1. Mesoscale inversions: from continental to local scales T. Lauvaux, C. Aulagnier, L. Rivier, P. Bousquet, P. Rayner, and others

2. Part 1: Comparison of transport models from the global to the continental scales LMDz (3° by 2°)  TM5 (1° by 1°)  Chimère-MM5 (50km by 50km) Part 2: Potential of a high resolution inversion in the South West of France Non-hydrostatic model Meso-NH (8km by 8km)

3. CO2 Balance in Europe with a mesoscale model : CHIMERE WHO: L. Rivier, C. Aulagnier, P. Rayner, M. Ramonet, P. Ciais, R. Vautard WHAT for: What is the added value of increased resolution ? From 100km grids down to a few kms… Improved models for improved inversions at the regional scale ?

4. Using of CHIMERE MM5 CTM = CHIMERE LMDZ Biospheric Fluxes +Inventaires (Oce- anic/Fossil Fluxes) Surface Fluxes TRANSCOM /FOSEXP Boundary Conditions Meteo Forcing // CO2 concentration Fossil98 Taka02 SiB_hr

5. Capacity of CHIMERE Model CHIMERE = French CTM developed by LMD/INERIS Multi-species et multi-scale CTM (Horizontal Resolution from 100km to 1km) Used for Ozone Daily Forecast in France (www.prevair.org) European Domain CONT3 used here = Resolution : 0.5 x 0.5 degrees (50Km) 20 vertical layers (1000 to 500hPa) Computation = 10 minutes CPU for 5 days

6. Sites of Comparison Mesure / Model ORL

7. Validation of BL Height parameter CHIMERE well capture of BL Height parameter, ORL CHIMERE BL Height vs Mesures, for 68 points in Europe, night & day

8. Schauinsland station Bio signal Fossil signal CO2 signal Well capture of seasonal cycle… FOSEXP, hourly … With CASA or ORCHIDEE, not with SIB which overestimate the summer 2003 (+) anomaly… … While in the same time Fos98, which is a dynamic tracer, shows night overmixing, not EDGAR_hr

9. Well capture of synoptic winter events…... Driven by meteo Cabauw200 station

10. FOSEXP, hourly Well capture of summer signal…... With EDGAR/IER, not with Fos98, too highly variable... Driven by vegetation… Heidelberg station

11. FOSEXP, hourly Well capture of mean summer diurnal cycle for plain sites… … With LMDZ-SIB-Fos98 … Or with CHIMERE-Orchidee-Edgar Heidelberg station Hungaria115 station

12. Mars mean diurnal fluxes & CO2 cycle… Orchidee begins to photo- synthetise too earlier, SIB & CASA OK. Hungaria115 station

13. Sept mean diurnal fluxes & CO2 cycle… SiB & CASA stops to photo-synthetise too lastly, Orchidee OK. Hungaria115 station

14. Conclusions CHIMERE and TM5 forced with TRANSCOM tracers have a similar behaviour and a better reactivity than LMDZ. CHIMERE well capture of BL Height « key » parameter CHIMERE forced with « highest spatio-temporal resolution tracers » like EDGAR hourly /ORCHIDEE 0.35deg is able to capture satisfiyingly CO2 seasonal cycle, synoptic signal, and mean diurnal cycle, in an improving way compared to global models (which seem to schow less difference between tracers, Cf. P. Peylin’s work …) … So CHIMERE seems to be better adapted than global models for inversion at continentales scales.

15. Toward a mesoscale flux inversion at high resolution in the South West of France T.Lauvaux, C. Sarrat, F. Chevallier, P. Ciais, M. Uliasz, A. S. Denning, P. Rayner

16. Observations + errors Aircrafts towers Sources and Sinks a priori + errors Forward Transport (meso-NH, Lafore et al., 98) Retro transport (surface and boundaries) Variationnal inversion (Chevallier et al., 2004) Large scale [CO2] Boundary conditions (LMDZ) Information on error coherence from eddy-flux data Particle Dispersion Model (LPDM, Uliasz, 94) Inversion of sources and sinks of CO2

