1. Potential of 14 CO 2 to constrain emissions at country scale Y. Wang, G. Broquet, P. Ciais, F. Vogel, F. Chevallier, N. Kadygrov, L. Wu, R. Wang, S. Tao LSCE – CEA/UVSQ, Gif-sur-Yvette, France Laboratory for Earth Surface Processes, Peking University, China Paris Workshop, Sino-French institute for earth system sciences (SOFIE) Oct. 13-14 2014

2. Different CO2 concentration Atmospheric transport model Use atmospheric CO 2 measurements to constrain emissions « verified » emissions + Observations Inversion ratio between IER and PKU maps

3. What is the best sampling strategy to verify emissions by atmospheric data ? Stations around the main emission areas : big cities Needs very good transport models at small scales Would require up-scaling to all cities Misses power plants, small cities Does not give a complete picture Emission plumes may not be captured if network is too sparse Signals get rapidly diluted by transport We need a tracer of emissions only OR Stations from dense regional networks, e.g. ICOS Shiga et al. 2014 Number of region-months can be detected by purely CO 2 measurements

4. Use of atmospheric 14 CO 2 measurements 14 CO 2 is a good tracer for FFCO 2 Not yet assimilated in an inversion system…… Levin et al.,2011 Fossil CO2 emissions in USA (Pacala et al., 2010) Uncertainties reduced down to 10% + : are stations proposed

5. Objectives Focus on impact of uncertainties for subgrids Monitor FFCO 2 emissions at large scales virtual ground based 14 CO 2 in-situ networks diagnostics: uncertainty reduction & misfit reduction Theoretic computation OSSE

6. Global configuration for the monitoring of FFCO 2 emissions at large scale 55 regions in total, focus on North America, Asia and Europe Control: monthly budgets over large regions during 1 year

7. Set-up for the monitoring of FFCO 2 emissions at large scale Observation vector an virtual “ICOS-like” ground-based 14 CO 2 in-situ network sampling of afternoon data: weekly / biweekly integrated 14 CO 2 measurements Assumptions fluxes from biosphere, ocean, soil and nuclear power plant ignored uncertainties of ∆FFCO 2 = 1 ppm Observation operator prior fluxes: global-IER at 1˚  1˚ / hourly resolution Global atmospheric transport model LMDZ: at 3.75˚  2.5˚ / 3 hourly resolution “ICOS-like” networkIER emissions (in kg C/m 2 /hour)

8. Prior uncertainty in anthropogenic emissions Two configurations: use of general assumptions (B th ) : 10% uncertainty at national scale 2 months temporal correlations use of statistics of the differences between PKU and IER inventories (B pract ) RMS of monthly (IER-PKU)/IER at regional scale uncertainties of monthly budgets at regional scale

9. Estimation of the observation errors Observation error: representation error: aggregation error 0.5˚/1h to 3˚/3h conc in 0.5˚ − conc in 3˚ = repr error (in ppmv) (in ppmv) (in ppmv) flux in 0.5˚ − flux in 3˚ agg error (pixel) (in kg C/m 2 /hour) (in kg C/m 2 /hour) (in ppmv) transport

10. monthly budget  PKU pattern − monthly budget  IER pattern agg error (large region) (in kg C/m 2 /hour) (in kg C/m 2 /hour) (in ppmv) Observation error: aggregation error 3˚/3h to region-month Estimation of the observation errors transport

11. Estimate of representation and aggregation errors RMS of 2-week mean concentrations between 1 month simulations Repr error (in ppmv) Agg error (in ppmv) (0.5˚/1h to 3˚/3h) Agg error (in ppmv) (0.5˚/1h to 3˚/3h) Winter Summer UrbanRuralUrbanRural Representation error 2.60.71.00.3 Aggregation error (0.5˚/1h to 3˚/3h) 0.2 0.1 Aggregation error (3˚/3h to control region-month) 0.1 Typical value for the different errors when considering biweekly sampling (in ppmv)

12. Results for Europe Results with a sampling at each site (quadratic means of the monthly results over 1 year) Theoretical uncertainty reduction using B th Theoretical uncertainty reduction using B pract Practical misfit reduction during the OSSE Uncertainty reduction (UR) of FR and GER > 40% Posterior uncertainty ~5% UR for other regions <20% Misfit reduction comparable to UR

13. Sensitivity of observation network Even with one station in each grid, few regions have a uncertainty reduction more than 50% Most regions have a uncertainty reduction <20% 5 sites, biweekly 38 sites, biweekly 121 sites, weekly Theoretical uncertainty reductions (quadratic means of the monthly results over 1 year)

14. Potential to monitor emission trends Region Post unc. of ann. budget 5-year trend10-year trend15-year trend20-year trend Ireland21%0.6%±6.6%2.3%±2.5%2.5%±1.4%2.3%±1.0% U.K.2.4%-1.9%±0.7%-0.9%±0.3%-0.3%±0.1%-0.2%±0.1% Benelux8.5%0.3%±2.7%0.7±1.0%0.6%±0.5%0.3%±0.3% Swiss/Austria3.8%-1.8%±1.2%-0.1%±0.4%-0.2%±0.2%0.4%±0.1% Italy6.0%-0.8%±1.9%0.4%±0.7%1.2%±0.4%1.0%±0.2% Balkan53%-5.2%±15%-0.3%±5%1.0%±3%1.1%±2% Germany3.9%-2.6%±1.2%-1.3%±0.4%-0.8%±0.2%-0.5%±0.1% Denmark6.6%1.0%±2.2%0.2%±0.8%-0.7%±0.4%-0.8%±0.3% France6.5%-1.1%±2.1%0.4%±0.7%0.7%±0.4%0.5%±0.3% Iberia8.7%1.2%±2.8%2.7%±1.1%3.7%±0.7%3.2%±0.4% North EU6.3%0.5%±2.0%0.8%±0.7%0.6%±0.4%0.2%±0.2% East EU5.1%-7.3%±1.4%-4.4%±0.4%-2.6%±0.2%-1.4%±0.2% Southeast EU14%-6.0%±3.9%-2.9%±1.3%-1.7%±0.7%-1.0%±0.4%

15. Emission change of France by inventories

17. 41 sites, biweekly 119 sites, weekly Results for North America Theoretical uncertainty reductions (quadratic means of the monthly results over 1 year) With 119 sites / weekly sampling, 3 regions UR>70% Most regions have a uncertainty reduction <30%

18. Summary 1.For few regions, the uncertainty reduction can reach as high as 50% and posterior uncertainties for annual budgets could be reduced to <5% To be challenged by more complex and less optimistic assumptions, e.g. other 14 C fluxes 2.For most regions, the uncertainty reductions will not exceed 20% Need for models at a high resolution to increase the capability of monitoring fossil fuel emissions Errors between plants and atmosphere Bozhinova et al., 2014 Nuclear ∆ 14 C gradients Graven and Gruber, 2011 CHIMERE (0.5°/hourly) LMDZ(3.75°×2.5°/3-hourly) Integrated biweekly afternoon FFCO 2 concentration

19. Thank you!