Bayesian Monte Carlo analysis applied to a regional scale transport chemistry model Deguillaume L., Beekmann M., Menut L., Derognat C. Improving emission uncertainties Characterizing ozone production and chemical regimes Over the Ile-de-France region 13/10/2006 - Gloream

Context  Photochemical air pollution CHEMISTRY NOx limited VOC limited TRANSPORT EMISSIONS

Airparif Context  Modelling those processes  Development of a chemistry transport model CHIMERE Ile-de-France region (IPSL/ INERIS/LISA) Model domain : 150×150km Horizontal resolution : 6×6km grid Vertical resolution : 8 layers in hybrid pressure coordinates Chemical mechanism : reduced Melchior Meteorology: ECMWF Anthropogenic emissions: EMEP, ARIA, AIRPARIF Biogenic emissions: Simpson et al. (1999) Airparif

Objectives (1) Uncertainties in emissions is always rather large and difficult to estimate  Uncertainties on activity factor, emission factor spatial distribution, temporal variability... (2) Evaluation  difficult since emitted pollutants undergo chemical transformation and are tranported away from sources Problems Observation : Simulated concentrations are very sensitive to emissions Inverse modelling of emissions from observations (ground based and satellite) Objectives  Improving emission uncertainties with a Bayesian approach Application to semi-climatologic (summers 1998+1999) period for generalization

Methods : inverse modelling To verify and improve available estimates of atmospheric pollutants emissions Alternative to bottom-up construction of emission cadastres To improve performance of atmospheric models, especially in diagnostic studies To develop a general observation-based methodology for estimating parameters of the atmosphere that cannot be observed directly Adjoint model Kalman filter Bayesian Monte Carlo analysis

Principle of the Bayesian Monte Carlo analysis A priori uncertainties in emissions A priori uncertainties of input parameters « Model uncertainties » Monte Carlo simulations A priori concentrations without constraints Weighting by observations A posteriori distributions of emissions Uncertainties in observations  Correction on a priori distributions of emissions (also on other perturbed input parameters) (-) Single correction factor over the whole grid domain and time period of the simulations (+) Information on the value but also uncertainty associated to the emissions

Mathematical formulation P(O|Yk) For each kth Monte Carlo simulation, the agreement function: « Probability to observe a vector of observations O given that the model output Yk is the true value for the kth Monte Carlo simulation » Hypothesis:  Observations present a normally distributed errors ε  N independent observations Oj Each simulation is weighted by P(O|Yk)  Cost function  A posteriori probability density function vs. a priori one Results ?  Cumulative probability density function (CPDFs) (probability that a given model prediction Xk stays below the limit X)

Perturbed model input parameters A priori uncertainties of input parameters « Model uncertainties » Parameters 1 σ Uncertainty Emissions Anthropogenic VOCs + 40 Anthropogenic NOx Biogenic VOCs + 50 Rate constants NO + O3 + 10 NO2 + OH NO + HO2 NO + RO2 + 30 HO2 + HO2 RO2 + HO2 RH + OH CH3COO2 + NO + 20 CH3COO2 + NO2 PAN + M Photolysis frequencies and radiation Actinic fluxes + 10 J(O3  2 OH) + 30 J(NO2  NO + O3) + 20 J(CH2O  CO + 2 HO2) + 40 J(CH3COCO  …) + 50 Meteorological parameters Zonal wind speed + 1 Meridional wind speed Mixing layer height Temperature + 1.5 Relative humidity Vertical mixing coefficient Others Deposition velocity + 25  Log-normal distribution  Uncertainty ranges  uncertainty assessment studies and expert judgements

Measurement constraints observations (1) Urban NO and O3 NO  direct forcing for NOx emissions O3  information on ozone precursor emissions (VOC, NOx) PARIS (2) Rural O3 buildup The two daily maximal [O3]  O3 plume The 3 lowest [O3]  O3 background For simulations and observations: + (1) Days where the maximal [O3] is observed and simulated at the same or neighbouring station (2) [O3 max] - [O3 back] > 10 ppb for simulations and observations AIRPARIF NETWORK

Results – Cumulative probability Simulated urban NO, O3 and O3 production in the plume- summers 1998 + 1999 Total constraints Red  Total constraints 2σ 2σ 2σ 2σ Uncertainties are reduced by a factor : 3.2 2.4 1.7 Blue  without constraints Deguillaume et al., in press, JGR, 2006

Results – Probability density functions Emissions of anthropogenic NOx and VOC – cumulative summers 1998 and 1999 Total constraints Red lines  a posteriori distribution Blue histogram  a priori distribution with 40% 1σ uncertainty  1σ uncertainty : 22 % for NOx , 31% for VOC emissions  NOx emissions remain nearly unchanged - VOC emissions are enhanced (+16%) Better fit the observations in 1999 vs. 1998 (in 1998, the constraints do not act in a similar way)

Other region ?  Marseille area (ESCOMPTE, AIRMAIX network) Conclusion & perspectives Bayesian Monte Carlo uncertainty analysis Semi-climatologic period Ile-de-France region  A posteriori PDF  NOx emission  unchanged average  reduced standard deviation (20% vs. 40%)  VOC emission  enhancement (+16%)  reduced standard deviation (30% vs. 40%)  Uncertainty in the simulated urban NO, urban O3 and O3 production in the plume are strongly reduced indirect constraints on VOC emissions (urban O3 and O3 production)  lower reduction  Adjustements in the other model input parameters (vertical mixing coefficient, 1998)  Better fit in 1999 than in 1998 because constraints in 1998 do not act in a similar way Other region ?  Marseille area (ESCOMPTE, AIRMAIX network) Satellite measurements

Perspectives… Better understand the buildup of pollution episodes around Paris region Objectives  Characterizing ozone production and chemical regimes Application to semi-climatologic (summers 1998+1999) period for generalization Methodology 2 approaches Direct simulation Bayesian Monte Carlo approach Constraints by observations  Emission reduced scenario (-30% NOx and -30% VOC emissions) Analysis of the chemical regime over the Ile de France region

(2) Characterizing ozone production and chemical regimes Preliminary results ... Daily maximum of O3 averaged over summers 1998 and 1999 Reference NOx -30% VOC -30% Anticyclone sur l’ocean  mouvement aiguille montre  vent vers le sud