sensitivity analysis of chemical mechanisms in the wrf ... · additionally, to analyse the impact...

Aerosol and Air Quality Research, 18: 505–521, 2018 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2017.05.0156 Sensitivity Analysis of Chemical Mechanisms in the WRF-Chem Model in Reconstructing Aerosol Concentrations and Optical Properties in the Tibetan Plateau Junhua Yang1, Shichang Kang1,3*, Zhenming Ji2*

1 State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (CAS), Gansu 730000,China 2 School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China 3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China ABSTRACT

To investigate the effect of gas-phase chemical schemes and aerosol mechanisms on the reconstruction of the concentrations and optical properties of aerosols in the Tibetan Plateau (TP) and adjacent regions, two simulation experiments using the mesoscale Weather Research and Forecasting (WRF) meteorological model with the chemistry module (WRF-Chem) were performed in 2013. The RADM2 gas-phase chemical mechanism and the MADE/SORGAM aerosol scheme were selected in the first configuration, whereas the CBMZ gas and MOSAIC aerosol reaction schemes were included in the second simulation. The comparison demonstrated that chemical mechanisms play a key role in affecting the evolution of gas-phase precursors and aerosol processes. Specifically, compared with RADM2, CBMZ revealed lower O3 and higher NO2 surface concentrations, because of more efficient O3-NO titration, and higher HNO3 concentrations owing to more effective NO2 + OH reaction. SO2 could easily form particulate sulfate through cloud oxidation in RADM2. The MADE/SORGAM module presented higher surface PM2.5 and PM10 concentrations than did the MOSAIC module over the TP and in surrounding regions, because of the difference in aerosol compounds and the distribution of computed aerosol concentrations between modes and bins. The aerosol optical depth at 550 nm indicated a potential correlation with surface secondary inorganic aerosols concentrations. Higher surface sulfate and nitrate concentrations appeared to determine higher AOD values in MADE/SORGAM than in MOSAIC. Finally, the comparison with observations suggested that, the simulation performed using the CBMZ gas-phase chemical mechanism and MOSAIC aerosol module could be suitable for the efficient reconstruction of aerosols and their optical depth over the TP. Keywords: Chemical schemes; Aerosol concentration; Aerosol optical properties; Tibetan Plateau.

INTRODUCTION

Aerosol is a colloid of solid or liquid particles in a gas, mainly containing sulfate, ammonium, nitrate, organic carbon, black carbon, sea salt, and dust. Aerosols can affect radiative transfer directly by absorbing and scattering solar radiation and indirectly by modifying the microphysical and optical properties of clouds (Kiehl et al., 1993; Jones * Corresponding author.

Tel.: 1-369-103-7228 E-mail address: [email protected]

** Corresponding author. Tel.: 1-851-360-5562 E-mail address: [email protected]

et al., 1994), thereby resulting in a cooling (e.g., sulphate (Andreae et al., 2005)) or heating (e.g., black carbon (Jacobson, 2001)) effect on the planet. In contrast to greenhouse gases, aerosols are not well-mixed, therefore, they are vulnerable to be affected by meteorological conditions, chemical mechanisms, and aerosol modules, leading to substantial heterogeneity in their spatial and temporal variations, optical properties, and subsequent radiative effect (Huang et al., 2009, Ji et al. 2016).

The Tibetan Plateau (TP), also referred to as the third pole because it contains the world's third-largest store of ice, has a considerable effect on radiative budgets and climate (Ye and Wu, 1998; Ma et al., 2009). According to instrumental temperature records and re-analysis datasets, the temperature in the TP significantly increased over the last 60 years (Kang et al., 2010). Therefore, glaciers have retreated rapidly in many parts of the TP (Yao et al., 2012).

Yang et al., Aerosol and Air Quality Research, 18: 505–521, 2018 506

Gas emissions, considered as the main cause of global warming, increasing the concentrations of aerosols (e.g., black carbon) in ambient regions appear to be another crucial anthropogenic driving force of these changes over the TP (Ramanathan and Carmichael, 2008; Lau et al., 2010; Ji et al., 2011; Cong et al., 2015; Ji et al., 2015; Kang et al., 2016; Ming et al., 2016).

The TP is located at the convergence of the two largest anthropogenic aerosol emission sources: southern Asia and eastern Asia. Once aerosols are transported to the TP, they may affect the thermal structure of the atmosphere, radiative balance, and surface albedo and further change hydrological cycle (Ji, 2016) and monsoon cycles in Asia (Huang et al., 2009; Xu et al., 2009; Lau et al., 2010; Ji et al., 2011). However, due to the harsh environment, difficult field works access, and limited instrumental stations, the measurement records are insufficient to quantitative study the spatial and temporal variations of aerosols and the climatic effects of aerosols over the TP and surrounding regions.

The mesoscale Weather Research and Forecasting (WRF, Grell et al., 2005) meteorological model with the Chemistry module (WRF-Chem) is a new generation of online-coupled meteorology and chemistry models. This model significantly reduces the inconsistency between meteorological and chemical processes and also takes feedback effects related to aerosol into account (Zhang et al., 2010; Chen et al., 2017). Nevertheless, the accurate modeling of aerosol feedback effects requires a reliable reproduction of the main driving process of the fate of atmospheric aerosols. A recent simulation study in air quality still showed considerable uncertainties in aerosol treatments (Solazzo et al., 2012), inducing significant discrepancies in modeling of aerosol concentrations and optical properties. Chemical mechanisms play a key role in the reconstruction of aerosols, which affects aerosol processes and the evolution of gas-phase precursors.

The WRF-Chem model enables the comparison and evaluation of different gas-phase chemistry and aerosol modules under the same meteorological driving data and chemical emissions (Balzarini et al., 2014). Balzarini et al. (2015) reported out that CMBZ showed higher gas concentration than did RADM2, and that MADE-SORGAM reproduced higher aerosol values than did MOSAIC over Europe in 2010. Yun et al. (2014) evaluated the sensitivity of aerosol schemes and dust options to predict particulate matters, and the scenario under the RADM2 gas-phase scheme and MADE/SORGAM aerosol module presented higher concentration values than did other scenarios. These studies mainly focused on low-altitude regions. However, the sensitivity of the WRF-Chem model to chemical mechanisms in high-altitude areas, such as the TP remains unclear.

In this study, two 1-year simulation experiments of the WRF-Chem model were designed for the TP and surrounding regions. We not only investigated the ability of different chemical mechanisms in reconstructing the concentrations and optical properties of aerosol, but also analyzed the reasons for the inconsistent performance levels of various schemes.

