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Randall Martin with contributions from

Top-Down Constraints on Emissions: Opportunities and Challenges from Satellite Observations of Atmospheric Composition. Randall Martin with contributions from Shailesh Kharol , Gray O’Byrne, Akhila Padmanabhan , Aaron van Donkelaar.

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Randall Martin with contributions from

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  1. Top-Down Constraints on Emissions: Opportunities and Challenges from Satellite Observations of Atmospheric Composition Randall Martin with contributions from ShaileshKharol, Gray O’Byrne, AkhilaPadmanabhan, Aaron van Donkelaar LokLamsal (Dalhousie  NASA), Chulkyu Lee (Dalhousie  KMA) Jintai Lin (PKU), DavenHenze (CU Boulder), GuannanGeng, Qiang Zhang, and Yuxuan Wang (Tsinghua) 2013 China Emissions Workshop, Beijing 28 June 2013

  2. Satellite-derived PM2.5 for 2001-2006 Evaluation in North America: r=0.77 slope = 1.07 N=1057 Outside Canada/US N = 244 (84 non-EU) r = 0.83 (0.83) Slope = 0.86 (0.91) Bias = 1.15 (-2.64) μg/m3 van Donkelaar et al., EHP, 2010

  3. PM2.5 Nearly as Sensitive to Emissions of NOx as to SO2 GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions ΔNOx Emissions ΔSO2 Emissions ΔNH3 Emissions ΔPM2.5 (ug m-3) -0.5 0 1 2 34% 41% 25% Kharol et al., GRL, 2013 But How Accurate are the Emissions Used in this Calculation? How Accurate are Emissions in General? What Can We Learn about Emissions from Satellite Observations?

  4. Major Nadir-viewing Space-based Measurements of Tropospheric Trace Gases and Aerosols (Not Exhaustive) Solar Backscatter&Thermal Infrared

  5. Close Relationship of NOxand SO2 Emissions With Satellite Tropospheric NO2 and SO2 Columns Satellite Tropospheric NO2 column ~ ENOx Tropospheric SO2column ~ ESO2 BOUNDARY LAYER NO2 NO/NO2  W ALTITUDE NO SO42- OH, cloud day SO2 lifetime hours HNO3 Deposition Emission Emission Nitrogen Oxides (NOx) Sulfur Dioxide (SO2)

  6. Top-Down (Mass Balance) Estimates of NOx& SO2Emissions 2004-2005 SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1) Martin et al., 2006 49.9 Tg S yr-1 2006 OMI SO2 (1016molec cm-2) SO2emissions (1011 atoms N cm-2 s-1) Lee et al., 2011

  7. Application of Satellite Observations for Timely Updates to NOx Emission Inventories Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions Forecast Inventory for 2010 Based on Bottom-up for 2005 and Monthly OMI NO2 for 2005-2010 23% decrease in North American emissions Lamsal et al., GRL, 2011 Streets et al., AE, in press 2.5% increase in global emissions 27% increase in Asian emissions

  8. Integration of Top-down Information In Bottom-up ApproachExample Evaluation of Spatial Proxies Population, Outdated Road Network Industrial GDP, New Road Network GuannanGeng (Tsinghua) et al. in prep

  9. ComplicationsSatellite Retrievals Inverse Modeling

  10. Need to Account for Average Kernel in IR Satellite RetrievalsIASI Provides Some Constraint on NH3 Emissions with Averaging Kernels Total Column Using NH3emissions from Streets et al. (2003) reduced by 30% following Huang et al. (2012) Kharol et al., GRL, 2013

  11. IB Io dt() EARTH SURFACE Need to Account for Vertical Profile and Atmospheric Scattering (Air Mass Factor; AMF) in UV-Vis Retrievals Radiative Transfer Model Atmospheric Chemistry Model “a-priori” Shape factor sigma () SO2 mixing ratio CSO2() Scattering weight () is temperature dependent cross-section INDIVIDUAL OMI SCENES • Calculate w() as function of: • solar and viewing zenith angle • surface albedo, pressure • cloud pressure, aerosol • OMI O3 column

