Sensitivity of nitrate aerosols to ammonia emissions and to nitrate chemistry: implications for present and future nitrate optical depth

Abstract

We update and evaluate the treatment of nitrate aerosols in the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model (AM3). Accounting for the radiative effects of nitrate aerosols generally improves the simulated aerosol optical depth, although nitrate concentrations at the surface are biased high. This bias can be reduced by increasing the deposition of nitrate to account for the near-surface volatilization of ammonium nitrate or by neglecting the heterogeneous production of nitric acid to account for the inhibition of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ reactive uptake at high nitrate concentrations. Globally, uncertainties in these processes can impact the simulated nitrate optical depth by up to 25 %, much more than the impact of uncertainties in the seasonality of ammonia emissions (6 %) or in the uptake of nitric acid on dust (13 %). Our best estimate for fine nitrate optical depth at 550 nm in 2010 is $0.006$ (0.005–0.008). In wintertime, nitrate aerosols are simulated to account for over 30 % of the aerosol optical depth over western Europe and North America. Simulated nitrate optical depth increases by less than 30 % (0.0061–0.010) in response to projected changes in anthropogenic emissions from 2010 to 2050 (e.g., $-$40 % for SO${}_{\mathrm{2}}$ and$+$38 % for ammonia). This increase is primarily driven by greater concentrations of nitrate in the free troposphere, while surface nitrate concentrations decrease in the midlatitudes following lower concentrations of nitric acid. With the projected increase of ammonia emissions, we show that better constraints on the vertical distribution of ammonia (e.g., convective transport and biomass burning injection) and on the sources and sinks of nitric acid (e.g., heterogeneous reaction on dust) are needed to improve estimates of future nitrate optical depth.

Introduction

Ammonium nitrate (${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$) aerosols are produced by the reaction of nitric acid (${\mathrm{HNO}}_{\mathrm{3}}$), a photochemical product of $\mathrm{NO}$ oxidation, and ammonia (${\mathrm{NH}}_{\mathrm{3}}$). Emissions of ${\mathrm{NH}}_{\mathrm{3}}$ and $\mathrm{NO}$ are primarily from anthropogenic origin: fossil fuel combustion for $\mathrm{NO}$ and agriculture for ${\mathrm{NH}}_{\mathrm{3}}$ (i.e., ). The formation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is favored by cold temperatures and high relative humidity . ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ production competes with that of ammonium sulfate, which is generally more thermodynamically stable , and that of coarse-mode nitrate via heterogeneous uptake of ${\mathrm{HNO}}_{\mathrm{3}}$ on dust and sea salt (i.e., ).

${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is an important component of surface particulate matter in the USA (i.e., ), Europe (i.e., ), and Asia (i.e., ), especially in winter. As ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ rapidly volatilizes away from sources of $\mathrm{NO}$ and ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ may become the largest contributor to anthropogenic AOD by the end of the twenty-first century , following the projected increase of ${\mathrm{NH}}_{\mathrm{3}}$ emissions and decrease of ${\mathrm{SO}}_{\mathrm{2}}$ emissions. Such an increase of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions, the heterogeneous chemistry of ${\mathrm{HNO}}_{\mathrm{3}}$, 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{NH}}_x\equiv {\mathrm{NH}}_{\mathrm{3}}+{\mathrm{NH}}_{\mathrm{4}}^{+}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{NH}}_{\mathrm{3}}$ 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${}^{\circ }$ N, 88.4${}^{\circ }$ W). In Sect. , we examine the response of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 $\mathrm{km}$ (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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ formation from gas-phase oxidation and the in-cloud reaction of ${\mathrm{SO}}_{\mathrm{2}}$ with ${\mathrm{O}}_{\mathrm{3}}$ and ${\mathrm{H}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{2}}$ . In-cloud production of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ is sensitive to cloud pH, which is calculated as a function of the concentration of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (assumed to be entirely in-cloud water), ${\mathrm{NH}}_{\mathrm{3}}$, ${\mathrm{SO}}_{\mathrm{2}}$, ${\mathrm{HNO}}_{\mathrm{3}}$, and ${\mathrm{CO}}_{\mathrm{2}}$. ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ 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 ${\mathrm{PM}}_{2.5}$ . In-cloud oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ is restricted to liquid clouds and we revise the calculation of cloud pH to account for the partitioning of ${\mathrm{HNO}}_{\mathrm{3}}/{\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NH}}_{\mathrm{3}}/{\mathrm{NH}}_{\mathrm{4}}^{+}$ between the gas phase and cloud water.