METHODOLOGY AND DATA WRF-Chem Model and Experimental Setup

The WRF-Chem model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. In this study, two experiments with different combinations of chemical mechanisms and aerosol modules were designed to investigate the modeling sensitivity levels of the WRF-Chem model version 3.6 over the TP. The first experiment, RS, selected the RADM2 (Regional Acid Deposition Model v2; Stockwell et al., 1990) gas-phase chemical mechanism and the MADE/SORGAM (Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model; Ackermann et al., 1998; Schell et al., 2001) aerosol module, whereas the second experiment CB selected the CBMZ (Carbon Bond Mechanism version Z; Zaveri and Peters, 1999) gas-phase chemistry and the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry; Zaveri et al., 2008) aerosol reaction scheme. Additionally, to analyse the impact of aerosol feedbacks on the simulation differences for the meteorological variables, we set up another experiment that has no aerosol feedbacks.

The RADM2 gas-phase mechanism, a condensed gas-phase photo-oxidation mechanism developed by Stockwell et al. (1990), uses a “lumped molecule” technique in which similar organic compounds are grouped together in different model categories (Grell et al., 2005), and it comprises 157 gas-phase reactions and 63 chemical species. The MADE/ SORGAM aerosol module divides aerosols into three log-normally modal distributions: the Aitken mode (< 0.1 µm diameter), the accumulation mode (0.1－2 µm diameter), and the coarse mode (> 2 µm diameter). Under this mechanism, aerosols are assumed to be internally mixed in the same mode but externally mixed among different modes (Zhao et al., 2010).

The CBMZ gas-phase mechanism, used in the second experiment, contains 67 species and 164 reactions in a lumped structure approach that classifies organic compounds according to their internal bond types (Gery et al., 1989). Rates for photolytic reactions are modified as described in DeMore et al. (1994). The MOSAIC aerosol module includes sulfate, nitrate, ammonium, sodium, calcium, chloride, black carbon, primary organic mass, liquid water, and other inorganic mass with 4 Bin size ranges: (1) 0.04–0.156 µm; (2) 0.156–0.625 µm; (3) 0.625–2.5 µm; (4) 2.5–10.0 µm. This mechanism simulates major aerosol processes such as thermodynamic equilibrium, condensation, binary nucleation, and coagulation.

The two WRF-Chem model configurations shared the same physical parameterizations, namely the Morrison 2-moment microphysical parameterization (Gustafson et al., 2007), RRTMG longwave and shortwave radiation scheme (Zhao et al., 2011), Mellor-Yamada-Janjic (MYJ) planetary boundary layer scheme (Schaefer, 1990), Noah land surface (Chen and Dudhia, 2001), and Grell–Devenyi cumulus scheme (Grell et al., 2002). WRF-Chem simulations were performed in 2013 mainly covering the TP and south Asia (Fig. 1). The first 6 days were used for model spin-up. The


Fig. 1. Model domain and topographic field (m); the black and red dots represent the CMDN and AERONET stations, respectively.

spatial configuration employed consists of one-way domain centered on latitude 33°N, and longitude 88°E, with horizontal resolution of 15 km, making 216 and 148 grid cells in west-east and north-south directions. There are 30 vertical sigma levels with the top at 50 hPa. The Lambert projection has been used according to the project specifications. The simulation was driven by the 6-h National Centers for Environmental Prediction (NCEP) final reanalysis data with 1° × 1° spatial resolution.

In the WRF-Chem model, improvement in modeling meteorological fields can lead to better performance in atmospheric chemistry simulation. Therefore, we added the Jimenez subgrid-scale orography parameterization scheme (Jiménez and Dudhia, 2012) and used the 2013 MODIS-based land-use data to replace the default 2001 land-use data (Fig. S1, from 2001 to 2013, land-use types are shown as marked changes over the TP). The 2013 MODIS-based land-use data could improve WRF modeling for meteorological fields over the northeastern TP (Yang et al., 2016). Additionally, we assimilated AMSUA /B radiance data (NOAA-15/16/17/18/19) by the WRF-3DVar assimilation system to improve the model’s initial field, according to our previous study (Yang et al., 2016). Emissions

Anthropogenic emissions of CO, VOC (Volatile Organic Compounds), NOx, BC (black carbon), OC (organic carbon), SO2, PM2.5 and PM10 are based on Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) with spatial resolution of 0.5 × 0.5 degree. As shown in supplement Fig. S2, the emission fluxes of the main anthropogenic aerosols components has low concentrations in the Tibetan Plateau but high values in South Asia and east China. The greenhouse gases emission database (e.g., CO2, CH4, and N2O) were derived from the Reanalysis of the TROpospheric

chemical composition (RETRO, http://retro.enes.org/index. shtml) with 0.5 × 0.5 degree resolution. The sea salt and volcanic ash emissions to the study domain is interpolated from a global emissions dataset (Freitas et al. 2011). The fire emissions inventory was based on the fire inventory from NCAR (FINN) (Wiedinmyer et al., 2010). Biogenic emission is from the Model of Emission of Gases and Aerosol from Nature (MEGAN, Guenther et al., 2006). Additionally, the mozbc utility and the Model for OZone and Related chemical Tracers (MOZART, http://www.ac om.ucar.edu/wrf-chem/mozart.shtml, Emmons et al., 2010) data were used to create improved initial and lateral chemical boundary conditions. Observation Data

In the study area, 231 national observation stations from the China Meteorological Data Network (CMDN, http://data.cma.cn/) were included to evaluate the model simulation of 2-m temperature (T2), 2-m relative humidity (RH2), and 10-m wind speed (U10) in 2013. Fig. 1 showed the 231 national stations with black dots. Additionally, for the spatial pattern, dataset from the Climate Research Unit (CRU, Mitchell and Jones, 2005) was used to validate the simulated surface air temperature, and modeled surface relative humidity. Simulated 500 hPa wind was evaluated by the ERA-Interim data provided by the European Centre for Medium-Range Weather Forecasts (Dee et al., 2011).

The near surface concentration of gaseous species from six in situ observation stations and aerosol compounds from seven in situ observation stations were collected for testing the model output. The identification of Aerosol Optical Depth (AOD) over the region is based on quality-assured data from the Aerosol Robotic Network (AERONET), which is a network of sum- and sky-scanning ground-based automated radiometers providing reliable and continuous


data on optical properties of various types of aerosols worldwide (Holben et al., 2001; Dubovik et al., 2002). In this study, we used the level 2.0 AOD data from 2013. Fig. 1 presented the fifteen AERONET sites with red dots. Specifically, at Namco site, the AOD data from AERONET were provided by Cong et al. (2009).

