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  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. Szopa161. 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. Future changes in composition related to emissions 1 year runs Future changes in composition related to climate change 5-10 year runs 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. 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 26 Participating Models CTMs driven by analyses CTMs coupled to GCMs CTMs driven by GCM output

  9. 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

  10. Year 2000 O3

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

  12. Year 2000 Ensemble meanof 25 models AnnualZonalMean 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 O32000-2030 under 3 scenarios Annual Zonal Mean ΔO3 / ppbv Annual Tropo-spheric Column ΔO3 / 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. Positive stratosphericinflux feedback Negative watervapour feedback Impact of Climate Change on Ozone by 2030(ensemble of 9 models) Mean + 1SD Mean - 1SD Mean Positive and negative feedbacks – no clear consensus

  19. Global budgets of O3 and CH4

  20. Results for asingle model,several scenarios Higher burdengoes withlonger lifetime MFR A2 As emissions rise, burden increases, lifetime falls Climate changeshortens lifetimebut burden canrise/fall Global O3 budget terms Colours signifydifferent models O3 lifetime / days Ensemble mean model (offset) O3 burden / Tg(O3)

  21. Colours signifydifferent models Climate changereduces CH4 Models with longer CH4 have lower O3 destruction rates: O(1D) + H2O → 2OH Results for asingle model,several scenarios Emissions haveminor influenceon CH4 IPCC TAR8.4 years 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@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

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