1. Itsushi UNO*, Youjiang HE, Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka, JAPAN Toshimasa OHARA, Jun-ichi KUROKAWA, Hiroshi TANIMOTO National Institute for Environmental Studies, Tsukuba, Ibaraki, JAPAN Kazuyo YAMAJI Frontier Research Center for Global Change, Yokohama, Kanagawa, JAPAN Interannual and Seasonal Variations of CMAQ-simulated tropospheric NO 2 in Asia and comparison with GOME satellite data - Combination of bottom-up and top-down analysis -

2. How can we understand this year-by-year trend ? Can we simulate these recent increase of NO 2 by CMAQ? Can we reproduce satellite observed horizontal distribution of NO 2 ?

3. NO 2 from GOME measurements Tropospheric NO 2 : Sources: –anthropogenic: transport, energy, biomass burning –natural: soil emissions, fires, lightning short lifetime, emission dominated Data: 8 years of global measurements (1996.1 - 2003.6) Retrieval V2.0 data using –monthly AMFs based on MOZART 1997 profiles –surface reflectivity climatology –Stratosphere contribution by SLIMSCAT model –Provide Tropospheric NO2 column densities aerosol a priori assumptions  a priori information is used, but no trend in a priori only daytime measurements （ 10:30LT ； 40x320km ； every 4 days)

4. Model simulation (Full calendar year calculation) Past: 1980,1985,1990,1995 Recent: 1996-2006.3

6. GOME-NO 2 swath data (40x320km ） Observation 10:30LT CMAQ NO 2 PS system (80km grid ） 3 hr interval REAS 1.1 Emission Inventory 0.5 x 0.5 ˚ Lon-Lat Mesh GOME-NO 2 interpolate into 0.5x0.5˚ Interpolate PS to Lon-Lat system. Use 3UTC data. Tropospheric NO2 column below 10km is integrated to get NO2 VCDS. Analysis Method

7. EyyMyyYear-by-year emission and meteorology Control run E00MyyFixed emission for year 2000Fixed emission run Sensitivity Experiments Examination Domain CEC(1000km x 1000km) Japan (Korea)

8. Comparison of year 2000 annual average GOME-CMAQ Model results under- estimate NO 2 VCDs over the large source region (especially Beijing Region), overestimate Taiwan and Korea.

9. GOME_NO 2 = -5.55E14 + 2.41 × CMAQ_NO 2 (molecule·cm 2 ) (R=0.919).

10. Japan China CEC region (7 year average) Seasonal variation of NO 2 VCDs Maximum values of the NO 2 columns occur in December even though the wind speed is higher. This indicates that the effect of the longer chemical lifetime of NO 2 is more important than that of strong wind. While the minimum value is observed in July and August because of the strong vertical mixing, the short lifetime of NO 2 and the inflow of relatively clean air from the Pacific Ocean side. For CEC, CMAQ VCDs corresponds to 64% of the value of GOME VCDs in July.

11. CMAQ CMAQ/REAS results under-estimate GOME. But almost same under-estimates were reported by many global CTMs inter-comparison paper by Noije et al. (2006; ACP) Intercomparion results for year 2000. Green lines are satellite retrieval from 3 different groups.

12. Scatter plot of monthly averaged value of GOME NO 2 and CMAQ NO 2 over China CEC. This figure shows the scatter of monthly averaged NO 2 VCDs for GOME and CMAQ EyyMyy. Red numbers represent data from CEC (last digit of the year). Blue symbols are data from Japan. This plot indicates that GOME NO 2 is more enhanced when the CMAQ NO 2 concentration becomes higher (i.e., emission becomes higher); most of these conditions occur after the year 2000. The exact reason why the relationship between CMAQ NO 2 and GOME NO 2 becomes nonlinear remains unclear

13. Emission Trend Analysis by GOME and CMAQ/REAS An increasing trend of 1996–1998 and 2000–2002 for GOME and CMAQ/REAS shows a good agreement (GOME is approximately 10–11%·yr -1, whereas CMAQ/REAS is 8–9%·yr -1 ). The greatest difference also can be found between 1998 and 2000. The CMAQ/REAS result shows only a few percentage points of increase, but GOME gives more than 8%·yr -1 of increase. The most likely explanation is that the REAS emission trend (based on Chinese data) underestimates the rapid growth of emissions. This result highlights that combinations of CTM based on bottom- up inventories with satellite top-down estimates can play an important role in improving emission inventory estimates and provide very useful information that advances the development of a reliable CTM simulation. Trend of GOME NO 2, CMAQ NO 2 and REAS NOx emission normalized to 2000.

14. O 3 Fields Tanimoto et al. (2006)

15. Systematic analyses of interannual and seasonal variations of tropospheric NO 2 vertical column densities (VCDs) based on GOME satellite data and the CMAQ were presented The horizontal distribution of annual averaged GOME NO 2 VCDs for 2000 generally agrees with CMAQ/REAS results. However, CMAQ results underestimate GOME retrievals by factors of 2–4 over polluted industrial regions such as Central East China Evolution of the tropospheric columns of NO 2 above Japan and CEC between 1996 and 2003 was examined. Recent trends of annual emission increases in CEC were examined. This study shows that the combinations of CMAQ based on bottom-up inventories with satellite top-down estimates can play an important role for air quality study. Concluding Remarks More detailed can be found in Uno et al. (2006, Atmos Chem. Phys. Submitted)

16. RAMS Grid 2 CMAQ Grid1 CMAQ Grid2 RAMS Grid 1 OMI NO2 (from TEMIS web page) Log 10 [CMAQ NO2] (20km grid2) Future Directions 1) High resolution CMAQ and Satellite (Aura/OMI NO 2 ) 2) Emission Inversion by adjoint