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  • View in gallery

    (top) Scatterplot of the simulated positive Niño-3.4 index (°C) during DJF0/+1 against (a) the following year’s DJF+1/2 and (b) equatorial surface wind anomalies (m s−1) over the WP (2°S–2°N, 135°–170°E). (c) As in (b), but for observed values. Red and blue dots denote selected events for the identical-twin experiments. (bottom) Evolution of the Niño-3.4 index (°C) overlaid from January0 to January+3 for the selected (d) El Niño and (e) La Niña events.

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    (top) Composited anomalies of observed SST (°C; contours and color shading) and surface wind (m s−1; vectors) for (a) El Niño and (b) La Niña during DJF0/+1. The anomalies are normalized by the Niño-3.4 index. (c),(d) As in (a),(b), but for simulated SST values derived from identical-twin experiments. The contour interval is 0.2°C. (bottom) Frequency distribution (%) of El Niño (red bars) and La Niña (blue bars) duration (months) after the December peak (months) from (e) ERSSTv3 and (f) MIROC5. The number of El Niño (La Niña) events in each 3-month bin has been divided by the total number of El Niño (La Niña) events.

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    Time evolution of the Niño-3.4 index (°C) for Ctrl (black solid line) and the ensemble mean of CIO (black dashed line) and NIO (red dashed line) overlaid from January0 to March+2 for (a),(b) El Niño and (c),(d) La Niña events. Spread of individual forecast members for NIO is denoted by yellow shading.

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    Time evolution of the ACC using ensemble-mean SST in the tropical Pacific region (15°S–15°N, 120°E–90°W) between Ctrl and CIO (solid line) and between Ctrl and NIO (dashed line) for (a) El Niño and (b) La Niña from October0 to March+2.

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    (a) SST anomalies (°C; contours and color shading) and surface wind (m s−1; vectors) for Ctrl simulation of one El Niño event from November0 to October+1. (b) As in (a), but for the difference between the CIO and NIO. The contour interval is 0.6°C.

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    (a) Longitude–time section of the difference in SST (°C; color shading) and surface zonal wind (m s−1; contours) between CIO and NIO along the equator (2°S–2°N) from Octorber0 to March+2 for one El Niño event. The contour interval is 0.7 m s−1. (b) As in (a), but for spatial distribution of the differences in OHC (°C; contours and color shading) and surface wind (m s−1; vector) during NDJ0/+1. The contour interval is 0.2°C.

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    Scatterplot of the ACC of the tropical Pacific SST between Ctrl and CIO against that of IO (lead 2 months) during October0 to August+1 for the (a) warm and (b) cold phases of ENSO.

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    Longitude–time section of the composited SST (°C; contour and color shading) and surface wind (m s−1; vectors) anomalies along the equator (2°S–2°N) from January0 to March+2 based on five strong (a) El Niño and (b) La Niña events in Ctrl. The anomalies are normalized by the Niño-3.4 index. (c),(d) As in (a),(b), but for the long-term NIO. The contour interval is 0.5°C.

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    One-sided auto-lag correlation of Niño-3.4 index from lag −24 to +13 months for the simulated (a) El Niño and (b) La Niña phases. That of the Niño-3 index is also plotted for El Niño phase. (c) Power spectra of Niño-3.4 index. The solid (dashed) line represents the Ctrl (NIO) simulation.

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    (a) The imposed heating rate (K day−1; contours and color shading) at 500 hPa for the linear atmospheric model and 850-hPa wind response (m s−1; vectors) to the heating. (b) Scatterplot between the center longitudes of the Pacific heating vs zonal wind anomaly at the equatorial WP derived from the linear atmospheric model (solid line). Five strong El Niño (La Niña) observed events are overlain by red (blue) circles based on NOAA interpolated OLR and NCEP–NCAR reanalysis. The dashed line represents the wind anomaly when the LBM is forced by the IO heating only.

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    Schematic diagram illustrating the asymmetric role of the IO in the transition of (top) El Niño and (bottom) La Niña after their mature phases. Red (black) shading indicates positive (negative) SST and subsurface temperature anomalies. White outlined arrows represent anomalous surface wind. Clouds (larger downward arrows) indicate enhanced (reduced) convective activity.

