Beijing Climate Center Earth System Model version 1 ( BCC-ESM 1 ) : 1 Model Description and Evaluation of Aerosol Simulations

Abstract. The Beijing Climate Center Earth System Model version 1 (BCC-ESM1) is the first version of a fully coupled Earth system model with interactive atmospheric chemistry and aerosols developed by the
Beijing Climate Center, China Meteorological Administration. Major aerosol
species (including sulfate, organic carbon, black carbon, dust, and sea salt)
and greenhouse gases are interactively simulated with a whole panoply of
processes controlling emission, transport, gas-phase chemical reactions,
secondary aerosol formation, gravitational settling, dry deposition, and wet
scavenging by clouds and precipitation. Effects of aerosols on radiation,
cloud, and precipitation are fully treated. The performance of BCC-ESM1 in
simulating aerosols and their optical properties is comprehensively
evaluated as required by the Aerosol Chemistry Model Intercomparison Project
(AerChemMIP), covering the preindustrial mean state and time evolution from
1850 to 2014. The simulated aerosols from BCC-ESM1 are quite coherent with
Coupled Model Intercomparison Project Phase 5 (CMIP5)-recommended data, in situ measurements from surface networks (such as
IMPROVE in the US and EMEP in Europe), and aircraft observations. A
comparison of modeled aerosol optical depth (AOD) at 550 nm with satellite
observations retrieved from the Moderate Resolution Imaging Spectroradiometer
(MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR) and surface AOD
observations from the AErosol RObotic NETwork (AERONET) shows reasonable
agreement between simulated and observed AOD. However, BCC-ESM1 shows
weaker upward transport of aerosols from the surface to the middle and upper
troposphere, likely reflecting the deficiency of representing deep
convective transport of chemical species in BCC-ESM1. With an overall good
agreement between BCC-ESM1 simulated and observed aerosol properties, it
demonstrates a success of the implementation of interactive aerosol and
atmospheric chemistry in BCC-ESM1.


Submit to Geosci. Model Dev. 1970s, when 3D transport of ozone and simple stratospheric chemistry were firstly 58 incorporated into a GCM to simulate global O 3 production and transport (e.g., Cunnold et al. 59 energy, water, carbon and other tracers at their interfaces. The coupling between the 108 atmosphere and the ocean is done every hour. 109 The atmospheric component BCC-AGCM3-Chem is able to simulate global atmospheric 110 composition and aerosols from anthropogenic emissions as forcing agents. Its resolution is T42 111 (approximately 2.8125º x2.8125º transformed spectral grid). The model has 26 levels in a hybrid 112 sigma/pressure vertical coordinate system with the top level at 2.914 hPa. Details of the model 113 physics are described in Wu et al. (2019). The BCC-AGCM3-Chem combines 66 gas-phase 114 chemical species and 13 bulk aerosol compounds as listed in Table 1. Apart from 3 gas-phase 115 species of dimethyl sulfide (DMS), SO 2 and NH3, the other 63 gas-phase species are the same 116 as those in the "standard version" of MOZART2 (Model for Ozone and Related chemical 117 Tracers, version 2), a global chemical transport model for the troposphere developed by the 118 National Center for Atmospheric Research (NCAR) driven by meteorological fields from 119 either climate models or assimilations of meteorological observations (Horowitz et al., 2003). 120 Advection of all tracers in BCC-AGCM3-Chem is performed through a semi-Lagrangian 121 scheme (Williamson and Rasch, 1989), and vertical diffusion within the boundary layer 122 follows the parameterization of Holtslag and Boville (1993). The gas-phase chemistry of the 123 63 MOZART2 gas-phase species as listed in Table 1 is treated in the same way as that in the 124 "standard version" of MOZART2 (Horowitz et al., 2003) SIS that are used in BCC-ESM1 may be found in Wu et al. (2013) and Wu et al. (2019). 169 In the following sub-sections, we will describe the treatments in BCC-ESM1 for 3 170 gas-phase species of DMS, SO 2 and NH 3 , 13 prognostic aerosol species including sulfate 171 to SO 4 2occur by gas phase reactions (Table 2) and by aqueous phase reactions in cloud 183 droplets. The dry deposition velocity of SO 2 follows the resistance-in-series approach of 184 Wesely (1989) using the formula, W SO2 = 1/( + + ), in which r a , r b, and r c are the 185 aerodynamic resistance, the quasi-laminar boundary layer resistance, and the surface 186 resistance, respectively and they are interactively computed in each model time step. The loss 187 rate of SO 2 due to wet deposition is computed following the scheme in the global Community  The sources of SO 2 mainly come from fuel combustion, industrial activities, and 191 volcanoes. SO 2 can also be formed from the oxidation of DMS as listed in Table 2  produced primarily by the gas-phase oxidation of SO 2 (in Table 2) and by aqueous phase 198 oxidation of SO 2 in cloud droplets. The gas phase reactions, rate constants, and gas-aqueous 199 equilibrium constants are given by Tie et al. (2001

