ACP Atmospheric Chemistry and Physics ACP Atmos. Chem. Phys. 1680-7324 Copernicus Publications Göttingen, Germany 10.5194/acp-16-2819-2016The impact of snow nitrate photolysis on boundary layer chemistry and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland in a global chemical transport model ZatkoMaria GengLei AlexanderBecky beckya@uw.edu https://orcid.org/0000-0001-9915-4621 SofenEric https://orcid.org/0000-0002-4495-2148 KleinKatarina https://orcid.org/0000-0002-6101-441X Department of Atmospheric Sciences, University of Washington, Seattle, USA Division of Glaciology, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany now at: MathWorks, Natick, MA, USA Becky Alexander (beckya@uw.edu)4March2016 16 5 28192842 19May2015 10July2015 9February2016 11February2016 This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ This article is available from https://acp.copernicus.org/articles/.html The full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf

The formation and recycling of reactive nitrogen (NO, NO2, HONO) at the air–snow interface has implications for air quality and the oxidation capacity of the atmosphere in snow-covered regions. Nitrate (NO3-) photolysis in snow provides a source of oxidants (e.g., hydroxyl radical) and oxidant precursors (e.g., nitrogen oxides) to the overlying boundary layer, and alters the concentration and isotopic (e.g., δ15N) signature of NO3- preserved in ice cores. We have incorporated an idealized snowpack with a NO3- photolysis parameterization into a global chemical transport model (Goddard Earth Observing System (GEOS) Chemistry model, GEOS-Chem) to examine the implications of snow NO3- photolysis for boundary layer chemistry, the recycling and redistribution of reactive nitrogen, and the preservation of ice-core NO3- in ice cores across Antarctica and Greenland, where observations of these parameters over large spatial scales are difficult to obtain. A major goal of this study is to examine the influence of meteorological parameters and chemical, optical, and physical snow properties on the magnitudes and spatial patterns of snow-sourced NOx fluxes and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland. Snow-sourced NOx fluxes are most influenced by temperature-dependent quantum yields of NO3- photolysis, photolabile NO3- concentrations in snow, and concentrations of light-absorbing impurities (LAIs) in snow. Despite very different assumptions about snowpack properties, the range of model-calculated snow-sourced NOx fluxes are similar in Greenland (0.5–11×108 molec cm-2 s-1) and Antarctica (0.01–6.4×108 molec cm-2 s-1) due to the opposing effects of higher concentrations of both photolabile NO3- and LAIs in Greenland compared to Antarctica. Despite the similarity in snow-sourced NOx fluxes, these fluxes lead to smaller factor increases in mean austral summer boundary layer mixing ratios of total nitrate (HNO3+ NO3-), NOx, OH, and O3 in Greenland compared to Antarctica because of Greenland's proximity to pollution sources. The degree of nitrogen recycling in the snow is dependent on the relative magnitudes of snow-sourced NOx fluxes versus primary NO3- deposition. Recycling of snow NO3- in Greenland is much less than in Antarctica Photolysis-driven loss of snow NO3- is largely dependent on the time that NO3- remains in the snow photic zone (up to 6.5 years in Antarctica and 7 months in Greenland), and wind patterns that redistribute snow-sourced reactive nitrogen across Antarctica and Greenland. The loss of snow NO3- is higher in Antarctica (up to 99 %) than in Greenland (up to 83 %) due to deeper snow photic zones and lower snow accumulation rates in Antarctica. Modeled enrichments in ice-core δ15N(NO3-) due to photolysis-driven loss of snow NO3- ranges from 0 to 363 ‰ in Antarctica and 0 to 90 ‰ in Greenland, with the highest fraction of NO3- loss and largest enrichments in ice-core δ15N(NO3-) at high elevations where snow accumulation rates are lowest. There is a strong relationship between the degree of photolysis-driven loss of snow NO3- and the degree of nitrogen recycling between the air and snow throughout all of Greenland and in Antarctica where snow accumulation rates are greater than 130 kg m-2 a-1 in the present day.

 Introduction

Nitrogen oxides (NOx= NO + NO2) emitted from fossil fuel combustion, biomass burning, soil microbial activity, and lightning have adverse respiratory effects, contribute to the formation of atmospheric acidity, and are a key ingredient in tropospheric oxidant cycling leading to the formation of ground-level ozone (O3). O3 also has adverse respiratory effects, is an effective greenhouse gas (UNEP, 2011), and its photolysis dominates hydroxyl radical (OH) production in much of the troposphere (Thompson, 1992). Oxidation to form nitrate (HNO3/ NO3-) is the main sink for NOx in the troposphere (Logan, 1983), and the lifetime of NOx against oxidation to nitrate is expected to be 1–3 days in polar regions (Levy et al., 1999). NO3- is lost from the atmosphere through dry and wet deposition to the Earth's surface, and has a global atmospheric lifetime of roughly 5 days (Xu and Penner, 2012). In Antarctica and Greenland, HNO3 and NO3- deposited to the snowpack originates from both the troposphere (e.g., long-range transport) (Geng et al., 2014a; Lee et al., 2014; Wespes et al., 2012) and stratosphere (Davidson et al., 1989; Frey et al., 2009; Savarino et al., 2007). In snow-covered regions, the deposition of HNO3 and NO3- is not a permanent sink for NOx, as the photolysis of snow NO3- returns reactive nitrogen (Nr= NOx, HONO) back to the atmosphere, with implications for other oxidants such as OH and O3 (Domine and Shepson, 2002).

Snow photochemistry significantly influences boundary layer chemistry and plays an important role in oxidant production and cycling, especially in pristine regions, such as Antarctica (Bloss et al., 2007; Chen et al., 2004, 2007; Grannas et al., 2007, Helmig et al., 2008; Sjostedt et al., 2007; Thomas et al., 2012). Snow photochemistry may have more widespread impacts since up to 40 % of land on Earth is snow-covered at a given time (Grannas et al., 2007). NO3- is not the only photochemically active species in snow. The photolysis of nitrite (NO2-) in snow and the photolysis of snow-sourced formaldehyde (CH2O), nitrous acid (HONO), and hydrogen peroxide (H2O2) provide additional sources of Nr and OH to the boundary layer. Bromine (Br2) is also produced in the snow via reactions involving bromide (Br-), photochemically active species (e.g., NO3-), and photochemically produced species (e.g., OH) within snow grains (Pratt et al., 2013).

In snow, NO3- photolysis likely occurs in the liquid-like region (LLR) on the surface of ice grains, in cracks between ice grains, or in brine pockets embedded within ice grains (Domine et al., 2013). There are two channels for NO3- photolysis at wavelengths (λ)=290–345 nm. In the aqueous phase, NO3- can photolyze to produce NO2 and OH (Eq. 1), or produce NO2- and O(3P) (Eq. 2), but Eq. (1) is thought to be the dominant pathway (Grannas et al., 2007; Mack and Bolton, 1999; Meusinger et al., 2014). NO3-(aq)+hν(+H+)→NO2(aq)+OH(aq)NO3-(aq)+hν→NO2-(aq)+O(3P)(aq)

The aqueous-phase NO2 produced in Eq. (1) can evaporate to the gas phase and be released into the interstitial air (Boxe et al., 2005) and subsequently be transported to the overlying atmosphere via diffusion and wind pumping (Zatko et al., 2013). The quantum yield (ϕ) in Eq. (1) is strongly influenced by the location of NO3- in an ice grain. Quantum yields are more weakly dependent on LLR temperature and pH; ϕ values increase with increasing temperature and pH (Chu and Anastasio, 2003). Chu and Anastasio (2003) froze NO3--doped water in the lab and measured the quantum yield for Eq. (1) (0.003 molec photon-1 at T=253 K) as frozen ice grains were exposed to ultraviolet (UV) radiation. Zhu et al. (2010) deposited HNO3 on an ice film and measured ϕ for Eq. (1) (0.6 molec photon-1 at T=253 K), as the frozen surface was irradiated with UV radiation. A recent study by Meusinger et al. (2014) found ϕ=0.003–0.44 molec photon-1 at T=258 K for Eq. (1), which nearly spans the full range of previously reported quantum yields. Results from Meusinger et al. (2014) suggest that ϕ is dependent on the length of time that snow is exposed to UV radiation, as well as the location of NO3- in the ice grain. Meusinger et al. (2014) suggest that two photochemical domains of NO3- exist: photolabile NO3- and NO3- buried within the ice grain. The NOx produced from the photolysis of photolabile NO3- can escape the ice grain, while the NOx produced from the photolysis of buried NO3- is likely to undergo recombination chemistry within the snow grain, thus lowering the quantum yield of NOx for NO3- photolysis. Recombination chemistry involves NO3- reformation from photo-products (e.g., NOx and OH) within the ice grain, which alters oxygen isotopes (e.g., Δ17O) of NO3-, but does not impact bulk snow NO3- concentrations nor its nitrogen isotopes (δ15N) (Frey et al., 2009; Erbland et al., 2013, 2015).

The NO2- produced in Eq. (2) is quickly photolyzed at longer wavelengths (λ=290–390 nm) in the LLR or can react with OH or H+ in the LLR to produce Nr (Grannas et al., 2007): NO2-(aq)+hν(+H+,aq)→NO(aq)+OH(aq),NO2-(aq)+OH(aq)→NO2(aq)+OH-(aq),NO2-(aq)+H+(aq)→HONO(aq).

HONO produced in Eq. (5) can rapidly photolyze to produce NO and OH in the interstitial air or the atmospheric boundary layer (Anastasio and Chu, 2009). Reactions involving NO2- are intermediate reactions for NO3- photolysis because NO3- photolysis is required for NO2- formation and the end products of Eqs. (1)–(5) are all Nr. Once produced, NO2 and NO can be efficiently transported to the overlying atmosphere via wind pumping (Zatko et al., 2013) and enter into rapid NOx-cycling reactions. In the atmosphere, the relative abundance of NO and NO2 will be determined by local atmospheric conditions, specifically oxidant concentrations (e.g., O3, HO2, RO2, BrO, and ClO) (Frey et al., 2013). The snow-sourced NOx can be re-oxidized to HNO3 via Eq. (6) under sunlit conditions. NO2(g)+OH(g)→HNO3(g)

The HNO3 produced in Eq. (6) can undergo wet or dry deposition to the snow surface (Dibb et al., 2004) within a day (Slusher et al., 2002; Wang et al., 2008). Evidence for HNO3 re-deposition is seen in the snow NO3- concentration profile at many polar locations, where NO3- concentrations may be over an order of magnitude higher in the top 2 cm of snow compared to NO3- concentrations below (Dibb et al., 2004; Frey et al., 2009; Mayewski and Legrand, 1990; Rothlisberger et al., 2000).

Once HNO3 is deposited back to the snow, it is available for photolysis again. NO3- can be recycled multiple times between the boundary layer and the snow before burial below the photochemically active region, known as the snow photic zone (Davis et al., 2008; Erbland et al., 2015). Only two previous studies have attempted to quantify the degree of nitrogen recycling between the air and snow. Davis et al. (2008) use estimates of atmospheric NOx overhead-column burdens, as well as average atmospheric NOx lifetimes along with primary nitrogen deposition measurements from Legrand and Kirchner (1990), to estimate that nitrogen is recycled 1.8 times on average between the air and snow in one photochemical season in East Antarctica, although this value may be 3–5 times higher due to uncertainties in primary nitrogen deposition estimates. Erbland et al. (2015) used a multi-layer, one-dimensional (1-D) snow model (TRANSITS, Transfer of Atmospheric Nitrate Stable Isotopes To the Snow) and calculated that nitrogen is recycled 4 times on average before burial beneath the snow photic zone. Erbland et al. (2015) observed an inverse relationship between snow accumulation rate and nitrogen recycling in regions where snow accumulation rates are greater than 50 kg m-2 a-1, and suggest that in these regions, the degree of NO3- recycling is governed by the time NO3- remains in the snow photic zone.

