for years 2008 and 2010
. HTAP_v2 aircraft emissions are
distributed vertically following . Daily
biomass burning emissions are based on the NCAR Fire INventory
FINNv1, and emitted in the model
surface layer. Average dust emissions are parameterized following
, as
Fp=CSspu10m2(u10m-ut)ifu10m>ut,
where C is a dimensional factor (µgs2m-5),
S is the source function based on topography, u10m
is the horizontal wind at 10 m (ms-1),
ut is the threshold velocity (ms-1), and
sp is the fraction of total dust emitted in the size class p
as defined by . Over the 2008–2010 period, dust
emission is 1640 Tga-1. This includes
1230 Tga-1 from natural sources (S from
, C=0.125µgs2m-5,
ut=1ms-1), similar to the AEROCOM
multi–model mean , and
410 Tga-1 from anthropogenic sources (primarily over
cropland and pasture from with updated MODIS
collection 6, C=0.219µgs2m-5,
ut=3ms-1). Isoprene emissions are
calculated using the Model of Emissions of Gases and Aerosols from
Nature MEGAN,.
NO emissions from lightning are calculated as a function of
subgrid convection . Differences in the
treatment of convection in AM3N result in greater NO emissions from
lightning in AM3N (5.6 TgNa-1) compared to AM3
(5.2 TgNa-1), with both estimates within the range
of emissions inferred from observations . Other natural emissions, including soil
NOx and soil and ocean NH3 emissions, are
described by and . Global
total emissions of SO2, NH3, and NOx
are listed in Table .
Simulated budget of SO4, NHx, and NOy in 2010.
AM3
AM3N
SO42-a
Production (Tg S a-1)
37.3
33.1
OH
10.4
7.7
H2O2
26.7
16.2
O3
0.1
4.5
dust
0.0
1.9
Loss (Tg S a-1)
37.4
33.3
Dry deposition
4.7
4.6
SO42-
4.7
3.8
SO42- on dust
0.0
0.8
Wet deposition
32.7
28.7
SO42-
32.7
27.5
SO42- on dust
0.0
1.1
Lifetime (days)
4.9
3.8
NHx
NH3 emission (Tg N a-1)b
54.5
54.5
Loss (Tg N a-1)
54.8
55.0
Dry deposition
14.4
23.5
NH4+
14.3
3.6
NH3
0.1
19.9
Wet deposition
40.4
30.7
NH4+
39.4
20.7
NH3
1.0
10.1
Gas oxidation
0.0
0.8
Lifetime (days)
5.5
2.5
NOy
NO emission (Tg N a-1)
51.4
51.8
Loss (Tg N a-1)
51.3
51.0
Dry deposition
25.4
23.1
HNO3
18.3
10.7
NO3- on dust
0.0
3.4
NH4NO3
0.7
0.8
Organic nitrogen
3.9
4.0
Wet deposition
25.6
27.6
HNO3
23.4
17.8
NO3- on dust
0.0
3.7
NH4NO3
0.5
3.5
Organic nitrogen
1.7
2.6
Lifetime (days)
22.7
13.4
a SO2 emissions are
74.0 TgSa-1 including 16.0 TgSa-1 from
dimethyl sulfide (DMS) oxidation.b including
39.9 TgNa-1 from anthropogenic sources,
3.9 TgNa-1 from biomass burning, and
10.7 TgNa-1 from natural sources (primarily from the ocean).
Sensitivity simulations
Considering the large uncertainty in the simulated nitrate optical
depth and surface concentrations, we design a set of sensitivity
simulations based on AM3N to characterize the sensitivity of nitrate
and sulfate to key uncertainties in chemistry and in NH3
emissions (Table ). All simulations are run
from 2007 to 2010, using 2007 to spin-up the model. To facilitate
the comparison with observations and limit meteorological
variability across model configurations, the model horizontal wind
is relaxed to 6 hourly values from the National Centers for
Environmental Predictions reanalysis as
described in .
NH3 emissions
Present-day – the largest source of NH3 to the
atmosphere is agriculture. Unlike anthropogenic emissions of other
compounds, which are dominated by fossil fuel emissions,
NH3 emissions exhibit large seasonal variations, which
reflect the seasonality of agricultural practices (e.g., fertilizer
application) as well as the decrease of NH3 solubility
with temperature . The HTAP_v2 inventory includes monthly
variations in anthropogenic NH3 emissions over North
America, Europe, and parts of Asia, including Japan and China, but
excluding India. Anthropogenic emissions of NH3 previously
used in AM3 simulations for ACCMIP and CMIP5 are constant throughout
the year . To evaluate the impact of the
seasonality of NH3 emissions on NO3-, we remove
all temporal variability in the anthropogenic emissions of
NH3 in simulation AM3N_ns. NH3 emissions also
exhibit diurnal variability , which may
affect the simulated concentrations of NH3 and
NH4NO3 . In AM3N_diu, we impose the NH3
diurnal cycle of the regional LOTOS (Long Term Ozone Simulation)
model globally . The ratio between maximum
emissions (13:00–14:00 local time) and minimum emissions (03:00–06:00)
is 5.7.
Average annual emissions of NH3 for 2010 (top row) and 2050
(bottom row) based on anthropogenic NH3 emissions from HTAP_v2
(left column) and from RCP8.5 (right column). Non anthropogenic emissions
(including biomass burning) are the same in all scenarios. Total annual
emissions are indicated inset.
