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Satellite and Aircraft Based Constraints on NO X Emissions

Satellite and Aircraft Based Constraints on NO X Emissions. Chris Sioris Kelly Chance. Randall Martin. Tom Ryerson Andy Neuman. CIRES. Ron Cohen. Aaron Swanson Frank Flocke. UC Berkeley. NCAR. I B. I o. d t (  ). EARTH SURFACE.

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Satellite and Aircraft Based Constraints on NO X Emissions

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  1. Satellite and Aircraft Based Constraints on NOX Emissions Chris SiorisKelly Chance Randall Martin Tom Ryerson Andy Neuman CIRES Ron Cohen Aaron Swanson Frank Flocke UC Berkeley NCAR

  2. IB Io dt() EARTH SURFACE Air Mass Factor Calculation in SCIAMACHY Retrieval Needs External Info on Shape of Vertical Profile RADIATIVE TRANSFER MODEL ATMOSPHERIC CHEMISTRY MODEL Shape factor sigma () NO2 mixing ratio CNO2() norm. by columnΩNO2 Scattering weight Individual SCIAMACHY Scenes () is temperature dependent cross-section • Calculate w() as function of: • solar and viewing zenith angle • surface albedo, pressure • cloud pressure, frac • aerosol profile, type

  3. Increased NOx Emissions from Midlatitude Improves GEOS-CHEM Simulation of NO2 Profiles Used in Retrieval Remaining Discrepancy In Vertical Profile of NOx Emissions (i.e. importance of cloud-cloud flashes) In Situ 0.4 Tg N yr-1 1.6 Tg N yr-1 Midlatitude lightning Mean Bias in AMF: 0.4 Tg N yr-1 12% 9% 3% 1.6 Tg N yr-1 1% 5% 3%

  4. Enhanced Midlatitude Lightning Reduces Discrepancy with SCIAMACHY over North AtlanticProfile of NOx Emissions (lifetime) May Explain Remaining Discrepancy SCIAMACHY NO2 (1015 molec cm-2) GEOS-Chem NO2 (1015 molec cm-2) 1.6 Tg N in Midlat GEOS-Chem NO2 (1015 molec cm-2) 0.4 Tg N in Midlat May-Oct 2004

  5. Significant Agreement Between Coincident Cloud-Filtered SCIAMACHY and In-Situ Measurements r = 0.79 slope = 0.8 1:1 line • Coincident measurements • 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 Cohen NO2 Ryerson NO2 Horizontal bars show 17th & 83rd percentiles Chris Sioris

  6. Cloud-filtered Tropospheric NO2 Columns Retrieved from SCIAMACHY May-Oct 2004 detection limit

  7. Conduct a Chemical Inversion & Combine Top-Down and Bottom-up Inventories with Error Weighting A Priori NOx Emissions SCIAMACHY NO2 Columns 1011 molec N cm-2 s-1 1015 molec cm-2 GEOS-CHEM model Error weighting A posteriori emissions Top-Down Emissions

  8. Global Optimal Emission Inventory RevealsMajor Discrepancy in NOx Emissions from Megacities May-Oct 2004 r2=0.83 vs a priori

  9. A Posteriori NOx Emissions from East Asia Exceed Those from Either North America or Europe

  10. Large Change in NOx Emissions Near New York City r2= 0.92 A posteriori – A priori A priori A posteriori 7.2 Tg N 7.8 Tg N 0.6 Tg N 1011 atoms N cm-2 s-1 1011 atoms N cm-2 s-1 1011 atoms N cm-2 s-1 Evaluate Each Inventory By Conducting GEOS-CHEM Simulation & Sampling Model Along Aircraft Flight Tracks Simulation with A Posteriori – Simulation with A Priori NOx (pptv) PAN (pptv) HNO3 (pptv)

  11. In Situ Airborne Measurements Support A Posteriori Inventory New England New England New England + Gulf GEOS-CHEM (A priori) GEOS-CHEM (A posteriori) In Situ P-3 Measurements from Tom Ryerson (NOAA) Aaron Swanson Andy Neuman Frank Flocke (NCAR) (CIRES/NOAA) Horizontal bars show 17th & 83rd percentiles

  12. Conclusions • Growing confidence in top-down constraint on NOx emissions • Underestimate in NOx emissions from megacities • North American lightning NOx emissions underestimated

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