Heterogeneous chemistry – we include the heterogeneous uptake of ${\mathrm{HNO}}_{\mathrm{3}}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$, ${\mathrm{SO}}_{\mathrm{2}}$, and ${\mathrm{H}}_{\mathrm{2}}{\mathrm{SO}}_{\mathrm{4}}$ on dust particles (Table S1 in the Supplement). The uptake of ${\mathrm{HNO}}_{\mathrm{3}}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ 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 $>$ $\mathrm{1}\mathrm{\mu }\mathrm{m}$), 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 ($\mathit{\gamma }$) of ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$, ${\mathrm{NO}}_{\mathrm{2}}$, and ${\mathrm{NO}}_{\mathrm{3}}$ on aerosols relative to AM3 (see Table S1 in the Supplement and Sect. ). The implications of these changes for the budget of ${\mathrm{HNO}}_{\mathrm{3}}$ and aerosol ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 $\mathrm{nm}$ of less than 20 % for relative humidity (RH) $<90\mathit{\%}$ and by less than 10 % between 90 and 95 % (Fig. S1 in the Supplement). The optical depth of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ associated with dust is expected to be small relative to fine-mode ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (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 ${\mathrm{H}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{2}}$ 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 ${\mathrm{HNO}}_{\mathrm{3}}$ . 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 ${0.5}^{\circ }\times {0.5}^{\circ }$ 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 $$F_p=CS{s}_p{u}_{10\mathrm{m}}^{\mathrm{2}}(u_{10\mathrm{m}}-u_{\mathrm{t}})\text{if}{u}_{10\mathrm{m}}>u_{\mathrm{t}},$$ where $C$ is a dimensional factor ($\mathrm{\mu }\mathrm{g}{\mathrm{s}}^{\mathrm{2}}{\mathrm{m}}^{-\mathrm{5}}$), $S$ is the source function based on topography, $u_{10\mathrm{m}}$ is the horizontal wind at 10 $\mathrm{m}$ ($\mathrm{m}{\mathrm{s}}^{-\mathrm{1}}$), $u_{\mathrm{t}}$ is the threshold velocity ($\mathrm{m}{\mathrm{s}}^{-\mathrm{1}}$), and $s_p$ is the fraction of total dust emitted in the size class $p$ as defined by . Over the 2008–2010 period, dust emission is 1640 $\mathrm{Tg}{\mathrm{a}}^{-\mathrm{1}}$. This includes 1230 $\mathrm{Tg}{\mathrm{a}}^{-\mathrm{1}}$ from natural sources ($S$ from , $C=0.125\mathrm{\mu }\mathrm{g}{\mathrm{s}}^{\mathrm{2}}{\mathrm{m}}^{-\mathrm{5}}$, $u_{\mathrm{t}}=\mathrm{1}\mathrm{m}{\mathrm{s}}^{-\mathrm{1}}$), similar to the AEROCOM multi–model mean , and 410 $\mathrm{Tg}{\mathrm{a}}^{-\mathrm{1}}$ from anthropogenic sources (primarily over cropland and pasture from with updated MODIS collection 6, $C=0.219\mathrm{\mu }\mathrm{g}{\mathrm{s}}^{\mathrm{2}}{\mathrm{m}}^{-\mathrm{5}}$, $u_{\mathrm{t}}=\mathrm{3}\mathrm{m}{\mathrm{s}}^{-\mathrm{1}}$). Isoprene emissions are calculated using the Model of Emissions of Gases and Aerosols from Nature MEGAN,. $\mathrm{NO}$ 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 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$) compared to AM3 (5.2 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$), with both estimates within the range of emissions inferred from observations . Other natural emissions, including soil ${\mathrm{NO}}_x$ and soil and ocean ${\mathrm{NH}}_{\mathrm{3}}$ emissions, are described by and . Global total emissions of ${\mathrm{SO}}_{\mathrm{2}}$, ${\mathrm{NH}}_{\mathrm{3}}$, and ${\mathrm{NO}}_x$ are listed in Table .

Simulated budget of SO${}_{\mathrm{4}}$, NH${}_x$, and NO${}_y$ in 2010.

			AM3	AM3N
${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$${}^{\text{a}}$
Production (Tg S a${}^{-\mathrm{1}}$)			37.3	33.1
	OH		10.4	7.7
	${\mathrm{H}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{2}}$		26.7	16.2
	${\mathrm{O}}_{\mathrm{3}}$		0.1	4.5
	dust		0.0	1.9
Loss (Tg S a${}^{-\mathrm{1}}$)			37.4	33.3
	Dry deposition		4.7	4.6
		${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$	4.7	3.8
		${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ on dust	0.0	0.8
	Wet deposition		32.7	28.7
		${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$	32.7	27.5
		${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ on dust	0.0	1.1
Lifetime (days)			4.9	3.8
${\mathrm{NH}}_x$
${\mathrm{NH}}_{\mathrm{3}}$ emission (Tg N a${}^{-\mathrm{1}}$)${}^{\text{b}}$			54.5	54.5
Loss (Tg N a${}^{-\mathrm{1}}$)			54.8	55.0
	Dry deposition		14.4	23.5
		${\mathrm{NH}}_{\mathrm{4}}^{+}$	14.3	3.6
		${\mathrm{NH}}_{\mathrm{3}}$	0.1	19.9
	Wet deposition		40.4	30.7
		${\mathrm{NH}}_{\mathrm{4}}^{+}$	39.4	20.7
		${\mathrm{NH}}_{\mathrm{3}}$	1.0	10.1
	Gas oxidation		0.0	0.8
Lifetime (days)			5.5	2.5
${\mathrm{NO}}_y$
NO emission (Tg N a${}^{-\mathrm{1}}$)			51.4	51.8
Loss (Tg N a${}^{-\mathrm{1}}$)			51.3	51.0
	Dry deposition		25.4	23.1
		${\mathrm{HNO}}_{\mathrm{3}}$	18.3	10.7
		${\mathrm{NO}}_{\mathrm{3}}^{-}$ on dust	0.0	3.4
		${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$	0.7	0.8
		Organic nitrogen	3.9	4.0
	Wet deposition		25.6	27.6
		${\mathrm{HNO}}_{\mathrm{3}}$	23.4	17.8
		${\mathrm{NO}}_{\mathrm{3}}^{-}$ on dust	0.0	3.7
		${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$	0.5	3.5
		Organic nitrogen	1.7	2.6
Lifetime (days)			22.7	13.4

${}^{\text{a}}$ ${\mathrm{SO}}_{\mathrm{2}}$ emissions are 74.0 $\mathrm{Tg}\mathrm{S}{\mathrm{a}}^{-\mathrm{1}}$ including 16.0 $\mathrm{Tg}\mathrm{S}{\mathrm{a}}^{-\mathrm{1}}$ from dimethyl sulfide (DMS) oxidation.${}^{\text{b}}$ including 39.9 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$ from anthropogenic sources, 3.9 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$ from biomass burning, and 10.7 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ 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 .