The photolysis of snow NO3- and subsequent recycling between the air and snow alters the concentration and isotopic (e.g., δ15N) signature of NO3- that is ultimately preserved in polar ice sheets, which hampers the interpretation of ice-core NO3- records (Wolff et al., 2008). Such records have been sought to reconstruct the past history of the abundance of NOx in the atmosphere (Wolff, 1995). It has also been suggested that the nitrogen (δ15N) and oxygen (Δ17O) isotopic composition of ice-core NO3- can provide information on past variability in atmospheric NOx sources and oxidant abundances (e.g., Alexander et al., 2004; Hastings et al., 2005). Different sources of NOx have different δ15N signatures (∼-19 to 25 ‰, Geng et al., 2014a; Walters et al., 2015), giving ice-core δ15N(NO3-) measurements the potential to track NOx-source changes over time.

Ice-core δ15N(NO3-) values will be altered if there is photolysis-driven loss of NO3- from the snow when snow-sourced NOx is transported away from the site of primary deposition. NO3- photolysis in snow is associated with a large fractionation constant (ε, e.g., -47.9 ‰; Berhanu et al., 2014), providing the boundary layer with a source of NOx that is highly depleted in δ15N(NO3-), leaving highly enriched δ15N(NO3-) in the snow. In Antarctica, atmospheric δ15N(NO3-) values at the coast are as low as -40 ‰, indicating transport of snow-sourced NOx from the continental interior (Morin et al., 2009), while on the East Antarctica plateau, snow δ15N(NO3-) up to 480 ‰ has been reported (Blunier et al., 2005; Erbland et al., 2013; Frey et al., 2009; Shi et al., 2014), indicating net loss of NO3--driven by photolysis. In Greenland, atmospheric δ15N(NO3-) values are much less depleted (as low as -15 ‰) and snow δ15N(NO3-) values are much less enriched (as high as 15 ‰) compared to these extreme values observed in coastal Antarctica and on the East Antarctic plateau (Geng et al., 2014a; Hastings et al., 2004; Jarvis et al., 2009).

Glossary of variables used in this paper.

Variable Unit Description λ nm Wavelength ϕ molecphoton-1 Quantum yield for NO3- photolysis σNO3- cm2 Absorption cross-section for NO3- photolysis I photons cm-2s-1nm-1 Actinic flux of UV radiation ze cm e-folding depth of UV actinic flux in snow z3e cm Depth of snow photic zone αr kgm-2year-1 Total annual snow accumulation rate CBC ngg-1 Annual mean snow black carbon concentration re µm Radiation equivalent mean ice grain radii ρsnow kg m-3 Snow density Kexttot cm-1 Bulk extinction coefficient for snow [NO3-]top ngg-1 Mean NO3- concentration in top 2 cm of snow [NO3-]bot ngg-1 Mean NO3- concentration in below 2 cm snow depth EF unitless NO3- enhancement factor in top 2 cm of snow Fp fraction Fraction of photolabile NO3- in snow [NO3-]bot×Fp ngg-1 Mean NO3- concentration from 2 cm depth to the bottom of the snow photic zone scaled by fraction of photolabile NO3- in snow ε ‰ Fractionation constant for NO3- photolysis FNOx‾ moleccm-2s-1 Mean austral summer flux of snow-sourced NOx FNOx ng N m-2year-1 Annual sum of snow-sourced NOx flux FPRI ng N m-2year-1 Annual sum of primary NO3- deposited to snow FR ng N m-2year-1 Annual sum of recycled NO3- to snow NRF unitless Metric to assess degree of nitrogen recycling in 1 year τNO3-burial years/months Lifetime of NO3- against burial below snow photic zone τNO3-photolysis months Lifetime of NO3- against photolysis in snow photic zone Fpphoto unitless Fraction of photolabile NO3- photolyzed rather than buried below the snow photic zone f fraction Fraction of photolysis-driven loss of NO3- from snow δ15N(NO3-) ‰ Nitrogen isotopic composition of NO3-

If snow-sourced NOx is simply re-deposited back to the snow surface at the site of emission, a vertical profile in δ15N(NO3-) within the snow photic zone will develop due to vertical redistribution of NO3- (Erbland et al., 2013; Frey et al., 2009); however, the depth-integrated δ15N(NO3-) will not be impacted, even with active photolysis-driven recycling between the atmosphere and the snow. Enrichment in ice-core δ15N(NO3-) requires photolysis-driven loss from snow from net atmospheric transport of snow-sourced NOx away from the locations of its production. In addition to photolysis, ice-core δ15N(NO3-) values are also influenced by evaporation of HNO3 (Mulvaney et al., 1998) from snow and by atmospheric processing, such as NOx cycling (Freyer et al., 1993) and gas-particle partitioning (Heaton et al., 1997; Geng et al., 2014a); however, these impose a fractionation in δ15N(NO3-) at least an order of magnitude smaller than photolysis, and are thus not able to explain the large enrichments in snow δ15N(NO3-) observed on the East Antarctic plateau (Blunier et al., 2005; Erbland et al., 2013; Frey et al., 2009; Shi et al., 2014).

Here we incorporate an idealized snowpack with a NO3- photolysis parameterization into a global chemical transport model. The idealized Antarctic and Greenland ice sheets in the model have similar properties as the real ice sheets, but are subject to assumptions about the chemical and physical properties of the snow. The idealized snowpacks in this modeling framework attempt to account for the spatial variability in parameters important to snow NO3- photolysis in order to investigate the potential spatial variability in snow-sourced NOx fluxes, reactive nitrogen recycling and redistribution, and preservation of ice-core NO3- across Antarctica and Greenland, where observations of these parameters over large spatial scales are difficult to obtain. A major advantage of using a global chemical transport model framework is the ability to examine the potential redistribution and loss of reactive nitrogen due to photolysis-driven loss of snow NO3- across large spatial scales. The sensitivity of many parameters, such as snow accumulation rate, on the flux of snow-sourced NOx, nitrogen recycling, and loss of snow NO3- is elucidated by comparing modeled results in Antarctica and Greenland. Section 2 describes the inclusion of an idealized snowpack with a snow NO3- photolysis parameterization into a global chemical transport model, Goddard Earth Observing System (GEOS) Chemistry model (GEOS-Chem). Section 3 explores the implications of photolysis-driven reactive nitrogen recycling and redistribution for boundary layer chemistry and the alteration of NO3- concentration and its nitrogen isotopes (δ15N) ultimately archived in ice cores. In Sect. 3, we also compare model results in Antarctica and Greenland to examine the sensitivity of the flux of snow-sourced NOx and associated photolysis-driven processes on meteorological parameters and various chemical, optical, and physical properties of snow. We end Sect. 3 by discussing results from our model sensitivity studies that highlight the largest uncertainties in our ability to model these processes. Section 4 summarizes our conclusions and provides recommendations for future laboratory and field studies based on our model sensitivity results.

 Methods Incorporating snow NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> photolysis into a global chemical transport model

Table 1 provides a glossary of the variables used throughout this paper and Table 2 shows the values of parameters used in this modeling study.

Global chemical transport model description

GEOS-Chem is a global 3-D model of coupled aerosol–oxidant chemistry with detailed HOx–NOx–VOC–O3–BrOx tropospheric chemistry originally described in Bey et al. (2001). The model uses assimilated meteorological data from the NASA Goddard Earth Observing System (GEOS-5) including winds, convective mass fluxes, boundary layer depths, temperature, precipitation, and surface properties. Meteorological data have 6 h temporal resolution (3 h for surface variables and mixing depths). The TPCORE advection algorithm (Lin and Rood, 1996) is the transport routine in GEOS-Chem and is based on the calculation of the slopes between neighboring grid boxes. At the poles, neighboring grid boxes are used to estimate transport of chemical species into and out of the circular polar grid box. In Figs. 2–11, data in each grid box are smoothed using bilinear interpolation. The spectral direct and diffuse downwelling surface irradiance and photolysis frequencies are calculated using the Fast-JX radiative transfer module (Bian and Prather, 2002; Mao et al., 2010; Wild et al., 2000). In GEOS-Chem, aerosols can be wet deposited via scavenging in convective updrafts and by rainout from convective anvils and large-scale precipitation (Liu et al., 2001). The wet deposition scheme for gases is described by Amos et al. (2012) and the scavenging of aerosol by snow and cold/mixed precipitation is described by Wang et al. (2011). Dry-deposition velocities for coarse-mode aerosols (radii between 1 and 10 mm) are calculated based on aerosol size and hydroscopic growth as described in Zhang et al. (2001). Aerosol deposition to snow and ice surfaces is described by Fisher et al. (2011). For smaller aerosols (radii less than 1 µm), dry deposition velocities are calculated with a standard resistance-in-series scheme (Wang et al., 1998; Wesely, 1989).

Anthropogenic NOx emissions are from the EDGAR (Emission Database for Global Atmospheric Research) 3.2-FT2000 global inventory for the year 2000 (Oliver et al., 2005), scaled by country on the basis of energy statistics as described by van Donkelaar et al. (2008). The monthly inventory of emissions from biomass burning are from the Global Fire Emissions Database (GFED2) (van der Werf et al., 2009). Soil NOx emissions are computed using a parameterization described in Hudman et al. (2012), which is a function of vegetation type, temperature, soil moisture, precipitation, and fertilizer emissions. Emissions of NOx from lightning are linked to deep convection following the parameterization of Price and Rind (1992) and are scaled globally as described by Murray et al. (2012) to match OTD/LIS climatological observations of lightning flashes. The stratospheric source of NOy (= NOx+ HNO3) utilizes monthly climatological 3-D production and loss rates from the Global Modeling Initiative (GMI) model (Allen et al., 2010), which captures the formation of the polar vortex and PSC (polar stratospheric cloud) sedimentation (Murray et al., 2012).

For this work, GEOS-Chem version v9-01-01 was run at 2∘×2.5∘ horizontal resolution with 72 hybrid vertical levels using GEOS-5 meteorology from May 2009 to May 2010. The model was spun-up for 6 months prior to May 2009. There are no sub-surface (snow) layers in GEOS-Chem and the three lowest vertical levels are each roughly 100 m in height above Antarctica. The boundary layer in GEOS-Chem is calculated using a parameterization involving the bulk Richardson number with surface friction, a turbulent velocity scale, and non-local fluxes of heat and moisture (Holtslag and Boville, 1993) as implemented by Lin and McElroy (2010). The mixing of emissions, dry deposition, and concentrations of individual species within the boundary layer are determined by static instability. In a stable boundary layer, the local scheme based on eddy diffusivity theory is used, and the mixing is weak. In an unstable boundary layer, boundary layer mixing is triggered by large eddies. Average boundary layer mixing ratios (ppbv) of species reported in this study (e.g., NO3-, NOx, OH, O3) are mixing ratios in the lowest vertical grid box (total height ∼100 m).

