2. Interpretation of station data with an adjoint Model Maarten Krol (IMAU) Peter Bergamaschi (ISPRA) Jan Fokke Meierink, Henk Eskes (KNMI) Sander Houweling (SRON/IMAU)

3. What is TM5? Global model with zoom option Two-way nesting Mass-conserving / Positive Atmospheric chemistry Applications Off-line ECMWF Flexible geometry

4. What is TM5? 6x4 3x2 1x1

5. Why an Adjoint TM5? Concentrations on a station depend on emissions Interesting quantity: dM(x,t)/dE(I,J,t’) –How does a ‘station’ concentration at t changes as a function of emissions in gridbox (I,J) at time t’? –Inverse problem: from measurements M (x,t) --> E(I,J,t’)

6. Adjoint TM5 dM(x, t)/dE(I,J) (constant emissions) can be calculated with the adjoint in one simulation M0(x, t) = f(E 0 (I,J)) M(x, t) = M 0 +dM(t)/dE(I,J)*(E(I,J)-E 0 (I,J)) Only if the system is linear!

7. Adjoint TM5 (4DVAR)

8. Finokalia MINOS 2001 measurements Dirty Clean

9. Finokalia Integrations from M(t) back to july, 15. Forcing at station rm(I,J,1) = rm(I,J,1) + f(t,t+dt) (during averaging period) Adjoint chemistry Adjoint emissions give analytically: dM(t)/dE(I,J)

12. Clean

13. Dirty

14. Clean

15. Dirty

16. Clean

17. Dirty

18. Clean

19. Dirty

20. Prior MCF emission distribution

21. Procedure Minimise With

22. Posterior MCF emissions: Negatives Emissions over sea BETTER CONSTRAIN THE PROBLEM

27. Conclusions Emissions seem to come from regions around the black sea! Results sensitive to prior information Not surprising: 8 observations 1300 unknowns Emissions required: 10-30 gG/year How to avoid negatives?

28. Next Steps (to be done) Prior Information –non-negative –full covariance matrix Full 4Dvar, starting with obtained solution as starting guess emissions Influence station sampling, BL scheme, …. All observations separately (Movie)