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Overlaps of AQ and climate policy

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Overlaps of AQ and climate policy global modelling perspectives David Stevenson Institute of Atmospheric and Environmental Science School of GeoSciences – PowerPoint PPT presentation

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Title: Overlaps of AQ and climate policy


1
Overlaps of AQ and climate policy global
modelling perspectives
  • 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, Peter Bergamaschi, Frank Raes
    (JRC Ispra)
  • Markus Amann, Janusz Cofala, Reinhard Mechler
    (IIASA)
  • NERC and the Environment Agency for funding

2
  • Material mainly from 2 current publications
  • The impact of air pollutant and methane emission
    controls on tropospheric ozone and radiative
    forcing CTM calculations for the period
    1990-2030
  • Dentener et al (2004) Atmos. Chem. Phys. Disc.
  • (currently open for discussion on the web)
  • Impacts of climate change and variability on
    tropospheric ozone and its precursors
  • Stevenson et al (2005) Faraday Discussions
  • (upcoming discussion meeting at Leeds in April)

3
Rationale
  • Regional-global scale AQ legislation has
    implications for climate forcing quantify these
    for current and possible future policies (use 2
    very different models to try and reduce model
    uncertainty)
  • Climate change will influence AQ use coupled
    climate-chemistry model to identify potentially
    important interactions

4
Modelling Approach
  • Global chemistry-climate model STOCHEM-HadAM3
    (also some results from TM3others)
  • Three transient runs 1990 ? 2030, following
    different emissions/climate scenarios
  • 1. Current Legislation (CLE)
  • Assumes full implementation of all current
    legislation
  • 2. Maximum Feasible Reductions (MFR)
  • Assumes full implementation of all available
    current emission reduction technology
  • 3. CLE climate change
  • For 1 and 2, climate is unforced, and doesnt
    change.
  • For 3, climate is forced by the is92a scenario,
    and shows a global surface warming of 1K between
    1990 and 2030.

5
STOCHEM-HadAM3
  • Global Lagrangian 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
  • CH4-CO-NOx-hydrocarbons (70 species)
  • includes S chemistry
  • Interactive lightning NOx, C5H8 from veg.
  • these respond to changing climate
  • 3 years/day on 36 processors (SGI Altix)

6
Global NOx emissions
SRES A2
CLE
MFR
Figure 1. Projected development of IIASA
anthropogenic NOx emissions by SRES world region
(Tg NO2 yr-1).
7
Global CO emissions
SRES A2
CLE
MFR
Figure 2 Projected development of IIASA
anthropogenic CO emissions by SRES world region
(Tg CO yr-1).
8
Global CH4 emissions
SRES A2
CLE
MFR
Figure 3 Projected development of IIASA
anthropogenic CH4 emissions by SRES region (Tg
CH4 yr-1).
9
Regional NOx emissions
Figure 4. Regional emissions separated for
sources categories in 1990, 2000, 2030-CLE and
2030-MFR for NOx Tg NO2 yr-1
10
Surface O3 (ppbv) 1990s
11
CLE
A large fraction is due to ship NOx
Change in surface O3, CLE 2020s-1990s
BAU
12
CLE Surface Annual Mean O3 2020s-1990s TM3 (top)
and STOCHEM (bottom)
Figure 13. Decadal averaged ozone volume mixing
ratio differences ppbv comparing the 2020s and
1990s for (a) TM3 CLE and STOCHEM CLE.
13
Surface ?O3 2030CLE2000 (NB July)
18 Models from IPCC-ACCENT intercomparison
14
Change in surface O3, MFR 2020s-1990s
MRF
BAU
15
MFR Surface Annual Mean O3 2020s-1990s TM3 (top)
and STOCHEM (bottom)
Figure 13(b) Decadal averaged ozone volume mixing
ratio differences ppbv comparing the 2020s and
1990s for TM3 MFR and STOCHEM MFR
16
Surface ?O3 2030MFR2000 (NB July)
18 Models from IPCC-ACCENT intercomparison
17
CH4, ?CH4 OH trajectories 1990-2030
CLE
CLEcc
18
If the world opts for MFR over CLE, net reduction
in radiative forcing of 0.2-0.3 W m-2 for the
period 2000-2030
19
Part 1 Summary
  • Co-benefits for both AQ and climate from some
    emissions controls
  • Methane offers the best opportunity (also CO and
    NMVOCs)
  • NOx controls (alone) benefit AQ, but probably
    worsen climate forcing (via OH and CH4)
    (Similarly for SO2)
  • AQ policies influence climate this study gives
    a quantitative assessment
  • Use of many models shows results are quite
    consistent

20
?O3 from climate change
Warmer temperatures higher humidities increase
O3 destruction over the oceans
But also a role from increases in isoprene
emissions from vegetation changes in lightning
NOx
2020s CLEcc- 2020s CLE
21
Zonal mean ?T (2020s-1990s)
22
Zonal mean H2O increase 2020s-1990s
23
Zonal mean change in convective updraught flux
2020s-1990s
24
C5H8 change 2020s (climate change fixed climate)
25
Lightning NOx change 2020s (climate change
fixed climate)
More lightning in N mid-lats Less, but higher,
tropical convection No overall trend in
Lightning NOx emissions
26
Zonal mean PAN decrease 2020s (climate change
fixed climate)
Colder LS
Increased PAN thermal decomposition, due
to increased T
27
Zonal mean NOx change 2020s (climate change
fixed climate)
Increased N mid-lat convection and lightning
Less tropical convection and lightning
Increased PAN decomposition
28
Zonal mean O3 budget changes 2020s (climate
change fixed climate)
29
Zonal mean O3 decrease 2020s (climate change
fixed climate)
30
Zonal mean OH change 2020s (climate change
fixed climate)
Complex function F(H2O, NOx, O3, T,)
31
Influence of climate change on O3 4 IPCC ACCENT
models
32
Part 2 Summary
  • Climate change will introduce feedbacks that
    modify air quality
  • These include
  • More O3 destruction from H2O
  • More stratospheric input of ozone
  • More isoprene emissions from vegetation
  • Changes in lightning NOx
  • Increases in sulphate from OH and H2O2
  • Wetland CH4 emissions (not studied here)
  • Changes in stomatal uptake? ()
  • These are quite poorly constrained different
    models show quite a wide range of response large
    uncertainties
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