1. 2020 vision: Modelling the near future tropospheric composition David Stevenson Institute of Atmospheric and Environmental Science School of GeoSciences The University of Edinburgh Thanks to: Ruth Doherty (Univ. Edinburgh) Dick Derwent (rdscientific) Mike Sanderson, Colin Johnson, Bill Collins (Met Office) Frank Dentener (JRC Ispra), Markus Amann (IIASA)

2. Talk Structure Chemistry-climate model: STOCHEM-UM Several transient runs: 1990 → 2030 the 1990s – how modellers use observations comparisons with ozone-sonde data the 2020s – what is needed to predict the future? a believable model (hence the first bit) a computationally efficient model future emissions climate change other things? What are the results telling us?

3. STOCHEM Global Lagrangian 3-D chemistry-climate model Meteorology: HadAM3 + prescribed SSTs GCM grid: 3.75° x 2.5° x 19 levels CTM: 50,000 air parcels, 1 hour timestep CTM output: 5° x 5° x 9 levels Detailed tropospheric chemistry − CH 4 -CO-NO x -hydrocarbons − detailed oxidant photochemistry Interactive lightning NO x, C 5 H 8 from veg. ~1 year/day on 36 processors (Cray T3E)

4. Model experiments Several transient runs: 1990 → 2030 Driving meteorology – Fixed SSTs (mean of 1978-1996) – SSTs from a climate change scenario (is92a) shows ~1K surface warming 1990s-2020s – Shorter run with observed SSTs 1990-2002 New IIASA* global emissions scenarios: – Business as usual (BAU) – Maximum reductions feasible (MRF) Stratospheric O 3 is a fixed climatology Vegetation (land-use) also a fixed climatology *IIASA: International Institute for Applied Systems Analysis (Austria)

5. IIASA Emissions scenarios Global totals – there are significant regional variations Courtesy of Markus Amann (IIASA) & Frank Dentener (JRC)

6. Compare with 1990s obs Model experiments BAU, observed SSTs 1990-2002 BAU, fixed SSTs 1990-2030 MRF, fixed SSTs 1990-2030 BAU, is92a SSTs 1990-2030 1990 2030

7. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Ozone-sonde data from Logan et al. (1999 - JGR) Chemical tropopause (O 3 =150 ppbv)

8. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR)

9. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) Model > obs Model < obs

10. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) ±1 std dev in obs

11. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) ±1 std dev in obs ±1 std dev in model

12. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) Model overestimates by >1 std dev Model underestimates by >1 std dev

13. Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O 3 =150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) Model underestimates by >1 std dev Identify where and when the model is wrong Model overestimates by >1 std dev

14. Ny Alesund (79N, 12E), Spitzbergen Ozone-sonde data from Logan et al. (1999 - JGR) Model O 3 too low in lower troposphere for all seasons except spring

15. Resolute (75N, 95W), Canada Ozone-sonde data from Logan et al. (1999 - JGR) Model O 3 too low in boundary layer in summer - autumn

16. Sapporo (43N, 141E), Japan Ozone-sonde data from Logan et al. (1999 - JGR) Surface O 3 generally too high Mid-troposphere in summer too low

17. Wallops Island (38N, 76W), Eastern USA Ozone-sonde data from Logan et al. (1999 - JGR) Mid- & upper-tropospheric O 3 too low in summer

18. Ascension (8S, 14W), Mid-Atlantic Ozone-sonde data from Thompson et al. (2003 - JGR) Major O 3 underestimate in tropical mid-troposphere – too much destruction? or not enough sources? OK at surface, but…

19. The model has some skill at simulating tropospheric ozone, but is far from perfect. Careful comparisons with other gases (NO x, NO y, etc.) also needed, but there is much less data. For climate-chemistry model validation, lengthy climatologies, including vertical profiles are most useful. If you want modellers to uses the data, provide it in easy-to-use formats (we’re lazy!) MOZAIC (operational aircraft data) and satellite data are examples of the sort of datasets needed. If you trust the model, it may be useful for future predictions…

20. Compare changes between the 1990s and 2020s Compare with 1990s obs Model experiments BAU, observed SSTs 1990-2002 BAU, fixed SSTs 1990-2030 MRF, fixed SSTs 1990-2030 BAU, is92a SSTs 1990-2030 1990 2030

21. Decadal mean values 1990s

22. BAU 2020s

23. BAU Change in surface O 3, BAU 2020s-1990s >+10 ppbv India +2 to 4 ppbv over N. Atlantic/Pacific A large fraction is due to ship NO x

24. MRF 2020s

25. MRFBAU Change in surface O 3, MRF 2020s-1990s Up to -10 ppbv over continents

26. BAU+climate change 2020s

27. MRFBAU BAU+cc Look at the difference between these two to see influence of climate change Change in surface O 3, BAUcc 2020s-1990s

28. ΔO 3 from climate change Warmer temperatures & higher humidities increase O 3 destruction over the oceans But also a role from increases in isoprene emissions from vegetation?

29. Zonal mean O 3 & ΔO 3 (2020s-1990s) 1990s BAU ΔO 3 MRF ΔO 3 BAUcc ΔO 3

30. Zonal mean OH & ΔOH(2020s-1990s) 1990s BAU ΔOH MRF ΔOH BAUcc ΔOH

31. CH 4,  CH4 & OH trajectories 1990-2030 Current CH 4 trend looks like MRF – coincidence? All scenarios show increasing OH

32. Radiative forcings from ΔO 3 and ΔCH 4 (2020s-1990s) BAUMRFBAUcc ΔO3+0.04-0.07+0.01 ΔCH4+0.140? +0.18-0.07?

33. Conclusions Model development and validation is ongoing, & is guided by observations Anthropogenic emissions will be the main determinant of future tropospheric O 3 − Ship NO x looks important Climate change will introduce feedbacks that modify air quality We can estimate the radiative forcing implications of air quality control measures NB: Many processes still missing