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Simulating the atmospheric composition during the last decades: Evaluation with long-term observational datasets and the impact of natural climate variability. Volker Grewe, Martin Dameris, Jens Grenzhäuser and Pieter Valks German Aerospace Center. ACCENT-GLOREAM, Paris, October, 2006.
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Simulating the atmospheric composition during the last decades: Evaluation with long-term observational datasets and the impact of natural climate variability Volker Grewe, Martin Dameris, Jens Grenzhäuser and Pieter Valks German Aerospace Center ACCENT-GLOREAM, Paris, October, 2006
Solar Cycle Air Quality Emissions ENSO Transport and Chemistry Ozone Production (Chapman) Ozone Intrusion NOx - Ozon Production Institutstag IPA 2006
NOx Emissions [Tg N/a] Surface, aircraft, lightning Photolysis Dynamics (ECHAM) T30, 39 layers, top layer centred at 10 hPa Chemistry (CHEM) Prognostic variables (vorticity, divergence, temperature, specific humidity, log-surface pressure, cloud water), hydrological cycle, diffusion, gravity wave drag, transport of tracers, soil model, boundary layer; sea surface temperatures. Methane oxidation Heterogeneous Cl reactions PSC I, II, aerosols Dry/wet deposition Feedback O3, H2O, CH4, N2O, CFCs Chemical Boundary Conditions Radiation Atmosphere: CFCs, at 10 hPa: ClX, NOy, Surface: CH4, CO Long-wave Short-wave CCM E39/C (Stratosphere-troposphere)- Model description Hein et al., 2001 Institutstag IPA 2006
Solar cycle and volcanoes Transiente Model simulation - Boundary Conditions QBO Dameris et al., 2005 Institutstag IPA 2006
Transiente Model simulation - Boundary Conditions Sea surface temperatures and ice coverage: Monthly means: UK Met Office Hadley Centre, hier: Beispiel für Juni 1985 (Rayner et al., 2003) Natural und anthropogenic NOx emissions: Source Reference Emissions: 1960 to 2000 Industry Benkovitz et al., 1996 12 - 33 TgN/a Lightning Grewe et al., 2001 ~5 TgN/a Air traffic Schmitt und Brunner, 97 0.1 - 0.7 TgN/a Surface Traffic Matthes, 2003 3.6 - 9.9 TgN/a Ships Corbett et al, 1999 1.2 - 3.2 TgN/a Biomass Burning Lee, pers. comm 6.3 - 7.2 TgN/a Institutstag IPA 2006
High variability Ozone hole Evolution of ozone column [DU]: 1960 - 2000 1960 1980 2000 1980 Institutstag IPA 2006
QBO clearly visible + + + + + - - - - 11y- Solar cycle recognizable, but QBO, volcanoes, trend overlaid De-seasonalized anomalies of the ozone columns [%] 1980 1960 Global Trend: ~20 DU 1980 2000 Institutstag IPA 2006
calm, stable winter situations Beginning of 90s: stronger ozone losses Individual strong events well represented E39/C vs. Observation: Anomalies of ozone column E39/C TOMS Ground stations (Bojkov and Fioletov, 1995; pers. com. Fioletov, 2004) Institutstag IPA 2006
Validation of E39C results: Tropospheric Ozone Mean annual cycle of ozone at 47°N, 11°E (1967-2000) Hohenpeißenberg OBS E39C minus OBS Too weak seasonal cycle E39C Cold bias too = high tropopause
Validation of E39C results Mean annual cycle of ozone at 40°N, 105°W (1979-2000) Boulder OBS E39C minus OBS E39C Similar conclusion
Validation of E39C results Evolution of ozone anomalies at distinct levels [in ppbv] Hohenpeißenberg Ozonesonde E39C Ozonesonde E39C Variability smaller: Sampling or real difference ? Evolution not well reproduced: - very rough assumptions on emission data - no interannual variability of bb emissions 47°N, 11°E; 300 hPa 47°N, 11°E; 700 hPa Ozonesonde E39C Ozonesonde E39C 47°N, 11°E; 500 hPa 47°N, 11°E; 850 hPa
Some agreement: Coincidience or period where changes are controll by processes, which are better described Validation of E39C results Evolution of ozone anomalies [in ppbv] Ozonesonde E39C 47°N, 11°E; 500 hPa
20°N Eq. Minimum Pacific 20°S 180°W 180°E Minimum South America Maximum Africa Average tropospheric tropiocal O3-Column below 200 hPa 1996-2003 E39/C GOME (TEMIS) Generally higher ozone values ! DJF General pattern in agreement: Minimum over Pacific Maximum over Africa MAM 15-40 DU 10-30 DU JJA However, ozone maximum less pronounced: Biomass burning? SON
How can we understand the simulated trends and the observed differences ? • Sensitivity studies (for selected periods) • e.g. rerun period without volcanic eruption (Pinatubo) • Additional diagnostics • Tracer: Ozone origin (Regions in Stratosphere/ Troposphere) • Tracer: Ozone 'source') (biomass burning, Lightning, ...) • Mass fluxes
Simulated ozone origin Grewe, 2006 Institutstag IPA 2006
Grewe, 2004 Institutstag IPA 2006
+ - + -+ - + - + Signal of solar cycle identifyable especially on SH Large interannual variability No trends recognizable Ozone influx from the stratosphere to the troposphere Estimate based on correlations with long-lived species: 475 Tg/year (Murphey and Fahey, 1994) and with flux calculations: NH: 252 Tg/a SH: 248 Tg/a (Olson et al., 2004) Monthly means x De-seasonalized Institutstag IPA 2006
Stratospheric ozone follows influx from stratosphere, producing ±2% variability out of a totale interannual var. of ±4% Lightning ozone correlated with Nino Index variability: ±1-2% De-seasonalized ozone changes in the tropical UT Institutstag IPA 2006
Evolution of de-seasonalized ozone in NH lower troposphere (30N-90N; 500-1000 hPa) ~25% ~30% -5% • Year-to-year variability strongly dominated by stratosphere (±5%) • Trend in ozone (25% increase): • - results from increase in NOx emissions (Industry and traffic) • Trend reduction in 80s caused by lower emissions and • lower stratospheric contribution. Institutstag IPA 2006
Conclusions - Outlook (I) • Stratosphere well reproduced • Troposphere: Some similarities with observational data • Main Discrepancies: • Too weak seasonal cycle: • Too strong influence from stratosphere (chem lifetime) • Too much transport of upper troposphere tropical air • Too weak seasonal cycle of O3 perturbation • from anthropogenic emissions • Less intense tropical ozone maximum • Solution: Rerun with revised emission data (RETRO) • biomass burning + anthrop. emission data • including interannual and regional variability
Conclusions - Outlook (II) • Discrepancies: • Less ozone in the upper troposphere: • Problem of cold bias = too high tropopause • Solution: Lagrangian transport scheme • → Realistic water vapor transport • → 80% Reduction of Cold Bias (Stenke&Grewe, 2006) • Despite discrepancies • Stratospheric ozone variability influences trend (Trend reduction in 80s) • Impact of stratospheric and tropospheric variability (El Nino) quantified.
Institut für Physik der Atmosphäre