Nonlinear atmospheric response to SST around the boreal winter-spring is responsible for the ENSO asym. Red: El Niño Blue: La Niña Role of the Indo-Pacific Interbasin Coupling in Predicting Asymmetric ENSO Transition and Duration in Predicting Asymmetric ENSO Transition and Duration Masamichi Ohba (Central Research Institute of Electric Power Industry, Abiko, Japan) Masahiro Watanabe (Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan) J. Climate in press. 2. Coupled GCM: MIROC5(T42 ver.) 1. Introduction El Nino-Southern Oscillation (ENSO) Jin and Kinter (2009,JC) NCEP CFS month ACC of Nino3 -1yr +1yr From Ohba and Ueda 2009 Easterly wind anom. in both phase after their mature phase Prediction skill of El Nino and La Nina for growth and decay (dash) phase of ENSO Purpose of this study is to evaluate the extent to which the interactive IO is responsible for the ENSO asymmetry in duration AGCM: DJF SST anom. (Ohba and Ueda 2009) EPAC IO+PAC (Okumura et al. 2011) Asymmetry of WP zonal wind is significantly reduced !! Surface wind response to SST anom. ENSO-related symmetric SST forcing precip Asymmetry of ENSO in CMIP3 & 5 Cor. DJF Nino34 vs DJF(+1yr) nino34 Selected case Four El Nino & Two La Nina 110-yr Ctrl simulation Oct 0 Start!! End TransitionDuration Experimental design Interactive Air-Sea Coupled-IO(CIO) V.S. Decoupled-IO(NIO) by prescribing the clim. SST a. Idealized twin forecast experiment & b. Long-term NIO experiment (100-yr) 7 member ensemble (LAF), 18 mon forecasts: 1st October 0 ~the end of April Asymmetric impact of IO on ENSO transition a. Perfect model experiment start Evolution of the Nino 3.4 index Coupled-IO hasting the El Niño transition consistent with the previous studies. (e.g., Kug et al. 2006; Ohba and Ueda 2007) The La Nina events endure in both simulation & the spread is much small The difference begins to spread after the spring Spread of individual forecast for NIO (yellow shade) About half of the ENSO asymmetry arise from the asymmetry of IO feedback Total asym. The other half is likely due to direct nonlinear atmospheric response to local CEP forcing (e.g. Hoerling et al. 2001; Ohba and Ueda 2009) Anomaly correlation for ensemble mean SST over the tropical Pacific Ocean (TPO) Solid: Ctrl vs coupled-IO Dash: Ctrl vs decouple-IO TPO: 120°E-90°W, 15°S-15°N Remarkable cooling with the anomalous easterlies Enhanced generation of Kelvin wave-like -> Acceleration of the ENSO transition WP easterly IO warming How the El Nino transition is accelerated? Shade: SST Counter: zonal wind HHH El Nino phase: the IO prediction skill is relatively collaborated with the following TPO predictability La Nina phase: the Indo-Pacific interbasin coupling is much weaker than El Nino Relationship of forecast skills between the TPO and IO Coupled-IO simulation from Oct 0 to Aug 1 :each ensemble Both the ACCs drop along the one-to-one line The decline of the ACCs swerves to the left b. Long-term IO-decoupled simulation IO-decoupled simulation shows El Niño：increase the duration period La Niña：relatively small difference Reduced WP easterly with the weakened transition Skewness of simulated SST and Tropospheric temp ( hPa) What causes the asymmetry of the IO feedback? 1. Skewness of IO SST (Hong et al.) The IO basin-wide warming is greater than that in the cooling 2. Asymmetry of the zonal distance of convection between El Nino and La Nina (Okumura et al. 2011; the Pacific precip. anom. during La Nina are displaced westward by °in longitude)  possibly change the sensitivity of WP zonal wind to IO Linear atmospheric model (LBM:T42L20) (Watanabe and Kimoto 2000) We check the wind response to change in the peak longitude of CEP heating Shade: SST, contour: Toropos. temp Circles: observed strong event 4. Summary and discussion a. Discussion Effect of the IO feedback is different between El Nino and La Nina About half of ENSO asymmetry arises from asymmetry of the Indo-Pacific interbasin coupling (the other half is possibly due to nonlinear atmospheric response to local SST in the Pacific as Ohba and Ueda 2009) IO SST variation is possibly one of regulation factor of “spring prediction barrier”. By improving the SST response of the IO, we can expect to overcome the spring prediction barrier of ENSO. Similar asymmetry of the IO-ENSO relationship is found in the 450-yr coupled-IO ctrl run b. Summary When El Nino-direct heating exists in the WP, the IO feedback(easterly anom.) is significantly interfered. → The zonal distance is important factor for the ENSO asymmetry The amplitude of the Indian Ocean SST warming is much stronger than that of the cooling. Reproduction of the ENSO asymmetry is difficult in most CGCMs (Ohba et al. 2010). However, MIROC5 well capture the both spatial and temporal asymmetry (i.e., El Niño rapidly turn into La Niña, while La Niña tend to remain La Niña state) The LBM responses to the heating located on various longitudes well capture the observed relationship. Most remarkable case: El Nino oct0037 simulation One-sided lag regression (cont) and correlation (shd) of equatorial SST onto the positive and negative DJF Nino-3.4 index “Spring barrier”: the prediction of the decay phase is very difficult. There are asymmetry of “spring barrier”: El Nino rapidly loose skill in spring, while La Nina loose gradually. Such a difference is possibly related with the asymmetry of ENSO transition system (e.g., Ohba and Ueda 2009; Ohba et al. 2010; Okumura and Deser 2010). ⇒El Niño tends to shift rapidly to La Niña after the mature phase, while La Niña tends to persist for up to two years (asymmetry in duration between El Niño and La Niña). The asymmetry in the ENSO transition system mainly arise from the asymmetry of WP wind response to SST anomalies over the Indo-Pacific. Recent studies show the “Impact of IO warming on the El Nino transition (e.g., Kug et al. 2006; Ohba and Ueda 2007) through the enhancement of WP easterly (Watanabe and Jin 2002; Annamalai et al. 2005). However, the importance of IO feedback on the ENSO prediction during the opposite phase has not been fully clarified. During La Niña, negative precipitation anomalies over the CEP shift westward compared to positive anomalies during El Niño. The zonal displacement of the Pacific precipitation anomalies may alter the balance of local and remote wind forcing over the WP between El Niño and La Niña. EPAC only vs IO+PAC IO SST warming The difference between the CIO vs NIO is very minor in La Niña Decoupled-IO: about 8mon The skill drops rapidly as seen in the “spring prediction barrier” Coupled-IO extends skillful prediction about 1.5 year