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

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  • 1 Central Research Institute of Electric Power Industry, Abiko, Japan
  • 2 Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
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Abstract

Warm and cold phases of El Niño–Southern Oscillation (ENSO) exhibit a significant asymmetry in their transition/duration such that El Niño tends to shift rapidly to La Niña after the mature phase, whereas La Niña tends to persist for up to 2 yr. The possible role of sea surface temperature (SST) anomalies in the Indian Ocean (IO) in this ENSO asymmetry is investigated using a coupled general circulation model (CGCM). Decoupled-IO experiments are conducted to assess asymmetric IO feedbacks to the ongoing ENSO evolution in the Pacific. Identical-twin forecast experiments show that a coupling of the IO extends the skillful prediction of the ENSO warm phase by about one year, which was about 8 months in the absence of the IO coupling, in which a significant drop of the prediction skill around the boreal spring (known as the spring prediction barrier) is found. The effect of IO coupling on the predictability of the Pacific SST is significantly weaker in the decay phase of La Niña. Warm IO SST anomalies associated with El Niño enhance surface easterlies over the equatorial western Pacific and hence facilitate the El Niño decay. However, this mechanism cannot be applied to cold IO SST anomalies during La Niña. The result of these CGCM experiments estimates that approximately one-half of the ENSO asymmetry arises from the phase-dependent nature of the Indo-Pacific interbasin coupling.

Corresponding author address: Masamichi Ohba, Central Research Institute of Electric Power Industry, Environmental Science Research Laboratory, 1646 Abiko, Abiko-shi, Chiba 270-1194, Japan. E-mail: oba-m@criepi.denken.or.jp

Abstract

Warm and cold phases of El Niño–Southern Oscillation (ENSO) exhibit a significant asymmetry in their transition/duration such that El Niño tends to shift rapidly to La Niña after the mature phase, whereas La Niña tends to persist for up to 2 yr. The possible role of sea surface temperature (SST) anomalies in the Indian Ocean (IO) in this ENSO asymmetry is investigated using a coupled general circulation model (CGCM). Decoupled-IO experiments are conducted to assess asymmetric IO feedbacks to the ongoing ENSO evolution in the Pacific. Identical-twin forecast experiments show that a coupling of the IO extends the skillful prediction of the ENSO warm phase by about one year, which was about 8 months in the absence of the IO coupling, in which a significant drop of the prediction skill around the boreal spring (known as the spring prediction barrier) is found. The effect of IO coupling on the predictability of the Pacific SST is significantly weaker in the decay phase of La Niña. Warm IO SST anomalies associated with El Niño enhance surface easterlies over the equatorial western Pacific and hence facilitate the El Niño decay. However, this mechanism cannot be applied to cold IO SST anomalies during La Niña. The result of these CGCM experiments estimates that approximately one-half of the ENSO asymmetry arises from the phase-dependent nature of the Indo-Pacific interbasin coupling.

Corresponding author address: Masamichi Ohba, Central Research Institute of Electric Power Industry, Environmental Science Research Laboratory, 1646 Abiko, Abiko-shi, Chiba 270-1194, Japan. E-mail: oba-m@criepi.denken.or.jp

1. Introduction

The El Niño–Southern Oscillation (ENSO), which consists of a quasi-periodic (3–7-yr time scale) warming (El Niño) and cooling (La Niña) of the tropical central and eastern Pacific Ocean (CEP), is the most dominant driver for the earth’s interannual climate variability. ENSO prediction is of practical interests, in addition to scientific, because of its large environmental and societal impacts. To predict and understand the variability of ENSO, a number of investigators (e.g., Schopf and Suarez 1988; Jin 1997; Weisberg and Wang 1997) have suggested conceptual theories, providing a comprehensive idea regarding the cyclic nature of ENSO (Wang 2001). The mechanisms in these conceptual theories effectively capture the observed phase transition from El Niño to La Niña and successfully reproduce the linear oscillation of ENSO.

However, several studies have reported that a type of break in the ENSO cycle occurs when La Niña shifts to El Niño. The air–sea coupled system over the Pacific somehow remains in a weak La Niña state for up to 2 yr (Kessler 2002; Larkin and Harrison 2002; Nagura et al. 2008; McPhaden and Zhang 2009), whereas El Niño tends to turn rapidly into La Niña after the mature phase. Recent studies (Ohba and Ueda 2009a; Okumura and Deser 2010; Ohba et al. 2010) have reported that the nonlinear atmospheric response to the CEP sea surface temperature (SST) forcing (Hoerling et al. 1997, 2001; Kang and Kug 2002) is a fundamental cause of the asymmetry in the transition. Because the duration of an ENSO episode can cause severe drought (e.g., the 1999–2001 drought in central Asia; Hoerling and Kumar 2003; Ueda and Kawamura 2004) and this duration is difficult to reproduce in most coupled general circulation models (CGCMs), understanding of the ENSO asymmetry is important for improving seasonal climate forecast skills (Ohba et al. 2010). Therefore, the asymmetry of transition/duration is an important aspect of ENSO, in addition to the asymmetry of the amplitude (e.g., An et al. 2005) and spatial distribution (e.g., Kang and Kug 2002).

