Transcript Atmospheric Chemistry and Climate

Multi-model
ensemble simulations
of present-day and nearfuture 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.
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
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
ACCENT intercomparison (Expt. 2)
• Focus on 2030 – of direct interest to policymakers
• Go beyond radiative forcing: also consider ozone AQ, Nand S-deposition, and the use of satellite data to
evaluate models
• Present-day base case for evaluation:
Future changes
– S1: 2000
in composition
• Consider three 2030 emissions scenarios:
related to
– S2: 2030 IIASA CLE (‘likely’)
emissions
– S3: 2030 IIASA MFR (‘optimistic’)
1 year runs
Future
changes
– S4: 2030 SRES A2 (‘pessimistic’)
in composition
• Also consider the effect of climate change:
related to
– S5: 2030 CLE + imposed 2030 climate
climate change
5-10 year runs
Global NOx emission scenarios
200.0
SRES A2
160.0
120.0
CLE
80.0
40.0
MFR
0.0
1990
2000
2000
Europe
Asia + Oceania
Africa + Middle East
SRES A2 - World Total
2010
2020
2030
2030
North America
Latin America
Maximum Feasible Reduction (MFR)
SRES B2 - World Total
Figure 1. Projected development of IIASA anthropogenic NOx emissions by SRES world region (Tg NO2 yr-1).
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)
Requested model diagnostics
• Monthly mean, full 3-D
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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
26 Participating Models
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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
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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 coupled to GCMs
CTMs driven by GCM output
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
Year 2000 O3
Year 2000 Annual Zonal Mean Ozone (24 models)
Year 2000
Ensemble mean
of 25 models
Annual
Zonal
Mean
Annual
Tropospheric
Column
Sonde data from Logan (1999) + SHADOZ data from Thompson et al (2003)
Model
± 1SD
UT: 250 hPa
Sonde
± 1SD
JFMAMJJASOND
MT: 500 hPa
LT: 750 hPa
90-30S
30S-EQ
EQ-30N
30-90N
Ensemble mean model closely resembles ozone-sonde measurements
Year 2000
Inter-model
standard deviation (%)
Annual
Zonal
Mean
Annual
Tropospheric
Column
O3 in 2030,
radiative forcing
& influence of
climate change
Multi-model ensemble mean change in
tropospheric O3 2000-2030 under 3 scenarios
Annual
Zonal
Mean
ΔO3 /
ppbv
Annual
Tropospheric
Column
ΔO3 / DU
‘Likely’
‘Optimistic’
‘Pessimistic’
IIASA CLE
SRES B2 economy +
Current AQ Legislation
IIASA MFR
SRES B2 economy +
Maximum Feasible
Reductions
IPCC SRES A2
High economic growth +
Little AQ legislation
Radiative forcing implications
Forcings (mW m-2) 2000-2030 for the 3 scenarios:
-23%
+37%
CLE
MRF
A2
CO2
795
795
1035
CH4
116
0
141
O3
63
-43
155
1500
mW / m2
1000
CO2
500
0
-500
CH4
O3
Impact of Climate Change on Ozone by 2030
(ensemble of 9 models)
Positive
stratospheric
influx
feedback
Negative water
vapour feedback
Mean - 1SD
Mean
Mean + 1SD
Positive and negative feedbacks – no clear consensus
Global budgets
of O3 and CH4
O3 lifetime / days
Global O3 budget terms
Higher burden
goes with
longer lifetime
Results for a
single model,
several scenarios
Colours signify
different models
MFR
A2
As emissions rise,
burden increases,
lifetime falls
O3 burden / Tg(O3)
Ensemble mean
model (offset)
Climate change
shortens lifetime
but burden can
rise/fall
O3 chemical loss / Tg(O3)/yr
O3 budget and CH4 lifetime
Colours signify
different models
Climate change
reduces CH4
Ensemble mean
model (offset)
Results for a
single model,
several scenarios
IPCC TAR
8.4 years
Models with longer
CH4 have lower
the interO3 destruction
rates:
EmissionsWhat
have causes
1D) + H O → 2OH
O(
2
minor influence
model differences?
on CH4Water vapour?
Lightning NOx?
Photolysis schemes?
CH4 lifetime / years
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]
Related Posters
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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