Development of the global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0: model description and evaluation

20 Chemistry plays an indispensable role in investigations of the atmosphere, however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed 25 tropospheric HOx-NOx-VOC-ozone-bromine-aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a threehttps://doi.org/10.5194/gmd-2019-240 Preprint. Discussion started: 28 October 2019 c © Author(s) 2019. CC BY 4.0 License.

prediction), and also represents an important step for the development of fully coupled earth system models (ESMs) in China. 75 Until recently, the offline GEOS-Chem CTM relied exclusively on fixed longitude-latitude grids and was designed for shared-memory (OpenMP) parallelization. With such features the GEOS-Chem CTM was not flexible to be coupled with BCC-CSM, which typically runs on spectral space (with adjustable options for the grid type and resolution dependent on the wave truncation number) and requires vast computational resources. Integration of GEOS-Chem 80 chemical module into CSMs has been enabled by separating the module (which simulates all local processes including chemistry, deposition, and emission) from the simulation of transport, and making it operate on 1-D (vertical) columns in a grid-independent manner (Long et al., 2015;Eastham et al., 2018). The GEOS-Chem chemical module can thus be coupled with a CSM on any grid, and the CSM simulation of dynamics then handles chemical transport. GEOS-Chem used as an online chemical module in CSMs shares the exact same code as the classic offline GEOS-Chem for local 85 processes (chemistry, deposition, and emission) (Long et al., 2015). This capability ensures that the scientific improvements of GEOS-Chem contributed from worldwide research community can be conveniently incorporated into CSMs, allowing the chemistry of BCC-GEOS-Chem to be trackable to the latest GEOS-Chem version. Previous studies have demonstrated the success of coupling GEOS-Chem into the NASA GEOS-5 Earth system model and more recently the Weather Research and Forecasting (WRF) mesoscale meteorological model as an online atmospheric chemistry 90 module (Long et al., 2015;Hu et al., 2018;Lin et al., 2020). This paper presents the overview of the BCC-GEOS-Chem v1.0 model, and evaluates the model simulation of presentday atmospheric chemistry. The model framework and its components are described in Section 2. We conducted a threeyear (2012-2014) model simulation to demonstrate the model capability and for model evaluation. In section 3, we 95 compare simulated gases and aerosols with satellite and in-situ observations, and also diagnose the global tropospheric ozone burden and budget. Future plans for model development and summary are presented in Section 4.

Development and description of the BCC-GEOS-Chem v1.0
Figure 1 presents the framework of the BCC-GEOS-Chem v1.0. BCC-GEOS-Chem v1.0 includes interactive atmosphere (including dynamics, physics, and chemistry) and land modules, and other components such as ocean and 100 sea ice are configured as boundary conditions for this version. Atmospheric dynamics and physics module (Section 2.1) and the land module (Section 2.2) come from the BCC-AGCM version 3 and the BCC Atmosphere and Vegetation Interaction Model version 2 (BCC-AVIM2), respectively. Atmosphere and land modules exchange the fluxes of momentum, energy, water, and carbon through the National Center for Atmospheric Research (NCAR) flux Coupler version 5. Dynamic and physical parameters from both the atmosphere (e.g., radiation, temperature, and wind) and the 105 land modules (e.g., surface stress and leaf area index) are then used to drive the GEOS-Chem chemistry (Section 2.3) and deposition (Section 2.4) of atmospheric gases and aerosols. Anthropogenic and biomass burning emissions are from the inventories used for the CMIP6 (Section 2.5.1). A number of climate-sensitive natural emissions such as biogenic and lightning emissions are calculated online in the model (Section 2.5.2). Boundary conditions, external forcing, and experiment design are described in Section 2.6. 110

