We use the GEOS-Chem model (Bey et al., 2001; www.geos-chem.org) v9-02 to simulate the impact of changes in vegetation on atmospheric chemistry. GEOS-Chem is a global 3-D atmospheric chemical transport model driven by assimilated meteorology from the NASA Global Modeling and Assimilation Office. Our simulations are driven by GEOS-5 meteorological data for the year 2010 and performed over North America at the nested horizontal resolution of 0.5∘×0.667∘, with dynamic boundary conditions supplied from a global simulation at 2∘×2.5∘.

The model includes a detailed HOx-NOx-VOC-O3 chemical scheme originally presented by Bey et al. (2001). The chemical mechanism includes over 90 species (including the following lumped categories: > C3 alkanes, > C2 alkenes, > C4 alkynitrates, > C1 aldehydes, > C1 alcohols, and > C1 organic acids), over 200 chemical reactions, and over 50 photolysis reactions, incorporating the latest JPL and IUPAC recommendations. Detailed isoprene oxidation chemistry is included, following Paulot et al. (2009a, b) as implemented for GEOS-Chem by Mao et al. (2013). Explicit oxidation pathways are not yet included for terpenes. Given that isoprene dominates biogenic OH reactivity over the continental US, we assume terpenes play a minor role outside of SOA formation (see below) in our land cover change simulations. Gas-aerosol partitioning in the sulfate-nitrate-ammonium system is described according to the thermodynamic ISORROPIA II equilibrium model (Fountoukis and Nenes, 2007).

Carbonaceous aerosol sources include primary emissions from fossil fuel, biofuel, and biomass burning (Park et al., 2003) and reversible SOA formation following Pye et al. (2010). Secondary organic aerosol are lumped into five species based on the parent hydrocarbons (terpenes, isoprene, light aromatics, and intermediate volatile organic compounds, semivolatile organic compounds (SVOCs), and oxidized SVOCs). Aerosol yields are parameterized using a volatility basis set (Donahue et al., 2006) for aerosol systems with multiple parent hydrocarbons or aerosol formation pathways, or an Odum 2-product approach (Odum et al., 1996) for systems with one parent hydrocarbon. Emitted biogenic parent hydrocarbons are lumped in the following manner: (1) α-pinene +β-pinene + sabinine + carene; (2) limonene; (3) t-β-ocimene + myrcene + other monoterpenes; (4) farnesene + caryophyllene + other sesquiterpenes; and (5) isoprene. SOA yields from ozonolysis (at high and low NOx) and nitrate radical oxidation are represented in the model for groups (1)–(4), while yields from photooxidation (initiated by OH) and nitrate radical oxidation are represented for isoprene. Further gas-aerosol phase coupling occurs for example through N2O5 uptake (Evans and Jacob, 2005) and HO2 uptake (Mao et al., 2013).

We use anthropogenic emission inventories according to the NEI-2005 inventory for the United States (http://www.epa.gov/ttnchie1/trends/), CAC for Canada (http://www.ec.gc.ca/pdb/cac/), and BRAVO (Kuhns et al., 2005) for Mexico, and scale these to the year 2010 following van Donkelaar et al. (2008). The model also includes biomass burning emissions (GFED3, Mu et al., 2011), lightning NOx (Murray et al., 2012), and volcanic SO2 emissions (Fisher et al., 2011). Soil NOx and BVOC emissions are described below.

Here we briefly describe the main mechanisms in the model by which vegetated land cover impacts atmospheric chemistry.

GEOS-Chem v9-02 includes the Berkeley-Dalhousie Soil NOx Parameterization (Hudman et al., 2012). In this parameterization, the flux of NOx from soils is a function of temperature, soil moisture, and emission coefficients which depend on available nitrogen and biome type. Biomes (and basal emission coefficients) are defined according to Steinkamp and Lawrence (2011), with 24 different land cover types. Dry spell length is also included to account for pulsing. A canopy reduction factor is calculated according to leaf area index (LAI), wind speed, and surface resistance, and is designed to simulate the uptake of NOx by vegetation following soil emission (Wang et al., 1998).

