1. WCRP-WWRP/THORPEX MJO Task Force Duane Waliser JPL/Caltech/USA Matthew Wheeler ABOM/Australia SSG-18, UNESCO Paris, Franca; May 2011 Membership => Established 2010 Follows from the US CLIVAR MJO Working Group

2. Figures: Maloney, PMEL/TAO, Nakazwa, MJO WG, Lin, Waliser The MJO is the dominant form of intraseasonal variability in the Tropics The MJO impacts a wide range of weather & climate phenomena o Monsoon Onset & Breaks o ENSO+IOD Interactions o Tropical Cyclone Modulation o Mid-latitude Weather Impacts o Organization of Chl, Aerosols, Ozone, etc. variability Our weather & climate models have a poor representation of the MJO Great benefit could be derived from better predictions of the MJO - Helps to bridge the gap between weather and seasonal predictions Motivation

3. TORs Overall goal: Facilitate improvements in the representation of the MJO in weather and climate models in order increase the predictive skill of the MJO and related weather and climate phenomena Develop process-oriented diagnostics/metrics to assess/guide physics and take advantage of more modern data (e.g. A-Train) Explore MJO multi-scale interactions and with emphasis on vertical structure and diabatic processes Expand MJO forecast metrics: e.g., boreal summer & multi-model ensemble development Coordinate community MJO activities (e.g. WCRP monsoon) In late 2009, follow-on WCRP/WWRP Task Force established: “…. the MJO Task Force should be formed within the framework of the joint WWRP/THORPEX/WCRP YOTC activity, and report to the JSC- WWRP, ICSC THORPEX and the SSG-CLIVAR.”

4. MJO Simulation Diagnostics for GCMs (MJOWG, J. Climate, 2009) Observation-Based Diagnostics Variability Life Cycle Mean-State Data Set Sensitivity Web Display and Code Availability Adopted by NCAR/NCL www.usclivar.org/mjo.php

5. Application of Diagnostics to GCMs (Kim et al. J. Climate, 2009) Applied to 8 GCMs CAM3.5, CAM-3Z, SPCAM, ECHAM4/OPYC, CFS, SNU, GFDL, GEOS5 SPCAM (uncoupled) ECHAM4/OPYC (coupled) Performed best. Still Challenges!

6. MJOTF: 3 sub-themes 1.MJO Forecasting and Impacts Experimental real-time MJO forecasts being made Project forecast data onto the observed MJO modes Skill assessment, multi-model approach, etc. 2.Boreal summer monsoon ISV diagnostics and metrics Goal of better capture the northward propagating component (especially over the western Pacific) 3.Development of process-oriented diagnostics To better understand the physics of the MJO/ISO Develop metrics to stratify model performance Develop metrics too be used for quick look diagnosis and as a contribution to the WGNE/WGCM Metrics Panel 1.MJO Forecasting and Impacts Experimental real-time MJO forecasts being made Project forecast data onto the observed MJO modes Skill assessment, multi-model approach, etc. 2.Boreal summer monsoon ISV diagnostics and metrics Goal of better capture the northward propagating component (especially over the western Pacific) 3.Development of process-oriented diagnostics To better understand the physics of the MJO/ISO Develop metrics to stratify model performance Develop metrics too be used for quick look diagnosis and as a contribution to the WGNE/WGCM Metrics Panel

7. Why an Operational MJO Forecast Metric? MJO - ? http://www.usclivar.org/mjo.php Use of a common forecast metric allows for: quantitative forecast skill assessment targeted model improvements friendly competition to motivate model improvements. developing a multi-model ensemble forecasts ENSO – “Nino 3.4” Weather – 500 mb heights ECMWF Based WH Indices With Support WGNE Operational Center Participation CPC/NCEP Host

8. Operational Forecast Metric (Gottschalck et al. BAMS, 2010) w/ WGNE http://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/CLIVAR/clivar_wh.shtml 10 operation centers, 20 data streams, 13 ensemble forecasts (with 4 – 51 members)

9. NCEPCPTEC Operational Forecasts (26 April 2011 – 10 May 2011) Differences in propagation rate, amplitude, and dispersion

10. NCEP: 26 April start Operational Forecasts: NCEP NCEP: 3 May start

11. Operational Skill Verification (Gottschalck et al. BAMS, 2010) w/ WGNE

12. MJO Modeling and Forecasts from ECMWF Promising gains from continued model improvements Resolution, Data Assimilation, Model Physics (Tomkins et al. 2007; Bechtold et al 2008) – M. Miller. OLR; 15-day Lead Time; 10N-10S; TOGA COARE Courtesy F. Vitart ECMWF

13. Forecast Skill of Combined RMM1 & RMM2 80 ensemble mean start dates, daily 1st Feb., May, Aug and Nov 1989-2008 MJO Forecasts from ECMWF Forecast Skill of RMM1 & RMM2 48 ensemble mean start dates - daily from 15 Dec 1992 to 31 Jan1993 Courtesy, F. Vitart ECMWF

14. Boreal Summer Monsoon ISV Compare MJO forecasts generated by projecting onto the near- equatorial basis functions of Wheeler and Hendon (2004) to those using the OLR spatial patterns from Annamalai and Sperber (2005)

15. Process Oriented / Vertical Structure Diagnostics & Metrics Zhu et al 2010 TRMM Diabatic Heating Moisture Precip Relationships Kim et al 2009 Jiang et al 2009 Jiang et al 2010 CloudSat Retrievals AIRS Temp and WV Tian et al 2010

16. YOTC MJOTF, GEWEX GCSS, AAMP MJO Diabatic Heating Experiment Long-Term Simulations & Initialized Hindcasts During YOTC MJOs Focus on Diabatic Heating Structure Utilize TRMM & Reanalyses Q1, QR Encourage HiRes Miura et al. 2007 Dec 2006 MJO

17. I. CLIVAR MJOWG Sponsored, Irvine, CA 2007 Sperber and Waliser BAMS Meeting Summary 2008 New Approaches to Understanding, Simulating, and Forecasting the Madden-Julian Oscillation II. WCRP/WWRP MJOTF + CLIVAR AAMP, Busan, 2010 MJO Workshops Monsoon Intraseasonal Variability Modeling Workshop BAMS Meeting Summary (in press)

18. ISVHE Intraseasonal Variability Hindcast Experiment Suited for MJO & other ISV Contacts: Bin Wang & June-Yi Lee Close Collaboration: AAMP, YOTC, AMY, APCC Partial Support: NOAA CTB