17. CarboEurope Regional Experiment Network Regional budget of CO2 in the South West of France from ground based observations and aircraft data observation sites: Flux and CO2 concentration Piper Aztec Flux tower Concentration tower

18. Mesoscale atmospheric modelling Meso-NH coupled with ISBA-A-gs: dynamical fields corresponding to wind and turbulence => Prognostic parameters: u, v, w, Tp, TKE => Diagnostic parameters: u*, LMO, Boundary layer top, … Resolution of 8km in a domain of about 700*700 km2 (South West of France) => Increased to 2km during the flight periods (two-way grid nesting) Coupling with a vegetation scheme ISBA-Ag-s, parameterised with a 250m resolution vegetation cover map: Transport of atmospheric CO2 based on ISBA- A-gs fluxes (12 patches) Transport and carbon fluxes from the 23rd to the 27th of May 2005 Surface scheme (Surfex) coupled on-line with hydrology and vegetation scheme => Momentum, heat, water, CO2

19. Direct modelling: Aircraft data comparison Dimona Piper Aztec Sarrat et al., 2006, JGR  Good correlation ( < 3ppm )  10ppm gradient between types  Low decrease from West to East

20. Lagrangian Particle Dispersion Model (Uliasz, 94) Off-line coupling of mesoNH dynamical fields with LPDM: determination of diagnostic physical parameters Particles backward in time from the receptors to the sources Particle releasing frequency, number, particle lost (sedimentation,...), time dependant dynamics Integration of instrumented tower data and aircraft data  4 vertical boundaries (N, S, E, W) with 2 vertical layers (BL, FT)  Surface grid (8km resolution) Particle distribution from the 2 towers (Biscarosse and Marmande) released between 6:30am and 7:30 am the 27th of May 2005

21. Influence function: surface and boundaries Surface grid: 90*90 grid cells (8km resolution) 4 lateral boundaries: 2 vertical levels with 5 horizontal grid cells (LMDz resolution) Low level = boundary layer High level = free troposphere Free troposphere Boundary layer Particles backward = multiple surface contacts => One single boundary contribution per particle State vector dimension at each hour = 90*90 + 4*5*2 (Surface) (boundaries)

22. Meteorological context during the 27th of may 27th may - 6pm 27th may – 2am 27th may - 2pm Early growth season for summer crops  Mainly influenced by the distant fluxes?

23. Tower vs aircraft for surface flux influence Flight 1 Flight 2 Marmande tower (normalised)

24. Vertical profiles of aircraft particle clouds Particles sheared by a main South Eastern wind closed to the ground and a western wind at higher altitudes (called Autan wind) 10 hours 20 hours 25 hours35 hours

25. Error reduction on the 4-day inversion 2 towers Biscarosse (20m) Marmande (70m) 1 aircraft flight (transect Brodeaux-Toulouse) 2 towers and 3 flights CERES domain  Error reduction > 30% for half of the domain  No spatial correlation on the prior flux error covariance

26. Boundary contribution Error reduction at the boundaries for the tower-only inversion around 5% => Initial offset concentration or extra flux unknowns Error reduction >90% on one or two grid cells at the boundaries with the flights Uncertainty in the prior error covariance for the boundaries has no impact on the error reduction at the surface

27. Footprint of a fictive tall tower Biscarosse tower (20m high) Fictive Biscarosse tower (300m high)

28. Optimizing flight trajectory ? 12 virtual flights based on a long transect over the domain, with constant altitudes from 100m to 2500m high => Can we optimize future campaigns to get maximum of informations from the aircraft data?

29. Time integration and space correlation Hourly distribution of the particles originating from the lateral boundaries Limited time window for the inversion 1 12 24 36 hours Ecosystemsdistance<50kmdistance>50km Homogeneous0.90.1 to 0.3 Heterogeneous0.3 to 0.50.1 Correlation coefficient from the linear regression

30. Conclusions and perspectives Significant error reduction on the domain to start the real-data inversion Uncertainties: Transport error by using the variability from an ensemble of simulations (coupling files from a global model run with perturbed initial files) Spatial correlation estimated from a long-term simulation of ISBA (5 weeks…) and the 11 flux towers of the campaign

31. Thanks for your attention Etna volcano’s eruption, Sicily