Biogenic VOC emissions from vegetation are calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.02: Guenther et al., 2006, with updates from Sakulyanontvittaya et al., 2008). In GEOS-Chem v9-02, mapped basal BVOC emission factors are provided as an input to the model and are modulated online by activity factors that are a function of temperature, LAI, photosynthetically active radiation (PAR), and average leaf age.

Dry deposition is calculated by the resistance-in-series scheme of Wesely (1989), using a “big-leaf” approximation where the deposition surface is treated as a single uniform surface (or leaf). Dry deposition velocities are calculated as a combination of aerodynamic resistance (Ra), laminar layer resistance (Rb), and surface resistance (Rc). Ra is calculated separately for unstable, moderately stable, and very stable atmospheric conditions, and is a function of roughness heights (which would be a function of land cover type) that are provided by the meteorological input data. Rb depends on meteorological data and the identity of the gas-phase species being deposited. The Rc parameterization depends on the solubility and reactivity of individual chemical compounds and on land type according to Wesely (1989), and is scaled by LAI. Land types are derived by the USGS global land characteristics database (http://edc2.usgs.gov/glcc/globdoc2_0.php), known also as the Olson Land Map. Over 70 land types are represented and mapped to the 11 deposition surface types given by Wesely (1989). Aerosol deposition is also parameterized by the resistance-in-series scheme according to Zhang et al. (2001), with deposition to snow and/or ice as presented by Fisher et al. (2011). Gravitational settling of dust and sea salt is described according to Fairlie et al. (2007) and Alexander et al. (2005) respectively.

As described above, the parameterizations of soil NOx emissions, BVOC emissions, and dry deposition all depend on LAI in some way. By default, GEOS-Chem uses a MODIS-derived monthly LAI product (Myneni et al., 2007) that is mapped to the GEOS-Chem grid (year-specific or a climatology), and linearly interpolated to daily values.

Here we document the development of a land use module to describe land–atmosphere exchange in GEOS-Chem and to facilitate simulations involving changes in land cover and land use, such as the tree mortality being explored here.

To increase the flexibility in the BVOC emissions, basal emission factors are now mapped at simulation initialization using input land cover data. As a base input, we use present-day (year 2000) land cover from the Community Land Model (CLM) v. 4 (http://www.cgd.ucar.edu/tss/clm/ and Lawrence et al., 2011). Vegetation is divided into 16 plant functional types (PFTs, Table A1 in the Appendix) and their fractional coverage is mapped globally at a native resolution of 0.23∘×0.3125∘. We incorporate updated emission factors following MEGAN v2.1 (Guenther et al., 2012).

We also eliminate the dependence of the dry deposition velocities on the Olson Land Map. Instead, the same PFTs that drive BVOC emissions are mapped directly to the 11 deposition types from Wesely (1989). We replace the roughness heights provided by the assimilated meteorological product with values that are specific to the land cover or plant functional type (Table A1). Furthermore, rather than basing dry deposition on the dominant land type at a certain native resolution, the complete sub-grid fractional coverage of all PFT/land types are accounted for. In this way, deposition in the model should be less dependent of the horizontal resolution of the simulation or land cover data set. For soil NOx emissions, we map the same set of PFTs to the 24 biomes of Steinkamp and Lawrence (2011) based on plant type and latitude (Fig. A1).

To achieve consistency between our land type description and the LAI used in the model, we replace the monthly MODIS-derived gridded LAI with the sub-grid PFT-specific monthly LAI from the CLM4 land cover description, also based on MODIS observations as well as additional cropping data (Lawrence et al., 2011).

In this way, BVOC emissions, soil NOx emissions, dry deposition, and surface roughness are all newly harmonized to the same land cover input and vegetation density. These changes make it possible to alter the specified PFT distributions and/or fractional coverages, and self-consistently investigate the impact on biosphere–atmosphere exchange.

