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

2. Outline 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Robust MJO features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions 4.Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings 5.Testing the hypotheses & improving the model

3. The MJO Madden and Julian 1972

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

5. Outline

6. Models have trouble with this stuff convection & 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 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Pick and study MJOs from 2 active seasons, 2 longitude sectors, two MERRA versions 4.Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings 5.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

13. 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”

14. Outline 1.What is the MJO? 2.What is assimilation-based science? 3.Robust features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions 4.Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings 5.Testing the hypotheses & improving the model

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

16. 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 integrations

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

18. time Obs vs. analyzed Z aside hypothesis: analyses lie toward free model’s bias

19. Outline 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Robust features from two active seasons, two longitude belts, two MERRA versions 4.Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings 5.Testing the hypotheses & improving the model

20. One possible worry If the bias is severe, so that ‘realistic’ states are too far off the model’s solution manifold, then Ż phys errors could start to be related nonlinearly to state variable errors. compromising interp. of averages and phase composites –hope not

21. every time step, build Ż ana = (Z obs - Z mod pred ) /  –Z obs is NWP analysis, interpolated to model timesteps really, is DART going to beat EC at raw-data assimilation? fine, try a few other analyses (MERRA, NCEP) if you like study Ż ana as above, and dependence on  –tiny  will keep model verrrrrry close to analysis including divergence/omega & sounding, despite convxn –moderate values just keep synoptic flow in phase –try different values for winds vs. thermo e.g. let model have its comfort sounding biases –get at worry of ‘far from manifold’ nonlinearities

22. Outline 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Robust features from two active seasons, two longitude belts, two MERRA versions 4.Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings 5.Testing the hypotheses & improving the model

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

24. MERRA analysis model’s OLR

25. 15NS u850 NCEP 10NS

26. 15NS u850 MERRA 10NS

27. Wheeler - Hendon RMM1-RMM2 view (Difficulties: WH removed a mean seasonal cycle, and the preceding 120- day mean at each time. I just removed the 120-day time means of my 4-month dataset, and offset RMM1 by 0.5 to match their figure more closely)

28. time-height sections: 60-110E, 130-180E

29. 60-110 130-180 Rather noisy - next let’s make a phase composite

30. MJO phase definition 0 9 0 5

31. 1990 MJO phase in time-lon space 0 9 5 IO WP

32. 1992-3 MJO phase in time-lon space 0 9 5 IO WP

33. COARE 1992-3

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

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

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

38. LWP: MERRA too low by half

39. 1990 wind speed vs. SSMI: good (assimilated) MERRA SSMI over water only

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

41. u200 and u850 1990 1992-3

42. 1990 1992-3 COARE -50 -5

43. tilt more obvious in div maybe

44. 1990 (not same color bars!) 1992-3

46. 1990 T 1992-3 COARE 850 250

47. IO WP 1990 1992-3

48. 1990 RH 1992-3 COARE 60 <40 60 <40 60 <40 60 <40

49. 1990 1992-3 COARE 0.45 0.5

50. 1992-3 COARE period in MERRA COARE OSA qv lag regression (Mapes et. al. 2006 DAO) ?

51. 1990 q cond 1992-3

52. CloudSat echo (2006-8 cases) thesis of Emily Riley – same MJO def. & techniques 25 +7% -6% < 7.5%

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

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

55. 1990 cloud 1992-3 55 60

56. Outline 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Robust features from two active seasons, two longitude belts, two MERRA versions 4.Analysis tendency based hypotheses about MJO mechanisms, and model shortcomings 5.Testing the hypotheses & improving the model

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

58. Analysis tendencies oppose humidity bias (with a little MJO dependence too) Ż ana ≅ -(error in Ż phys ) zonal mean qv bias 1990 JFMA MJOs DJFM 1992-3 COARE

59. Bias stripes correspond to Moist Phys tend. Ż ana ≅ -(error in Ż phys ) + - + - + -

60. Beyond the bias: phase dependence Moist physics Qv tendencies MINUS MEAN:

61. 1990 1992-3 COARE analysis Qv tend.

62. Benedict and Randall schematic

63. deep Mc

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

65. 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

66. MERRA Temperature biases (DJF) 2 different years, 3 different reference reanalyses -NCEP2 -ERA -JRA

67. 1990 1992-3 Again: analysis tendencies fight the bias

68. T budget: DYN-PHYS balance mostly MST sharp ‘shelf’ in moist heating profile may be bias source. Again the shallow to deep convection transition issue?

70. 1990 1992-3 zonal mean u biases wrt 3 other reanalyses easterly biases westerly analysis tendencies westerly biases easterly mean analysis tendencies

71. Outline 1.What is the MJO? 2.Why does it require assimilation-based science? 3.Robust features from two active seasons, two longitude belts, two MERRA versions 4.Analysis tendency based hypotheses about MJO mechanisms, and model shortcomings 5.Testing hypotheses / improving the model

72. closing the loop 1.Adjust model based on hypotheses –convection scheme formulations »after learning them (what i’m here for) 2.Re-run in assimilation mode –or replay »? advice ? 3.Remake diagrams and evaluate –mean AND variability »will interplay make results inscrutable? 4.Focus on improved aspects, declare victory. 5.Refine hyp., go to 1. Progress, if not victory...

73. Poor man’s version 1.Learn inside scoop on model convection –what i’m here for 2.Make diagrams for existing alt. model versions –starting with ½ deg regular MERRA e.g. does resolution matter, via conv-LS partitioning? 3.Publish speculative interpretations