1. Roles of Madden-Julian Oscillation on ENSO Onset Wang Guomin ( 王國民 ) Centre for Australian Weather and Climate Research: A partnership between the Bureau of Meteorology and CSIRO Shi, L., O. Alves, H.H. Hendon, G. Wang, and D. Anderson, 2009: The Role of Stochastic Forcing in Ensemble Forecasts of the 1997/98 El Niño. J. Climate, 22, 2526–2540.

2.  MJO’s role in ENSO onset controversial: ZC model vs observational studies (Hendon et al, 2007)  At least two MJO events in the winter of 1996/97 coincided with the onset of the 1997/98 El Niño. (McPhaden, 1999)  MJO might be relevant to the timing, growth and strength of El Niño but not the event itself. (Kleeman and Moore 1997)  MJO-ENSO relation early studies: artificial MJO forcing. (Flugel et al. 2004)  Australian Bureau of Meteorology POAMA seasonal forecast coupled model is capable producing reasonable MJO, an ideal framework for MJO-ENSO study Introduction

3. Obs. of the onset & development of 1997/98 El Niño

4. Introduction  MJO’s role in ENSO onset controversial: ZC model vs Observational studies (Hendon et al, 2007)  At least two MJO events in the winter of 1996/97 coincided with the onset of the 1997/98 El Niño. (McPhaden, 1999)  MJO might be relevant to the timing, growth and strength of El Niño but not the event itself. (Kleeman and Moore 1997)  MJO-ENSO relation early studies: artificial MJO forcing. (Flugel et al. 2004)  Australian Bureau of Meteorology POAMA seasonal forecast coupled model is capable of producing reasonable MJO, an ideal framework for MJO-ENSO study

5. Brief description of POAMA and the Ensemble Method The Bureau of Meteorology’s Dynamical Seasonal Prediction System POAMA First version went operational in 2002. This version is used in present study. The current version is POAMA1.5 and a new version (POAMA2) is ready POAMA.BOM.GOV.AUWebpage POAMA.BOM.GOV.AU

6. Atmos IC Realistic Ocean IC Perturbing SST i i=1,90 i=1 i=2 i=90 Forecasts from 1 Dec 96 to 31 Aug 97 POAMA Mean of the 36 integration. Remove stochastic noise. 36-member AMIP-style integration Ensemble Experiment Design Such defined atmos IC will no longer be a solution thus not in balance with ocean

7. Nino3.4 Ensemble Forecasts Spread grows rapidly to 1.5C in 4 months, and further to 2.2C at 9 months.

8. Hovmoller plots of Ensemble Mean Anomalies OLR U10 SST D20

9. Methodology Nino3.4 from 90 ensembles are equally divided into three categories: STRONG, Intermediate and WEAK, relative to their ensemble mean, for each lead time. Track every member in each category and count how many remain forwards at 9 th month. Same can be done but backwards. It is found for members in STRONG and WEAK at 4 th month, more than half remain so at 9 th month. This suggests SST spread in the first 4 months has decisive impact on the strength of El Nino at 9 th month. Thus how MJO evolves in the first 4 months plays crucial role on El Nino strength in mature phase.

10. 2 nd Mon3 rd Mon4 th Mon5 th Mon6 th Mon7 th Mon8 th Mon9 th Mon Jan3021 6 3 17 8 5 16 8 6 15 8 7 14 7 9 17 4 9 15 7 8 Feb 3022 6 2 20 7 3 19 5 6 18 6 4 17 7 6 14 10 6 Mar 3021 8 1 19 7 4 19 6 5 18 7 5 17 7 6 Apr 3023 6 1 21 7 2 17 10 3 16 9 5 May 3024 6 21 8 1 19 10 1 Jun 3024 6 22 8 Jul 3026 4 2 nd Mon3 rd Mon4 th Mon5 th Mon6 th Mon7 th Mon8 th Mon9 th Mon Jan 3016 13 1 11 14 5 11 14 5 10 14 6 12 10 8 12 12 6 12 10 8 Feb 3022 7 1 18 8 4 18 9 3 15 12 3 16 11 3 14 14 3 Mar 3024 5 1 21 7 2 16 12 2 17 11 2 15 14 1 Apr 3025 5 22 7 1 22 6 2 19 10 1 May 3025 5 24 5 1 21 9 Jun 3028 2 24 6 Jul 3026 4 Sorting and Tracking Ensemble Forecasts

11. Methodology Nino3.4 from 90 ensembles are equally divided into three categories: STRONG, Intermediate and WEAK, relative to their ensemble mean, for each lead time. Track every member in each category and count how many remain at 9 th month. It is found for members in STRONG and WEAK at 4 th month, more than half of them remain so at 9 th month. This suggests SST spread in the first 4 months has decisive impact on the strength of El Nino at 9 th month. Thus how MJO evolves in the first 4 months plays crucial role on El Nino strength in mature phase.

12. Hovmoller of Differences (STONG-WEAK) OLR U10 SST D20

13. Combined Multivariate (U10a & OLR) EOF decomposition (Similar to Wheeler and Hendon 2004 but use model anomalies) EOF1 EOF2 OLR: Solid line U10a: Dashed line

14. STRONG WEAK MJO Projections in STRONG and WEAK

15. Band-pass filtering (periods of 20-120 days and eastward propagating wavenumbers 1-4) The base point (referred as day 0) is determined by the minimum of the filtered OLR anomalies along 140ºE in the first 60 days. Lagtime – Longitude Composites

16. Lag time-longitude composites of the first MJO event during the first 4 months forecast (Base longitude is 140E) Lag (days)

17. Lag time-Longitude Composites of Surface Wind Anomalies Lag (days)

18. Time-longitude Composites of Isotherm Depth and SST (day 0 onwards)

19. Lag time-long evolution of the differences between the strong group composite and the weak group composite Lag time-longitude evolution of the difference (STRONG-WEAK) of 20ºC isotherm depth anomalies (shaded, interval 3 metres), U10 anomalies (superimposed bold blue contours, interval 0.8m/s) and SSTA (thin contour, interval 0.2ºC)

20. Summary  Using 90-member 9-month forecast ensemble (Dec 1996 - Aug 1997), roles of MJO on 1997/98 El Niño onset were examined.  By design no MJO exists at the beginning. All spread comes from internal stochastic variability.  All forecast lead to warm conditions, but strength varies from 0.5C to 2.7C in Nino3.4  Strong or weak warming depends on MJO evolution in the first 4 months.  The westerly anomalies associated with the MJO in SRONG case shifted further east and stronger than those in WEAK case.  Subsequent downwelling ocean Kelvin waves, driven by wider zonal fetch and stronger westerly anomalies, were also stronger, leading to warmer SST in STRONG case.  Suggests MJO related westerlies are cause of stronger El Nino  Implication for EL Nino prediction: if there is limited predictability on MJO, there will be limited scope for accurate prediction of El Nino strength.

21. Wavenumber-Frequency Spectral Analysis OLR (Ensemble mean and Time mean removed) OLR (Ensemble mean and Time mean removed) Strong cases (30) Weak cases (30) To effectively extract the MJO-like signals from the first four months, the ‘artificial’ time series (padding sufficient zeros in bath the head and end of OLR ensemble mean anomalies in the first four months for each forecast) are used to compute the wavenumber-frequency power spectral of the strong group and weak group respectively. In order to capture the MJO-scale signals, a band-pass filter method would be used hereafter. All spectral power that did not fall within periods 20-120 days for eastward zonal wavenumbers 1-4 was set equal to zero. And then, inverse FFTs were performed for each wavenumbers and frequencies.