An Evaluation of O 3 Dry Deposition Simulations in East Asia Printer-friendly Version Interactive Discussion Atmospheric Chemistry and Physics an Evaluation of O 3 Dry Deposition Simulations in East Asia an Evaluation of O 3 Dry Deposition Simulations in East Asia Printer-friendly Version Interactiv

Discussions This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Abstract We used a 3-D regional atmospheric chemistry transport model (WRF-Chem) to examine processes that determine O 3 in East Asia; in particular, we focused on O 3 dry deposition, which is an uncertain research area due to insufficient observation and numerical studies in East Asia. Here, we compare two widely used dry deposition pa-5 rameterization schemes, Wesely and M3DRY, which are used in the WRF-Chem and CMAQ models, respectively. The O 3 dry deposition velocities simulated using the two aforementioned schemes under identical meteorological conditions show considerable differences (a factor of 2) due to surface resistance parameterization discrepancies. The O 3 concentration differed by up to 10 ppbv for the monthly mean. The simulated 10 and observed dry deposition velocities were compared, which showed that the Wesely scheme model is consistent with the observations and successfully reproduces the observed diurnal variation. We conduct several sensitivity simulations by changing the land use data, the surface resistance of the water and the model's spatial resolution to examine the factors that affect O 3 concentrations in East Asia. As shown, the model 15 was considerably sensitive to the input parameters, which indicates a high uncertainty for such O 3 dry deposition simulations. Observations are necessary to constrain the dry deposition parameterization and input data to improve the East Asia air quality models.


Introduction
Ozone (O 3 ) is a harmful air pollutant in surface air and the primary chemical oxidation driver in the free troposphere, and O 3 tropospheric concentrations are largely determined by the balance between net chemical production, influx from the stratosphere and physical-loss processes (Wu et al., 2007).O 3 dry deposition is a dominant physical loss process and accounts for approximately 25 % of the total O 3 lost in the troposphere (Lelieveld and Dentener, 2000).Introduction

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Full In typical chemical transport models, dry deposition is calculated as a first-order process that uses dry deposition velocity, which is parameterized as a function of surface type and atmospheric stability conditions (Wesely, 1989).However, in models, its parameterization is highly uncertain due to complexities from surface conditions at sub-grid scales (Wu et al., 2011).Thus, previous studies on dry deposition calculations have primarily focused on the United States and Europe, for which observations on ozone fluxes or dry deposition velocities were available to validate either simulated O 3 losses or dry deposition velocity parameterization (Charusombat et al., 2010;Gerosa et al., 2007;Rannik et al., 2012;Wu et al., 2011).
East Asia (China, Japan, and Korea) has recently experienced rapid economic growth, during which anthropogenic emissions have increased and deteriorated air quality (Ohara et al., 2007).Thus, air quality model use has also gradually increased in East Asia to understand the air pollutant spatial and temporal distributions and examine the impact of the increased anthropogenic emissions on air quality degradation for East Asian countries.A critical role for such models includes quantifying the regional air quality sources, including trans-boundary transport of air pollutants and their precursors in East Asia.In this context, the dry deposition simulation is important for accurately assessing the contribution from a source to regional air pollutant concentrations.
However, air quality model evaluations have been relatively limited due to the lack of long-term regional observations in East Asia.In particular, evaluating individual processes, including dry deposition calculations, has not been rigorous for East Asia.Several O 3 dry deposition simulation studies have been conducted for a tropical forest in Southeast Asia (Matsuda et al., 2005(Matsuda et al., , 2006)), but the vegetation type differs from East Asia.
The purpose of this study is to evaluate the O 3 dry deposition simulations (schemes) in the most widely used regional air chemistry models in East Asia: the Weather Research and Forecasting-Chemistry (WRF-Chem) and the Community Multiscale Air Quality (CMAQ) models.We conducted multiple model simulations to understand the Introduction

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Full differences between the two models as well as two different dry deposition schemes and factors that affect dry deposition and O 3 concentrations in East Asia.We also evaluated the O 3 concentration and dry deposition velocity model results by comparing such results with observations to constrain the model simulations.Finally, several sensitivity simulations were performed using different input datasets to demonstrate the models' uncertainty, which should be considered in assessing O 3 spatial and temporal distribution and contribution from a source to the a particular region, including trans-boundary transport of its precursors in East Asia.

