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ACCENT Experiment 2. 25 different models perform same experiments 15 Europe: 4 UK (STOCHEM x2, UM_CAM, TOMCAT) 3 Germany (MATCH-MPIC x2, MOZECH) 2 France (LMDzINCA x2) 2 Italy (TM5, ULAQ) 1 Switzerland (GEOS-CHEM) 1 Norway (UIO_CTM2) 1 Netherlands (TM4) 1 Belgium (IASB) 7 US:

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accent experiment 2
ACCENT Experiment 2
  • 25 different models perform same experiments
    • 15 Europe:
      • 4 UK (STOCHEM x2, UM_CAM, TOMCAT)
      • 3 Germany (MATCH-MPIC x2, MOZECH)
      • 2 France (LMDzINCA x2)
      • 2 Italy (TM5, ULAQ)
      • 1 Switzerland (GEOS-CHEM)
      • 1 Norway (UIO_CTM2)
      • 1 Netherlands (TM4)
      • 1 Belgium (IASB)
    • 7 US:
      • GMI (x3), NCAR (MOZART4), GFDL (MOZART2), LLNL, GISS
    • 3 Japan:
      • JAMSTEC – CHASER (x2), FRSGC/UCI
  • Large ensemble reduces uncertainties, and allows them to be quantified
accent expt 2
ACCENT Expt 2
  • Consider 2030 – ‘the next generation’ – of direct interest for policymakers
  • 3 Emissions scenarios
    • ‘Likely’: IIASA CLE (‘Current Legislation’)
    • ‘Low’: IIASA MFR (‘Maximum technically Feasible Reductions’)
    • ‘High’: IPCC SRES A2
  • Also assess climate feedbacks
    • expected surface warming of ~0.7K by 2030
  • Target IPCC-AR4
people organisation

Climate change/deposition

CO

People & Organisation
  • Co-ordination; N+S-deposition, Tropospheric O3
    • F. Dentener, D. Stevenson
  • Surface O3 - impacts on health/vegetation; web-site
    • K. Ellingsen
  • NO2 columns – comparison of models and satellite data
    • T. van Noije, H. Eskes
  • Emissions
    • M. Amann, J. Cofala, L. Bouwman, B. Eickhout
  • Data handling and storage (SRB; ~1 TB of model output)
    • J. Sundet
  • Other modellers and contributors:
    • C.S. Atherton, N. Bell, D.J. Bergmann, I. Bey, T. Butler, W.J. Collins, R.G. Derwent, R.M. Doherty, J. Drevet, A. Fiore, M. Gauss, D. Hauglustaine, L. Horowitz, I. Isaksen, M. Krol, J.-F. Lamarque, M. Lawrence, V. Montanaro, J.-F. Müller, G. Pitari, M.J. Prather, J. Pyle, S. Rast, J. Rodriguez, M. Sanderson, N. Savage, M. Schultz, D. Shindell, S. Strahan, K. Sudo, S. Szopa, O. Wild, G. Zeng
slide4

IPCC-AR4-ACCENT ‘High’ Ship Emission Scenario

  • Scenario S4: IPCC A2, but with ship emissions of the year 2000
  • Scenario S4s: "Worst" case ship emission scenario in conjunction with S4.
slide5

SO2 High ship emissions: A2s "2030"

NOx High ship emissions: A2s "2030"

SO2 emissions: A2 "2000"

NOx emissions: A2 "2000"

slide6

IPCC-AR4-ACCENT ‘High’ Ship Emission Scenario

Characteristics:

  • The idea of comparing A2 to A2s:
  • What is the influence of ship emissions on tropospheric chemistry in 2030 if they were unabated?
  • Does an ensemble of models give approximately the same answer regarding the influence of ship emissions?
  • Status: Data analysis recently started
  • Thanks to everybody who sent data so far (FRSGC_UCI, LMDz/INCA, MATCH-MPIC, TM4)
  • We invite all other model groups to join in the inter-comparison
  • If you are interested, please contact [email protected] and [email protected]
slide7

Year 2000 Anthropogenic NOx Emissions

EDGAR database: Jos Olivier et al., RIVM

Plot: Martin Schultz, MPI

year 2000 tropospheric no 2 columns
Year 2000 tropospheric NO2 columns

Observed (GOME)(mean of 3 methods)

Model(ensemble mean)

(10:30am local sampling in both cases)

Courtesy Twan van Noije, Henke Eskes – figure from Dentener et al, submitted

global no x emission scenarios
Global NOx emission scenarios

SRES A2

CLE

MFR

Figure 1. Projected development of IIASA anthropogenic NOx emissions by SRES world region (Tg NO2 yr-1).

slide12

1990

2000

2030 CLE

2030 MFR

Regional NOx emissions

Ships/aircraft:

unregulated;

may become

larger than any

regional source

by 2030

USA:

~flat

Europe:

falling

Asia:

rising

Figure 4. Regional emissions separated for sources categories in 1990, 2000, 2030-CLE and 2030-MFR for NOx [Tg NO2 yr-1]

slide13

Emission Changes 2030 CLE - 2000

Plots: Martin Schultz, MPI

IIASA RAINS model: Markus Amann et al.

slide15

Year 2000

Ensemble meanof 25 models

AnnualZonalMean

Annual TroposphericColumn

slide16

%

Standard Deviationof 25 models

Absolute

Standard Deviationof 25 models

Ensemble meanof 25 models

Year 2000 Annual Mean O3

comparison of ensemble mean model with o 3 sonde measurements
Comparison of ensemble mean model with O3 sonde measurements

UT250 hPa

Model ±1SD

Observed ±1SD

J F M A M J J A S O N D

MT

500

hPa

LT

750

hPa

30°S-Eq

30°N-Eq

90-30°N

90-30°S

slide18

+10 ppbv

+5 ppbv

-5 ppbv

2030 A2 - 2000

2030 MRF - 2000

2030 CLE - 2000

radiative forcing implications
Radiative forcing implications

Forcings (mW m-2) 2000-2030 for the 3 scenarios:

+37%

-23%

CO2

CH4

O3

slide21

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

slide25

Higher H2O

Higher LNOx ?

Lower H2O

Lower LNOx ?

Highest H2O

+High Lightning NOx (8 TgN/yr)

O3 chemical loss / Tg-O3 yr-1

More complicated- other factors

CH4 lifetime / years

slide26

Tropospheric water vapour in 6 GCMs

Differences of

± 10% in tropics

Tropospheric H2O column / g(H2O) m-2

90S Eq 90N

slide27

AOT40, May-June-July, mean model, ppb*hours

3000 ppb.h !!!

Courtesy Kjerstin Ellingsen

conclusions
Conclusions
  • Logistics:
    • Large group participation – partly due to IPCC-AR4
    • Lot of work involved – relies on funding ‘goodwill’
    • Need well defined experiments and diagnostics
    • Central database and strict data format
    • Assume mistakes will be made in first attempts
    • Enforce deadlines if possible
  • Science:
    • Multi-model ensemble allows uncertainties to be assessed
    • Sample large model parameter space
    • Get hints about the controls on internal model processes
    • Future work:
    • Water vapour, convection, lightning NOx, isoprene schemes
    • STE, biomass burning
    • Global HOx/NOx/NOy budgets, as well as O3 and CH4
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