RESULTS AND DISCUSSION Model Performance in Meteorological Fields

As shown in Figs. 2(a)–2(c), the T2 modeled by two WRF-Chem experiments modeled was in proximity to the T2 reported in the CRU dataset in 2013. The T2 was below 0°C over the TP. Warmer areas surrounded the TP, such as South Asia, which had a T2 of above 24°C. In contrast to the T2, the RH2 over the TP was higher than that in adjacent regions (Figs. 2(d)–2(f)). The model showed favorable spatial consistency with the ERA-Interim reanalysis data in terms of the RH2. Compared with the CB experiment (Fig. 2(s)),

the RS experiment showed higher RH2 in high-value centers, namely the southeastern and northwestern TP (Fig. 2(d)). As presented in Figs. 2(g)–2(i), westerly winds prevailed over the study area and partially divided into two branches due to the topography. One branch flowed westward, and the other branch was forced by a high terrain and followed a northwesterly path. The two branches converged to form westerly winds at longitude of approximately 95°E. In South Asia, the simulated U10 in CB (Fig. 2(g)) was higher than that in RS (Fig. 2(h)).

Additonally, we analyzed the aerosol-induced mean changes in temperature and relative humidity at near-surface (Fig. S3) and vertical profile (Fig. S4). The differences of aerosol-induced mean changes in 2-m temperature between the CB and RS were shown in Fig. S5. The CB with lower 2-m temperature increment (Fig. 5(a)) due to aerosol feedbacks can reduce the simulation overestimation of 2-m temperature in some regions, e.g., in Turpan basin and the inland Tibetan Plateau. At the vertical direction, aerosols

Fig. 2. Mean surface air temperature (a–c), surface relative humidity (d–f) and wind at 500 hPa for CB (left), RS (center) simulation and CRU(ERA-Interim) data (right) in 2013.


led to increased temperature in lower atmosphere but reduced temperature in upper atmosphere. There is an inverse correlation between temperature and humidity in both Figs. S3 and S4, because the decrease (increase) in temperature can lead to a decrease (increase) in the saturation pressure of water vapor thus an increase (decrease) in relative humidity.

To quantitatively evaluate model performance in surface meteorological elements, the meteorological data of 231 national stations were used. Fig. 3 presents a box plot of

the daily mean variation of observed and simulated T2, RH2, and U10 at meteorological stations. Compared with the observation, the two simulation experiments showed a slightly higher T2 from January to March and a lower T2 from July to September (Fig. 3(a)). As shown in Fig. 3(b), the model can represent the annual variance of the RH2 and showed underestimation from January to March and overestimation from July to September. Considering the subgrid-scale orography, the model performance in simulating the U10 was not as satisfactory as that in

‐15

‐10

‐5

0

5

10

15

20

25

30

2013/1/1 2013/4/1 2013/7/1 2013/10/1

T2(℃

)

Local time

(a)

Observed

CB

RS

‐15

‐10

‐5

0

5

10

15

20

25

30

Observed CB RS

T2(℃

)

0

10

20

30

40

50

60

70

80

2013/1/1 2013/4/1 2013/7/1 2013/10/1

RH

2(%

)

Local time

(b)

20

30

40

50

60

70

80

observed CB RS

RH

2(%

)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2013/1/1 2013/4/1 2013/7/1 2013/10/1

U10

(m/s

)

Local time 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Observed CB RS

U10

(m/s

)

Fig. 3. Daily mean variation of measured and simulated (a) T2, (b) RH2, and (c) U10 with box plot at meteorological stations in study domain in 2013.


simulating the T2 and RH2. The CB experiment displayed higher U10 than did the RS experiment (Fig. 3(c)). The corresponding statistics between observations and simulations are shown in Table 1. Sensitivity Study and simulation Evaluation in Gaseous Species, Aerosol Compounds, and Optical Properties Gaseous Species

The spatial pattern of the annual mean concentrations of gas-phase compounds leading to secondary inorganic aerosols (SIA) is presented in Fig. 4. The differences between CB and RS are calculated by subtracting the substance concentrations of RS from that of CB. The CB and RS experiments revealed a quite consistent performance in the spatial distribution of gas-phase species. Surface O3 concentrations in the CBMZ gas-phase scheme were generally lower (up to 5 ppb) than those in the RADM2 scheme (Fig. 4(a)). The difference in the surface NO2 concentration is mostly associated with the discrepancy in the O3 concentration (Fig. 4(b)). The O3 and NO2 concentrations are strongly affected by the O3-NO titration process (Pirovano et al., 2012). The CBMZ scheme appeared to present more efficient O3-NO titration than did the RADM2 scheme; thus, the CB experiment showed a higher NO2 concentration than did the RS experiment, particularly in South Asia (up to 1.2 ppb) with high concentrations of NOx (Fig. S2).

The annual mean SO2 concentrations in the CB experiment was generally higher than that in the RS experiment (Fig. 4(c)). This discrepancy may be related to the chemical oxidation processes of SO2, because the two experiments shared the same anthropogenic emissions. SO2 was more likely to form particulate sulfate through cloud oxidation in the RADM2 gas-phase scheme (Brugh et al., 2010). Therefore, a decrease in the surface SO2 concentration in the RS experiment can be expected. Furthermore, we analyzed the distribution of H2O2, which is one of the most efficient oxidants of sulfuric compounds in fogs and clouds (Seinfeld and Pandis, 1998). As shown in Fig. 4(d), the CB experiment showed a higher concentration of H2O2 (up to 0.4 ppb) with respect to regions having a lower SO2 concentration, suggesting inefficient SO2 aqueous oxidation. Additionally, we compared the vertical distribution of SO2 between the CB and RS experiments. As shown in Fig. S6(c), the mean SO2 concentrations in the CB experiment was generally higher than that in the RS experiment.

The CB experiment generally predicted higher HNO3 concentration than did the RS experiment, and the largest differences appeared in south Asia (Fig. 4(e)). This can be due to different reaction coefficients in the photochemical reaction of NO2 with OH. When the temperature reached

300 K, the constant of reaction rate in NO2 + OH + M → HNO3 was approximately 1.3 times higher in the CBMZ scheme than in the RADM2 scheme (Balzarini et al., 2015). Furthermore, in vertical direction, the HNO3 concentration in CB was also higher than that in RS, especially in South Asia (Fig. S6(e)).

We summarized gaseous surface concentrations from six sites for comparison with the model simulations. The observed and simulated mean gaseous surface concentrations were shown in Table 2 and the corresponding normalized mean bias (NMB) were presented in Table S1. The comparison results indicated that both experiments overestimated the mean O3 concentrations at Lhasha (NMBs are equal to 20.9 in CB and 52.7 in RS) and Kanpur (NMBs are equal to 56 in CB and 61.3 in RS), with the CB experiment exhibiting higher performance, whereas the O3 concentration was underestimated at Namco, with the RS experiment exhibiting higher performance (NMB = –13.4). Our daily O3 concentration measurement at Namco also illustrated the aforementioned finding (Fig. S7). The CB experiment showed higher performance for the surface NO2 concentration at Lhasa, Aksu, and Lanzhou, whereas RS experiment revealed a closer agreement with observations at Kanpur. For the surface SO2 concentration, the two experiments presented underestimation at Aksu and Lanzhou but overestimation at other sites. At Lhasha, the SO2 concentration was clearly overestimated by 6.8 and 5.2 µg m–3 in CB and RS simulations, respectively. The simulation biases in NO2 and SO2 may partly be attributed to the deviation in modeling meteorological variables. In this study, the simulation underestimation of the surface wind speed (Fig. 4(c)) may not be beneficial for the dilution of pollutants and resulted in higher concentrations for gaseous species.