General description
We used the WRF-Chem model (version 3.3) to simulate O 3 in East Asia.The model is a fully coupled meteorology-chemistry model, which was developed by the National Center for Atmospheric Research (NCAR) (Grell et al., 2005) to account for the interaction between meteorological and chemical processes at each time step (Chapman et al., 2009).The model is described in detail elsewhere (Grell et al., 2005).Herein, we primarily describe our model simulations.
The model has the horizontal resolutions 45 km × 45 km with 14 eta vertical grids and a 50 hPa top.The model domain for our simulations is shown in Fig. 1, which includes the nested grid domain that focuses on the Korean peninsula.For meteorology simulations, we used physics modules in the WRF, as shown in Table 1.In particular, turbulent mixing at the surface and within the planetary boundary layers was calculated using schemes developed by Chen andDudhia (2001) andHong et al. (2006), respectively.
We used anthropogenic emissions from the Sparse Matrix Operator Kernel Emissions-Asia (SMOKE-Asia), which was developed by Woo et al. (2011) to operate the CMAQ model (Byun and Ching, 1999)  Full anthropogenic emissions based on the Carbon Bond 05 (CB05) chemical mechanism (Appel et al., 2007), which slightly differs from the Carbon Bond mechanism Z (CBz) used in WRF-Chem.We used the chemical mapping in Table 2 to match the emission species between CB05 and CBz.A few species do not precisely correspond between the two schemes, but such species are relatively unimportant for our O 3 simulations below.The total NO x , CO, and VOC emissions in the domain are 24.6 Tg yr −1 , 150.2 Tg yr −1 , and 96.0 Tg yr −1 , respectively.The initial and lateral boundary conditions for the meteorology simulations were determined using a WRF preprocessing system with the NCEP Final Operational Model Global Tropospheric Analyses data (National Centers for Environmental Prediction, 2000).Climatological values were used to generate the initial and boundary values for the chemical species concentrations (Grell et al., 2005).

Dry deposition parameterization
Chemical species loss due to dry deposition in air chemistry models is typically computed as a first-order process, the dry deposition velocity for which is parameterized as shown in Eq. (1).
v d indicates the dry deposition velocity, and C represents the species concentrations in the lowest model layer.Therefore, the species lost to dry deposition is directly proportional to the dry deposition velocity, which is also parameterized in such models.
The dry deposition velocity is computed as the reciprocal of the sum for aerodynamic resistance (R a ), quasi-laminar resistance (R b ), and surface resistance (R c ) as follows: Therefore, in Eq. ( 2), the resistance with the largest value is the most important factor that determines dry deposition velocity.Generally, surface resistance is the largest Introduction

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Full among the three resistances, and it determines the dry deposition velocity (Erisman et al., 1994); we will discuss the surface resistance formulation in Sect.2.3.
Here, we compare two widely used dry deposition schemes: the Wesely and M3DRY schemes.The first scheme was developed by Wesely (1989) and is used in WRF-Chem as the default method (hereinafter, the Wesely method).The latter scheme was proposed by Pleim et al. (2001) and is used as a default scheme in CMAQ; it is a part of the meteorological transport module Meteorology-Chemistry Interface Processor version 3.3 used in CMAQ, (Otte and Pleim, 2010) (hereinafter, M3DRY).We implemented M3DRY in WRF-Chem to examine the O 3 simulation sensitivity for the two different dry deposition schemes using identical input data.We found that both schemes use fairly similar parameterization for the aerodynamic and quasi-laminar resistances, but their surface resistance parameterization differed considerably, as discussed below.

Surface resistance parameterization
The surface resistance represents the surface uptake of chemical species and depends on the surface chemical and physical characteristics.As the surface resistance decreases, surface uptake of chemical species increases.The surface resistance can be further classified through four specific resistances: the stomata • mesophyll resistance (R sm ), cuticle resistance (R cut ), in-canopy resistance (R inc ), and ground resistance (R gnd ).The first three are related to physical and chemical characteristics of vegetation, and the last resistance is related to ground conditions.The four resistances combine in parallel to yield the surface resistance, as follows: Therefore, the resistance with the smallest value largely determines the surface resistance.Typically, the stomata • mesophyll and ground resistances are the smallest.
The stomata • mesophyll resistance is related to vegetation photosynthetic activity, and Introduction

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Full thus, it is a function of solar radiation.During the day, the stomata • mesophyll resistance substantially decreases, and it has the smallest value among the four, although its diurnal variation differs depending on the vegetation type.However, at night, its value becomes higher than the ground resistance, which plays a key role in determining surface resistance without solar radiation.In models, the four resistances shown in Eq. ( 3) are calculated using complex parameterizations; a detailed discussion on this subject is beyond the scope of our work.
We conducted two WRF-Chem simulations for March-May 2004 in East Asia using the two dry deposition schemes, Wesely and M3DRY.Identical boundary and initial conditions were used for the model, including species emissions, except for the dry deposition scheme.Therefore, the differences in the results are entirely due to the dry deposition calculation differences from each method.The model simulation for March-April was used for spin-up, and we primarily focused our analysis on the results for May here and elsewhere.