Sea salt aerosols 229
As shown in Table 3, sea salt aerosols in the model are classified into four size bins (0.2 where is a scaling factor and set to 4.05x10 -15 , 4.52x10 -14 , 1.15x10 -13 , 1.20x10 -13 for four 236 size bins of sea salt aerosols in BCC-ESM1, respectively. 237 Dry deposition of sea salts depends on the turbulent deposition velocity in the lowest 238 atmospheric layer using aerodynamic resistance and the friction velocity, and the settling 239 velocity through the whole atmospheric column for each bin of sea salts. The turbulent 240 deposition velocity and settling velocity depend on particle diameter and density (listed in 241 Table 3). In addition, the fact that the size of sea salts changes with humidity is also 242 considered. The wet deposition of sea salts follows the scheme for soluble aerosols used in 243 CAM4, and depends on prescribed solubility and size-independent scavenging coefficients. 244

Dust aerosols 245
Dust aerosols behave in a similar way as sea salts. Their variations involve three major 246 processes: emission, advective transport, and wet/dry depositions. The dust emission is based 247 on a saltation-sandblasting process, and depends on wind friction velocity, soil moisture, and 248 vegetation/snow cover (Zender et al., 2003). The vertical flux of dust emission is corrected by 249 a surface erodible factor at each model grid cell which has been downloaded from NCAR 250 website (https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/atm/cam/dst/). Soil 251 erodibility is prescribed by a physically-based geomorphic index that is proportional to the 252 properties and precipitation, and ultimately impact the hydrological cycle. 263 Prognostic aerosol masses are used to estimate the liquid cloud droplet number 264 concentration cdnc (cm -3 ) in BCC-AGCM3-Chem. cdnc is explicitly calculated using the 265 empirical function suggested by Boucher and Lohmann (1995)  where w is the liquid water density and the cloud liquid water content (g cm -3 ). 281 Aerosols also exert impacts on precipitation efficiency (Albrecht, 1989), which is taken 282 into account in the parameterization of non-convective cloud processes. There are five 283 processes that convert condensate to precipitate: auto-conversion of liquid water to rain, and collection of liquid by snow. The auto-conversion of cloud liquid water to rain (PWAUT) 286 is dependent on the cloud droplet number concentration and follows a formula that was 287 originally suggested by Chen and Cotton (1987) (8) 293 atmosphere, and estimating the global-to-regional climate response from these changes. 301 Modelling groups with full chemistry and aerosol models are encouraged to perform all 302 AerChemMIP simulations (Collins et al., 2017). To assess the ability of our model to simulate 303 aerosols (mean and variability), we have followed the historical simulation designed by 304  to the atmospheric composition of reactive gases and aerosols may affect the temperature 316 response to a given WMGHG concentration level (Collins et al., 2017). Three members of 317 historical experiments are conducted and the first member is analyzed in this work. 318