Aerosol Compounds

The CB and RS experiments showed a similar spatial distribution of the annual mean surface PM10 and PM2.5 concentration (Fig. 5). A higher aerosol concentration was observed in South Asia whereas a lower aerosol concentration was observed over the TP. The PM10 concentration was higher in the RS experiment than in the CB experiment, particularly in South Asia, with the difference being up to 33 µg m–3 (Fig. 5(b)). This is partly due to the difference in aerosol compounds such as SIAs produced by gaseous compounds, and partly due to different distribution of the calculated concentrations between bins and modes. In the MADE/SORGAM aerosol scheme, dust emissions were mainly distributed in the fine fraction, and the smaller size of aerosol leads to a lower sedimentation rate and a higher aerosol concentration (Wei et al., 2011).

Table 1. Statistical performances of simulated and observed T2 (°C), RH2 (%), and U10 (m s–1) over the study domain in 2013.

Observation RS CB mean mean NMB RMSE R mean NMB RMSE R

T2 9.23 9.27 0.4 1.77 0.95 9.30 0.7 1.8 0.96 RH2 55.05 52.78 –4.58 7.27 0.61 52.52 –4.12 6.57 0.68 U10 1.91 1.85 11.4 0.39 0.46 1.70 9.8 0.38 0.43


Fig. 4. O3 (a), NO2 (b), SO2 (c), H2O2 (d) and HNO3 (e) yearly mean concentrations at the ground for CB (left), RS (center) simulation and the difference between CB and RS (right) in 2013.


Surface SO4, NO3, and NH4 concentrations for both simulations are presented in Figs. 6(a)–6(c). The annual mean SO4 concentration was generally lower in CB than that in RS (Fig. 6(a)). As discussed previously, one of the most likely reasons of this difference was that the CBMZ gas-phase scheme in the CB experiment did not contain the aqueous-phase oxidation of SO2 by H2O2. In the vertical profile, the SO4 concentration in CB is lower than that in RS (Fig. S8(a)). The atmospheric NO3 concentration is mainly determined by its precursor HNO3 and the equilibrium between HNO3 and nitrate. As shown in Fig. 5(b), these two experiments showed a quite similar distribution, and the differences were mainly appeared in regions with higher relative humidity (such as the southern TP and its south slope, Fig. 6(b)). Because of higher relative humidity, the gas-particle partitioning process from HNO3 to NH4NO3 was more efficient in the MADE/SORGAM scheme than in MOSAIC scheme. Thus, the Mozurkewich (1993) approach available in the MADE/SORGAM scheme depicted higher NO3 concentrations over the southern TP and its south slope than did the Zaveri (2008) method available in the MOSAIC scheme (Fig. 6(c)). Higher NO3 concentrations in the CB experiment also appeared in the vertical profile in the south slope of the TP. The CB experiment exhibited a lower annual mean NH4 concentration than did the RS experiment (Fig. 6(d)) because of the underestimation of sulfate and nitrate concentrations in the MOSAIC aerosol module. The gas-phase HNO3 and NH3 can convert to particulate NH4NO3. Finally, the annual average concentrations of OC and BC are shown in Figs. 6(e) and 6(f), respectively. Differences between the two experiments were 1.0 µg m–3 for OC and 1.25 µg m–3 for BC, with concentrations being always higher in the CB experiment. Higher BC concentration also appears in the vertical profile (Fig. S8(d)). Because of the same emission source, vertical dispersion, and dry deposition equation, discrepancies between the CB and RS experiments could be due to differences in aerosol dynamics and aerosol concentrations calculations between bins and modes; for example, the MOSAIC scheme in this study divides aerosols into four particle ranges, whereas the MADE/SORGAM scheme has only three modes.

A comparison of observed and simulated mean aerosol and its compounds at several sites is presented in Table 3 and the corresponding NMBs were presented in Table S2. The model showed underestimation for the aerosol concentration at Aksu and Lanzhou but overestimation at Lhasa. The CB and RS experiments showed simulation biases of 7.4 (NMB = 26.6) and 13.8 µg m–3 (NMB = 49.6) in the PM2.5 concentration at Lhasa, respectively. For aerosol compounds at three sites in the TP (Lhasa, Namco, and Qomolangma), the CB experiment showed a closer agreement with observations. Over the TP, the RS configuration generally displayed a higher positive bias for surface SO4, NO3, and NH4 concentrations and a higher negative deviation for surface BC and OC concentrations than did the CB configuration. Both the experiments underestimated the concentrations of aerosol compounds (SO4, NO3, and NH4) at Lanzhou where the concentration of pollutants was higher.

Tab

le 2

. Obs

erve

d an

d si

mul

ated

gas

eous

sur

face

con

cent

rati

on (

µg

m–3

).

Nam

e L

ocat

ion

m.a

.s.l

Tim

e O

3 N

O2

SO

2

OB

S C

BR

S

OB

S C

B

RS

OB

SC

BR

S

Kan

pur

26.4

6°N

, 80.

33°E

12

5 20

13, 1

–5

43.2

± 1

5.6

67.4

69

.79.

5 ±

3.2

15.6

12

.4

7.1

± 2.

913

.5

10.4

G

aur

et a

l. (2

014)

L

hasa

29

.65°

N, 9

1.03

°E

3640

20

13, 2

–3

68.1

± 2

2.5

79.6

82

.422

.4 ±

16.

610

.6

8.7

9.9

± 7.

116

.7

15.1

B

u et

al.

(201

5)

Aks

u 40

.47°

N, 8

0.82

°E

1114

20

13, 1

–12

22.7

± 7

.76.

2 3.

3 16

.1 ±

7.4

14

.7

12.2

A

ihem

aiti

(20

15)

Gw

alio

r 26

.22°

N, 7

8.18

°E

207

2013

, 12

14.2

± 1

.4

19.7

17

.2

Ahm

ad e

t al.

(201

4)L

anzh

ou

36.0

3°N

, 103

.5°E

15

20

2013

, 1–5

32

.3 ±

11.

918

.8

14.3

35

.3 ±

21.

726

.7

19.4

L

iu e

t al.

(201

4)N

amco

30

.77°

N, 9

0.98

°E

4730

20

13, 1

–9

97.5

± 3

4.1

78

.284

.5


Fig. 5. PM10 (a) and PM2.5 (b) yearly mean concentrations at the ground for CB (left), RS (center) experiments and the discrepancy between CB and RS (right).