Observations
We used observations from the Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H 2 O, Organics, and Nitrogen-Rocky Mountain Organic Carbon Study (BEACHON-ROCS) campaign conducted at the Manitou forest observatory in the United States by NCAR 7-31 August 2010.Details on this campaign are at the following website (https://wiki.ucar.edu/display/mfo/Manitou+Forest+Observatory).We used the gradient method from Tsai et al. (2010) to generate the measured O 3 dry deposition velocity, as shown below.We first estimated ozone flux as a product of the friction velocity and the ozone eddy concentration.The ozone eddy concentration (c * ) can be calculated using Eq. ( 4) as follows:

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Full where k is the von Karman constant, and ∆c represents the ozone concentration difference between two different observation levels, z 1 (12 m) and z 2 (25 m).d 0 is the zero-plane displacement height, L is the Monin-Obukhov length, and integrated stability function (Ψ h ) is from Businger et al. (1971).After calculating the ozone flux, the dry deposition velocity was calculated by dividing the ozone flux by the ozone concentration at level 2 (z 2 ).Herein, we only considered the measured dry deposition velocities in the range 0 to 2.0 cm s −1 , which is a typical O 3 dry deposition velocity range in the literature (Padro, 1996).The variation in zero-plane displacement height (d 0 ) can generate a large uncertainty that is proportional to the vegetation height (15 m at the Manitou forest observatory).This variation can be accounted for by applying linear coefficients that range from 0.55 to 0.78 for the vegetation height (Garratt, 1994;Lovett and Reiners, 1986;Perrier, 1982).We computed a range of measured dry deposition velocities with minimum and maximum linear coefficients.We also used O 3 dry deposition velocities directly measured using the eddy covariance method at a Niwot Ridge AmeriFlux site in the Roosevelt National Forest in the Rocky Mountains of Colorado (Turnipseed et al., 2009).Details for this site are at the following website: http://ameriflux.ornl.gov/fullsiteinfo.php?sid=34.
To evaluate the O 3 simulation, we used surface air O 3 observations at sites from the National Institute of Environmental Research (NIER) in Korea and from the Acid Deposition Monitoring Network in East Asia (EANET, http://www.eanet.asia).The Korean sites are primarily located in polluted urban regions, including Seoul, the capital of South Korea, and Pusan, the second largest city in South Korea, whereas the EANET sites are primarily in islands, rural regions and mountains and were selected to avoid a direct influence from local pollution (Fig. 3).Despite the observation sites in China, O 3 observations are not available to the public, which limits our discussion on observed O 3 spatial patterns.Therefore, we primarily focused on the downwind regions of the continental pollution outflow, which was successfully used in a previous analysis during the TRACE-P campaign to chemically characterize East Asia (Jacob et al., 2003).Introduction

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Full The observations were averaged over the model grid boxes for comparison with the model.

O dry deposition velocity spatial and diurnal patterns in East Asia
Figure 1 compares the calculated monthly mean O 3 dry deposition velocities using the Wesely and M3DRY schemes for East Asia.The values are typically high on the continent relative to the ocean, which reflects the decrease in surface resistance due to vegetation.However, as shown in Fig. 1c, we found substantial differences in calculated dry deposition velocities between the two methods.The Wesely method typically yields in greater values compared with M3DRY due to the lower surface resistances in the Wesely method.We also used the two methods to compare the calculated stomata • mesophyll and ground resistances, which are two of the most important factors that affect surface resistance (not shown).We found that the Wesely method typically yields lower values for both the stomata-mesophyll and ground resistance compared with M3DRY.As a result, the Wesely method dry deposition velocity is greater than for the M3DRY method both during the day and at night.The dry deposition velocity domain mean difference between the two methods is 0.14 cm s −1 , which is 1.4 greater than the M3DRY method domain mean dry deposition velocity (0.10 cm s −1 ), implying a more rapid O 3 dry deposition loss with the Wesely method.We evaluated the dry deposition velocities calculated using the two schemes by comparing such values with the observations and primarily focusing on the diurnal variability.The observation values were acquired from the BEACHON_ROCS and Niwot Ridge AmeriFlux sites.Figure 2 compares the hourly measured and simulated O 3 dry deposition velocities using the Wesely and M3DRY methods.The measured values at the BEACHON_ROCS site were greatest in the early morning and decreased toward the afternoon, which reflects the friction velocity diurnal variation that depends on solar radiation.The measured values from the AmeriFlux site also show similar diurnal variation; the greatest values were from the afternoon.The simulation results from Introduction