${\mathrm{NH}}_{\mathrm{3}}$ emissions

Present-day – the largest source of ${\mathrm{NH}}_{\mathrm{3}}$ to the atmosphere is agriculture. Unlike anthropogenic emissions of other compounds, which are dominated by fossil fuel emissions, ${\mathrm{NH}}_{\mathrm{3}}$ emissions exhibit large seasonal variations, which reflect the seasonality of agricultural practices (e.g., fertilizer application) as well as the decrease of ${\mathrm{NH}}_{\mathrm{3}}$ solubility with temperature . The HTAP_v2 inventory includes monthly variations in anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ emissions over North America, Europe, and parts of Asia, including Japan and China, but excluding India. Anthropogenic emissions of ${\mathrm{NH}}_{\mathrm{3}}$ previously used in AM3 simulations for ACCMIP and CMIP5 are constant throughout the year . To evaluate the impact of the seasonality of ${\mathrm{NH}}_{\mathrm{3}}$ emissions on ${\mathrm{NO}}_{\mathrm{3}}^{-}$, we remove all temporal variability in the anthropogenic emissions of ${\mathrm{NH}}_{\mathrm{3}}$ in simulation AM3N_ns. ${\mathrm{NH}}_{\mathrm{3}}$ emissions also exhibit diurnal variability , which may affect the simulated concentrations of ${\mathrm{NH}}_{\mathrm{3}}$ and ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ . In AM3N_diu, we impose the ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ for 2010 (top row) and 2050 (bottom row) based on anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ 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.

[Figure omitted. See PDF]

2050 – anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ emissions for 2050 are estimated by scaling HTAP_v2 surface anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions from agriculture as noted by . Our approach results in 18 % more anthropogenic emissions (60 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$) than in RCP8.5 for 2050.

Configurations of AM3N used in this study.

	Temporal variation	Heterogeneous	Heterogeneous	Dry deposition
	of ${\mathrm{NH}}_{\mathrm{3}}$ emissions	chemistry on dust	production of ${\mathrm{HNO}}_{\mathrm{3}}$	of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$
AM3N	Monthly	Yes	Yes	${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
AM3N_fdep	Monthly	Yes	Yes	${\mathrm{HNO}}_{\mathrm{3}}$
AM3N_diu	Monthly + diurnal	Yes	Yes	${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
AM3N_ns	No	Yes	Yes	${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
AM3N_nhet	Monthly	Yes	No	${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
AM3N_ndust	Monthly	No	Yes	${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
AM3N_fdep_diu	Monthly + diurnal	Yes	Yes	${\mathrm{HNO}}_{\mathrm{3}}$

Heterogeneous chemistry

Wintertime production of ${\mathrm{HNO}}_{\mathrm{3}}$ in the northern midlatitudes' boundary layer is dominated by the uptake of ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ on aerosols (e.g., ). The probability for the heterogeneous conversion of ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ to ${\mathrm{HNO}}_{\mathrm{3}}$ ($\mathit{\gamma }$) 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 ${\mathrm{HNO}}_{\mathrm{3}}$ on aerosol ${\mathrm{NO}}_{\mathrm{3}}^{-}$, we neglect the heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ via ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ aerosol uptake in AM3N_nhet. We also neglect the productions of ${\mathrm{HNO}}_{\mathrm{3}}$ by ${\mathrm{NO}}_{\mathrm{3}}$ and ${\mathrm{NO}}_{\mathrm{2}}$ reactive uptake, as they may modulate the wintertime budget of ${\mathrm{NO}}_{\mathrm{y}}$ in polluted region . Note that previous characterizations of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{HNO}}_{\mathrm{3}}$, ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$, ${\mathrm{NO}}_{\mathrm{3}}$, ${\mathrm{H}}_{\mathrm{2}}{\mathrm{SO}}_{\mathrm{4}}$, and ${\mathrm{SO}}_{\mathrm{2}}$ on dust.

Surface removal of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$

In AM3N, the dry deposition of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is slow, similar to other fine aerosols. Several field observations have reported steeper vertical gradients and faster deposition velocities ($v_{\mathrm{d}}$) for ${\mathrm{NO}}_{\mathrm{3}}^{-}$ than for ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ . This difference stems from gradients in temperature, RH, and ${\mathrm{HNO}}_{\mathrm{3}}$ within the boundary layer, which reduce the stability of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ near the surface. The volatilization of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ may result in an underestimate of the surface deposition of ${\mathrm{TNO}}_{\mathrm{3}}\equiv {\mathrm{HNO}}_{\mathrm{3}}+{\mathrm{NO}}_{\mathrm{3}}^{-}$, since $v_{\mathrm{d}}\left({\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}\right)\ll v_{\mathrm{d}}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$. As an upper bound, we assume that the surface removal of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ is limited by turbulent transport by setting $v_{\mathrm{d}}\left({\mathrm{NO}}_{\mathrm{3}}^{-}\right)=v_{\mathrm{d}}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$ in AM3N_fdep.

Budget and global distribution

Table  shows the budgets of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NH}}_x$, and ${\mathrm{NO}}_y$ in AM3 and AM3N for 2010. Here ${\mathrm{NO}}_y$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NH}}_x$, and ${\mathrm{NO}}_y$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ with respect to convective removal decreases from 44 to 18 days.

For ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, the increased effectiveness of convective removal is partly offset by reduction in the removal by snow (Sect. ). The ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ via in-cloud oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ by ${\mathrm{O}}_{\mathrm{3}}$. The production of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ via ${\mathrm{SO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{3}}$ is 4.5 $\mathrm{Tg}\mathrm{S}{\mathrm{a}}^{-\mathrm{1}}$ in AM3N, greater than the recent estimate of (1.5 $\mathrm{Tg}\mathrm{S}{\mathrm{a}}^{-\mathrm{1}}$). This discrepancy may reflect differences in cloud pH and lower ${\mathrm{H}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{2}}$ concentrations in AM3N because of faster dry deposition for ${\mathrm{H}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{2}}$ and efficient removal of ${\mathrm{HO}}_{\mathrm{2}}$ via aerosol uptake . AM3N does not include production of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ via the aqueous reaction of ${\mathrm{SO}}_{\mathrm{2}}$ with ${\mathrm{O}}_{\mathrm{2}}$ catalyzed by iron and manganese or by the oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ by stabilized Criegee intermediates . The lifetime of ${\mathrm{SO}}_{\mathrm{2}}$ is 1.3 days in both AM3 and AM3N, similar to and . The overall conversion from ${\mathrm{SO}}_{\mathrm{2}}$ to ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (excluding ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ on dust) is reduced compared to AM3 from 50 to 42 % and lower than the AEROCOM multi-model mean (62 %).