Our modifications to GEOS-Chem impact the emissions, deposition, and simulated concentrations compared to the default model, demonstrating the important role of land cover on atmospheric chemistry. GEOS-Chem and other chemical transport models have previously shown a large sensitivity to land cover data sets (Li et al., 2013) and biogenic emission models (Fiore, 2005; Kim et al., 2014; Zare et al., 2012). Globally, we find annual emissions of isoprene decrease by 14 % from 531 to 459 Tg yr-1 with land use harmonization and updated emission factors. The emissions of some monoterpenes decrease (e.g., β-pinene, limonene, sabinene, and carene by 10 % or less; ocimene by 36 %), while others increase significantly (α-pinene by 64 %, myrcene by 145 %). Sesquiterpene emissions increase between 20 and 60 % depending on the species. These changes result from the new maps of PFTs, the updated emission factors from MEGAN v2.1 (Guenther et al., 2012), and the new LAI values used. Our modified global emissions are generally consistent with those for MEGAN v2.1 as formulated by Guenther et al. (2012). For example, our α-pinene emissions increase from 40 to 66 Tg yr-1, compared to 66 Tg yr-1 estimated by Guenther et al. (2012). Global soil NOx emissions, which depend on biome mapping from the PFT data set and LAI, decrease by 2 % compared to the default model (from 9.8 to 9.6 Tg yr-1).

Figure 1 shows how all of the modifications impact predicted global monthly mean O3 concentrations for August 2010. The spatial agreement between the simulations is very high (r=0.99), suggesting that our modifications have not resulted in significant changes to predicted O3. While the changes that we made to the model were not in principle intended to improve the accuracy of the GEOS-Chem O3 simulation (rather the priority was to more easily enable land-cover change experiments), the updated land cover data and the new consistency in the descriptions modestly improve the spatial correlation (from r= 0.54inthedefaultmodeltor= 0.56 in the modified model) between the simulated and gridded monthly mean O3 observed over North America, Europe, and other locations worldwide (Evans and Sofen, 2015) for the whole year. The modifications tend to decrease the high O3 concentrations at midlatitudes of the Northern and Southern hemispheres. In particular, the high summer bias in monthly mean O3 drops by 0.5–0.9 ppb (e.g., from RMSE =15.6–14.8 in August) while making little difference to winter month O3 (RMSE changed by < 0.3 ppb).

Figure 3 shows the simulated emissions of isoprene, total monoterpenes, and total sesquiterpenes, and the change in emissions due to tree mortality. The impact to total emissions across the US is a 6–7 % decrease for isoprene, monoterpenes, and sesquiterpenes, with much larger impacts locally. Over the continental US, isoprene emissions are projected to decrease by more than 5 % for more than 25 % of the model grid boxes (762 out of a total of 2693). The highest relative impact to isoprene emissions occurs in the Rocky Mountain forests of the northwestern US, where mortality is projected to be high. For example, the largest relative decrease occurs in Idaho [46.0∘ N, 115.3∘ W] where isoprene emissions decrease by 47 % (1.8 µmol m-2 h-1), compared to the base simulation. These pine-, spruce-, and fir-dominated forests of the northwest are relatively low isoprene emitters compared to the deciduous forests of the eastern US. The reduction in mean OH reactivity due to tree mortality-induced isoprene changes in the northwest is ∼0.2–0.5 s-1 at most. In the oak-dominated Ozarks of Arkansas and Missouri [∼36∘ N, 92∘ W], and the central Appalachian region [∼38∘ N, 81∘ W], baseline isoprene emissions are an order of magnitude higher; the corresponding reduction in mean OH reactivity due to tree mortality-induced isoprene changes exceeds 3 s-1. The highest absolute impact of mortality on isoprene emissions occurs at the border of West Virginia and Virginia [38.0∘ N, 80.0∘ W], where emissions decrease by 8.6 µmol m-2 h-1 (relative decrease of 26 %).