2050 – anthropogenic NH3 emissions for 2050 are
estimated by scaling HTAP_v2 surface anthropogenic NH3
emissions with national projections from the Representative
Concentration Pathway 8.5 (RCP8.5) from 2010 to 2050
(Fig. ), while keeping natural and
biomass burning emissions at their present-day levels. We use the
RCP8.5 scenario for 2050 as it most closely
resembles emissions from regional inventories over the 2000–2010
period . However, we do not use the RCP8.5
spatial distribution of NH3 sources, as it differs notably
from HTAP_v2 over many source regions such as India, the Nile
delta, the Benelux, the California Central Valley, and the
Saskatchewan (Fig. ). These differences
may reflect mapping errors for RCP8.5 NH3 emissions from
agriculture as noted by . Our approach
results in 18 % more anthropogenic emissions (60 TgNa-1) than in
RCP8.5 for 2050.
Configurations of AM3N used in this study.
Temporal variation
Heterogeneous
Heterogeneous
Dry deposition
of NH3 emissions
chemistry on dust
production of HNO3
of NH4NO3
AM3N
Monthly
Yes
Yes
SO42-
AM3N_fdep
Monthly
Yes
Yes
HNO3
AM3N_diu
Monthly + diurnal
Yes
Yes
SO42-
AM3N_ns
No
Yes
Yes
SO42-
AM3N_nhet
Monthly
Yes
No
SO42-
AM3N_ndust
Monthly
No
Yes
SO42-
AM3N_fdep_diu
Monthly + diurnal
Yes
Yes
HNO3
Heterogeneous chemistry
Wintertime production of HNO3 in the northern
midlatitudes' boundary layer is dominated by the uptake of
N2O5 on aerosols (e.g.,
). The
probability for the heterogeneous conversion of N2O5 to
HNO3 (γ) remains uncertain ,
with field and laboratory observations showing that it is inhibited
by aerosol nitrate and organics , but enhanced by
cold temperatures . To
quantify the impact of the heterogeneous production of HNO3
on aerosol NO3-, we neglect the heterogeneous production
of HNO3 via N2O5 aerosol uptake in AM3N_nhet.
We also neglect the productions of HNO3 by NO3
and NO2 reactive uptake, as they may modulate the
wintertime budget of NOy in polluted region
. Note that previous characterizations of
NO3- optical depth also neglected the heterogeneous
chemistry of oxidized nitrogen (e.g., ).
We also evaluate the impact of the heterogeneous chemistry on dust
as it is not included in all models (e.g.,
). In AM3N_ndust, we
neglect the uptake of HNO3, N2O5,
NO3, H2SO4, and SO2 on dust.
Surface removal of fine NO3-
In AM3N, the dry deposition of NH4NO3 is slow, similar to
other fine aerosols. Several field observations have reported
steeper vertical gradients and faster deposition velocities
(vd) for NO3- than for
SO42-
.
This difference stems from gradients in temperature, RH, and
HNO3 within the boundary layer, which reduce the stability
of NH4NO3 near the surface. The volatilization of
NH4NO3 may result in an underestimate of the surface
deposition of TNO3≡HNO3+NO3-,
since vd(NH4NO3)≪vd(HNO3). As an upper bound, we assume that the
surface removal of fine NO3- is limited by turbulent
transport by setting vd(NO3-)=vd(HNO3) in AM3N_fdep.
Budget and global distribution
Table shows the budgets of SO42-,
NHx, and NOy in AM3 and AM3N for 2010. Here
NOy is defined as the sum of all species that contained
oxidized nitrogen. The budgets for all simulations are given in
Table S2.
The lifetimes of SO42-, NHx, and
NOy are significantly shorter in AM3N than in AM3. This
decrease is driven in part by greater convective removal associated
with changes in finer vertical discretization of convective
plumes. For instance, the lifetime of SO42- with respect
to convective removal decreases from 44 to 18 days.
For SO42-, the increased effectiveness of convective
removal is partly offset by reduction in the removal by snow
(Sect. ). The SO42- lifetime
in both AM3 and AM3N falls within the range of AEROCOM models
3–5.2 days. Unlike AM3, AM3N includes
ammonium in the calculation of cloud pH, which reduces the acidity
of cloud droplets and favors the production of SO42-
via in-cloud oxidation of SO2 by O3. The
production of SO42- via SO2+O3 is
4.5 TgSa-1 in AM3N, greater than the recent estimate
of (1.5 TgSa-1). This
discrepancy may reflect differences in cloud pH and lower
H2O2 concentrations in AM3N because of faster dry
deposition for H2O2 and efficient removal of
HO2 via aerosol uptake . AM3N does not
include production of SO42- via the aqueous reaction of
SO2 with O2 catalyzed by iron and manganese or
by the oxidation of SO2 by stabilized Criegee
intermediates . The lifetime of
SO2 is 1.3 days in both AM3 and AM3N, similar to
and . The overall
conversion from SO2 to SO42- (excluding
SO42- on dust) is reduced compared to AM3 from 50 to
42 % and lower than the AEROCOM multi-model mean (62 %).
In AM3, NH3 uptake by SO42- is solely controlled by
kinetics without any thermodynamic limit, such that NH3
burden is small (0.005 TgN) and NH3 generally limits the
formation of NH4NO3. In AM3N, the uptake of NH3
by SO42- aerosols cannot exceed the thermodynamic limit
calculated by ISORROPIA, which results in a greater NH3
burden (0.11 TgN) and favors the production of NH4NO3. The
shorter lifetime of NHx in AM3N than in AM3 reflects the
change in the speciation of NHx and the faster dry
deposition of NH3 relative to NH4+. The lifetime
of NHx in AM3N (2.5 days) is similar to that derived by
and (2.3 days).
AM3N and AM3 differ most strikingly in their simulations of
NOy. The contribution of HNO3 to the removal
of NOy decreases from 81 % (AM3) to 56 % (AM3N).