In AM3, ${\mathrm{NH}}_{\mathrm{3}}$ uptake by ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ is solely controlled by kinetics without any thermodynamic limit, such that ${\mathrm{NH}}_{\mathrm{3}}$ burden is small (0.005 TgN) and ${\mathrm{NH}}_{\mathrm{3}}$ generally limits the formation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$. In AM3N, the uptake of ${\mathrm{NH}}_{\mathrm{3}}$ by ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ aerosols cannot exceed the thermodynamic limit calculated by ISORROPIA, which results in a greater ${\mathrm{NH}}_{\mathrm{3}}$ burden (0.11 TgN) and favors the production of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$. The shorter lifetime of ${\mathrm{NH}}_x$ in AM3N than in AM3 reflects the change in the speciation of ${\mathrm{NH}}_x$ and the faster dry deposition of ${\mathrm{NH}}_{\mathrm{3}}$ relative to ${\mathrm{NH}}_{\mathrm{4}}^{+}$. The lifetime of ${\mathrm{NH}}_x$ 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 ${\mathrm{NO}}_y$. The contribution of ${\mathrm{HNO}}_{\mathrm{3}}$ to the removal of ${\mathrm{NO}}_y$ decreases from 81 % (AM3) to 56 % (AM3N). In contrast, the contribution of aerosols to ${\mathrm{NO}}_y$ removal increases from 2 to 22 %. Recent studies have found an even greater contribution of aerosols to the removal of ${\mathrm{NO}}_y$ ($>30$ %); this difference may reflect the lack of ${\mathrm{HNO}}_{\mathrm{3}}$ uptake by sea salt in AM3N. Organic nitrogen contributes 10 % of ${\mathrm{NO}}_y$ removal in both AM3 and AM3N. The much lower fraction of ${\mathrm{NO}}_y$ deposited as ${\mathrm{HNO}}_{\mathrm{3}}$ in AM3N relative to AM3 reflects both the increased production of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ and the uptake of ${\mathrm{HNO}}_{\mathrm{3}}$ on dust. The total heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ by ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ (9.7 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$), ${\mathrm{NO}}_{\mathrm{2}}$ (0.6 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$), and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (0.4 $\mathrm{Tg}\mathrm{N}{\mathrm{a}}^{-\mathrm{1}}$) uptake on fine aerosols is reduced by 50 % in AM3N relative to AM3. This decrease is primarily driven by reduced reaction probabilities for ${\mathrm{NO}}_{\mathrm{2}}$ and ${\mathrm{NO}}_{\mathrm{3}}$ uptake. In contrast, the change of ${\mathit{\gamma }}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ from 0.1 (AM3) to 0.01 (AM3N) reduces the heterogeneous uptake of ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ by only 20 % because of the large increase in the sulfate surface area in winter (see Sect. ). The magnitude of the ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ source of ${\mathrm{HNO}}_{\mathrm{3}}$ in AM3N is 3 times as large as reported by . This may reflect greater reactive aerosol surface area in AM3N, as ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ hydrolysis can take place on ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, BC, OC, and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ aerosols, while only ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ is considered by . Reduction in the simulated ${\mathrm{HNO}}_{\mathrm{3}}$ burden – driven by faster ${\mathrm{NO}}_{\mathrm{3}}^{-}$ deposition (AM3N_fdep), heterogeneous uptake of ${\mathrm{HNO}}_{\mathrm{3}}$ on dust (AM3N_ndust), or reduced heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ (AM3N_nhet) – increase cloud pH, which favors the oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ by ${\mathrm{O}}_{\mathrm{3}}$ (Table S2).

Annual mean burden of ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ on dust, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, and ${\mathrm{NH}}_{\mathrm{3}}$ in $\mathrm{mg}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}$ 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.

[Figure omitted. See PDF]

Observed (black) and simulated monthly concentrations of ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, and ${\mathrm{NH}}_{\mathrm{3}}$ at Bondville (40.1${}^{\circ }$ N, 88.4${}^{\circ }$ 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 omitted. See PDF]

Figure  shows the burden of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ on dust, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, and ${\mathrm{NH}}_{\mathrm{3}}$ in AM3N. The simulated global burdens fall within the range of previous estimates for fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (0.04–0.11 $\mathrm{Tg}\mathrm{N}$), ${\mathrm{NO}}_{\mathrm{3}}^{-}$ on dust (0.07–0.41 $\mathrm{Tg}\mathrm{N}$), ${\mathrm{NH}}_{\mathrm{4}}^{+}$ (0.21–0.27 $\mathrm{Tg}\mathrm{N}$), and ${\mathrm{NH}}_{\mathrm{3}}$ (0.07–0.29 $\mathrm{Tg}\mathrm{N}$). The burden of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ peaks over China where it reaches over 5 $\mathrm{mg}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}$, with a secondary maximum over India. Fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions (Fig. ). AM3N simulates large enhancements in ${\mathrm{NH}}_{\mathrm{3}}$ column over source regions such as India (where the burden reaches 12 $\mathrm{mg}{\mathrm{m}}^{-\mathrm{2}}$), northern China, the Netherlands, and the US Midwest, as supported by satellite observations . This lends some support to the spatial allocation of anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ emissions in HTAP_v2 inventory, although observed enhancements in ${\mathrm{NH}}_{\mathrm{3}}$ 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${}^{\circ }$ N, 88.4${}^{\circ }$ W; 213 $\mathrm{m}\mathrm{a}.\mathrm{s}.\mathrm{l}.$). Bondville is located in the vicinity of large sources of ${\mathrm{NH}}_{\mathrm{3}}$ and ${\mathrm{NO}}_x$, which result in elevated surface ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ concentrations (from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network), surface ${\mathrm{NH}}_{\mathrm{3}}$ concentrations (Ammonia Monitoring Network (AMoN)), ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, and ${\mathrm{NH}}_{\mathrm{3}}$ (${\mathrm{NH}}_{\mathrm{4}}^{+}$ for wet deposition) for AM3 and different AM3N configurations. Both ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{NH}}_{\mathrm{3}}$ concentrations are higher year round in AM3N than in AM3, as ISORROPIA enforces thermodynamic limitation on the uptake of ${\mathrm{NH}}_{\mathrm{3}}$ by ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$. Observations show a spring peak in surface ${\mathrm{NH}}_{\mathrm{3}}$ concentrations, while both AM3 and AM3N simulate a summer peak. Bondville is surrounded by corn and soybean fields and ${\mathrm{NH}}_{\mathrm{3}}$ emissions associated with spring fertilizer application may be underestimated . In summer, more efficient convective removal of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ in AM3N reduces the AM3 high bias for ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ surface concentration and low bias for ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ wet deposition. In winter, the low bias for surface ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ concentration in AM3 is reduced as a result of less efficient removal by snow and increased in-cloud oxidation of ${\mathrm{SO}}_{\mathrm{2}}$. AM3N_nhet and AM3N_fdep produce greater ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ concentrations in winter than AM3N consistent with increased in-cloud oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ by ${\mathrm{O}}_{\mathrm{3}}$ (Table S2).