Likewise, the highest relative impacts to total monoterpene and total sesquiterpene emissions occur in the Rocky Mountain forests of the western and northwestern US (the largest relative decrease occurs in Colorado [38∘ N, 106.7∘ W] where the monoterpene and sesquiterpene emissions decrease by 48–50 %). Significant relative impacts are also predicted in the pine forests of the Sierra Nevada (10–20 %). In terms of absolute magnitude, the difference in monoterpene and sesquiterpene emissions is largest in pine-dominated forests of the southern US. The highest absolute impacts in the country occur in eastern Texas [31.0∘ N, 94.7∘ W] where the monoterpene emissions decrease by 1.4 µmol m-2 h-1 (or 22 %), and in Arkansas [33.5∘ N, 92.7∘ W] where sesquiterpene emissions decrease by 0.4 µmol m-2 h-1 (or 18 %) compared to the base simulation.

Figure 4 shows the baseline emissions of NOx from soils and the simulated change resulting from tree mortality. The highest soil NOx emissions occur in the central US where crops contribute significantly to the land cover. Soil NOx emissions are also appreciable in the needleleaf evergreen forests of the northwest and southern US. These forests map to biomes with high NOx emission factors (about four times greater than for deciduous biomes), resulting in baseline emissions approaching several µmol m-2 h-1. The relative impact of tree mortality on soil NOx emissions exceeds 10 % in some of these areas (the largest relative difference occurs in western Montana [46∘ N, 115.3∘ W] where soil NOx emissions increase by 15 %). In projecting changes to soil NOx emissions, we allow the canopy reduction factor to respond to changes in LAI, but we assume that the tree mortality does not impact the basal soil NOx emission factors (nor soil temperature or moisture). The increase in net emission therefore arises from a decrease in canopy reduction factor only, representing the impact of less NO2 uptake by the canopy before export into the lower mixed layer. A better understanding of the canopy reduction factor, and accounting for canopy chemistry, would facilitate a more thorough assessment of these projected increases in soil NOx emissions.

Figure 5 shows the dry deposition velocity of O3 in the baseline scenario, and the simulated change resulting from tree mortality. In the northeast, where deciduous forests dominate and vegetation is dense, O3 deposition velocities are highest (0.6–0.7 cm s-1), whereas the deposition velocity over needleleaf forests is lower (0.3–0.4 cm s-1). Lowest deposition velocities occur over the arid and sparsely vegetated regions of the country. Where projected tree mortality is high, O3 deposition velocity decreases by up to 0.08 cm s-1 due to reduced stomatal uptake and change in roughness height. The highest absolute impact occurs in the eastern US, along the border of Virginia and West Virginia [38.0∘ N, 80.0∘ W]. On a relative basis the impact is largest in the northwest (deposition velocity in northern Idaho [47.5∘ N, 116.0∘ W] decreases by 16 %, or 0.06 cm s-1). Spatially, the impact on the deposition velocity for other constituents is similar. For example, the deposition velocity of HNO3 (which is largely limited by aerodynamic resistance instead of surface resistance as in the case for O3) also decreases in the same regions due to the change in roughness heights in the tree mortality scenario. In this case, decreases in HNO3 deposition velocity exceeding 20 % are predicted in the northwest and eastern US.

Figure 6a shows the June-July-August mean surface O3 concentrations simulated in the base scenario (Simulation 1). The high concentrations in the western US are consistent with previous work and are a consequence of the elevation and the dry climate resulting in a deep boundary layer and slow deposition velocities (Fiore et al., 2002; Wu et al., 2008). High concentrations are also simulated in the eastern US. Figure 6b shows the change in simulated O3 concentrations as a result of changes in BVOC and soil NOx emissions in a tree mortality scenario where deposition is calculated using baseline land cover (Simulation 3 – Simulation 1). Changes in soil NOx emissions alone increase O3 slightly (Simulation 3 – Simulation 2), but this response is an order of magnitude smaller (or less) than the response to decreased BVOC emissions. The result is a net decrease in O3 on the order of 0.2–0.4 ppb across a large area of the eastern US and in parts of the northwest and California. The largest change occurs in eastern Texas [32.5∘ N, 94.7∘ W] where mean O3 decreases by 0.44 ppb. Concentrations increase slightly over the Ozarks of Arkansas and Missouri [∼36∘ N, 92∘ W] and the Appalachian region in West Virginia [∼38∘ N, 81∘ W].