Statistical analyses of historical SST records indicate that the anomalous surface conditions of the Indian Ocean (IO) correlate strongly with ENSO events (Tourre and White 1995; Klein et al. 1999; Yu and Rienecker 1999; Alexander et al. 2002; Lau and Nath 2003). Several of these studies report that the most dominant SST variations in the IO are basin-wide warming (BW) and cooling (BC) that typically appear several months after the mature phase of El Niño and La Niña, respectively (Hong et al. 2010). This SST variation is mainly due to the net surface heat flux (Klein et al. 1999; Ohba and Ueda 2005, 2009b) over large parts of the IO, with the exception of the southwestern basin, where downwelling ocean Rossby waves are predominant (Xie et al. 2002). The ENSO-induced BW persists until the boreal spring–summer and enhances the anticyclonic anomalies developing in the lower troposphere over the northern west Pacific (WP) (Watanabe and Jin 2002, 2003; Annamalai et al. 2005; Xie et al. 2009; Chowdary et al. 2011). The anticyclonic anomalies over the WP generate anomalous equatorial easterlies that can contribute to the propagation of upwelling Kelvin waves (Wang et al. 2000). Recent studies using a CGCM and an atmospheric general circulation model (AGCM) show that the IO warming accelerates the transition from El Niño to La Niña through an enhancement of the surface easterly anomalies in the WP (Kug and Kang 2006; Kug et al. 2006; Ohba and Ueda 2007). Therefore, the effect of the IO SST anomalies should be considered when predicting ENSO (Yamanaka et al. 2009; Annamalai et al. 2010; Luo et al. 2010; Izumo et al. 2010).

As a feedback from the IO to the Pacific, these studies reveal the impacts of the IO SST anomalies on the transition from El Niño to La Niña. 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. By use of observational data, Kug and Kang (2006) pointed out that the IO feedback is relatively weak for the cold events. Okumura et al. (2011) also found in their AGCM experiments that the impact of IO cooling during La Niña on the WP wind could be much weaker than that during El Niño. The question we will address is how the IO affects the predictability of ENSO in a CGCM. The ENSO-related SST anomalies over the tropical IO co-occur with those in the tropical Pacific; therefore, separate quantification of their effects by standard control simulation alone would be difficult. To study their individual effects, two sets of experiments were conducted by using a state-of-the-art CGCM: one with interactive air–sea coupling in the IO and the other without coupling in the IO by prescribing SST to climatology. The high sensitivity of ENSO predictability to the IO BW during El Niño is consistent with that reported in previous studies (e.g., Ohba and Ueda 2007), but the present study detects the asymmetry in the La Niña phase. The goal of the present study is to evaluate the extent to which the ENSO asymmetry in duration caused by the interactive IO. The use of a state-of-the-art air–sea coupled model, simulating a more realistic ENSO transition system, enables us to present more concrete evidence for the role of IO asymmetry in predicting ENSO after the mature phase, as suggested by the previous studies (Kug and Kang 2006; Okumura et al. 2011).

Often called “spring prediction barrier,” a significant drop in prediction skill during the boreal spring is a well-known feature of ENSO forecasting (e.g., Goswami and Shukla 1991; Landsea and Knaff 2000; Jin and Kinter 2009). The spring prediction barrier is closely and most often associated with the difficulty of ENSO transition (e.g., Torrence and Webster 1998; Clarke and Van Gorder 1999; Yu 2005; Jin and Kinter 2009; Wu et al. 2009). A possible explanation for this deterioration in forecast skill is the systematic model error (e.g., Jin and Kinter 2009), which is largely attributed to the WP wind error at the surface (Wu et al. 2009). Investigating the phase dependency of the Indo-Pacific interbasin coupling for the simulated ENSO transitivity–persistency could also contribute to the understanding in the regulation factor of model predictability.

The rest of this paper is organized in the following manner: Section 2 presents a brief description of the CGCM and experimental design. Section 3 examines the asymmetric response of the ENSO in the CGCM to positive–negative SST anomalies in the IO. Section 4 presents the summary and discussion.

