MICS-Asia II: Model intercomparison and evaluation of ozone and relevant species

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Atmospheric Environment 42 (2008) 3491–3509 www.elsevier.com/locate/atmosenv

MICS-Asia II: Model intercomparison and evaluation of ozone and relevant species Z. Hana,b,, T. Sakuraib, H. Uedab, G.R. Carmichaelc, D. Streetsd, H. Hayamie, Z. Wanga, T. Hollowayf, M. Engardtg, Y. Hozumib, S.U. Parkh, M. Kajinoi, K. Sarteletj, C. Fungk, C. Bennetg, N. Thongboonchooc, Y. Tangc, A. Changk, K. Matsudal, M. Amannm a

Institute of Atmospheric Physics, Chinese Academy of Science, 100029 Beijing, China b Acid Deposition and Oxidant Research Center, Niigata, Japan c Center for Global and Regional Environmental Research, University of Iowa, Iowa, USA d Argonne National Laboratory, Illinois, USA e Central Research Institute of Electric Power Industry, Chiba, Japan f University of Wisconsin-Madison, Wisconsin, USA g Swedish Meteorological and Hydrological Institute, Norrkoping, Sweden h Seoul National University, Seoul, Korea i Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan j Centre d’Enseignement et de Recherche en Environnement Atmospherique, Marne la Valle´e, France k Hong Kong Environmental Protection Department, Hong Kong SAR, China l Meisei University, Hino, Tokyo, Japan m International Institute for Applied System Analysis, Laxenburg, Austria Received 21 March 2007; received in revised form 13 July 2007; accepted 13 July 2007

Abstract Eight regional Eulerian chemical transport models (CTMs) are compared with each other and with an extensive set of observations including ground-level concentrations from EANET, ozone soundings from JMA and vertical profiles from the TRACE-P experiment to evaluate the models’ abilities in simulating O3 and relevant species (SO2, NO, NO2, HNO3 and PAN) in the troposphere of East Asia and to look for similarities and differences among model performances. Statistical analysis is conducted to help estimate the consistency and discrepancy between model simulation and observation in terms of various species, seasons, locations, as well as altitude ranges. In general, all models show a good skill of simulating SO2 for both ground level and the lower troposphere, although two of the eight models systematically overpredict SO2 concentration. The model skills for O3 vary largely with region and season. For ground-level O3, model results are best correlated with observations in July 2001. Comparing with O3 soundings measured in the afternoon reveals the best consistency among models in March 2001 and the largest disparity in O3 magnitude in July 2001, although most models produce the best correlation in July as well. In terms of the statistics for the four flights of TRACE-P experiment, most models appear to be able to accurately capture the variability in the lower troposphere. The model performances for NOx are relatively poor, with lower correlation and with almost all models tending to underpredict NOx levels, due to Corresponding author. Institute of Atmospheric Physics, Chinese Academy of Science, 100029 Beijing, China. Tel.: +86 10 82995158; fax: +86 10 62045230. E-mail address: [email protected] (Z. Han).

1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.07.031

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larger uncertainties in either emission estimates or complex chemical mechanism represented. All models exhibit larger RMSE at altitudes o2 km than 2–5.5 km, mainly due to a consistent tendency of these models towards underprediction of the magnitude of intense plumes that often originate from near surface. Relatively lower correlation at altitudes 2–5.5 km may be attributed to the models’ limitation in representing convection or potential chemical processes. Most of the key features in species distribution have been consistently reproduced by the participating models, such as the O3 enhancement in the western Pacific Ocean in March and in northeast Asia in July, respectively, although the absolute model values may differ considerably from each other. Large differences are found among models in the southern parts of the domain for all the four periods, including southern China and northern parts of some Southeast Asia countries where the behaviors of chemical components and the ability of these models are still not clearly known because of a lack of observational databases. r 2007 Elsevier Ltd. All rights reserved. Keywords: Chemical transport model; EANET; TRACE-P; Evaluation; Model intercomparison; O3 and relevant species; Seasonality

1. Introduction East Asia has been experiencing continuous economic growth in the past two decades (Streets et al., 2003), but brought about noticeable degradation of air quality and ecosystem at the same time. Significant increases in emissions and changes in environment in East Asia also have important implications for atmospheric chemical cycling and climate change at both regional and global scales. A large number of projects have been launched by Asian countries to promote our understanding of air pollution problems and to conserve our living environment in a practical and effective way. Chemical transport models (CTMs) serve as useful tools in scientific research and policy making. These models are normally composed of a series of modules, which represent complex physical, chemical and biological processes. Due to limitations in our understanding and computational resources, many processes are usually handled by parameterization. Various treatments and numerical approaches at differing levels of complexity have been incorporated into models of different structure, and modelers apply what they think the most appropriate parameters and inputs for specific model simulation. As a result, models may provide a variety of simulation results, and such disparity among models might be intensified due to nonlinear interactions among many kinds of processes. To better understand model performance and accompanying uncertainty, model evaluation is needed and model intercomparison is a good way for promoting model’s ability through identifying potential uncertainties. A number of air quality models or CTMs have been developed and widely used to explore a series of concerned problems regarding the transport,

transformation and deposition mechanism of chemical components and source-receptor relationship in East Asia (An et al., 2002; Carmichael et al., 2003; Chang and Park, 2004; Engardt, 2000; Han et al., 2005; Hayami and Ichikawa, 2001; Holloway et al., 2002; Ichikawa et al., 2001; Kajino et al., 2004; Ueda et al., 2000; Wang et al., 2002). Several intercomparisons of global scale models have been conducted with focuses on radon, carbon monoxide, sulfur dioxide and sulfate, as well as photochemistry (Jacob et al., 1997; Kanakidou et al., 1999; Barrie et al., 2001; Olson et al., 1997), some of which are supported by IPCC and IGAC. However, model intercomparison study specifically for Asia is very limited (Phadnis and Carmichael, 1998; Kiley et al., 2003) and require more efforts. The MICSAsia project was initiated in 1998. Carmichael et al. (2002) introduced the findings and outcome of MICS-Asia phase I, which focused on long-range transport and deposition of sulfur in East Asia. MICS-Asia phase II was initiated in 2003, as an extension of phase I, it includes more species of concern, such as ozone, nitrogen compounds, aerosols in additional to sulfur. Detailed information on this project is referred to an overview paper (Carmichael et al., 2008). The main objectives of this paper are to evaluate the abilities of eight regional scale CTMs in simulating ozone and relevant chemical species, to identify the consistency and discrepancy among models, and to explore the potential factors responsible for the difference among models and deviation of model results from observations, as well as to reveal the characteristics of key chemical components in the troposphere of East Asia. A wide variety of observations are collected from EANET (Acid Deposition Monitoring Network in East Asia), JMA (Japan meteorological agency) and

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TRACE-P (transport and chemical evolution over Pacific) experiment that allows for a rigorous and comprehensive evaluation of model performances. Several key trace species are investigated, namely SO2, O3, NO, NO2, HNO3 and PAN, with focus on O3, NOx and SO2 due to relatively abundant observations and their significance in atmospheric chemistry. This paper appears to be the first attempt to provide a perspective of model intercomparison and evaluation for ozone and relevant species in East Asia. It is expected to provide valuable insights into the abilities and limitations of current regional CTMs and to indicate directions for future model development. 2. Information on participating models and numerical experiment 2.1. Model description and relevant parameters In total, eight model groups participated in MICS-Asia phase II. All of the models are regional 3-D Eulerian models. These models are from Seoul