${\mathrm{NO}}_{\mathrm{3}}^{-}$ shows a large positive bias in AM3N in winter ($>70$ % in February). This bias can be reduced by either neglecting the heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ via ${\mathrm{NO}}_{\mathrm{2}}$, ${\mathrm{NO}}_{\mathrm{3}}$, and ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ (AM3N_nhet) or treating the deposition of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ like that of ${\mathrm{HNO}}_{\mathrm{3}}$ (AM3N_fdep). Conversely, neglecting the seasonality of ${\mathrm{NH}}_{\mathrm{3}}$ emissions (AM3N_ns), similar to simulations performed for ACCMIP and CMIP5, increases the bias for ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in winter.

To analyze the factors controlling ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ to its gas-phase precursors ${\mathrm{NH}}_{\mathrm{3}}$ and ${\mathrm{HNO}}_{\mathrm{3}}$ and is defined as $$\text{GR}={\displaystyle \frac{\left[{\mathrm{NH}}_{\mathrm{3}}\right]+\left[{\mathrm{NH}}_{\mathrm{4}}^{+}\right]-\mathrm{2}\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]}{\left[{\mathrm{HNO}}_{\mathrm{3}}\right]+\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]}}.$$ GR defines three different regimes: (a) GR $>$ 1, in which ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation is limited by the availability of ${\mathrm{HNO}}_{\mathrm{3}}$, (b) 0 $<$ GR $<$ 1, in which ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is limited by the availability of ${\mathrm{NH}}_{\mathrm{3}}$, and (c) GR $<$ 0, in which ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is inhibited by ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$. We define the degree of limitation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ by ${\mathrm{HNO}}_{\mathrm{3}}$ ($\mathcal{L}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$) as the fraction of the time when GR $>$ 1. In winter, ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is most frequently limited by ${\mathrm{HNO}}_{\mathrm{3}}$ ($\mathcal{L}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$) $=$ 78 % in AM3N). Figure  (bottom panel) shows $\mathcal{L}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$ binned by ${\mathrm{NO}}_{\mathrm{3}}^{-}$ concentrations. ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is most limited by ${\mathrm{HNO}}_{\mathrm{3}}$ availability at low $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$, while ${\mathrm{NH}}_{\mathrm{3}}$ becomes more limiting at high $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$. This suggests that even in an environment that is generally ${\mathrm{NH}}_{\mathrm{3}}$-rich with respect to ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation, ${\mathrm{NH}}_{\mathrm{3}}$ emissions modulates ${\mathrm{NO}}_{\mathrm{3}}^{-}$ production during high ${\mathrm{NO}}_{\mathrm{3}}^{-}$ episodes (AM3N_ns).

Observed and simulated distribution of daily ${\mathrm{NO}}_{\mathrm{3}}^{-}$ concentration at Bondville (40.1${}^{\circ }$ N, 88.4${}^{\circ }$ W) in winter (top panel) from 2006 to 2012 (observations) and 2008 to 2010 (model). The degree of ${\mathrm{HNO}}_{\mathrm{3}}$ limitation for ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation (GR $>$ 1) is shown in the bottom panel. The different model sensitivity experiments are described in Table .

[Figure omitted. See PDF]

Figure  also shows that AM3N_nhet and AM3N_fdep produce different distributions of daily $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ although they have similar mean monthly $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ (top panel). AM3N_fdep reproduces observations at low ${\mathrm{NO}}_{\mathrm{3}}^{-}$ concentrations well but underestimates the frequency of high ${\mathrm{NO}}_{\mathrm{3}}^{-}$ events, when ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ exhibits significant sensitivity to ${\mathrm{NH}}_{\mathrm{3}}$. Under these conditions, less volatilization of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ near the surface is expected as ${\mathrm{NH}}_{\mathrm{3}}$ is not depleted near the surface like ${\mathrm{HNO}}_{\mathrm{3}}$. AM3_nhet $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ is most consistent with observations at high $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$, conditions under which ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ heterogeneous uptake has been observed to be inhibited both in laboratory and field settings . The ability of AM3N_fdep and AM3N_nhet to capture ${\mathrm{NO}}_{\mathrm{3}}^{-}$ under different conditions emphasizes the need to represent the dynamic nature of $\mathit{\gamma }\left({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right)$ and ${\mathrm{TNO}}_{\mathrm{3}}$ surface removal.

Observed and simulated aerosol optical depth at 550 $\mathrm{nm}$ at Bondville (40.1${}^{\circ }$ N, 88.4${}^{\circ }$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (red), ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (cyan), OC (green), BC (purple), dust (brown), and sea salt (blue) for AM3N_fdep_diu (2008–2010 average).

