Observations and Model Analysis of Recent Asian Dust Events Nobuo Sugimoto (National Institute for Environmental Studies) Itsushi Uno (Research Institute for Applied Mechanics, Kyushu University) Atsushi Shimizu, Ichiro Matsui (National Institute for Environmental Studies) Kimio Arao (Nagasaki University) Hao Quan, Yan Cheng (CJFCEP, China) Jun Zhou (AIOFM, China) C-H Lee (Kyung Hee University, Korea) APAN Conference, Fukuoka Jan 21-23, 2003

Heavy dust event in Beijing on March 20, 2002.

Dust Project in the Global Environment Research Program of the Ministry of the Environment (1) Observation of distribution and movement of Asian dust using lidars (2) Chemical analysis of Asian dust (3) Modeling study

NIES lidar observation network Tsukuba (36.05N, E) Nagasaki (32.78N, E) Mar Beijing, China (39.9N, 116.3E) Mar Sri Samrong, Thailand (17.15N, 99.95E) Oct Suwon, Korea (37.14N, E) Amami-Ohshima (28.44N, E) Miyakojima (24.7N, 125.3E) Fukue (32.63N, E)Oct Hefei, China (31.90N, E) Oct Research Vessel “Mirai”1999--

MapNew

Purpose of the lidar network observations - Climatology of aerosols and clouds - To understand aerosol phenomena including effects of Asian dust and anthropogenic aerosols on the environment and climate - To validate chemical transport models - Monitoring of Asian dust and anthropogenic aerosols in the regional and global scales

NIES Compact Mie Lidar

Beijing Nagasaki Tsukuba NIES Lidar Network for Asian Dust Observation

NIES Compact Mie Lidar

Lidar data Beijing 2002

Target classification method Scattering intensity Depolarizati on ratio water cloud ice cloud spherical aerosols dust Laser P // P⊥P⊥ Depolarization ratio  = P ⊥ /P // dust spherical aerosol

Target classification 2 dust rain ice cloud water cl. aerosols unknown no obs. April 2001 Target classification using the signal intensity and the depolarization ratio.

Histogram 2001

Histogram 2002

Tsukuba

The Chemical Forecast System (CFORS), (I. Uno) (A RAMS based regional model including chemistry) Comparison with Models

Chemical Forecast System (CFORS)

Lidar Chemical Transport Model signal intensity (depolarization) Which parameter shall we compare? dust extinction coefficient extinction coefficient dust density S1 assumption on external mixing mass/extinction conversion factor distribution and characteristics of other aerosols optical characteristics of dust

Ratio of dust is estimated by the following equations when we consider external mixture of dust and other spherical aerosols. R={(1-  2 ’)  -  2 ’}/{(  1 ’-  2 ’)(1+  )} ……………………..(1)  1 ’ ＝  1 /(1+  1 ） ………………………(2)  2 ’ ＝  2 /(1+  2 ） ………………………(3) where  1 is depolarization ratio of dust, and  2 is depolarization ratio of other aerosols. Empirically,  1 ~0.35,  2 ~0.05.

dust air pollution aerosols dust Distributions of dust and spherical (air-pollution) aerosols estimated from the signal intensity and depolarization ratio Beijing March 2001 Day (UTC)

Comparison with CFORS

Chemical Forecast System (CFORS)

Dust Number(Lidar)

Dust Number

Asian dust source regions

XZ2001Apr

CFORS 2001, 2002

Summary We conducted continuous observations in Beijing, Nagasaki, and Tsukuba with automated polarization lidars since March A statistical analysis showed that the frequency of dust events in 2002 and 2001 was not very different in Beijing, but the frequency was much higher in 2002 in Tsukuba. We studied the dust source regions and transport paths using the regional chemical transport model CFORS. The results showed that most major dust events originated in Inner Mongolia and/or Mongolia. The dust was transported rapidly with the strong westerly of the storm, and the main part was transported northeast near the coast of China. In 2002, the location of dust streams were shifted slightly to the east, and this caused heavy dust events in Korea and northern Japan. This is probably related with the climate change.

Dust event on November 12, 2002 RIAM-NIES CFORS

Lidar- CFORS1 Suwon Beijing

Lidar- CFORS2 FukueTsukuba

Lidar- CFORS3 Miyako-jima Hefei

Perspective Understanding dust phenomena Constructing dust monitoring network Dust forecast Ground based observation network Satellite data (surface, dust) Chemical transport model

ふろく

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