1. Emissions of CO from Asia during TRACE-P Paul Palmer, Daniel Jacob, Dylan Jones, Colette Heald David Streets, Glen Sachse, Hanwant Singh http://www.people.fas.harvard.edu/~ppalmer Global emissions of CO are highly uncertain KEY QUESTIONS: 1)Are bottom-up estimates of CO sources consistent with measured concentrations of CO? 2)How much information do concentration measurements of CO provide on CO sources?

2. Forward model (GEOS-CHEM) Inverse model P3B, DC8 observations D. Streets’ Emissions * FF BB BF Modeling Overview * * Logan/Heald Emissions * * X s = X b + S b K (K T S b K + S Y ) -1 (Y – HX b ) S S = (K T S Y -1 K + S b -1 ) -1

3. GEOS-CHEM Tagged CO simulation for TRACE-P (also state vector) China India Japan Southeast Asia Korea Rest of World Some sources/regions are combined due to interdependency of emissions Global 3D CTM 2x2.5 deg resolution

4. GEOS-CHEM CO [ppb] Lat [deg] Observation A priori A priori emissions have a large negative bias in the boundary layer

5. o Emission uncertainties for Asia: Anthropogenic (D. Streets): China (78%), Japan (17%), Southeast Asia (100%), India (100%), other (42%) Biomass burning: 50% o Measurement uncertainty: Observation accuracy (1%) Representativeness (14ppb or 25%) Estimated: 1 sigma value about mean observed 2x2.5 value Model error GEOS-CHEM 2x2.5  cell TRACE-P Detailed error specification for inverse model

6. GEOS-CHEM All latitudes (measured-model) /measured Altitude [km] Model error: (y*RRE) 2 ~38ppb (>70% of total measurement error) Mean bias RRE

7. A posteriori emissions are insensitive to assumptions made in inverse analysis Korea + Japan India Rest of World/10 Southeast Asia China (BB) China (anthropogenic) A priori A posteriori 1-sigma uncertainty

8. GEOS-CHEM CO [ppb] Lat [deg] Observation A priori A posteriori A posteriori emissions improve agreement with observations

9. [10 18 molec cm -2 ] MOPITT shows low CO columns over Southeast Asia during TRACE-P GEOS-CHEMMOPITT MOPITT – GEOS-CHEM [10 18 molec cm -2 ] c/o Heald, Emmons, Gille Largest difference

10. Conclusions 1)A priori emissions are inconsistent with BL data 2)Error analysis is crucial for accurate determination of emissions from concentration data 3) Inverse model shows: * increased anthropogenic emissions (30% from China) *decreased biomass burning (results inconclusive) Multi-species inversion is the next important step