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National University (Chang and Park, 2004), PATH (pollutants in the atmosphere and their transport over Hongkong) from Hongkong Environmental Protection Department, RAQM (regional air quality model) from Acid Deposition and Oxidant Research Center, Japan (Han et al., 2004), MSSP (model system for soluble particles) from Disaster Prevention Research Institute, Kyoto University (Kajino et al., 2004), STEM (the sulfur transport Eulerian model) from Center of Global and Regional Environmental Research (CGRER), Iowa University (Carmichael et al., 2003), MATCH (multiscale atmospheric transport and chemistry model) from Swedish Meteorological and Hydrological Institute (Engardt, 2000), CMAQ utilized by Central Research Institute of Electric Power Industry, Japan (http://www.epa.gov/asmdnerl/CMAQ/) and Polair3D From CEREA (Centre d’Enseignement et de Recherche en Environnement Atmospherique), France (Boutahar et al., 2004). Table 1 summarizes the basic information on the eight participating models. We deliberately hide the name of each model because the main objective of this study is not ranking these models.

Table 1 Basic structures, schemes and relevant parameters of the eight participating models Models

M1

Domain

10.41S–54.31N 2.51S–451N 201N–501N 201N–501N 56.61E–1681E 751E–1501E 901E–1451E 901E–1451E 45 km 40.5 km 0.51 0.51

7.91N–52.91N 12.51S–541N 7.251S–53.71N 19.71N–48.81N 74.71E–163.41E 75.51E–1581E 56.71E–153.31E 88.61E–150.41E 80 km 0.51 45 km 45 km

25sp levels

21sp levels 12sz levels

12sz levels

16sz levels

25sp levels

10sp levels

9Z levels

17 m

10 m

50 m

50 m

75 m

10 m

7m

10 m

12 km Lambert

9 km Lambert

9 km Lat/lon

9 km Lat/lon

12 km Polarsterographics RAMS/ ECMWF Finite diff.

7 km Lat/lon

6 km Lambert

5.5 km Lambert

ECMWF

MM5/ GANAL Piecewise parabolic

Same as M7

Horizontal resolution Vertical resolution Depth of first layer Model top Projection

M2

M3

Meteorology MM5/ NCEP MM5/ MM5/ ECMWF GANAL Advection Bott (1989) Bott (1989) Walcek and Aleksic (1998) Vertical K-theory K-theory K-theory diffusion Dry Wesely (1989) Wesely Wesely deposition (1989) (1989) Wet Henry’s law RADM RADM scavenging Gas CIT CB-IV Condensed chemistry mechanism CB-IV Aqueous RADM RADM RADM chemistry

M4

MM5/NCEP Walcek and Aleksic 1.5 order TKE Wesely (1989) Henry’s law

M5

M6

Bott (1989)

K-theory

Holtslag et al. (1995) Wesely (1989) Engardt (2000) Fixed rate Berge(1993)

Simplified CB-4 SAPRC 99 Chameides(1984) Fixed rate

Simpson et al. (1993) Berge(1993)

M7

M8

3rd-order Spee (1998)

K-theory

Troen and Mahrt (1986) Wesely (1989) Wesely (1989) RADM CB-IV

Sportisse and Du Bois (2002) RACM

RADM

RACM

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The participating models employ various domains with a variety of horizontal and vertical extensions. Fig. 1a shows a reference domain previously recommended by MICS-Asia secretariat, and a smaller domain, which is common to all the models (901–1451E, 201–501N). Also shown are locations of ground-level monitoring stations of EANET, most of which (open circle) are concentrated in the common domain, with remaining located sporadically over Southeast Asia, Mongolia and Russia. Fig. 1b presents the locations of O3 sounding sites and the four flight tracks of TRACE-P. In this study, all models utilized a common data set for anthropogenic and biomass burning emissions from Streets et al. (2003) in order to rule out the potential discrepancy that might arise from using different emission estimates. This emission inventory prepared by CGRER is monthly varied and aggregated on a grid of 0.51. The volcanic emission is derived from Kajino et al. (2004). The release heights were prescribed at an altitude of about 300 m for large point source and 1500 m for volcanic emission. Natural emissions (biogenic VOCs, soil and lighting NOx, dust and seasalt aerosol) are optional, but have not been taken into account by most models in this study. All models except M5 used a similar horizontal grid

resolution of about 45 km on Lambert or 0.51 on spherical projection, whereas the vertical structure of the models varied significantly (shown in Table 1). Most models use a common data source for boundary conditions, which is derived from a global CTM, namely MOZART-II (Holloway et al., 2008). M5 used its own lateral boundary conditions based on recent observations and an assumption of O3-PV relationship to reflect stratospheric O3 influence. Meteorological fields were derived from MM5 (Grell et al., 1994) with different options (M1, M2, M3, M4, M7 and M8), RAMS (Pielke et al., 1992) (M5) and ECWMF reanalysis data (M6), respectively (Table 1). 2.2. Data protocol for comparison Four periods were selected for MICS-Asia phase II experiment (March 2001, July 2001, December 2001, March 2002). All model groups reported their model results according to the data protocol. A standard data set of model predictions was requested and analyzed, which includes: (1) Gridded monthly mean concentrations of SO2, NO, NO2, O3, HNO3 and PAN in the lowest

Fig. 1. (a) Model domains and locations of the ground-level monitoring sites of EANET (open circle denotes sites within the common domain, open triangle means sites outside, ID numbers of each site are also shown). (b) Locations of the O3 sounding sites and the four flight tracks during the TRACE-P campaign adopted for this study.

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model layer and at four specific altitudes (300, 1500, 3000 and 6000 m) for four periods. (2) Daily mean concentrations of the above species in the lowest model layer at 43 monitoring sites of EANET for four periods. (3) O3 vertical profiles at four monitoring sites (Sapporo, Tateno, Kagoshima, Naha), at 06:00 UTC of specific days in the four periods. (4) Gridded hourly concentrations of SO2, NO, NO2, O3, HNO3 and PAN within the prescribed domain (1101E–1401E, 201N–401N, below 12 km) for specific days (17, 18, 21 and 27) of March 2001. These data sets were compared with available observations from EANET, JMA and the TRACEP experiment. Mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) are calculated to help evaluate and interpret model performances. 3. Results and discussions 3.1. Monthly mean concentrations 3.1.1. Comparison with ground-level observations of SO2, NO2 and O3 Table 2 shows the statistics for monthly mean concentrations of SO2, NO2 and O3 over all available sites for the combination of four periods. Results of seven models and ensemble means are compared with observations. M5 is not included because it only reports one period. A common domain of 901E–1451E, 201N–501N (shown in Fig. 1) is chosen to ensure an identical statistical analysis. Each of the models generally contains 69,

