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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? Robert Vautard Laboratoire des Sciences du Climat et de l’Environnement And all colleagues from CityDelta and EuroDelta
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
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
Hourly ozone values Slight improvement in mean values No improvement in correlation
PM10 simulation skill • General underestimation • Improvement in mean values • Intercity variability not reproduced • Correlations 0.5-0.6
EuroDelta Experiment • Regional, european scale • 6 models • Comparison with rural stations (EMEP or AIRBASE)
Mean diurnal cycles Ozone Ox
Seasonal Skill scores Table 5: Correlation coefficients for daily average and daily maximum O3.
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 ,
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
Rank Histograms Not true for individual stations to be further studied
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