Atmospheric Chemistry and Climate

1
Multi-model ensemble simulations of present-day
and near-future tropospheric ozoneD.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.
2
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 didnt include ships new datasets
• SRES biomass burning(?) new satellite data

3
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

4
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

5
Global NOx emission scenarios
SRES A2
CLE
MFR
2000
2030
Figure 1. Projected development of IIASA
anthropogenic NOx emissions by SRES world region
(Tg NO2 yr-1).
6
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)

7
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

8
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 coupled to GCMs
CTMs driven by GCM output
9
Analysis of O3 results

• Masked at tropopause using O3150 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

10
Year 2000 O3
11
Year 2000 Annual Zonal Mean Ozone (24 models)
12
Year 2000 Ensemble meanof 25 models AnnualZonal
Mean Annual TroposphericColumn
13
Sonde data from Logan (1999) SHADOZ data from
Thompson et al (2003)
Sonde 1SD
Model 1SD
UT 250 hPa
J F M A M J J A S O N D
MT 500 hPa
LT 750 hPa
90-30S 30S-EQ EQ-30N
30-90N
Ensemble mean model closely resembles ozone-sonde
measurements
14
Year 2000 Inter-model standard deviation
() AnnualZonalMean Annual
TroposphericColumn
15
O3 in 2030, radiative forcing influence of
climate change
16
Multi-model ensemble mean change intropospheric
O3 2000-2030 under 3 scenarios
Annual Zonal Mean ?O3 / ppbv
Annual Tropo-spheric Column ?O3 / DU
Optimistic IIASA MFR SRES B2 economy Maximum
Feasible Reductions
Likely IIASA CLE SRES B2 economy Current AQ
Legislation
Pessimistic IPCC SRES A2High economic growth
Little AQ legislation
17
Radiative forcing implications
Forcings (mW m-2) 2000-2030 for the 3 scenarios
37
-23
CO2
CH4
O3
18
Impact of Climate Change on Ozone by
2030(ensemble of 9 models)
Mean
Mean - 1SD
Mean 1SD
Positive and negative feedbacks no clear
consensus
19
Global budgets of O3 and CH4
20
Global O3 budget terms
Colours signifydifferent models
O3 lifetime / days
Ensemble mean model (offset)
O3 burden / Tg(O3)
21
O3 budget and CH4 lifetime
Ensemble mean model (offset)
O3 chemical loss / Tg(O3)/yr
What causes the inter-model differences?Water
vapour?Lightning NOx? Photolysis schemes?
CH4 lifetime / years
22
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.
• dstevens_at_met.ed.ac.uk

23
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