Optical Properties Fig. 7 presents the spatial distribution of the annual mean

simulated aerosol optical depth at a wavelength of 550 nm (AOD550). The highest modeled AOD550 values were found in South Asia, whereas TP showed lower values in both experiments. Differences between the two simulations were up to 0.24 in South Asia, where the RS experiment revealed a higher amount of fine particle of aerosols than did the CB experiment (Fig. 5(b)). This finding suggests the crucial role of fine particles in AOD550 estimation, which have higher scatter efficiency than coarse particles (Seinfeld et al., 1998). In the TP, differences in the magnitude between the two experiments were less remarkable, with the values ranging from –0.03 to 0.03. Fig. 8 shows the time series of simulated and observed daily average AOD550 values at 15 AERONET sites, and the corresponding statistics are presented in Table S3. The two experiments efficiently represented the temporal variability of observed AOD550 values with the correlation ranging from 0.45 (RS) to 0.50 (CB). The RS experiment overestimated the observed values (mean NMB = 25.7), whereas the CB experiment underestimated the observed values (mean NMB = –7.4). This was primarily because most AERONET stations are located in Southern Asia and the RS experiment showed

overestimation at these sites; that is, the simulated AOD550 value showed biases of –0.06 and 0.09 in the CB and RS experiment, respectively.

The considerable simulation differences in AOD550 values between the CB and RS experiments may partially be due to differences in the aerosol composition. A previous study indicated that fine (NH4)2SO4 and NH4NO3 can scatter light more efficiently (Seinfeld et al., 1998). As evident in Fig. 5, the RS simulation showed higher nitrate and sulfate concentrations than did CB experiment in north India, whereas the two experiments presented similar magnitudes and spatial patterns over the TP. Thus, we selected Kanpur (located in north India) and Namco (located in the TP) to specifically analyze the connection between SIA differences and AOD550. Fig. 9 shows the scatterplots of daily differences in AOD550 values between the two experiments versus the corresponding differences in daily fine nitrate and sulfate concentrations, calculated in 2013. The correlations between SIA differences and AOD550 were beyond 0.6 in Kanpur and around 0.5 in Namco. This suggests that the two SIA compounds were relevant in AOD550 estimations, and higher surface sulfate and nitrate concentrations might lead to higher AOD550 values in RS than in CB, particularly in South Asia.


Fig. 6. SO4 (a), NO3 (b), NH4 (c), BC (d) and OC (e) yearly mean concentrations at the ground for CB (left), RS (center) experiments and the discrepancy between CB and RS (right).


Tab

le 3

. Obs

erve

d an

d si

mul

ated

aer

osol

and

its

com

poun

ds’

conc

entr

atio

n (µ

g m

–3).

Nam

e L

hasa

A

ksu

Shi

gats

e Q

omol

angm

a L

anzh

ou

Nam

co

Hul

ugou

Loc

atio

n 29

.65°

N, 9

1.03

°E

40.4

7°N

, 80.

82°E

29

.15°

N, 8

8.53

°E

28.3

6°N

, 86.

95°E

36.0

3°N

, 103

.5°E

30

.77°

N, 9

0.98

°E38

.23°

N, 9

0.48

°Em

.a.s

.l 36

40

1114

38

36

4276

1520

47

30

4730

T

ime

2013

, 1–1

2 20

13, 1

–12

2013

, 1–9

20

13, 1

–12

2013

, 1–5

, 10

2013

, 1–1

220

13, 1

–11

PM

2.5

OB

S

27.8

± 5

.4

129

± 47

C

B

35.2

65.4

RS

41

.679

.8P

M10

O

BS

42

.4 ±

7.5

22

6.9

± 13

4 17

3 ±

79.2

C

B

49.3

87.6

74

.3R

S

58.6

98.7

89

.8S

O4

OB

S

0.58

± 0

.28

0.74

± 0

.54

15.5

± 5

.8

0.45

± 0

.24

CB

0.

790.

98

9.5

1.04

R

S

1.54

2.31

12

.41.

63

NO

3 O

BS

0.

79 ±

0.2

4 0.

75 ±

0.5

913

.1 ±

6.7

1.

33 ±

0.8

9C

B

0.64

0.56

3.

50.

57

RS

1.

211.

04

5.7

0.54

N

H4

OB

S

0.18

± 0

.16

1.6

± 0.

6 10

.7 ±

3.5

C

B

0.53

0.91

7.

8R

S

0.76

2.67

8.

4O

C

OB

S

5.0

± 1.

7 C

B

0.86

RS

0.

62B

C

OB

S

0.46

± 0

.33

0.17

± 0

.14

0.95

± 0

.72

0.76

± 0

.49

CB

0.

270.

19

0.29

0.57

R

S

0.19

0.14

0.

120.

42

L

i et a

l. (2

015)

; W

an e

t al.

(201

6)

Aih

emai

ti (2

015)

Y

ang

et a

l. (2

016)

L

iu e

t al.

(201

4);

Wan

g et

al.

(201

6)

Li e

t al.

(201

5)


Fig. 7. Yearly mean AOD550 at the ground for CB (left), RS (center) simulation and the discrepancy between RS and CB (right).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

2013/1/1 3-2 5-1 6-30 8-29 10-28 12-27

AO

D55

0

Local time

Observed CB RS

0

0.2

0.4

0.6

0.8

1

Observed CB RS

AO

D55

5

Fig. 8. AOD550 time series of daily mean values at all AERONET stations (left) and box whisker plots (right) in 2013.

Particle size is another crucial aspect affecting the simulation of AODs. The angstrom exponent at the interval of 440–675 nm, an indicator of particle distribution, was analyzed together with AODs, where values ranging from 1 to 2 suggest that small aerosols dominate in the accumulation mode, whereas values close to zero represent the existence of coarse particles (Eck et al., 1999). Specifically, the majority of angstrom exponent values were lower than 1 at Kanpur (Figs. 10(a) and 10(b)), indicating to dominance of coarse-mode aerosols in this region. The overestimation of AOD550 values in the RS experiment may be due to the distribution of dust emissions mainly in the fine fraction in the MADE/SORGAM scheme, and the fine particles had a higher scattering efficiency. By contrast, at the Namco station, which had a high angstrom exponent value, there was a better agreement with the measured AOD550 value in the CB experiment than in the RS experiment (Figs. 10(c) and 10(d)). This is mainly because the MOSAIC scheme in CB divides aerosols into four particle ranges (MADE/SORGAM has only three modules), which can adequately represent fine particles at Namco. Taken together, deficiencies in AODs between the simulation and observation increased for the low angstrom exponent value and the CB simulation

performed better for AODs in the TP.