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53 and 42 pairs of samples (modeled and observed monthly means) for SO2, NO2 and O3 for all four periods, respectively. All O3 monitoring sites are from Japan, except for March 2002 when three sites from South Korea, one site from Mongolia and one from Thailand are also available. For SO2 and NO2, a number of monitoring sites in China, Korea, Japan, Russia, Mongolia and Vietnam are included where observational data are available. It can be found from Table 2 that all models are able to generally capture the variability of SO2, with R ranging from 0.36 of M2 to 0.61 of M8, but almost all models tend to overpredict SO2 concentrations. Although M8 exhibits the best correlation (0.61), it has the largest MBE and RMSE. M2 shows a substantial overprediction of SO2 as well. Most models exhibit considerably lower correlations for NO2 than for SO2, whereas M1 and M3 show similar correlations for the two species. Most models tend to underpredict NO2 concentration, whereas M2 distinctly overpredict NO2, with the largest MBE and RMSE among all. The correlations for O3 vary from nearly 0.0 to as high as 0.77. M6, M7 and M8 exhibit notably larger correlations and smaller RMSE than the rest, but together with M2 and M4, they all tend to underpredict O3 levels, with MBE being 2.2 to 18.8 ppbv. Such negative biases are mainly due to the significant underprediction of O3 in March (not shown). M2 predicts notably lower O3 concentration. This can be partly explained by the modeled too high NOx concentration (Table 2), which mainly results from the less vertical diffusivity near surface compared with that in the other models (discuss later). The correlations between the ensemble means and observations are

Table 2 Statistics for monthly mean concentrations for the combination of four periods Species

Statistics

Model M1

M2

M3

M4

M6

M7

M8

EMS

SO2 (2.8)

R MBE RMSE

0.54 0.30 4.28

0.36 8.80 25.72

0.48 0.35 5.00

0.46 1.80 9.63

0.55 0.97 6.27

0.51 1.30 7.49

0.61 10.74 26.21

0.51 2.89 10.32

NO2 (7.6)

R MBE RMSE

0.48 2.54 6.20

0.10 3.43 10.32

0.51 0.13 5.31

0.21 0.10 7.68

0.01 0.70 7.48

0.27 2.28 6.46

0.31 0.66 3.46

0.29 0.64 6.17

O3 (40.3)

R MBE RMSE

0.36 2.52 11.60

0.25 18.83 23.04

0.41 0.91 11.55

0.06 3.95 14.35

0.70 5.17 9.62

0.77 2.22 7.58

0.63 6.73 10.97

0.62 4.28 9.78

Unit: ppbv, values in bracket denote observations, EMS represents the ensemble mean statistics.

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0.62 for O3, 0.51 for SO2, and 0.29 for NO2, respectively. The tendencies of model ensemble towards overprediction of SO2 and underprediction of O3 and NO2 are also reflected. 3.1.2. Modeled and observed monthly means of SO2, NO2 and O3 at EANET sites in four periods Fig. 2 illustrates the modeled and observed ground level monthly mean concentrations of SO2, NO2 and O3 at EANET monitoring sites where observational data are available. Ensemble means, derived from averaging all the participating models are also presented to exhibit a composite of model performances. A logarithmic scale is used for SO2 and NO2. From Fig. 2: (1) all models show good skill in simulating SO2 spatial distribution, reflecting the location and intensity of SO2 emission, with higher levels over China, moderate levels over Japan, and very low levels over the western Pacific. The models generally tend to overpredict SO2 at site 2 (Jinyunshan), site 4 (Weishuiyuan) of China, site 22 (Ijira) of Japan, and all urban sites of Thailand, and to predict lower values at Russian sites for all periods. (2) There exists a tendency for all models to underpredict NO2 at most of the urban sites, such as site 6 (Hongwen) and 8 (Xiangzhou) of China, site 24 (Petaling-Jaya) of Malaysia, and site 37, 38 (Bangkok, Samutprakarn) of Thailand. All models except M4 predict lower NO2 levels at site 16 (Ogasawara) and there is large variance among models (as much as an order of magnitude) at this site. (3) There is relatively less deviation of model prediction from observation for O3. The major distribution patterns are generally captured by most of the models, such as the lower values at sites 16 (Ogasawara) and 21 (Hedo) in July and elevated levels at Hedo site in March and December as a result of the influences of predominant marine air masses and continental outflow, respectively. However, most models appear to have difficulties in capturing the O3 maximum at site 18 (Happo), and exhibit large scatter at this site as well. This might be associated with the model vertical resolution and mixing of ozone from the upper troposphere. M2 consistently underpredicts ground-level O3 for all periods, whereas M5, M4, M1 and M3 obviously overpredict O3 in the four different periods, respectively (Fig. 2). The statistics for SO2 and NO2 also indicates (not shown) that for M1, M2, M6 and M7 (which covers relatively larger domain (Table 1), inclusion of the sites outside of the common domain consistently

degrade their statistics (especially for correlation), suggesting more uncertainties in emission inventory, meteorology, as well as inherent model’s ability for Southeast Asia and Russia. It is worthwhile to note that all models consistently underpredict observed O3 at site 30 of South Korea (Kangwha) in March 2002. This is mainly due to the overprediction of NOx concentration, hence too strong titration process. The ensemble means are reasonably consistent with observations for the three species. The spatial and seasonal variability of O3 ensemble mean are clearly demonstrated in Fig. 2 and agree with the observed trends quite well except for Tappi (15), Happo (18) and Kuangwha (30) sites. In the western Pacific, an overall aspect of higher O3 levels in March, followed by relatively lower level in December and the lowest in July, has been both reflected in simulation and observation. 3.2. Daily mean concentrations Table 3 shows the statistics for ground level daily mean concentrations for all four periods and for each of the periods. The common domain was used for statistical analysis. There are in total 1062, 949 and 657 pairs of samples for SO2, NO2 and O3 for all four periods. The observations for O3 daily concentration are only from six Japanese sites, whereas SO2 and NO2 daily observations are also available at four sites of China besides Japanese sites. The correlation coefficients for SO2 vary from 0.3 to 0.59 for the combined four periods, a similar range as that in Table 1. M3 shows the best correlation (0.59), and the smallest MBE and RMSE. Systematic overprediction is found in M2 and M8, as shown in the statistics for monthly mean. It is noted that all models but M2 exhibit their best correlations with observation in March 2001, with a range of 0.47–0.65, although the MBE and RMSE are not always the least among the four periods. This indicates that most models did a good job in reproducing the spatial and day-to-day variability of SO2 in this period. Model performances for NO2 are poorer than for SO2 in terms of variability, although the MBE and RMSE are relatively smaller. The correlation coefficients for all periods range from 0.01 of M2 to 0.48 of M3. For the combination of all four periods, correlations for O3 range from 0.1 of M2 to 0.74 of M7, with MBE of 2.7 to 19.1 ppbv and RMSE of

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Obs M5

100

M1 M6

M2 M7

M3 M8

1000

M4 EMS

Mar. 2001

10 1 0.1 0.01

SO2 concentration (ppb)

SO2 concentration (ppb)

1000

1 0.1

100 10 1 0.1

2 4

6

NO2 concentration (ppb)

NO2 concentration (ppb)

