1. DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign Climate Variability & Predictability University of Hawaii at Manoa

2. 3 THE ULTIMATE GOAL of this project: To improve the prediction skill of Madden-Julian Oscillation (MJO) in the national climate forecast model (NOAA/NCEP CFSv2)  ANALYZE the MJOs observed during DYNAMO period  REVIEW operational models’ forecasting of the DYNAMO MJOs  ASSESS the capability of CFSv2, GFS, and UH models in MJO forecasts  QUANTIFY the impacts of air-sea coupling on MJO forecasting  Experiment for cumulus parameterizations and SST uncertainty  Categorize MJO types: coupled and uncoupled Outline

3. Observed MJO events during DYNAMO period

4. SST and MJO-filtered OLR Anomalies in DYNAMO Period Oct-MJO Nov-MJO SST (shading); OLR (contours) IOP Five MJO events Thanks-giving TC during Nov. MJO Only two MJO events (Nov. & Mar.) with robust coherent positive SST anomalies leading the convection Air-sea ‘coupling strength’ varies with individual MJO events 4

5. Prediction of the observed MJO by operational models

6. Good forecasts of two successive MJO events IC: Oct_17IC: Nov_07 Courtesy of NCEP MJO Discussion Summary led by Jon Gottschalck et al. Bad forecasts of Sep. primary MJO event 5 IC: Sep_20IC: Sep_12

7. IC: Oct_03 IC: Oct_10 Maritime Continent Barrier Weak Intensity IC: Oct_24 IC: Nov_27 IC: Mar_05 IC: Mar_12 9 Slow Propagation

8. Prediction by GFS, CFSv2 and UH models

9. Nov. MJO initiation in CFSv2&UH models IC: Nov_04 6 Both CFSv2 and UH models capture the development of November MJO. Propagation in UH model quite realistic. Propagation in CFSv2 too slow. Shadings: Observation Contours: Forecast Red arrows: Observed minimum values. Green arrows: Forecast minimum values. OLR anomalies

10. Extended-range forecasts of Nov. MJO initiation 7

11. Forecasts Initialized on Nov. 18, 2011 OBS CFSv2 UH 13 Propagation in UH model quite realistic. Propagation in CFSv2 too slow. Shadings: Observation Contours: Forecast OLR anomalies

12. Acknowledgment: Observational surface flux data from Revelle during DYNAMO period are provided by Chris Fairall, Simon de Szoeke, Jim Edson, and Ludovic Bariteau

13. MJO Skills of GFS, CFSv2, and UH during DYNAMO GFS: 13 days CFSv2&UH: 25/28 days CFSv2&UH MME: 36 days Fu et al. (2013) (Wheeler-Hendon Index, Lin et al. 2008) 14

14. MJO prediction in CFSv2 hindcasts (1999-2010)

15. Composites forecast for each initial phase Observation CFSv2 Initial Phase12345678 Obs (CFSv2-obs) 6.9 (-1.7) 6.7 (-1.2) 7.4 (-1.2) 7.6 (-0.5) 6.7 (-1.3) 7.2 (-2.0) 7.2 (-1.2) 6.4 (-1.3) Phase speed (Degree/day) Initial phases: 1, 3, 5, 7Initial phases: 2, 4, 6, 8 11 Wang et al. 2013. Climte Dyn.

16. Composite from initial phase 3 OLR (shading); U850 (contours) Forecast Observation 12

17. Bivariate correlation of Wheeler-Hendon index as a function of target phase (MJO Days) lead time (days) IO Africa Atl MC WP 10 (Based on CFSv2 1999-2010 hindcasts) Wang et al. 2013. Climte Dyn.

18. Courtesy of Owen Shieh 12 UTC Nov 28 November MJO & Thanks-giving TC (TC05A) 15

19. Forecasts of GFS, CFSv2 and UH with IC on Nov. 11 Observed and forecasted U850 and OLR averaged for days-13-15 U850 (contours) OLR (shading) 16

20. Forecasts of GFS, CFSv2 and UH with IC on Nov. 18 U850 (contours) OLR (shading) Observed and forecasted U850 and OLR averaged for days-13-15 18

21. What caused the dramatic differences in MJO prediction between GFS and CFSv2/UH? Air-sea coupling Model physics What are needed for an improved MJO prediction in GFS and CFS?

