Inverse modelling of CO emissions

1. Inverse modelling of CO emissions J.-F. Müller and T. Stavrakou Belgian Institute for Space Aeronomy Avenue Circulaire 3, 1180 Brussels jfm@aeronomie.be EVERGREEN International Workshop 19-20 January 2006, KNMI, De Bilt, The Netherlands

2. Outline • Carbon monoxide: sources and sinks • Inverse modeling of emissions using the adjoint model • State-of-the-art in CO inversion • The IMAGES model used in two inversion exercises constrained by: • a) 1997 CMDL data & GOME NO2 columns • b) the 2000-2001 MOPITT CO columns • Big-region vs. grid-based inversion approach • Comparison to independent observations and past studies • Conclusions and outlook

3. Carbon monoxide: sources and sinks (units: Tg C/year) deposition deposition 85 30 OH OH, hv OH CH2O CO2 CO CH4 1100 570 360 CO2 340 410 deposition 100 ??? OH,O3 80 NMVOC(non-methane volatile organic compounds) 250 SOA= Secondary Organic Aerosols 200 50 700 100

4. Inverse modelling of emissions Cost function: measures the bias between the model and the observations J(f)=½Σi (Hi(f)-yi)TE-1(Hi(f)-yi) + ½ (f-fB)TB-1(f-fB) Vector of the control parameters Matrix of errors on the observations Matrix of errors on the control parameters observations Model operator acting on the control parameters 1st guess values of the control parameters For what values of f is the cost function minimal?

5. The adjoint model Control variables f Forward CTM Integration from t0 to t Adjoint model Integration from t to t0 Transport Adjoint transport Observations Chemistry Current informations 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

6. Inverting for CO emissions – State-of-the-art

7. Advantages from the use of the adjoint The calculated derivatives are exact The full (transport/chemistry) adjoint allows to take non-linearities into account, e.g. the non-linear relationship between CO concentrations and surface emissions The emissions of different compounds can be optimized simultaneously, their chemical interactions being taken into account The computational time to determine the model sensitivity does not depend on the number of control variables  grid-based inversions can be addressed BUT: the exact posterior error estimation is not possible within this framework Instead, iterative approximations of the inverse Hessian can be used

8. The IMAGES model • Provides the global distribution of 60 chemical compounds at 5°x5° resolution and 25 vertical levels (Müller and Brasseur, 1995) • A priori anthropogenic emissions : 1997 EDGAR v3 inventory (Peters and Olivier, 2003) • Biomass burning emissions : GFED database (Van der Werf et al., 2003) or the POET inventory (Olivier et al., 2003) • Biogenic emissions for isoprene and monoterpenes from Guenther et al., 1995, and for CO from Müller and Brasseur, 1995 • Model time step : 1 day, spin-up time : 4 months, 1 year simulation

9. A. Big-region inversion of the 1997 CO emissions The inversion is constrained by: • NOAA/CMDL CO mixing ratios • Ground-based FTIR CO vertical column abundances • GOME tropospheric NO2 columns Simultaneous optimization of the total annual CO & NOx emissions over large regions (39 flux parameters) • chemical feedbacks via the adjoint • constant seasonality of the sources • B is assumed diagonal Müller and Stavrakou,ACP, 2005

11. Impact of emission changes on OH

12. Comparison to aircraft observations

13. Estimation of the posterior errors • Direct calculation of the Hessian matrix using finite differences on the adjoint model • Use of the inverse BFGS formula and the output of the minimization algorithm at each iteration • Use of the DFP update formula

14. B. Big-region vs. grid-basedinversion for optimizing the 2000-01 CO&VOC emissions • The inversion is constrained by the MOPITT daytime CO columns from May 2000 to April 2001 • The columns and averaging kernels are binned onto the IMAGES grid and monthly averaged  total : ~ 6000 observations • Error on the column is assumed 50% of the observed value « Big-region approach »: optimize the global CO fluxes over large regions as in case A (18 variables) « Grid-based » inversion: optimize the fluxes emitted from every model grid cell by month ( ~30000 param.) seasonality and geographical distribution varied source-specific correlations among prior errors on the flux parameters  B non-diagonal In both cases, • distinguish between anthropogenic, biomass burning and biogenic emissions Stavrakou and Müller, 2006, submitted

15. The error correlation setup Anthropogenic emissions errors: • highly correlated within the same country • weakly correlated within large world zones • uncorrelated in any other case • constant temporal correlation Vegetation fire and biogenic emissions: • spatial correlations decrease with geographical distance • they are further reduced when the fire or ecosystem type differ • temporal correlations

16. MOPITT column Grid-based setup Big-region setup Optimization results • Both solutions succeed in reducing the model/MOPITT bias over most regions • Larger cost reduction in the grid-based case (4.6) as compared to the big-region setup (2.2)

17. Big-region setup Grid-based setup Anthropogenic emission updates • Optimized global anthropogenic emissions : 664 Tg CO/yr (+16%) • More significant increase over the eastern China in the grid-based (110%), compared to the big-region setup (80%) • Reduced South Asian emissions by ~40% • Small changes over America, Europe and Oceania

18. Anthropogenic emissions by region

19. Seasonal variation • Remarkable convergence of optimizations using either GFED or POET prior emissions • Important changes in seasonality of biomass burning emissions • Increased S. African emissions in September, reduction in June when using GFED prior GFED prior POET big-region GFED grid-based GFED grid-based POET Vegetation fire emission updates Big-region setup Grid-based setup

20. prior big-region grid-based Biogenic emission updates Seasonal variation grid-based inversion • Global enhancement of biogenic VOC emissions (~ +15%) • Higher NMVOCs oxidation source by 10%

21. prior big-region grid-based Comparison to independent data (CMDL, FTIR, aircraft campaigns)

22. Comparison of our results to past inverse modelling studies

23. Conclusions and perspectives Feasibility of the multi-compound inversion Higher performance of grid-based inversion for reactive compounds Importance of the error correlation setup for better constraining thelarge number of emission parameters in the grid-based framework The posterior uncertainty analysis (using the DFP approximation) shows important error reductions for large-scale fluxes (e.g. Chinese anthropogenic emissions, African biomass burning), but small error reductions for individual grid cells Large increases of anthropogenic emissions over Far East Synergetic use of different datasets is required to better quantify emissions, in particular the CO production from the NMVOCs