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

model ensembles?

Robert VautardLaboratoire des Sciences du Climat et de

l’EnvironnementAnd all colleagues from CityDelta and EuroDelta

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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

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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

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Hourly ozone valuesSlight improvement in mean values No improvement in correlation

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PM10 simulation skill

•General underestimation

•Improvement in mean values

•Intercity variability not reproduced

•Correlations 0.5-0.6

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EuroDelta Experiment• Regional, european scale

• 6 models

• Comparison with rural stations (EMEP or AIRBASE)

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The Seven Models (EuroDelta) Model Horizontal resolution1 and number of

cells Vertical resolution Approx. depth 1st layer (m).

EMEP(EMEP-MSC-W)

50x50km110x100

20 sigma levels up to 100 hPa 90

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/pressure 50

DEHM(NERI)

Eur: 50x50kmNorthen hemisph: 150x150km : 96x96

20 sigma levels up to 100 hPa 50

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Mean diurnal cycles

30

40

50

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Observed EMEP LOTOS MATCH CHIMERE

RCG DEHM TM5 Ensemble

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Observed EMEP LOTOS MATCH CHIMERE

RCG DEHM TM5 Ensemble

Ozone Ox

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Percentiles

0

10

20

30

40

50

60

70

1p 2p 5p 10p 20p 30p 40p

Observed EMEP LOTOS MATCH CHIMERE RCG DEHM TM5 Ensemble

0

20

40

60

80

100

120

140

160

50p 60p 70p 80p 90p 95p 99p

Observed EMEP LOTOS MATCH CHIMERE RCG DEHM TM5 Ensemble

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Seasonal Skill scoresTable 5: Correlation coefficients for daily average and daily maximum O3.

  daily average daily maximum

  year DJF MAM JJA SON year DJF MAM JJA SON

EMEP0.72 0.67 0.55 0.50 0.55 0.75 0.60 0.59 0.61 0.53

LOTOS0.70 0.49 0.54 0.49 0.43 0.76 0.47 0.70 0.66 0.48

MATCH0.80 0.68 0.66 0.60 0. 0.81 0.58 0.68 0.7 0.61

CHIMERE0.76 0.62 0.58 0.64 0.60 0.84 0.62 0.71 0.77 0.62

RCG0.71 0.58 0.59 0.52 0.36 0.76 0.56 0.70 0.61 0.44

DEHM0.64 0.45 0.41 0.56 0.31 0.75 0.45 0.60 0.68 0.45

TM50.67 0.69 0.44 0.35 0.62 0.72 0.63 0.47 0.51 0.58

Ensemble0.79 0.74 0.66 0.68 0.58 0.84 0.69 0.76 0.78 0.59

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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, 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

2211 bK

RMSEens

,

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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

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Rank Histograms

Not true for individual stationsto be further studied

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Variability of Spread and Probabilistic Skill

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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