Schematic showing the nitrogen recycling associated with NO3- photolysis as included in the model. FPRI (ng N m-2 yr-1) is the downward, primary flux of NO3- to Antarctica and Greenland originating from long-range transport and the stratosphere, FNOx (ng N m-2 yr-1) is the upward flux of snow-sourced NOx to the boundary layer, and FR (ng N m-2 yr-1) is downward, recycled flux of HNO3 to the snow surface. The surface snow layer (top 2 cm) is distinguished from the rest of the photic zone because 30–65 % of snow-sourced NOx is produced in the top 2 cm of snowpack (Zatko et al., 2013), and because both NO3- concentrations and actinic flux are much higher in the top surface layer compared to deeper layers.

Figure 1 illustrates the nitrogen recycling associated with snow NO3- photolysis as included in the model. The total flux of snow-sourced NOx from the snow, FNOx (molec cm-2 s-1), is calculated using the wavelength-dependent absorption cross section for NO3- photolysis (σNO3-, cm2 molec-1), the temperature (T)-, and pH-dependent quantum yield for NO3- photolysis (ϕ,  molec photon-1), the depth- and wavelength-dependent actinic flux in the snow photic zone (I, photons  cm-2 s-1 nm-1), and the average NO3- concentration [NO3-], molec cm-3) integrated over the depth of the photic zone. FNOx (molec cm-2 s-1) is calculated in Eq. (7) and converted into units of ng N m-2 yr-1 in Eqs. (9) and (10).

FNOx=∫λ0λ1∫z0z3eJ(λ,z)⋅NO3-dλdz, where NO3- is the average photolabile NO3- concentration over the depth of the snow photic zone (z=z0 to z=z3e) and J is the photolysis rate constant (s-1), which is calculated in Eq. (8): J=σNO3-(λ)⋅ϕ(T,pH)⋅Iλ,z.

In Eq. (8), σNO3- is from Sander et al. (2006). The temperature-dependent ϕ from Chu and Anastasio (2003) (assumed constant pH of 5) is calculated using the modeled air temperature in the lowest vertical grid box. The actinic flux (I) is calculated at 1 cm intervals and integrated from the snow surface (z0) to the depth of the photic zone (z3e). The snow photic zone is defined as 3 times the e-folding depth of ultraviolet (UV) actinic flux in snow (z3e), where 1 e-folding depth is ze. Below z3e, more than 95 % of the radiation has been attenuated and minimal photochemistry occurs. The flux of snow-sourced NOx is integrated over several ultraviolet wavelength bands (298–307, 307–312, 312–320, 320–345 nm), which are then summed to calculate total FNOx from the photolysis of snow NO3- between λ=298–345 nm. We assume that all NOx formed in Eq. (7) is immediately evaporated and transported into the overlying boundary layer via wind pumping and diffusion (Zatko et al., 2013).

 Calculating radiative transfer in snow

A two-stream, plane parallel snowpack actinic flux parameterization based on a four-stream radiative transfer model (Grenfell, 1991) was developed and described in detail in Zatko et al. (2013) and has been implemented into GEOS-Chem for the purposes of this study. The parameterization is simple, broadly applicable, and allows for variation in snow and sky properties (e.g., solar zenith angle, cloud fraction) over time. Ice grains are assumed to be spherical in shape and light-absorbing impurities (LAIs), including black carbon, brown carbon, dust, and organics, are assumed to be homogeneously distributed throughout the snow and always external to the ice grain. The snowpack actinic flux parameterization is used to calculate the UV actinic flux (photons cm-2 s-1 nm-1) and the mean summer e-folding depths (cm) across Antarctica and Greenland, which are used to calculate FNOx. The snowpack actinic flux parameterization is most sensitive to radiation equivalent mean ice grain radii (re) and insoluble LAIs in snow (Zatko et al., 2013); higher concentrations of LAIs in the snow and smaller re lead to shallower e-folding depths (ze). Vertical re and snow density (ρsnow) profiles at Dome C, Antarctica, from Gallet et al. (2011) are used across Antarctica for all seasons, which range from 86 to 235 µm and 260 to 360 kg m-3, from the snow surface to 300 cm depth, respectively. Vertical re and ρsnow profiles at Summit, Greenland, from Carmagnola et al. (2013) are used in Greenland, ranging from 73 to 211 µm and 235 to 350 kg m-3, from the snow surface to 300 cm depth, respectively.

The concentration of black carbon (BC) concentrations in snow (CBC) are calculated using modeled total annual black carbon (hydrophilic + hydrophobic) deposition (ng BC m-2 yr-1) and total annual snow accumulation rates (g H2O m-2 yr-1) in GEOS-Chem. At some locations in coastal Antarctica, high accumulation rates (up to 700 kg m-2 yr-1) lead to unrealistically low CBC (as low as 0.01 ng g-1), therefore the minimum CBC value used in the model is 0.08 ng g-1, which is comparable to the CBC values measured in high snow accumulation rate regions, such as in the East Antarctic sea ice zone (0.1 ng g-1) (Zatko and Warren, 2015). Insoluble non-black carbon species (nonBC) including dust, brown carbon, and organics, are responsible for the majority (up to 89 % at λ=305 nm) of the absorption of radiation at UV wavelengths (Zatko et al., 2013) in snow. These nonBC species and their concentrations have not been well quantified in snow. Based on observations reported in Zatko et al. (2013), we scale UV absorption by insoluble nonBC to the absorption by insoluble black carbon in snow by assuming that at λ=650–700 nm, which is a wavelength range where black carbon dominates absorption, insoluble black carbon is responsible for 70 % of the particulate absorption. We also assume that nonBC material has an absorption Ångström exponent of 5 (Doherty et al., 2010).

We neglect the influence of soluble LAIs in the snow and only consider the influence of insoluble LAIs for calculations of actinic flux profiles in snow. To determine whether soluble LAIs contribute significantly to light-absorption in the snow, we calculate the total extinction coefficient for insoluble BC, insoluble nonBC, and soluble LAIs following Sect. 2.1 of Zatko et al. (2013) and using the absorption coefficients for soluble material in snow reported in Beine et al. (2011) in northern Alaska. To our knowledge, observations of soluble LAI in Antarctic and Greenland snow are unavailable. We use soluble LAI observations from Alaska to provide a relative estimate of the importance of soluble LAIs in polar snow. The absorption coefficients (0.028 m-1 at λ=307 nm) from Beine et al. (2011) are identical to the extinction coefficients because it is assumed that there is no scattering by soluble species. Insoluble CBC (9 ng g-1) from Barrow, Alaska (Doherty et al., 2010), were used to calculate extinction coefficients for insoluble BC and nonBC material and therefore the amount of nonBC absorption in the UV and near-visible wavelengths following Zatko et al. (2013). Insoluble nonBC material is responsible for 9–14 times more absorption than soluble material in the wavelength range λ=298–345 nm. Insoluble BC material is responsible for 1.5–10 times more absorption than soluble material in the wavelength range λ=298–345 nm. The extinction coefficient is not influenced by the addition of a soluble absorber because scattering by snow grains dominates the extinction in snow. The effective co-albedo of single scattering is increased by 6–15 % when soluble absorbers are included. The resulting change in ze is at most 0.5 cm, which represents an increase of 4–9 % in the wavelength region of λ=298–345 nm.

 Calculating NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> concentrations in snow

The median value of sub-surface snow NO3- concentrations is 60 ng g-1 in Antarctica (Bertler et al., 2005) and 132 ng g-1 in Greenland (Burkhart et al., 2009). Snow NO3- was collected over depths corresponding to between 1 and 70 year(s) of snow accumulation in Antarctica and between 1 and 148 year(s) of snow accumulation in Greenland. The observed median values of sub-surface snow NO3- concentrations are used for modeled sub-surface (from 2 cm depth to the bottom of the snow photic zone, z3e) snow NO3- concentrations ([NO3-]bot) across Antarctica and Greenland. Although there is a large variation in observed snow NO3- concentrations from the ITASE campaign (Bertler et al., 2005), there is no clear spatial pattern across Antarctica. In Greenland, the relationship between snow accumulation rate and snow NO3- concentrations is non-linear and snow accumulation alone cannot account for the spatial variability in NO3- concentrations in lower snow accumulation rate regions (Burkhart et al., 2009). In Antarctica, snow NO3- concentrations in the top 2 cm of snow are up to 10 times higher than NO3- concentrations below 2-cm depth (Dibb et al., 2004; Erbland et al., 2013; Frey et al., 2009; Mayewski and Legrand, 1990; Rothlisberger et al., 2000), while in Greenland, surface snow layers are at most 2 times higher compared to sub-surface snow layers (Dibb et al., 2007). In this study, NO3- concentrations in the top 2 cm of snow ([NO3-]top) in Antarctica are calculated by enhancing [NO3-]bot by a factor of 6 in the top 2 cm of snow, which is the median of observed NO3- enhancement factors (EF) in Antarctica (Dibb et al., 2004; Erbland et al., 2013; Frey et al., 2009; Mayewski and Legrand, 1990; Rothlisberger et al., 2000). Since NO3- concentrations in Antarctica are enhanced by a factor of 6 in the top 2 cm of snow, an equal amount of NO3- has been removed from the remainder of the photic zone depth to maintain mass balance of NO3- within the snow column. In the modeled Greenland snowpack, [NO3-]top is not enhanced (EF = 1) although EF is varied in a sensitivity study to assess the impact of EF on snow-sourced NOx in Greenland (Sect. 3.7).

As mentioned in the introduction, the measured quantum yields for the dominant NO3- photolysis pathway (Eq. 1) range from 0.003 molec photon-1 (Chu and Anastasio, 2003) to 0.6 molec photon-1 (Zhu et al., 2010) at T=253 K and exhibit a dependency on temperature (see temperature-dependent equation in Chu and Anastasio, 2003). A higher fraction of NO3- was likely present on ice surfaces in the Zhu et al. (2010) study compared to the Chu and Anastasio (2003) study due to the different sample preparation methods, and likely explains the 3 order-of-magnitude difference in quantum yields. This interpretation suggests NO3- on the surface of ice grains is much more photolabile compared to NO3- embedded within ice grains, consistent with results from Meusinger et al. (2014). In this study, we assume that buried NO3-, i.e., wet deposited to the snow surface, is more likely to be embedded in the interior of a snow grain compared to NO3- that is dry deposited to the surface of the snow grain. To simulate this effect in an idealized snowpack, we do not adjust the quantum yields, but instead scale snow NO3- concentrations by the fraction of dry relative to total (wet + dry) deposition to the Antarctic and Greenland snow surface, assuming that only the fraction of dry deposited NO3- is photolabile (Fp). The degree of migration of NO3- within a snow grain after deposition due to snow metamorphism is unknown, which may influence the photolability of NO3- (Domine and Shepson, 2002).

