1. F-gases emissions through inverse modelling Stefan Reimann Dominik Brunner Christoph A. Keller

2. -170 world-wide network for continuous halocarbons observations AGAGE and collaborative networks

3. Halocarbon measurements in Europe System for observation of halogenated greenhouse gases in Europe Mace Head Jungfraujoch GC-MS with preconcentration unit (ADS / MEDUSA) Ny Alesund Advanced Global Atmospheric Gases Experiment Monte Cimone

4. Montreal Protocol CFCs: Chlorofluorocarbons CFC-11CFC-12, CFC-13 CFC-113CFC-114CFC-115 Halones: (containing bromine) H-1301H-1211H-2402 Halogenated solvents and CH 3 Br CH 3 CCl 3 CCl 4 CH 3 Br HCFCs: Hydrochlorofluorocarbons HCFC-141bHCFC-124HCFC-22 HCFC-142b Kyoto Protocol HFCs: Hydrofluorocarbons HFC-32 HFC-23 HFC-125 HFC-134a HFC-152a HFC-227ea HFC-143a HFC-236fa HFC-365mfc HFC-245fa HFC-43-10mee PFCs: Perfluorocarbons & SF 6 CF 4 PFC-116 PFC-218 PFC-318 C 4 F 10 C 6 F 14 SF 6 Air Pollution/Environmental Technology Laboratory Others CH 3 ClCH 3 ICHCl 3 CH 2 Cl 2 SO 2 F 2 CH 2 Br 2 CHBrCl 2 CH 2 BrClCHBr 3 SF 5 CF 3 COS C 2 – C 7 hydrocarbonsHFC-1234ze(E) HFC-1234yf Measured substances

5. -170 HFC-134a used in car air conditioners

6. Examples of source allocation and quantification: HFC-23 Inversion model Measurements Emissions Modeling Combine 2-hour measurements with dispersion modeling results to estimate the amplitude / distribution of regional emissions

7. J = (z model - z obs ) T. σ z -1. (z model - z obs ) + (x post - x prior ) T. σ x -1. (x post - x prior ) Method 1: Bayesian inversion Minimization of cost function J (Stohl et al., ACP 2009): model – observation mismatchdeviation from a priori emissions z obs : observations z model : model predicted observations ( z model = M. x ) x prior : a priori emissions x post : optimized emissions (to be estimated) σ z / σ x : uncertainties of observations / a priori emissions M: Surface residence times ("footprint") of air masses computed with the ECMWF - FLEXPART Lagrangian Particle Dispersion model A priori emissions: - Reported data from UNFCCC Final setup: - Background concentration included in state vector x Uncertainty of x post : σ x,post = ( M T σ z -1 M + σ x -1 ) -1

8.  Concept: all variability is attributed to plant emissions Method 2: Time variable source analysis 1. Assume that observed variability only originates from (short) emission bursts from one of the HCFC-22 production plants 2. Calculate plant emissions for all events (and plants) individually 3. Fit obtained emissions to a log-normal distribution

9. European HFC-23 emissions (Keller at al., GRL, 2011) Year 2009July 2008 – July 2010 UNFCCCA prioriBayesian inversionPoint source analysis BLX1313 (0-29)36 (31-41)41 (26-46) FRA1515 (0-30)27 (22-34)33 (31-58) GER22 – 8652 (31-73)40 (32-48)63 (51-84) IT2.62.6 (0-18)26 (23-29)56 (41-74) UK5.55.5 (0-21)12 (10-14)21 (13-29)

10. x k = (x e k, x b k, x o k ) State vector: emissions station backgrounds ‘other’ Observations: y k 3. Kalman filter: Application to emission estimation

11. Observation operator H k : y k = H k x k = F k x e k + x b k footprint x emissions xbkxbk F k x e k FkFk background ykyk 3. Kalman filter: Application to emission estimation

12. 1 1 xexe F JFJ F MHD xbxb y k = H k x k y 0 0 = 3. Kalman filter: Application to emission estimation Advantage of Kalman filter method: Sequential: better handling of large inversion problems

13. Results for real observations Measured HFC-125 (black) versus simulated (red). Blue lines are the smooth backgrounds estimated by the filter. Jungfraujoch Mace Head 3. Kalman filter: Application to emission estimation

14. Results for real observations 3. Kalman filter: Application to emission estimation Brunner et al., submitted to ACP, 2011

15. Results for real observations 3. Kalman filter: Application to emission estimation Brunner et al., submitted to ACP, 2011

16. Summary and outlook  Continuous ground-based measurements have been exploited for real- world emission verification for F-gases  Different methods have been developed for combining meteorological transport models with measurements  Emission inventories are used as a-priori information  Earth observation EO products have not been used until now, no satellite observations for F-gases in the troposphere.  Potential future usage of EO/GMES products:  High-resolution inventories (spatial/temporal)…and faster!  CO measurements from satellites for source allocation  For using tracer-tracer ratios (F-gases vs. CO)  For providing missing a-priori information  Direct measurements of F-gases from satellites in the future?

17. Thank you for your attention