View
39
Download
0
Category
Tags:
Preview:
DESCRIPTION
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. - PowerPoint PPT Presentation
Citation preview
13 / 10 / 2006
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
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
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
13 / 10 / 2006
Hourly ozone valuesSlight improvement in mean values No improvement in correlation
13 / 10 / 2006
PM10 simulation skill
•General underestimation
•Improvement in mean values
•Intercity variability not reproduced
•Correlations 0.5-0.6
13 / 10 / 2006
EuroDelta Experiment• Regional, european scale
• 6 models
• Comparison with rural stations (EMEP or AIRBASE)
13 / 10 / 2006
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
13 / 10 / 2006
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
13 / 10 / 2006
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
13 / 10 / 2006
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
13 / 10 / 2006
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
,
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 / 10 / 2006
Rank Histograms
Not true for individual stationsto be further studied
13 / 10 / 2006
Variability of Spread and Probabilistic Skill
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
Recommended