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
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