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Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston

Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and Regional Air Quality Models. Daewon W. Byun, Nankyoung Moon, Heejin In. Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston. Daniel Jacob, Rokjin Park. Harvard University.

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Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston

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  1. Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and Regional Air Quality Models Daewon W. Byun, Nankyoung Moon, Heejin In Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston Daniel Jacob, Rokjin Park Harvard University

  2. Introduction Some US regional air quality problems may be originated from long-range transport processes (eg. Transport of EC/OC/CO/dust from Sahara & biomass burning from Central America) One of key problems of regional air quality models is finding accurate initial and boundary conditions for the simulations. Distribution of surface air chemistry and PM monitoring sites is limited both in the spatial density and in the physical and chemical details. Current method: Run a regional air quality model at a coarser resolution with seasonal profile data and use emissions input for a long period for the spin-up process. The fixed profile BCs are never accurate and cannot account for changes due to air pollution long-range transport events. It could be different at each side of domain reflecting certain regional differences.

  3. Three Areas of Inter-linkage Issues • Dynamic Representations in Global and Regional Models • - Chemical Representations in Global and Regional Models • Mechanics of Linkage • Linkage of scales: grid structure and scales of data representation (generation of IC/BCs) • Linkage of chemical species • Linkage of dynamics Study Objectives Provide tools/methods to link regional and global modeling systems (e.g.) Set the boundary of the domain that outside areas do not have much direct emissions and no high concentration blobs already existing What is the sensitivity of the simulations to the different IC/BCs?

  4. Mechanics of Linkage • Linkage of scales: Currently, grid structures of the global and regional models are not “consistent” • Requires less preferable horizontal & vertical interpolation Implementation Example MODEL3 CMAQ(Multi-pollutant Air Quality model) GEOS-CHEM (Goddard Earth Observing System-CHEMisrty) LAMBERT CONFORMAL 108 km X 108 km 23 layers in Sigma Po LAT-LON 2 degree X 2.5 degree 20 layers in Sigma P Initial & Boundary Condition IO/API Format in 108 km resolution Future – requires “geocentric” coordinates (from a flat-earth to a spherical earth, if not spheroid)

  5. Horizontal distribution of O3 concentration from GEOS-CHEM global output at Layer 1 For 2000 August episode 2 X 2.5 degree resolution 108km resolution

  6. Mechanics of Linkage • Chemical species:Currently, chemical mechanisms in global and regional models are not “consistent” MAPPING Table GEOS-CHEM O3-NOX-Hydrocarbon chemistry : 24 species CMAQ CB4 : 16 species Un-used species : ACET, ALD2

  7. Linkage of Chemistry GEOS-CHEM Mapping Table CMAQ SAPRAC-99

  8. Emission comparison

  9. Chemical Mechanism VOC Definition CB4 VOC = PAR + 2OLE + 2ETH + 2ALD2 + 7TOL + 8XYL + 5ISOP + FORM GEOS-CHEM VOC = C2H6 + C3H8 + ALK4 + PRPE + ISOP + CH2O + ALD2 + RCHO The emissions inputs used for the GEOS-CHEM and CMAQ for the NOx and VOC species were compared. Table presents how total VOC in mechanisms are calculated and the values of NOx and VOC for GEIA represent smaller than NEI99-SMOKE in maximum values.

  10. GEOS-CHEM OUTPUT, Layer-1, Ox Summer

  11. GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox Summer

  12. GEOS-CHEM OUTPUT, Layer-1, Ox Winter

  13. GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox Winter

  14. O3 and SO4 seasonal boundary condition time series (Col:56,Row:114) (Col:1,Row:88) (Col:73,Row:1)

  15. O3 time series at different vertical layers : Western Boundary Summer Winter

  16. O3 time series at different vertical layers : Southern Boundary Summer Winter

  17. O3 time series at different vertical layers : Northern Boundary Summer Winter

  18. SO4 seasonal boundary condition time series Summer Winter

  19. CMAQ simulation Emission NEI99 ( SMOKE ) Chemical mechanism CB4 / SAPRC99 MET. DATA MCIP (MM5) Domain CONUS 36-km Simulation IC & BC with Original profile data IC & BC with GEOS-CHEM output

  20. Comparison of simulated O3 concentration with AIRS

  21. Comparison of CMAQ results in different IC and BC (2000.08.25. 09, 21UTC) 03AM CST 03PM CST Profile Data Case GEOS-CHEM Data Case

  22. In CMAQ simulations, the results using GEOS-CHEM output for boundary condition have smaller value from 16 ppb to 20 ppb than the results using profile data around western and northeast boundary area. On the other hand, there is opposite results at south boundary area, which is related with positive bios of GEOS-CHEM over the GULF of Mexico. It is necessary to investigate the chemical mechanism differences in CMAQ simulation with GEOS-CHEM boundary condition .

