1. 13 / 10 / 2006 Uncertainty and regional air quality model diversity: what do we learn from model ensembles? Robert Vautard Laboratoire des Sciences du Climat et de l’Environnement And all colleagues from CityDelta and EuroDelta

2. 13 / 10 / 2006 Hopes from ensembles  Better air quality simulations and forecasts by « averaging errors » McKeen et al., 2005  Representation of the uncertainty (in forecasts, in scenarios) - Ensembles with perturbed model or input (Mallet and Sportisse 2006) - Model ensembles (Delle Monache et al 2003; McKeen et al. 2005)  Improve understanding by intercomparison: Condition: Models must be developed independently

3. 13 / 10 / 2006 CityDelta : only intercomparison Urban Scale (4 cities: Milan, Paris, Berlin, Prague) 9 models or model resolutions (3 models with 2 resolutions) REM, LOTOS, CHIMERE, EMEP, OFIS, CAMX Summer 1999 for ozone, Year 1999 for PM10

4. 13 / 10 / 2006 Hourly ozone values Slight improvement in mean valuesNo improvement in correlation

5. 13 / 10 / 2006 PM10 simulation skill General underestimation Improvement in mean values Intercity variability not reproduced Correlations 0.5-0.6

6. 13 / 10 / 2006 EuroDelta Experiment Regional, european scale 6 models Comparison with rural stations (EMEP or AIRBASE)

7. 13 / 10 / 2006 The Seven Models (EuroDelta) Model Horizontal resolution 1 and number of cells Vertical resolution Approx. depth 1 st layer (m). EMEP (EMEP-MSC-W) 50x50km 110x100 20 sigma levels up to 100 hPa90 RCG (UBA) 0.5°x0.25° 82x125 5 layers, surface layer fixed, 4 dynamical layers moving with MH 20 MATCH (SMHI) 0.4°x0.4° 84 x 106 14 layers (eta coordinates) up to 6 km 60 LOTOS-EUROS (TNO) 0.5°x0.25° 100x140 4 layers, surface layer fixed, 4 dynamical layers moving with MH 25 CHIMERE (INERIS, IPSL) 0.5°x0.5° 64x46 8 layers up to 500 hPa TM5 (JRC) Eur: 1°x1° Glob: 6°x4° 25 levels / hybrid sigma/pressure50 DEHM (NERI) Eur: 50x50km Northen hemisph: 150x150km : 96x96 20 sigma levels up to 100 hPa50

8. 13 / 10 / 2006 Mean diurnal cycles OzoneOx

9. 13 / 10 / 2006 Percentiles

10. 13 / 10 / 2006 Seasonal Skill scores Table 5: Correlation coefficients for daily average and daily maximum O 3. daily averagedaily maximum yearDJFMAMJJASONyearDJFMAMJJASON EMEP 0.720.670.550.500.550.750.600.590.610.53 LOTOS 0.700.490.540.490.430.760.470.700.660.48 MATCH 0.800.680.660.600.0.810.580.680.70.61 CHIMERE 0.760.620.580.640.600.840.620.710.770.62 RCG 0.710.580.590.520.360.760.560.700.610.44 DEHM 0.640.450.410.560.310.750.450.600.680.45 TM5 0.670.690.440.350.620.720.630.470.510.58 Ensemble 0.790.740.660.680.580.840.690.760.780.59

11. 13 / 10 / 2006 The skill of the ensemble mean Let us assume that the ensemble of K values x k is drawn from a distribution of physically possible states:  Then the observation x a has the same statistical properties than any member of the ensemble, and the RMSE of the ensemble average can be written: b is the ensemble bias, s is the ensemble spread (standard deviation)  The RMSE is a decreasing function of the number of members K  The RMSE (ensemble skill) is linearly linked to the ensemble spread,

12. 13 / 10 / 2006 Uncertainty All these concepts work only in the assumption of the representativeness of the ensemble: Method to measure representativeness: The rank histogram: count the rank of the observation among the ensemble members

13. 13 / 10 / 2006 Rank Histograms Not true for individual stations to be further studied

14. 13 / 10 / 2006 Variability of Spread and Probabilistic Skill

15. 13 / 10 / 2006 Conclusions We learn a lot from model intercomparisons Ensemble averages allow more accurate predictions of air quality for the present The diversity of the models studied allows representation of uncertainty. Hypotheses valid only for the present. How about scenarios? Needs to be studied