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Full the Wesely method were consistent with the observations and reproduced the diurnal variation, whereas the M3DRY method results were significantly lower than the observations.As discussed above, during the day, the stomata resistance was the most dominant factor for determining the dry deposition velocity and was better resolved in the Wesely method than the M3DRY method.
Our comparison in Fig. 2 shows that the Wesely method is better than M3DRY.Although this result is based on a comparison from the United States, we believe that this evaluation can be applied to East Asia.The Manitou forest observatory is a ponderosa pine plantation in the middle of shrub land (Kim et al., 2010), which is prevalent in East Asia, especially in the middle of China, as shown in Fig. 5a.We acknowledge that our evaluation is limited to a single type of vegetation, which does not represent East Asia in its entirety.In-situ O 3 dry deposition velocity measurements are critical and necessary for enhancing our understanding of O 3 loss and modeling capability for East Asia.

O 3 concentration spatial and diurnal patterns in East Asia
Figure 3 shows the observed and simulated monthly mean O 3 concentrations in the surface air over East Asia in May 2004 using the Wesely and M3DRY schemes.The observed values show a spatial gradient, wherein values at polluted urban sites in Korea are lower than at clean rural sites in Japan.O 3 loss through high NO emissions from automobiles in large megacities explains this observed spatial pattern with low values in Korea.
The simulated O 3 concentrations also show a clear spatial gradient, which is high over the downwind ocean and relatively low over the continent, where its precursor emissions are generated.Generally, the model reflects the observed spatial pattern with low values in Korea and high values in Japan.
However, the most striking feature is that the simulated values from the Wesely scheme are lower than from the M3DRY scheme.The O 3 difference between the two Introduction

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Full methods is up to 10 ppbv for the monthly mean and 4.7 ppbv averaged over the entire domain (Fig. 3c).The largest differences are in the Yellow Sea and northwestern Pacific, wherein the O 3 loss through dry deposition is weaker relative to the continent, as shown in Sect.3. The largest dry deposition velocity differences that we calculated were on the continents; however, the O 3 concentrations differences were greatest for the downwind ocean (Fig. 3).These data indicate that O 3 efficiently exports from the polluted continent to the downwind oceans, where O 3 accumulates due to relatively inefficient dry depositional loss.In addition, the O 3 differences over the ocean may partially be attributed to excessively high surface water resistance (low deposition loss) in the M3DRY scheme relative to the Wesely scheme, which is not clearly shown in Fig. 1.This issue is discussed in Sect. 5.
Figure 4 shows the hourly mean observed and simulated O 3 concentrations at the NIER sites in Korea and EANET sites in Japan.The values were computed by averaging the hourly data from individual sites.We also sampled the corresponding modelgrid values at individual sites for this comparison.The diurnal O 3 concentration variation differs between the two networks such that the observed O 3 concentrations in Korea show a strong diurnal variation, a peak in the afternoon and a minimum at night, which reflects direct influence from local pollution.However, such diurnal variation is less pronounced at the EANET sites.
The model reflects the observed temporal variation well.However, we also found considerable discrepancies between the model and observations.For example, at the NIER sites in Korea, the M3DRY method overestimated the observations by 4.4-17.1 ppbv.This high bias was lower when we used the Wesely method; although, the model did not reflect the lowest O 3 concentrations in the early morning.The lowest O 3 levels observed might be due to the chemical loss through NO during the early morning rush hour.We further examine this issue in Sect. 5.
The O 3 concentration values from the model are generally lower than the observed values at the EANET sites.This bias toward lower values is also exhibited by the model Introduction