Surface emissions 319
Surface emissions of chemical species from different sources are summarized in Table  320 4. They include anthropogenic emissions from fossil fuel burning and other industrial 321  Table 4). Monthly lumped emissions of 333 black carbon and organic carbon aerosols from 1850 to 2014 are downloaded from 334 CMIP6-recommended data, but we used 80% (for BC) and 50% (for OC) of them in their 335 hydrophobic forms (BC1 and OC1) and the rest in their hydrophilic forms (BC2 and OC2), 336 following the work of Chin et al. (2002). 337 Five tracers of ISOP, ACET (CH 3 COCH 3 ), C 2 H 4 , C 3 H 8 , and Monoterpenes (C 10 H 16 ) in 338 Table 1 belong to biogenic volatile organic carbons (VOCs). As shown in Table 4

Volcanic eruption, lightning and aircraft emissions 353
As there is no stratospheric aerosol scheme in BCC-ESM1, concentrations of sulfate 354 aerosol at heights from 5 to 39.5 km, which volcanic origin, are directly prescribed using the 355

Upper boundary of the atmosphere 368
As no stratospheric chemistry is included in the present version of BCC-AGCM3-Chem, 369 it is necessary to ensure a proper distribution of chemically-active stratospheric species.

Evaluation of O 3 and aerosol simulations in the 20 th century 434
The rate of sulfate formation is dependent on the levels of oxidants in the troposphere.   The DMS burden is 0.12 Tg with a lifetime of 0.78 days, which is within the range of other 514 models reported in the literature. As shown in Table 5, the total SO 2 production averaged for 515 the period of 1991 to 2000 is 76.93 Tg(S)· yr −1 . A rate of 13.2 Tg(S)· yr -1 (about 17%) SO 2 is 516 produced from the DMS oxidation, only 0.1 Tg(S)· yr -1 SO 2 from airplane emissions to the 517 atmosphere, and the rest (63.63 Tg(S)· yr -1 , near 82.7%) from anthropogenic activities and 518 volcanic eruption at surface. The amount of SO 2 produced from the DMS oxidation is in the 519 range of other works (10.0 to 24.7 Tg(S)· yr -1 ) reported in Liu et al (2005). All the SO 2 520 production is balanced by SO 2 losses by dry and wet deposition, and by gas-and 521 aqueous-phase oxidation. Half of its loss (38.74 Tg(S)· yr -1 ) occurs via its aqueous-phase 522 oxidation to form sulfate. Other losses through dry and wet depositions and gas-phase oxidation to form SO 4 2are also important (Table 2). All the sinks are in the range from the 524 literature (Liu et al., 2005). The global burden of SO 2 in the atmosphere is 0.48 Tg with a 525 lifetime of 1.12 days, consistent with values in literature (Liu et al., 2005). 526 Sulfate aerosol is mainly produced from aqueous-phase SO 2 oxidation (38.73 Tg(S)· yr -1 ) 527 and partly from gaseous phase oxidation of SO 2 (10.32 Tg(S)· yr -1 ), and is largely lost by wet

Aerosol Optical Properties 637
Aerosol optical depth (AOD) is an indicator of the reduction in incoming solar 638 radiation (at a particular wavelength) due to scattering and absorption of sunlight by aerosols. 639 In this study, we calculate the AOD at 550 nm for all aerosols including sulfate, BC, organic  In those regions, BCC-ESM also slightly overestimates MODIS and MISR AOD observations 660 (Fig. 17). 661                      of sulfate, organic carbon, black carbon, dust, and sea salt averaged for December-January-February (DJF), June-July-August (JJA), and annual respectively. The radial coordinate shows the standard deviation of the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modelled spatial pattern with the observed spatial pattern. Analysis is for the whole globe. The reference dataset is CMIP5-prescribed dataset.   carbon (BC) for the regional mean and spatial standard deviation, minimum and maximum values at IMPROVE and EMEP network sites, and the spatial correlation between observed and simulated multi-years averaged annual means. Simulated values are selected for the same locations and same valid observation time. The data used same as those in Figure 12.