1. Randall Martin Applying Space-Based Measurements of Ultraviolet and Visible Radiation to Understand Tropospheric Composition With contributions from: Aaron Van Donkelaar, Rongming Hu (Dalhousie University) Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory) Lyatt Jaeglé, Linda Steinberger (Univerisity of Washington) Yunsoo Choi, Yuhang Wang, Yongtao Hu, Armistead Russell (Georgia Tech) Arlene Fiore (GFDL) Tom Ryerson (NOAA) Ron Cohen (Berkeley) Bill Brune (Penn State)

2. Major Challenges in Tropospheric Chemistry More Accurate Emission Inventories Understand Aerosol Sources and Properties

3. Relative Uncertainty Global Surface NO x Emissions Uncertain to Factor of 2 Implications for Tropospheric Ozone, Aerosols, and Indirect Effect Here in Tg N yr -1 (based on) Fossil Fuel 24 (GEIA) Biomass Burning 6 (Duncan et al., 2003) Soils 5 (Yienger and Levy, 1995) NOx Emissions (Tg N yr -1 ) Fossil Fuel (20-33) Biomass Burning (3-13) Soils (4-21)

4. Top-Down Information from the GOME and SCIAMACHY Satellite Instruments Nadir-viewing solar backscatter instruments including ultraviolet and visible wavelengths Low-elevation polar sun-synchronous orbit, late morning observation time GOME 1995-2002 Spatial resolution 320x40 km 2 Global coverage in 3 days SCIAMACHY 2002-present Spatial resolution 60x30 km 2 Global coverage in 6 days

5. Spectral Fit of NO 2 Scattering by Earth surface and by atmosphere Backscattered intensity I B Solar I o Distinct NO 2 Spectrum Nonlinear least-squares fitting Ozone NO 2 O 2 -O 2 Albedo A Also Weak H 2 O line Based on Martin et al., 2002

6. Total NO 2 Slant Columns Observed from SCIAMACHY Dominant stratospheric background (where NO 2 is produced from N 2 O oxidation) Also see tropospheric hot spots (fossil fuel and biomass burning) May-October 2004

7. Perform a Radiative Transfer Calculation to Account for Viewing Geometry and Scattering  RcRc RoRo I B,o I B,c PcPc RsRs GOMECAT (Kurosu) & FRESCO Clouds Fields [Koelemeijer et al., 2002] Surface Reflectivity [Koelemeijer et al., 2003] LIDORT Radiative Transfer Model [Spurr et al., 2002] GEOS-CHEM NO 2 & aerosol profiles dd IoIo Based on Martin et al., 2002, 2003 Cloud Radiance Fraction I B,c / (I B,o + I B,c )

8. Cloud-filtered Tropospheric NO 2 Columns Determined from SCIAMACHY (Data  NASA) May-Oct 2004 detection limit

9. USE RETRIEVED NO 2 COLUMNS TO MAP NO x EMISSIONS Emission NO NO 2 HNO 3 lifetime ~hours NITROGEN OXIDES (NO x ) BOUNDARY LAYER GOME SCIAMACHY NO / NO2   W ALTITUDE Tropospheric NO 2 column ~ E NOx

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

11. Interpret Satellite Observations Using GEOS-CHEM Chemical Transport Model Assimilated Meteorology (GEOS) 2 o x2.5 o horizontal resolution, 30 vertical layers O 3 -NO x -VOC chemistry SO 4 2- -NO 3 - -NH 4 + -H 2 O, dust, sea-salt, organic & elemental carbon aerosols Interactive aerosol-chemistry Anthropogenic and natural emissions Cross-tropopause transport Deposition Calculated Mean Surface Ozone for August 1997

12. May-Oct 2004 48 Tg N yr -1 48 - 38 Tg N yr -1 Global Top-Down Emission Inventory Reveals Major Discrepancy in NOx Emissions from Megacities GEIA

13. ICARTT: COORDINATED ATMOSPHERIC CHEMISTRY CAMPAIGN OVER EASTERN NORTH AMERICA AND NORTH ATLANTIC IN SUMMER 2004 International, multi-agency collaboration targeted at regional air quality, pollution outflow, transatlantic transport, aerosol radiative forcing Terra ERS MISR, MODIS, MOPITT ERS-2 GOME Envisat SCIAMACHY Aqua AIRS, MODIS NASA DC-8 UK BAE-143 DLR Falcon NOAA-P3 Canada Convair NASA Proteus

14. North American NOx Emissions (May – October) Largest Change in Northeastern US Coast 10 11 atoms N cm -2 s -1 GEOS-CHEM (NAPAP)SCIAMACHY SCIAMACHY - NAPAP 7.6 Tg N 8.4 Tg N 0.8 Tg N r 2 = 0.85