In contrast, the contribution of aerosols to NOy removal
increases from 2 to 22 %. Recent studies
have found an even
greater contribution of aerosols to the removal of NOy
(>30 %); this difference may reflect the lack of
HNO3 uptake by sea salt in AM3N. Organic nitrogen
contributes 10 % of NOy removal in both AM3 and
AM3N. The much lower fraction of NOy deposited as
HNO3 in AM3N relative to AM3 reflects both the increased
production of NH4NO3 and the uptake of HNO3 on
dust. The total heterogeneous production of HNO3 by
N2O5 (9.7 TgNa-1), NO2
(0.6 TgNa-1), and NO3-
(0.4 TgNa-1) uptake on fine aerosols is reduced by
50 % in AM3N relative to AM3. This decrease is primarily driven
by reduced reaction probabilities for NO2 and
NO3 uptake. In contrast, the change of
γN2O5 from 0.1 (AM3) to 0.01 (AM3N) reduces the
heterogeneous uptake of N2O5 by only 20 % because of
the large increase in the sulfate surface area in winter (see
Sect. ). The magnitude of the N2O5
source of HNO3 in AM3N is 3 times as large as reported by
. This may reflect greater reactive
aerosol surface area in AM3N, as N2O5 hydrolysis can take
place on SO42-, BC, OC, and NO3- aerosols,
while only SO42- is considered by
. Reduction in the simulated
HNO3 burden – driven by faster NO3- deposition
(AM3N_fdep), heterogeneous uptake of HNO3 on dust
(AM3N_ndust), or reduced heterogeneous production of HNO3
(AM3N_nhet) – increase cloud pH, which favors the oxidation of
SO2 by O3 (Table S2).
Annual mean burden of NO3-, NO3- on dust,
NH4+, and NH3 in mgNm-2 in AM3N from
2008 to 2010. Global burdens are indicated inset. The location of
the Bondville site is indicated by a black cross in the upper left panel.
Observed (black) and simulated monthly concentrations of
NO3-, SO42-, and NH3 at Bondville
(40.1∘ N, 88.4∘ W) in surface air (left panel) and
precipitated water (right panel). Observations are averaged from
2006 to 2012, while model output is from 2008 to 2010. The vertical
bars denote 1 standard deviation of the mean monthly observations.
The different model sensitivity experiments are described in
Table .
Figure shows the burden of fine NO3-,
NO3- on dust, NH4+, and NH3 in
AM3N. The simulated global burdens fall within the range of previous
estimates for fine NO3-
(0.04–0.11 TgN), NO3- on dust
(0.07–0.41 TgN), NH4+
(0.21–0.27 TgN), and NH3
(0.07–0.29 TgN). The burden of fine NO3- peaks
over China where it reaches over 5 mgNm-2, with
a secondary maximum over India. Fine NO3- burden is also
elevated over northern Europe and the US Midwest, where agricultural
activities are located close to large sources of oxidized nitrogen.
Compared with the fine nitrate distribution from
for 2000, AM3N simulates greater
nitrate burden over Asia but lower burdens over Europe and the USA.
These differences may reflect different spatial distributions of
NH3 emissions (Fig. ). AM3N
simulates large enhancements in NH3 column over source
regions such as India (where the burden reaches
12 mgm-2), northern China, the Netherlands, and the
US Midwest, as supported by satellite observations
. This lends some support to the spatial
allocation of anthropogenic NH3 emissions in HTAP_v2
inventory, although observed enhancements in NH3 burden
over the Po Valley and California are not captured by AM3N.
Evaluation
Bondville
We first evaluate the model against an extensive suite of
observations collected at Bondville (40.1∘ N,
88.4∘ W; 213 ma.s.l.). Bondville is located in
the vicinity of large sources of NH3 and NOx,
which result in elevated surface NO3- concentrations
(Fig. ) and make this
site well-suited to evaluate the representation of nitrate aerosols
in AM3 and AM3N. Here we compare the model against observations of
surface NO3- and SO42- concentrations (from
the Interagency Monitoring of Protected Visual Environments
(IMPROVE) network), surface NH3 concentrations (Ammonia
Monitoring Network (AMoN)), SO42-, NO3-, and
NH4+ wet deposition (National Atmospheric Deposition
Program (NADP)), surface dry aerosol extinction NOAA Earth
System Research Laboratory (ESRL),, and AOD
(Aerosol Robotic Network (AERONET)). Vertical profiles of aerosol
extinction were also collected by NOAA ESRL Airborne Aerosol
Observing (AAO) program from 2006 to 2009 . Temperature and humidity profiles are measured
twice daily by the ESRL Surface Radiation Budget Network (SURFRAD).
Figure shows the
observed (black) and simulated monthly concentrations in surface air
(left column) and in precipitated water (right column) for
NO3-, SO42-, and NH3
(NH4+ for wet deposition) for AM3 and different AM3N
configurations. Both NO3- and NH3
concentrations are higher year round in AM3N than in AM3, as
ISORROPIA enforces thermodynamic limitation on the uptake of
NH3 by SO42-. Observations show a spring peak
in surface NH3 concentrations, while both AM3 and AM3N
simulate a summer peak. Bondville is surrounded by corn and soybean
fields and NH3 emissions associated with spring fertilizer
application may be underestimated . In
summer, more efficient convective removal of SO42- in
AM3N reduces the AM3 high bias for SO42- surface
concentration and low bias for SO42- wet deposition. In
winter, the low bias for surface SO42- concentration in
AM3 is reduced as a result of less efficient removal by snow and
increased in-cloud oxidation of SO2. AM3N_nhet and
AM3N_fdep produce greater SO42- concentrations in
winter than AM3N consistent with increased in-cloud oxidation of
SO2 by O3 (Table S2).
