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Understanding the MJO through the MERRA data assimilating model system

and. Understanding the MJO through the MERRA data assimilating model system. Brian Mapes RSMAS, Univ. of Miami and Julio Bacmeister NASA GSFC. Outline. What is the MJO? Why does it require assimilation-based science?

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Understanding the MJO through the MERRA data assimilating model system

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  1. and Understanding the MJO through the MERRA data assimilating model system Brian Mapes RSMAS, Univ. of Miami and Julio Bacmeister NASA GSFC

  2. Outline • What is the MJO? • Why does it require assimilation-based science? • Robust MJO features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions • Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings • Testing the hypotheses & improving the model

  3. The MJO • Madden and Julian 1972

  4. Eastward moving, 40-50 day period Wheeler and Kiladis 1999 Distinct from c-c Kelvin wave MJO in OLR data

  5. Outline • Why does it require assimilation-based science? • Low frequency means small time rate of change. • many processes (tendencies) & small imbalances important • Slow speed • even weak tropical background flow may be important • Large scale yet longitudinal confinement • need realistic geography & spatially varying basic flow • MJO power does not lie along linear wave theory dispersion lines (like the c-c Kelvin wave etc.) • no reason to believe it is tractable to toy modeling • free-running GCMs don’t simulate it realistically • true understanding implies explaining this fact too

  6. Models have trouble with this stuffconvection & cloud problems Obs Dominant modes: MJO, Kelvin, ER, WIG Dispersion curves correspond to equivalent depth 8, 12, 25, 50, 90m. Larger depth –faster phase speed. All modes: 25 m. Lin et al. 2005

  7. Outline • What is the MJO? • Why does it require assimilation-based science? • Pick and study MJOs from 2 active seasons, 2 longitude sectors, two MERRA versions • Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings • Testing hypotheses & improving a model

  8. Choosing MJO cases Filtered OLR variance

  9. Meanwhile (when I started project)

  10. Choosing a case in MERRA streams best avail Next (COARE)

  11. Satellite OLR 15N-15S, & filtered COARE Dec 1992- Mar 1993 Jan-Apr 1990

  12. MERRA data used • Scout runs (~2 degree) – for convenience • so actually, all other cases are available. • trying not to make ‘scout’ an object of research though • Real MERRA (1/2 x 2/3 degree) • will the parameterized-resolved rain partition differ? • will heating profiles differ in a corresponding way? • “convective vs. stratiform”

  13. Outline • What is the MJO? • What is assimilation-based science? • Robust features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions • Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings • Testing the hypotheses & improving the model

  14. Incremental Analysis Update (IAU) i cannot understand this diagram

  15. 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 integrations

  16. is nudging a bad word (or boring)? • not if we STUDY the analysis tendencies • (ΔZ/Δt)obs = (Żdyn + Żphys)+ Żana • If state is accurate (flow & gradients), then Żdyn will be accurate and thus Żana ≅ -(error in Żphys)

  17. Outline • What is the MJO? • Why does it require assimilation-based science? • Robust features from two active seasons, two longitude belts, two MERRA versions • Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings • Testing the hypotheses & improving the model

  18. Satellite observed OLR 1990 Jan-Apr 15NS 10NS

  19. MERRA analysis model’s OLR

  20. 15NS u850 NCEP 10NS

  21. 15NS u850 MERRA 10NS

  22. MJO phase definition 9 5 0 0

  23. 1990 MJO phase in time-lon space WP IO 9 0 5

  24. 1992-3 MJO phase in time-lon space WP IO COARE Dec 1992- Mar 1993 9 0 5

  25. Line checks: 1990 OLR vs. satellite IO WP MERRA biased high 10-20W in active phase misses ~10W IO-WP difference

  26. Rainrate compared to SSMI (SSMI is over water only) too rainy here x 10-4 mm/s MERRA SSMI 0

  27. PW: MERRA has humid bias, too little IO-WP difference WP 1990 SSMI IO 1990 MERRA IO too humid especially here

  28. LWP: MERRA too low by half

  29. Total rain:convective:anvil:large-scale cloud: 1992-3

  30. 1990 1992-3 COARE -5 -5 -50 -50

  31. 1990 T 1992-3 COARE 250 850

  32. 1990 RH 1992-3 COARE 60 60 <40 <40 <40 <40 60 60

  33. 1990 1992-3 COARE 0.5 0.45

  34. 1992-3 COAREperiod in MERRA COARE OSA qv lag regression (Mapes et. al. 2006 DAO) ?

  35. 1990 qcond 1992-3

  36. MERRA “Cloud fraction” Cloudsat echo coverage 50% 25% from Emily Riley MS thesis -15% +15% -6% +7%

  37. MERRA “Cloud fraction” Cloudsat echo coverage 50% 25% from Emily Riley MS thesis -15% +15% -6% +7%

  38. Outline • What is the MJO? • Why does it require assimilation-based science? • Robust features from two active seasons, two longitude belts, two MERRA versions • Analysis tendency based hypotheses about MJO mechanisms, and model shortcomings • Testing the hypotheses & improving the model

  39. MERRA has a Dry bias at 850, humid bias at 600 [qv] DJF 1990 minus JRA – typical of MERRA vs. all others

  40. Analysis tendencies oppose humidity bias(with a little MJO dependence too) DJFM 1992-3 COARE 1990 JFMA MJOs zonal mean qv bias • Żana ≅ -(error in Żphys)

  41. Bias stripes correspond to Moist Phys tend. + + - - + • Żana ≅ -(error in Żphys) -

  42. 1990 1992-3 COARE analysis Qv tend.

  43. Benedict and Randall schematic

  44. deep Mc

  45. Hypothesis: model convection scheme acts too deep too soon in the early stages of the MJO. • (Hypothesis for improving it is another seminar)

  46. Hypothesis: model convection scheme acts too deep too soon in the early stages of the MJO. • (Hypothesis for improving it is another seminar) • Might be entangled with the mean state biases. • “Improving” the model must consider both

  47. MERRA Temperature biases (DJF) • 2 different years, 3 different reference reanalyses -JRA -NCEP2 -ERA ----------------------cool < 200 mb---------------------- -----------------------warm 550----------------------- ------------------------cool 700-----------------------------

  48. 1990 1992-3 Again: analysis tendencies fight the bias ----------------------cool < 200 mb---------------------- -----------------------warm 550----------------------- ------------------------cool 700-----------------------------

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