10 1 0.1

10 1 0.1

2

4

6

8

July 2001

10 1 0.1 0.01

14 15 16 17 18 19 20 21 22 23 24 25 37 38 Monitoring sites

100 NO2 concentration (ppb)

10 1 0.1

2

4

6

8

14 15 16 17 18 19 20 21 22 23 24 25 37 38 Monitoring sites

14

15 16

17 18 19 20 22 Monitoring sites

23

24

25 37

38

1 0.1 0.01

40 30 20 14

15

16

17

18 19 Monitoring sites

20

21

22

70

6

8

14

15

16

17

18 19 20 21 Monitoring sites

22

23

24

37

38

July 2001

60 50 40 30 20 10

23

80

14

15

16

17

18 19 Monitoring sites

20

21

22

23

80 Dec. 2001

O3 concentration (ppb)

O3 concentration (ppb)

8

Mar. 2002

O3 concentration (ppb)

O3 concentration (ppb)

50

60 50 40 30 20 10

6

80 Mar. 2001

60

70

4

10

80

10

2

100 Dec. 2001

70

2 4 6 8 141516 1718 192021 2223 2425 272829 3031 323334 3536 373840 4142 43 Monitoring sites

100 Mar. 2001

NO2 concentration (ppb)

Mar. 2002

100

0.01

8 14 15 16 17 18 19 20 21 22 23 24 28 29 34 35 37 38 41 42 43 Monitoring sites

100

0.01

2 4 6 8 15 17 18 19 20 21 22 23 24 25 26 28 29 33 34 35 37 38 40 41 42 43 Monitoring sites

1000 SO2 concentration (ppb)

SO2 concentration (ppb)

10

2 4 6 8 14 15 16 17 18 19 20 21 22 23 24 25 27 28 33 34 35 37 38 41 42 43 Monitoring sites Dec. 2001

0.01

July 2001

100

0.01

1000

0.01

3497

14

15

16

17

18 19 Monitoring sites

21

22

23

70

Mar. 2002

60 50 40 30 20 10

14

15

16

17

18

19

20 21 22 Monitoring sites

23

30

31

32

33

40

Fig. 2. The modeled and observed monthly mean concentrations of SO2, NO2 and O3 at EANET sites in the four periods, solid line represents ensemble mean.

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Table 3 Statistics for daily mean ground level concentrations for individual and combined periods (unit: ppbv) Species periods SO2 Four periods

Statistics

M1

M2

M3

M4

R MBE RMSE

0.52 0.52 5.94

0.30 15.54 39.44

0.59 0.49 5.23

0.43 3.87 13.79

Mar-01

R MBE RMSE

0.59 1.29 6.22

0.31 15.45 40.76

0.65 0.95 5.85

0.47 3.86 13.95

Jul-01

R MBE RMSE

0.55 0.29 3.85

0.32 11.62 28.34

0.56 0.57 3.79

Dec-01

R MBE RMSE

0.49 0.10 7.77

0.33 23.50 53.54

Mar-02

R MBE RMSE

0.59 0.66 5.13

R MBE RMSE

Mar-01

M5

M6

M7

M8

0.52 2.25 9.00

0.44 2.39 10.90

0.49 14.87 33.18

0.60 1.87 8.09

0.49 2.11 9.90

0.53 14.04 29.62

0.44 4.38 13.03

0.55 2.00 6.73

0.45 1.87 7.77

0.52 12.66 26.69

0.59 0.42 5.79

0.44 4.29 15.59

0.49 3.23 11.62

0.46 4.05 15.07

0.50 19.41 43.91

0.25 12.47 32.11

0.59 0.02 5.20

0.40 2.95 12.30

0.48 1.84 8.73

0.40 1.46 9.23

0.46 13.16 29.11

0.33 1.89 4.74

0.01 3.23 9.08

0.48 0.18 4.13

0.14 2.01 9.01

0.13 0.15 5.68

0.26 1.54 4.85

0.26 0.56 5.03

R MBE RMSE

0.36 1.57 4.32

0.09 3.50 8.37

0.46 0.01 3.94

0.16 2.40 8.94

0.16 0.49 5.28

0.26 1.29 4.53

0.25 0.25 4.78

Jul-01

R MBE RMSE

0.36 1.65 3.85

0.06 3.09 9.31

0.40 0.06 3.62

0.24 1.62 5.92

0.17 0.10 4.57

0.26 1.53 4.01

0.28 0.73 3.96

Dec-01

R MBE RMSE

0.20 2.22 5.63

0.03 4.41 10.20

0.46 0.45 4.67

0.11 3.08 11.92

0.10 0.47 6.67

0.25 1.29 5.44

0.22 0.25 5.90

Mar-02

R MBE RMSE

0.44 2.08 4.87

0.03 2.14 8.60

0.55 0.22 4.18

0.14 0.92 7.97

0.12 0.46 5.87

0.30 2.04 5.19

0.30 1.01 5.19

R MBE RMSE

0.31 2.68 16.33

0.08 19.05 28.07

0.45 1.55 13.94

0.36 8.29 15.30

0.49 6.44 14.03

0.74 3.52 9.90

0.69 8.06 12.79

Mar-01

R MBE RMSE

0.06 1.85 15.79

0.11 23.19 29.74

0.14 6.84 14.80

0.47 15.81 18.38

0.05 6.53 14.51

0.30 4.13 11.12

0.30 10.72 14.78

Jul-01

R MBE RMSE

0.49 2.92 15.67

0.23 13.78 28.08

0.51 4.47 16.35

0.62 6.97 14.91

0.55 6.20 15.37

0.85 5.50 10.72

0.79 2.32 10.61

Dec-01

R MBE RMSE

0.17 12.27 17.24

0.26 14.21 23.52

0.12 5.05 9.25

0.68 9.33 10.77

0.08 5.63 10.99

0.01 0.37 7.36

0.03 7.49 10.12

Mar-02

R MBE RMSE

0.03 4.03 16.41

0.16 23.05 29.40

0.10 2.24 14.96

0.46 14.11 16.58

0.21 7.54 15.25

0.39 4.65 10.29

0.29 11.54 15.09

NO2 Four periods

O3 Four periods

0.61 0.65 6.07

0.13 2.22 4.82

0.56 4.22 10.68

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9.9–28.1 ppbv. All models show a tendency to underpredict O3 by 4–36%, with an exception of M1 which overpredicts O3 by 6%. M7 produces the best correlation and the least RMSE among models. As to the seasonal dependence of model performance, M5 exhibits the best skill for O3 among models in March 2001, with the largest correlation coefficient and the least RMSE. However, M5 is the unique model overpredicting O3 (MBE: 4.22 ppbv), which could be associated with the altitudinal difference between the lowest model layer (about 75 m in M5) and sampling height (generally at 20 m) or its relatively coarse grid resolution both in horizontal and vertical extensions. It is interesting to note that nearly all models consistently exhibit their best correlations for O3 in July, ranging from 0.23 to 0.85, with R40.5 for most of the models, despite the MBE and RMSE are not consistently the least. It needs explanation here that the samples used for statistics for SO2 and NO2 in Table 3 also include the data from four sites of China, which do not correspond to those for O3 statistics exactly. When statistics are calculated for Japanese sites only (not shown), all models show the best correlations for NO2 in July as well. Model performances for December O3 are poor, with six models showing small correlations ( 0.26 to 0.17). Six of the seven models produce negative MBE ( 0.37 to 14.21 ppbv), whereas M1 considerably overpredict O3, with MBE being 12.27 ppbv. Statistics for March are generally between those for July and December. Unlike SO2, the correlations for ground-level O3 in March are notably smaller than that in July, implying O3 behavior in springtime is dominated by more complex processes, which have not been well represented by these models. Comparison between M7 and M8 is meaningful because both models use the same data set of meteorological fields derived from MM5. Although M7 and M8 are quite different in vertical structure and parameterizations, it is interesting to note that there exists a good agreement between their correlations for all the three species in all four periods, implying the importance of meteorology in modeling long-range chemical transport. 3.3. Vertical profile of O3 in the western Pacific Rim 3.3.1. Comparison of model simulation with O3 sounding at four aerological stations Model performance on afternoon O3 vertical profiles was examined through comparison with

