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 YAMAJIFrontier Research Center for Global Change, Yokohama, Kanagawa, JAPAN

Interannual and Seasonal Variations of CMAQ-simulated tropospheric NO2 in Asia and comparison with GOME satellite data- Combination of bottom-up and top-down analysis -

How can we understand this year-by-year trend ?Can we simulate these recent increase of NO2 by CMAQ?Can we reproduce satellite observed horizontal distribution of NO2 ?

NO2 from GOME measurements

Tropospheric NO2:• 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)

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

GOME-NO2 swath data 　 (40x320km ） Observation 10:30LT

CMAQ NO2 PS system (80km grid ）3 hr interval

REAS 1.1 Emission Inventory0.5 x 0.5 ˚ Lon-Lat Mesh

GOME-NO2 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

EyyMyy Year-by-year emission and meteorology

Control run

E00Myy Fixed emission for year 2000 Fixed emission run

Sensitivity Experiments

Examination Domain

CEC(1000km x 1000km)

Japan

(Korea)

Comparison of year 2000 annual average

GOME-CMAQ

Model results under-estimate NO2 VCDs over the large source region (especially Beijing Region), overestimate Taiwan and Korea.

GOME_NO2 = -5.55E14 + 2.41 × CMAQ_NO2 (molecule·cm2) (R=0.919).

JapanChina CEC region (7 year average)

Seasonal variation of NO2 VCDs

Maximum values of the NO2 columns occur in December even though the wind speed is higher. This indicates that the effect of the longer chemical lifetime of NO2 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 NO2 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.

CMAQCMAQ

CMAQ/REAS results under-estimate GOME.But almost same under-estimates were reportedby 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.

Scatter plot of monthly averaged value of GOME NO2 andCMAQ NO2 over China CEC.

This figure shows the scatter of monthly averaged NO2 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 NO2 is more enhanced when the CMAQ NO2 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 NO2 and GOME NO2 becomes nonlinear remains unclear

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 NO2, CMAQ NO2 and REAS NOx emission normalized to 2000.

O3 Fields

Tanimoto et al. (2006)

Systematic analyses of interannual and seasonal variations of tropospheric NO2 vertical column densities (VCDs) based on GOME satellite data and the CMAQ were presented

The horizontal distribution of annual averaged GOME NO2 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 NO2 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)

RAMS Grid 2

CMAQ Grid1

CMAQ Grid2

RAMS Grid 1

OMI NO2 (from TEMIS web page)

Log10 [CMAQ NO2] (20km grid2)

Future Directions

1) High resolution CMAQ andSatellite (Aura/OMI NO2)

2) Emission Inversion by adjoint