2. Model

a. MIROC5i

The fifth version of the Model for Interdisciplinary Research on Climate (MIROC5; Watanabe et al. 2010), which will be employed for the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), was used in the present study. The design of MIROC5 was based on MIROC3 (Hasumi and Emori 2004), which employs a global spectral dynamical core and implements a standard physics package for the atmosphere. The ocean and sea ice models comprise the updated Center for Climate System Research ocean component model (Hasumi 2006). In addition, a land model that includes a river module is coupled to the system. In MIROC5, many of the schemes have been replaced by implementing recent ones, and the preindustrial control experiment showed a remarkable improvement in the ENSO amplitude and structure compared with that recorded by MIROC3 (Watanabe et al. 2010). In the present study, we use an interim version of MIROC5, called MIROC5i, which contains several minor differences from the official MIROC5 (for details, see Watanabe et al. 2011). The standard resolution for MIROC5 is T85L40 for the atmosphere. Because the model’s mean state and variability were not seriously altered when the horizontal resolution of the atmosphere was reduced, we decided to use a coarser resolution of T42L40 for MIROC5i in this study. Our decision was based solely on the increased computational burden of the new physics package in MIROC5.

b. Sensitivity experiments

We initially performed a 110-yr control simulation, referred to here as Ctrl. Regarding Ctrl as a “true” solution, two sets of idealized “twin” forecast experiments (or so-called perfect model study) were conducted. One forecast used the same model as in Ctrl [coupled-IO run (CIO)], and the other was based on the model without air–sea coupling in the IO [decoupled-IO run (NIO)]. The NIO run was performed by restoring the SST in the IO (30°S–30°N) to the monthly climatology derived from Ctrl. The identical-twin experiments eliminated model errors and hence provided a maximum predictability. Because of the computational burden, we selected four (two) strong to moderate El Niño (La Niña) events that showed peculiar transition (duration) features in Ctrl. Initial conditions were produced by the lagged average forecast (LAF) method. Months in the ENSO developing years are denoted by a superscript 0, and those in the succeeding years are represented as +1 and +2. Seven-member, 18-month ensemble forecasts were performed from October0 through the end of April+2. To confirm the result of the perfect model experiment, we conducted an additional long-term simulation of the decoupled-IO run for 90 yr.

Figures 1a,b show a comparison of the scatter diagrams of the simulated December0–February+1 (DJF0/+1) Niño-3.4 index with that recorded in the following year (Fig. 1a) and with the surface zonal wind over the equatorial WP (Fig. 1b). The Niño-3.4 index is defined as the averaged SST anomaly in the 5°S–5°N and 170°–120°W regions. The anomalies presented are defined by considering the deviation from the climatology of Ctrl for a simple estimation of the relative contribution of the BW–BC for the ENSO evolution. A nonlinear relationship existed between the ENSO and the following SST (Fig. 1a) relative to the asymmetry of the WP zonal wind response (Fig. 1b). The selected six events are plotted by red and blue dots representing El Niño and La Niña, respectively. The model accurately captured the expected nonlinear response of the zonal wind as seen in the observation (Fig. 1c). Figures 1d,e show the Niño-3.4 index of the four El Niño and two La Niña events, respectively. Although the El Niño events shifted to La Niña in the following year (Fig. 1d), the La Niña events were prolonged for up to 2 yr (Fig. 1e), suggesting that the model reproduced the strong asymmetry of transitivity, as reported in previous studies (Ohba and Ueda 2009a; Okumura and Deser 2010).

Fig. 1.
Fig. 1.

(top) Scatterplot of the simulated positive Niño-3.4 index (°C) during DJF0/+1 against (a) the following year’s DJF+1/2 and (b) equatorial surface wind anomalies (m s−1) over the WP (2°S–2°N, 135°–170°E). (c) As in (b), but for observed values. Red and blue dots denote selected events for the identical-twin experiments. (bottom) Evolution of the Niño-3.4 index (°C) overlaid from January0 to January+3 for the selected (d) El Niño and (e) La Niña events.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

Figure 2 shows the simulated and observed composite SST anomalies of the El Niño and La Niña events with surface wind. The five strongest El Niño (1965, 1972, 1982, 1987, and 1997) and La Niña (1955, 1973, 1988, 1998, and 1999) events based on the DJF Niño-3.4 index were used as observed values for the composite. The SST anomalies were derived from the Extended Reconstruction SST version 3 (ERSSTv3; Smith et al. 2008), and the surface zonal wind anomalies were obtained through National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996).