The atmospheric model BCC-AGCM3
BCC-AGCM3 is a global atmospheric spectral model. It has adjustable horizontal resolution and 26 vertical hybrid layers extending from the surface to 2.914 hPa. In this study we use the default horizontal spectral resolution of T42 (approximately 2.8° latitude × 2.8° longitude). The dynamical core and physical processes of the BCC-AGCM3 have been described comprehensively in Wu et al. (2008Wu et al. ( , 2010 with recent updates documented in Wu et al. (2012Wu et al. ( , 2019. 115 Wu et al. (2019) showed that the BCC-CSM2 (BCC-AGCM3 as the atmospheric model) well captured the global patterns of temperature, precipitation, and atmospheric energy budget. BCC-CSM2 also showed significant improvements in reproducing the historical changes of global mean surface temperature from 1850s and climate variabilities such the quasi-biennial oscillation (QBO) and the El Niño-Southern Oscillation (ENSO) compared with its previous version BCC-CSM1.1m (Wu et al., 2019). Here we present a brief summary of the main features in BCC-120 AGCM3.
The governing equations and physical processes (e.g., clouds, precipitation, radiative transfer, and turbulent mixing) of BCC-AGCM3 are originated from the Eulerian dynamic framework of the Community Atmosphere Model (CAM3) (Collins et al., 2006), but substantial modifications have been incorporated. Wu et al. (2008) introduced a stratified 125 reference of atmospheric temperature and surface pressure to the governing equations. In this way, prognostic temperature and surface pressure in the original governing equation can be derived from their prescribed reference plus the prognostic perturbations relative to the reference. Resolving algorithms (e.g., explicit and semi-implicit time difference scheme) were adapted accordingly. The modified dynamic framework reduced the truncation errors in the model as well as the bias due to inhomogeneous vertical stratification, and therefore improved the descriptions of the 130 pressure gradient force and the vertical temperature structure (Wu et al., 2008). BCC-AGCM3 also implements a new mass-flux cumulus scheme to parameterize deep convection (Wu, 2012). The revised deep convection parameterization by including the entrainment of environment air into the uplifting parcel better captured the realistic timing of intense precipitation (Wu, 2012) and the Madden-Julian Oscillation (MJO) (Wu et al., 2019). Other important updates of atmospheric physical processes in BCC-AGCM3 relative to CAM3 include a new dry adiabatic adjustment to conserve 135 the potential temperature, a modified turbulent flux parameterization to involve the effect from waves and sea spray on ocean surface latent and sensible heat, a new scheme to diagnose cloud fraction, a revised cloud microphysics scheme to include the aerosol indirect effects based on bulk aerosol mass, and modifications for radiative transfer and boundary layer parameterizations (Wu et al., 2010;.

The land model BCC-AVIM2 140
BCC-AVIM2 is a comprehensive land surface model originated from the Atmospheric and Vegetation Interaction Model (AVIM) (Ji, 1995;Ji et al., 2008), and serves as the land component in BCC-CSM2. It includes three submodules: the biogeophysical module, plant ecophysiological module, and soil carbon-nitrogen dynamic module. The biogeophysical module simulates the transfer of energy, water, and carbon between the atmosphere, plant canopy, and soil. It has 10 soil layers and up to 5 snow layers. The ecophysiological module describes the ecophysiological activities such as 145 photosynthesis, respiration, turnover, and mortality of vegetation, and diagnoses the induced changes of biomass. The soil carbon-nitrogen dynamic module describes the biogeochemical process such as the conversion and decomposition of soil organic carbon. The vegetation surface in BCC-AVIM2 is divided into 15 plant functional types (PFTs) as shown in Table 1, and each grid cell contains up to 4 PFTs types. Wu et al. (2013) showed that the model well captured the spatial distributions, long-term trends, and interannual variability of global carbon sources and sinks compared to 150 observations and other models. Recent improvements in BCC-AVIM2, such as the introduction of a variable temperature threshold for the thawing/freezing of soil water, and improved presentations of snow surface albedo and snow cover fraction, are described in Li et al. (2019). Biogenic emissions and dust mobilizations are also implemented in BCC-AVIM2 interactively with the atmosphere, as will be described later in Section 2.5.

Atmospheric chemistry 155
We implement in this study the GEOS-Chem v11-02b "Tropchem" mechanism as the atmospheric chemistry module of BCC-GEOS-Chem v1.0. As described in the introduction, GEOS-Chem used as an online chemical module in ESMs shares the exact same codes for local terms (chemistry, deposition, and emission) as the classic offline GEOS-Chem.

