Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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) Talk Structure • • • Chemistry-climate model: STOCHEM-UM Several transient runs: 1990 → 2030 the 1990s • • comparisons with ozone-sonde data the 2020s • • a believable model (hence the first bit) • a computationally efficient model • future emissions • climate change • other things? What are the results telling us? – how modellers use observations – what is needed to predict the future? 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 • • Interactive lightning NOx, C5H8 from veg. ~1 year/day on 36 processors (Cray T3E) − CH4-CO-NOx-hydrocarbons − detailed oxidant photochemistry 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 O3 is a fixed climatology Vegetation (land-use) also a fixed climatology *IIASA: International Institute for Applied Systems Analysis (Austria) IIASA Emissions scenarios Global totals – there are significant regional variations Courtesy of Markus Amann (IIASA) & Frank Dentener (JRC) Model experiments Compare with 1990s obs BAU, observed SSTs 1990-2002 BAU, fixed SSTs 1990-2030 MRF, fixed SSTs 1990-2030 BAU, is92a SSTs 1990-2030 1990 2030 Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Model > obs Model < obs Ozone-sonde data from Logan et al. (1999 - JGR) Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Ozone-sonde data from Logan et al. (1999 - JGR) ±1 std dev in obs Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) ±1 std dev in obs ±1 std dev in model Ozone-sonde data from Logan et al. (1999 - JGR) Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Model overestimates by >1 std dev Model underestimates by >1 std dev Ozone-sonde data from Logan et al. (1999 - JGR) Hohenpeissenberg Ozone-sonde model vs observations (monthly data for the 1990s) Chemical tropopause (O3=150 ppbv) Identify where and when the model is wrong Ozone-sonde data from Logan et al. (1999 - JGR) Model overestimates by >1 std dev Model underestimates by >1 std dev Ny Alesund (79N, 12E), Spitzbergen Model O3 too low in lower troposphere for all seasons except spring Ozone-sonde data from Logan et al. (1999 - JGR) Resolute (75N, 95W), Canada Model O3 too low in boundary layer in summer - autumn Ozone-sonde data from Logan et al. (1999 - JGR) Sapporo (43N, 141E), Japan Surface O3 generally too high Mid-troposphere in summer too low Ozone-sonde data from Logan et al. (1999 - JGR) Wallops Island (38N, 76W), Eastern USA Mid- & upper-tropospheric O3 too low in summer Ozone-sonde data from Logan et al. (1999 - JGR) Ascension (8S, 14W), Mid-Atlantic OK at surface, but… Major O3 underestimate in tropical mid-troposphere – too much destruction? or not enough sources? Ozone-sonde data from Thompson et al. (2003 - JGR) • • • • • • The model has some skill at simulating tropospheric ozone, but is far from perfect. Careful comparisons with other gases (NOx, NOy, 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… Model experiments Compare with 1990s obs BAU, observed SSTs 1990-2002 BAU, fixed SSTs 1990-2030 MRF, fixed SSTs 1990-2030 BAU, is92a SSTs 1990-2030 1990 Compare changes between the 1990s and 2020s 2030 1990s Decadal mean values BAU 2020s +2 to 4 ppbv over N. Atlantic/Pacific >+10 ppbv India A large fraction is due to ship NOx Change in surface O3, BAU 2020s-1990s BAU MRF 2020s Up to -10 ppbv over continents Change in surface O3, MRF 2020s-1990s MRF BAU BAU+climate change 2020s Change in surface O3, BAUcc 2020s-1990s MRF BAU BAU+cc Look at the difference between these two to see influence of climate change ΔO3 from climate change Warmer temperatures & higher humidities increase O3 destruction over the oceans But also a role from increases in isoprene emissions from vegetation? Zonal mean O3 & ΔO3 (2020s-1990s) 1990s BAU ΔO3 MRF ΔO3 BAUcc ΔO3 Zonal mean OH & ΔOH(2020s-1990s) 1990s BAU ΔOH MRF ΔOH BAUcc ΔOH CH4, CH4 & OH trajectories 1990-2030 Current CH4 trend looks like MRF – coincidence? All scenarios show increasing OH Conclusions • • • • • Model development and validation is ongoing, & is guided by observations Anthropogenic emissions will be the main determinant of future tropospheric O3 − Ship NOx 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