Fig. 2.
Fig. 2.

(top) Composited anomalies of observed SST (°C; contours and color shading) and surface wind (m s−1; vectors) for (a) El Niño and (b) La Niña during DJF0/+1. The anomalies are normalized by the Niño-3.4 index. (c),(d) As in (a),(b), but for simulated SST values derived from identical-twin experiments. The contour interval is 0.2°C. (bottom) Frequency distribution (%) of El Niño (red bars) and La Niña (blue bars) duration (months) after the December peak (months) from (e) ERSSTv3 and (f) MIROC5. The number of El Niño (La Niña) events in each 3-month bin has been divided by the total number of El Niño (La Niña) events.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

Overall, the simulated SST and surface wind accurately captured the ENSO-related anomalies over the Indo-Pacific sector, with the asymmetry between El Niño and La Niña similar to that in the observation. The location of the maximum SST and wind anomaly during La Niña is shifted to the west by about 30° in the CEP relative to that of El Niño. The model also generally reproduces the IO warming/cooling collaborated with the El Niño/La Niña events. As in the observation, the magnitude of IO cooling is weaker than that of IO warming from boreal winter to spring (Hong et al. 2010). These realistic features produced in the simulation determine the value of testing the IO BW/BC impact on the WP and evaluating the predictability of ENSO after its mature phase.

We should also document about some discrepancies between the model and observation that can potentially affect to the following result. For example, stronger negative SST anomalies during El Niño are overestimated in the western North Pacific. The SST anomalies associated with ENSO over the Pacific extend too far westward in the model, especially during the cold phase. Such discrepancies can mainly be attributed to the excessive cold tongue (Capotondi et al. 2006; Ohba et al. 2010). Although the simulated El Niño tends to have relatively large El Niño amplitude compared with the observation (not shown), the sensitivity of ENSO–IO coupling is very similar (Fig. 2). In the observation data (ERSSTv3; 1950–2010), a regression coefficient of the IO SST (20°S–20°N, 40°–120°E) on the Niño-3.4 index during DJF reveals 0.143 (the correlation coefficient is 0.68). That in the model is very similar (0.142 and the correlation coefficient is 0.65). This result implies that the model well reproduce the sensitivity of the IO SST to the ENSO seen in the observation.

To easily confirm how well the model can simulate the observed ENSO asymmetry in duration, we also show frequency distributions of individual ENSO events (Figs. 2e,f) as conducted in Deser et al. (2012). El Niño (La Niña) event duration is defined as the number of consecutive months for which the Niño-3.4 index exceeds 0.33 standard deviations (is less than −0.33 standard deviations) after December0, using the criteria above for event selection. The results show that about 90% (60%) of observed El Niño (La Niña) events last less than (more than) 8 months after December0. MIROC5 exhibits a similar shift in the frequency distribution of event duration: about 60% (90%) of El Niño (La Niña) events last less (more than) 8 months after December0. The model also simulate La Niña event duration exhibits a double peak in the frequency distribution.

3. Asymmetric impact of IO on ENSO transition

a. Perfect model experiment

We initially reported the differences in the predictability of simulated SST anomalies between the coupled- and decoupled-IO experiments. Figure 3 shows the evolution of the ensemble-mean Niño-3.4 index in the case of the Ctrl, CIO, and NIO runs for the two El Niño and La Niña cases. During the El Niño event (Figs. 3a,b), the index in the Ctrl and CIO runs (represented by solid and dashed lines, respectively) decrease rapidly during the following boreal spring and turn into a negative value with a narrow margin. However, aforementioned decrease is much slower in the NIO run (red dashes) than in the other. The difference in the indexes between the Ctrl and NIO run s begins to increase gradually during the period March–May+1 (MAM+1). In the following winter+1/2, the NIO simulations exhibited a widespread variability among the ensemble members, some of which reproduce the El Niño condition (Figs. 3a,b, yellow line). The Niño-3.4 SST anomaly during DJF+1/+2 in Ctrl is −2.1°C (Fig. 3a), whereas the ensemble mean in NIO is only −0.2°C. The difference in the Niño-3.4 index during DJF+1/2 between the coupled- and decoupled-IO simulations is above the 95% significant level. This result is a common feature of El Niño cases and implies that IO coupling simulates the rapid ENSO transition after the warm phase, as presented in previous studies (e.g., Ohba and Ueda 2007). In contrast, such a difference could not be seen for the La Niña events (Figs. 3c,d). The La Niña events enduring in both the CIO and NIO simulation share similar values with each other. In addition, the spread of the variability in NIO is much smaller than that for the El Niño event.