Other modeling studies have attempted to calculate the fraction of photolabile NO3- in snow by estimating the concentration of NO3- contained within the LLR on the surface of ice grains (e.g., Thomas et al., 2012). In this work, we do not explicitly calculate NO3- photolysis within the LLR because there are still many unknowns about the LLR (Domine et al., 2013), including the distribution of NO3- between the bulk snow and the LLR. This distribution is better understood for some species, such as chloride (Cho et al., 2002), but it is unclear if NO3- behaves similarly. In this study, we have assumed that all NOx formed from the photolysis of photolabile NO3- is transferred to the boundary layer, which neglects any recombination chemistry (e.g., Erbland et al., 2015) that prevents NOx from leaving the ice matrix, and may lead to overestimates in the modeled FNOx values presented in this study. The quantum yield for NO3- photolysis is dependent on the location of NO3- in snow, and although there are uncertainties surrounding the location of NO3- in snow, we perform model sensitivity studies using the full range of measured quantum yields to provide bounds for the amount of NOx produced from snow NO3- photolysis.

Value(s) of parameters used in the model.

Variable Value(s) used in model References ϕ 0.0015–0.0052 molec photon-1a (Antarctica) Chu and Anastasio (2003) 0.0032–0.0069 molec photon-1a (Greenland) σNO3- 2.7×10-20 cm2 (λ=298–307 nm) Sander et al. (2006) 2.4×10-20 cm2 (λ=307–312 nm) 1.9×10-20 cm2 (λ=312–320 nm) 2.3×10-21 cm2 (λ=320–345 nm) ε -47.9 ‰ Berhanu et al. (2014) re 86.0–235.0 µmb (Antarctica) Carmagnola et al. (2013) 73.0–211.0 µmb (Greenland) Gallet et al. (2011) ρsnow 260–360 kg m-3b Gallet et al. (2011) 235–350 kg m-3b Carmagnola et al. (2013) EF 6c (Antarctica) Dibb et al. (2004, 2007) 1c (Greenland) Erbland et al. (2013) Frey et al. (2009) Mayewski and Legrand (1990) Rothlisberger et al. (2000) [NO3-]bot 60 ng g-1d Bertler et al. (2005) 132 ng g-1e Burkhart et al. (2009) Dibb et al. (2007) Honrath et al. (2002) Geng et al. (2014b)

a Temperature-dependent equation from Chu and Anastasio (2003) used. The modeled temperatures ranged from 237 to 271 K across Antarctica and 257 to 280 K across Greenland. b Vertical profiles of re range from 86 µm at the snow surface to 332 µm at 300 cm depth in Antarctica and from 73 µm at the snow surface to 211 µm at 300 cm depth in Greenland and are not varied spatially or temporally. Vertical profiles of ρsnow range from 260 kg m-3 at the snow surface to 360 kg m-3 at 300 cm depth in Antarctica and from 235 kg m-3 at the snow surface to 350 kg m-3 at 300 cm depth in Greenland and are not varied spatially or temporally. Observations from Gallet et al. (2011) (Dome C) are used across Antarctica and from Carmagnola et al. (2013) (Summit) are used across Greenland. The deepest observed re and ρsnow values are used for all modeled re and ρsnow values at deeper snow depths. c Median of observed NO3- enhancement factors in Antarctica (1–17) and low end of observed NO3- enhancement factors in Greenland (1–2). d Median of observed sub-surface snow NO3- mixing ratios from the ITASE campaign (Bertler et al., 2005). e Average NO3- concentrations from buried snow layer presented in Table 3 are used.

 Model sensitivity studies

Model results from Greenland and Antarctica are compared in Sect. 3, which sheds light on the influence of chemical and meteorological parameters on FNOx, nitrogen recycling and redistribution, and NO3- loss from snow. Additionally, due to uncertainties in our understanding of snow photochemistry (Domine et al., 2013), we perform a variety of model sensitivity studies, as shown in Table 3. The range of values used for the parameters varied in the sensitivity studies reflects our estimates of their uncertainties. The majority of these sensitivity studies focus on Antarctica, but the sensitivity of FNOx to EF is tested in Greenland. The quantum yield is varied from 0.002 molec photon-1 (corresponding to T = 244 K) (Chu and Anastasio, 2003) to 0.6 molec photon-1 (Zhu et al., 2010). Snow NO3- concentrations below 2 cm ([NO3-]bot) are halved and doubled with respect to the base-case scenario and the impact of scaling NO3- concentrations by the fraction of photolabile NO3- (Fp) is investigated. The NO3- enhancement factor in the top 2 cm of snowpack is varied from 1 to 10, based upon the range of reported observations (Dibb et al., 2004; Frey et al., 2009; Mayewski and Legrand, 1990; Rothlisberger et al., 2000). CBC is halved and doubled with respect to the base-case scenario. Since field and satellite measurements suggest significant increases in surface re throughout austral summer in Antarctica (Jin et al., 2008; Klein, 2014), the impact of increasing surface re during austral summer on FNOx in Antarctica is evaluated in this study. The re profiles are varied in three sensitivity studies to examine its influence on FNOx. The bulk extinction coefficient for snow (Kexttot) is increased and decreased by 20 % with respect to the base-case scenario because Libois et al. (2013) suggest that the spherical snow grain assumption overestimates e-folding depths by a factor of 1.2. These sensitivity studies are used to provide estimates of the influence the uncertainty in these parameters on calculations of FNOx.

 Estimating the impact of snow NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> photolysis on boundary layer chemistry and ice-core NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> records

NO3- photolysis, followed by oxidation, recycling, and redistribution of snow-sourced reactive nitrogen, influences both boundary layer chemistry and the concentration and isotopic signature of NO3- that is ultimately preserved in ice-core records. The preservation of NO3- in ice cores is most dependent on the amount of NO3- lost from the snow through photolysis via transport of snow-sourced NOx away from the site of primary deposition. The methods used to explore and quantify nitrogen recycling and photolysis-driven loss of NO3- in snow are described in the following sections.

Reactive nitrogen recycling between the air and snow

The nitrogen recycling factor (NRF) is a metric originally proposed by Davis et al. (2008) to quantify the degree of reactive nitrogen recycling in snow over 1 year. The NRF is calculated in Eq. (9): NRF=FNOxFPRI.

In Eq. (9), FNOx (ng N m-2 yr-1) is the annual sum of NOx released from the snow and FPRI (ng N m-2 yr-1) is the annual sum of primary NO3- deposited to the snow. Davis et al. (2008) use the NRF to describe nitrogen recycling on both macro-scale (e.g., across the East Antarctic plateau) and micro-scale (e.g., the number of times one molecule of NO3- is recycled) levels. An NRF greater than 1 suggests that multiple nitrogen recycling events occur in the snow. NRF represents the average, or “bulk” degree of nitrogen recycling in snow because it does not resolve the degree of nitrogen recycling on a molecular level in the snow; some NO3- molecules may never be photolyzed while other NO3- molecules may be photolyzed and recycled many times greater than NRF. The NRF has implications for boundary layer chemistry because the continual re-emission of NOx enhances the effective concentration of NOx in the boundary layer (Davis et al., 2008). Additionally, nitrogen recycling between the air and snow may alter the preservation of NO3- in ice-core records.

 Export of snow-sourced NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> away from the original site of photolysis

Once snow-sourced NOx is emitted to the atmosphere, it is subject to transport away from the original site of photolysis. If snow-sourced NOx is oxidized to HNO3 and re-deposited back to the snow surface, then there is no net photolysis-driven loss of NO3- from the snow. However, if some of the snow-sourced NOx is transported away from the site of primary deposition, there is a net photolysis-driven loss of NO3- from the snow. The fraction (f) of total NO3- (photolabile + non-photolabile) lost from the snow driven by photolysis is calculated in Eq. (10): f=FRFNOxτNO3-burial-1×FP×FPphoto.

In Eq. (10), negative values of f represent a loss of NO3- from the snow and positive values of f represent a gain of NO3- to the snow. In Eq. (10), FR (ng N m-2 yr-1) is the total annual flux of recycled NO3- to the snow surface and FNOx (ng N m-2 yr-1) is the total annual flux of NOx released from the snow from photolysis of snow NO3-. FR is calculated by subtracting the depositional flux of NO3- from a model run without snow photochemistry from the depositional flux of NO3- from a model run with snow photochemistry. The ratio of FR to FNOx represents the fraction of photolabile NO3- remaining in the snow after 1 year. As long as NO3- remains in the photic zone, NO3- can continually be lost from the snow by photolysis-driven processes. The preservation of NO3- in ice cores is dependent on the fraction of NO3- lost from the snow through photolysis during the entire time that NO3- remains in the photic zone. Provided that there are no major changes in parameters that influence snow photochemistry (e.g., LAI, overhead ozone abundance) from year to year, the fraction of photolabile NO3- lost from the snow will be stable from year to year.

τNO3-burial represents the time that NO3- remains in the photic zone (years), and in Eq. (10), τNO3-burial accounts for the loss of NO3- that occurs during the entire time that it remains in the photic zone. When NO3- remains in the photic zone for less than a year (τNO3-burial<1), τNO3-burial in Eq. (10) is set equal to 1. τNO3-burial is calculated according to Eq. (11), where both the depth of the photic zone (cm) and the total annual snow accumulation (αr) (cm yr-1) are considered. τNO3-burial=zeαr

In Eq. (11), ze (cm) is 1 e-folding depth of UV actinic flux and is used instead of z3e because 87–91 % of snow-sourced NOx is produced within the top 1 e-folding depth. To convert total annual snow accumulation rate from kg m-2 yr-1 to cm, a typical polar snow density (0.36 g cm-3) (Grenfell et al., 1994) is assumed. τNO3-burial is the minimum amount of time on average that NO3- remains in the top one-third of the snow photic zone before burial beneath because nitrogen recycling, which effectively redistributes NO3- upwards in the snow, is not factored into Eq. (11). τNO3-burial thus represents the lifetime of NO3- in snow in an average sense and does not resolve photolysis and recycling of individual NO3- molecules.

In Eq. (10), FRFNOxτNO3-burial-1 represents the fraction of photolabile NO3- lost from the snow through photolysis. This fraction is multiplied by Fp to calculate the fraction of total (photolabile + non-photolabile) NO3- lost from the snow through photolysis (f). If the lifetime of NO3- against burial is shorter than the lifetime of NO3- against photolysis, Fp is multiplied by Fpphoto, which represents the fraction of photolabile NO3- that is buried below the snow photic zone before photolysis. Fpphoto is calculated in Eq. (12): Fpphoto=τNO3-burialτNO3-photolysis, where τNO3-burial represents the lifetime of NO3- against burial below the photic zone and τNO3-photolysis is lifetime of NO3- against photolysis. τNO3-photolysis is calculated in Eq. (13): τNO3-photolysis=1J.

In Eq. (13), J (Eq. 8) has been integrated from λ=298–345 nm and averaged over the top e-folding depth in snow (ze). τNO3-photolysis represents the average lifetime of NO3- in the snow photic zone against loss by photolysis.

