1. Toward understanding the MJO through the MERRA data-assimilating model Brian Mapes, U. Miami Stefan Tulich, CIRES Julio Bacmeister, GSFC and

2. 37 years of studying the MJO: Progress in description, but still no widely accepted theory Madden and Julian 1972 Benedict and Randall 2007

3. 37 years of studying the MJO: Progress in description, but still no widely accepted theory Madden and Julian 1972 Benedict and Randall 2007

4. Outline 1. Previous GCM studies of moisture preconditioning & the MJO 2.Using novel MERRA data-assimilating model to study this and other MJO science issues 3.Structure of the MJO in MERRA Not new, but shows model biases “Analysis tendencies” provide a new aspect to the problem 4.Future work: Model improvement as a path towards understanding

5. One of the first GCM moisture preconditioning experiments Tokioka et al. (1988): The equatorial 30-60 oscillation and the Arakawa-Schubert cumulus parameterization (J. Meteor. Soc. Japan) Control No non-entraining plumes

6. One of the first GCM moisture preconditioning experiments Tokioka et al. (1988): The equatorial 30-60 oscillation and the Arakawa-Schubert cumulus parameterization (J. Meteor. Soc. Japan) Control No non-entraining plumes

7. This modification also improves the MJO in the CAM 3.1 Maloney (2009)

8. This modification also improves the MJO in the CAM 3.1 Maloney (2009)

9. Still the model is not perfect

10. Even worse when looking at rainfall variance Maloney (2009)

11. Improvements are also model dependent Lee et al. (2009; in press)

12. How do we proceed further? Standard approach: Tinker with the model physics, run long time integration, diagnose model performance/feedbacks, repeat –Drawback: Time-consuming, tedious, feedbacks may impact other aspects of the simulation in unintended ways Our alternative: Assimilation-based science to study the MJO in global models (illustration of concept here)

13. MERRA Modern Era Reanalysis for Research and Applications (GEOS-5 based) NASA’s new atm. reanalysis, 1979-present Still running (3 streams), ~90% available Attractive features: - nowOpenDAP access (you needn’t download) - many budget terms, not just state variables - “analysis tendencies” available

14. time analyzed variable Z at discrete times free model solution: Ż ana = 0 (biased, unsynchronized, may lack oscillation altogether) initialized free model ΔZ/Δt = Ż model + Ż ana ΔZ/Δt = (Ż dyn + Ż phys ) + Ż ana use piecewise constant Ż ana (t) to make above equations exactly true in each time interval* Modeling system integrates: *through clever predictor-corrector time integration

15. time (aside) (analyses generated using same model are already biased toward model’s attractor...)

17. Learning from analysis tendencies (ΔZ/Δt) obs = (Ż dyn + Ż phys ) + Ż ana If state is accurate (including flow & gradients), then (ΔZ/Δt) obs and advective terms Ż dyn will be accurate and thus Ż ana ≅ -(error in Ż phys )

18. Choosing MJO cases good (COARE) MJO amplitude index MERRA data available when I started MERRA stream 2 best avail MERRA stream 3

19. Satellite OLR 15N-15S & MJO-filtered (contours) – used as reference lines below Filtered OLR courtesy G. Kiladis eastward wavenumbers 0-9, 30-96 days I averaged this over 15N-15S

20. 15N-15S GIBBS image archive

21. MJO phase definition 0 5

22. excluded IO WP Objective MJO phase categories PHASE

23. 10 phases relative to Benedict and Randall (2007) 9 8 7 6 5 4 3 2 1 0 ‘back (W)’ ‘front (E)’ 5 = filtered OLR min. Benedict & Randall 2007

24. MERRA rainrate compared to SSMI (SSMI over water only) MERRA SSMI 0 x 10 -4 mm/s too rainy phase 1-2

25. MERRA’s rain: convective: anvil: large-scale cloud: premature rain in phase 2 is mainly convective

26. deep Mc Phase dependent mass flux

27. 9 8 7 6 5 4 3 2 1 0 ‘back’ (W) ‘front (E)’ 5 = filtered OLR min. Model seems to be choking on the shallow-to-deep transition (even with Tokioka modification) Impact? Look at analysis tendencies

28. Phase dependent part of qv analysis tendency 1990 1992-3

29. Blame the convection scheme! seems to act too deep too soon in the early stages of the MJO. Analysis qv tendency has to compensate with moistening

30. Future work: Improving the model as path towards understanding Convection parameterization seems to be too insensitive to low- and mid-level moisture (even with Tokioka modification) Question: can we somehow further tighten/adjust the Tokioka limiter to reduce model errors? Strategy: perform short assimilation runs; does Ż ana get smaller? If so, something scientific learned from this technical activity.

31. Future work: Use analysis tendencies to develop a better forecast tool? Consider MJO index of Wheeler and Hendon (2004):

32. Future work: Use analysis tendencies to develop a better forecast tool? Idea: First, composite model analysis tendencies in this phase space

33. Future work: Use analysis tendencies to develop a better forecast tool? Idea: First, composite model analysis tendencies in this phase space Next, perform multi- day forecasts with these composite tendencies added during runtime. Forecast improved?