Red: El Niño Blue: La Niña

1. H H H Role of the Indo-Pacific Interbasin Coupling 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. Circles: observed strong event 3. Asymmetric impact of IO on ENSO transition a. Perfect model experiment 1. Introduction Prediction skill of El Nino and La Nina for growth and decay (dash) phase of ENSO Relationship of forecast skills between the TPO and IO Coupled-IO simulation from Oct0 to Aug1 :each ensemble El Nino-Southern Oscillation (ENSO) Evolution of the Nino 3.4 index 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 • “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 importanceof 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. Spread of individual forecast for NIO (yellow shade) ACC of Nino3 start The decline of the ACCs swerves to the left NCEP CFS Both the ACCs drop along the one-to-one line month Jin and Kinter (2009,JC) The La Nina events endure in both simulation& the spread is much small +1yr The difference begins to spread after the spring Similar asymmetry of the IO-ENSO relationship is found in the 450-yr coupled-IO ctrl run Coupled-IO hasting the El Niño transitionconsistent with the previous studies. (e.g., Kug et al. 2006; Ohba and Ueda 2007) b. Long-term IO-decoupled simulation Easterly wind anom. in both phase after their mature phase -1yr From Ohba and Ueda 2009 One-sided lag regression (cont) and correlation (shd) of equatorial SST onto the positive and negative DJF Nino-3.4 index Total asym. EPAC only vs IO+PAC 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) About half of the ENSO asymmetry arise from the asymmetry of IO feedback Surface wind response to SST anom. AGCM: DJF SST anom. IO-decoupled simulationshows El Niño：increase the duration period La Niña：relatively small difference IO+PAC EPAC precip Anomaly correlation for ensemble mean SST over the tropical Pacific Ocean (TPO) Reduced WP easterly with theweakened transition ENSO-related symmetric SST forcing Asymmetry of WP zonal wind is significantly reduced !! TPO: 120°E-90°W, 15°S-15°N Solid: Ctrl vs coupled-IO Dash: Ctrl vs decouple-IO 4. Summary and discussion a. Discussion Skewness of simulated SST and Tropospheric temp (850-250 hPa) Coupled-IO extends skillful prediction about 1.5 year Nonlinear atmospheric response to SST around the boreal winter-spring is responsible for the ENSO asym. (Ohba and Ueda 2009) (Okumura et al. 2011) Purpose of this study is to evaluate the extent to which the interactive IO is responsible for the ENSO asymmetry in duration Decoupled-IO: about 8mon The skill drops rapidlyas seen in the “spring prediction barrier” • 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 10-40 °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 2. Coupled GCM: MIROC5(T42 ver.) Shade: SST , contour: Toropos. temp • 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 Niñorapidly turn into La Niña, whileLa Niña tend to remainLa Niña state) The difference between the CIO vs NIO is very minorin La Niña The amplitude of the Indian Ocean SST warming is much stronger than that of the cooling. Asymmetry of ENSO in CMIP3 & 5 Cor. DJF Nino34 vs DJF(+1yr) nino34 How the El Nino transition is accelerated? 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 Most remarkable case: El Nino oct0037 simulation b. Summary IO warming Remarkable cooling with the anomalous easterlies Red: El Niño Blue: La Niña 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) 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) The LBM responses to the heating located on various longitudes well capture the observed relationship. Enhanced generationof Kelvin wave-like -> Acceleration of the ENSO transition Selected case Four El Nino & Two La Nina IO SST warming 110-yr Ctrl simulation End WP easterly • 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. Shade: SST Counter: zonal wind Oct0 Start!! Duration Transition 7 member ensemble (LAF), 18 mon forecasts: 1st October0 ~the end of April+2