NO3- shows a large positive bias in AM3N in winter
(>70 % in February). This bias can be reduced by either neglecting
the heterogeneous production of HNO3 via NO2,
NO3, and N2O5 (AM3N_nhet) or treating the
deposition of fine NO3- like that of HNO3
(AM3N_fdep). Conversely, neglecting the seasonality of NH3
emissions (AM3N_ns), similar to simulations performed for ACCMIP and
CMIP5, increases the bias for NO3- in winter.
To analyze the factors controlling NH4NO3 in the model,
we calculate the gas ratio (GR) at each model time step. The GR was
first proposed by to diagnose the
sensitivity of NH4NO3 to its gas-phase precursors
NH3 and HNO3 and is defined as
GR=[NH3]+[NH4+]-2[SO42-][HNO3]+[NO3-].
GR defines three different regimes: (a) GR > 1, in which
NH4NO3 formation is limited by the availability of
HNO3, (b) 0 < GR < 1, in which NH4NO3
is limited by the availability of NH3, and (c) GR < 0,
in which NH4NO3 is inhibited by SO42-. We
define the degree of limitation of NH4NO3 by
HNO3 (L(HNO3)) as the fraction of the
time when GR > 1. In winter, NH4NO3 is most
frequently limited by HNO3
(L(HNO3)) = 78 % in AM3N).
Figure (bottom panel) shows
L(HNO3) binned by NO3-
concentrations. NH4NO3 is most limited by HNO3
availability at low [NO3-], while NH3 becomes
more limiting at high [NO3-]. This suggests that even in
an environment that is generally NH3-rich with respect to
NH4NO3 formation, NH3 emissions modulates
NO3- production during high NO3- episodes
(AM3N_ns).
Observed and simulated distribution of daily NO3- concentration
at Bondville (40.1∘ N, 88.4∘ W) in winter (top panel)
from 2006 to 2012 (observations) and 2008 to 2010 (model). The degree
of HNO3 limitation for NH4NO3 formation (GR > 1) is
shown in the bottom panel. The different model sensitivity experiments
are described in Table .
Figure also shows that
AM3N_nhet and AM3N_fdep produce different distributions of daily
[NO3-] although they have similar mean monthly
[NO3-] (top panel). AM3N_fdep reproduces observations at
low NO3- concentrations well but underestimates the
frequency of high NO3- events, when NH4NO3
exhibits significant sensitivity to NH3. Under these
conditions, less volatilization of NH4NO3 near the
surface is expected as NH3 is not depleted near the
surface like HNO3. AM3_nhet [NO3-] is most
consistent with observations at high [NO3-], conditions
under which N2O5 heterogeneous uptake has been observed
to be inhibited both in laboratory and field settings
. The ability of AM3N_fdep and AM3N_nhet to
capture NO3- under different conditions emphasizes the need
to represent the dynamic nature of γ(N2O5) and
TNO3 surface removal.
Observed and simulated aerosol optical depth at
550 nm at Bondville (40.1∘ N, 88.4∘ W) in
AM3 and AM3N_fdep_diu. Observations (black crosses) are averaged
from 2006 to 2012 and the thin vertical black bars denote 1 standard
deviation of the mean. Thick color bars show the simulated optical
depth of SO42- (red), NO3- (cyan), OC
(green), BC (purple), dust (brown), and sea salt (blue) for
AM3N_fdep_diu (2008–2010 average).
Mean seasonal observed (black dots) and simulated surface
and vertical profiles of aerosol dry extinction at Bondville
(40.1∘ N, 88.4∘ W). The vertical profile show the
average of all observations by the Airborne Aerosol Observatory from
2006 to 2009 collected during daytime (10:00–16:00 local time).
Surface observations reflect the average of all daytime observations
at the ESRL BND station from 2006 to 2012 with no local pollution.
The model is averaged for daytime from 2008 to 2010. Horizontal
lines show the 25th to 75th percentiles of observed dry aerosol
extinctions. Dry extinctions are reported at standard temperature
and pressure (273.15 K, 1 atm). We multiply the
modeled nitrate extinction by 0.8 to account for the evaporation of
ammonium nitrate in the nephelometer . The
different model sensitivity experiments are described in
Table .
Figure shows the observed and simulated
monthly AOD at Bondville. Observed AOD peaks in summer and reaches
a minimum in winter. This seasonality is well captured by AM3 (top
panel), while AOD in AM3N_fdep_diu (bottom panel) peaks in spring
and is biased high in winter and fall. Biases in AOD may be caused by
errors in aerosol abundance and speciation but also by errors in
aerosol hygroscopic growth. Their relative contribution can be
estimated by comparing observed and simulated aerosol extinction
profiles, under dry conditions (RH < 40 %) .
Figure shows that AM3N overestimates aerosol
dry extinction in spring and fall, which suggests that the simulated
aerosol abundance is overestimated. This bias may be caused by organic
carbon or dust, which contribute over 30 % of the simulated
aerosol dry extinction throughout the column in spring, summer, and
fall (Fig. S2 in the Supplement). In winter and
summer, AM3N is more consistent with the observed aerosol dry
extinction profile than AM3. In particular, AM3 exhibits a low bias in
winter and a high bias in summer, consistent with the biases for
surface [SO42-] and with the lack of extinction from
NO3-, the largest contributor to AM3N dry aerosol
extinction below 1000 m in winter
(Fig. S2). The different biases of
AM3 and AM3N against AOD and dry extinction in winter and summer
suggest errors in the hygroscopic growth of aerosols. This is
consistent with comparisons with twice daily soundings of temperature
(Fig. S3) and relative humidity
(Fig. S4) over Bondville, which show that
AM3N is on average too humid in winter and spring and too dry in
summer. In particular, AM3N overestimates the occurrence of
high-humidity periods (RH > 90 %,
Fig. S5), when aerosol hygroscopic
growth is especially large. Modeled AOD would be especially sensitive
to positive RH biases in winter since AOD winter is primarily
controlled by SO42- and NO3-, which have
stronger hygroscopic growth than organic carbon and dust.
