1. Regional Inversion of continuous atmospheric CO 2 measurements A first attempt ! P., P., P., P., and P. Philippe Peylin, Peter Rayner, Philippe Bousquet, Philippe Ciais, Philippe Heinrich, F. hourdin

2. Outline Measurements over Europes Requirements for regional inversions Time resolution in an inversion ? LMDz transport model : - direct approach - retro-plume approach European set up : primarily results

3. The European observing system : AEROCARB database : http://www.aerocarb.cnrs-gif.fr/database.html Flasks In-situ Aircraft Aircraft (project)

4. Aircraft measurements - Orleans, France Free troposphere CO2 concentrations for all flights PBL

5. Continuous measurements At Mace Head How to use such information ?

6. How to assimilate continental sites ? Transport models are to be improved : - Higher resolution in time and space - Parameterization of PBL Mesoscale models : boundary problems ! Nested models : computing time ! Global with zoom : LMDz model Data selection in models : - position in space and time properly represented Prior land fluxes should be improved : - Fossil fuel - Diurnal cycle of biospheric fluxes Inverse procedure need to be updated : - Spatial resolution of fluxes : pixel ? - Time resolution : identical for fluxes / obs ?

7. Monthly mean 24 hr Monthly mean Flask timing Concentrations (ppm) Diurnal rectification effect : Selection of model output according to flask data timing (TM3)

8. [ ppm ] CO 2 - fossils Spatial position of Schauinsland station in mesoscale model REMO REMO 30m REMO 130m Observations DAYS (Chevillard, 2001) Data selection

9. A 2GtC/yr Sink over Europe, consistent with all Observations (kaminsky et al.) Spatial resolution of fluxes Few large regionsAll pixels ? Compromise needed OR all pixel + correlations Aggregation error Estimation error

10. Time discretisation Estimation of flux X ={xi, i=1,n} with a temporal distribution xi Observations Y = {yi, i =1, m} with same errors R None Bayesien Question : Should we average data at the time-resolution of the fluxes we solve for ? - Annual flux : Monthly data ? - Monthly flux : Daily data ? - Satellite : assimilate individual shot ? Little derivation : with error

11. Same weight for all data Data weight proportional to Hi Averaged data Y All data {yi, i =1, m} Hi = Transport o Flux-distribution {xi} High values of Hi correspond to “ low mixing by transport ” and / or “Peak of the flux time-distribution”

12. Error estimates We can show Uncertainties are always smaller with all individual data

13. month Concentration (ppmv) Temperate N. Amer. (3rd yr) Annual response function sampled each month Time pattern : total respiration (SiB2) Pulse of 1GtC / year 5 10 15 20 25

14. Days Concentration (ppmv) Monthly response function sampled each day Time pattern : flat Pulse of 1GtC / month Western Europe (July pulse) 0 10 20 30 40 -10

15. Summary of time discretisation ? Need some caution when using data at higher time resolution than that of the fluxes Individual terms Hi need to be compared ! Annual flux / Monthly data is not adapted Monthly flux / daily data ?? Solution is probably : solve fluxes at the resolution of the data + time-correlation (equivalent to the “spatial aggregation problem”)

16. LMDz transport model GCM from the LMD laboratory (Paris) Nudged with ECMWF Global with possible zoom - 0.5 x 0.5 degree in zoom - 4 x 4 degree at the lowest 19 vertical levels Backward mode possible grid zoomed over Europe

17. Inverse Transport : “retro-plume” approach Frederic Hourdin Direct approach : J: measure = mean CO2 per kg of air C: concentration of CO2 (kg / kg air)  : density of air  : distribution of the measure  : spatial and temporal domain C is govern by : With - surface flux :  = - C = Ci at t0

18. Inverse approach : Rewrite measure using the advection/diffusion equation like in lagrange multipliers c*: distribution to be determined integration by parts c* that satisfy 0 = Using : We obtain : Contribution from Initial conditions Flux contribution

19. Retro-plume approach Simply run transport backward in time (need to save all mass flux in a forward run) C* is the sensitivity to both surface fluxes / initial conditions in ppmv / kgC Direct mode / Inverse mode Source  variable Sample c* according to  distribution Emission according To selected data Selection of data J

20. Example of retro-plumes Day 1 Day 2 Day 4 Day 8 Schauinsland station in November 1998

21. Mace Head retro-plume Day 4 longitude Day 4

22. European Inversion, using continuous data, with high temporal/spatial flux resolution, for a short period (campaign) Only a first attempt !

23. methodological experiment Data : 2 sites Mace Head / Schauinsland Daily average values Period : Campaign type experiment one month : November 1998 Regions : - Pixels for Western Europe - Rest of the world with large regions (18) Time resolution of fluxes : - Daily for pixels - monthly for the other large regions Priors Pixels Large regions Flux: Bousquet et al. Bousquet et al. Error: 100 % of mean 1GtC + correlations (0.8) Special treatment for initial conditions

24. Map of regions + 2 sites Mace HeadSchauinsland November

25. Treatment of initial conditions Add additional unknowns corresponding to initial conditions Reduce the size of initial condition problem by projecting on main directions in the data space using SVD. C init = H x Pprior (60 x 50000) (50000) (SVD decomposition) U. W. V T x Pprior H’ x P’ (60 x 60) (60) Solve for P’ with : - prior value from global simulation (using bousquet et al. fluxes) - error corresponding to 3 ppm

26. Fit to the data

27. Model components : MHD Europe Pixels Other big regions Initial conditions days posterior prior ppmv -4 8 8 8

28. Model components : SCH Initial conditions days posterior prior ppmv -4 8 8 8 Europe Pixels Other big regions

29. Summary Regional CO 2 flux estimates require complete and permanent monitoring of CO2 Transport models have to be improved over the continents ! Selection of the data (time and space) is crucial Inverse scheme : - High spatial resolution with correlations - Temporal resolution of fluxes adapted to the resolution of the data Initial conditions seem to be important for campaign based inversions (10 day !) => Need for other constraints : O2/N2, C14, C13, O18, Radon, …

30. AEROCARB project: European Inversion - 5 models - European domain for regional models - Boundary from global model (TM3) - Use continuous data over Euro-Siberia (12 sites) - Account for “diurnal rectifier” / selection (during aircraft profiles) Monthly inversion first Daily inversion in a second step

32. Days Concentration (ppmv) Monthly response function sampled each day Time pattern : flat Pulse of 1GtC / month Western Europe (January pulse)

33. month Concentration (ppmv) Annual response function sampled each month Time pattern : total respiration (SiB2) Pulse of 1GtC / year Boreal N. Amer. (3rd yr)

35. Conclusions  Regional CO 2 flux estimates require a complete and permanent monitoring of “regional” air using atmospheric data (flask & continuous sites, towers, airplane, …) ---> Monitoring of air is essential to estimate European carbon budget.  Currently data limited inversions are becoming model limited inversion as we intend to assimilate continental measurements ----> Transport models have to be improved over the continents.  There is only one carbon cycle. There is no reason to assimilate only atmospheric data.assimilate ----> A global carbon assimilation system should be developed Addressing these issues should allow to provide Kyoto relevant estimates of European carbon budget within the next decade.