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Table 4 Statistics for O3 vertical concentrations for all four periods in terms of various altitude regions (unit: ppbv) Models Statistics o2 km 2–5.5 km 45.5 km o5.5 km All levels M1

R MBE RMSE

0.01 7.72 22.21

0.09 12.54 23.17

0.04 5.97 56.36

0.12 10.78 22.82

0.20 8.62 41.42

M2

R MBE RMSE

0.44 2.73 18.38

0.47 3.91 24.53

0.10 2.91 41.59

0.44 1.49 22.49

0.31 2.04 31.40

M3

R MBE RMSE

0.46 9.24 18.91

0.42 13.68 20.05

0.02 11.91 26.53

0.47 12.07 19.64

0.40 12.02 21.69

M4

R MBE RMSE

0.27 20.30 30.83

0.01 8.59 22.85

0.06 8.17 37.77

0.10 12.85 26.04

0.10 4.74 31.09

M5

R MBE RMSE

0.67 11.71 15.27

0.44 7.97 11.33

0.30 12.55 84.18

0.57 9.33 12.90

0.38 0.51 57.27

M6

R MBE RMSE

0.74 4.81 12.21

0.56 6.22 13.72

0.22 5.23 18.97

0.66 5.71 13.19

0.60 5.59 14.80

M7

R MBE RMSE

0.56 1.75 13.22

0.60 0.44 11.21

0.30 3.60 17.23

0.59 0.36 11.98

0.58 0.22 12.20

M8

R MBE RMSE

0.63 1.43 12.30

0.65 4.22 11.20

0.65 2.17 11.61

0.65 2.17 11.61

available O3 sounding data at four aerological stations (Fig. 1b). In total, 61 profiles were obtained and used for comparison. The launch time was around 06:00 UTC, when the western Pacific was in late afternoon (15:00 LST). Table 4 gives a summary of the statistics for each model for the combined four periods (M5 is only available for March 2001) in terms of various altitude ranges. The model results and observations were interpolated into prescribed levels at 50 m increments up to 10 km. An altitude of 5.5 km (Table 1) is taken as an interface, below which the number of sample points are generally identical among the models; but above this altitude, the samples vary, depending on the vertical extension of each model. At altitudes below 5.5 km, the correlation coefficients range from 0.1 to 0.66, with MBE and RMSE in the range of 0.36–12.85 ppbv and 11.61–26.04 ppbv, respectively. M6 and M8 share the best correlations (0.65–0.66), and M8 produces the least RMSE as well.

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Some models, such as M4, M5 and M6, show the best correlations in the lowest 2 km, whereas the rest do not show a clear altitudinal dependence within 5.5 km. At altitudes 45.5 km, all models exhibit the largest RMSE and the lowest correlations compared with those in other altitude regions (o2 km and 2–5.5 km), indicating poor skill of these models in simulating upper tropospheric ozone. The effect of boundary conditions is discussed in detail by Holloway et al. (2008). The seasonal dependence of model skill varies from model to model (Table not shown). For altitudes o5.5 km, four of seven models consistently produce the best correlations in July, whereas the rest show the best in the other periods. Noticeably, each model shows the largest RMSE in July, and the scatter of MBE of each model is the largest as well, ranging from –20.8 ppbv of M2 to 43.2 ppbv of M4, indicating large deviation from observation in magnitude in summertime, although correlation can be better produced. For all altitude ranges, these models are consistent in producing the lowest correlations in March periods, mainly due to inability of these models to well reproduce O3 behavior in the upper troposphere as discussed above. 3.3.2. Vertical distribution of modeled and observed O3 at four sites in four periods The modeled and observed O3 vertical profiles at four sites for four periods are illustrated in Fig. 3. Each panel is composed of observation and corresponding ensemble mean for each site and period. The horizontal bar represents 71s, indicating the deviation of model results from the ensemble mean for each 0.5 km bin. Major characteristics in O3 vertical distribution are obtained. The models systematically underpredict upper tropospheric ozone at Sapporo for all periods except for March 2002. This is expected because high latitudes are frequently influenced by stratospheric intrusion, which is difficult to be well reproduced by the models. March 2002 might be a special period experiencing less stratospheric influence in this area. In general, ensemble mean best represents ozone sounding in March 2001 in the lower and middle troposphere. The best agreement between ensemble means and observations and the least differences among models appear in March 2001 at Sapporo. In most cases, the differences among models increase with height, with more consistency in the boundary layer or the lower troposphere. But in some cases, these models show the least scatter at a height of 2–4 km, such as at

Tateno, Kagoshima and Naha in March 2001 and at Kagoshima in December 2001. Fig. 3 clearly shows the largest differences among models in afternoon O3 levels in July, though the correlations between ensemble means and observations are generally good at the same time. Above discrepancy is presumably caused by a series of factors. One is associated with the ozone production efficiency by different chemical mechanisms of individual models in summertime when solar radiation is strongest and photochemical processes are most active. Another could be the differing model skills in simulating cloud activity and convective mixing, which often occurs during summertime. The above explanation appears to be sound with respect to the decreasing differences among models from the low latitudes (Naha) to higher latitudes (Sapporo). The seasonality of O3 vertical profile is reasonably reproduced by the models in most cases. Such as the lower O3 concentration in the low troposphere at Naha, and the increasing O3 level with latitude and altitude in July and the higher O3 level in the lower troposphere in March at all sites except for Tateno (close to Tokyo) where maximum occurs in summer. 3.4. Comparison of model results with TRACE-P data The TRACE-P experiment was conducted in the western Pacific in March–April 2001, with the aim to characterize the chemical composition and evolution of Asian outflow. This data set provides a good opportunity for further evaluation of model performances in this study. In total, four flights (Flight 12, 13, 15 of DC-8 and Flight 13 of P-3B) were selected for comparison, which were associated with frontal lifting. Model results were interpolated to the exact locations and times along flight tracks using trilinear interpolation. Model results for SO2, NOx and O3 were extracted every 2 min along each track. Sensitivity test for data extraction every 5 min was made, which shows similar statistics. Within 5.5 km, the pairs of samples are nearly identical for each model, which ensures comparability of statistics. Table 5a, 5b presents the statistics for individual model performance in terms of various species and altitude ranges to help understand the models’ abilities in the boundary layer, middle and upper troposphere. All models consistently show a good skill of simulating SO2. Models agree with each other and