Dry and wet deposition
Dry and wet deposition for both gas and aerosols are parameterized following GEOS-Chem algorithms. Dry deposition is calculated online based on the resistance-in-series scheme (Wesely, 1989). The scheme describes gaseous dry 180 deposition by three separate processes, i.e., the turbulent transport in aerodynamic layer, molecular diffusion through the quasi-laminar boundary layer, and uptake at the surface. Aerosol dry deposition further considers the gravitational settling of particles as described in Zhang et al. (2001). Variables needed for the dry deposition calculation such as the friction velocity, Monin-Obukhov length, and leaf area index (LAI) are obtained from the atmospheric dynamics/physics modules or the land module BCC-AVIM, based on which GEOS-Chem calculates the aerodynamic, boundary-layer, 185 and surface resistances. The impacts of some other short-term land variables, such as stomatal conductance, on dry deposition are not included yet. We have also reconciled the land use types (LUT) used in dry deposition with those used in BCC-AVIM, following Geddes et al. (2016) and Zhao et al. (2017). The LUTs from BCC-AVIM are mapped directly to the 11 deposition surface types used that in GEOS-Chem as shown in Table 1. Dry deposition velocity is calculated as the weighted average over all LUTs in each grid box. 190 Wet deposition of aerosols and soluble gases by precipitation in BCC-GEOS-Chem v1.0 includes the scavenging in convective updrafts, in-cloud rainout, and below-cloud washout (Liu et al., 2001). Following the implementation of GEOS-Chem chemical module to GEOS-5 ESM , convective transport of chemical tracers and scavenging in the updrafts in BCC-GEOS-Chem v1.0 is performed using the GEOS-Chem convection scheme but with 195 convection variables diagnosed from BCC-AGCM. This takes advantages of the existing capability of the GEOS-Chem scheme to describe gas and aerosol scavenging (Liu et al., 2001;Amos et al., 2012).

Offline emissions 200
Historical anthropogenic emissions used in this study are mostly obtained from the Community Emissions Data System (CEDS) emission inventory (Hosely et al., 2018). CEDS is an updated global emission inventory which provides sectoral, gridded, and monthly emissions of reactive gases and aerosols from 1750-2014 for use in the CMIP6 experiment (Eyring et al. 2016;Hosely et al., 2018). Here we use the CEDS anthropogenic emissions of NOx, CO, SO2, NH3, non-methane volatile organic compounds (NMVOCs), and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) 205 (Table 2). We also include three-dimensional aircraft emissions of several gases and aerosols in the model.

Online emissions 225
BCC-GEOS-Chem v1.0 includes a number of climate-sensitive natural sources. Biogenic emissions of NMVOCs are calculated online using the Model of Gases and Aerosols (MEGAN) algorithm (Guenther et al., 2012) in the land module.
MEGAN estimates biogenic emissions as a function of an emission factor at standard condition, a normalized emission activity factor relative to the standard condition, and a scaling ratio which accounts for canopy production and loss. The emission activity factor is further determined by surface or plant parameters such as leaf age and LAI diagnosed in 230 BCC-AVIM, as well as meteorological variables such as radiation and temperature. The annual biogenic isoprene emissions calculated in BCC-GEOS-Chem v1.0 are 410.0 Tg year -1 averaged for 2012-2014 period with a relatively small interannual variability (404.6 to 415.2 Tg year -1 ). This is close to but lower than estimates from the literature (500-750 Tg, Guenther et al., 2012). The model captures the hot spots of biogenic isoprene emissions in the tropical continents and the southeastern US (Fig. 2c). 235 Parameterization of lightning NO emissions follows Price and Rind (1992). The model diagnoses the lightning flash frequency in deep convection as a function of the maximum cloud-top-height (CTH). Lightning NO production is then calculated as a function of lightning flash frequency, fraction of intracloud (IC) and cloud-to-ground (CG) lightning based on the cloud thickness, and the energy per flash (Price et al., 1997). year -1 as summarized in Schumann and Huntrieser, 2007). The emissions are centered near the tropics due to strong convection as shown in Figure 2d.

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The model also includes wind-driven sea salt and mineral dust emissions. Emission fluxes of sea salt aerosols are dependent on the sea salt particle radius and proportional to the 10-meter wind speed with a power of 3.41 following the empirical parameterization from Monahan et al. (1986) and Gong et al. (1997). Mineral dust emissions are determined by wind friction speed, soil moisture, and vegetation type following the Dust Entrainment and Deposition (DEAD) scheme as described by Zender et al. (2003).

