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, T.P.C. van Noije 5, O. Wild 6, G. Zeng 7, M. Amann 8, C.S. Atherton 9, N. Bell 10, D.J. Bergmann 9, I. Bey 11, T. Butler 12, J. Cofala 8, W.J. Collins 13, R.G. Derwent 14, R.M. Doherty 1, J. Drevet 11, H.J. Eskes 5, A.M. Fiore 15, M. Gauss 4, D.A. Hauglustaine 16, L.W. Horowitz 15, I.S.A. Isaksen 4, M.C. Krol 2, J.-F. Lamarque 17, M.G. Lawrence 12, V. Montanaro 18, J.-F. Müller 19, G. Pitari 18, M.J. Prather 20, J.A. Pyle 7, S. Rast 3, J.M. Rodriguez 21, M.G. Sanderson 13, N.H. Savage 7, D.T. Shindell 10, S.E. Strahan 21, K. Sudo 6, and S. Szopa 16 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 didn’t 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 Future changes in composition related to emissions 1 year runs Future changes in composition related to climate change 5-10 year runs

5. Global NO x emission scenarios Figure 1. Projected development of IIASA anthropogenic NO x emissions by SRES world region (Tg NO 2 yr -1 ). CLE SRES A2 MFR 20002030

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 CH 4 for each case (from earlier transient runs)

7. Requested model diagnostics Monthly mean, full 3-D –O 3, NO, NO 2, CO, OH, … –O 3 budget terms –CH 4 + OH –NO y, NH x and SO x deposition fluxes –T, Q, etc. for climate change runs Daily NO 2 column (GOME comparison) Hourly surface O 3 (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 driven by GCM output CTMs coupled to GCMs

9. Analysis of O 3 results Masked at tropopause using O 3 =150 ppbv Interpolated to common vertical and horizontal grid Ensemble mean model and standard deviations calculated Compared to sonde measurements Other ongoing validation work: NO 2 columns, surface O 3, CO, deposition fluxes Global tropospheric O 3 and CH 4 budgets, radiative forcings

10. Year 2000 O 3

11. Year 2000 Annual Zonal Mean Ozone (24 models)

12. Year 2000 Ensemble mean of 25 models Annual Zonal Mean Annual Tropospheric Column

13. Ensemble mean model closely resembles ozone-sonde measurements UT: 250 hPa MT: 500 hPa LT: 750 hPa 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)

14. Year 2000 Inter-model standard deviation (%) Annual Zonal Mean Annual Tropospheric Column

15. O 3 in 2030, radiative forcing & influence of climate change

16. Annual Zonal Mean ΔO 3 / ppbv Annual Tropo- spheric Column ΔO 3 / 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 in tropospheric O 3 2000-2030 under 3 scenarios

17. Radiative forcing implications Forcings (mW m -2 ) 2000-2030 for the 3 scenarios: -23% +37% CO 2 CH 4 O3O3

18. Impact of Climate Change on Ozone by 2030 (ensemble of 9 models) Mean Mean - 1SD Mean + 1SD Negative water vapour feedback Positive stratospheric influx feedback Positive and negative feedbacks – no clear consensus

19. Global budgets of O 3 and CH 4

20. Global O 3 budget terms O 3 lifetime / days O 3 burden / Tg(O 3 ) Results for a single model, several scenarios Colours signify different models Ensemble mean model (offset) Higher burden goes with longer lifetime Climate change shortens lifetime but burden can rise/fall As emissions rise, burden increases, lifetime falls MFR A2

21. O 3 chemical loss / Tg(O 3 )/yr O 3 budget and CH 4 lifetime IPCC TAR 8.4 years CH 4 lifetime / years Results for a single model, several scenarios Colours signify different models Ensemble mean model (offset) Models with longer  CH4 have lower O 3 destruction rates: O( 1 D) + H 2 O → 2OH Climate change reduces  CH4 Emissions have minor influence on  CH4 What causes the inter- model differences? Water vapour? Lightning NO x ? Photolysis schemes?

22. Conclusions Ensemble mean model O 3 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@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