J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels jfm@aeronomie.be

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

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

3. Correlations for anthropogenic emission errors Basic assumption: errors on emissions from different subcategories, or from different large regions of the world are uncorrelated. 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 En,k= total emission of countryn in subcategory k (=1,… 7) σ En,k= standard error for this country/subcategory Φi= total flux emitted in grid cell I = fraction of flux Φidue to country n and subcategory k Anm= 1 whenn=m, =0 if n andmbelong to different large regions (Western Europe, Eastern Europe, FSU, etc.) Cijnm = 1 when i=j, <1 otherwise + Temporal correlations

4. Correlations for pyrogenic and BVOC emission errors Basic assumptions: errors on biogenic emissions decrease exponentially with geographical distance; and the errors on emissions from different vegetation types are uncorrelated. 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 dij = geographical distance between grid cells i and j decorrelation lengths : 500 kmfor pyrogenic / 3000 kmfor biogenic xin: fraction of the flux Φiemitted by vegetation typen (forest/savannafor pyrogenic, 7 plant functional typesfor biogenic VOC emissions) + Temporal correlations

5. Minimizing the cost Control variables f Forward CTM Integration from t0 to t Adjoint model Integration from t to t0 Transport Adjoint transport Observations Chemistry Checkpointing Adjoint chemistry Cost function J(f) Adjoint cost function Gradient of the cost function Calculation of new parameters f with a descent algorithm Minimum of J(f) ? no yes Optimized control parameters > 20 iterations needed to decrease the norm of the gradient by factor of ~100

6. The IMAGES (v2) model • 70 chemical compounds, 5°x5° resolution, 40 σ-p levels (Stavrakou andMüller, 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 H2O • 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

7. Optimisations • 10-year inversion of NOx emissions based on GOME/SCIAMACHY NO2 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

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 factor of 2 decrease over the Eastern U.S. when GEOS-Chem isoprene mechanism is used Large increase in Southern Africa, esp. over shrubland when GFEDv2 is used

10. Comparison with aircraft campaigns

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

12. Conclusions • 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 NO2 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