Boundary conditions, external forcing, and experiment design
BCC-GEOS-Chem v1.0 is configured using prescribed ocean and sea ice as boundary conditions. Historical sea surface temperature and sea-ice extents are obtained from (https://esgf-node.llnl.gov/search/input4mips/, last access: 2 June 255 2019). These prescribed datasets are also used in CMIP6 atmosphere-only simulations. External forcing data, including historical greenhouse gas concentrations (CO2, CH4, N2O, CFCs) (Meinshausen et al., 2017), land use forcing, and solar forcing, are also accessed from (https://esgf-node.llnl.gov/search/input4mips/). BCC-CSM2 has implemented the radiative transfer effects of greenhouse gases and aerosols as well as the aerosol-cloud interactions based on bulk aerosol mass concentrations (Wu et al., 2019). Since BCC-CSM2 does not include interactive atmospheric chemistry, the 260 calculation of radiative transfer and aerosol-cloud interactions are based on historical gridded ozone concentrations from CMIP5 and CMIP6-recommended anthropogenic aerosol optical properties (Stevens et al., 2017). Here for BCC-GEOS-Chem v1.0, we follow BCC-CSM2 and use these prescribed ozone and aerosols rather than model online calculated values for feedback calculation. This is meant to focus on modeling and evaluation of atmospheric chemistry in this work as the first step of the coupling. Interactive coupling of chemistry and climate through radiation and aerosol-cloud 265 interactions will be considered in the next version of BCC-GEOS-Chem.

Observations used for model evaluation
We use an ensemble of surface, ozonesonde, and satellite observations to evaluate the BCC-GEOS-Chem v1.0 275 simulation of present-day atmospheric chemistry (Table 3). Ozonesonde measurements are obtained from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; http://woudc.org/data.php, last access: 2 June 2019) operated by the Meteorological Service of Canada. The network also includes sites from the Southern Hemisphere Additional Ozonesondes (SHADOZ, Thompson et al., 2003). To derive the monthly mean ozone profiles, only sites and months with more than three observations per month are considered, and simulated monthly mean ozone profiles are sampled 280 over the corresponding model grids (Lu et al., 2019b). We further categorize the WOUDC observations into ten regions following Tilmes et al. (2012) and Hu et al. (2017) for model evaluation as shown in Figure 3. We also use the TOAR surface ozone database (Schultz et al., 2017a) that provides ozone metrics (e.g. monthly mean) for more than 9000 monitoring sites around the world from the 1970s to 2014 (Schultz et al., 2017b). Surface aerosol measurements (sulfate, nitrate, OC, BC) over the US are obtained from the Interagency Monitoring of Protected Visual Environments 285 (IMPROVE) network. These aerosol measurements are 24-hour averages every 3 days.
Satellite products from the NASA Earth Observing System (EOS) Aura satellite's Ozone Monitoring Instrument (OMI) are also used. We use the OMI PROFOZ ozone profiles with 24 layers extending from the surface to 60 km retrieved by Liu et al. (2005;2010) based on the optimal estimation technique (Rodgers, 2000). The OMI PROFOZ dataset has 290 been comprehensively validated by comparisons with ozonesondes (Zhang et al., 2010;Hu et al., 2017;Huang et al., 2017) and satellite products (Huang et al., 2018). We also use the OMI gridded monthly mean tropospheric column of nitrogen dioxide (NO2) (Krotkov et al., 2013), formaldehyde (CH2O) (De Smedt et al., 2015), and planetary boundary layer (PBL) sulfur dioxide (SO2) column (Krotkov et al., 2015). Other satellite observations include carbon monoxide  Figure 4 shows the spatial and seasonal distributions of mid-tropospheric ozone (700-400 hPa) from OMI satellite observations and BCC-GEOS-Chem v1.0 simulation averaged over 2012-2014, as well as their differences. We analyze ozone at 700-400hPa where OMI satellite has the peak sensitivity (Zhang et al., 2010). Model outputs are sampled along the OMI tracks and smoothed with OMI averaging kernels for proper comparison to the observations (Zhang et al., 2010;Hu et al., 2017Hu et al., , 2018Lu et al., 2018). 305