Figure 6c shows the simulated change in surface O3 due to tree mortality including the impact of changes to dry deposition (Simulation 4 – Simulation 1). The increase in concentrations due to slower deposition velocities counteracts the decrease in O3 concentrations that result from changes in BVOC emissions alone. In some regions these influences are predicted to be roughly equal so that the net change in O3 is close to zero. However, in many parts of the country including the northeast (e.g., Vermont, New Hampshire, and Maine), and the northwest (northern Idaho and western Montana), the predicted change in deposition is large compared to the change from emissions alone, resulting in net increases to O3 approaching 1 ppb or greater. Over the central Appalachian region (most notably West Virginia) and Ozarks the predicted change including dry deposition is also very large compared to the small increase from emissions alone. The highest increase in O3 occurs at the tristate intersection of Kentucky, West Virginia and Virginia [37.5∘ N, 82.0∘ W], where mean O3 is 1.4 ppb higher than in the base simulation. The substantial effect of slower dry deposition underscores the importance of understanding canopy deposition and the potential impact of canopy processes on chemical losses in predictions of land cover change impacts. Given the influence of changes in dry deposition in our simulations, exploring the uncertainties in dry deposition calculations should be a priority for model development.

Since regions where the impact on tree cover is largest are heavily forested and removed from pollution sources, they tend to have relatively low NOx concentrations. In such situations, O3 production is expected to be NOx-limited so that decreases in VOC emissions weakly impact O3 formation. This is the case over the central Appalachian and Ozarks regions, where NOx concentrations are below 1 ppb and BVOC emissions decreased by 10–20 %, but where O3 is minimally impacted in the scenario with altered emissions only (Fig. 6b). In these forest environments, the change to dry deposition velocity will be the dominant mechanism impacting O3 concentrations, and indeed we find that O3 increases when all mechanisms are considered (Fig. 6c). On the other hand, in high-NOx (or polluted) regions, O3 production can be expected to be more sensitive to changes in VOC emissions, and since these areas tend to be more developed, deposition plays a smaller role. As a result, in the scenario considering only changes in emissions we find that the predicted impact to O3 concentrations is relatively large in the heavily populated regions along the coast of the mid-Atlantic (Fig. 6b, ∼40∘ N, 74∘ W).

In general, we find that the ratio of NOx to VOC concentrations (ppb NOx/ ppb C) in a grid box can explain some of the O3 response to changes in tree cover across the US, despite varying degrees of predicted land cover change. Figure 7 shows histograms of the change in surface O3 concentrations for two populations of grid boxes that had changes in isoprene emissions of at least 0.1 µmol m-2 h-1 (N=1115 grid boxes from a total of 2693 grid boxes in the continental US). These two distributions (N=111 in both) are grid boxes with the lowest 10 % NOx : VOC concentrations in the base scenario, and grid boxes with the highest 10 % NOx : VOC concentrations in the base scenario. These distributions are statistically different (p< 0.001, Wilcoxon rank-sum test), and represent the general pattern of impact on “clean” and “polluted” regions respectively. The top panel displays results based on the scenario where only biogenic emissions change (Simulation 3 – Simulation 1). Grid boxes with the highest NOx to VOC ratios tend towards stronger changes in O3 concentrations than the grid boxes with lowest NOx to VOC ratios. This suggests more generally that in addition to the extent of land cover change, the impacts of tree mortality on O3 can depend on whether the conditions are NOx-limited (low NOx : VOC) or VOC-limited (high NOx : VOC). This NOx-dependence of the regional chemistry impacts resulting from land system changes has also been identified by Wiedinmyer et al. (2006) and Hardacre et al. (2013) for example. The bottom panel displays the results based on the scenario where changes to biogenic emissions and dry deposition are accounted for (Simulation 4 – Simulation 1). Here we find that the change in O3 is more frequently positive (increasing O3 compared to the base scenario) in the low NOx : VOC grid boxes, since the deposition response tends to be large compared to the impact of emissions. In contrast, while slower deposition counteracts some of the decrease in O3 concentrations in the more polluted grid boxes, the net impact remains largely negative (decreasing O3 compared to the base scenario).

