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

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

4. 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

5. ACE-Asia (NSF) & TRACE-P (NASA) Spring 2001 Experiments NASA/GTE DC-8

6. Ace-Asia April/May 2001

8. DC8P3  Two aircrafts – DC8 and P3 urban plumes  Chemical evolution during continental outflow, biomass burning, dust outbreaks, and urban plumes  22  22 flights out of Hong Kong, Okinawa and Tokyo  O 3, CO, SOx, NOx, HOx, RH and J  100m to 12000m China NASA GTE TRACE-P Mar’01- Apr’01

10. Model Overview Regional Transport Model: STEM Modular  Structure: Modular (on-line and off-line mode) RAMSMM5ECMWFNCEP  Meteorology: RAMS - MM5 - ECMWF - NCEP  Emissions  Emissions: Anthropogenic, biogenic and natural SAPRC’99  Chemical mechanism: SAPRC’99 (Carter,2000)  93 Species, 225 reactions, explicit VOC treatment NCAR-TUV 4.1  Photolysis: NCAR-TUV 4.1 (30 reactions) Flexible  Resolution: Flexible 80km x 80km for regional and 16km x 16km for urban

11. Photochemistry : STEM-TUV Y. Tang (CGRER), 2002

14. Regional Emission Estimates: Anthropogenic Sources Industrial and Power Sector Coal, Fuel Oil, NG SO 2, NO x, VOC, and Toxics Domestic Sector Coal, Biofuels, NG/LPG SO 2, CO, and VOC Transportation Sector Gasoline, Diesel, CNG/LPG NO x, and VOC

15. Regional Emission Estimates: Natural Sources Biomass Burning In-field and Out-field combustion CO, NO x, VOC, and SPM Volcanoes SO 2, and SPM Dust Outbreaks SPM

16. The Emissions Vary Greatly by Region – Reflecting Many Social/Economic Factors

17. The TRACE-P/Ace-Asia emission inventory shows the important sources of each type of air pollutant in Asia

18. For Southeast Asia and Indian Sub-Continent Original Fire Count(FC) data(AVHRR) “Fill-up” Zero Fire Counts using Moving Average(MA) “Fill-up” Zero Fire Count using TOMS AI Satellite Coverage Cloudiness Mask Grid (Landcover) Precipitation(NCEP) “Extinguish” Fire Count using Mask Grids Mask Grid (Never Fire) Moving Averaged Fire Count data (Level 2) AI Adjusted Fire Count data (Level 3) 5-day Fire Count Regress. Coeff.(AI/FC) Regional Emission Estimates: Biomass Burning Emissions

19. Open Burning Emissions of CO – Based on AVHRR Fire-count Data

20. Comparison of country surveys with various AVHRR fire-count adjustments reveals problem areas for further investigation Xinjiang Mongolia Indonesia Vietnam fire count > country surveys fire count < country surveys India It remains difficult to make the link between satellite observations of fire and atmospheric emissions

22. The Importance of Fossil, Biofuels and Open Burning Varies by Region

23. Uncertainty analysis has revealed wide differences in our knowledge of the emissions of particular species in particular parts of Asia …

24. 3/9 March 9 --forecast Example of Forecast Used in Flight Planning

25. Propane data from Blake et al.

27. 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)

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

29. Using Measurements and Model – We Estimate Contributions of Fossil, Biofuel and Open Burning Sources

30. Testing Model:CO under-prediction under 1000m for TRACE-P ---WHY? What doe this tell us ? CO data from Sacshe

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

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

33. Urban Photochemistry OH Radical Cycle Air Toxics Ozone Acid Rain Visibility PM2.5 WaterQuality. OH NOx + VOC + OH + hv ---> O 3 SOx [or NOx] + NH 3 + OH ---> (NH 4 ) 2 SO 4 [or NH 4 NO 3 ] SO 2 + OH ---> H 2 SO 4 NO 2 + OH ---> HNO 3 VOC + OH ---> Orgainic PM OH Air Toxics (POPs, Hg(II), etc.) Fine PM (Nitrate, Sulfate, Organic PM) NOx + SOx + OH (Lake Acidification, Eutrophication)

