1. Recent Advances in Chemical Weather Forecasting in Support of Atmospheric Chemistry Field Experiments Gregory R. Carmichael Department of Chemical & Biochemical Engineering Center for Global & Regional Environmental Research and the University of Iowa

2. TRACE-P EXECUTION Emissions -Fossil fuel -Biomass burning -Biosphere, dust Long-range transport from Europe, N. America, Africa ASIA PACIFIC P-3 Satellite data in near-real time: MOPITT TOMS SEAWIFS AVHRR LIS DC-8 3D chemical model forecasts: - ECHAM - GEOS-CHEM - Iowa/Kyushu - Meso-NH FLIGHT PLANNING Boundary layer chemical/aerosol processing ASIAN OUTFLOW Stratospheric intrusions PACIFIC

3. Models are an Integral Part of Field Experiments Flight planning Provide 4-D context of the observations Facilitate the integration of the different measurement platforms Evaluate processes (e.g., role of bomass burning, heterogeneous chemistry….) Evaluate emission estimates (bottom-up as well as top-down)

4. CFORS/STEM Model Data Flow Chart Meteorological Outputs from RAMS or MM5 Meteorological Preprocessor CFORS Forecast Model with on-line TUV Normal meteorological variables: wind velocities, temperature, pressure, water vapor content, cloud water content, rain water content and PV et al Dust and Sea Salt emissions Emission Preprocessor Biomass Emissions Volcanic SO 2 Emissions Anthropogenic Area Emissions Biogenic Emissions Large Point Sources Satellite Observed total O 3 (Dobson Unit) Post Analysis

5. CFORS/STEM Model Data Flow Chart Meteorological Outputs from RAMS or MM5 Meteorological Preprocessor CFORS Forecast Model with on-line TUV Normal meteorological variables: wind velocities, temperature, pressure, water vapor content, cloud water content, rain water content and PV et al Dust and Sea Salt emissions Emission Preprocessor Biomass Emissions Volcanic SO 2 Emissions Anthropogenic Area Emissions Biogenic Emissions Large Point Sources Satellite Observed total O 3 (Dobson Unit) Post Analysis Tracers/Markers: SO2/SulfateDMS BCOC VolcanicMegacities CO fossilCO-Biomass EthaneEthene Sea SaltRadon Lightning NOx Dust 12 size bins

6. 3/9 March 9 --forecast

7. Frontal outflow of biomass burning plumes E of Hong Kong Observed CO (G.W. Sachse, NASA/LaRC) Observed aerosol potassium (R. Weber, Georgia Tech) Biomass burning CO forecast (G.R. Carmichael, U. Iowa)

8. DC8 #8 (2:30-3:30 GMT)

9. VGEO-Langley

10. Obs M M (w/o bb) % b b

11. Measured and Modeled Ethane (Blake et al.) as a Function of Latitude DC8 & P3 Flights

12. Data from Avery and Atlas

13. Summary of the TRACE-P analysis (A) from SPCvsSPC-OBS-MOD.pdf (S-final) (4) O 3 vs. CCHO We couldn’t reproduce the high CCHO at low O 3 condition. HNO 3 vs. SO 2 High SO 2 and Low HNO 3. (Volcano Signal) Also NOy vs. SO 2 etc have same feature.

14. CO under-prediction under 1000m for TRACE-P What doe this tell us ? CO data from Sacshe

15. Back Trajectories from High CO point. --- CO > 700 --- CO > 600 --- CO > 500 --- CO > 450 --- CO > 400

16. Back Trajectories from High CO point (Zoom & CO > 500 ppbv) --- CO > 700 --- CO > 600 --- CO > 500

17. 16km-resolution forecasted SO 2 (ppbv) at 1km layer at 3GMT, 04/11/2001 80km-resolution forecasted SO 2 (ppbv) at 1km layer at 3GMT, 04/11/2001 Effect of Model Resolution

18.  1000 ppbv of CO, 10 ppbv of HCHO, 100 ppbv of O 3 Shanghai  Fresh plumes out of Shanghai, < 0.5 day in age % Urban HCHO Flight Path Back Traj. Characterization of Urban Pollution Flight DC8-13 : 03/21/2001

19. We run back-trajectories from each 5 minute leg of merge data set. Keep track of each time a trajectory passes in the grid cell of the city and below 2 km. Classification of trajectory by the Source of Megacity. Age as determined by trajectory is also shown Before After Big difference !!! We catch more number of fresh airmass from Shanghai and Seoul.

20. Comparing Modeled and Measured Ratios We extract all points associated with a specified city and plot measured ratios and plot modeled ratios.