Fig. 3.
Fig. 3.

Time evolution of the Niño-3.4 index (°C) for Ctrl (black solid line) and the ensemble mean of CIO (black dashed line) and NIO (red dashed line) overlaid from January0 to March+2 for (a),(b) El Niño and (c),(d) La Niña events. Spread of individual forecast members for NIO is denoted by yellow shading.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

To quantitatively measure the difference in the predictability between CIO and NIO, we calculated the anomaly correlation coefficient (ACC) using the ensemble-mean SST in the tropical Pacific region (15°S–15°N, 120°E–90°W) in the warm (Fig. 4a) and cold (Fig. 4b) ENSO phases. The solid lines represent the ACC between Ctrl and CIO, whereas the dashed lines represent the ACC between Ctrl and NIO. In the coupled-IO case, the ACCs extend the skillful (ACC > 0.6) prediction of both ENSO phases by 1–1.5 yr. However, that of the warm ENSO phase in NIO is approximately 8 months. The forecast skills rapidly deteriorated to below 0.6 near the boreal spring–summer (Fig. 4a), which is very similar to the spring prediction barrier. These changes in skill occurred only in the warm phase. As expected from Figs. 3c,d, the difference between CIO and NIO is negligible for the cold phase (Fig. 4b).

Fig. 4.
Fig. 4.

Time evolution of the ACC using ensemble-mean SST in the tropical Pacific region (15°S–15°N, 120°E–90°W) between Ctrl and CIO (solid line) and between Ctrl and NIO (dashed line) for (a) El Niño and (b) La Niña from October0 to March+2.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

To confirm the process of El Niño transition acceleration, we adopt one of the El Niño cases that respond remarkably. Figure 5 presents the spatial distribution of the simulated SST anomalies and surface wind for Ctrl (Fig. 5a) and the difference in the CIO and NIO runs (Fig. 5b). In the Ctrl run, the simulated anomalies during the November0–January+1 period (NDJ0/+1) showed a large-scale structure: that is, IO and CEP warming and cooling in the northern WP. The SST anomalies are accompanied by an anomalous anticyclonic circulation centered over the northern WP that induces enhanced equatorial trade winds. After the mature phase in NDJ0/+1, the equatorial warming associated with El Niño decays rapidly and is replaced by weak equatorial cooling over the CEP near the boreal summer (Fig. 5a, May+1–July+1). The equatorial SST cooling and easterly anomalies associated with La Niña persist through the following season. This feature of the simulated turnabout from El Niño to La Niña resembles the observations well, such as those experienced in 1973, 1983, and 1998.

Fig. 5.
Fig. 5.

(a) SST anomalies (°C; contours and color shading) and surface wind (m s−1; vectors) for Ctrl simulation of one El Niño event from November0 to October+1. (b) As in (a), but for the difference between the CIO and NIO. The contour interval is 0.6°C.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

It is widely accepted that ENSO variability exerts a significant impact on the IO. Positive SST anomalies appear over the IO around the mature phase of warm ENSO events and persist through the following summer. During El Niño, atmospheric deep convection is suppressed over the tropical IO by a change in the Walker circulation. The resulting increase in the incoming solar radiation is mainly responsible for the warming of the IO (Klein et al. 1999), with ocean dynamics also playing an important role in the southwestern part of the basin (Xie et al. 2002). The warming of the IO and its persistence are well simulated in the Ctrl run (Fig. 5a). The difference between CIO and NIO (Fig. 5b) sheds light on the remote impact of IO SST anomalies on the El Niño transition. The results presented here are based on ensemble averages over the seven individual integrations. Although the difference in wind and SST patterns during NDJ0/+1 is miniscule, except for the IO warming, the SST anomalies clearly exhibit a strong presence in IO coupling, resulting in rapid ENSO transition during the following seasons. Strong SST cooling with anomalous easterlies is established over the equatorial Pacific, which is a mechanism associated with the generation of La Niña in CIO. The SST difference between the two experiments is much stronger than that in Ctrl (Fig. 5a), suggesting that the El Niño state is enduring in NIO and hence the transition is not established.