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Fig. 3. The modeled and observed O3 profiles at four sites of Japan in the four periods. Coarse line represents observation, thin line represents model ensemble mean averaged over every 0.5 km altitude bin. Horizontal bar indicates 71s (deviation of model results from ensemble mean). For each panel, x-axis denotes O3 concentration (unit: ppbv), y-axis means altitude (unit: km).

with observations quite well, with correlations ranging 0.48–0.71 for altitudes o5.5 km and 0.51–0.75 for o2 km. M8 predict too high SO2 concentrations at altitudes o5.5 km, the same tendency as that in ground-level simulation. Noticeably, M2 exhibits a negative bias for altitudes o2 km, which is completely opposite to the tendency towards overprediction for ground-level SO2, and its overall performance appears to be better in comparison with the TRACE-P data. Most models show a distinctly higher correlation in the lowest 2 km than

in other altitude ranges. The negative MBE in the lowest 2 km in contrast to the positive one at altitudes 2–5.5 km, implying more SO2 could be ventilated into the middle troposphere from the boundary layer or predicted larger influence from elevated sources, such as volcanic emission. The correlations for NOx (NO+NO2) are lower than for SO2 at almost all altitudes. For altitudes o5.5 km, the correlations range from 0.03 to 0.58, with MBE being –235.4 to +150.4 pptv and RMSE 481.0–641.9 pptv. The correlations are even worse

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Table 5 M1

M2

M3

M4

M5

M6

(a) Statistics for SO2, NOx (pptv) and O3 (ppbv) for the combination of four flights in terms of various altitude regions SO2 R o2 km 0.75 0.51 0.51 0.67 0.57 2–5.5 km 0.35 0.33 0.54 0.26 0.51 o5.5 km 0.48 0.51 0.50 0.69 0.53

M7

0.72 0.28 0.71

M8

0.69 0.25 0.70

0.71 0.36 0.65

343.9 231.5 83.6

207.3 633.3 404.0

1028.3 123.2 610.4

74.4 637.1 254.1

642.3 53.4 321.1

298.3 31.9 145.8

2081.9 1917.6 2001.9

RMSE o2 km 2–5.5 km o5.5 km

3031.6 1075.9 2341.2

3061.0 585.2 2299.1

3016.5 1071.2 2329.7

3330.6 193.9 2447.1

3023.6 847.1 2291.8

2911.6 293.2 2145.4

2647.3 288.4 1952.1

3383.6 2452.9 2967.0

NOx R o2 km 2–5.5 km o5.5 km 45.5 km

0.54 0.28 0.58 0.12

0.36 0.27 0.42 0.08

0.26 0.28 0.32 0.02

0.34 0.14 0.03 0.12

0.38 0.06 0.47 0.13

0.36 0.06 0.45 0.48

0.22 0.26 0.39 0.39

0.01 0.28 0.20

MBE o2 km 2–5.5 km o5.5 km 45.5 km

258.0 54.3 165.6 123.3

39.1 74.0 54.8 66.0

192.7 24.9 116.4 60.2

227.0 58.7 150.4 10.4

193.0 74.3 138.9 141.9

301.7 72.0 197.1 67.2

361.7 84.2 235.4 40.9

221.2 64.0 148.3

RMSE o2 km 2–5.5 km o5.5 km 45.5 km

630.1 177.9 481.0 219.9

760.2 185.5 574.4 88.8

735.0 217.8 562.2 88.2

846.3 217.3 641.9 68.2

633.4 189.6 484.9 224.0

685.4 188.7 521.8 84.9

738.8 191.4 560.6 51.3

750.5 186.0 564.6

O3 R o2 km 2–5.5 km o5.5 km 45.5 km MBE o2 km 2–5.5 km

0.01 0.03 0.02 0.12 13.2 18.3

0.62 0.73 0.64 0.53 12.7 3.4

0.27 0.59 0.41 0.41 9.1 14.5

0.25 0.00 0.18 0.05

0.79 0.78 0.77 0.71

0.56 0.21 0.40 0.50

4.4 5.2

2.2 0.1

9.5 3.2

0.78 0.75 0.75 0.51 10.0 10.2

0.63 0.40 0.37

7.7 6.6

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MBE o2 km 2–5.5 km o5.5 km

o5.5 km 45.5 km

15.6 0.1

8.2 5.0

11.7 5.5

4.8 2.4

1.2 13.2

6.5 3.1

10.1 18.6

0.5

RMSE o2 km 2–5.5 km o5.5 km 45.5 km

25.0 25.2 25.1 75.9

18.7 13.1 16.3 29.4

20.0 18.5 19.3 15.5

19.2 16.6 18.0 21.4

10.6 8.5 9.6 64.2

16.9 13.0 15.1 10.1

14.6 13.6 14.1 25.6

14.6 12.9 13.8

0.67 0.35 0.65 0.39

0.15 0.04 0.20 0.08

0.21 0.01 0.31 0.20

0.65 0.29 0.67 0.29

0.02 0.11 0.09 0.38

0.08 0.07 0.18

553.0 228.8 402.1 129.6

468.8 199.4 343.9 117.3

293.1 213.2 255.5 188.3

269.7 1211.6 426.8 2174.0

351.9 164.7 263.9 118.6

434.6 203.5 325.9 153.8

447.6 174.4 319.1 144.5

676.5 259.2 471.5

RMSE o2 km 2–5.5 km o5.5 km 45.5 km

976.8 282.5 739.7 177.9

1651.5 565.5 1269.3 177.8

1334.5 667.8 1073.8 505.2

1157.6 1804.5 1497.0 2592.8

975.4 256.9 731.5 180.2

882.9 268.7 668.6 168.5

1057.9 330.2 802.6 147.9

1185.8 316.5 874.3

PAN R o2 km 2–5.5 km o5.5 km 45.5 km

0.71 0.62 0.64 0.29

0.57 0.86 0.62 0.54

0.04 0.22 0.10 0.07

0.24 0.47 0.34 0.08

0.71 0.82 0.73 0.45

0.48 0.57 0.52 0.53

0.47 0.61 0.49 0.25

0.36 0.39 0.32

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145.7 66.5 66.5 123.3

298.2 214.4 214.4 87.5

170.5 87.5 87.5 121.9

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99.1 110.9 110.9 76.7

80.2 101.1 101.1 172.6

101.9 12.8 12.8 18.0

105.8 269.6 269.6

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433.3 366.6 366.6 189.5

578.2 434.0 434.0 157.9

635.2 497.7 497.7 257.1

594.2 454.3 454.3 259.3

420.5 333.7 333.7 149.3

541.0 418.3 418.3 198.7

519.8 396.3 396.3 126.2

585.8 549.6 549.6

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Z. Han et al. / Atmospheric Environment 42 (2008) 3491–3509

(b) Same as (a) except for HNO3 and PAN (pptv) HNO3 R o2 km 0.67 0.65 2–5.5 km 0.36 0.32 o5.5 km 0.69 0.66 45.5 km 0.13 0.31