If f is 0, then all snow-sourced NOx is redeposited to the snow and there is no net loss of NO3-. f is also 0 if the net export of snow-sourced NOx away from the site of original photolysis is balanced by net import of snow-sourced NOx from other Antarctic or Greenland locations. If -1≤f≤0, the export of local snow-sourced NOx is higher than the deposition of snow-sourced NOx from elsewhere in Antarctica or Greenland, resulting in net photolysis-driven loss of NO3- from the snow. If f>0, the export of local snow-sourced NOx is lower than the deposition of snow-sourced NOx from elsewhere in Antarctica or Greenland, resulting in net photolysis-driven gain of NO3- to the snow.

f is used to calculate the enrichment in ice-core δ15N(NO3-) due solely to the impact of photolysis-driven loss of NO3- in snow when -1≤f<0. We use a Rayleigh fractionation equation to calculate δ15N(NO3-) (Blunier et al., 2005): δ15NNO3-=((δ15NNO3-air+1)1+fε)-1. In Eq. (14), δ15NNO3-air is the annual-averaged δ15N value of boundary layer NO3- and ε is the fractionation constant (-47.9 ‰; Berhanu et al., 2014). In this work, we set δ15NNO3-air equal to 0 ‰ to investigate the enrichment in δ15N(NO3-) only from photolysis-driven loss of NO3- from snow. The model-calculated δ15N(NO3-) enrichments represent values in snow below the photic zone (i.e., at depths where sunlight is minimal and NO3-photolysis is not occurring) because these values are indicative of the loss of snow NO3- over the total time that it spent in the snow photic zone. When NO3- is buried below the snow photic zone, no more photolysis or alteration of δ15N(NO3-) occurs; hence, the δ15N(NO3-) is further unaltered and is preserved in the ice-core record. The chemical and physical properties of snow within the photic zone are used to estimate what the enrichment in δ15N(NO3-) would be in ice cores at depths below the snow photic zone.

Modeled meteorological variables in GEOS-Chem. (a, b) Mean summer (DJF, Antarctica; JJA, Greenland) air temperature in lowest vertical grid box. (c, d) Annual total snow accumulation rate (kg m-2 yr-1) from May 2009 to May 2010. (e, f) Annual mean surface wind divergence (s-1) from May 2009 to May 2010. Blue regions indicate regions of convergence.

(a, b) Modeled annual mean snow black carbon concentrations (CBC). (c, d) Calculated mean summer (DJF, Antarctica; JJA, Greenland) UV e-folding depth (ze).

 Results and discussion Parameters that influence <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:msub><mml:mtext>NO</mml:mtext><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> and its spatial redistribution

Figure 2a and b present modeled air temperature in the lowest vertical grid boxes, which range from 237 to 271 K in Antarctica and 257 to 280 K in Greenland; lowest temperatures are located at the highest elevations. Figure 2c and d show modeled total annual snow accumulation rates from GEOS-Chem (kg m-2 yr-1), ranging from 10 to 700 kg m-2 yr-1 in Antarctica and 60 to 1400 kg m-2 yr-1 in Greenland. In both regions, the decrease in snow accumulation rate from the coast to the top of the plateau is attributed to increased distance from the ocean (moisture source), increased elevation, and lower temperatures. Figure 2e and f show modeled annual mean surface wind divergence from May 2009 to May 2010. Figure 2e is consistent with Antarctic Mesoscale Prediction System surface wind output (Fig. 3 in Parish and Bromwich, 2007), indicating that the large-scale airflow pattern in Antarctica flows from the East Antarctic plateau downslope towards the coast (katabatic winds). There are three major regions of wind convergence in Antarctica, located near the Ross, Ronne, and Amery ice shelves. In Greenland, air generally flows downwards from the continental interior towards the coasts.

Figure 3a and b present modeled snow CBC, ranging from 0.08 to 0.6 ng g-1 in Antarctica and 0.8 to 5.5 ng g-1 in Greenland. In Antarctica, the highest CBC values are found on the East Antarctic plateau and the spatial pattern of CBC is governed by the snow accumulation rate; higher snow accumulation rates dilute CBC (Doherty et al., 2013). The modeled boundary layer black carbon concentrations are relatively uniform across Antarctica (0.1–0.6 pptv) because the majority of black carbon reaches Antarctica through long-range transport (with the exception of local production from Antarctic research stations). In Greenland, the highest CBC values are found at the coasts due to their proximity to pollution sources, even though snow accumulation rates are highest at the coasts as well. Figure 3c and d show mean summer (DJF, Antarctica; JJA, Greenland) e-folding depths of UV actinic flux in snow (ze), which range from 24 to 69 cm in Antarctica and 2 to 17 cm in Greenland. The shallowest e-folding depths are located in regions of relatively high CBC, which is on the plateau in Antarctica and at the coasts in Greenland. Higher CBC in snow results in shallower ze because UV absorption in snow is enhanced as the concentration of LAIs increases (Zatko et al., 2013). In this study, coastal grid boxes are a mixture of water, sea ice, and snow-covered surfaces, and since actinic flux profiles are only calculated for snow-covered surfaces, the average ze in coastal grid boxes are artificially shallow.

(a, b) Ratio of annual dry-deposited NO3- to annual total deposited NO3-, FP. (c, d) Annual sub-surface snow NO3- concentrations ([NO3-]bot×Fp) used in the model.

Figure 4a and b show the fraction of dry-deposited NO3- compared to total deposited NO3- across Antarctica and Greenland. The ratio of dry deposition to total deposition ranges from 0.05 in coastal Antarctica and coastal Greenland to 0.92 in central Greenland and 0.99 on the East Antarctic plateau. Figure 4c and d show annual mean sub-surface (from 2 cm depth to the bottom of the photic zone, z3e) snow NO3- concentrations ([NO3-]bot) in the model scaled by Fp, ([NO3-]bot×Fp),which ranges from 13 to 60 ng g-1 across Antarctica and 8 to 121 ng g-1 across Greenland.

 Emission, recycling, and redistribution of nitrogen across Antarctica and Greenland

Figure 5 shows model-calculated mean summer snow-sourced NOx flux, FNOx‾, across Antarctica and Greenland. The spatial patterns of FNOx‾ in Fig. 5 are governed by the depth of the photic zone (ze) (especially in Antarctica) and also the concentration of photolabile NO3- (Fig. 4a and b), which is lowest at the coasts in both Antarctica and Greenland in the model. Additionally, spatial patterns of FNOx‾ are influenced by the temperature-dependent quantum yield; the highest quantum yield values are located at the coasts where temperatures are highest. FNOx‾ ranges from 0.01–6.4×108 molec cm-2 s-1 in Antarctica and 0.5–11×108 molec cm-2 s-1 in Greenland.

Mean summer (DJF, Antarctica; JJA, Greenland) flux of snow-sourced NOx from the snow (FNOx‾).

(a, b) Annual wet plus dry deposition flux of primary NO3- to the snow (FPRI). (c, d) Annual wet plus dry deposition flux of recycled NO3- to the snow (FR). (e) Ratio of FR to the total downward NO3- flux (FRFPRI+FR) for the base-case scenario. (f) Ratio of FPRI to the total downward NO3- flux (FPRIFPRI+FR).

Figure 6a and b present the total annual depositional flux of primary NO3- (FPRI), which ranges from 0.9–35×105 ng N m-2 yr-1 in Antarctica and 14–1000×105 ng N m-2 yr-1 in Greenland and is highest at the coasts due to its relative proximity to NOx-source regions in lower latitudes. An adjoint modeling study by Lee et al. (2014) suggests that boundary layer NO3- abundance in Antarctica is dominated by NO3- transport to Antarctica originating from NOx emissions from 25 to 65∘ S during austral winter and by thermal decomposition of peroxyacyl nitrate (PAN) as it descends from the free troposphere in all other seasons. In Greenland, boundary layer nitrate is predominately in the gas phase (HNO3) (Bergin et al., 1995; Dibb et al., 1994) and it has been suggested that NO3- in Greenland snow originates from both the troposphere (Geng et al., 2014a; Wespes et al., 2012) and stratosphere (Davidson et al., 1989).

Nitrogen recycling factor (NRF; Eq. 9).

Figure 6c and d show the total annual depositional flux of recycled NO3- (FR), which ranges from 0.5–11×105 ng N m-2 yr-1 in Antarctica and 0.4–9×105 ng N m-2 yr-1 in Greenland and is highest in areas of wind convergence (Greenland coasts and Antarctic ice shelves). Figure 6e shows the ratio of deposition of recycled nitrogen (FR) to total deposition (FPRI+FR) in Antarctica. In Antarctica, recycled nitrogen (FR) is the dominant (50–85 %) form of NO3- deposition along the Transantarctic Mountains and in regions of wind convergence, such as the Ronne, Ross, and Amery ice shelves. Along the Antarctic coast, FR represents as little as 11 % of the deposition flux. Figure 6f shows the ratio of total annual primary NO3- deposition to total NO3- deposition (primary + recycled) in Greenland. Primary NO3- deposition is responsible for at least 85 % of total NO3- deposition across most of Greenland, and up to 100 % in southern Greenland. This ratio is set equal to 0 over the ocean and sea ice, which leads to artificially low ratios at the coast because the model grid boxes at the coasts are an average of ocean, sea ice, and continental values.

Figure 7 shows the NRF, which ranges from 0.2 to 12 across Antarctica and 0.01 to 1.6 across Greenland. Nitrogen is recycled multiple times over the course of 1 year across most of Antarctica, with the exception of the coasts. In contrast, NRF values are less than 1.5 across all of Greenland. The spatial pattern of NRF is governed by the flux of snow-sourced NOx, which is influenced by the depth of the photic zone (ze), the concentration of photolabile NO3-, and the temperature-dependent quantum yield for NO3- photolysis. The spatial pattern of NRF is also dependent on FPRI, which is highest at the coasts and lowest at the top of the plateaus. In both Antarctica and Greenland, NRF values are lowest near the coast because the fraction of photolabile NO3- is small and FPRI values are high. The maximum NRF values generally occur in regions corresponding to maximum FNOx values. Our calculated NRF value at Dome C is 6, compared to a value of 4 calculated by Erbland et al. (2015). Davis et al. (2008) estimate an NRF of 1.8, which is roughly 3 to 6 times lower than the modeled East Antarctic NRF values in this study (NRF = 5–10), although Davis et al. (2008) state that their estimated NRF value could be factors of 3–5 times higher due to uncertainties in primary nitrogen deposition estimates. Erbland et al. (2015) found a relationship between inverse snow accumulation rates and nitrogen recycling in regions where the snow accumulation rates (αr) are higher than 50 kg m-2 a-1 in Antarctica. The relationship between NRF and 1/αr in our model simulations for αr > 50 kg m-2 a-1 in Antarctica suggests that only 22 % of the spatial variability of NRF can be explained by αr (Fig. S1 in the Supplement), likely due to the redistribution of snow-sourced reactive nitrogen across Antarctica by winds. In Greenland, which is a region where snow accumulation rates are greater than 50 kg m-2 a-1, our model simulations suggest that only 30 % of the spatial variability of NRF can be explained by αr (Fig. S2).

 Impact of reactive nitrogen recycling on boundary layer chemistry

The height of the boundary layer will strongly influence the abundance of NO3-, reactive nitrogen oxides, and oxidants emitted or formed at or near the surface. At many polar stations (e.g., Neumayer, South Pole, Dome C, Halley, Kohnen, Summit) there is a wide range of observed boundary layer heights during summer (10–600 m; Casasanta et al., 2014; Cohen et al., 2007; Davis et al., 2004; Drue and Heinemann, 2007; Handorf, 1996; Helmig et al., 2002; Jones et al., 2006, 2008; King et al., 2006; Kodama et al., 1985; Konig-Langlo et al., 1998; Neff et al., 2008; Oncley et al., 2004; Travouillon et al., 2008; Weller et al., 1999), and although modeled boundary layer heights are not systematically biased in one direction compared to observations, they often do not agree well. Therefore, only the relative impacts of snow photochemistry on reactive nitrogen and oxidant abundances are evaluated in this study. The impact of snow NO3- photolysis on boundary layer chemistry can be examined by considering factor changes in boundary layer NOx, total nitrate (gas-plus aerosol-phase), OH, and O3 mixing ratios between simulations with and without snow NO3- photolysis. Other snow photochemical reactions mentioned in the introduction but not included in this modeling study will also impact oxidant abundances, but the effects of each photochemical reaction are not additive due to the highly non-linear nature of oxidant cycling.

