1. J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels jfm@aeronomie.be AGU Fall meeting, Dec. 2007 Multi-year emission inversion for reactive gases using the adjoint model method

2. Inversion methodology Prior emission distributions : base functions (one per grid cell, category and month) f j are the emission parameters, which minimize the cost function: Optimized emissions : anthropogenic biomass burning biogenic

3. Correlations for anthropogenic emission errors E n,k = total emission of country n in subcategory k (=1,… 7) σ En,k = standard error for this country/subcategory Φ i = total flux emitted in grid cell I E n,k = total emission of country n in subcategory k (=1,… 7) σ En,k = standard error for this country/subcategory Φ i = total flux emitted in grid cell I = fraction of flux Φ i due to country n and subcategory k A nm = 1 when n=m, = 0 if n and m belong to different large regions (Western Europe, Eastern Europe, FSU, etc.) C ij nm = 1 when i=j, <1 otherwise Subcategories for NOx: 1. Road transport 2. Power generation 3. Fossil fuel use in industry 4. Biofuel (residential) 5. Cement 6. Non-road land transport 7. Other Basic assumption: errors on emissions from different subcategories, or from different large regions of the world are uncorrelated. + Temporal correlations

4. Correlations for pyrogenic and BVOC emission errors + Temporal correlations Plant types considered in the MEGAN model for isoprene: 1. Needleleaf evergreen 2. Needleleaf deciduous 3. Broadleaf deciduous 4. Broadleaf evergreen 5. Shrub 6. Grass 7. Crops Basic assumptions: errors on biogenic emissions decrease exponentially with geographical distance; and the errors on emissions from different vegetation types are uncorrelated. d ij = geographical distance between grid cells i and j decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic x i n : fraction of the flux Φ i emitted by vegetation type n (forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions) d ij = geographical distance between grid cells i and j decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic x i n : fraction of the flux Φ i emitted by vegetation type n (forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions)

5. Gradient of the cost function Calculation of new parameters f with a descent algorithm Minimum of J(f) ? Observations Forward CTM Integration from t 0 to t Transport Chemistry Cost function J(f) Adjoint model Integration from t to t 0 Adjoint transport Adjoint chemistry Adjoint cost function Checkpointing Control variables f yes no Optimized control parameters Minimizing the cost > 20 iterations needed to decrease the norm of the gradient by factor of ~100

6. 70 chemical compounds, 5°x5° resolution, 40 σ-p levels (Stavrakou and Müller, JGR, 2006) Updated oxidation mechanism for isoprene and pyrogenic NMVOCs, so that the HCHO yields match MCM-derived values Monthly wind fields from ECMWF, impact of wind variability represented as horizontal diffusion Daily ECMWF fields for convective fluxes, PBL mixing, cloud fields, T and H 2 O Biomass burning emissions : GFED versions 1 and 2 (Van der Werf et al., 2003, 2006) Biogenic isoprene emissions from MEGAN model driven by ECMWF meteorological fields (Müller et al., ACPD, 2007) 10-year simulations, plus spin-up The IMAGES (v2) model

7. 10-year inversion of NOx emissions based on GOME/SCIAMACHY NO 2 columns (TEMIS dataset): cf. presentation A34B-04 by Stavrakou et al. on Wednesday afternoon  Use averaging kernels 10-year inversion of biogenic and pyrogenic NMVOC emissions based on GOME/SCIAMACHY HCHO columns (new dataset developed by I. De Smedt and M. Van Roozendael, IASB-BIRA)  the HCHO retrievals use HCHO vertical profile shapes from IMAGES model In both cases, the observations are binned onto the CTM grid and monthly averaged accounting for the actual sampling times of the observations at each location Optimisations

8. Results: NOx Optimized / prior emission ratio for anthropogenic NOx (here, July 2000) Inferred anthropogenic emission trend 1997- 2006, %/year

9. Results: Biogenic VOCs Biogenic emission ratio for July 1997 when GFEDv1 is used when GFEDv2 is used factor of 2 decrease over the Eastern U.S. when GEOS-Chem isoprene mechanism is used Large increase in Southern Africa, esp. over shrubland

10. Comparison with aircraft campaigns

11. Improving biomass burning inventories? Prior, GFEDv2 Prior, GFEDv1 Optimized, GFEDv2 Optimized, GFEDv1 Bad timing and amplitude in GFEDv1, optimization fails Strong overestimation in GFEDv2 Strong underestimation in GFEDv2, optimization wrongly increases biogenic emissions to compensate

12. Both the chemical observations and the prior information on the emissions (distributions, errors) determine the optimization results Ideally, emission models should be coupled to the CTM and incoporated in the optimization system; but even then, characterization of the error co(variances) remain difficult Multi-year emission inversions make possible to estimate the interannual variability of the emissions (e.g. for biomass burning) and their long-term trends (for anthropogenic NOx) Anthropogenic emission trends can be determined from 10-year NO 2 dataset – caution is needed due to the indication of temporal drifts in the data Biogenic NMVOC emissions determined from the HCHO retrievals developed at IASB-BIRA (De Smedt &Van Roozendael) are generally lower than previously estimated based on another HCHO retrieval and on the GEOS-Chem model – most of the difference is apparently related to the retrievals Intercomparisons of the HCHO retrievals are clearly needed! Biomass burning inventories can be evaluated and even improved based on HCHO retrievals Conclusions