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Satellite and Aircraft Based Constraints on NO X Emissions Randall Martin Chris Sioris Kelly Chance Tom Ryerson Andy Neuman Ron Cohen UC Berkeley Aaron.

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Presentation on theme: "Satellite and Aircraft Based Constraints on NO X Emissions Randall Martin Chris Sioris Kelly Chance Tom Ryerson Andy Neuman Ron Cohen UC Berkeley Aaron."— Presentation transcript:

1 Satellite and Aircraft Based Constraints on NO X Emissions Randall Martin Chris Sioris Kelly Chance Tom Ryerson Andy Neuman Ron Cohen UC Berkeley Aaron Swanson Frank Flocke NCAR CIRES

2 Air Mass Factor Calculation in SCIAMACHY Retrieval Needs External Info on Shape of Vertical Profile d()d() IoIo IBIB EARTH SURFACE RADIATIVE TRANSFER MODEL Scattering weight ATMOSPHERIC CHEMISTRY MODEL Shape factor Calculate w(  ) as function of: solar and viewing zenith angle surface albedo, pressure cloud pressure, frac aerosol profile, type Individual SCIAMACHY Scenes NO 2 mixing ratio C NO2 (  ) norm. by column Ω NO2  (  ) is temperature dependent cross-section sigma (  )

3 Increased NO x Emissions from Midlatitude Improves GEOS-CHEM Simulation of NO 2 Profiles Used in Retrieval Remaining Discrepancy In Vertical Profile of NOx Emissions (i.e. importance of cloud-cloud flashes) 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

4 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

5 Significant Agreement Between Coincident Cloud-Filtered SCIAMACHY and In-Situ Measurements r = 0.79 slope = 0.8 1:1 line Ryerson NO 2 Cohen NO 2 Chris Sioris 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 Horizontal bars show 17 th & 83 rd percentiles

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

7 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 cm -2 A Priori NOx Emissions SCIAMACHY NO 2 Columns 10 11 molec N cm -2 s -1 GEOS-CHEM model

8 May-Oct 2004 Global Optimal Emission Inventory Reveals Major Discrepancy in NOx Emissions from Megacities r 2 =0.83 vs a priori

9 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 Asia6.89.0 North America8.18.8 Europe6.58.3 Africa7.18.2 SE Asia & India5.05.2 South America4.45.1 Australia1.11.8 Total39.146.4

10 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)

11 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) 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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