The changes in monthly mean ozone mask even larger impacts on shorter timescales (hours) that may be of importance to air quality standards. The magnitude of the impact on surface O3 in the scenario that considered changes to both emissions and deposition is highest during the day and less significant at night due to the diurnal pattern of chemical O3 production and biogenic emissions, and to the strong dependence of modeled deposition velocities on time of day. As a result, the number of days with O3 above a specific threshold changes in many locations depending on the land cover scenario. We consider for example daily maximum 8 h averages. The EPA has recently revised the O3 air quality standard to be based on 8 h averages exceeding a threshold of 70 ppb instead of the previous 75 ppb (http://www3.epa.gov/ttn/naaqs/standards/ozone/s_o3_index.html), so we investigate the number of days during June-July-August in each grid box of the US where the 8 h average O3 exceeds 70 ppb. In the scenario considering only a change in emissions (Simulation 3 – Simulation 1), the number of days exceeding an 8 h O3 concentration of 70 ppb decreases in 16 % of the grid boxes in the lowest NOx : VOC decile (“clean” regions of the US), and in 45 % of the grid boxes in the highest NOx : VOC decile (“polluted” regions of the US). Across the US, the number of exceedances decreases by 4 or more days in several regions such as central South Carolina (34.0∘ N, 81.3∘ W), central Kentucky (37.5∘ N, -86.0∘ W), central Indiana (38.5∘ N, -90.7∘ W), northern Ohio (41.5∘ N, 83.3∘ W), and southwest Michigan (42.0∘ N, 71.3∘20 W). In the scenario considering both the change in biogenic emissions and also the change to deposition rates (Simulation 4 – Simulation 1), many grid boxes experience a net increase in the number of days exceeding an 8 h O3 concentration of 70 ppb. The increase impacts clean regions disproportionately (30 % of lowest NOx : VOC grid boxes) compared to polluted regions (5 % of high NOx : VOC grid boxes). The largest increase is 4 days, which occurs north of Richmond, VA (38.0∘ N, 77.3∘ W). In the same scenario, less than 1 % of the low NOx : VOC grid boxes experiences a decrease in the number of days exceeding an 8 h O3 concentration of 70 ppb, compared to 26 % of the high NOx : VOC grid boxes.

Figure 8 shows the mixing ratios of reactive nitrogen oxides in the base scenario (Simulation 1), and the simulated changes resulting from tree mortality (Simulation 4 – Simulation 1) on a relative scale (% change). We plot the results for the sum of all reactive nitrogen oxides (NOy, Fig. 8a), in addition to the individual contributions from NOx (Fig. 8c), HNO3 (Fig. 8e) and the sum of all alkyl-, peroxy-, and acylperoxy-nitrates (or “organic nitrates”, Fig. 8g). We find that the relative impacts on NOy and its partitioning as a result of the tree mortality could be locally significant, and are a complex result of all three mechanisms (changes in BVOC emissions, changes in soil NOx, and changes to the deposition velocities), depending on the chemical species. Total NOy increases by up to 8 % in the northwest (the largest relative increase of 120 ppt is along the Idaho-Montana border [47.5∘ N, 115.3∘ W]). The increases here consist of roughly equal increases in NOx (79 ppt) and HNO3 (66 ppt) mixing ratios with a smaller decrease in organic nitrates (29 ppt). Over the rest of the country, changes in total NOy are small, in part because the increases in NOx and HNO3 are counteracted by decreases in organic nitrate species. Significant changes in NOx abundance and NOy partitioning could alter the transport and removal of O3 precursors, and alter the peroxy radical chemistry involved in O3 production.