22. Impacts of air-sea coupling on the prediction

23. Names of Experiments SST Settings CPL Atmosphere-ocean coupled forecasts. Fcst_SST (or fsst) Atmosphere-only forecasts driven by daily SST derived from the ‘cpl’ forecasts. Pers_SST (or psst) Atmosphere-only forecasts driven by persistent SST. TMI_SST (or osst)Atmosphere-only forecasts driven by observed daily TMI SST. UH Forecast Experiments with Different SSTs 19

24. SST-Feedback Significantly Extends MJO Prediction Skill Persistent SST CPL Forecasted Daily SST Observed Daily SST Potential 20

25. Dependence on convection parameterization and SST uncertainty

27. NCEP GFS Forecast Experiments 1. Model Atmosphere-only GFS (May 2011 version) T126/L64 2. SSTs Clim NCDC OI analysis TMI (TRMM Microwave Imager) 3. Convection parameterizations SAS (Simplified Arakawa Schubert (Pan&Wu 1995)): Operational CFSv2 SAS2 (Revised Simplified A-S (Han&Pan 2011)): Operational GFS RAS (Relaxed A-S (Moorthi and Suarez (1999)) 4. Forecast runs Initial conditions: CFSR Initial dates: 1 Oct 2011 to 15 Jan 2022 (4 runs from 00, 06, 12, 18Z each day) 31 target days 21

28. 22 (Wang et al. 2014)

29. 23 (Wang et al. 2014)

30. OLR RMM index Anomaly Correlation (Wang et al. 2014)

31. Differences in forecast q (RAS – SAS2) with TMI SST from 7 Nov 2011 The lower troposphere above PBL with SAS2 is consistently drier than that with RAS, even before Nov 12 when rainfall rate is small. The drier lower troposphere with SAS2 is related to the larger rainfall rate during the first few days, indicating that the SAS2 convection scheme tend to drive the atmosphere to a drier state to maintain the balance between convection and large-scale dynamics Establishment of such a drier lower troposphere with SAS2 results in a less strong convection response to the underlying SST anomalies. 24 Why does RAS scheme produce better MJO?

32. CFS Use an alternative convection scheme, e.g., replacing SAS2 with RAS Improve SST accuracy with better intra-seasonal and diurnal variability What can we do to improve MJO prediction in CFS and GFS? 25 GFS Use an alternative convection scheme, e.g., replacing SAS2 with RAS Specify SSTs from another coupled forecast system (e.g., CFS), or couple GFS to a mixed-layer ocean model.

33. Categorization of MJO types: Coupled and uncoupled

34. Different Roles of Air-sea Coupling on the Oct. and Nov. MJO Events (UH) Fu et al. (2014) 26

35. Different Roles of Air-sea Coupling on the Oct. and Nov. MJO Events (GFS) Oct-MJO Nov-MJO Dec-MJO Need Daily SST Forcing 28

36. 29 Summary  Only two of five observed MJOs during DYNAMO have robust coherent positive SST anomalies leading MJO convections.  The initiation of successive MJO is more predictable than primary MJO. Major MJO forecasting problems include: slow eastward propagation, the Maritime Continent barrier and weak intensity.  During DYNAMO period, the MJO forecasting skills for the GFS, CFSv2, and UH models are 13, 25, and 28 days. The equal- weighted MME of the CFSv2 and UH reaches 36 days.  Air-sea coupling is important for MJO forecasting and still has plenty rooms to be improved.

37. 31 Summary  The interactions between the Nov-MJO and Thanksgiving-TC have been much better represented in the UH and CFSv2 coupled models than that in the atmosphere-only GFS.  CFSv2 MJO forecasting may be improved with an alternative cumulus parameterization (e.g., RAS) and more accurate SST prediction.  GFS MJO forecasting with an alternative cumulus parameterization (e.g., RAS) and SSTs from CFS, or couple GFS to an mixed-layer ocean model.  Two-type MJOs exist: strongly coupled to underlying ocean or largely determined by atmospheric internal dynamics.

38. 31 Publications Fu, X., J.-Y. Lee, P.-C. Hsu, H. Taniguichi, B. Wang, W. Q. Wang, and S. Weaver, 2013: Multi-model MJO forecasting during DYNAMO/CINDY period. Clim. Dyn., 41, 1067-1081. Wang, W. Q., M.-P. Hung, S. Weaver, A. Kumar, and X. Fu, 2013: MJO prediction in the climate forecast system version 2 (CFSv2). Clim. Dyn. Fu, X., W. Q. Wang, J.-Y. Lee and et al.: Distinctive roles of air-sea coupling on different MJO events: A new perspective revealed from the DYNAMO/CINDY field campaign. submitted. Wang, W. Q., A. Kumar, and X. Fu: Dependence of MJO prediction on sea surface temperatures and convection schemes. to be submitted.

39. Group Meeting, Honolulu, Mar 02, 2012

40. MJO Initiation MJO-I MJO-II MJO-III One Primary MJO Event Three Successive MJO Events

41. 10S-10N average OLR anomalies (Wm -2 ) Observation SAS2 SAS NCDC SST Day 12 forecast RAS