Evaluation of tropospheric ozone with observations 300
The model well captures the main features of tropospheric ozone distribution and seasonal variation. Both satellite observations and BCC-GEOS-Chem v1.0 model results show high mid-tropospheric ozone levels over the northern mid-latitudes in boreal spring due to stronger stratospheric influences and in summer due to higher photochemical production, and over the Atlantic and southern Africa during boreal autumn driven by strong biomass burning emissions 310 (Fig. 2), lightning NOx and dynamical processes (e.g., Sauvage et al., 2007). The spatial patterns of observed and in a similar period . We find that BCC-GEOS-Chem v1.0 tends to overestimate tropospheric ozone 315 levels over tropical oceans by 3-12 ppbv and underestimate ozone over the northern mid-latitudes by 3-9 ppbv, similar to the patterns simulated by the classic GEOS-Chem and G5NR-Chem models (Hu et al., 2017. Comparisons with global ozonesonde observations further demonstrate that BCC-GEOS-Chem v1.0 has no significant biases in the tropospheric ozone simulation. As shown in Figure 5, the model well reproduces the observed annual mean 320 ozone vertical structures, e.g., the slow increase of ozone with increasing altitude in the troposphere, and the sharp ozone gradient near and above the tropopause. Figure 6 compares seasonal variations of ozone concentrations in different regions at three tropospheric levels (800 hPa, 500 hPa, and 300 hPa). Overall, the model reproduces the ozone annual cycles driven by different chemical and dynamical processes. The model captures the springtime and summertime ozone peaks at the northern mid-latitudes (Japan, US, Europe, Canada) (r=0.53~0.94 for different layers), but only fairly 325 reproduces the annual ozone cycle in the Southern Hemisphere (SH) and the tropics. Mean model biases at the three layers are mostly within 10 ppbv, with small low biases over the northern mid-latitudes (-6.0~-0.6 ppbv), and high biases over the tropics in the lower and middle troposphere (e.g., about 10 ppbv at 800 hPa over the SH tropics), consistent with the comparison with satellite observations (Fig.4). We find that the model has large low ozone bias in the upper troposphere (300 hPa) particularly over the northern polar regions (~-30 ppbv). The underestimation extends to the 330 stratosphere globally except for the extratropical Southern Hemisphere (Fig. 5). These negative model biases are likely due to the use of a simplified stratospheric ozone scheme and/or errors in modeling dynamics of ozone exchange between the stratosphere and the troposphere as will be discussed later, or the low model vertical resolution (26 layers).  Inclusion of urban and suburban sites slightly decreases the spatial correlations (r=0.34~0.60, N=292) and enlarges the annual mean high bias (10.2 ppbv). We find again that the high biases are more prominent in the tropics (e.g., coastal sites in the western Pacific and Indonesia) and in summer. Although the above comparison is heavily weighted toward the US, Europe, Japan and South Korea due to the density of observations in these regions, our results demonstrate the overall good performance for BCC-GEOS-Chem v1.0 in simulating ground-level ozone at least for rural and remote 345 regions.