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for altitudes 45.5 km, where the maximum correlation is just 0.13. An important feature is found that all models but M4 produce negative MBE ( 54.8 to 235.4 pptv) at all altitude ranges. The underprediction varies from 24% of M2 to as high as 81% of M7. The relatively poor model performance for NOx could be attributed to the larger uncertainty in NOx emissions and the shorter lifetime of NOx associated with complex chemical mechanism. M4 produces apparently poor correlations for NOx, mainly due to uncertainty in calculating partition between aerosol and gas phase species, and consequently, this also degrade its skills for other related species, such as HNO3, nitrate, as well as O3. The statistics for O3 differ largely among the individual models, with correlations varying from 0.02 to 0.77, MBE of 10.1 to 15.6 ppbv, and RMSE of 9.6–25.1 ppbv for altitudes o5.5 km. For altitudes 45.5 km, most models show smaller correlations and larger RMSE, although the samples are different among models (Table 1). The dependence of model performance on altitude varies among models. Most models exhibit better correlations at altitudes o2 km than those at 2–5.5 km, while M2 and M3 show an opposite aspect. Below 2 km, four of the eight models (M2, M6, M7 and M8) underpredict the observed O3 mean by 26%, 19%, 21% and 9%, whereas the remaining models (M1, M3, M4, M5) overpredict by 27%, 19%, 9% and 4%, respectively. Of the eight models, for altitudes o5.5 km, M5 exhibits the best correlation, together with the least MBE and RMSE, whereas M1 consistently shows the lowest correlation and the largest RMSE among models. M1 and M5 exhibit much larger RMSE in the 45.5 km region, mainly due to inappropriate treatment of top boundary condition and higher model top compared with the other models. It is interesting to note that M5 has a relatively coarser resolution (80 km), but the statistics are the best among all, implying some options adopted superior to that in the other models. Model performances for HNO3 are clearly separated into two groups. M1, M2, M3 and M6 are more consistent in producing higher correlations around 0.67 for altitudes o5.5 km, and the rest show much lower values ranging from 0.15 to 0.31. Five of the eight models (M1, M5, M6, M7 and M8) show negative MBE for all altitudes. It is worthwhile to notice that all models exhibit better correlations in the lowest 2 km than those in the

2–5.5 km range. This could be explained in part by the poor skill of these models to well represent heterogeneous reactions on aerosol surface, which is thought to be a major sink of nitric acid in aerosolrich air mass (Carmichael et al., 2003), such as soil dust outflow, which often appeared at altitude 2–5.5 km during March 2001. Below 5.5 km, the correlations for PAN range from 0.1 of M3 to 0.73 of M5. M4, M5, M7 and M8 exhibit apparent higher correlations for PAN than those for HNO3, whereas M3 shows a distinct opposite aspect. It is interesting to find that all models except M1 generally show a better correlation at altitude 2–5.5 km than that in the lowest 2 km. Six models exhibit the negative MBE for all altitudes, whereas the rest (M5, M8) show the positive MBE within 5.5 km. A principal feature is found from Table 5 for all species analyzed that is RMSE at altitudes o2 km is greater than that at 2–5.5 km. This is mainly due to the difficulty of these models in quantitatively representing the magnitude and variation amplitude of intense plumes, which are mostly originated from near surface. Concurrently, most models show better correlations at altitudes o2 km, indicating the ability of these regional models in capturing the timing and location of those plumes in the boundary layer. The overall model skills generally decrease with increasing altitude, likely due to the uncertainties in representing convective mixing, potential chemical reaction, as well as stratospheric intrusion processes. The model performances vary largely among individual flights (figure not shown). All models consistently exhibit the best correlations for SO2 in DC-8 Flight 13, ranging from 0.42 of M3 to 0.86 of M4, but show the largest MBE and RMSE among flights at the same time, due to significant underprediction of two intense plumes in the boundary layer. Model performances for NOx are worst during DC-8 Flight 15, with most models showing the smallest correlations and negative MBE (table not shown). The correlations for O3 at altitudes o5.5 km for DC-8 Flight 15 are quite low for all models (0.0–0.5) and six of the eight models showing the lowest correlations among the four flights. This could be partly associated with the poor model performance for NOx and the models’ uncertainties in simulating convection during this flight, which was studied in detail by Kiley et al. (2003). All models show the best correlations for O3 in either DC-8 Flight 13 or

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P-3B Flight 13, although the MBE and RMSE are not consistently the least. For DC-8 Flight 13, five models produce correlations 40.8 at altitudes o2 km, and the models are more consistent with each other and with aircraft observations. 3.5. Spatial distribution of O3 and relevant species concentrations The predicted spatial distributions of groundlevel SO2 and NOx concentrations are quite similar among models (not shown), with high levels in or in the vicinity of major cities or industrial regions. However, the maximum near surface differ considerably due in part to the various depths of the first model layer and different vertical diffusivity simulated by each model. M2 and M8 consistently produce much higher SO2 levels than the rest. Through an analysis of the fraction of SOx near surface relative to the total within 3 km, it is found that M2 has a distinct tendency to retain more quantity in the lowest layer over the source regions than the other models, suggesting that the smaller vertical mixing of emissions is an important reason. The overprediction of M8 is mainly due to possible uncertainty in projecting SO2 emission into model grid and the smaller conversion rate from SO2 to sulfate as discussed by Hayami et al. (2008). For NOx, there is no apparent disparity among models except for M2, which consistently shows much higher concentration than the others for all periods, due to insufficient vertical mixing as discussed above. M5 produces notably lower SO2 and NOx levels than the others over source regions in March, which is mainly attributed to its coarser grid resolution. All models did a good job in reproducing the seasonality of near surface SO2 and NOx concentrations. The models consistently predict higher levels in December and lower levels in July, with moderate levels in March, associated with the seasonality in emission amount, vertical mixing and chemical reaction activity. The predicted distribution patterns of SO2 and NOx are very alike between March 2001 and March 2002, due to quite similar meteorology and emissions used. O3 is one of the major focuses in this study. Fig. 4 presents the model-predicted spatial distribution of monthly mean ground-level O3 concentrations for the three periods. For March 2001, most models similarly predict quite lower O3 levels in source regions of northern China and major cities of South

3505

Korea and Japan. A major feature consistently produced by all models except M4, is the elevation of O3 concentration over the western Pacific from the China East Sea to south of Japan, due to the continental outflow. However, the magnitude and position of the enhanced O3 are different among models. M5 predicts the highest O3 levels of 60–65 ppbv, whereas M8 predicts the lowest 45–50 ppbv. M3, M6 and M7 are more consistent in reproducing this feature, whereas M1 shows the O3 enhancement further south of Japan compared with other results. The models are also consistent in predicting elevated ozone levels over Tibet, which is mainly contributed by the prescribed western and top boundary conditions from the global model. However, O3 levels in this area vary by 30 ppbv among models, with M3 showing the highest, both due to larger vertical diffusivity in the free troposphere and potentially stronger O3 production efficiency by the condensed chemical mechanism. M2 notably underpredicts O3 levels, especially over eastern China and northern parts of the model domain, either due to inadequate application of boundary conditions or the predicted too high NOx concentrations near surface, which may limit efficient O3 formation around source regions. The largest differences among models appear in the southwestern parts of the domain including the northern parts of Vietnam, Myanmar and Lao, and the eastern parts of India, where M1 and M3 predicts much higher ozone levels (60–70 ppbv), in contrast to the small values predicted by M2 and M6 (o20 ppbv). The above discrepancy among models might result from a combined influence of a series of factors including the diversity in simulated dry deposition (Wang et al., 2008), cloud information, the various chemical mechanism employed, as well as the boundary conditions specified for the models with different domain. Olson et al. (1997) indicates that even for extremely simple chemical situation, there can be considerable difference among model simulations, due to differences in calculated photolysis rates, specific chemical reaction rates, and various treatments of NMHCs. More detailed information from each model is needed if we want to know clearly the exact reasons for above large divergence among models because of the complex interaction of atmospheric physics and chemistry. And observational data are needed to help understand the actual O3 behavior and model’s uncertainty in these regions.