[Figure omitted. See PDF]

Mean seasonal observed (black dots) and simulated surface and vertical profiles of aerosol dry extinction at Bondville (40.1${}^{\circ }$ N, 88.4${}^{\circ }$ 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 $\mathrm{K}$, 1 $\mathrm{atm}$). 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 omitted. See PDF]

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 $\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$ and with the lack of extinction from ${\mathrm{NO}}_{\mathrm{3}}^{-}$, the largest contributor to AM3N dry aerosol extinction below 1000 $\mathrm{m}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ and ${\mathrm{NO}}_{\mathrm{3}}^{-}$, 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{HNO}}_{\mathrm{3}}$, ${\mathrm{NH}}_{\mathrm{3}}$ and ${\mathrm{NH}}_x$, concentrations of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in rain, and total aerosol optical depth at 550 $\mathrm{nm}$ from AERONET, MISR, and MODIS${}^{*}$.

			AM3	AM3N	AM3N_fdep_diu
${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$
	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)
${\mathrm{NO}}_{\mathrm{3}}^{-}$
	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)
$\mathrm{NH}$${}_x$
	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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$	$-$0.15 (0.83)	0.11 (0.87)	0.09 (0.87)
		High ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$	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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$	$-$0.12 (0.84)	0.21 (0.87)	0.18 (0.87)
		High ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$	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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$	$-$0.50 (0.87)	$-$0.01 (0.76)	$-$0.07 (0.70)
		High ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$	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 ${\mathrm{NH}}_{\mathrm{3}}$ 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 $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$, $\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$, and $\left[{\mathrm{NH}}_{\mathrm{3}}\right]$ in the USA (IMPROVE and AMoN) and Europe (European Monitoring and Evaluation Programme (EMEP)), $\left[{\mathrm{NH}}_x\right]$ and $\left[{\mathrm{HNO}}_{\mathrm{3}}\right]$ (EMEP), and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (Figs. S6 and S12). As previously noted, the improvement in the simulated $\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$ in AM3N reflects increased removal in summer by convective precipitation, greater production of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ via ${\mathrm{O}}_{\mathrm{3}}+{\mathrm{SO}}_{\mathrm{2}}$, and less efficient removal by snow in winter. The increased removal of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ 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 ${\mathrm{HNO}}_{\mathrm{3}}$ and ${\mathrm{NH}}_{\mathrm{3}}$ also reduces the low bias in simulated summer wet deposition for ${\mathrm{NO}}_{\mathrm{3}}^{-}$ ($-$50 to $-$23 %, Fig. S10) and ${\mathrm{NH}}_{\mathrm{4}}^{+}$ ($-$46 to $-$16 %, Fig. S10). Greater in-cloud oxidation of ${\mathrm{SO}}_{\mathrm{2}}$ by ozone in AM3N_fdep and AM3N_nhet reduces the low biases for surface $\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$ relative to AM3N (from $-$11 to $-$5 % in the USA and $-$22 to $-$13 % in Europe).

Surface $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ is generally overestimated in AM3N, especially over the USA ($+$100 %). Recent studies using a range of ${\mathrm{NH}}_{\mathrm{3}}$ emissions and different representations of aerosol thermodynamics and heterogeneous chemistry have also found large positive biases in simulated surface $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ . Figure  shows the annual distribution of $\mathcal{L}\left({\mathrm{HNO}}_{\mathrm{3}}\right)$ in AM3N. At the surface, ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation is primarily limited by the availability of ${\mathrm{HNO}}_{\mathrm{3}}$ over continental regions, such as Europe, India, or northern China. Under ${\mathrm{HNO}}_{\mathrm{3}}$-limited conditions, our analysis at Bondville suggests that increasing the deposition of ${\mathrm{TNO}}_{\mathrm{3}}$ (AM3N_fdep) can improve the simulation of surface $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$. On a continental basis, we also find that AM3N_fdep_diu better captures surface $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ ($+$17 % bias in the USA) and we will focus on this configuration in the following. Note that the diurnal cycle of ${\mathrm{NH}}_{\mathrm{3}}$ emissions has a small impact on the simulated mean surface $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ concentration, but reduces surface $\left[{\mathrm{NH}}_{\mathrm{3}}\right]$ and increases its export to the free troposphere. Figure S20 shows the observed and simulated diurnal cycle of $\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]$ at the YRK site from the SouthEastern Aerosol Research and Characterization Network. ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions peak in the afternoon, the magnitude of the ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ diurnal cycle in AM3N_diu is lower than in AM3N. Higher daytime concentrations of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ in AM3N_diu suggest that accounting for the diurnal cycle of ${\mathrm{NH}}_{\mathrm{3}}$ emissions may increase the magnitude of the radiative forcing associated with ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$.

Simulated degree of limitation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation by ${\mathrm{HNO}}_{\mathrm{3}}$ (GR $>$ 1) weighted by ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ concentration at different pressure levels in AM3N for 2010.

[Figure omitted. See PDF]

Observed and simulated monthly AOD at 550 $\mathrm{nm}$ 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).

[Figure omitted. See PDF]

Contribution of different aerosol types to the global mean annual aerosol optical depth at 550 $\mathrm{nm}$ in AM3, AM3N, and other climate models considering ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and total AOD across AM3N configurations are shown by red, light blue, and black horizontal bars respectively. Note that changes in the parameterization of ${\mathrm{NH}}_{\mathrm{3}}$ convective removal reduce the simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth by GISS to 0.005 (S. Bauer, personal communication, 2015).

[Figure omitted. See PDF]

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 $\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$. 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 $30\mathit{\%}$ of AOD over North America.