34.  Tropospheric chemistry is characterized by reaction cycles  OH  OH plays a key role in tropospheric chemistry removalgeneration  Reactions lead to removal as well as generation of pollutants  NO x to VOC ratio  NO x to VOC ratio governs Ozone production Urban/Regional Photochemistry

35. Urban/Regional Photochemistry NO x -VOC-Ozone Cycle  Organic radical production and photolysis of NO 2  VOC’s and N-species compete for OH radical

36. Urban/Regional Photochemistry NO x -VOC-Ozone Cycle  In polluted environment, CO contributes to O 3 production

37. Urban/Regional Photochemistry NO x -VOC-Ozone Cycle  HCHO – primary intermediate in VOC-HO x chemistry  Short lived and indicator of primary VOC emissions

39. Comparison of Observed and Modeled OH Provides a Direct Check on Models

40.  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

41. 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 Big difference !!! We catch more number of fresh airmass from Shanghai and Seoul.

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

43. 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

44. Age in days calculated from back trajectories along the flight path Units: ppbv-HCHO/ ppbv-CO Urban Photochemistry HCHO to CO Ratios City Plume Age (days) Ratio (Obs.) Ratio (Mod.) All Points < 1 day 0.01020.0079 1 to 2 days0.00690.0068 2 to 3 days0.00610.0066 3 to 4 days0.00610.0069 4 to 6 days0.0070 Shanghai< 1 day0.01140.0079 1 to 2 days0.00740.0066 2 to 4 days0.00390.0047 4 to 6 days 0.0043 Beijing0.00650.0071 Seoul< 1 day 0.0120 1 to 6 days0.0078 Pusan< 1 day0.0116 1 to 6 days0.0077 Hong Kong0.00630.0062 Tokyo0.0102 Manila0.0192

45. 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)

46. Urban Photochemistry NO x -VOC Sensitivity to O 3 Production VOC sensitive NOx sensitive Loss(N)/(Loss(N)+Loss(R)) Model NOx (ppbv) Model results along the flight path Megacity points from back trajectories Klienman et al., 2000 Less than 2 day old plumes

47. These Results Also Have Air Quality Management Implications

48. 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

49. Fly here to sample high O 3

50. Climate : Air Quality Analysis Framework

51. Development of a General Computational Framework for the Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints (NSF ITR/AP&IM 0205198 – Started Fall 2002) A collaboration between: Greg Carmichael (Dept. of Chem. Eng., U. Iowa) Adrian Sandu (Dept. of Comp. Sci., Mich. Inst. Tech.) John Seinfeld (Dept. Chem. Eng., Cal. Tech.) Tad Anderson (Dept. Atmos. Sci., U. Washington) Peter Hess (Atmos. Chem., NCAR) Dacian Daescu (Inst. of Appl. Math., U. Minn.)

52. Goal: To develop general computational tools, and associated software, for assimilation of atmospheric chemical and optical measurements into chemical transport models (CTMs). These tools are to be developed so that users need not be experts in adjoint modeling and optimization theory.

53. Approach: Develop novel and efficient algorithms for 4D-Var data assimilation in CTMs; Develop general software support tools to facilitate the construction of discrete adjoints to be used in any CTM; Apply these techniques to important applications including: (a) analysis of emission control strategies for Los Angeles; (b) the integration of measurements and models to produce a consistent/optimal analysis data set for the AceAsia intensive field experiment; (c) the inverse analysis to produce a better estimate of emissions; and (d) the design of observation strategies to improve chemical forecasting capabilities.

54. Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimilation of chemical data.

55. Application: The Design of Better Observation Strategies to Improve Chemical Forecasting Capabilities. Example flight path of the NCAR C-130 flown to intercept a dust storm in East Asia that was forecasted using chemical models as part of the NSF Ace-Asia (Aerosol Characterization Experiment in Asia) Field Experiment Data Assimilation Will help us Better Determine Where and When to Fly and How to More Effectively Deploy our Resources (People, Platforms, $s) Shown are measured CO along the aircraft flight path, the brown isosurface represents modeled dust (100 ug/m3), and the blue isosurface is CO (150 ppb) shaded by the fraction due to biomass burning (green is more than 50%).

56. http://www.cgrer.uiowa.edu/people/carmichael/GURME/GURME.html

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