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M1

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Fig. 4. Ground-level O3 spatial distributions from the eight models for (a) March 2001, (b) July 2001 and (c) December 2001 (unit: ppbv).

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Fig. 5. Model composites of ground-level monthly mean O3 concentrations for the four months: (a) March 2001, (b) July 2001, (c) December 2001 and (d) March 2002 (unit: ppbv).

For July 2001, high ozone concentrations mainly occur in northeast Asia, whereas the southern parts of the domain exhibit quite lower O3 concentrations, mainly due to more cloud activity and precipitation, and the influence of marine air masses from lower latitudes. The distribution patterns of O3 are quite alike among models, with high concentrations over parts of northern China, the Yellow Sea, Bohai Bay and the eastern parts of the Sea of Japan. O3 maxima in above regions may differ by 20 ppbv among models, with M3 showing the highest value (55–60 ppbv), M2 showing the lowest (35–40 ppbv), and the rest being consistently in a middle range (40–55 ppbv). All models except for M2 and M4 generally show reduced O3 levels over South Korea and Japan, due to frequent intrusion of marine air masses from the western Pacific Ocean under southwesterlies. Large differences among models mainly occur in the areas of lower latitudes, with the most difference in the southwestern parts of the domain. For December 2001, five of the seven models predict discernable O3 elevation in the western Pacific, with O3 concentration ranging 40–60 ppbv. Considerably low O3 levels (o25 ppbv) are predicted over central China by most models. Relatively larger disparity is found among models for December compared with that in other periods. O3 distributions are very alike between March 2001 and March 2002 (not shown). For March periods, all models predict high HNO3 concentration in the western Pacific Rim (not shown), including the coastal area of eastern China and southern Japan. But the maxima can differ by an order of magnitude among models, probably due to more complex mechanism involved, for instance, aerosol chemistry. All models produce a similar

pattern for PAN, showing a long band extending from southern China to Japan islands, with concentrations generally decreasing from southwest to northeast. The maximum concentration of PAN in southern China may differ by as high as a factor of three among models, with M6 showing the highest value (3 ppbv). Differences among models in HNO3 and PAN concentrations in other periods are larger than those in March. Fig. 5 shows the ensemble means of ground-level monthly mean O3 concentrations for the four periods. The major features in different seasons (or periods) discussed above can be more clearly identified. Fig. 2 shows that the model composites are generally in a better agreement with observations than any of the individual models. This indicates that Fig. 5 well represents the seasonality of O3 distribution at least in the western Pacific Rim where most O3 observations occur. 4. Conclusions Evaluation and intercomparison of eight regional scale CTMs were conducted with a wide variety of observations from EANET, JMA and the TRACEP experiment. The ability of CTMs in simulating O3 and relevant species in the troposphere of East Asia were rigorously evaluated in terms of different seasons, locations and altitude ranges. Potential factors responsible for deviation of models from observations and disparity among models were investigated. Important features in spatial distribution were consistently reproduced by the participating models in the western Pacific Rim where most observations are concentrated. Considerably larger differences were found among models in the

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southern parts of the model domain, such as southern China and northern parts of Southeast Asia where observational data is very limited. The models generally show good skill of simulating SO2 and reproduce day-to-day variability best in March 2001. Most of the models exhibit relatively poor skill for NO2 or NOx and consistently exhibit a tendency towards underprediction over the TRACE-P region. Model performance for O3 varies largely among models. All models consistently show their best correlations for ground-level O3 and NO2 in July over the western Pacific Rim, indicating the day-to-day variability is better captured in summer compared to the other periods. However, the disparity in afternoon O3 level among models reaches a maximum in July, although the patterns are still correlated with observed O3 profiles reasonably well. All models show difficulty in correctly representing O3 behavior in the upper troposphere. Statistical analysis with the TRACE-P data shows that most models are able to capture the spatial and temporal variability of trace species in the region of o2 km, showing the largest correlation for SO2, followed by O3, and the smallest for NOx in general. However, all of the models consistently show difficulty in quantitatively representing the intensity of major plumes originated from surface and show a distinct tendency towards underprediction, resulting in greater RMSE in the lowest 2 km than that at altitudes 2–5.5 km. In most cases, the models show poorer performance at altitudes 2–5.5 km than o2 km, which is likely due to the limitation of the models in simulating convection and potential chemical or aerosol processes. The model-predicted horizontal distribution patterns for SO2 and NOx, which are mainly subject to source location and intensity, are more alike among models than for the other species, and the seasonality of these two species are well captured by all models as well. Chemical transport patterns downwind of Asian continent are consistently reproduced by most of the models reasonably well, which are mainly characterized by pronounced O3 enhancement over the western Pacific in March and elevated O3 levels over northeast Asia in July. The largest disparities among models have been identified in southern China and northern parts of Southeast Asia for all four periods. The limitations and uncertainties consistently reflected in most of the participating models have been pointed out, which would also be regarded as potential directions for future modeling study for

East Asia. For instance, all models except M5 consistently underpredict ground-level O3 concentrations at sites in Japan (most of which are located at latitudes 4351N), with relatively poor correlations in March and December. The models appear to be more consistent in the regions of O3 elevation (such as the western Pacific Ocean), in contrast to the large disparity over southern China and northern parts of Southeast Asia. All models exhibit poor skills of simulating O3 in the upper troposphere and of quantitatively representing intense plumes in the boundary layer. While we have obtained some confidence in the participating models’ abilities and have gained considerable understanding on the characteristics of key trace species over the western Pacific, our knowledge are still limited for some other regions, like the southern and western parts of the model domain. More efforts are required for further exploring these uncertainties and improving the models’ abilities. Moreover, observations of a series of key atmospheric chemical components over the Asian continent are desirable to provide assistance both in a better understanding of the real atmosphere and in a comprehensive evaluation of current CTMs for those regions. Acknowledgements This research is sponsored by Acid Deposition and Oxidant Research Center (ADORC) and partly supported by National 973 project (2005CB422205), in collaboration with International Institute for Applied System Analysis (IIASA), Austria. The first author was also supported by the 100-Talent Project of Chinese Academy of Sciences. We would like to express our sincere thanks to all the model groups and participants for their great efforts and contributions to this project.

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