Normalized mean bias and correlation coefficient (in parentheses)
of monthly model results vs. measurements of surface concentrations
of SO42-, NO3- and HNO3, NH3
and NHx, concentrations of SO42-, NH4+,
and NO3- in rain, and total aerosol optical depth at 550 nm
from AERONET, MISR, and MODIS*.
AM3
AM3N
AM3N_fdep_diu
SO42-
Aerosol
USA
0.07 (0.81)
-0.11 (0.89)
-0.06 (0.89)
Europe
-0.43 (0.24)
-0.22 (0.62)
-0.13 (0.64)
Wet deposition
USA
0.00 (0.42)
-0.07 (0.59)
-0.08 (0.57)
Europe
-0.18 (0.53)
-0.32 (0.57)
-0.32 (0.53)
NO3-
Aerosol
USA
-0.61 (0.64)
1.03 (0.64)
0.17 (0.65)
Europe
-0.78 (0.62)
0.32 (0.62)
-0.30 (0.58)
Gas + aerosol
Europe
-0.18 (0.61)
0.17 (0.75)
-0.29 (0.57)
Wet deposition
USA
0.14 (0.33)
0.23 (0.52)
0.11 (0.54)
Europe
-0.32 (0.57)
-0.29 (0.54)
-0.39 (0.54)
NHx
Gas
USA
-0.75 (0.50)
-0.10 (0.54)
-0.22 (0.53)
Europe
-0.65 (0.48)
0.23 (0.54)
0.17 (0.50)
Gas + aerosol
Europe
0.69 (0.66)
0.18 (0.64)
0.02 (0.64)
Wet deposition
USA
-0.20 (0.50)
-0.20 (0.69)
-0.15 (0.69)
Europe
-0.23 (0.52)
-0.36 (0.58)
-0.32 (0.58)
AOD
MODIS
World
0.09 (0.57)
-0.08 (0.68)
-0.08 (0.68)
High NO3-
-0.15 (0.83)
0.11 (0.87)
0.09 (0.87)
High SO42-
0.57 (0.83)
0.06 (0.87)
0.06 (0.87)
MISR
World
-0.03 (0.53)
-0.16 (0.59)
-0.16 (0.59)
High NO3-
-0.12 (0.84)
0.21 (0.87)
0.18 (0.87)
High SO42-
0.54 (0.86)
0.12 (0.88)
0.12 (0.88)
AERONET
World
-0.03 (0.72)
-0.10 (0.82)
-0.11 (0.82)
High NO3-
-0.50 (0.87)
-0.01 (0.76)
-0.07 (0.70)
High SO42-
0.33 (0.47)
-0.10 (0.74)
-0.10 (0.71)
* Model results are averaged from 2008
to 2010, while we use observations from 2006 to 2012, except for MODIS
and MISR (2008–2010) and NH3 observations in the USA (2007–2014). Detailed seasonal comparisons are presented in the Supplement.
Global evaluation
We broaden our evaluations of AM3 and AM3N using observations of
surface [NO3-], [SO42-], and [NH3]
in the USA (IMPROVE and AMoN) and Europe (European Monitoring and
Evaluation Programme (EMEP)), [NHx] and [HNO3]
(EMEP), and SO42-, NO3-, and NH4+
concentrations in precipitated water (NADP and EMEP). We compare the
model monthly means from 2008 to 2010 to the average monthly
observations from 2006 to 2012. For AMoN, we consider all
observations (2007–2014) to take advantage of the ongoing expansion
of the network. We apply Grubbs' test for
each station to filter out possible outliers (95 % critical
value). Table shows the normalized mean
bias (ratio of the mean difference between the model and
observations to the mean observed value) and the correlation between
the model and observations for each data set for AM3, AM3N.
Evaluations of all AM3N configurations and seasonal comparisons
(Table S3 and Figs. S6 to S18) are provided in the Supplement.
Table shows that AM3 and AM3N exhibit
similar normalized mean biases for SO42- surface
concentrations and wet deposition in the USA and Europe. However,
AM3N exhibits better correlation with observations, which reflects
a large improvement in the simulated seasonality of surface SO42- (Figs. S6 and S12). As previously noted, the
improvement in the simulated [SO42-] in AM3N reflects
increased removal in summer by convective precipitation, greater
production of SO42- via O3+SO2, and
less efficient removal by snow in winter. The increased removal of
SO42- by convective precipitation in AM3N improves the
simulation of summer wet deposition in the USA, although it remains
biased low (Fig. S9). Increased convective removal of HNO3
and NH3 also reduces the low bias in simulated summer wet
deposition for NO3- (-50 to -23 %, Fig. S10) and
NH4+ (-46 to -16 %, Fig. S10). Greater in-cloud
oxidation of SO2 by ozone in AM3N_fdep and AM3N_nhet
reduces the low biases for surface [SO42-] relative to
AM3N (from -11 to -5 % in the USA and -22 to -13 % in
Europe).
