Overview of the first results of the EURODELTA3 model intercomparison project

1. Overview of the first results of the EURODELTA3 model intercomparison project Bertrand BESSAGNET (INERIS) on behalf of the EURODELTA team

2. The Eurodelta III exercize • Two phases: • Simulation of campaigns (2009, 2008, 2007, 2006) • validation • Retrospectiveanalysis (2008, 1999, 1990) • Ability of models to reproduce the difference of concentrations for the threekeyyears, how modelsworkunderdifferentchemicalregimes • Common inputs for models : meteorology (IFS), emissions (EC4MACS dataset), boundary conditions (MACC), domain (except CMAQ) • CAMx (PSI/RSE), CHIMERE (INERIS), CMAQ (HZG), EMEP (Met.no), LOTOS-EUROS (TNO), MINNI (ENEA), RCG (FUB) • Others participants: • DG JRC, CIEMAT, BSC, IPSL-CNRS, Univ of Brescia, NILU • TOTAL, CONCAWE, LWA

3. Status of the exercize • Start of the exercize in April 2012 • April  June : work on input data • June  October : work on models • November : production of runs • December : first discussion on 2009 campaign • January February : consolidation of observation datasets (some surprises in data: outliers , daily obs starts often at 8:00) • April : discussion on last results • June 2013 : preparation of a report on the 2009 campaign • End of 2013 : other campaigns + start of retrospective analysis

4. 2009 campaign (25/02 – 26/03) • Available observations : hourly and daily obs background data (EMEP & campaigns : EBAS) • Comparisons on: • NO2, O3, SO2, PM10, PM2.5, HNO3, NO3, NH3, NH4, Dust, TOM, EC, Na • AMS data are compared with PM2.5 modelled results • TOM = 1.6 x OC • Wet deposition S, N • PBL, T2M, U10, RAIN

5. PM10 average concentrations • Visible windblowndust for EMEP • LOTO and CMAQ have the lowest PM10 concentrations • Po Valley: differentpatterns for RCGC and LOTO • High concentrations for MINNI

6. Total nitrate average concentrations • CHIM has no coarse nitrate (toolow in Spain) • MINNI and CMAQ have toohigh concentrations • Differentpattern over the Po valley for LOTO and RCGC

7. Dust average concentrations • Dust as 8xCa • EMEP, MINNI, RCGC and LOTO have windblowndustemission modules • Ability of EMEP to reproduce a dustevent in Cyprus

8. Sulphate average concentrations • CHIM has the highest concentrations (toohigh) • Similar gradient W/E for all models

9. Cumulated NHx wet depositionover the campaign

10. Diurnal cycles • O3 : CMAQ, EMEP and CHIM have the lowestbias • NO2 : LOTO has the lowestbias, best diurnal cycle for MINNI • PM10: MINNI has the best diurnal cycle

11. Diurnal cycles • Na : CHIM & MINNI overshoot, best performances for RCGC, EMEP and LOTO • TOM : Underestimate by models • NO3 : CHIM & EMEP have the best performances

12. Meteorology Near Paris • Samemeteorology but differentwayto calculatemixing and PBL • On average for the PBL at 12:00 : • CHIM & CAMX have the lowestnegativebias (-100 m) • MINNI : large negativebias ( -400 m)

13. Best performances based on RMSEModel ranking based on daily basis data * ɸ < 10 µm All daily obs.10% highest obs. • O3 CHIM, CAMX, EMEP CAMX, EMEP, CHIM • NO2 CAMX, CHIM, EMEPEMEP, MINNI, LOTO • SO2 CHIM, LOTO, EMEPEMEP, LOTO, CMAQ • PM10 EMEP, CHIM, MINNI EMEP, MINNI, CHIM • PM25 CHIM, MINNI, EMEP MINNI, CHIM, CAMX • SO4* EMEP, CMAQ, LOTO MINNI, CAMX, CHIM • NH4* EMEP, LOTO, CHIM CHIM, CMAQ, MINNI • NO3* CHIM, EMEP, LOTO CHIM, MINNI, RCGC • TOM* CHIM, MINNI, RCGC CAMX, CHIM, MINNI • EC* EMEP, MINNI, LOTO EMEP, MINNI, LOTO • DUST* EMEP, LOTO, CAMX RCGC, MINNI, CAMX • NA* EMEP, LOTO, RCGC EMEP, RCGC, CHIM

14. « Deposition capability » in the models Indicator: (Deposition/Concentration) for dry deposition  (Deposition/Concentration/Rain) for wet deposition ×

15. Chemical regime for SIA chemistry Free ammonia (mol) = TNH4 – TNO3 – 2 x SO4

16. Identified problems and general comments • By model: • CAMX : Problem (or not!) with SOA chemistry • CHIM : Overestimate of sea salts and sulphates (Boundary conditions?) • CMAQ : Underestimates PM10 concentrations • MINNI : Underestimates the PBL • EMEP : Low TOM concentrations • LOTO : Low TOM and PM10 concentrations • RCGC : Noisy wind blown dust parameterization • In general : • underestimate of TOM concentrations, not specially during episodes (negative bias of 50 to 80%) : SVOC emissions ? • General underestimate of PM peaks (Dust events, TOM event in some countries CH, IT, ES). Wood burning emissions? • SIA are well modelled • Dust parameterizations remain uncertain but EMEP catches well the CY dust event • Still a large model variability (dispersion and mixing, chemistry, biogenic, deposition schemes, landuse)

17. Remaining todos for the 2009 campaign • Need to evaluate the MACC boundary conditions (MOZART : O3, Dust, SO4) • Ozone profiles will be tested • Focus on episodes on highly instrumented sites • Understand the differences between SOA models and check the emissions of precursors • Need to compare with BSOA, BBOA measurements if available • Diffusion of results : report, web site