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ACCENT-TROPOSAT-2 (AT-2)

ACCENT-TROPOSAT-2 (AT-2). Task Group 2 Synergistic use of models and observations Achievements and Prospects Aims and objectives Outlook of final reports-highlights Prospects.

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ACCENT-TROPOSAT-2 (AT-2)

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  1. ACCENT-TROPOSAT-2 (AT-2) Task Group 2 Synergistic use of models and observations Achievements and Prospects Aims and objectives Outlook of final reports-highlights Prospects

  2. The synergistic use of models and observations to improve our understanding of tropospheric chemistry and dynamics.Leader: Martin Dameris, DLR, Oberpfaffenhofen ; 17 PIs (12+1 final reports) • Investigation of physical, dynamical, and chemical processes in the troposphere. • Development of methods for using satellite data from the troposphere as part of model validation strategy. • Use the combination of model results, satellite observations, ground based and airborne measurements in a synergistic way to improve our knowledge about individual tropospheric processes, such as: source attribution and impact assessment of gaseous and particulate pollutants; cloud occurrence and the hydrological cycle. • Use model results to help bridge the gap between point measurements and the satellite view footprint for evaluating satellite retrievals.

  3. Investigation of physical, dynamical, and chemical processes in the troposphere Dehydration of the polar vortex Global Measurements of Water Vapour in the TropopauseRegion and Upper Troposphere with MIPAS/ENVISAT (2002-2008) M.Milz & G.P. Stiller, IMK, Univ. Karlsruhe September/October 2002: unusual sudden stratospheric warming and subsequent vortex split  comparatively weak dehydration and early recovery of the Antarctic stratospheric water vapour compared to the next years Validation against other satellite measurements : lower relative humidity in MIPAS than in Microwave Limb Sounder (MLS) collocated measurements of the Japanese instrument ILAS-II  good agreement up to 40 km & against in situ and LIDAR measurements (TROCCINOX & SCOUT –O3 campaigns)

  4. Impact of Climate Change on Dynamics and Chemistry of the UTLS: Investigations with a Climate-Chemistry Model Dameris et al. DLR Motivation: improve on the ‘cold bias’ of the extra tropical lowermost stratosphere 74°N, 95°W A Lagrangian approach (ATTILA) for transport of tracers has been extended to the CCM E39C, resulting in the upgraded model version E39C-A Ozone partial pressure profiles : Means of simulations over 1983-1989 from E39C-A and E39C. Dotted lines : standard deviation (±1σ). Black solid lines: average radiosonde observations (Resolute: 1983-1989 & South Pole: 1986-1987 ). Stenke, A., M. Dameris, V. Grewe, and H. Garny, Implications of Lagrangian transport for coupled chemistry-climate simulations, Atmos. Chem. Phys. Discuss., accepted in September 2008. A non diffusive advection scheme  better description of the ozonopause

  5. Development of methods for using satellite data from the troposphere as part of model validation strategy. Account for: Spatial and temporal sampling of the atmosphere Assumptions on vertical distribution van Noije et al. (2006) Ensemble average annual mean tropospheric NO2 column density for three different GOME retrievals (left panel) and the full model ensemble (right panel). These quantities have been calculated after smoothing the data to a horizontal resolution of 5×5 deg. van Noije et al. (2006). Differences in GOME retrievals are in many instances (10-50%) as large as the spread in model results. Emission estimates depend on the choice of model and retrieval!

  6. Combine model results, & … observations to improve our knowledge about individual tropospheric processes,such as: source attribution impact assessment of gaseous and particulate pollutants; cloud occurrence and the hydrological cycle.

  7. Glyoxal : First observations from space & secondary biogenic contribution to its levels source attribution Wittrock et al., GRL, 2006

  8. source attribution: Natural & anthropogenic CHOCHO Myriokefalitakis et al., ACP, 8, 4965, 2008

  9. source attribution: The influence of natural and anthropogenic secondary sources onthe glyoxal global distribution Sec.+prim. Only land Secondary. land + ocean Hot spots All land Secondary source: 56 Tg/y (70% biogenic,17% C2H2, 11% aromatics)  missing source ~20 Tg/y (lifetime ~ 3h) Primary –combustion : 7 Tg/y leads to an overestimate over hot spots Myriokefalitakis et al., ACP, 8, 4965, 2008

  10. Use model results to help bridge the gap between point measurements and the satellite view footprint for evaluating satellite retrievals. Inversions: Retrievals of atmospheric concentrations from observed radiances optimisation of estimates of model parameters Data assimilation Data assimilation: filtering the signal from noisy observations interpolation in space and time completion of state variables that are not sampled by the observation network Account for a process that is not (fully) resolved in the model

  11. Assimilation of Tropospheric Species into a Chemistry Transport Model J.-L attié1, L. El Amraoui2 , P. Ricaud1, V.-H. Peuch2, M. Claeyman1, S. Massart3, B. Barret1, N. Semane2 and A. Piacentini31LA, 2Meteo France, 3Cerfacs, Toulouse CO data from Terra/MOPITT and Aura/MLS have been assimilated into the MOCAGE-PALM chemistry-transport model by using the 3-DFGAT scheme via the PALM software developed by the CERFACS – Dual assimilation Zonal mean of CO calculated over India from MOCAGE without assimilation (left) and with dual assimilation (right). Grey areas represent larger values