Surface [NO3-] is generally overestimated in AM3N,
especially over the USA (+100 %). Recent studies using a range
of NH3 emissions and different representations of aerosol
thermodynamics and heterogeneous chemistry have also found large
positive biases in simulated surface [NO3-]
. Figure shows the annual
distribution of L(HNO3) in AM3N. At the surface,
NH4NO3 formation is primarily limited by the availability
of HNO3 over continental regions, such as Europe, India,
or northern China. Under HNO3-limited conditions, our
analysis at Bondville suggests that increasing the deposition of
TNO3 (AM3N_fdep) can improve the simulation of surface
[NO3-]. On a continental basis, we also find that
AM3N_fdep_diu better captures surface [NO3-] (+17 %
bias in the USA) and we will focus on this configuration in the
following. Note that the diurnal cycle of NH3 emissions
has a small impact on the simulated mean surface [NO3-]
concentration, but reduces surface [NH3] and increases its
export to the free troposphere. Figure S20 shows the observed and
simulated diurnal cycle of [NO3-] at the YRK site from the SouthEastern Aerosol Research and Characterization Network.
NO3- exhibits a pronounced diurnal cycle with a maximum in
the early morning and a minimum in the late afternoon (as a result
of both thermodynamics and boundary layer height). AM3N and
AM3N_diu capture the timing of the diurnal cycle well. As
NH3 emissions peak in the afternoon, the magnitude of the
NH4NO3 diurnal cycle in AM3N_diu is lower than in AM3N.
Higher daytime concentrations of NH4NO3 in AM3N_diu
suggest that accounting for the diurnal cycle of NH3
emissions may increase the magnitude of the radiative forcing
associated with NH4NO3.
Simulated degree of limitation of NH4NO3 formation by HNO3
(GR > 1) weighted by NH4NO3 concentration at different pressure
levels in AM3N for 2010.
Observed and simulated monthly AOD at 550 nm in
different regions averaged over the 2008–2010 period. Circles show
observations from MODIS (open circles) and MISR (filled circles).
The solid and dashed black lines show the AOD simulated by
AM3N_fdep_diu and AM3 respectively. We also show the simulated
optical depths of sulfate (red), nitrate (cyan), dust (brown),
organic carbon (green), black carbon (purple), and sea salt (blue)
in AM3N_fdep_diu. The model is sampled to match the location
and time of valid measurements by both MODIS and MISR in each
region. Correlations between simulated and observed AOD are shown
inset for AM3N_fdep_diu and AM3 (in parentheses).
Contribution of different aerosol types to the global mean
annual aerosol optical depth at 550 nm in AM3, AM3N, and
other climate models considering NO3- aerosol
(all-sky except clear-sky for GISS). AM3 and AM3N AOD are representative of
2010 conditions, while other models reflect 2000 conditions. The
range of SO42-, NO3-, and total AOD across
AM3N configurations are shown by red, light blue, and black
horizontal bars respectively. Note that changes in the
parameterization of NH3 convective removal reduce the
simulated NO3- optical depth by GISS to 0.005 (S. Bauer,
personal communication, 2015).
Figure shows the average monthly variation of
AOD from 2008 to 2010 over different regions as observed by MODIS
and MISR and simulated
by AM3 and AM3N_fdep_diu. Although AM3 does not exhibit a large bias
on a global scale (normalized mean biases lower than 10% for both MODIS and MISR), it fails to capture the seasonality of AOD over
most continental regions. Over North America, AOD is biased low in
winter and high in summer in AM3, consistent with the biases in
surface [SO42-]. The spring bias may be exacerbated by
insufficient transport of aerosols from Asia. AM3 is biased high over
tropical land masses, consistent with insufficient convective removal
of aerosols. AM3N_fdep_diu AOD shows improved correlations with
observations over most continental regions (see also
Fig. S19). The increased AOD in winter and
spring can be partly attributed to nitrate optical depth, which
accounts for over 30% of AOD over North America.
Following and , we
further evaluate the performances of AM3 and AM3N in locations
within the top decile of simulated NO3- and
SO42- burden against observations from MODIS, MISR, and
AERONET. AM3 AOD is biased high over high SO42- regions
(+30 to 50 %) and low over high NO3- regions (-10
to -50 %) consistent with the analysis of
. The bias over high SO42-
regions is greatly reduced in AM3N (<10 %), while the model
exhibits a high bias against satellite AOD observations (10–20 %)
but little bias against AERONET observations in high NO3-
regions. More detailed comparisons with AERONET show that AM3N
better captures AOD at high latitudes in spring (Fig. S19), which
lends support to the changes made to the representation of in-cloud
sulfate production and wet deposition.
Sensitivity of nitrate optical depth
Present-day emission
Figure compares the contributions of
SO42-, NO3-, OC, BC, dust, and sea salt to
the global mean AOD in AM3 and AM3N_fdep_diu with previous
estimates .
Present-day global mean AOD in AM3N_fdep_diu is 0.136, 16 %
less than in AM3. All AOD components decrease as a result of
more efficient convective removal, with the largest decrease for
SO42- (-36 %). SO42- optical depth
decreases most from AM3 to AM3N_fdep_diu over tropical regions,
while it increases at high latitudes, consistent with changes in
SO42- chemistry and removal. NO3- optical
depth ranges from 0.0052 (AM3N_nhet) to 0.0078 (AM3N_ndust). Our
best estimate is 0.0060 (AM3N_fdep_diu). The different treatment
of reactive nitrogen results in similar changes in
SO42- (0.002) and NO3- optical depth
(0.003). The range of NO3- optical depths derived from
AM3N (0.0052–0.0078) encompasses recent estimates by
and , but
differs significantly from the Goddard Institute for Space Studies (GISS) (0.023) and
the Centre for International Climate and Environmental Research – Oslo (CICERO) (0.002) models.
reported that convective transport of NH3 to the free
troposphere, where NH4NO3 is stable and sensitive to
NH3 (Fig. ), is responsible for the elevated
nitrate in the GISS model. Revisions of the treatment of
NH3 convective removal in GISS reduce the simulated
present-day NO3- optical depth to 0.005 (S. Bauer,
personal communication, 2015).
also showed that CICERO may overestimate
SO42- optical depth, which would inhibit the production
of NH4NO3 by decreasing the amount of free ammonia
([NHx]-2[SO42-]).
