Monte Carlo uncertainty analysis 

1. Part 1 Monte Carlo uncertainty evaluationof emission reduction scenarios constrained by observations from the ESQUIF campaignM. Beekmann (LISA), C. Derognat (Aria-Technologies)

2. Part 2 Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite dataI. Konovalov (Institute of Appplied Physics, Nizhny Novgorod) M. Beekmann (LISA)R. Vautard (LMD/IPSL)A. Richter (IUP, University of Bremen)J. Burrows (IUP, University of Bremen),

3. What is the uncertainty in the simulation of emission reduction scenarios ?Case of Paris agglomeration Monte Carlo uncertainty analysis  Model output uncertainty due to uncertainty ininput parameters  Constraint by measurements (ESQUIF campaign) (Bayesian Monte Carlo uncertainty analysis)  Reduced uncertainty

4. METHODOLOGY (1)SET-up of the CHIMERE model for the Paris region (version 2002) OX, NOy 16/7/99 14h POI6 • Domain 150 km x 150 km with 6 km horizontal resolution • 5 vertical levels from surface to ~3 km • Forced by ECMWF first guess or forecast • Gas phase chemistry: MELCHIOR with 82 compounds, 338 reactions • Emissions, refined for regional scale from AIRPARIF, also biogenic • Boundary conditions: from CHIMERE at continental scale

5. METHODOLOGY (2)Definition of the probability density function for input parameters

6. METHODOLOGY (3) Constraints from ESQUIF observations From circular flights (DIMONA, MERLIN) • DOX, DNOy, DNOx, (DVOC) DC = C (plume) – C (background) From airquality network (AIRPARIF) • DOX = OX (urban) – OX (background)

7. Flight tracks around the Paris agglomeration during ESQUIF

8. METHODOLOGY (3) Constraints from ESQUIF observations From circular flights (DIMONA, MERLIN) • DOX, DNOy, DNOx , (DVOC) DC = C (plume) – C (background)

9. METHODOLOGY (4)mathematical formulation of the constraint For each Monte Carlo simulation k: Likelihood L for model output Yk to be correct for observations Oi(Bayesian Monte Carlo analysis Bergin and Milford, 2000): 1 (Oi – Yk,i)2 L(YkY | Oi) = _____________ EXP [ -0.5 _______________ ] (2p)0.5si si2 L(Yk | O) = L(Yk,,1 | O1) * L(Yk,2 | O2) * ……. Measurement errors siof observations Oi are assumed as • normally distributed • independent They stem from • instrumental errors • uncertainty in representativity for model grid

10. METHODOLOGY (5) Simulations performed • For 3 days in POI’s 2 and 6: August7, 1998 and July 16,17 • 500 Monte Carlo simulations with base line emissions • 500 Monte Carlo simulations with reduced emissions • - 50 % anthropogenic VOC • - 50 % anthropogenic. NOx • - 50 % anthro. VOC + NOx

11. RESULTS (1) • Cumulative probability plots Surface O3 maxima for baseline and 50% reduced emissions With (____) and without (- - - -) constraint

12. RESULTS (2) Surface O3 maxima for baseline and 50% reduced emissions

13. RESULTS (3) Chemical regime averaged over the pollution plume: Difference in surface O3 between a • NOx emissions –50 % and a • VOC emissions –50% scenario Positive values : VOC limited chemical regime Average over 1998/1999 : VOC sensitive or intermediate chemical regime (thesis C. Derognat)

14. RESULTS (4) OH averaged over the pollution plume at 14 UT (layer 2 50-600 m):

15. RESULTS (5) A posteriori and a priori probability of input parameters : NOx and VOC emissions

16. CONCLUSIONS • Uncertainty in simulated max. ozone (for baseline and reduced emissions) reduced by a factor 1.5 to 3 due to measurement constraint • Uncertainty in VOC limited regime is reduced for two days, shift from slightly VOC limited to slightly NOx limited for anaother day • For OH, the uncertainty is less reduced, but very low values are rejected, remaining uncertainty factor 1.5 – 2.5 • Weighting procedure through likelihood function changes distribution in input parameters namely NOx emissions

17. Limitations of this study: • Uncertainty in model formulation is neglected (transport, model chemistry) • Uncertainty in the definition of pdf’s for input parameters • Uncertainty in error distribution of observations (covariance always zero ?) Perspectives : • Application to continental scale • Application to air quality forecast

18. Part 2 Extension of CHIMERE to Eastern Europe and evaluation with surface and satellite dataI. Konovalov (Institute of Appplied Physics, Nizhny Novgorod) M. Beekmann (LISA)R. Vautard (LMD/IPSL)A. Richter (IUP, University of Bremen)J. Burrows (IUP, University of Bremen),

19. Model set up • Domain covering EU to Ural + Mediterranean regions with 0.5 ° horizontal resolution • 8 vertical levels from surface to 500 hPa • Forced by NCEP forecast (2.5°) and MM5 (1° res.) • Gas phase chemistry: MELCHIOR reduced • Emissions from EMEP and EDGAR, if needed • Boundary conditions: from MOZART

20. Time series

21. Error statistics

22. Comparison between GOME and CHIMERE derived tropospheric NO2 columns, June – August 1997 University of Bremen, GOME version V2 320 * 40 km resolution I. B. Konovalov, M. Beekmann, R. Vautard, J. P. Burrows, A. Richter, H. Nüß, N. Elansky, ACP, 2005

23. CHIMERE tropospheric NO2 columns versus GOME tropospheric NO2 columnsAverage June – August 1997 Western Europe Eastern Europe Slope = 0.75 R = 0.91 Slope = 0.70 R = 0.77

24. differences in GOME / CHIMERE tropospheric NO2 columns versus tropospheric NO2 columns (1015mol.) Western Europe • Random error in monthly mean (in a spatial sens) is mainly of multiplicative nature (25-30%), no attribution to GOME or CHIMERE possible

25. differences in GOME / CHIMERE tropospheric NO2 columns versus tropospheric NO2 columns (1015mol.) Eastern Europe • Random error in monthly mean (in a spatial sens) is less clearly of multiplicative nature for Eastern Europe than for Western Europe

26. CONCLUSIONS • CHIMERE domain has been extended to Eastern EU and Mediteranean region • Correlation with surface O3 obs. larger in WE (>80%) than in Central and EE <60-70%) • Comparison with GOME tropospheric NO2 :* No bias* slope 0.70-0.75* multiplicative spatial random error 15% EE – 30% WE