1. Modelling of ozone and precursors M. Beekmann Laboratoire Interuniversité des Systèmes Atmosphériques (LISA) CNRS / Univ. Paris 12 and 7 Créteil, France TFMM Uncertainty workshop, Dublin 23/ 10 / 2007

2. Questions ? 1)Can European ozone concentration increases (or decreases) be attributed to hemispheric transport or to European emissions changes ? 2)Which formal ways to quantify model uncertainty ?

3. Hemispheric transport versus European emissions changes ? Regional modelling study (CHIMERE) comparing observed and simulated surface ozone concentrations Period : 1990 – 2002 Emissions : EMEP (Vestreng., 2004) Are decadal anthropogenic emission reductions in Europe consistent with surface ozone observations ? Vautard R., S. Szopa, M. Beekmann, L. Menut, D. A. Hauglustaine, L. Rouil, M. Roemer (2006), Geophys. Res.Lett., 33, 1747-2038. Locations of the ozone (shaded circles) and nitrogen dioxide (solid circles) sites From EMEP network

4. Constant emission run Variable emission run Variable emission + variable boundary run (+0.4 ppb O3 per year from Mace Head background climatology)  high correlation, interannual variability well depicted,  decreasing RMS with time,  no clear trend in obs., look seperataly at low and high percentiles Correlation coefficient RMS average O3 daily max µg/m 3

5. Results for 90 % percentile  significant negative trend in P90 observations  significant trend in difference between constant emissions run and observations  no significant trend in difference between variable emissions run and observations (consistency between emissions / model / observations)  small impact of boundary condition trend on P90 µg/m 3

6. Results for 10% percentile  no significant trend in observations  small impact of emission trend on P10  significant trend in differences if constant boundary condition increase is applied apparently, + 0.4 ppb/yr trend should not be applied for whole boundary µg/m 3

7. Spatial structure of high percentile surface ozone trends Impact of 1990 to 2002 EU emission changes on surface ozone P99 EMEP model simulation - meteorological year 2002 Jonson, J. E., Simpson, D., Fagerli, H., and Solberg, S.: Can we explain the trends in European ozone levels?, Atmos. Chem. Phys., 6, 51-66, 2006  Strong decrease of P99 surface O3 over NW – EU, but small changes over SW and SE EU ppb

8. Is this picture coherent with emission changes ?  Surface NO2 trend analysis shows decrease of emissions in ninetees over North-Western / Central Europe  Analysis of satellite derived NO2 tropospheric column data is useful to close gaps in spatial coverage in surface data, GOME (1996 – 2002), SCIAMACHY (2003 – 2005)  Inverse modelling estimation of NOx emission trends, using EMEP trends as a priori and CHIMERE simulations I.B Konovalov, М. Beekmann, A. Richter and J. Burrows, Satellite measurement based estimated decadal changes in European nitrogen oxides emissions, in preparation

9. SPATIAL DISTRIBUTION OF NO x EMISSION TRENDS Trends in the a posteriori NO x emissions (%/yr) Trends in EMEP emissions (%/yr)  Negative trends over NW + central EU confirmed  Positive trends over SW EU and for shipping emissions confirmed  Differences mainly for Eastern Europe

10. TRENDS OF NO x EMISSIONS FOR DIFFERENT COUNTRIES Trends in percent per year Values in [ ] are uncertainty of a linear fit

11. Information from global modelling studies GEOS-CHEM simulations (4° x 5° deg.) Auvray, M. and I. Bey, JGR 2005 Surface O3 (total) European surface O3 Background surface O3 1997 vs. 1980

12. Contributions to surface O3 changes from GEOS- CHEM study (Auvrey and Bey, 2005) 1997 1980  During summer, changes in EU and Asian contribution are of same order, but with contrary sign, changes in North American contribution are weak 1997 1980  E(NOx) - 16 % + 122 % - 6 %  E(CO) - 37 % + 159 % - 13 %  E(VOC) - 39 % + 126 % - 13 %

13.  too many uncertainties to state on origin of background surface ozone changes Emissions Transport * convection for intercontinental transport * transport from stratosphere * vertical dispersion for regional scale Chemistry (non-linear O3 precursor relationship) Dry, wet deposition => => Model resolution  No formal framework yet to assess these uncertainties in a coupled global / regional frame  Go back to continental (european scale )

14. Global uncertainty estimation Ensemble techniques :  Estimate model uncertainty from an ensemble (order of 10 members) of different models  Hope that models are sufficiently different to span the overall uncertainty range Monte Carlo analysis  Perturb model parameters in a random and simultaneous way  Typically several hundreds of runs to construct pdf of model output  Bayesian MC : Weight individual simulations by comparison with observations

15. Ensemble modelling Example from European scale ensemble modelling for year 2001 including 7 state of the art models  Summertime O3 max Within EURODELTA Vautard et al., 2006, Van Loon et al., 2007

