15 / 05 / 2008 model ensembles for the simulation of air quality over europe robert vautard...
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15 / 05 / 2008
Model ensembles for the simulation of air quality over Europe
Robert VautardLaboratoire des Sciences du Climat et de
l’EnvironnementAnd many colleagues from IPSL, LISA, INERIS,
EURODELTA and TRANSCOM projects
15 / 05 / 2008
Why air quality modelling?
Short-term forecasts (0-3 days)
Long-term predictions of emission scenarios (climate?): 2010 or 2020 or more
Increase knowledge on processes together with observations…
15 / 05 / 2008
Air Quality forecastingPrevention
• 10 Years ago: statistical models, actions based on observations
• Now many deterministic forecasting systems
• Data assimilation in some cases
• In France, PREV’AIR system
• European GEMS/MACC projects (GMES)
15 / 05 / 2008
What are regional AQ models?
Transport
Chemistry
CTMWeather
BoundaryConditions
Emissions
LanduseConcentrations
Many many uncertainties…
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Regional air quality forecastnot really an initial value problem
Timmermans et al 2007
Assimilation experiments
without assimilation
with assimilation
Blond et al 2004
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What are the skill of regional AQ forecasts?
PREV’AIR Operational AQ forecasts (3 Summers):Average skill over >200 stations in Europe
Honoré et al. 2008
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Ensembles with perturbed meteorology (ARPEGE), chemistry
Carvalho et al., in preparation
20 MEMBERS
0.00
0.10
0.20
0.30
0.40
0.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Bin
Rel
ativ
e fr
equ
ency
RUR_D1 RUR_D2 PUR_D1 PUR_D2 URB_D1 URB_D2
CHEMISTRY
0.000.100.200.300.400.500.600.700.80
1 2 3 4 5 6 7 8 9 10 11
Bin
Rel
ativ
e fr
equ
ency
RUR_D1 RUR_D2 PUR_D1 PUR_D2 URB_D1 URB_D2
METEOROLOGY
0.000.100.200.300.400.500.600.700.80
1 2 3 4 5 6 7 8 9 10 11 12
Bin
Rel
ativ
e fr
equ
ency
RUR_D1 RUR_D2 PUR_D1 PUR_D2 URB_D1 URB_D2
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Emissions controlAction
Loss in life expectancy attributable to PM2.5, and 2020simulation with current legislation, Amann et al 2005
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But some species are very poorly simulated
PM Episode intercomparisonStern et al. 2008
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Hopes from ensembles
Represent the « unpredictable part » of the system
Meteorological/emission « noise », knowledge gaps
Provide better deterministic predictions by « error cancelation »Delle Monache and Stull 2003; Galmarini et al., 2004; McKeen et al., 2005
Predict the uncertainty (in forecasts, in scenarios), using the rangeUsing one perturbed mode Hanna et al., 2001; Mallet and Sportisse 2006, Deguillaume et al., 2008, … or a
model ensemble; Vautard et al., 2006;
How to evaluate ? Easy for deterministic predictions More difficult for uncertainty: tools borrowed from ensemble
weather forecasting
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EuroDelta Experiment
• Regional, european scale evaluation of emission scenarios for 2010 or 2020
• Control experiment: simulation of Year 2001
• 7 models: CHIMERE, DEHM, EMEP, LOTOS-EUROS, MATCH, RCG, TM5,
• Comparison with rural stations (EMEP or AIRBASE)
• Results in– Van Loon et al., 2007 (Atmos. Env.)– Schaap et al., 2008 (in revision…)– Vautard et al., 2006 (Geophys. Res. Lett.)– Vautard et al., 2008 (AE, submitted)
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Example of improvement by ensemble averaging: 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=O3+NO2
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Seasonal skill scores for ozoneTable 5: Correlation coefficients for daily average and daily maximum O3.
daily average daily maximum
year DJF MAM JJA SON year DJF MAM JJA SON
EMEP
0.72 0.67 0.55 0.50 0.55 0.75 0.60 0.59 0.61 0.53
LOTOS
0.70 0.49 0.54 0.49 0.43 0.76 0.47 0.70 0.66 0.48
MATCH
0.80 0.68 0.66 0.60 0. 0.81 0.58 0.68 0.7 0.61
CHIMERE
0.76 0.62 0.58 0.64 0.60 0.84 0.62 0.71 0.77 0.62
RCG
0.71 0.58 0.59 0.52 0.36 0.76 0.56 0.70 0.61 0.44
DEHM
0.64 0.45 0.41 0.56 0.31 0.75 0.45 0.60 0.68 0.45
TM5
0.67 0.69 0.44 0.35 0.62 0.72 0.63 0.47 0.51 0.58
Ensemble
0.79 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
• Perfect ensemble: 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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Evaluation of uncertaintyConcepts and tools borrowed from ensemble weather forecasting
• Reliability: observation could be one of the members– Observation compatible with predicted distribution
Rank histogram: count the times the rank is 1, 2, …, n:frequencies should be equal
But predicted distributions can have no information content (random or climatological)
• Resolution: the smaller the ensemble spread, the higher the resolution
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Reliability and Resolution
Resolution index: Normalized spread = spread/stdevReliability index: (extreme counts – central counts) / total counts
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CO2 Modelling : TRANSCOMWork in progress
CO2 modeling important for understanding and inverting fluxes
TRANSCOM ensemble (Law et al., 2008) : Evaluation of model ability to simulate CO2 at regional scale
2 Simulation Years: 2002 and 2003
17 atmospheric models/model versions differing by resolution, input biospheric fluxes (2), anthropogenic CO2 fluxes (2)
6 monitoring sites from CARBOEUROPE-IP
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Conclusions
• Develop methods to evaluate uncertainty prediction
• European ensemble displays relatively complementary aspects
• For ozone, poor resolution in Atlantic areas, poor reliability in complex terrain, balanced ensemble in Northern Europe.
• For NO2, poor reliability, for secondary inorganic aerosols reliable ensemble. For nitrate, poor reliability in gaz/solid balance.
• For CO2: model ensemble mean spread too small. Analysis coming soon.
15 / 05 / 2008
European papers on evaluation and AQ model ensembles(several missing, most probably!)
Many individual model evaluations (to be reviewed)
EUROTRAC reports…
Tilmes et al., 2002: Forecasts over 1 month of ozone in Germany
Galmarini et al., 2004a,b; 2007 (ENSEMBLE project, dispersion models, ETEX)
EMEP review report Van Loon et al., 2004
Vautard et al., 2007, AE (CityDelta project): City-Scale (5 EU cities, 1 year), eulerian approach
Thunis et al., 2007, AE (CityDelta): Scenario ensembles at city scale
Van Loon et al., 2007, AE (EuroDelta project): Regional scale, Eulerian, ozone, 1 year
Vautard et al., 2006, GRL (EuroDelta, ozone): Ensemble uncertainty
Schaap et al., 2008, AE (EuroDelta): PM10 and components evaluation
Stern et al., 2008 (UBA exercise): PM10 extreme case in Germany