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Uncertainty and regional air quality model diversity: what do we learn from model ensembles?

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. Hopes from ensembles.

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Uncertainty and regional air quality model diversity: what do we learn from model ensembles?

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  1. 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. 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. 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. Hourly ozone values Slight improvement in mean values No improvement in correlation

  5. PM10 simulation skill • General underestimation • Improvement in mean values • Intercity variability not reproduced • Correlations 0.5-0.6

  6. EuroDelta Experiment • Regional, european scale • 6 models • Comparison with rural stations (EMEP or AIRBASE)

  7. The Seven Models (EuroDelta)

  8. Mean diurnal cycles Ozone Ox

  9. Percentiles

  10. Seasonal Skill scores Table 5: Correlation coefficients for daily average and daily maximum O3.

  11. The skill of the ensemble mean • Let us assume that the ensemble of K values xk is drawn from a distribution of physically possible states:  Then the observation xa 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. 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. Rank Histograms Not true for individual stations to be further studied

  14. Variability of Spread and Probabilistic Skill

  15. 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

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