Factor increase in mean austral summer (DJF) boundary layer (a) NOx, (b) gas + aerosol-phase nitrate, (c) OH, and (d) O3 mixing ratios between model runs with FNOx compared to without FNOx.

As shown in Fig. 8, the inclusion of a snow NOx source leads to factor increases in boundary layer mixing ratios of NOx from 1.2 to 24.5, gas-plus aerosol-phase nitrate from 1.0 to 11.8, OH from 1.8 to 5.3, and O3 from 1.1 to 1.8 in Antarctica. The largest factor increases are in West Antarctica, particularly near the Ross and Ronne ice shelves, where winds carrying photo-produced species converge. The surface transport pattern is especially important for the redistribution of the longer-lived species NO3- and O3. Figure 9 shows that the inclusion of a snow NOx source leads to factor increases in boundary layer mixing ratios of NOx from 1.0 to 6.3, gas-plus aerosol-phase nitrate from 1.0 to 2.2, OH from 1.1 to 2.4, and O3 from 1.0 to 1.14 in Greenland. The largest factor increases for short-lived species (NOx and OH) are in central Greenland where FNOx is highest (Fig. 5), while the largest factor increases for longer-lived species (NO3- and O3) are located in regions of wind convergence.

Factor increase in mean summer (JJA) boundary layer (a) NOx, (b) gas + aerosol-phase nitrate, (c) OH, and (d) O3 mixing ratios between model runs with FNOx compared to without FNOx.

 Implications for ice-core records of NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> concentrations and isotopes

Figure 10a and b show the minimum amount of time that snow NO3- remains in the photic zone on average, τNO3-burial (Eq. 11) as calculated in the model. NO3- remains in the snow photic zone for 4 months near the Antarctic coasts and up to 6.5 years on the East Antarctic plateau before burial below the photic zone. In Greenland, NO3- remains in the photic zone for 0.1 months at the coasts and up to 7 months in central Greenland. The spatial pattern of τNO3-burial is governed by the snow accumulation rate, both directly and indirectly through its influence on CBC. The spatial patterns of τNO3-burialare in agreement with the expectation that NO3- remains in the photic zone the longest in areas with low snow accumulation rates.

(a, b) Minimum lifetime of NO3- against burial below snow photic zone (τNO3-burial, Eq. 11). (c) Average lifetime of NO3- against photolysis in the photic zone in Greenland (τNO3-photolysis, Eq. 13). (d) Ratio of τNO3-burial to τNO3-photolysis in Greenland (Fpphoto, Eq. 12).

In Antarctica, the lifetime of NO3- against burial is always longer than the lifetime of NO3- against photolysis. However, in Greenland, the lifetime of NO3- against burial may be shorter than the lifetime against photolysis due to the much shorter τNO3-burial values. Figure 10c presents the lifetime of NO3- against photolysis in Greenland (τNO3-photolysis, Eq. 13), which ranges from less than a month in northern Greenland to 37 months in southern Greenland. The spatial pattern of τNO3-photolysis is dependent on the mean summer (JJA) daily averaged downwelling surface irradiance values (not shown), which are highest in northern Greenland. Figure 10d shows Fpphoto (Eq. 12), which ranges from 0.003 to 1 across Greenland. Larger Fpphoto values in north-central Greenland suggest that the lifetime of photolabile NO3- against photolysis is sufficiently short relative to its lifetime in the snow photic zone, allowing for a potentially large fraction of snow NO3- to be photolyzed before burial beneath the photic zone. In southeastern Greenland, the lifetime of photolabile NO3- against photolysis is longer than its lifetime in the snow photic zone, which will reduce post-depositional photolysis of snow NO3- in this region.

Figure 11a and b show the fraction of NO3- gained or lost from the snow through photolysis (f, Eq. 10), which ranges from -0.99 to 0.30 in Antarctica and -0.83 to 0.02 in Greenland. Throughout most of Antarctica and Greenland, values of f are negative indicating that the photolysis of snow NO3- leads to net loss of NO3- from the snowpack in most locations. Positive f values indicate regions with net gain of NO3- to the snow resulting from the spatial redistribution of NO3- driven by snow photochemistry. In some regions of convergence, such as over the Ronne Ice Shelf in Antarctica, there is a net gain of snow-sourced NO3-. There are sharp gradients in f between the plateaus and the coasts. The largest loss of snow NO3- occurs at the top of the plateaus, where most photolyzed NO3- is transported away by katabatic winds. Along the coasts, the photolysis-driven loss of NO3- from the snow is minimal due to high snow accumulation rates and transport of snow-sourced NO3- from higher elevations. The spatial pattern of f is influenced by the time that NO3- remains in the photolytic zone (τNO3-burial), the concentration of photolabile NO3- (Fp), and wind patterns across Antarctica and Greenland.

(a, b) Fraction of NO3- gained (positive values) or lost (negative values) from the snow through photolysis (f, Eq. 10). (c, d) Modeled enrichment in ice-core δ15N(NO3-) (Eq. 14) due to photolysis-driven loss of NO3- in snow. Note the varied color bar scale in (a) and (b).

Figure 11c and d show modeled enrichments in ice-core δ15N(NO3-) from photolysis-driven loss of NO3- in snow. The δ15N(NO3-) enrichments presented in Fig. 11c and d represent enrichments in δ15N(NO3-) below the snow photic zone where NO3- photolysis is not occurring, effectively integrating the total loss of NO3- during its lifetime in the snow photic zone. Model-calculated ice-core δ15N(NO3-) values range from 0 to 363 ‰ in Antarctica and 0 to 90 ‰ in Greenland and are highest at the tops of the plateaus. The modeled ice-core δ15N(NO3-) enrichments resulting from the photolysis-driven loss of snow NO3- are sensitive to the fractionation constant (ε). In this study, the fractionation constant is varied over the full range of values reported in Erbland et al. (2013), Frey et al. (2009), and Shi et al. (2015); an ε of -90 ‰ increases modeled ice-core δ15N(NO3-) by a factor of 2 and an ε of -10 ‰ decreases modeled ice-core δ15N(NO3-) by a factor of 5 across Antarctica and Greenland.

 Relationship between nitrogen recycling and photolytic loss of NO<inline-formula><mml:math display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> in snow

NRF versus f values across Antarctica. (a) Regions where NO3- remains in the photic zone for 2 years or less. (b) Regions where NO3- remains in the photic zone for more than 2 years. The color scale represents the number of years NO3- remains in the photic zone (τNO3-burial).

The degree of photolysis-driven loss of snow NO3- is determined by both rates of photolysis and transport patterns across Antarctica and Greenland. The spatial patterns of recycling (NRF, Fig. 7) and loss (f, Fig. 11a and b) differ across Antarctica and Fig. 12 shows the relationship between f and NRF across Antarctica. The magnitude of nitrogen recycling and degree of photolysis-driven loss of snow NO3- in Antarctica are well correlated (r2=0.74, p<0.001) in regions where NO3- remains in the photic zone for less than 2 years (τNO3-burial<2) (Fig. 12a). The relationship between recycling and loss breaks down (r2=0.03, p<0.001) in locations where NO3- remains in the photic zone for more than 2 years (Fig. 12b). The relationship between recycling and loss weakens with increasing τNO3-burial because recycling of reactive nitrogen occurs at or near the surface only, while loss of NO3- occurs throughout the depth of snow photic zone. The time that NO3- remains in the snow photic zone is dependent on snow accumulation rates and the concentrations of LAIs in snow, the latter of which is partially governed by snow accumulation rates. In the present climate, τNO3-burial less than 2 years corresponds to snow accumulation rates higher than 130 kg m-2 a-1 in Antarctica. Figure 13 shows the relationship between f and NRF in Greenland. Nitrogen recycling and the degree of photolysis-driven loss of snow NO3- are well correlated (r2=0.71, p<0.001) in Greenland, likely because NO3- remains in the photic zone for less than 2 years across all of Greenland. Snow accumulation rates are higher than 130 kg m-2 a-1 across much of Greenland, except in parts of central Greenland, where snow accumulation rates are between 60 and 100 kg m-2 a-1. Differences in the relationship between snow accumulation rate and τNO3-burial between Greenland and Antarctica are due to the higher concentrations of LAIs in Greenland snow.

NRF versus f values across Greenland. The color scale represents the minimum number of months that NO3- remains in the photic zone (τNO3-burial).

 Comparison between Greenland and Antarctica

Comparing model results in Greenland and Antarctica reveals information about how different parameters influence snow-sourced NOx fluxes, nitrogen recycling, and photolysis-driven loss of snow NO3-. Air temperatures (Fig. 2a, b) and annual snow accumulation rates (Fig. 2c, d) are generally higher in Greenland compared to Antarctica, and Greenland is also closer to pollution sources, which lead to differences in the magnitudes and spatial patterns of FNOx, NRF, and f between these two regions.

The magnitude and spatial patterns of FNOx are influenced by snow photic zone depths (which are governed by snow LAI concentrations), snow NO3- concentrations, and the quantum yield for NO3- photolysis. The e-folding depths of UV actinic flux are shallower in Greenland (2–17 cm) compared to Antarctica (24–69 cm) because concentrations of light-absorbing impurities in snow are higher in Greenland (CBC=0.8–5.5 ng g-1) compared to Antarctica (CBC=0.08–0.6 ng g-1). Similar to snow CBC, the proximity of Greenland to natural and anthropogenic NOx sources leads to higher observed snow NO3- values; the median of observed sub-surface snow NO3- concentrations in Greenland (132 ng g-1, Burkhart et al., 2009) is over 2 times higher than the median of observed sub-surface snow NO3- concentrations in Antarctica (60 ng g-1; Bertler et al., 2005). Similarly, modeled sub-surface snow NO3- concentrations ([NO3-]bot×Fp) are higher across much of Greenland compared to Antarctica. However, in coastal Antarctica and Greenland, modeled sub-surface snow NO3- concentrations ([NO3-]bot×Fp) are similar in magnitude because the fraction of dry-deposited NO3- to total deposited NO3- (Fp) is lower in Greenland, which lowers the concentration of photolabile NO3- in the model. Additionally, modeled snow NO3- in the top 2 cm of snow ([NO3-]top) is generally higher across Antarctica ([NO3-]top=78–360 ng g-1) compared to Greenland ([NO3-]top=8–121 ng g-1) because EF = 6 in Antarctica and EF = 1 in Greenland. Lastly, summer temperatures are higher in Greenland (257–280 K) compared to Antarctica (237–271 K), which leads to higher quantum yields for NO3- photolysis in Greenland (ϕ=0.0032–0.0069) compared to Antarctica (ϕ=0.0015–0.0052).

