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DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign

DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign. Application of DYNAMO/AMIE observations to validate and improve the representation of MJO initiation and propagation in the NCEP CFSv2 Joshua Xiouhua Fu (University of Hawaii) & Wangqiu Wang (NCEP)

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DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign

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  1. DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign Application of DYNAMO/AMIE observations to validate and improve the representation of MJO initiation and propagation in the NCEP CFSv2 Joshua Xiouhua Fu (University of Hawaii) & Wangqiu Wang (NCEP) Wednesday, July 23 @ 2pm Climate Variability& Predictability University of Hawaii at Manoa

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

  3. Observed MJO events during DYNAMO period

  4. SST and MJO-filtered OLR Anomalies in DYNAMO Period • 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 Oct-MJO Nov-MJO IOP SST (shading); OLR (contours) 4

  5. Prediction of the observed MJO by operational models

  6. Good forecasts of two successive MJO events IC: Oct_17 IC: Nov_07 Bad forecasts of Sep. primary MJO event IC: Sep_20 IC: Sep_12 Courtesy of NCEP MJO Discussion Summary led by Jon Gottschalck et al. 5

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

  8. Prediction by GFS, CFSv2 and UH models

  9. Nov. MJO initiation in CFSv2&UH models OLR anomalies • 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. IC: Nov_04 6

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

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

  12. Acknowledgment: Observational surface flux data from Revelleduring DYNAMO period are provided by Chris Fairall, Simon de Szoeke, Jim Edson, and LudovicBariteau

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

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

  15. Composites forecast for each initial phase Observation CFSv2 Initial phases: 1, 3, 5, 7 Initial phases: 2, 4, 6, 8 Phase speed (Degree/day) 11 Wang et al. 2013. ClimteDyn.

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

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

  18. November MJO & Thanks-giving TC (TC05A) 12 UTC Nov 28 Courtesy of Owen Shieh 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 Observed and forecasted U850 and OLR averaged for days-13-15 U850 (contours) OLR (shading) 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. UH Forecast Experiments with Different SSTs 19

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

  25. Dependence on convection parameterization and SST uncertainty

  26. 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

  27. (Wang et al. 2014) 22

  28. (Wang et al. 2014) 23

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

  30. Differences in forecast q (RAS – SAS2) with TMI SST from7 Nov 2011 Why does RAS scheme produce better MJO? • 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

  31. What can we do to improve MJO prediction in CFS and GFS? • CFS • Use an alternative convection scheme, e.g., replacing SAS2 with RAS • Improve SST accuracy with better intra-seasonal and diurnal variability • 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. 25

  32. Categorization of MJO types: Coupled and uncoupled

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

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

  35. 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. 29

  36. 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. 31

  37. 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. 31

  38. Group Meeting, Honolulu, Mar 02, 2012

  39. MJO Initiation One Primary MJO Event MJO-I MJO-II MJO-III Three Successive MJO Events Group Meeting, Honolulu, Mar 02, 2012

  40. 10S-10N average OLR anomalies (Wm-2) NCDC SST Day 12 forecast RAS Observation SAS2 SAS

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