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![Page 1: Multi-model ensemble simulations of present-day and near- future tropospheric ozone D.S. Stevenson 1, F.J. Dentener 2, M.G. Schultz 3, K. Ellingsen 4,](https://reader036.fdocuments.in/reader036/viewer/2022062422/56649f265503460f94c3dc4d/html5/thumbnails/1.jpg)
Multi-model ensemble simulations
of present-day and near-future tropospheric ozone
D.S. Stevenson1, F.J. Dentener2, M.G. Schultz3, K. Ellingsen4, T.P.C. van Noije5, O. Wild6, G. Zeng7, M. Amann8, C.S. Atherton9, N. Bell10, D.J. Bergmann9, I. Bey11, T. Butler12,
J. Cofala8, W.J. Collins13, R.G. Derwent14, R.M. Doherty1, J. Drevet11, H.J. Eskes5, A.M. Fiore15, M. Gauss4, D.A. Hauglustaine16, L.W. Horowitz15, I.S.A. Isaksen4, M.C. Krol2,
J.-F. Lamarque17, M.G. Lawrence12, V. Montanaro18, J.-F. Müller19, G. Pitari18, M.J. Prather20, J.A. Pyle7, S. Rast3, J.M. Rodriguez21, M.G. Sanderson13, N.H. Savage7, D.T.
Shindell10, S.E. Strahan21, K. Sudo6, and S. Szopa16 1. University of Edinburgh, School of GeoSciences, Edinburgh, United Kingdom. 2. Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy.
3. Max Planck Institute for Meteorology, Hamburg, Germany. 4. University of Oslo, Department of Geosciences, Oslo, Norway. 5. Royal Netherlands Meteorological Institute (KNMI), Atmospheric Composition Research, De Bilt, the Netherlands.
6. Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan. 7. University of Cambridge, Centre of Atmospheric Science, United Kingdom. 8. IIASA, International Institute for Applied Systems Analysis, Laxenburg, Austria. 9. Lawrence Livermore National Laboratory, Atmos. Science Div., Livermore, USA.
10. NASA-Goddard Institute for Space Studies, New York, USA. 11. Ecole Polytechnique Fédéral de Lausanne (EPFL), Switzerland. 12. Max Planck Institute for Chemistry, Mainz, Germany. 13. Met Office, Exeter, United Kingdom. 14. rdscientific, Newbury, UK. 15. NOAA GFDL, Princeton, NJ, USA. 16. Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France.
17. National Center of Atmospheric Research, Atmospheric Chemistry Division, Boulder, CO, USA. 18. Università L'Aquila, Dipartimento di Fisica, L'Aquila, Italy. 19. Belgian Institute for Space Aeronomy, Brussels, Belgium.
20. Department of Earth System Science, University of California, Irvine, USA 21. Goddard Earth Science & Technology Center (GEST), Maryland, Washington, DC, USA.
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Background
• ‘OxComp’ model intercomparison for IPCC TAR sampled models in ~1999
• OxComp focussed on SRES A2 in 2100.• Models and emissions have developed in the
last 5 years – time for an update• New scenarios from IIASA include AQ legislation
measures (not in SRES)• SRES didn’t include ships – new datasets• SRES biomass burning(?) – new satellite data
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Scope of IPCC-AR4
• Chapter 2: Changes in atmospheric constituents and in radiative forcing
• Chapter 7: Couplings between changes in the climate system and biogeochemistry– Includes a section on Air Quality
• Design intercomparison to be of direct use to IPCC-AR4
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ACCENT intercomparison (Expt. 2)
• Focus on 2030 – of direct interest to policymakers• Go beyond radiative forcing: also consider ozone AQ, N-
and S-deposition, and the use of satellite data to evaluate models
• Present-day base case for evaluation:– S1: 2000
• Consider three 2030 emissions scenarios:– S2: 2030 IIASA CLE (‘likely’)– S3: 2030 IIASA MFR (‘optimistic’)– S4: 2030 SRES A2 (‘pessimistic’)
• Also consider the effect of climate change:– S5: 2030 CLE + imposed 2030 climate
Future changes in composition related to emissions1 year runsFuture changes in composition related to climate change5-10 year runs
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Global NOx emission scenarios
0.0
40.0
80.0
120.0
160.0
200.0
1990 2000 2010 2020 2030
Europe North AmericaAsia + Oceania Latin America
Africa + Middle East Maximum Feasible Reduction (MFR)
SRES A2 - World Total SRES B2 - World Total
Figure 1. Projected development of IIASA anthropogenic NOx emissions by SRES world region (Tg NO2 yr-1).
