1 / 31

Toward understanding the MJO through the MERRA data-assimilating model

Explore MJO using the MERRA data-assimilating model and its biases. Study moisture preconditioning and the MJO structure, with future work aimed at improving model understanding.

marcoscochran
Download Presentation

Toward understanding the MJO through the MERRA data-assimilating model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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 Growing consensus that moisture “preconditioning” is a key factor

  4. Outline • Previous GCM studies of moisture preconditioning & the MJO • Using novel MERRA data-assimilating model to study this and other MJO science issues • Structure of the MJO in MERRA Not new, but shows model biases “Analysis tendencies” provide a new aspect to the problem • 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) No non-entraining plumes Control

  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) No non-entraining plumes Control

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

  15. Learning from analysis tendencies (ΔZ/Δt)obs = (Żdyn + Żphys)+ Żana • If state is accurate (including flow & gradients), then (ΔZ/Δt)obs and advective terms Żdyn will be accurate • and thus Żana ≅ -(error in Żphys)

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

  17. Satellite OLR 15N-15S& MJO-filtered (contours) – used as reference lines below COARE Dec 1992- Mar 1993 Jan-Apr 1990 Filtered OLR courtesy G. Kiladis eastward wavenumbers 0-9, 30-96 days I averaged this over 15N-15S

  18. 15N-15S Jan 1990 GIBBS image archive

  19. MJO phase definition 5 0

  20. Objective MJO phase categories excluded WP IO PHASE

  21. 10 phases relative to Benedict and Randall (2007) 5 = filtered OLR min. 9 8 7 6 5 4 3 2 1 0 ‘back (W)’ ‘front (E)’ Benedict & Randall 2007

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

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

  24. Phase dependent mass flux deep Mc

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

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

  27. 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

  28. 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 Żana get smaller? If so, something scientific learned from this technical activity.

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

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

  31. 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?

More Related