21. Comparison of Modeled and Observed Results from China’s Mega Cities Shanghai model measured Shanghai emissions Hong Kong model measured Hong Kong emissions Beijing model measured Beijing emissions HCHO/CO.0072.0080.002490.0045 0.00180.00960.007 0.00720.00251 C2H6/CO.0106.01010.004560.0043 0.00490.011430.0058 0.00510.00452 SO2/C2H2 4.613 3.7116.262.251 1.15038.6724.07 4.108.076 SO2/CO.0179.01950.10490.0031 0.26180.0236 0.02140.0575 N0x/SO2.222.2290.9970.468 0.4162.7050.299 0.2960.884 C2H6/C2H2 1.18 1.140.70571.657 0.7361.6891.21 1.220.634 BC/CO.0105.01120.008380.0058 0.00550.010.0074 0.00790.0080 BC/SO2.245.300.07991.299 1.3010.060.138 0.1860.14

22. Trace-P Observed - O 3 vs NO z DC8 P3

23. Ratio Analysis by Back trajectory region category. (1) Only from 01-05GMT Japan RegionOBS RatioModel Ratio Biomass (SEA)3.234.89 Philippine25.620.6 South China21.04.98 Middle China3.034.92 N. China, Korea0.452.76 Japan16.311.5 ΔO3/ΔNOz Central China (Shanghai etc)

24. The CFORS forecast (upper left) of the two dust systems are shown above. The dust plume (pink) represents the region with dust concentrations greater than 200  grams/m 3. White indicates clouds. The SeaWifs satellite image (upper right) also clearly shows the accumulation of dust spiraling into the Low Pressure center. Also note the strong outflow of dust in the warm sector “ahead” of the front over the Japan Sea. The two systems are clearly seen in the satellite derived TOMS-AI (aerosol index) (lower right). The dust event is clearly seen in the China SEPA air pollution monitoring network. Lower left hand panel shows extremely large ground level concentrations (http://www.ess.uci.edu/~oliver/tracep/airqual/index.html). The sandstorm and sand-drifting weather, which swept across most parts of China caused severe visibility and air quality problems http://news.xinhuanet.com/english/20010409/395181.htm NASA-Seawifs

25. legend Sea Salt PM10 PM2.5 DUST Sulfate OC BC Data from Clarke et al. 8 9 10 11 12 1314 15 16 17 18 TRACE-P Extinction

26. Simulations for Sensitivity Study  NORMAL: standard STEM simulation. Aerosol and cloud optical properties are explicitly considered  NOAOD: STEM simulation without aerosol optical properties, but with cloud impacts.  CLEARSKY: STEM simulation without aerosol or cloud optical properties. J[O 3  O 1 D+O2]J[NO 2  O 3 P+NO] For TRACE-P all DC-8 and P-3 Flights: Data from Shetter et al.

27. Cloud and Aerosol Impacts on Photolysis Rates for All TRACE-P Flights Aerosol Impacts = NORMAL – NOAOD Cloud Impacts = NOAOD – CLEARSKY Aerosol Extinction J[NO 2 ] J[O 1 D]

28. Cloud and Aerosol Impacts on Chemical Species via Photolysis Rates for All TRACE-P Flights OH O3O3 NO x Ethane

29. Observed and calculated O3 on C- 130 flight 6 (April11): Red line w/o heterogeneous chemistry; light blue with.

30. DUST[μg/m 3 ] SO 4 [μg/m 3 ] BC[μg/m 3 ] OC[μg/m 3 ] APRIL Lev =0.1,0.24,0.36,0.48,0.6,0.72,0.84,0.96,1.08 Lev =1,3,6,9,12,15,18,21 Lev =10,30,60,90,120,150,180,210 Rishiri 0 2 4 6 8 Height[km] 0 2 4 6 8 0 2 4 6 8 Lev =0.1,0.4,0.8,1.2,1.6,2.0,2.4,2.8,3.2 0 2 4 6 8 Height[km] AOD ： BC+OC ： DUST ： Sulfate ： Sea salt 10227 121.2 0.11.15 0.13.48 E.Q. N30 E120E90 Rishiri Okinawa Fukuoka Beijing Nagasaki & Fukue E150 Harbin Amami Tsukuba Sado Shanghai Hachijo Ogasawara Tarukawa Qingdao

32. IGAC ITCT Y2K Experiment http://www.cgrer.uiowa.edu/people/ytang/itct-2k2.html

33. ITCT-2K2 WP-3 Flight #9: Los Angeles Plume Study

34. Trinidad Head Surface Measurements during ITCT Y2K– Model Forecasts were Provided Daily

35. Trinidad Head, Ozone, May 3 Ozonesonde May 3, 1850Z MOZART (Larry Horowitz, GFDL) Apr 30 - May 5 (May 3, 19Z = -47)

36. Where do we go from here? Example of Use of 3-D CFORS modeling system at TRACE-P Information Day in Hong Kong

37. Goal: Better Integration of Measurements and Model Products More evolvement of models in the design of the experiments Data assimilation What are the most useful forecast products? More “sophisticated” use of measurements and models – e.g., aerosol issues. Better coupling between global and regional models Measurers and modelers need to work even more closely as collaborators With a goal of developing optimally merged data sets.

38. U. Iowa/Kyushu/Argonne/GFDL With support from NSF, NASA (ACMAP,GTE), NOAA, DOE

40. Fly here to sample high O 3

42. Integration of Measurements and Models

43. 6 7 8 9 10 11 12 Dust (  g/m 3 ) 6 7 8 9 10 11 12 64206420 Sulfate (  g/m 3 ) Black Carbon (  g/m 3 ) 6 7 8 9 10 11 12 64206420 64206420 Beijing Lidar Measurements and CFORS Model Results

44. E.Q. N30 E120E90 Rishiri Okinawa Fukuoka Beijing Nagasaki & Fukue E150 Harbin Amami Tsukuba Sado Shanghai Hachijo Ogasawara Tarukawa Qingdao