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M. Van Roozendael, I. De Smedt, J. Stavrakou, J.-F. Muller Belgian Institute for Space Aeronomy

On the usability of space nadir UV-visible observations for the inverse modeling of NMVOC emissions. M. Van Roozendael, I. De Smedt, J. Stavrakou, J.-F. Muller Belgian Institute for Space Aeronomy Brussels, Belgium T. Kurosu SAO-Harvard, Cambridge M.A., USA. Outline.

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M. Van Roozendael, I. De Smedt, J. Stavrakou, J.-F. Muller Belgian Institute for Space Aeronomy

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  1. On the usability of space nadir UV-visible observations for the inverse modeling of NMVOC emissions M. Van Roozendael, I. De Smedt, J. Stavrakou, J.-F. Muller Belgian Institute for Space Aeronomy Brussels, Belgium T. Kurosu SAO-Harvard, Cambridge M.A., USA

  2. Outline • HCHO and non-methane volatile organic compounds (NMVOCs) • HCHO retrieval from UV-Vis nadir sounders • Improved NMVOC emission inventories using HCHO measurements and inverse modeling • Current limitations • What’s next ?

  3. Impact of NMVOCs on O3 mixing ratios July 1997 without NMVOCs with NMVOCs Simulations performed with the IMAGES global CTM J. Stavrakou and J.-F. Muller, BIRA

  4. What are the main sources of NMVOCs ? Large uncertainties about these emissions NMVOCs 3% 85% 12%

  5. Link between NMVOCs and HCHO • HCHO directly emitted from fossil fuel combustion and biomass burning • Also formed as a high-yield secondary product in the CH4, and NMVOC oxidation • Both HCHO and NMVOCs are short-lived: lifetime on the order of a few hours  HCHO is a good tracer of NMVOC emissions

  6. 2008 1995 2003 GOME 320x40 km2 SCIAMACHY 60x30 km2 OMI 15x25 km2 GOME-2 40x80 km2 today HCHO retrieval from current nadir UV-Vis sounders  DOAS technique (328.5-346 nm)

  7. Apr.1996 – Dec.2002 Jan.2003 – Jun.2007 GOME and SCIAMACHY HCHO products SCIAMACHY GOME Homogenised analysis settings Comprehensive error analysis De Smedt et al., ACPD, 2008

  8. CF < 40% Error analysis • Meridian dependence driven by systematic errors on slant columns • AMF errors in the range of 10-25% (cloud fractions < 40%) • For single pixels, error largely dominated by slant column random error (30-100 %)

  9. OMI HCHO monthly means show reduced noise owing to: Improved coverage (6x better than SCIAMACHY) More selective cloud screening (smaller pixels) What’s new with OMI ?

  10. Improved NMVOC emission inventories using HCHO measurements and inverse modeling • 3D-CTM with a chemical scheme optimized with respect to HCHO production from VOCs • Bottom-up inventories of NMVOCs emissions • Pyrogenic VOCs (fires)  GFED v1 and v2 data bases • Biogenic VOCs (50% isoprene)  MEGAN data base • Inversion method relates observed HCHO to emitted VOCs • Empirical linear relationships • Adjoint model of 3D-CTM (available for IMAGES model) • Published work so far mostly based on GOME observations. Most recent studies also use SCIAMACHY and OMI.

  11. Prior, GFEDv2 Prior, GFEDv1 Optimized, GFEDv2 Optimized, GFEDv1 Example of optimisation of pyrogenic VOCs emissions using IMAGES model and GOME data Stavrakou and Muller, 2007

  12. Inverse modeling of BVOC emissions using GOME and IMAGES model Biogenic emission ratio for July 1997 when GFEDv1 and MEGAN are used Large decrease over the Eastern U.S. Stavrakou and Muller, 2007 Large increase in Southern Africa, esp. over shrubland

  13. Isoprene emissions for USA using OMI + GEOSCHEM Millet et al., JGR, 113, D02307, doi:10.1029/2007JD008950, 2008

  14. Main current limitations • Accuracy of satellite data • Validation needed • More intercomparison exercises between satellites • Accuracy of HCHO production schemes in models • Resolution and S/N ratio of satellite data • E.g. anthropogenic emissions can hardly be identified, even with the OMI instrument (partly due to low HCHO production efficiency from anthropogenic sources)

  15. How to enhance the sensitivity and detect smaller amounts of HCHO ? • Noise largely dominated by photon noise (shot noise) • Improving sensitivity means improving photon collection • What can we do? • Increase instrument throughput (limited by weight and size !) • Multiply instruments in space • Increase integration time  trade-off to be made between coverage/ time resolution/ sensitivity • Simple calculation for GEO: assuming revisit time of 0.5 hour and 12 hours integration, S/N on daily averages can be increased by an order of magnitude compared to SCIAMACHY baseline (of course at the expense of a lost in global coverage)

  16. Complement HCHO observations by glyoxal retrievals ? Wittrock et al., GRL, 2006

  17. What are glyoxal measurements bringing more ? • CHOCHO show larger anthropogenic signal than HCHO • HCHO and CHOCHO both emitted by fire events but with different paths  potential to improve pyrogenic NMVOC emission estimates • New science questions: • Enhanced satellite columns of CHOCHO over the tropical oceans  missing marine source of glyoxal or unknown glyoxal precursors ? • Glyoxal might be a significant source of SOA, currently not taken into account by models (cf. Fu et al., 2008)

  18. Summary • Satellite retrievals of HCHO by GOME, SCIAMACHY and OMI provide useful information to test and improve bottom-up NMVOC emission data bases • Owing to its higher resolution and better coverage, OMI displays enhanced sensitivity to HCHO in comparison to both GOME and SCIAMACHY • Further improvements in sensitivity are needed to allow addressing anthropogenic emissions (possibly achievable from GEO) • Great interest in combing HCHO with glyoxal measurements from same sensors

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