Figure shows the annual AM3N
nitrate optical depth and its sensitivity to the treatment of
NH3 emissions and NO3- chemistry in AM3N. The
sensitivity of NO3- optical depth to NH3
seasonality is small and follows the patterns of NH4NO3
limitations by NH3, with largest sensitivity over the
eastern USA and in the outflow of continents The global sensitivity
to NH3 seasonality is a lower bound, since the seasonality
of anthropogenic NH3 emissions is not represented in
important source regions (e.g., India, South America) in HTAPv2. We
find greater sensitivity to the diurnal cycle of NH3
emissions, which is attributed to increased transport of
NH3 into the free troposphere, where NH4NO3 is
more sensitive to NH3 (Fig. ) and more stable
because of colder temperature. Decreasing HNO3 production,
either by neglecting its heterogeneous production (AM3N_nhet) or
increasing the deposition of NO3- (AM3N_fdep), reduces
the annual mean NO3- optical depth by 25 % globally.
Regionally, NO3- in polluted regions is more sensitive to
the heterogeneous production of HNO3 because of the large
aerosol surface area in these regions. Neglecting heterogeneous
chemistry on dust results in a large relative increase of
NO3- optical depth in dusty regions, but the increase of
the global mean NO3- optical depth is small (13 %).
This muted response is caused by low NH3 sources near
major natural dust sources. A notable exception is anthropogenic
dust, whose sources are primarily associated with agriculture
. The proximity of NH3 and
anthropogenic dust sources results in 35 % greater sensitivity
of NO3- optical depth to anthropogenic dust than to
natural dust (per kilogram of dust).
2050 emissions
Figure shows the contributions of sulfate, nitrate,
organic carbon, black carbon, dust, and sea salt to the global mean AOD in
AM3 and AM3N_fdep_diu using 2050 emission as described in
Sect. . Sulfate optical depths decrease by 20 %
from 2010 to 2050 in both AM3 and AM3N_fdep_diu, similar to
. In all configurations, AM3N produces a small
increase of the global mean NO3- optical depth in response to
changes in anthropogenic emissions from 2010 to 2050 (<30 %), with
NO3- optical depth ranging from 0.0061 (AM3N_fdep) to 0.01
(AM3N_ndust). In AM3N, the conversion rate from NH3 to
NO3- (excluding dust) defined as the molar ratio of the fine
NO3- burden to NH3 emissions decreases by 10 % from
0.34 day-1 to 0.29 day-1. NH4NO3
lifetime with respect to deposition increases by 25 % under the 2050
emissions, which suggests that the increase in NO3- optical depth
in AM3N is driven by reduced sinks rather than increased production. The
response of NO3- to changes in anthropogenic emissions is weaker than
reported in recent studies. For instance,
reported a NO3- optical depth of 0.01 for 2050 and an increase of
the conversion rate from NH3 to
NO3- from 0.36 day-1 to 0.57 day-1 from
2000 to 2050. Using the same anthropogenic emissions, the simulated NO3-
optical depth in AM3N in 2050 (the configuration closest to that used by
) is 0.077 and the conversion rate from
NH3 to NO3- is 0.33 day-1.
Annual mean NO3- optical depth at 550 nm
in AM3N (top left panel) and its relative sensitivity to the
treatment of NH3 emissions, NO3- production,
and loss in % for 2008–2010 conditions. The change in
NO3- optical depth relative to AM3N is indicated in the
bottom left for each configuration. The sensitivity is only shown in
regions where NO3- optical depth is greater than 0.005.
Nitrate optical depth at 550 nm over the United States, Europe, China,
and India for 2008–2010 (white bars) and 2050 (black bars) anthropogenic
emissions for different configurations of AM3N. The thin red bar indicates
the nitrate optical depth calculated using RCP8.5 2050 NH3
emissions in AM3N. The relative changes between 2008–2010 and 2050
in NO3- optical depth and surface emissions of NH3,
SO2, and NO are indicated for each region.
Figure shows that the simulated
NO3- optical depth decreases in all AM3N configurations
over Europe and China, increases over India, and exhibits little
change over the USA. In all regions SO2 emissions are
projected to decrease. This results in greater sensitivity of
NO3- optical depth to HNO3, which is reflected
in the increase of the sensitivity of NO3- optical depth
to the uptake of HNO3 by dust and lower sensitivity to
temporal variations of NH3 emissions (seasonality, diurnal
cycle). The sensitivity of NO3- optical depth to the
heterogeneous production of HNO3 is reduced despite the
increased sensitivity of NO3- to HNO3. This
follows the decrease in aerosol surface area associated with the
reduction of the SO42- burden.
The simulated changes in NO3- optical depth from the
present day to 2050 over the USA, China, and Europe are consistent
with surface NH4NO3 limitations. For instance, surface
NH4NO3 is primarily limited by HNO3 in Europe
and China and the decrease of NO3- optical depth is
driven by the reduction of NO emissions. In these regions,
AM3N simulates similar NO3- optical depth using different
anthropogenic emissions of NH3 for 2050, which is also
consistent with the reduced sensitivity to NH3 emissions.