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Full using both dry deposition schemes; although, the M3DRY scheme is more consistent with the observations.In general, the model and observation discrepancies are due to the model's inability to simulate steep sub-grid land-to-sea gradients at a mixing depth (Gao and Wesely, 1994) that is shallower over the ocean compared with the continent.Our model with 45 km × 45 km spatial resolution may not adequately reflect the shallow mixing depth at the EANET sites, which are near the sea.Although the model reflects certain observed features, using the comparisons in Figs. 3 and 4, it is difficult to determine the scheme with the best performance at reproducing the observed O 3 concentration spatial and temporal variations in East Asia.However, as discussed in Sect.3, the model using the Wesely scheme reproduced the observed dry deposition velocities better than M3DRY.Therefore, we used the Wesely scheme results for our subsequent analysis below, where we examined the model's sensitivity for other input parameters.

Surface-type uncertainty effects on the simulated O 3 concentrations
The dry deposition velocity spatial distribution closely resembles the land-use data, which indicates that the model is highly sensitive to land-use data.The land-use information from the United States Geological Survey (USGS) was used as the default for WRF-Chem.Here, we compare the USGS data with the MODIS land-use data (Friedl et al., 2002), which is widely used in meteorological research; we also explore the model sensitivity to the different surface-type specifications.First, the two datasets use a different coding system for denoting each surface type.For example, the USGS uses eight for the shrub land, whereas the MODIS uses six.The two data sets were difficult to compare due to the different coding systems.Therefore, as shown in Table 4, we developed a mapping table for the surface type using the two datasets, and this mapping information was used to implement the MODIS land-use data in the WRF-Chem simulations below.Introduction

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Full Figure 5 shows the USGS and MODIS land-use data.The color-coding system used for the individual surface types is consistent with the USGS coding system (Tables 3  and 4).In general, vegetation types identified by the two datasets are consistent for East Asia, but the datasets yield certain differences, especially for south China.One notable difference is that USGS classifies the Korean peninsula as a Savanna (code 10 in Table 3), which differs from the MODIS classification (mixed forest, code 5 in Table 4).The different surface-type classifications affect O 3 dry deposition in the model, and we examine the simulated O 3 sensitivity to the surface types below.
Figure 6 shows the dry deposition velocity and O 3 concentration differences in the model using two different surface-type datasets: MODIS and USGS.Here, we use the Wesely dry deposition scheme, for which the simulated dry deposition velocities were consistent with the observations and which was more sensitive to surface types than M3DRY.Therefore, the dry deposition velocity differences reflect the different surface-type classifications between the two datasets.Generally, we calculated lower dry deposition velocities for East Asia using MODIS compared with USGS.Southern China yields the greatest decrease, for which the surface type was changed from cropland/pasture, cropland/grassland mosaic, shrubland, and savanna to mixed forest (Fig. 5).The surface-type differences increased the surface resistances and, thus, decreased the dry deposition velocity.On the other hand, the calculations for Manchuria and Republic of the Union of Myanmar showed higher dry deposition velocities because the surface type was changed from mixed forest to cropland/pasture or evergreen broadleaf.
The dry deposition velocity changes significantly increased the O 3 concentration up to a 1.2 ppbv monthly mean in southern China and the downwind regions, including Korea, Japan and the north Pacific.The average O 3 concentration over the domain using the MODIS land-use data was slightly higher (by 1.3 %) compared with the USGS data.However, in the urban and industrialized regions, the O 3 increase from MODIS was greater (by 5.1 ppbv; 12.7 %) compared with the USGS data, which indicates that the surface-type classification is important for the O 3 simulations.Introduction

