Sébastien WAGNER sebastien.wagner@jrc.it Emissions and Health Unit – JRC IES

1. Numerical modelling of atmospheric pollutant dispersion over the ESCOMPTE area. Comments on the preliminary results for the IOP2a+b Multiscale Air Pollution Model MAPOM Sébastien WAGNER sebastien.wagner@jrc.it Emissions and Health Unit – JRC IES Escompte Modelling Exercise Worshop - Toulouse 5-6 May 2004

2. The Joint Research Centre‘s Integrated Modelling System for Air Quality

3. What is MAPOM? MAPOM = Multiscale Air Pollution Model (FORTRAN90/95) (Wagner, 2003) Two-way nested mesoscale air quality model solving: Transport Diffusion (only the vertical diffusion) Gas phase chemistry Radiation Dry deposition for gases Automatic handling of the grid structure in case of nested grids What is not in MAPOM yet? Aerosol thermodynamics Wet deposition + dry deposition for aerosols Effects of clouds on radiation and aerosols  Under process of implementation Description of MAPOM - 1 - Physics and chemistry

4. Structure of the resolution based on an operator splitting scheme (Strang’s scheme): Dry deposition Emissions Transport (XYZ) Diffusion Chemistry Emissions Diffusion Transport (ZYX) Dry deposition Transport scheme: finite volumes + operator splitting (XYZ). Fluxes calculated by the corrected PPM method (A. Clappier, 1998) Diffusion: 1D two-point finite volume scheme Gas phase chemistry: RACM (76 species - 237 reactions) (Stockwell, Kirchner, and al., 1997) Radiation: TUV model (S. Madronich, S. Flocke, 1998) Dry deposition (based on Wesely, 1986) Description of MAPOM - 2 - Numerical aspects t/2 t/2 + t/2 t t/2

5. D1 D2 D3 Model configuration 3 grids of 72 by 72 cells, centred on Marseille: • D1: 18 km by 18 km  1296 km by 1296 km • D2: 6 km by 6 km  432 km by 432 km • D3: 2 km by 2 km  144 km by 144 km Same vertical structure: 18 levels, up to about 6500m above ground level (finest resolution: 33m) Domains D1 + D2 + D3  meteorology (two-way nested simulation) Domain D1  air quality (to build boundary conditions) Domains D2 + D3  air quality (two-way nested simulation)

6. Boundary conditions Boundary conditions for the meteorology:  NCEP analysis (6-hour frequency) • ECMWF analysis (12-hour frequency, interpolation to have 6-hour frequency) Lateral boundary conditions  D1 Initial conditions + ground conditions  D1 + D2 + D3 Boundary conditions for the chemistry: D1  Constant boundary conditions (for incoming fluxes) (use of the GCM TM5 model output for BC under evaluation) D2  Boundary conditions varying in time and space (coming from simulation on D1) D3  Two-way nesting procedure Spin-up: Meteo + air quality on D1  2 days before the beginning of the IOPs Air quality on D2 + D3  1 day (as requested) D1 D2 D3

7. Meteorology - Emissions The meteorology:  Off-line meteorological driver = MM5  Same vertical structure than the air quality model  Two-way nesting procedure for D2 and D3  Output frequency = 1 hour NO data assimilation The emissions  Projected EMEP emission inventories for D1 and D2 (reference year 2000) • no use of the ESCOMPTE inventories for D1 • indirect use of the ESCOMPTE inventories for D2 through the two-way nesting method For EMEP emissions, conversion from yearly to hourly emissions using activities tables provided by EMEP Effective height? “Stack emissions” distributed onto 4 levels between 50m and 400m above ground  ESCOMPTE emission inventories for D3 (completed with EMEP for outer cells)

8. Table for effective heights by sector Tables for monthly / daily / hourly activities Yearly EMEP distributed at various height (sector wise) Yearly EMEP Hourly values EMEP Speciation table • 2nd lumping of the VOCs • RACM species + • NOx 85% NO+ 15% NO2 • SO2 , CO, NH3 VOC speciation for the various sectors  227 species (AEAT/ENV/0545 Report “Speciation of UK emissions of NMVOC”, Passant 2002) 1st lumping of the VOCs  42 species (Middleton nomenclature) Middleton table Conversion to ppm.m/s EMEP Emissions

9. Comparison to observations • TERRAIN:  Data retrieved from MM5 database (resolutions = 5min for D1, 2min for D2 and 30s for D3)  No data assimilation / no comparison yet with Escompte data on elevation • COMPARISON WITH OBSERVATIONS:  Under process Methodology for ground observations: from latitude-longitude co-ordinates, the grid cells are retrieved. Direct comparison between the observation and the model results (no interpolation) Methodology for airborne observations: from latitude-longitude co-ordinates + altitude for each record, the grid cells are retrieved (“trajectory”). Direct comparison between the observation and the model results (no interpolation)

10. Model strengths / Weakness STRENGTH: • Automatic gridding system and data management in the case of nested grids • Large flexibility in the choice of the process modelling • Easy setting of the various IOPs WEAKNESS: • CPU time: model relatively slow (BUT code optimisation under process) • Land use:  Retrieved from MM5. Some important inconsistencies were noticed (e.g. for water bodies and wood lands) • Large emission sources close to the shoreline:  Problem with dispersion processes in case of extremely weak horizontal wind (dispersion relies only on the vertical diffusion) • Mapping between emissions, meteorological fields and air quality not straightforward

11. General comments on comparisons with observations In general, overestimation of the O3 concentrations with respect to measurements • Effects of the sea:  Large reservoir for O3 • Dry deposition: is it strong enough? In general, in MAPOM, small part of ozone is removed  Modelling? Bad land use? • Radiation? Photolysis rate too high?  Investigation needed on the treatment of the absorption of the incoming radiation by compound such as O3, NO2, etc General comments on the exercise • Short information about the various data sets would be nice (rural, urban, etc.) • A lot of information to handle...