CONCLUSIONS

In this study, two annual WRF-Chem modeling configurations were performed to investigate the effect of different chemical mechanisms on the reproduction of aerosol concentrations and aerosol optical properties over the TP and surrounding regions. Simulated results were compared with observations from CMDN, CRU, ERA-Interim, AERONET, and in situ stations collected from other papers. The results indicated that WRF-Chem could capture the spatial and temporal distribution of the surface temperature, relative humidity, and wind over the study area. The CBMZ gas-phase mechanism demonstrated lower O3 and higher NO2 and HNO3 concentrations than did the RADM2, and this is because of more efficient O3-NO titration and a higher reaction rate constant for NO2 + OH + M → HNO3 in CBMZ. Lower SO2 and higher H2O2 surface concentration were observed in the RS experiment, because SO2 could easily form particulate sulfate through cloud oxidation in RADM2.

Surface PM2.5 and PM10 concentrations obtained from CB


‐0.6

‐0.4

‐0.2

0

0.2

0.4

0.6

-10 -5 0 5 10AO

D55

0

NO3(ug/m3)

Kanpur

CB‐RSR=0.65

‐0.4

‐0.3

‐0.2

‐0.1

0

0.1

0.2

0.3

0.4

-4 -3 -2 -1 0 1 2 3 4AO

D55

0

SO4(ug/m3)

Nam_co

CB‐RSR=0.52

-0.5

-0.3

-0.1

0.1

0.3

0.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

AO

D55

0

NO3 (ug/m3)

Nam_co

CB-RSR=0.48

Fig. 9. Scatter plot diagram of AOD550 daily differences versus SO4 (left) and NO3 (right) daily differences at Kanpur (upper) and Namco (under) AERONET stations in 2013. Differences are calculated as CB-RS.

and RS configurations presented a similar spatial pattern, and the RS experiment showed a higher aerosol concentration. This is partly due to the difference in aerosol compounds, such as SIAs produced by gaseous compounds. In particular, the annual mean SO4, NO3 and NH4 concentrations were higher in RS than in CB, and BC and OC concentrations were generally lower in RS. Additionally, different distribution of the calculated concentrations between bins and modes contributed to the discrepancy in aerosol concentrations. The CB experiment revealed a closer agreement with the observed values of aerosol and its compounds in the TP.

The model sufficiently captured the spatial and temporal pattern of AOD550 values and had a correlation ranging from 0.45 (RS) to 0.50 (CB) with observations at 15 AERONET sites. In the two simulation configurations, AOD550 showed potential positive correlation with SIA (e.g., sulfate and nitrate). Particle size was another crucial aspect in modeling AODs. At Namco, the CB experiment more satisfactorily represented AODs than did the RS, because the MOSAIC scheme divides aerosols into four particle bins that can better reflect the particle size distribution over the TP, whereas MADE/SORGAM has only three bins.

The results of this study suggest that the selection of chemical mechanisms is a key aspect of model simulation,

even when the online-coupled WRF-Chem model is concerned. The CBMZ gas-phase chemical mechanism and MOSAIC aerosol scheme could satisfactorily reconstruct the reconstruction the concentrations and optical depth of aerosols over the TP. Nonetheless, coarse particles were still poorly represented by the chosen aerosol schemes. Therefore, a revision of the treatment concerning the coarse aerosol size distribution is highly recommended in the future, such as the MOSAIC aerosol scheme with eight sectional bins. ACKNOWLEDGMENTS

This work was supported by the Key Research Program of the Chinese Academy of Sciences (KJZD-EW-G03-04), the National Natural Science Foundation of China (41630754, 91644225, 41701074), the Open Program (SKLCS-OP-2017-02) from State Key Laboratory of Cryospheric Science, and the China Postdoctoral Science Foundation (2016M602896). The computer resources were supported by the Supercomputing Center, Big Data Center of Cold and Arid Region Environment and Engineering Research Institute, Chinese Academy of Sciences and we are also grateful to Guohui Zhao for his help of installing software.


‐1

‐0.6

‐0.2

0.2

0.6

1

0 0.5 1 1.5 2

AO

D55

0(C

B-O

BS

)

ANGSTROM EXP(440-675nm)

kanpur

-1

-0.6

-0.2

0.2

0.6

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

AO

D55

0(C

B-O

BS

)


Nam_co

-1

-0.6

-0.2

0.2

0.6

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

AO

D55

0(R

S-O

BS)


Nam_co

Fig. 10. Scatter plot diagram of AOD550 daily biases versus daily Angstrom exponent at Kanpur (upper) and Namco (under) AERONET stations in 2013.

SUPPLEMENTARY MATERIAL

Supplementary data associated with this article can be found in the online version at http://www.aaqr.org. REFERENCES Ackermann, I.J., Hass, H., Memmesheimer, M., Ebel, A.,

Binkowski, F.S. and Shankar, U. (1998). Modal aerosol dynamics model for Europe: Development and first applications. Atmos. Environ. 32: 2981–2999.

Aihemaiti, M. (2015). Study on Environmental air quality and change trend of Aksu city. Master Dissertation, Xinjiang University. (in Chinese)

Andreae, M.O., Jones, C.D. and Cox, P.M. (2005). Strong present-day aerosol cooling implies a hot future. Nature 435: 1187–1190.

Balzarini, A., Honzak, L., Pirovano, G., Riva, G.M. and Zabkar, R. (2014). WRF-Chem model sensitivity analysis to chemical mechanism choice. Air pollution modeling and its application XXIII. Springer International Publishing, pp. 557–561.

Balzarini, A., Pirovano, G., Honzak, L., Žabkar, R., Curci, G. and Forkel, R. (2015). WRF-Chem model sensitivity

to chemical mechanisms choice in reconstructing aerosol optical properties. Atmos. Environ. 115: 604–619.

Brugh, J.M. J.A.D., Schaap, M., Vignati, E., Dentener, F., Kahnert, M., Sofiev, M., Huijnen, M.C. and Krol, M.C. (2011). The European aerosol budget in 2006. Atmos. Chem. Phys. 11: 1117–1139.

Bu, D., Zhang, Y., Kong, L., Fu, H., Hu, Y., Chen, J., Li, L. and Qiong, A. (2015). Individual particle analysis of aerosols collected at Lhasa City in the Tibetan Plateau. J. Environ. Sci. 29: 165–177.

Chen, D.S., Xie, X., Zhou, Y., Lang, J.L., Xu, T.T., Yang, N., Zhao, Y.H. and Liu, X.X. (2017). Performance evaluation of the WRF-Chem model with different physical parameterization schemes during an extremely high PM2.5 pollution episode in Beijing. Aerosol Air Qual. Res. 17: 262–277.

Chen, F. and Dudhia, J. (2001). Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Weather Rev. 129: 569–585.