Fluxes of snow-sourced NOx (FNOx) are somewhat higher in Greenland (0.5–11×108 molec cm-2 s-1) compared to Antarctica (0.01–6.4×108 molec cm-2 s-1) because ϕ, and often sub-surface snow NO3- concentrations ([NO3-]bot×Fp), are higher in Greenland compared to Antarctica, even though snow photic zones are shallower across Greenland due to higher snow LAI concentrations. In Antarctica, the highest FNOx values occur midway up the plateau because photic zone depths and the quantum yields for NO3- photolysis increase towards the coast while the concentrations of photolabile NO3- decrease towards the coast. In Greenland, the highest FNOx values occur at the top of the plateau because both photic zone depths and concentrations of photolabile NO3- are at a maximum there. The quantum yield of NO3- photolysis increases towards the coast, partially dampening the decreasing FNOx gradient towards the coast.

The NRF are much smaller in Greenland (NRF = 0.01–1.6) compared to Antarctica (NRF = 0.2–12), suggesting that the degree of nitrogen recycling is much lower in Greenland. Although FNOx values are generally higher in Greenland compared to Antarctica, the degree of nitrogen recycling is lower in Greenland because of its proximity to pollution sources. Primary deposition of NO3- is over an order of magnitude larger in Greenland compared to Antarctica. The total nitrate, NOx, OH, and O3 boundary layer mixing ratio factor increases in Greenland are 16, 7, 3, and 2 times lower, respectively, compared to Antarctica due to its proximity to pollution sources.

NO3- remains in the snow photic zone for a shorter period of time in Greenland (up to 7 months) compared to Antarctica (up to 6.5 years), because photic zones are shallower and snow accumulation rates are higher in Greenland. The assumed fraction of photolabile NO3- (Fp) in the model is lower in Greenland compared to Antarctica because a higher fraction of NO3- is wet deposited in Greenland. Additionally, the lifetime of photolabile NO3- against burial beneath the snow photic zone (τNO3-burial) is generally shorter than its lifetime against photolysis (τNO3-photolysis) in Greenland, further limiting post-depositional loss and recycling of snow NO3-.

Dependence of mean austral summer (DJF) flux of snow-sourced NOx (FNOx‾) in Antarctica on quantum yield (ϕ), the fraction of photolabile NO3- (Fp), snow NO3- concentrations below 2 cm ([NO3-]bot), the radiation equivalent ice grain radius (re), the bulk snow extinction coefficient (Kexttot), the NO3- concentration enhancement factor in the top 2 cm (EF), and snow black carbon concentration.

Parameter Base-case values Values used in FNOx‾ range in sensitivity studies (where FNOx‾= 0.5–7.8×108 sensitivity studies (×108 molec cm-2 s-1) ) molec cm-2 s-1) (Factor changes compared to FNOx‾ in base case, unitless) Quantum yield (ϕ) 0.02 molec photon-1a 0.6 molec photon-1 5–2600 (10.0–333.0) Fraction of photolabile NO3- (Fp) 0.01–0.99 Set to 1 everywhere 3.7–9.6 (1.2–7.4) (spatial variation, Fig. 3c) Sub-surface snow NO3- ([NO3-]bot) 60.0 ng g-1b 30–120 ng g-1 0.3–15.8 (0.6–2.0) Radiation equivalent mean Jan: 332.0 µmc Study 1: 332.0 µmd 0.5–10.2 ice grain radii (re) Dec–Feb: 198–332.0 µmc Study 2: 198–332.0 µmd (1.0–1.3) Mar–Nov: 86.0–332.0 µmc Study 3: 86.0-332.0 µmd Bulk snow extinction 1.7–6.9×103 m-1 ±20 % with respect to 0.5–9.4 coefficient (Kexttot) (spatial variation) base-case values (1.0–1.2) NO3- enhancement 6.0e 1–10 0.5–9.3 factor in top 2 cm (EF) (1.0–1.2) Snow black carbon 0.08–0.6 ng g-1 ± factor of 2 with respect 0.5–8.6 (CBC) (spatial variation, Fig. 3b) to base-case values (1.0–1.1)

a From Chu and Anastasio (2003) at T=244 K. b Median of ITASE campaign (Bertler et al., 2005). c re is varied vertically and temporally, but uniformly across Antarctica based on Gallet et al. (2011) and Klein (2014). In January, re is constant with depth (332 µm), in December and February, re ranges from 198 µm at the snow surface to 332 µm at 300 cm depth, and from March to November, re ranges from 86 µm at the surface to 360 µm at 300 cm depth. d In re sensitivity study 1, the base-case “January” re profile is applied for every month. In re sensitivity study 2, the base-case “December and February” re profile is applied for every month. In re sensitivity study 3, the base-case “March–November” re profile is applied for every month. e Median of observed EF (Dibb et al., 2004; Frey et al., 2009; Mayewski and Legrand, 1990; Rothlisberger et al., 2000).

In both Greenland and Antarctica, the highest fractions of photolysis-driven loss of snow NO3- (f) are located at the top of the plateaus and the lowest f values are found at the coasts. Photolysis-driven loss of snow NO3- is generally lower in Greenland compared to Antarctica because the fraction of photolabile NO3- (Fp) and the time spent in the snow photic zone (τNO3-burial) are lower in Greenland. Similar to the spatial patterns of f, modeled enrichments in ice-core δ15N(NO3-) are highest on the plateau and lowest at the coasts in both Antarctica and Greenland. δ15N(NO3-) values in Antarctica are up to 4 times larger compared to Greenland because a higher fraction of NO3- is lost from the snow via photolysis in Antarctica.

 Exploring the influence of chemical, optical, and physical parameters in snow on <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:msub><mml:mtext>NO</mml:mtext><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>

Snow accumulation rates have a major influence on snow-sourced NOx fluxes, nitrogen recycling and redistribution, and photolysis-driven loss of snow NO3- via two mechanisms. By impacting burial rates, the snow accumulation rate impacts the time NO3- spends in the snow photic zone. Snow accumulation rates influence light-absorbing impurity concentrations in snow, which impact the depth of the snow photic zone. Snow accumulation rates also influence concentrations of photolabile NO3- in snow; both directly through dilution effects and indirectly by controlling the partitioning of wet and dry deposited NO3-. Temperature also influences FNOx via the temperature-dependent quantum yield (ϕ), with higher temperatures leading to higher values of ϕ.

In addition to exploring the sensitivity of snow NO3- photolysis to meteorological parameters, the sensitivity of mean summer snow-sourced NOx fluxes (FNOx‾ ) to a variety of chemical, optical, and physical parameters in snow is explored in this section. Although the vast majority of these sensitivity studies focus on Antarctica because model computation time was limited, one sensitivity study was performed in Greenland and will be described below.

Table 3 shows the dependence of FNOx‾ on uncertainties in the quantum yield for NO3- photolysis (ϕ), the fraction of photolabile NO3- (Fp), sub-surface snow NO3- concentrations ([NO3-]bot), radiation equivalent mean ice grain radii (re), the bulk snow extinction coefficient (Kexttot), the NO3- concentration enhancement factor in the top 2 cm of snow (EF), and snow black carbon concentrations in Antarctica. The range of values for each of these parameters is determined by their estimated degree of uncertainty, in order to highlight the largest uncertainties in calculations of FNOx‾ The sensitivity study results are compared to FNOx‾ from the standard scenario, which is also described in Table 3. The FNOx‾ values from the standard scenario are slightly different than the FNOx‾ values presented in Fig. 5a because a spatially uniform ϕ is used in the standard scenario while a temperature-dependent ϕis used in the rest of the manuscript (Figs. 5–13).

FNOx‾ is most sensitive to uncertainties in ϕ, which increases FNOx‾ by up to a factor of 330 when ϕ from Zhu et al. (2010) (0.6 molec photon-1) is used compared to the ϕ in the standard scenario (ϕ=0.002 molec photon-1 (Chu and Anastasio, 2003)). The second most influential parameter is the concentration of photolabile NO3- ([NO3-]bot×Fp). Assuming that all NO3- is photolabile (Fp=1) increases FNOx‾ by up to a factor of 7.4 (at the coasts) with respect to the standard scenario. Use of the fraction of dry-deposited NO3- (Fp) to scale the concentration of photolabile NO3- lowers FNOx‾ by up to 85 % along the coast, but has little impact on the East Antarctic plateau due to the high fraction of dry deposited NO3-. Uncertainties in re, Kexttot, EF, and CBC influence FNOx‾ by up to a factor of 1.3 compared to the standard scenario. In Greenland, EF is varied between 1 and 2, which is the range of observed EF presented in Dibb et al. (2007). Similar to Antarctica, varying EF from 1 to 2 increases FNOx‾ in Greenland by at most a factor of 1.2.

Calculated snow-sourced NOx fluxes are by far most sensitive to uncertainties in the quantum yield for NO3- photolysis and the concentration of photolabile NO3-, which are likely related to one another. The results of Chu et al. (2003), Zhu et al. (2010), and Meusinger et al. (2014) suggest that the quantum yield is largely influenced by the location of NO3- in the ice grain; quantum yields tend to be lowest when NO3- is buried within the ice grain and highest when NO3- is on the ice grain surface. Although we have not explicitly varied the quantum yield based upon NO3- location in ice grains due to lack of information regarding the location, we alter the concentration of photolabile NO3- based upon the ratio of dry to total deposited NO3- This effectively assumes that only the dry-deposited fraction of NO3- is on the surface of the snow grain and is photolabile, and that the wet-deposited NO3- is trapped within an ice grain and thus is not photolabile. Using conservative ϕ values (Chu and Anastasio, 2003) and assuming that only dry-deposited NO3- is photolabile may suggest that our calculated FNOx values represent a lower limit. However, no field-or laboratory-based information is available to assess our assumption that only dry-deposited NO3- is photolabile; it is possible that the fraction of NO3- that is photolabile is higher or lower than what we assume.

These sensitivity studies highlight the need for field, laboratory, and modeling studies to investigate factors influencing the quantum yield and concentration of photolabile NO3-, such as the location of NO3- within ice grains. We note that FNOx is also sensitive to the depth of the snow photic zone, which in turn is most sensitive to snow LAI concentrations and re (Zatko et al., 2013). By scaling the concentrations of total snow LAIs to CBC in the model based on observations in Greenland and Antarctica (Zatko et al., 2013), we assume the same factor of 2 uncertainty in total LAI concentrations as we do for CBC. More measurements of snow LAIs other than black carbon are needed to confirm our assumptions about snow LAI concentrations and their associated uncertainty.

 Conclusions

We have incorporated an idealized snowpack along with a snow radiative transfer model into a global chemical transport model (GEOS-Chem) and used this modeling framework to simulate the photolysis of snow NO3- and calculate the associated snow-sourced flux and redistribution of nitrogen across Antarctica and Greenland. An important goal of this study is to explore the sensitivity of various chemical, optical, and physical properties of snow, as well as meteorology, on fluxes of snow-sourced NOx and related nitrogen recycling, redistribution, and loss of NO3- from the snow. This modeling framework is also used to examine the impact of snowpack NO3- photolysis on boundary layer chemistry and the preservation of NO3- concentration and nitrogen isotopes in ice cores across Antarctica and Greenland.