  12. Local Air Mass Factor and Offset Correction Improves Agreement with Aircraft Observations (INTEX-A and B) OMI SCIAMACHY Orig:slope = 1.6, r = 0.71 New: slope = 0.95, r = 0.92 Orig:slope = 1.3, r=0.78 New: slope = 1.1, r=0.89 SCIAMACHY OMI Lee et al., JGR, 2009

  13. Need to Account for Multiple Effects of Aerosols on UV-Vis Trace Gas Retrievals Accounting for Aerosol Haze Can Increase R2 (0.720.96) of OMI NO2vs Ground-based DOAS Observations in China Jintai Lin (PKU) et al., in prep, ACP

  14. Expected OMI NO2 Retrieval Bias for Snow-Covered ScenesDue to Errors in Accounting for Transient Snow & Ice All Cloud Fractions With Cloud Fraction Threshold (f < 0.3) -0.5 0 1.0 0.5 O’Byrne et al., JGR, 2010

  15. Aerosol Retrievals Susceptible to Bias over Bright SurfacesAerosol Optical Depth (AOD) from MODIS and MISR over 2001-2006 MODIS 1-2 days for global coverage (w/o clouds) AOD retrievals at 10 km x 10 km Requires assumptions about surface reflectivity MISR 6-9 days for global coverage (w/o clouds) AOD retrievals at 18 km x 18 km Simultaneous retrieval of surface reflectance and aerosol optical properties 0 0.1 0.2 0.3 AOD [unitless] van Donkelaar et al., EHP, 2010

  16. Can Remove Biased Data Using Sunphotometer Observations Excluded Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20% 0.3 0.25 0.2 0.15 0.1 0.05 0 Combined MODIS/MISR r = 0.61(vs. in-situ PM2.5) AOD [unitless] MODIS r = 0.39 (vs. in-situ PM2.5) MISR r = 0.39 (vs. in-situ PM2.5) van Donkelaar et al., EHP, 2010

  17. Adjoint Reduces Inversion Error vs Mass BalanceTest to Recover 30% Increased NOx Emissions in Four Locations Using a Week of Synthetic Observations of NO2 Columns November July Mass Balance NME=6x10-3 NME=3x10-3 Adjoint NME=4x10-4 NME=5x10-4 Inversion – Truth (ΔNOx Emissions molec cm-2 s-1) Δ NME = Normalized Mean Error Padmanabhan et al., in prep

  18. How Well Do Models Represent SO2 Lifetime in China?Evaluation of GEOS-Chem SO2 Lifetime vs Calculations from In Situ Measurements in Eastern US U Maryland Research Flights for Eastern U.S. C is SO2 from EPA Network H is GEOS Mixed Layer Depth June - August Hains, Dickerson, et al., 2007 Lee et al., JGR, 2011

  19. Inversion Relies on Relative Error in Bottom-up and Top-down Approaches: Embrace Uncertainty Inverse problem seeks emissions E that minimize cost function J a priori emissions Need information on uncertainty (σ) Observed Trace Gas a posteriori emissions Observed NO2Columns (Ω) observational error A Priori NOx Emissions (Ea) a priori error Error weighting Model F(E) σa σ A posteriori emissions E

  20. Uncertainty in SO2 Retrievals Due to Clouds, Surface Reflectance, SO2 Vertical Profile, and Aerosols Cloud-free Fraction of Scene Cloudy Fraction of Scene Lee et al., JGR, 2009

  21. Most Satellites Observe at Specific Times of DayRequires Attention to the Diurnal Profile of Emissions

  22. Conclusions • Substantial opportunities and challenges • Integrate top-down and bottom-up methods & communities • Account for retrieval assumptions in inversion (e.g. trace gas profile) • Avoid bias (e.g. aerosol, snow) in satellite data products and algorithms • Quantify uncertainty in both top-down and bottom-up methods Acknowledgements:NSERC, Environment Canada

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