  12. Derivation of Tropospheric Composition from Satellites using a 3-D CTM Gunn, Richards, Chipperfield, Univ Leeds Data assimilation in the 3-D chemical transport model (TOMCAT/SLIMCAT) to accurate represent the stratosphere (HALOE data) and improve the quantitative derivation of tropospheric composition. Comparison of assimilated and non-assimilated SLIMCAT/TOMCAT vertical column NO2 with ground based DOAS observations for 4 NDACC sites: The black crosses represent the observations, the dashed blue line represents the free running model and the yellow dashed line the model with assimilation. The red dashed line is a version of the assimilation model which does not assimilate O3. Gunn et al., ACPD submitted 2008

  13. 4-Dimensional Variational Assimilation of Satellite Data into a Chemistry Transport ModelH. Elbern, L. Nieradzik, A. StrunkRIER, Univ. Cologne While benefits can be observed for all species assimilated, the signatures of assimilation results are more sustained for aerosols, and least for NO2 Difference of Analysis and Background [µg/m³] Analysis field [µg/m³] EC EURAD-IV model Assimilation per aerosol type: increase in soot (EC) Slight depletion in WAter SOlubles (sulphate) SO4= Necessity to use an assimilation system capable of resolving aerosol types

  14. Scientific Interpretation of SCIAMACHY CO, CO2 and CH4 Measurements S. Houweling, M. Krol, J.-F. Meirink and I. Aben SRON, IMAU, WUR SCIAMACHY- CH4 observations bias corrected (ppbv) @ use of the 4D-VAR technique to infer CH4 sources and sinks from a combination of surface and satellite measurements (TM5- modeling) @ progress in the spectroscopic parameters that are used in the SCIAMACHY CH4 retrieval (Frankenberg et al GRL, 2008 & ACPD,2008) improved agreement between the satellite-optimized model and in situ measurements. Model CH4 difference: a posteriori- a priori (ppbv) Meirink et al., JGR 10.1029/2007JD009740, 2008

  15. Scientific Interpretation of SCIAMACHY CO, CO2 and CH4 Measurements S. Houweling, M. Krol, J.-F. Meirink and I. Aben SRON, IMAU, WUR aircraft measurements vs TM5 simulated CH4 AMAZON region – Brazil Observations Miller et al GRL 2007 blue, TM5 using prior fluxes; green, TM5 using posterior fluxes obtained using surface measurements; blue and red, TM5 using surface measurements and SCIAMACHY retrievals. Meirink et al., JGR 10.1029/2007JD009740, 2008

  16. Integrating Chemical Modelling and Satellite Observations for monitoring Tropospheric Chemistry and Air QualityM. Beekmann, I. Konovalov, G. Dufour, A. Hodzic et al.LISA, LMD, LSCE, IAP, NCAR, LIV • @ use of GOME and SCIAMACHY tropospheric NO2 and HCHO measurements (1996-2005) for inverse modelling of European NOx and biogenic VOC emissions (CHIMERE model). • set of empirical models Trop_column_NO2=f(Flux NO2) • NO2 satellite data +ground based O3 • Uncertainty estimate Bayesian Monte-Carlo experiments • @ use of POLDER and MODIS aerosol optical depths for constraining fire emissions. Konovalov et al., ACP, 2008

  17. Integrating Chemical Modelling and Satellite Observations for monitoring Tropospheric Chemistry and Air QualityM. Beekmann, I. Konovalov, G. Dufour, A. Hodzic et al.LISA, LMD, LSCE, IAP, NCAR, LIV Comparison of time averaged HCHO columns over land for summer (June to August) 2003, CHIMERE simulations, SCIAMACHY observations @ use of GOME and SCIAMACHY tropospheric NO2 and HCHO measurements (1996-2005) for inverse modelling of European NOx and biogenic VOC emissions (CHIMERE model). Remaining systematic observation errors and model errors are limiting factors for biogenic isoprene emission inversion, but nevertheless use of satellite data is shown to reduce biogenic emissions uncertainties by more than 50 % in the most sensitive regions Dufour, Wittrock, et al., ACPD submitted 2008

  18. More results in the presentations that follow!

  19. What did we gain? From M. Beckmann – final report ACCENT/AT2 stimulated and catalysed our research work in a variety of ways: • Strong collaboration with satellite retrieval groups was crucial, because expertise on satellite retrieved observations is mandatory to ensure proper use of data. • For inverse modelling studies, detailed information on observation errors is needed and was provided within AT2 project. • Last not least, annual AT meetings and specific workshops provided stimulating forums to present and discuss results, and to plan further studies.

  20. Summary remarks • Synergistic use of satellite observations with CTMs has opened new horizons in air pollution control and climate change evaluation. • Continuous dialogue between satellite retrieved observations of atmospheric trace constituents and model results enables improvingi) process understanding, ii) models, iii) retrieval algorithms and constructing a concise picture of our changing atmosphere • Data assimilation contributes in improving prognostic models, in the past for weather forecasts and recently for chemical weather forecasts. • Estimates of emissions from satellite retrievals strongly depend on the choice of model & of retrieval.

  21. Looking forward • Need for developments towards results independent from the applied tools • Higher spatial resolution models and satellite observations will better resolve air pollution from various sources • Improvements are also needed in the temporal sampling of the atmosphere by the satellite based sensors that has to be taken into account in the models when satellite retrievals are used. • Substantial efforts are required in optimizing assimilation algorithm parameters

  22. Thank you! ευχαριστώ

  23. A Synergistic Approach for Deriving Regional Tropospheric NO2 T. Erbertseder1, J.Meyer-Arnek1, P.J. M.Valks2 and F. Baier1DF, DLR • Use of SCIAMACHY observations & stratospheric & tropospheric NO2 forecasts. The method tackles the stratospheric NO2 variability and its contribution, the tropospheric vertical NO2 distribution and the influence of the cloud-top height

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