Following and , we further evaluate the performances of AM3 and AM3N in locations within the top decile of simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ burden against observations from MODIS, MISR, and AERONET. AM3 AOD is biased high over high ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ regions ($+$30 to 50 %) and low over high ${\mathrm{NO}}_{\mathrm{3}}^{-}$ regions ($-$10 to $-$50 %) consistent with the analysis of . The bias over high ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ ($-$36 %). ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ optical depth decreases most from AM3 to AM3N_fdep_diu over tropical regions, while it increases at high latitudes, consistent with changes in ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ chemistry and removal. ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (0.002) and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth (0.003). The range of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ to the free troposphere, where ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is stable and sensitive to ${\mathrm{NH}}_{\mathrm{3}}$ (Fig. ), is responsible for the elevated nitrate in the GISS model. Revisions of the treatment of ${\mathrm{NH}}_{\mathrm{3}}$ convective removal in GISS reduce the simulated present-day ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to 0.005 (S. Bauer, personal communication, 2015).

also showed that CICERO may overestimate ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ optical depth, which would inhibit the production of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ by decreasing the amount of free ammonia ($\left[{\mathrm{NH}}_x\right]-\mathrm{2}\left[{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}\right]$).

Figure  shows the annual AM3N nitrate optical depth and its sensitivity to the treatment of ${\mathrm{NH}}_{\mathrm{3}}$ emissions and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ chemistry in AM3N. The sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to ${\mathrm{NH}}_{\mathrm{3}}$ seasonality is small and follows the patterns of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ limitations by ${\mathrm{NH}}_{\mathrm{3}}$, with largest sensitivity over the eastern USA and in the outflow of continents The global sensitivity to ${\mathrm{NH}}_{\mathrm{3}}$ seasonality is a lower bound, since the seasonality of anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ emissions is not represented in important source regions (e.g., India, South America) in HTAPv2. We find greater sensitivity to the diurnal cycle of ${\mathrm{NH}}_{\mathrm{3}}$ emissions, which is attributed to increased transport of ${\mathrm{NH}}_{\mathrm{3}}$ into the free troposphere, where ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is more sensitive to ${\mathrm{NH}}_{\mathrm{3}}$ (Fig. ) and more stable because of colder temperature. Decreasing ${\mathrm{HNO}}_{\mathrm{3}}$ production, either by neglecting its heterogeneous production (AM3N_nhet) or increasing the deposition of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (AM3N_fdep), reduces the annual mean ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth by 25 % globally. Regionally, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in polluted regions is more sensitive to the heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ because of the large aerosol surface area in these regions. Neglecting heterogeneous chemistry on dust results in a large relative increase of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth in dusty regions, but the increase of the global mean ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth is small (13 %). This muted response is caused by low ${\mathrm{NH}}_{\mathrm{3}}$ sources near major natural dust sources. A notable exception is anthropogenic dust, whose sources are primarily associated with agriculture . The proximity of ${\mathrm{NH}}_{\mathrm{3}}$ and anthropogenic dust sources results in 35 % greater sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth in response to changes in anthropogenic emissions from 2010 to 2050 ($<30$ %), with ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth ranging from 0.0061 (AM3N_fdep) to 0.01 (AM3N_ndust). In AM3N, the conversion rate from ${\mathrm{NH}}_{\mathrm{3}}$ to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (excluding dust) defined as the molar ratio of the fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden to ${\mathrm{NH}}_{\mathrm{3}}$ emissions decreases by 10 % from 0.34 ${\mathrm{day}}^{-\mathrm{1}}$ to 0.29 ${\mathrm{day}}^{-\mathrm{1}}$. ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ lifetime with respect to deposition increases by 25 % under the 2050 emissions, which suggests that the increase in ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth in AM3N is driven by reduced sinks rather than increased production. The response of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ to changes in anthropogenic emissions is weaker than reported in recent studies. For instance, reported a ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth of 0.01 for 2050 and an increase of the conversion rate from ${\mathrm{NH}}_{\mathrm{3}}$ to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ from 0.36 ${\mathrm{day}}^{-\mathrm{1}}$ to 0.57 ${\mathrm{day}}^{-\mathrm{1}}$ from 2000 to 2050. Using the same anthropogenic emissions, the simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth in AM3N in 2050 (the configuration closest to that used by ) is 0.077 and the conversion rate from ${\mathrm{NH}}_{\mathrm{3}}$ to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ is 0.33 ${\mathrm{day}}^{-\mathrm{1}}$.

Annual mean ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth at 550 $\mathrm{nm}$ in AM3N (top left panel) and its relative sensitivity to the treatment of ${\mathrm{NH}}_{\mathrm{3}}$ emissions, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ production, and loss in % for 2008–2010 conditions. The change in ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth relative to AM3N is indicated in the bottom left for each configuration. The sensitivity is only shown in regions where ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth is greater than 0.005.

[Figure omitted. See PDF]

Nitrate optical depth at 550 $\mathrm{nm}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions in AM3N. The relative changes between 2008–2010 and 2050 in ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth and surface emissions of ${\mathrm{NH}}_{\mathrm{3}}$, ${\mathrm{SO}}_{\mathrm{2}}$, and $\mathrm{NO}$ are indicated for each region.

[Figure omitted. See PDF]

Figure  shows that the simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth decreases in all AM3N configurations over Europe and China, increases over India, and exhibits little change over the USA. In all regions ${\mathrm{SO}}_{\mathrm{2}}$ emissions are projected to decrease. This results in greater sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to ${\mathrm{HNO}}_{\mathrm{3}}$, which is reflected in the increase of the sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to the uptake of ${\mathrm{HNO}}_{\mathrm{3}}$ by dust and lower sensitivity to temporal variations of ${\mathrm{NH}}_{\mathrm{3}}$ emissions (seasonality, diurnal cycle). The sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to the heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ is reduced despite the increased sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ to ${\mathrm{HNO}}_{\mathrm{3}}$. This follows the decrease in aerosol surface area associated with the reduction of the ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ burden.

The simulated changes in ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth from the present day to 2050 over the USA, China, and Europe are consistent with surface ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ limitations. For instance, surface ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is primarily limited by ${\mathrm{HNO}}_{\mathrm{3}}$ in Europe and China and the decrease of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth is driven by the reduction of $\mathrm{NO}$ emissions. In these regions, AM3N simulates similar ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth using different anthropogenic emissions of ${\mathrm{NH}}_{\mathrm{3}}$ for 2050, which is also consistent with the reduced sensitivity to ${\mathrm{NH}}_{\mathrm{3}}$ emissions. However, surface ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ limitation patterns cannot explain the increase of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth over India.