We find that the increases in NOx are largely a result of elevated soil NOx emissions (Simulation 3 – Simulation 1). On the other hand, the increases in HNO3, which are up to 18 % on a relative scale, are due to both slower deposition and increasing soil NOx emissions (Simulation 4 – Simulation 1). Small increases in HNO3 (locally up to 3–4 %) are also observed in the BVOC emissions only scenario (Simulation 2 – Simulation 1). Broad decreases in the organic nitrate concentrations (approaching 10 %) are found across large parts of the country. This result is nearly entirely due to the reduction in BVOC emissions alone, with only a small counteracting effect of lower deposition velocities. For example, where the relative impact was largest (a 10 % decrease near Missoula MT [47∘ N, 114.7∘ W]), the decrease from the BVOC emissions alone is 36 ppt, while the decrease after accounting for dry deposition and soil NOx emissions is 29 ppt.

Figure 9a shows the predicted biogenic SOA (BSOA) surface mass concentrations in the base simulation (Simulation 1). The dominant contributors to BSOA over the United States in these simulations are terpenes, consistent with the results of Pye et al. (2010). This results from nitrate radical oxidation included in the SOA mechanism, since terpenes are emitted at night (in addition to during the day) and model aerosol yields from nitrate oxidation are relatively high. The baseline simulation predicts BSOA greater than 3 µg m-3 throughout most of the southeast US, approaching 10 µg m-3 near the Mississippi–Alabama and Missouri–Arkansas borders. Biogenic SOA contributes 80 % or more of the modeled total OA mass concentration in this region and season. In parts of the northeast and on the west coast, BSOA can also exceed 3 µg m-3 and the model predicts the biogenic contribution to total organic aerosol to exceed 50 % there. In the northwest, BSOA approaches 1–2 µg m-3.

Figure 9b shows the change in BSOA predicted due to tree mortality (Simulation 4 – Simulation 1). In contrast to O3 and NOy species (where the relative importance of deposition and chemical production could vary), the simulation predicts consistent decreases in BSOA from the tree mortality scenario as a result of decreasing BVOC emissions. The change in atmospheric lifetime as a result of slower dry deposition is negligible (Simulation 4 – Simulation 3). Across the eastern US, BSOA decreases by 5–10 %. The relative impacts are highest where terpene emissions are significant and projected tree mortality is high due to the dominance of terpenes as precursors to BSOA in these simulations. The impact on BSOA due to tree mortality generally exceeds 10 % where the contributions of terpene emissions represent 50 % or more of total BVOC emissions (in mass carbon). The spatial pattern in ΔBSOA corresponds most to the relative contribution of the lumped MTPA category of terpenes (α-pinene +β-pinene + sabinene + carene). In some parts of the southeast the change exceeds 25 % (1–2 µg m-3 in terms of absolute mass). The largest absolute impact occurs in southern Arkansas [33.5∘ N, 92.7∘ W], where BSOA decreases by 2.0 µg m-3 (or 20 %). The relative impact is also high in the northwest, where BSOA decreases by 0.5–1 µg m-3 (the highest relative difference of 39 % occurs in northern Idaho [46.0∘ N, 115.3∘ W]).

Given the dominance of BSOA in much of the US, these changes appreciably impact total OA (and consequently total aerosol mass). Relative impacts to the sum of all organic aerosol are on the order of 20 % or greater in many parts of the south, northwest, and northern California. These simulations suggest that tree mortality and the concomitant change in biogenic emissions has the potential to impact background aerosol levels by up to 2 µg m-3 in some regions. This may be of particular relevance to the EPA Regional Haze Program, aimed at improving visibility in national parks and wilderness areas (http://www3.epa.gov/visibility/program.html).