Tropospheric ozone and OH budgets in BCC-GEOS-Chem v1.0
We then diagnose the global tropospheric ozone burden and its driving terms (Table 4 and Figure 8). BCC-GEOS-Chem v1.0 estimates the global tropospheric ozone burden to be 336.0 Tg averaged over 2012-2014. This is consistent with the results from the classic offline GEOS-Chem CTM and the G5NR-Chem (~350 Tg) with an earlier version (v10-01) 350 of GEOS-Chem as chemical module (Hu et al., 2017;2018), and also in agreement with the recent model assessments of 49 models (320-370 Tg, Young et al., 2018). We divide the global tropospheric ozone burden into different regions following Young et al. (2013)  We find that the global tropospheric mean OH concentration in BCC-GEOS-Chem v1.0 is 1.16×10 6 molecule cm -3 , close to the offline GEOS-Chem v10-01 (1.25×10 6 molecule cm -3 , Hu et al., 2018) and well within the range of 16 ACCMIP models (1.11±0.16×10 6 molecule cm -3 , Naik et al., 2013). Figure 8c and 8d compares the distribution of simulated OH concentrations with the climatology derived from previous studies (Spivakovsky et al., 2001;Emmons et 370 al., 2010). We find that the model shows notable high bias in the lower troposphere (below 750 hPa) particularly in the tropics (2.04 to 2.45 molecule cm -3 in BCC-GEOS-Chem v1.0 compared to 1.44 to 1.52 molecule cm -3 in Spivakovsky et al., 2001). Discrepancies in modeling climate and concentrations of methane, ozone, NOx, and CO can all contribute to the OH bias in climate-chemistry models (Nicely et al., 2020). We calculate the methane chemical lifetime against OH loss to be 8.3 years in BCC-GEOS-Chem v1.0, which falls in the low end of the range reported from ACCMIP 375 multi-model assessments (9.7±1.5 years) (Naik et al., 2013).
We now diagnose the budget of global tropospheric ozone in BCC-GEOS-Chem v1.0. Following the classic GEOS-Chem, BCC-GEOS-Chem v1.0 diagnoses the chemical production and loss of the odd oxygen family (Ox, including O3, NO2, NOy, several organic nitrates and bromine species) to account for the rapid cycling among Ox constituents. Ozone 380 accounts for more than 95% of the total Ox (Hu et al., 2017). The global annual ozone chemical production and loss are 5486 Tg and 4983 Tg, respectively (Table 4); both are higher than the classic GEOS-Chem (Hu et al., 2017) and fall in the high quartile of multi-model assessments (Young et al., 2018). The high tropospheric ozone production is due at least in part to the high precursor emissions used in this study particularly for NOx emissions The model shows strong chemical production over northern mid-latitude continents in summertime, and large chemical loss over the tropical 385 oceans driven by high water vapor content (figure not shown).
The global annual mean ozone dry deposition flux diagnosed in BCC-GEOS-Chem v1.0 is 873 Tg averaged for 2012-2014. It is consistent with recent reviews by Hardacre et al. (2015) and Young et al. (2018) (700-1500 Tg from 33 model estimates). Figure 9 presents the global ozone dry deposition velocity and flux for January and July 2012-2014. Both 390 hemispheres show larger ozone dry deposition velocities in summer than winter due to stronger atmospheric turbulence and larger vegetation cover. Large ozone dry deposition velocity (> 0.5 cm s -1 ) can be seen over the tropical continents, while over the oceans and glaciers ozone dry deposition is very weak.
We then diagnose the annual amount of ozone stratosphere-troposphere exchange (STE) of 370 Tg as the residual of 395 mass balance between tropospheric chemical production, chemical loss, and deposition as previous studies did Hu et al., 2017). This value is lower than most of other model estimates (400-680 Tg, Young et al., 2018). The low STE in BCC-GEOS-Chem v1.0 appears to be the main factor causing ozone underestimates in the upper troposphere as seen above. This may reflect a number of model limitations, for example, the representation of stratospheric chemistry, inadequate STE due to model meteorology (e.g., biases in wind and tropopause), and the low 400 model vertical resolution. Given the tropospheric ozone burden and its loss to chemistry and deposition, we derive the lifetime of tropospheric ozone of 20.9 days, consistent with the multi-model estimates (Young et al., 2013). Figure 10 compares the spatial distributions of annual mean simulated CO, NO2, SO2, and CH2O with satellite 405 observations. We evaluate CO at 700 hPa where MOPITT satellite has generally high sensitivity (Emmons et al., 2004;Pfister et al., 2005), and apply averaging kernel to smooth the modelled CO. As shown in Figure 10, BCC-GEOS-Chem v1.0 reproduces the high CO levels over the northern mid-latitudes driven by higher anthropogenic sources, and over the central Africa driven by biomass burning emissions (spatial correlation coefficient r=0.92) with some overestimates.

Evaluation of other atmospheric constituents
It also captures the observed hotspots of tropospheric NO2 (r=0.87) and PBL SO2 columns (r=0.32) over the East Asia 410 that generally follow the distribution of anthropogenic sources. The sharp land-ocean gradients for both tracers reflect their short chemical lifetime. We find low biases in the modelled PBL SO2 especially over the volcanic eruption regions (e.g., Central Africa) but high biases in the industrialized regions such as East Asia, a pattern consistent with previous comparisons between the OMI and GEOS-Chem PBL SO2 columns, which may reflect inappropriate ship and volcanic emissions in the model (Lee et al., 2009) and/or the model bias in the PBL height. High levels of tropospheric CH2O 415 column are simulated over the Amazon, the central Africa, tropical Asia, and the southeastern US, typical regions where CH2O oxidized from large biogenic emissions of VOCs (r=0.67), but the model shows notable overestimates. Previous studies  showed that satellite CH2O retrievals are biased low by 20-51% compared to aircraft measurements which would partly explain the model bias. Future assessments are required to correct the biases of these gaseous pollutants. 420 We evaluate model simulated AOD at 550 nm with the MODIS AOD observations in Figure 11. High AOD values over the East Asia due to high anthropogenic emissions, and over Africa and the adjacent oceans due to dust emissions are shown in both MODIS observations and BCC-GEOS-Chem v1.0, although the model tends to underestimate the observed hotspots likely due to the coarse model resolution. Figure 12 further shows the comparison of simulated surface 425 aerosol components (sulfate, nitrate, OC and BC) with the observations from the IMPROVE network over the US. The model fairly reproduces the spatial and seasonal patterns for all analyzed aerosol components, e.g., high sulfate and nitrate concentrations over the eastern US. Among all the components, the simulation of sulfate in the US shows best agreement with biases of -10%~20% and spatial correlation coefficients of 0.76-0.87 over model grids covering the measurement sites (N=77). The model also captures the high summertime OC and BC concentrations in the mid-western 430 US driven by active wildfire activities (r=0.20-0.57 for different seasons). However, the model shows high biases in wintertime nitrate in the eastern US as found in previous GEOS-Chem evaluations .

