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TFMM Uncertainty workshop, Dublin 23/ 10 / 2007

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. Questions ?.

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TFMM Uncertainty workshop, Dublin 23/ 10 / 2007

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

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

  3. Hemispheric transport versus European emissions changes ? 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. Regional modelling study (CHIMERE) comparing observed and simulated surface ozone concentrations Period : 1990 – 2002 Emissions : EMEP (Vestreng., 2004) Locations of the ozone (shaded circles) and nitrogen dioxide (solid circles) sites From EMEP network

  4. average O3 daily max µg/m3 • 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

  5. Results for 90 % percentile µg/m3 • significant negative trend in P90 observations • significant trend in difference between constant emissions runand 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

  6. Results for 10% percentile µg/m3 • no significant trend in observations • small impact of emission trend on P10 • significant trend in differences if constant boundary conditionincrease is applied apparently, + 0.4 ppb/yr trend should not be applied for whole boundary

  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 ppb • Strong decrease of P99 surface O3over NW – EU, but small changes over SW and SE EU

  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 NOx EMISSION TRENDS Trends in the a posteriori NOx 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 NOx EMISSIONS FOR DIFFERENT COUNTRIES Values in [ ] are uncertainty of a linear fit Trends in percent per year

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

  12. Contributions to surface O3 changes from GEOS-CHEM study (Auvrey and Bey, 2005) • During summer, changes in EU and Asian contribution are of same order, but with contrary sign, changes in North American contribution are weak 19971980 19971980 DE(NOx) - 16 % + 122 % - 6 % DE(CO) - 37 % + 159 % - 13 % DE(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. 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 Global uncertainty estimation

  15. 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 Ensemble modelling

  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 O3, 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. Emissions Anthropogenic VOC + 40 % Anthropogenic NOx + 40 % Biogenic VOC + 50 % Rate constants NO + O3 + 10 % NO2 + OH + 10 % NO2 + OH + 10 % NO + HO2 + 10 % NO + RO2 + 30 % HO2 + HO2+ 10 % RO2 + HO2 + 30 % RH + OH + 10 % CH3COO2 + NO + 20 % CH3COO2 + NO2 + 20 % PAN + M + 30 % 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 % 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 % Uncertainty ranges (1s) adopted for model input parameters (log-normal distribution)

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

  19. Cumulative probability density function (CPDF) for Monte Carlo simulations with and without constraints by observations average over summers 1998 and 1999. 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).

  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 NOx EMISSION TRENDS Trends in the a posteriori NOx emissions (%/yr) Trends in the (new) EMEP emissions (%/yr) Magnitudes of the NOx emissions specified in CHIMERE (108 cm-2s-1yr-1) Trends in the (old) EMEP emissions (%/yr)

  27. COMPARISON WITH INDEPENDENT MESUREMENTS NOx (UK NAQN): weighed and centered t.s. O3 (EMEP): average of 90th 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 NOx emissions which are, at least, as accurate than available emission inventory data • The inverse modeling results confirm predominantly negative NOx 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 O3, NOx observations  Cost function (agreement Monte Carlo simulations vs. obs.)  Conditional uncertainty

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