15. Evaluate Top-Down and Bottom-Up NOx Inventories Conduct GEOS-CHEM Simulation For Each Inventory Sampled GEOS-CHEM Along Flight Tracks  NO x (ppbv) Simulation with SCIAMACHY – Original NOx Emission Inventory  HNO 3 (ppbv)

16. In Situ Airborne Measurements Support Top-Down Inventory In Situ GEOS-CHEM (Bottom-up) GEOS-CHEM (Top-Down) New EnglandNew England + GulfRemote P-3 Measurements from Tom Ryerson (NOAA)

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. 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. Transient Enhancements In GOME NO 2 Columns from Lightning Choi et al., GRL, 2005

21. SCIAMACHY Shows Elevated NOx Export from North America May-Oct 2004 SCIAMACHY NO 2 (10 15 molec cm -2 ) GEOS-CHEM NO 2 (10 15 molec cm -2 ) May-Oct 2004

22. Explained by Model Bias in Upper Tropospheric NO x GEOS-CHEM NO 2 Cohen NO 2 Errorbars Show 17 th and 83 rd percentiles West of -60 degrees lon, “land”East of -60 degrees lon, “ocean” GEOS-CHEM NO Brune NO GEOS-CHEM low by 7.5% in column GEOS-CHEM low by factor of 2 in column

23. EMISSION CONTROL STRATEGY FOR OZONE POLLUTION: ARE NO x OR VOCs THE LIMITING PRECURSORS? HCHO/NO 2 < 1 (blue)  VOC-limited HCHO/NO 2 > 1 (green-red)  NO x -limited Use GOME observations of HCHO/NO 2 ratio to determine ozone production regime [Sillman, 1995] Martin et al. [2004]

24. Aerosol Single Scattering Albedo Major Source of Uncertainty in Global Radiative Forcing Estimates IPCC [2001]

25. Maximum Sensitivity to Aerosol Optical Thickness Over Dark Surfaces More Sensitive to Single Scattering Albedo Over Bright Surfaces King et al., BAMS, 1999 Saharan Dust Plume Staten Island Refinery Fire

26. TOMS Aerosol Index Measures Absorbing Aerosols In Ultraviolet Where Rayleigh Scattering Acts as Bright Surface July 2000 TOA spectral albedo measured by GOME

27. MODIS Aerosol Optical Depth Includes Both Scattering and Absorbing Aerosols July 2000 1.5 1.1 0.8 0.4 0 3.0 2.2 1.6 0.8 0 July 2000 MODIS TOMS

28. Retrieval of Aerosol Single Scattering Albedo Determined with Radiative Transfer Calculation as Retrieval of Aerosol Single Scattering Albedo Determined with Radiative Transfer Calculation as SSA that reproduces TOMS Aerosol Index Rongming Hu July 2000

29. Modeled Organic Carbon Too Low in Southeast in July Ground-Based Measurements from IMPROVE (ug m -3 ) GEOS-CHEM model calculation (ug m -3 ) Aaron Van Donkelaar

30. IMPROVE minus GEOS-CHEM OC [ug/m 3 ], 2001 Summer Organic Carbon Bias GEOS-CHEM already accounts for primary and secondary sources of organic aerosol Aaron Van Donkelaar

31. Distribution of Isoprene Emissions Similar to OC Bias Mounting Field and Laboratory Evidence of OC Yield from Isoprene Oxidation Products 2001 Isoprene Emission (10 12 moles C cm -2 s -1 ) Aaron Van Donkelaar

32. IMPROVE minus GEOS-CHEM OC [ug/m 3 ], 2001 Organic Carbon from Isoprene Oxidation Products Largely Corrects Bias Aaron Van Donkelaar

33. Conclusions Growing confidence in top-down constraint on NOx emissions Gross-underestimate in NOx emissions from megacities Soil NOx emissions underestimated, especially from Northern Equatorial Africa North American lightning NOx emissions underestimated Promise for global retrieval of aerosol single scattering albedo Low yield of organic carbon from isoprene oxidation products reduces model bias

34. Acknowledgements Aaron Van Donkelaar, Rongming Hu (Dalhousie U.) Chris Sioris, Kelly Chance (Smithsonian) Lyatt Jaeglé, Linda Steinberger (U. of Washington) Yunsoo Choi, Yuhang Wang, Yongtao Hu, Armistead Russell (Georgia Tech) Arlene Fiore (GFDL) Tom Ryerson (NOAA) Ron Cohen (Berkeley) Bill Brune (Penn State) Funding: National Aeronautics and Space Administration (NASA) Canadian Foundation for Innovation (CFI) Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) Natural Sciences and Engineering Research Council of Canada (NSERC) Nova Scotia Research and Innovation Trust (NSRIT)