Summary and future plans
This study describes the framework and evaluation of the new global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0. The development of the BCC-GEOS-Chem v1.0 takes advantage of grid-independent structure 435 of the GEOS-Chem chemical module, which allows the exact same GEOS-Chem chemistry and deposition algorithms to be performed on any external grid and supported by MPI. BCC-GEOS-Chem v1.0 includes interactive atmospheric and land modules. It simulates the evolution of atmospheric chemical interactive constituents through a detailed mechanism of HOx-NOx-VOCs-ozone-bromine-aerosol tropospheric chemistry as well as online wet and dry deposition schemes. The model also implements a number of climate-sensitive natural emissions such as biogenic VOCs and 440 lighting NO.
We conduct a three-year (2012-2014) model simulation with year-specific CMIP6 anthropogenic and biomass burning emissions. We evaluate the model with a focus on tropospheric ozone using surface, ozonesonde, and satellite observations. We show that BCC-GEOS-Chem v1.0 can well capture the spatial distributions (r=0.79~0.93 with OMI 445 satellite observations of ozone at 700-400hPa) and seasonal cycles of tropospheric ozone. The model shows no significant biases in the lower and middle tropospheric ozone compared to satellite observations (0.4~2.2 ppbv at 700-400 hPa), ozonesonde (within 10 ppbv at 800, 500, and 300 hPa except for the polar upper troposphere), and surface measurements (4.9 ppbv). We calculate a global tropospheric ozone burden of 336 Tg year -1 and OH burden of 1.

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The development of BCC-GEOS-Chem v1.0 for online atmospheric chemistry simulation represents an important step for the development of fully-coupled earth system models in China. There are still several limitations in this version that should be addressed in future model development. The current version of BCC-GEOS-Chem does not include full stratospheric chemistry mechanism, which is important for accurately modeling the evolution of ozone and its climate influences (Lu et al., 2019a). We plan to implement the unified tropospheric-stratospheric chemistry extension (UCX) 460 (Eastham et al., 2014), which is now the "Standard" mechanism for GEOS-Chem chemistry, into the next version of BCC-GEOS-Chem. Diagnosing radiative transfer and aerosol-cloud interactions will be the next priority for model evaluation, and it can take advantage of the GEOS-Chem aerosol microphysics module (TwO-Moment Aerosol Sectional (TOMAS) module (Kodros and Pierce, 2017) or Advanced Particle Microphysics (APM) (Yu and Luo, 2009)).
Updates of emissions (e.g., application of new or regional anthropogenic emissions inventories) could be merged to 465 BCC-GEOS-Chem with the future implementation of the GEOS-Chem emission module (Harvard-NASA Emissions Component, HEMCO) (Keller et al., 2014). BCC-GEOS-Chem is ready to be updated to higher horizontal and vertical resolution of T106 (about 110km, 46 layers up to 1.5hPa) or T266 (about 45 km, 56 layers up to 0.09 hPa) with recent BCC-CSM-MR and BCC-CSM-HR (Wu et al., 2019), which enables applications on air quality prediction in the future.

Code and data availability
The GEOS-Chem model is maintained at the Harvard Atmospheric Chemistry Modeling group  Zhang (zhanglg@pku.edu.cn) and Tongwen Wu (twwu@cma.gov.cn). Lin Zhang, Tongwen Wu, Daniel Jacob, and Jun Wang led the project. Xiao Lu, Lin Zhang, Tongwen Wu, Michael Long, Fang Zhang and

Competing interests
The authors declare that they have no conflict of interest.        shows climatological tropospheric OH burden reported by Spivakovsky et al. (2000) and summarized by Emmons et al. (2010).