To better describe the time evolution of the ENSO transition, we plotted a longitude–time section, highlighting the differences in the SST and surface zonal wind between CIO and NIO near the equator (Fig. 6a). A close examination of the mature phase reveals equatorial easterly anomalies along the equator in 140°–160°E during NDJ0/+1. Lagged behind the weak easterly anomalies by a few months, the difference in SST in the eastern Pacific reveals remarkable negative values and expands to the westward after the boreal spring+1. From the succeeding summer to winter, the difference in the SST evolves rapidly to ultimately show strong cooling with easterly wind anomalies, indicating that the transition from El Niño to La Niña is established in the CIO run.

Fig. 6.
Fig. 6.

(a) Longitude–time section of the difference in SST (°C; color shading) and surface zonal wind (m s−1; contours) between CIO and NIO along the equator (2°S–2°N) from Octorber0 to March+2 for one El Niño event. The contour interval is 0.7 m s−1. (b) As in (a), but for spatial distribution of the differences in OHC (°C; contours and color shading) and surface wind (m s−1; vector) during NDJ0/+1. The contour interval is 0.2°C.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

The most plausible explanation for the easterly generation over the WP during the NDJ0/+1 is its link to the IO BW, which increases tropospheric temperatures and forces an atmospheric Kelvin wave into the WP (Annamalai et al. 2005; Terao and Kubota 2005; Ohba and Ueda 2007). The easterly anomalies induced by this Kelvin wave in NDJ0/+1 reinforce the climatological trade wind over the WP. The enhanced wind anomalies are expected to contribute to the ENSO variability because the eastern Pacific SST is closely related to the equatorial zonal wind through the generation of oceanic upwelling Kelvin waves. The resultant eastward propagation of the oceanic Kelvin wave induces thermocline displacement, which potentially contributes to the demise of CEP warming.

Presented in Fig. 6b are the differences in the ocean heat content (OHC) and surface wind between CIO and NIO during NDJ0/+1. In this study, the OHC is defined as the vertically averaged temperature from the sea surface to a depth of 300 m, which represents a variation of the thermocline. Consistent with the BW-enhanced easterly during the mature phase of El Niño (Fig. 6a), the difference between CIO and NIO reveals enhanced generation of oceanic Kelvin waves along the equatorial CEP (Fig. 6b). The cold OHC signal propagates eastward from the WP at a speed of 40°–50° longitude per month (not shown), which is roughly the propagation speed of the first and second Kelvin modes (e.g., Boulanger et al. 2003). The consecutive generation and passage of upwelling Kelvin waves into the eastern Pacific can effectively erode equatorial surface cooling. Therefore, CEP SST cooling can be attributed to the activated generation of cold Kelvin waves.

The results of the aforementioned El Niño phase are consistent with those of previous studies (e.g., Ohba and Ueda 2007). However, no significant differences exist in the tropical SST and surface wind between CIO and NIO during the La Niña phase (not shown). To confirm the asymmetry of the atmospheric sensitivity to the IO SST between the warm and cold phases of ENSO, we examine the relationship between the forecast skills in the tropical Pacific and IO. Presented in Fig. 7 is a scatter diagram of the ACCs of SST in the tropical Pacific versus the IO in CIO, derived from October0 to August+1. The IO prediction skill in the warm phase of ENSO collaborates strongly with the following prediction skill for the Pacific. Both ACCs drop from the top-right-hand corner along the one-to-one line (i.e., the solid diagonal line). However, the decline of the ACCs in the cold phase swerves to the left from the one-to-one line, suggesting that the Indo-Pacific interbasin coupling could be significantly weaker than that during the warm phase in the cases. The result presented here enhances the possibility of significant asymmetry in the sensitivity of ENSO transition to the IO SST between El Niño and La Niña.

Fig. 7.
Fig. 7.

Scatterplot of the ACC of the tropical Pacific SST between Ctrl and CIO against that of IO (lead 2 months) during October0 to August+1 for the (a) warm and (b) cold phases of ENSO.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