The calculated fluxes of snow-sourced NOx in Antarctica and Greenland range from 0.01–6.4×108 molec cm2 s-1 and 0.5–11×108 molec cm2 s-1, respectively. The modeled spatial patterns of snow-sourced NOx fluxes are determined by the spatial patterns of light-absorbing impurity (e.g., insoluble black carbon, dust, organics) concentrations in snow, photolabile NO3- concentrations, and temperature-dependent quantum yields for NO3- photolysis. In the model, the spatial patterns of light-absorbing impurities are influenced by snow accumulation rates and proximity to pollution sources, the spatial patterns of photolabile NO3- in the model are influenced by the amount of wet-deposited NO3- compared to total deposited NO3-, and the spatial patterns of quantum yields of NO3- photolysis are influenced by modeled surface air temperatures. Model sensitivity studies suggest that the magnitude of the snow-sourced NOx flux is most sensitive to uncertainties in the quantum yield for NO3- photolysis and the concentration of photolabile NO3-, which are likely related to one another. The concentration of light-absorbing impurities in snow is also important for the flux of snow-sourced NOx, but we assume a much smaller degree of uncertainty in this parameter relative to uncertainties in the quantum yield and the concentrations of photolabile NO3-. Fluxes of snow-sourced NOx are somewhat higher in Greenland compared to Antarctica because the quantum yields, and often photolabile NO3- concentrations, are higher in Greenland because Greenland is warmer and closer to anthropogenic NOx sources (e.g., northern hemisphere mid-latitudes). This is counteracted by the higher concentrations of snow light-absorbing impurities leading to shallower show photic zones in Greenland.

The nitrogen recycling factor (NRF) is dependent on the magnitude and spatial patterns of fluxes of primary NO3- to the snow relative to fluxes of NOx from the snow. NRF values greater than 1 suggest that nitrogen is recycled multiple times between the air and snow. NRF values range from 0.2 to 12 in Antarctica and are greater than 1 across most of Antarctica, with the exception of the coasts. NRF values range from 0.01 to 1.6 in Greenland and are only larger than 1 in central Greenland. The degree of nitrogen recycling is lower in Greenland because NO3- deposition to snow is dominated by primary NO3- deposition in Greenland, largely because Greenland is closer to pollution sources. Similarly, boundary layer mixing ratios of total nitrate, NOx, OH, and O3 are less influenced by snow-sourced NOx in Greenland because of its proximity to pollution sources.

This modeling framework can also be used to examine the impact of NO3- photolysis on the preservation of NO3- in ice cores. The time that NO3- remains in the snow photic zone is dependent on snow accumulation rates both directly, and indirectly through their influence on light-absorbing impurity concentrations in snow. NO3- remains in the snow photic zone for a much shorter period of time in Greenland (up to 7 months) compared to Antarctica (up to 6.5 years) because snow accumulation rates are higher in Greenland. The fraction of NO3- lost from the snow through photolysis (f) ranges from -0.99 to 0.30 in Antarctica and -0.83 to 0.02 in Greenland, where negative values indicate net loss of NO3- from the snow. The fraction of NO3- lost from the snow is dependent on the time that NO3- remains in the snow photic zone, the concentration of photolabile NO3-, and wind patterns across Antarctica and Greenland. In both Antarctica and Greenland, net loss of snow NO3- is highest on top of the plateaus and lowest at the coasts; some regions (e.g., Ronne and Ross Antarctic ice shelves) experience net gain of snow NO3- due to the redistribution of snow-sourced nitrogen. The fraction of photolysis-driven loss of snow NO3- is lower in Greenland compared to Antarctica because the fraction of photolabile NO3- and the time spent in the snow photic zone (τNO3-burial) is lower in Greenland due to the higher snow accumulation rates and higher concentrations of snow light-absorbing impurities. The fraction of NO3- lost from the snow through photolysis is used to calculate the enrichment in ice-core δ15N(NO3-) solely from photolysis-driven NO3- loss in snow. The modeled enrichment in ice-core δ15N(NO3-) ranges from 0 to 363 ‰ in Antarctica and 0 to 90 ‰ in Greenland; the highest δ15N(NO3-) enrichments are calculated in central Antarctica and Greenland.

A significant relationship exists between nitrogen recycling and photolysis-driven loss of snow NO3- in Antarctica when NO3- remains in the photic zone for less than 2 years (τNO3-burial<2), corresponding to a snow accumulation rate greater than 130 kg m-2 a-1 in the present day. There is also a significant relationship between nitrogen recycling and photolysis-driven loss of snow NO3- throughout all of Greenland, which is a region where snow accumulation rates are generally higher than 130 kg m-2 a-1. Since the spatial variability of ice-core δ15N(NO3-) is mainly determined by the fractional loss of snow NO3- due to the large fractionation factor associated with its photolysis (Berhanu et al., 2014), observations of δ15N(NO3-) in snow and ice can be used to estimate both the degree of recycling and loss of snow NO3- in Antarctica and Greenland as long as this condition is met. The relationship between recycling and loss can be useful for the interpretation of the oxygen isotopic composition of ice-core NO3- (e.g., Sofen et al., 2014), which is impacted by recycling of snow NO3-. We note that the relationship between τNO3-burial and snow accumulation rate may vary in different climates depending on the concentrations of light-absorbing impurities in snow (Geng et al., 2015).

This is the first modeling study to incorporate an idealized snowpack along with a snow radiative transfer model into a global chemical transport model. This modeling framework is used to investigate the impacts of snow-sourced NOx on boundary layer chemistry and nitrogen recycling and redistribution, and its spatial variability, across Antarctica and Greenland. The modeled spatial patterns of nitrogen recycling, photolysis-driven loss of NO3- from snow, and ice-core δ15N(NO3-) are each sensitive to multiple meteorological and chemical parameters, some of which are interdependent. Model sensitivity studies suggest that future field, laboratory, and modeling studies continue to focus on gaining a better understanding of the quantum yield for NO3- photolysis and the concentration of photolabile NO3-, which are likely related. More observations of the concentration of UV light-absorbing impurities in snow are also needed. Updated information about the quantum yield for NO3- photolysis and the concentration of photolabile NO3- in snow along with additional snow photochemical reactions can be incorporated into this modeling framework in the future, which will continue to improve our understanding of the impacts of snow photochemistry on boundary layer chemistry and the preservation of NO3- and other photochemically active species in ice cores.

 Acknowledgements

We acknowledge support from NSF PLR 1244817, NSF PLR 0944537, NSF PLR 1446904, and an EPA STAR graduate fellowship to M. C. Zatko. The authors thank Steve Warren, Sarah Doherty, Thomas Grenfell, and Quentin Libois for helpful discussions about light-absorbing impurities in snow and their influence on snow photochemistry. We thank Joseph Erbland for many helpful comments and discussions about nitrogen recycling. Joel Thornton and Lyatt Jaeglé also provided many helpful comments about this work. We also thank Paul Hezel and Yanxu Zhang for helping M. C. Zatko learn GEOS-Chem. We thank Qianjie Chen for helpful feedback on paper drafts and Martin Schneebeli for providing useful advice about snow grain profiles in Antarctic snow. The authors thank the editor and two anonymous reviewers for helpful comments. Edited by: E. Browne

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We have incorporated an idealized snowpack with a NO<sub>3</sub><sup>−</sup> photolysis parameterization into a global chemical transport model (Goddard Earth Observing System (GEOS) Chemistry model, GEOS-Chem) to examine the implications of snow NO<sub>3</sub><sup>−</sup> photolysis for boundary layer chemistry, the recycling and redistribution of reactive nitrogen, and the preservation of ice-core NO<sub>3</sub><sup>−</sup> in ice cores across Antarctica and Greenland, where observations of these parameters over large spatial scales are difficult to obtain. A major goal of this study is to examine the influence of meteorological parameters and chemical, optical, and physical snow properties on the magnitudes and spatial patterns of snow-sourced NO<sub><i>x</i></sub> fluxes and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland. Snow-sourced NO<sub><i>x</i></sub> fluxes are most influenced by temperature-dependent quantum yields of NO<sub>3</sub><sup>−</sup> photolysis, photolabile NO<sub>3</sub><sup>−</sup> concentrations in snow, and concentrations of light-absorbing impurities (LAIs) in snow. Despite very different assumptions about snowpack properties, the range of model-calculated snow-sourced NO<sub><i>x</i></sub> fluxes are similar in Greenland (0.5–11 × 10<sup>8</sup> molec cm<sup>−2</sup> s<sup>−1</sup>) and Antarctica (0.01–6.4 × 10<sup>8</sup> molec cm<sup>−2</sup> s<sup>−1</sup>) due to the opposing effects of higher concentrations of both photolabile NO<sub>3</sub><sup>−</sup> and LAIs in Greenland compared to Antarctica. Despite the similarity in snow-sourced NO<sub><i>x</i></sub> fluxes, these fluxes lead to smaller factor increases in mean austral summer boundary layer mixing ratios of total nitrate (HNO<sub>3</sub>+ NO<sub>3</sub><sup>−</sup>), NO<sub><i>x</i></sub>, OH, and O<sub>3</sub> in Greenland compared to Antarctica because of Greenland's proximity to pollution sources. The degree of nitrogen recycling in the snow is dependent on the relative magnitudes of snow-sourced NO<sub><i>x</i></sub> fluxes versus primary NO<sub>3</sub><sup>−</sup> deposition. Recycling of snow NO<sub>3</sub><sup>−</sup> in Greenland is much less than in Antarctica Photolysis-driven loss of snow NO<sub>3</sub><sup>−</sup> is largely dependent on the time that NO<sub>3</sub><sup>−</sup> remains in the snow photic zone (up to 6.5 years in Antarctica and 7 months in Greenland), and wind patterns that redistribute snow-sourced reactive nitrogen across Antarctica and Greenland. The loss of snow NO<sub>3</sub><sup>−</sup> is higher in Antarctica (up to 99 %) than in Greenland (up to 83 %) due to deeper snow photic zones and lower snow accumulation rates in Antarctica. Modeled enrichments in ice-core <i>δ</i><sup>15</sup>N(NO<sub>3</sub><sup>−</sup>) due to photolysis-driven loss of snow NO<sub>3</sub><sup>−</sup> ranges from 0 to 363 ‰ in Antarctica and 0 to 90 ‰ in Greenland, with the highest fraction of NO<sub>3</sub><sup>−</sup> loss and largest enrichments in ice-core <i>δ</i><sup>15</sup>N(NO<sub>3</sub><sup>−</sup>) at high elevations where snow accumulation rates are lowest. There is a strong relationship between the degree of photolysis-driven loss of snow NO<sub>3</sub><sup>−</sup> and the degree of nitrogen recycling between the air and snow throughout all of Greenland and in Antarctica where snow accumulation rates are greater than 130 kg m<sup>−2</sup> a<sup>−1</sup> in the present day.</p></abstract-html> <ref-html id="bib1.bib1"><label>1</label><mixed-citation> Allen, D., Pickering, K., Duncan, B., and Damon, M.: Impact of lightning NO emissions on North American photochemistry as determined using the Global Modeling Initiative (GMI) model, J. Geophys. Res., 115, D22301, <a href="http://dx.doi.org/10.1029/2010JD014062" target="_blank">doi:10.1029/2010JD014062</a>, 2010. </mixed-citation></ref-html> <ref-html id="bib1.bib2"><label>2</label><mixed-citation> Alexander, B., Savarino, J., Kreutz, K. J., and Thiemens, M. H.: Impact of preindustrial biomass burning emissions on the oxidation pathways of tropospheric sulphur and nitrogen, J. Geophys. 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