16. Bayesian Monte Carlo analysis study for Greater Paris region Fix a priori uncertainties for input parameters Perform 1000 Monte Carlo simulations for baseline emissions Compare with observations, here urban, background, plume surface O 3, NOx routine measurements from the AirParif network; calculate weighting factor Perform additional 100 simulations with either flat reduced (-30 %) NOx or VOC emissions (for the most “probable” model configurations) Construct cumulative probability density functions from weighted model output

17. Uncertainty ranges (1  ) adopted for model input parameters (log-normal distribution) Emissions –Anthropogenic VOC+ 40 % –Anthropogenic NOx+ 40 % –Biogenic VOC+ 50 % Rate constants –NO + O 3 + 10 % –NO 2 + OH+ 10 % –NO + HO 2 + 10 % –NO + RO 2 + 30 % –HO 2 + HO 2 + 10 % –RO 2 + HO 2 + 30 % –RH + OH+ 10 % –CH 3 COO 2 + NO+ 20 % –CH 3 COO 2 + NO 2 + 20 % –PAN + M+ 30 % Photolysis frequencies and radiation –Actinic fluxes+ 10 % –J(O 3  2 OH)+ 30 % –J(NO 2  NO + O 3 )+ 20 % –J(CH 2 O  CO + 2 HO 2 )+ 40 % –J(CH 3 COCO  …)+ 50 % –J(unsaturated carbonyl  …) +40 % Meteorological parameters –Zonal wind speed+ 1 m/s –Meridional wind speed+ 1 m/s –Mixing layer height+ 40 % –Temperature+ 1.5 K –Relative humidity+ 20 % –Vertical mixing coefficient+ 50 % –Deposition velocity + 25 %

18. Reference simulations Deguillaume et al., 2007, JGR, in press

19. Red  Daily absolute maximum of ozone over the model domain (O3AbsMax) Green  Daily maximum of ozone in the Paris area (O3MaxParis) Blue  Average of the 60 grid cells with the most elevated daily ozone maxima (O3Max10%, intended to reflect the plume average). Cumulative probability density function (CPDF) for Monte Carlo simulations with and without constraints by observations average over summers 1998 and 1999.

20. Uncertainty in photochemical ozone production Factor of two difference in 10% and 90 % cumulative probability in photochemical ozone production

21. Cumulative probability density functions from constrained Monte Carlo simulations for the Ile de France region (summers 1998 and 1999) Daily ozone maximum Paris + plume Daily ozone maximum Paris Daily ozone maximum average over plume Blue colour -> Base line emissions minus reduced NOx emissions (-30%); Red colour -> Base line emissions minus reduced VOC emissions (-30%); Green colour -> Reduced NOx emissions (-30%) minus reduced VOC emissions (-30%) Deguillaume et al., 2007, JGR, in press.

22. Next steps …. apply method to European domain –take into account spatial decorrelations in parameter errors –use European observations as a constraint

23. Conclusions Past decreases in high percentile ozone values in NW and Central Europe are clearly related to emission reductions Changes in background ozone are not yet fully explained, but hemispheric transport is important Ensemble modelling allows estimation of model uncertainty Bayesian Monte Carlo analysis gives a constraint on photochemical ozone production in Greater Paris region (uncertainty of a factor of two), and allows robust estimation of uncertainty with respect to emission reduction scenarios

24. Extra slides

25. 1990-2002 ozone daily maxima 90% percentile bias (simulation minus observation) trends at each station used in ug/m3/y. Stations where trends are significant at the p<=0.1 level are marked with a solid circle inside.

26. SPATIAL DISTRIBUTION OF NO x EMISSION TRENDS Trends in the a posteriori NO x emissions (%/yr)Trends in the (new) EMEP emissions (%/yr) Magnitudes of the NO x emissions specified in CHIMERE ( 10 8 cm -2 s -1 yr -1 ) Trends in the (old) EMEP emissions (%/yr)

27. COMPARISON WITH INDEPENDENT MESUREMENTS NO x (UK NAQN): weighed and centered t.s.O 3 (EMEP): average of 90 th percentile of daily max

28. MAIN CONCLUSIONS FOR INVERSE TREND STUDY available satellite data combined with modeling results can help in obtaining obtaining independent estimates of decadal changes in NO x emissions which are, at least, as accurate than available emission inventory data The inverse modeling results confirm predominantly negative NO x emission trends in Western Europe; considerable differences between our results and EMEP data are revealed, especially outside of Western Europe.

29.  Principle of Bayesian Monte Carlo analysis Random perturbation of model input parameters and parameterizations  Global uncertainty of simulated concentrations with respect to model uncertainty  Observational constraint -Here : urban, background, urban and plume surface O 3, NOx observations  Cost function (agreement Monte Carlo simulations vs. obs.)  Conditional uncertainty