1. In Cooperation with the IAMAS Commission on Atmospheric Chemistry and Global Pollution (CACGP) The International Global Atmospheric Chemistry Project A Core Project of the International Geosphere- Biosphere Programme (IGBP) Randall Martin Airborne Platforms and Satellite Observations

2. Major Challenge in Tropospheric Chemistry More Accurate Emission Inventories

3. Major Space-based Measurements of Tropospheric Composition (Not Exhaustive) SensorTOMSGOMEMOPITTMISRMODISAIRSSCIA- MACHY TESOMICALIOP Platform (launch) multi (1979-) ERS-2 (1995) Terra Aqua (1999) ( 2002) Envisat (2002) Aura (2004) Calipso (2005) ozoneX (low lat) XXXX COXXXX NO 2 XXX HCHOXXX BrOXXX SO 2 XXX aerosolXXXXXXX

4. Tropospheric NO 2 Retrieval from the GOME and SCIAMACHY Satellite Instruments GOME 1995-2002 Spatial resolution 320x40 km 2 SCIAMACHY 2002-present Spatial resolution 60x30 km 2 Spectral Fit Remove Stratosphere Total NO 2 Slant Column Tropospheric NO 2 Slant Column Calculate AMF Tropospheric NO 2 Column Martin et al., 2002, 2005 Martin et al., 2002 Palmer et al. 2001 Martin et al., 2002, 2003, submitted 5-10x10 14 molec cm -2 2-10x10 14 molec cm -2 40% Pixel Uncertainty Mean Total ±(5x10 14 molec cm -2 + 30%)

5. ICARTT Campaign Over and Downwind of Eastern North America in Summer 2004 Aircraft Flight Tracks and Validation Locations Overlaid on SCIAMACHY Tropospheric NO 2 Columns NASA DC-8NOAA WP-3D May-Oct 2004

6. Air Mass Factor Calculation in SCIAMACHY Retrieval Needs External Info on Shape of Vertical Profile Increased NO x Emissions from Midlatitude Improves GEOS- CHEM Simulation of NO 2 Profiles Remaining Discrepancy In Vertical Profile of Emissions Midlatitude lightningMean Bias in AMF: 0.4 Tg N yr -1 12%9%3% 1.6 Tg N yr -1 1%5%3% In Situ 0.4 Tg N yr -1 1.6 Tg N yr -1 NO 2 Measurements from Cohen (DC-8) and Ryerson (WP-3D)

7. Enhanced Midlatitude Lightning Reduces Discrepancy with SCIAMACHY over North Atlantic Profile of NOx Emissions (lifetime) May Explain Remaining Discrepancy May-Oct 2004 SCIAMACHY NO 2 (10 15 molec cm -2 ) GEOS-Chem NO 2 (10 15 molec cm -2 ) 1.6 Tg N in Midlat GEOS-Chem NO 2 (10 15 molec cm -2 ) 0.4 Tg N in Midlat

8. Significant Agreement Between Coincident Cloud- Filtered SCIAMACHY and In-Situ Measurements r = 0.78 slope = 0.82 1:1 line Ryerson (WP-3D) Cohen (DC-8) Chris Sioris Cloud-radiance fraction < 0.5 In-situ measurements below 1 km & above 3 km Assume constant mixing ratio below lowest measurement Add upper tropospheric profile from mean obs Horizontal bars show 17 th & 83 rd percentiles

9. Cloud-filtered Tropospheric NO 2 Columns Retrieved from SCIAMACHY May-Oct 2004 detection limit

10. A. Richter et al. Nature, 437, 129-132, 2005 1996 - 2002 Annual changes in tropospheric NO 2 observed with GOME

11. Error weighting Conduct a Chemical Inversion & Combine Top-Down and Bottom-up Inventories with Error Weighting A posteriori emissions Top-Down Emissions 10 15 molec N cm -2 A Priori NOx Emissions SCIAMACHY NO 2 Columns 10 11 molec N cm -2 s -1 GEOS-CHEM model GEIA

12. May-Oct 2004 Global Optimal Emission Inventory Reveals Major Discrepancy in NOx Emissions from Megacities r 2 =0.82 vs a priori

