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 ?

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 ?

Hemispheric transport versus European emissions changes ?

Regional modelling study(CHIMERE) comparing observed and simulated surface ozone concentrationsPeriod : 1990 – 2002Emissions : 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) sitesFrom EMEP network

• 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/m3

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/m3

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/m3

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 O3over NW – EU, but small changes over SW and SE EU

ppb

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

SPATIAL DISTRIBUTION OF NOx EMISSION TRENDSTrends 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

TRENDS OF NOx EMISSIONS FOR DIFFERENT COUNTRIES

Trends in percent per year Values in [ ] are uncertainty of a linear fit

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

Contributions to surface O3 changes from GEOS-CHEM study (Auvrey and Bey, 2005)

19971980

During summer, changes in EU and Asian contribution are of same order, but with contrary sign, changes in North American contribution are weak

19971980

E(NOx) - 16 % + 122 % - 6 %E(CO) - 37 % + 159 % - 13 %E(VOC) - 39 % + 126 % - 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 )

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

Ensemble

modelling

• Example from European scale ensemble modelling for year 2001 including 7 state of the art models

Summertime O3 max Within EURODELTAVautard et al., 2006, Van Loon et al., 2007

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

Uncertainty ranges (1) adopted for model input parameters (log-normal distribution)

• 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 %

Reference simulations

• Deguillaume et al., 2007, JGR, in pressdays

0 14 28 42 56 70 84 98 112 126

O3

conc

entr

atio

ns (

ppb)

0

20

40

60

80

100

120

140

160

180

200

220

O3AbsMaxO3MaxParis O3Max10%

summer 1998 summer 1999

Maximum of ozone (ppb)

47

44

45

46

47

48

49

50

51

52

53

54

48.5

49.0

1.5 2.0 2.5 3.0

7 0

1

2

3

4

5

6

7

8

9

10

48.5

49.0

1.5 2.0 2.5 3.0

Production of ozone (ppb)

35 50 15

20

25

30

35

40

45

50

55

60

65

70

48.5

49.0

1.5 2.0 2.5 3.0

EXCEED_90ppb(number of days where O3 max > 90ppb)

Paris

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.

Uncertainty in photochemical ozone production

• Factor of two difference in 10% and 90 % cumulative probability in photochemical ozone production

3

3

4

5 0123456789

1011121314151617181920

48.5

49.0

1.5 2.0 2.5 3.0

78

0123456789

1011121314151617181920

48.5

49.0

1.5 2.0 2.5 3.0

Production of ozone (ppb)

P10 P90

Production of ozone (ppb)

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

48.5

49.0

1.5 2.0 2.5 3.0

65

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

48.5

49.0

1.5 2.0 2.5 3.0

EXCEED_90ppbindicator (hours) EXCEED_90ppbindicator (hours)

P10 P90

Cumulative probability density functions from constrained Monte Carlo simulations for the Ile de France region

(summers 1998 and 1999)

Concentrations (ppb)

0

0.2

0.4

0.6

0.8

1

Ref-NOx

Ref-VOC

NOx-VOC

-8 -4 16 20 24

Concentrations (ppb) Concentrations of ozone (ppb)12840

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

-8 -4 16 20 2412840 -8 -4 16 20 2412840

Ref-NOx

Ref-VOC

NOx-VOC

Ref-NOx

Ref-VOC

NOx-VOC

O3AbsMax O3Max10%

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.

Next steps ….

• apply method to European domain– take into account spatial decorrelations in

parameter errors– use European observations as a constraint

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

Extra slides

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.

SPATIAL DISTRIBUTION OF NOx EMISSION TRENDSTrends 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)

COMPARISON WITH INDEPENDENT MESUREMENTSNOx (UK NAQN): weighed and centered t.s. O3 (EMEP): average of 90th percentile of daily max

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.

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

2

,

,

,,1 5.0exp

1

2

1)Y|p(O

j

jkj

jNjk

YO