Cong, Z.Y., Kang, S.C., Smirnov, A. and Holben, B. (2009). Aerosol optical properties at Nam Co, a remote site in central Tibetan Plateau. Atmos. Res. 92: 42–48.

Cong, Z.Y., Kang, S.C., Kawamura, K., Liu, B., Wan, X.,


Wang, Z., Gao, S. and Fu, P. (2015). Carbonaceous aerosols on the south edge of the Tibetan Plateau: Concentrations, seasonality and sources. Atmos. Chem. Phys. 15: 1573–1584.

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.N. and Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137: 553–597.

Demore, W.B., Margitan, J.J., Molina, M.J., Watson, R.T., Golden, D.M., Hampson, R.F., Kurylo, M.J., Howard, C.J. and Ravishankara, A.R. (1994). Chemical kinetics and photochemical data for use in stratospheric modeling. National Aeronautics and Space Administration, Jet Propulsion Laboratory, Pasadena, Calif, pp. 1135–1151.

Dubovik, O., Holben, B., Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanre, D. and Slutsker, I. (2002). Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 59: 590–608.

Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'Neill, N.T., Slutsker, I. and Kinne, S. (1999). Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. Atmos. 27: 333–331.

Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39: 6957–6975.

Emmons, L.K., Walters, S., Hess, P.G., Lamarque, J.F., Pfister, G.G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S.L. and Kloster, S. (2010). Description and evaluation of the model for ozone and related chemical tracers, version 4 (mozart-4). Geosci. Model Dev. 3: 43–67.

Freitas, S.R., Longo, K.M., Alonso, M.F., Pirre, M., Marecal, V., Grell, G., Stockler, R., Mello, R.F. and Sánchez Gácita, M. (2011). PREP-CHEM-SRC – 1.0: A preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models. Geosci. Model Dev. 4: 419–433.

Gaur, A., Tripathi, S.N., Kanawade, V.P., Tare, V. and Shukla, S.P. (2014). Four-year measurements of trace gases (SO2, NOx, CO, and O3) at an urban location, Kanpur, in Northern India. J. Atmos. Chem. 71: 1–19.

Gery, M.W., Whitten, G.Z., Killus, J.P. and Dodge, M.C. (1989). A photochemical kinetics mechanism for urban and regional scale computer modeling. J. Geophys. Res. 94: 12925–12956.

Grell, G.A. and Devenyi, D.A. (2002). Generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett. 29: 38-1–38-4.

Grell, G.A., Peckham, S.E., Schmitzc, R., McKeenb, S.A., Frostb, G., Skamarockd, W.C. and Eder, B. (2005). Fully coupled ‘‘online’’ chemistry within the WRF model. Atmos. Environ. 39: 6957–6975.

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I. and Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (model of emissions of gases and aerosols from nature). Atmos. Chem. Phys. 6: 3181–3210.

Gustafson, Jr., W.I. Chapman, E.G., Ghan, S.J., Easter, R.C. and Fast, J.D. (2007). Impact on modeled cloud characteristics due to simplified treatment of uniform cloud condensation nuclei during NEAQS 2004. Geophys. Res. Lett. 34: 255–268.

Holben, B.N., Tanre, D. Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., Castele, J.V., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karneli, A., Neill, N.T., Pietras, C., Pinker, R.T., Voss, K. and Zibordi, G. (2001). An emerging groundbased aerosol climatology: Aerosol optical depth from AERONET. J. Geophys. Res. 106: 12067–12097.

Huang, J., Fu, Q., Su, J. and Tang, Q. (2009). Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liou radiation model with CERES constraints. Atmos. Chem. Phys. 9: 4011–4021.

Jacobson, M.Z. (2001). Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature 409: 695–697.

Ji, Z., Kang, S., Zhang, D., Zhu, C., Wu, J. and Xu, Y. (2011). Simulation of the anthropogenic aerosols over south Asia and their effects on Indian summer monsoon. Clim. Dyn. 36: 1633–1647.

Ji, Z., Kang, S., Cong, Z., Zhang, Q. and Yao, T. (2015). Simulation of carbonaceous aerosols over the third pole and adjacent regions: Distribution, transportation, deposition, and climatic effects. Clim. Dyn. 45: 2831–2846.

Ji, Z. (2016). Modeling black carbon and its potential radiative effects over the Tibetan Plateau. Adv. Clim. Change Res. 7: 139–144.

Ji, Z., Kang, S., Zhang, Q., Cong, Z., Chen, P. and Sillanpää, M. (2016). Investigation of mineral aerosols radiative effects over High Mountain Asia in 1990–2009 using a regional climate model. Atmos. Res. 178–179: 484–496.

Jiménez, P.A. and Dudhia, J. (2012). Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J. Appl. Meteorol. Climatol. 51: 300–316.

Jones, A., Roberts, D.L. and Slingo, A. (1994). A climate model study of the indirect radiative forcing by anthropogenic sulphate aerosols. Nature 370: 450–453.

Kang, S.C., Xu, Y., You, Q.L., Flügel, W.A., Pepin, N. and Yao, T. (2010). Review of climate and cryospheric change in the Tibetan Plateau. Environ. Res. Lett. 5: 75–82.

Kang, S.C., Chen, P.F., Li, C.L., Liu, B. and Cong, Z.Y. (2016). Atmospheric aerosol elements over the inland


Tibetan Plateau: Concentration, seasonality, and transport. Aerosol Air Qual. Res. 16: 789–800.

Kiehl, J.T. and Briegleb, B.P. (1993). The relative roles of sulfate aerosols and greenhouse gases in climate forcing. Science 260: 311–314.

Lau, W.K.M., Maengki, K., Kim, K.M. and Wooseop, L. (2010). Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols. Environ. Res. Lett. 5: 302–307.

Li, C.L., Chen, P.F., Kang, S.C., Yan, F.P., Hu, Z.H., Qu, B. and Sillanpää, M. (2016). Concentrations and light absorption characteristics of carbonaceous aerosol in PM2.5 and PM10 of Lhasa city, the Tibetan Plateau. tmos. Environ. 127: 340–346.

Li, Z.J., Li, Z.X., Wang, T.T., Gao, Y., Cheng, A.F., Guo, X.Y., Guo, R., Jia, B., Song, Y.X., Han, C.T. and Theakstone, W.H. (2015). Composition of wet deposition in the central Qilian Mountains, China. Environ. Earth Sci. 73: 7315–7328.

Liu, Y.M. (2014). The spatial and temporal variations of atmospheric pollutants in China and the relationship with meteorological factors. Master dissertation, Linzhou University, (Chinese)

Ma, Y., Wang, Y., Wu, R., Hu, Z., Yang, K., Li, M., Ma, W., Zhong, L., Sun, F., Chen, X., Zhu Z., Wang, S. and Ishikawa, H. (2009). Recent advances on the study of atmosphere-land interaction observations on the Tibetan Plateau. Hydrol. Earth Syst. Sci. 13: 11033–1111.

