Nonlinear atmospheric response to SST around the boreal winter-spring is responsible for the ENSO asym.

Red: El NiñoBlue: La Niña

Role of the Indo-Pacific Interbasin CouplingRole of the Indo-Pacific Interbasin Coupling in Predicting Asymmetric ENSO Transition and in Predicting Asymmetric ENSO Transition and

DurationDurationMasamichi OhbaMasamichi Ohba (Central Research Institute of Electric Power Industry, Abiko, Japan)(Central Research Institute of Electric Power Industry, Abiko, Japan)

Masahiro WatanabeMasahiro Watanabe (Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan)(Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan)

J. Climate in press.J. Climate in press.

2. Coupled GCM: MIROC5(T42 2. Coupled GCM: MIROC5(T42 ver.) ver.)

1. Introduction1. IntroductionEl Nino-Southern Oscillation (ENSO)

Jin and Kinter (2009,JC)

NCEP CFS

month

AC

C o

f N

ino3

-1yr

+1yr

From Ohba and Ueda 2009

Easterly wind anom. in both phaseafter 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 windis significantly reduced !!

Surface wind response to SST anom.

ENSO-related symmetric SST forcing

precip

Asymmetry of ENSO in CMIP3 & 5Cor. DJF Nino34 vs DJF(+1yr)

nino34

Selected caseFour El Nino & Two La Nina

110-yr Ctrl simulation

Oct0

Start!!

End

TransitionDuration

Experimental designInteractive 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 October0 ~the end of April+2

3. Asymmetric impact of IO on ENSO transition3. Asymmetric impact of IO on ENSO transitiona. Perfect model experimenta. 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 asymmetryarise 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-IODash: 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

H

H

H

El Nino phase: the IO prediction skill is relatively collaborated with the following TPO predictabilityLa 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 Oct0 to Aug1 :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 simulationb. Long-term IO-decoupled simulation

IO-decoupled simulation showsEl Niño ： increase the duration periodLa Niña ： relatively small difference

Reduced WP easterly with the weakened transition

Skewness of simulated SST

and Tropospheric

temp (850-250 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 10-40 °in longitude)

possibly change the sensitivity of WP zonal wind to IOLinear 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 discussion4. Summary and discussiona. Discussiona. Discussion

Effect of the IO feedback is different between El Nino and La NinaAbout 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. Summaryb. 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 8monThe skill drops rapidly as seen

in the “spring prediction barrier”

Coupled-IO extends skillful prediction about 1.5 year