However, surface NH4NO3 limitation patterns cannot
explain the increase of NO3- optical depth over India.
Figure shows that the
NO3- burden is projected to shift equatorward in the
Northern Hemisphere in response to changes in anthropogenic
emissions from the present day to 2050. NH4NO3 increases in
the free troposphere but decreases near the surface, a vertical
redistribution also noted by . The
decrease of surface NO3- in the midlatitudes is primarily
driven by lower NO emission. Large differences in the seasonality,
spatial distribution, and magnitude of anthropogenic NH3
emissions in RCP8.5 (dotted line) and scaled HTAPv2 for 2050 have
little impact on the simulated NO3- burden (<10 %),
which reflects the diminishing sensitivity of surface
NH4NO3 to NH3. However, NO3- remains
sensitive to NH3 in the free troposphere, where it can
persist longer than in the boundary layer thanks to lower
temperature. The solid line in
Fig. shows the impact of lower
convective removal of NH3 (achieved by neglecting the impact
of pH on NH3 solubility) on the NO3- burden. Over
the 2008–2010 period, this results in a 40 % increase of the
NO3- burden with a near quadrupling in the tropics,
qualitatively matching the results of
in this region. In 2050, the impact is much more pronounced and the
simulated burden is more than twice as large as in 2010, a similar
response to that found by . Note that
increasing NH3 emissions from biomass burning and
distributing these emissions vertically also
increases tropical NO3- (not shown) but to a much lower
degree (<50 %). These results suggest that differences in the
transport of NH3 to the free troposphere across models
contribute to the variability in the projected NO3- burden
and optical depth. Such differences may arise from differences in
the parameterizations of convection as
suggested by the much lower tropical NO3- burden in AM3N
than in the LMDz-INCA model but also
from changes in the tropical circulation in response to climate
change (e.g., ).
Conclusions
We have developed a new configuration of AM3 (AM3N) with revised
treatment of nitrate and sulfate chemistry and deposition. We showed
that AM3N better captures observed AOD than a configuration of AM3
similar to that used for ACCMIP and CMIP5. AM3N overestimates
surface NO3- concentration especially in the USA. This
bias may reflect neglect in AM3N of the dynamic nature of
N2O5 uptake and near-surface volatilization of
NH4NO3.
We have evaluated the sensitivity of NO3- optical depth to
poorly constrained aspects of NO3- chemistry (heterogeneous
production of HNO3, uptake of HNO3 by natural and
anthropogenic dust, surface removal of NH4NO3) and
NH3 emissions (diurnal cycle, seasonality). Globally, the
formation of NH4NO3 is more limited by HNO3 than
NH3, such that NO3- optical depth is more
sensitive to the representation of the heterogeneous chemistry of
HNO3 than to uncertainties in NH3
emissions. Simulated present-day NO3- optical depth ranges
from 0.0054 to 0.0082, depending on the treatment of reactive
nitrogen. Differences in the treatment of reactive nitrogen alone are
unlikely to account for the large spread in estimates of present-day
NO3- optical depth (0.0023–0.025).
Annual zonal mean distribution of NO3- in AM3N
with 2008–2010 anthropogenic emissions (top) and 2050 anthropogenic
emissions (from RCP8.5 except for NH3; see text). The
blue, green, red, and cyan regions denote the NO3- burden
located above 800 hPa, between 600 and 800 hPa, between 400 and
600 hPa, and below 400 hPa, with the partial burden in each
pressure range indicated inset. The annual mean zonal burdens of
NO3- simulated using AM3N_fdep_diu (dashed line),
using AM3N with anthropogenic emissions from RCP8.5 for
NH3 (dotted line), using AM3N_ndust (dashed dotted line),
and using AM3N with reduced convective removal of NH3
(solid line) are also shown. The white circles in the top panel
indicate the 2000 annual zonal mean NO3- burden simulated
by .
We have examined the response of simulated NO3- optical depth to
projected changes in anthropogenic emissions from 2010 to 2050 in RCP8.5.
Depending on the configuration of AM3N (Table ),
NO3- optical depth varies from 0.0061 to 0.01 in 2050. The increase
of NO3- (<30 % relative to 2008–2010) is partly inhibited by
greater limitation of NH4NO3 production by HNO3 at the
surface due to lower NO emissions, more efficient removal of
HNO3 by dust, and a large decrease in the heterogeneous production of
HNO3 by N2O5 (associated with lower aerosol surface area).
In the Northern Hemisphere, the NO3- burden is projected to shift
southward, following the increase of tropical NH3 emissions and the
decrease of NO emissions in the midlatitudes. This shift is associated with
an increase of the NO3- burden in the free troposphere, where
NH4NO3 formation is limited by NH3. We suggest that the convective
transport of NH3 and its response to climate change (not considered
here) play an important role in modulating the response of NO3- optical
depth to changes in anthropogenic emissions. The complexity of the response
of NO3- to changes in surface processes, chemistry, and convection
indicates that the global trends of NH3 emissions may not be
a suitable proxy to estimate the future forcing from NO3- aerosols
.
We conclude that in addition to improvements to NH3 emission inventories
(e.g., bidirectional exchange of NH3, ),
observational constraints on the processes controlling the vertical
redistribution of NH3 and the response of NO3- to NH3 in
the free troposphere (e.g., magnitude of NH3 emissions in the tropics
, biomass burning injection height
, transport and removal of NH3 in
convective updrafts, heterogeneous chemistry on dust) and sensitivity studies
to characterize their response to climate change are needed to improve
estimates of present and future NO3- optical depth.