Ming, J., Xiao, C.D., Wang, F.T., Li, Z.Q. and Li, Y.M. (2016). Grey Tienshan Urumqi Glacier No.1 and light-absorbing impurities. Environ. Sci. Pollut. Res. Int. 23: 9549–9558.

Mitchell, T.D. and Jones, P.D. (2005). An improved method of constructing a database of monthly climate observations and associated high resolution grids. Int. J. Climatol. 25: 693–712.

Mozurkewich, M. (1993). The dissociation constant of ammonium nitrate and its dependence on temperature, relative humidity and particle size. Atmos. Environ. 27A: 261–270.

Pirovano, G., Balzarini, A., Bessagnet, B., Emery, C., Kallos, G., Meleux, F., Mitsakou, C., Nopmongcol, U., Riva, G.M. and Yarwood, G. (2012). Investigating impacts of chemistry and transport model formulation on model performance at European scale. Atmos. Environ. 53: 93–109.

Ramanathan, V. and Carmichael, G. (2008). Global and regional climate changes due to black carbon. Nat. Geosci. 1: 221–227.

Schaefer, J.T. (1990). The critical success index as an indicator of warning skill. Weather Forecasting 5: 570–575.

Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S. and Ebel, A. (2001). Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J. Geophys. Res. 106: 28275–28293.

Seinfeld, J.H., Pandis, S.N. and Noone, K. (1998). Atmospheric chemistry and physics, from air pollution to climate change. Phys. Today 51: 88–90.

Solazzo, E., Bianconi, R., Vautard, R., Appel, K.W., Moran, M.D., Hogrefe, C., Bessagnet, B., Brandt, J., Christensen, J.H., Chemel, C., Coll, I., Denier van der Gon, H., Ferreira, J., Forkel, R., Francis, X.V., Grell, G., Grossi, P., Hansen, A.B., Jeričević, A., Kraljević, L., Miranda, A.I., Nopmongcol, U., Pirovano, G., Prank, M., Riccio, A., Sartelet, K.N., Schaap, M., Silver, J.D., Sokhi, R.S., Vira, J., Werhahn, J., Wolke, R., Yarwood, G., Zhang, J., Rao, S.T. and Galmarini, S. (2012). Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII. Atmos. Environ. 53: 60–74.

Stockwell, W.R., Middleton, P., Chang, J.S. and Tang, X. (1990). The second generation regional acid deposition model chemical mechanism for regional air quality modeling. J. Geophys. Res. Atmos. 95: 16343–16367.

Wan, X., Kang, S.C., Xin, J.Y., Liu, B., Wen, T.X., Wang, P.L., Wang, Y.S. and Cong, Z.Y. (2016). Chemical composition of size-segregated aerosols in Lhasa city, Tibetan Plateau. Atmos. Res. 174: 142–150.

Wei, L.F., Zhang, L., Ji, X.U., Pan, X.S., Zhu, Y.F. and Lin, J.M. (2011). Research on distribution of particle size and stability of bio-aerosol. Chin. J. Disinfect. 28: 158–160.

Wiedinmyer, C., Akagi, S.K., Yokelson, R.J. and Emmons, L.K. (2010). The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. Discuss. 3: 2439–2476.

Xin, W., Yan, N., Hong, C., Bo, W., Tao, H. and Xia, D.S. (2016). Pollution characteristics and source apportionment of PM2.5 in Lanzhou city. Environ. Sci. 37: 1619–1628. (in Chinese)

Xu, B.Q., Cao, J.J., Hansen, J., Yao, T.D., Joswia, D.R., Wang, N.L., Wu, G.J., Wang, M., Zhao, H.B., Yang, W., Liu, X.Q. and He, J.Q. (2009). Black soot and the survival of Tibetan glaciers. Proc. Natl. Acad. Sci. U.S.A. 106: 22114–22118.

Yang, J. and Duan, K. (2016). Effects of initial drivers and land use on WRF modeling for near-surface fields and atmospheric boundary layer over the northeastern Tibetan Plateau. Adv. Meteorol. 2016: 1–16.

Yang, J.H., Duan, K.Q., Wu, J.K., Qin, X., Shi, P.H, Liu, H.C., Xie, X.L., Zhang, X. and Sun, J.Y. (2015). Effect of data assimilation using WRF-3DVAR for heavy rain prediction on the northeastern edge of the Tibetan plateau. Adv. Meteorol. 2015: 1–14.

Yang, Y., Zhou, R., Yan, Y., Yu, Y., Liu, J., Di, Y., Du, Z. and Wu, D. (2016). Seasonal variations and size distributions of water-soluble ions of atmospheric particulate matter at Shigatse, Tibetan Plateau. Chemosphere 145: 560–567.

Yao, T.D., Thompson, L., Yang, W., Yu, W.S., Yang, G., Guo, X.J, Yang, X.X., Duan, K.Q., Zhao, H.B., Xu, B.Q., Pu, J.C., Lu, A.X., Xiang, Y., Kattel, D.B. and Joswiak D. (2012). Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Change 2: 663–667.

Ye, D.Z. and Wu, G.X. (1998). The role of the heat source


of the Tibetan Plateau in the general circulation. Meteorol. Atmos. Phys. 67: 181–198.

Yun, S.M., Koo, Y.S., Jung, O.J., Yun, S.M., Koo, Y.S. and Jung, O.J. (2014). Analysis of sensitivity to prediction of particulate matters and related meteorological fields using the WRF-Chem model during Asian dust episode days. J. Korean Earth Sci. Soc. 35: 1–18.

Zaveri, R.A. and Peters, L.K. (1999). A new lumped structure photochemical mechanism for large-scale applications. J. Geophys. Res. Atmos. 104: 30387–30415.

Zaveri, R.A., Easter, R.C., Fast, J.D. and Peters, L.K. (2008). Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. Atmos. 113: 1395–1400.

Zhang, Y., Wen, X.Y. and Jang, C.J. (2010). Simulating chemistry–aerosol–cloud–radiation–climate feedbacks

over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmos. Environ. 44: 3568–3582.

Zhao, C., Liu, X., Leung, L.R. and Johnson, B. (2010). The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: Modeling sensitivities to dust emissions and aerosol size treatments. Atmos. Chem. Phys. 10: 8821–8838.

Zhao, C., Liu, X., Ruby Leung, L. and Hagos, S. (2011). Radiative impact of mineral dust on monsoon precipitation variability over West Africa. Atmos. Chem. Phys. 11: 91–103.

Received for review, May 29, 2017

Revised, July 30, 2017 Accepted, August 17, 2017

sensitivity analysis of chemical mechanisms in the wrf ... · additionally, to analyse the impact...

Documents