  23. Comparison of O3 production rate profile BC GEOS-CHEM BC CMAQ/CB4 CMAQ/CB4 GEOS-CHEM BC CMAQ/SAPRC

  24. Comparison of wind field MM5 NASA-GMAO General patterns of wind fields are well Some difference shows in circled area. - CMAQ/MM5 shows parallel to the grid - GEOS-CHEM/NASA-GMAO shows inflow This difference can be cause the uncertainty to regional air quality simulations. Let’s see how big the problem is:

  25. GEOSCHEM : Easterly and northerly MM5 : Clock wise rotation motion

  26. MM5 GMAO

  27. PREGRID MYPREGRID REGRIDDER Study importance of the dynamic consistency Comparison of the first guess field used in MM5: between ETA and GMAO EDAS DAO

  28. Comparison of wind fields among three different MM5 results. Case 1; MM5 results with EDAS first guess Case 2; MM5 results with ETA first guess and GMAO objective analysis Case 3; MM5 results with GMAO first guess ~ trying to get closer wind fields to GMAO

  29. MM5 Results August 25. 00 UTC CASE 1 CASE 2 CASE 3 GMAO

  30. Comparison of Root Mean Square Error (RMSE) RMSE is for MM5 result of each case and GMAO According to the evaluation result of numerical models, RMSE was 1.63, 1.57 and 1.41 for wind speed and 68.37, 66.66 and 69.49 for wind direction for RAMS, MM5 and Meso-Eta respectively (Zhong and Fast, 2003). In that evaluation, RMSE was for observation data and simulation results for different meteorological model outputs.

  31. Case 2 (ETA first guess and objective analysis with GMAO) shows the most closest results to GMAO filed in three cases from RMSE analysis. • Even if MM5 use GMAO data for the first guess in case3, MM5 can not simulate closer values to initial filed (GMAO) with the lower resolution of GMAO in time(6 hourly) and space(2X2.5). • The best case is use of ETA data of high resolution in time and space for the first guess and use of objective analysis with GMAO data in INTERPF.

  32. CMAQ Simulation Emission ; EPA-NEI99 Chemical Mechanism ; CB4 Meteorology ; MM5 results of three cases (Each case has corresponding case of MM5) • Comparison of Ozone difference • CASE2 – CASE1 • ; (ETA_first guess & GMAO_objective analysis) – (ETA_first guess) • CASE3 – CASE1 ; (GMAO_first guess) – (ETA_first guess)

  33. CMAQ Simulation Results: Ozone Concentration Differences MM5 with DAO - MM5 with EDAS for August 25, 2000

  34. Comparison of O3 difference Case2 – Case1

  35. May 15 1998 May 13 1998 May 14 1998 May 16 1998 Biomass burning due to ENSO-related drought in Mexico and Central America during April ~ June 1998 TOMS Aerosol Index

  36. GEOS2CMAQ Interface Aerosol species Mapping GEOS-CHEM CMAQ OC OC_hydrophilic + OC_hydrophobic EC_hydrophilic + EC_hydrophobic AORGI+AORGJ+AORGPAI+AORGPAJ AORGBI+AORGBJ EC AECI+AECJ Coordinate transformation 36 km × 36 km Rambert Conformal 2.5°× 2° Simple Interpolation 30 vertical layer (Ptop= 10 mb) 23 Sigma vertical layer (Ptop= 50 mb)

  37. GEOS-CHEM Global simulation ( 2.5°× 2° ) GEOS2CMAQ Interface OC EC ICON BCON

  38. EMISSION DATA SOURCE : US EPA NEI 99 Processed with SMOKE EC OC

  39. Spatial Evolutional Feature of OC CMAQ ver 4.3 Grids : 133 × 91 × 23 Resolution : 36 km × 36 km Science Process : CB4-AERO3- EBI solver Meteorogical data from MM5 ver3.6

  40. Simulated Monthly CON Evaluation by IMPROVE Monitoring W/ fixed profile BC EC OC W/ GEOS-CHEM BC Difference A B B C

  41. Daily concentrations of Simulated vs. Observed OC Region A OR WA NV CA Region B UT CO FL VT

  42. IMPROVE Network Improving of OC concentration Region C TX AZ TN AR

  43. Daily Mean CON A OC EC B Simulation R= 0.59 G G R= 0.60 C R= 0.33 P P R= 0.35 Observation

  44. Monthly concentrations of Simulated vs. Observed EC OC G R= 0.75 G R= 0.79 Simulation P P R= 0.33 R= 0.68 Observation

  45. Conclusion Linkage issues between global tropospheric chemistry model and regional air quality model has been studied. To investigate the effects of using GEOS-CHEM output as initial and boundary conditions instead of the profile data on regional simulations, we have conducted 4 sensitivity CMAQ simulations with the CB4 and SAPRC99 as the chemical mechanisms. We observe significant differences between profile vs. GEOSCHEM IC/BC. Global-regional scale linking is the best when direct emission source is little outside the regional domain boundary; e.g., US-continental domain. It is necessary to quantify and minimize the effects of different dynamics between the global and regional meteorological data used and to study the issues of consistency in chemical mechanisms.

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