13. A Posteriori NOx Emissions from East Asia Exceed Those from Either North America or Europe A priori (Tg N yr -1 ) A posteriori (Tg N yr -1 ) East Asia6.89.2 North America8.18.8 Europe6.58.5 Africa7.18.2 SE Asia & India5.05.3 South America4.45.1 Australia1.11.9 Total39.147.0

14. Large Change in NOx Emissions Near New York City 10 11 atoms N cm -2 s -1  10 11 atoms N cm -2 s -1 A prioriA posteriori A posteriori – A priori 7.8 Tg N 0.6 Tg N r 2 = 0.92 Evaluate Each Inventory By Conducting GEOS-CHEM Simulation & Sampling Model Along Aircraft Flight Tracks  NO x (pptv) Simulation with A Posteriori – Simulation with A Priori  HNO 3 (pptv) 7.2 Tg N  PAN (pptv)

15. In Situ Airborne Measurements Support A Posteriori Inventory In Situ GEOS-CHEM (A priori) GEOS-CHEM (A posteriori) New England New England + Gulf P-3 Measurements from Tom Ryerson (NOAA) Aaron Swanson Andy Neuman Frank Flocke (NCAR) (CIRES/NOAA)

16. Error weighting EMIS: Emissions Mapping Integration Science Optimize NO x Emissions A posteriori emissions Top-Down Emissions May-Oct 2004 10 15 molec N cm -2 NOx Emissions (SMOKE/G.Tech) SCIAMACHY NO 2 Columns 10 11 molec N cm -2 s -1 Aug 2004 CMAQ

17. Algorithm for partitioning top-down NO x inventory (2000) Algorithm tested using synthetic retrieval GOME NO x emissions Fuel Combustion 1. Spatial location of FF- dominated regions in a priori (>90%) 1 Biomass Burning 2. Spatiotemporal distribution of fires used to separate BB/soil VIRS/ATSR fire counts Soils No fires + background 2 Jaeglé et al., 2005

18. Biomass Burning (2000) A priori A posteriori  Good agreement with BB seasonality from Duncan et al. [2003] (±200%) r 2 = 0.72 (±80%) SE Asia/India N. Eq. Africa S. Eq. Africa N. Eq. Africa: 50% increase SE Asia/India: 46% decrease Line: A priori (BB) Bars: A posteriori (BB) 10 10 atoms N cm -2 s -1 A posteriori total Jaeglé et al., 2005

19. Speciated Inventory for Soil emissions A posteriori 70% larger than a priori! A priori A posteriori Largest soil emissions: seasonally dry tropical + fertilized cropland ecosystems (±200%) (±90%) r 2 = 0.62 Soils Onset of rainy season: Pulsing of soil NO x ! North Eq. Africa Jaeglé et al., 2005 Soils East Asia

20. Liu et al., submitted Direct Retrieval of Tropospheric Ozone from GOME Using Optimal Estimation in Ultraviolet with TOMS V8 a priori GOMEGEOS-CHEM Tropospheric Ozone Column (Dobson Units)

21. Northern Tropics Remain a Challenge for Satellites and Models GOMEGEOS-CHEM RBiasR Caracas0.570.80.548.7 Dakar-0.37-3.80.815.2 Tel Aviv0.96-1.50.941.4 Bangkok0.83-2.40.947.2 Comparison with Ozone Measurements from the MOZAIC aircraft campaign Liu et al., submitted

22. Conclusions Synergy from integration of aircraft and satellite observations Growing confidence in top-down constraint on NOx emissions Underestimate in NOx emissions from megacities, in soils, and North American lightning Direct global retrieval of tropospheric ozone; puzzling ozone distribution in northern tropics

23. Acknowledgements Chris Sioris, Xiong Liu, Kelly Chance (Smithsonian Astrophysical Observatory) Lyatt Jaeglé, Linda Steinberger (Univerisity of Washington) Yongtao Hu, Armistead Russell (Georgia Tech) Tom Ryerson, Andy Neuman (NOAA/CIRES) Ron Cohen (Berkeley) Aaron Swanson, Frank Flocke (NCAR) Andreas Richter (University of Bremen) Funding: National Aeronautics and Space Administration (NASA) Canadian Foundation for Innovation (CFI) Nova Scotia Research and Innovation Trust (NSRIT)