CLE
SRES A2
MFR
2000 2030
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Other emissions categories
• EDGAR3.2 ship emissions, and assumed 1.5%/yr growth in all scenarios
• Biomass burning emissions from van der Werf et al. (2003) – assumed these remained fixed to 2030 in all scenarios
• Aircraft emissions from IPCC(1999)• Modellers used their own natural emissions
• Specified fixed global CH4 for each case (from earlier transient runs)
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Requested model diagnostics
• Monthly mean, full 3-D – O3, NO, NO2, CO, OH, …
– O3 budget terms
– CH4 + OH
– NOy, NHx and SOx deposition fluxes
– T, Q, etc. for climate change runs
• Daily NO2 column (GOME comparison)
• Hourly surface O3 (for AQ analysis)
• NETCDF files submitted to central database
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26 Participating Models• CHASER_CTM• CHASER_GCM• FRSGC/UCI• GEOS-CHEM• GISS• GMI/CCM3• GMI/DAO• GMI/GISS• IASB• LLNL-IMPACT• LMDz/INCA-CTM• LMDz/INCA-GCM• MATCH-MPIC/ECMWF
• MATCH-MPIC/NCEP • MOZ2-GFDL• MOZART4• MOZECH• MOZECH2• p-TOMCAT• STOCHEM-HadAM3• STOCHEM-HadGEM• TM4• TM5• UIO_CTM2• ULAQ• UM_CAM
CTMs driven by analyses
CTMs driven by GCM outputCTMs coupled to GCMs
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Analysis of O3 results
• Masked at tropopause using O3=150 ppbv• Interpolated to common vertical and horizontal
grid• Ensemble mean model and standard deviations
calculated• Compared to sonde measurements• Other ongoing validation work: NO2 columns,
surface O3, CO, deposition fluxes• Global tropospheric O3 and CH4 budgets,
radiative forcings
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Year 2000 O3
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Year 2000 Annual Zonal Mean Ozone (24 models)
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Year 2000Ensemble meanof 25 models
AnnualZonalMean
Annual TroposphericColumn
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Ensemble mean model closely resembles ozone-sonde measurements
UT
: 250
hP
aM
T: 5
00 h
Pa
LT
: 75
0 h
Pa
J F M A M J J A S O N D
Sonde± 1SD
Model± 1SD
90-30S 30S-EQ EQ-30N 30-90N
Sonde data from Logan (1999) + SHADOZ data from Thompson et al (2003)
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Year 2000Inter-modelstandard deviation (%)
AnnualZonalMean
Annual TroposphericColumn
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O3 in 2030, radiative forcing
& influence of climate change
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Annual Zonal MeanΔO3 / ppbv
Annual Tropo-spheric ColumnΔO3 / DU
‘Likely’IIASA CLE
SRES B2 economy +Current AQ Legislation
‘Optimistic’IIASA MFR
SRES B2 economy +Maximum Feasible
Reductions
‘Pessimistic’IPCC SRES A2
High economic growth +Little AQ legislation
Multi-model ensemble mean change intropospheric O3 2000-2030 under 3 scenarios
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Radiative forcing implications
-500
0
500
1000
1500
mW
/ m
2
CO2 795 795 1035
CH4 116 0 141
O3 63 -43 155
CLE MRF A2
Forcings (mW m-2) 2000-2030 for the 3 scenarios:
-23% +37%
CO2
CH4
O3
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Impact of Climate Change on Ozone by 2030(ensemble of 9 models)
MeanMean - 1SD Mean + 1SD
Negative watervapour feedback
Positive stratospheric
influx feedback
Positive and negative feedbacks – no clear consensus
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Global budgets of O3 and CH4
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Global O3 budget terms
O
3 lif
etim
e / d
ays
O3 burden / Tg(O3)
Results for asingle model,several scenarios
Colours signifydifferent models
Ensemble mean model (offset)
Higher burdengoes with
longer lifetime
Climate changeshortens lifetimebut burden canrise/fall
As emissions rise,burden increases,
lifetime falls
MFR
A2
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O
3 ch
emic
al lo
ss /
Tg
(O3)
/yr
O3 budget and CH4 lifetime
IPCC TAR8.4 years
CH4 lifetime / years
Results for asingle model,several scenarios
Colours signifydifferent models
Ensemble mean
model (offset)Models with longer
CH4 have lowerO3 destruction rates:O(1D) + H2O → 2OH
Climate changereduces CH4
Emissions haveminor influence
on CH4
What causes the inter-model differences?Water vapour?Lightning NOx?Photolysis schemes?
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Conclusions
• Ensemble mean model O3 closely resembles observations
• Inter-model standard deviations highlight where models differ the most
• Quantitative assessment of 2030 scenarios provide clear options for policymakers (radiative forcing and AQ)
• Influence of climate change uncertain• Global budgets reveal interesting and fundamental
model differences• Analysis is ongoing – please come to meeting on
Thursday night for more information.• [email protected]
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Related Posters
• D155a Szopa et al.
• G186a Dentener et al.
• G190b Rast et al.
• G193 Gauss et al.
• G204 Van Dingenen et al.
• G205 Ellingsen et al.
• G210 Sudo & Akimoto