Figure  shows that the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden is projected to shift equatorward in the Northern Hemisphere in response to changes in anthropogenic emissions from the present day to 2050. ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ increases in the free troposphere but decreases near the surface, a vertical redistribution also noted by . The decrease of surface ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in the midlatitudes is primarily driven by lower NO emission. Large differences in the seasonality, spatial distribution, and magnitude of anthropogenic ${\mathrm{NH}}_{\mathrm{3}}$ emissions in RCP8.5 (dotted line) and scaled HTAPv2 for 2050 have little impact on the simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden ($<10$ %), which reflects the diminishing sensitivity of surface ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ to ${\mathrm{NH}}_{\mathrm{3}}$. However, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ remains sensitive to ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ (achieved by neglecting the impact of pH on ${\mathrm{NH}}_{\mathrm{3}}$ solubility) on the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden. Over the 2008–2010 period, this results in a 40 % increase of the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NH}}_{\mathrm{3}}$ emissions from biomass burning and distributing these emissions vertically also increases tropical ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (not shown) but to a much lower degree ($<50$ %). These results suggest that differences in the transport of ${\mathrm{NH}}_{\mathrm{3}}$ to the free troposphere across models contribute to the variability in the projected ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden and optical depth. Such differences may arise from differences in the parameterizations of convection as suggested by the much lower tropical ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ concentration especially in the USA. This bias may reflect neglect in AM3N of the dynamic nature of ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ uptake and near-surface volatilization of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$.

We have evaluated the sensitivity of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to poorly constrained aspects of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ chemistry (heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$, uptake of ${\mathrm{HNO}}_{\mathrm{3}}$ by natural and anthropogenic dust, surface removal of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$) and ${\mathrm{NH}}_{\mathrm{3}}$ emissions (diurnal cycle, seasonality). Globally, the formation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ is more limited by ${\mathrm{HNO}}_{\mathrm{3}}$ than ${\mathrm{NH}}_{\mathrm{3}}$, such that ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth is more sensitive to the representation of the heterogeneous chemistry of ${\mathrm{HNO}}_{\mathrm{3}}$ than to uncertainties in ${\mathrm{NH}}_{\mathrm{3}}$ emissions. Simulated present-day ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth (0.0023–0.025).

Annual zonal mean distribution of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in AM3N with 2008–2010 anthropogenic emissions (top) and 2050 anthropogenic emissions (from RCP8.5 except for ${\mathrm{NH}}_{\mathrm{3}}$; see text). The blue, green, red, and cyan regions denote the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ simulated using AM3N_fdep_diu (dashed line), using AM3N with anthropogenic emissions from RCP8.5 for ${\mathrm{NH}}_{\mathrm{3}}$ (dotted line), using AM3N_ndust (dashed dotted line), and using AM3N with reduced convective removal of ${\mathrm{NH}}_{\mathrm{3}}$ (solid line) are also shown. The white circles in the top panel indicate the 2000 annual zonal mean ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden simulated by .

[Figure omitted. See PDF]

We have examined the response of simulated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to projected changes in anthropogenic emissions from 2010 to 2050 in RCP8.5. Depending on the configuration of AM3N (Table ), ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth varies from 0.0061 to 0.01 in 2050. The increase of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ ($<30$ % relative to 2008–2010) is partly inhibited by greater limitation of ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ production by ${\mathrm{HNO}}_{\mathrm{3}}$ at the surface due to lower $\mathrm{NO}$ emissions, more efficient removal of ${\mathrm{HNO}}_{\mathrm{3}}$ by dust, and a large decrease in the heterogeneous production of ${\mathrm{HNO}}_{\mathrm{3}}$ by ${\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}$ (associated with lower aerosol surface area). In the Northern Hemisphere, the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden is projected to shift southward, following the increase of tropical ${\mathrm{NH}}_{\mathrm{3}}$ emissions and the decrease of NO emissions in the midlatitudes. This shift is associated with an increase of the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ burden in the free troposphere, where ${\mathrm{NH}}_{\mathrm{4}}{\mathrm{NO}}_{\mathrm{3}}$ formation is limited by ${\mathrm{NH}}_{\mathrm{3}}$. We suggest that the convective transport of ${\mathrm{NH}}_{\mathrm{3}}$ and its response to climate change (not considered here) play an important role in modulating the response of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth to changes in anthropogenic emissions. The complexity of the response of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ to changes in surface processes, chemistry, and convection indicates that the global trends of ${\mathrm{NH}}_{\mathrm{3}}$ emissions may not be a suitable proxy to estimate the future forcing from ${\mathrm{NO}}_{\mathrm{3}}^{-}$ aerosols .

We conclude that in addition to improvements to ${\mathrm{NH}}_{\mathrm{3}}$ emission inventories (e.g., bidirectional exchange of ${\mathrm{NH}}_{\mathrm{3}}$, ), observational constraints on the processes controlling the vertical redistribution of ${\mathrm{NH}}_{\mathrm{3}}$ and the response of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ to ${\mathrm{NH}}_{\mathrm{3}}$ in the free troposphere (e.g., magnitude of ${\mathrm{NH}}_{\mathrm{3}}$ emissions in the tropics , biomass burning injection height , transport and removal of ${\mathrm{NH}}_{\mathrm{3}}$ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$ optical depth.

The Supplement related to this article is available online at doi:10.5194/acp-16-1459-2016-supplement.

Acknowledgements

We are grateful to J. Ogren and B. Andrews for guidance with observations from the NOAA AAO program. We thank D. Ward for helpful discussions. This study was supported by NOAA Climate Program Office's Atmospheric Chemistry, Carbon Cycle, and Climate program and by NASA under grant NNH14ZDA001N-ACMAP to P. Ginoux and F. Paulot. Edited by: B. Ervens