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Full Figure 7 compares the model results from the two different surface datasets with the observations.We found that the simulated O 3 concentrations were highly sensitive at NIER sites in Korea, where the surface type changes from Savanna to the mixed forest, urban and built-up land generates a 3.9 ppbv monthly mean increase, which is slightly worse for the model results compared with the observations.The model spatial resolution was too coarse to represent surface-type inhomogeneity in Korea at the NIER sites, which are primarily in urban regions.The surface-type sub-grid scale variability may also be a potentially important source for model uncertainty.On the other hand, the model shows minimal changes in O 3 at the EANET sites located near the sea.
We further examine sensitivity for the simulated O 3 at different surface water resistances using the dry deposition schemes.The Wesely scheme used 2000 s m −1 for the water resistance, which was lower than the M3DRY scheme water resistance (10 5 ∼ 10 6 s m −1 ).We conducted a model simulation using the Wesely scheme by switching the water surface resistance values from the Wesley to the M3DRY values.
Figure 8 shows the resulting dry deposition velocity and O 3 concentration differences from the model.We found that the dry deposition velocity increased up to 0.043 cm s −1 , which yielded an O 3 decrease as low as 8.7 ppbv in the ocean; this change is 76 % of the previous overall O 3 concentration difference between the two schemes for the ocean.Although the O 3 dry deposition loss is lower in the ocean compared with the continent, this result indicates that the model is highly sensitive to water surface resistance, which has an important implication for estimating long-range O 3 transport from a source to receptor.Finally, we conducted a nested model simulation using a finer spatial resolution (15 km) and focusing on the Korean peninsula to examine the effect of NO titration on O 3 concentrations in polluted urban cities. Figure 9  compared with the coarse model.We found the greatest reduction during the early morning, when the NO emission from rush hour traffic is greatest, as shown in Fig. 9b.However, the model's high bias for the early morning remains, which indicates that the nested model is still too coarse to represent the concentrated plume from traffic.

Conclusions
We used the WRF-Chem with two widely used dry deposition schemes (Wesely and M3DRY) to evaluate such schemes and examine the sensitivity of the simulated surface air O 3 concentrations to dry deposition calculations for East Asia.We found significant differences in O 3 concentrations up to 10 ppbv for the monthly mean, primarily due to the dry deposition velocity differences between the two schemes.The Wesely scheme generates 1.4 times greater dry deposition velocity values compared with M3DRY under identical meteorological conditions due to discrepancies in the surface resistance parameterization.Among the surface resistances, the mesophyll and stomata resistances were the most importance factors during the day, whereas the ground resistance was an important factor for the continent at night.In addition, the surface resistance of the sea can yield considerable O 3 concentration differences over the ocean.We compared the simulated dry deposition velocities with the observed dry deposition velocity from the BEACHON-ROCS campaign.The Wesely scheme reproduced the observed dry deposition velocity diurnal variation better than the M3DRY scheme.The Wesely scheme also reproduced the observed diurnal O 3 concentration variation at polluted urban sites in Korea but failed to reflect the observations at relatively clean sites in Japan, which indicates that other factors are important for O 3 simulations in East Asia, such as emission, chemistry, and meteorology.
We conducted several sensitivity simulations for the land-use data, water surface resistance and model spatial resolution to examine uncertainty in O 3 concentration simulations for East Asia.The model results showed considerable changes in the simulated O 3 concentrations, which suggests that the model is highly sensitive to such Introduction

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Full input data and the model resolution.Observations are necessary to constrain the dry deposition parameterization and input data and improve East Asia air quality models.
The roles of vegetation have primarily been discussed for reactive BVOC emissions and tropospheric photochemistry that enhances O 3 and SOA production in East Asia air quality research (e.g., Kim et al., 2013;Bao et al., 2011;Ran et al., 2011;Tie et al., 2013).Therefore, the comprehensive evaluation of dry deposition model schemes herein clearly indicates that deposition is also a critical physical process, which must be precisely constrained in regional and global air quality assessments because O 3 has tremendous implications for public health (Levy et al., 2001) and climate change (IPCC, 2007).In addition, a number of experimental studies have clearly suggested that a substantial level of unknown/unobserved reactive BVOCs may enhance nonstomatal O 3 dry deposition rates (Kurpius and Goldstein, 2003;Hogg et al., 2007); chemical processes should also be empirically evaluated and incorporated in regional models.Introduction

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Full  Full  Full  Full  3).We used the mapping information (Table 4) to illustrate the MODIS data.Introduction

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Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 .
Fig. 1.The O 3 monthly mean dry deposition velocities in East Asia for May 2004 from WRF-Chem using the Wesely (left) and M3DRY schemes (middle).The different values from the two simulations are shown in the right panel.

Fig. 6 .Fig. 7 .Fig. 8 .
Fig. 6.Differences in dry deposition velocity (left) and monthly mean O 3 concentration in the surface air (right) between the MODIS and USGS land-use data using the Wesely scheme for May 2004.
over East Asia.SMOKE-Asia calculates Introduction

Table 1 .
Physics options used in the WRF-Chem simulations.

Table 2 .
Species mapping using the CB05 and CBz chemical schemes.

Table 4 .
Land-use mapping using the 20-category IGBP-Modified MODIS and 24-category USGS schemes.