b. Long-term decoupled simulation

To confirm the result presented in the precious section, we compare the ENSO properties in the long-term coupled- and decoupled-IO simulations. There are several studies using the decoupled-IO experiment (Yu et al. 2002; Wu and Kirtman 2004; Kug et al. 2006; Yu et al. 2009) that examined the impacts of the Indian Ocean on the ENSO variability. The present study analyzes the experiment in view of the asymmetry of ENSO transition. We first draft composites of five strong El Niño and La Niña events and compare them between Ctrl (Fig. 8, top) and NIO (Fig. 8, bottom). Figure 8 shows the longitude–time section of the composited SST and surface wind anomalies over the equatorial Pacific from 12 months before to 14 months after the mature phases of warm and cold events. The standard deviation of ENSO in NIO is about 1.5 times larger than that in Ctrl. As documented in previous papers (e.g., An and Wang 2000; An et al. 2008; Choi et al. 2011), the ENSO characteristics (especially amplitude) are controlled to a large extent by the mean atmosphere–ocean state in the tropical Pacific. The sudden decoupling of the IO can cause the change in the mean state over the Pacific for a slightly longer time. In fact, we can find the slight change in the mean state relatively similar to Choi et al. (2011, Fig. 3) that can potentially contribute to the change in the amplitude with zonal shift in the center of SST maximum. The coupled-IO climate shows a significant difference in the ENSO duration between El Niño and La Niña (Fig. 8, top) relative to the asymmetry in the WP wind during the mature phase. When the IO is decoupled from the atmosphere, the duration of El Niño is significantly longer than that in Ctrl. Strong positive SST anomalies exceeding 2°C are maintained for the following winter, particularly over the eastern Pacific, and hence the resultant negative Niño-3.4 SST anomalies recorded during DJF+1/2 are nearly one-half of that in Ctrl. In contrast, such a difference is not observed in the La Niña phase. In both Ctrl and NIO, the cold SST anomalies are maintained for the following year with equatorial surface easterlies.

Fig. 8.
Fig. 8.

Longitude–time section of the composited SST (°C; contour and color shading) and surface wind (m s−1; vectors) anomalies along the equator (2°S–2°N) from January0 to March+2 based on five strong (a) El Niño and (b) La Niña events in Ctrl. The anomalies are normalized by the Niño-3.4 index. (c),(d) As in (a),(b), but for the long-term NIO. The contour interval is 0.5°C.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

Figures 9a,b show a one-sided autocorrelation of the Niño-3.4 index as a function of the lag derived from the coupled- and decoupled-IO simulations. In the El Niño phase (Fig. 9a), the correlation coefficient after the mature (+0) phase rapidly decreases from a positive value to a negative value. In the decoupled-IO run, the reduction of the correlation is relatively weaker than that in Ctrl, which is consistent with the results in Fig. 8. In addition, the correlation coefficient in the developing phase of the warm ENSO shows higher values, particularly 1–2 yr forward of the mature phase. The most plausible cause of this difference is the increase in the postponement of the El Niño transition in relative to the lack of IO SST anomalies, which could also result in enhancement of the El Niño amplitude. However, these differences are much weaker in the cold ENSO phase, as expected.

Fig. 9.
Fig. 9.

One-sided auto-lag correlation of Niño-3.4 index from lag −24 to +13 months for the simulated (a) El Niño and (b) La Niña phases. That of the Niño-3 index is also plotted for El Niño phase. (c) Power spectra of Niño-3.4 index. The solid (dashed) line represents the Ctrl (NIO) simulation.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

The lengthening of the warm ENSO period appears to cause a change in the overall feature of the ENSO frequencies. Power spectra of ENSO in Ctrl and NIO are presented in Fig. 9c. The power spectrum in NIO is somewhat stronger than that in Ctrl at lower frequencies, especially near the 8-yr point, around which it is statistically different from the Ctrl at a 95% confidence level. It is reasonable to suspect that the change in frequency is due to the reduced El Niño transitivity. The differences between the coupled- and decoupled-IO simulations presented in this section share certain similarities to the result of the identical-twin forecast experiments detailed in the previous section. The results of our additional long-term simulation strengthen our hypothesis that the decoupled-IO state leads to reduced ENSO asymmetry, thereby modifying the ENSO characteristics.

4. Summary and discussion

a. Discussion

We consider that the asymmetric feedback of the IO SST may be attributed to the following two factors: First, the amplitude of the IO SST warming is significantly stronger than that of the cooling. As for the reason, Hong et al. (2010) have explained that the asymmetry of the mixed layer depth between the warm and cold events is a major contributing factor to the positive skewness of the IO SST. In a simple linear regression model, the asymmetric intensity of the IO warming and cooling directly result in the asymmetric IO feedback. MIROC5 reproduces positive SST skewness results similar to the observation (not shown). This distortion is especially strong in the northern IO (Figs. 2c,d). Thus, the asymmetry in the intensity of the IO BW and BC is a plausibly one of the primary explanations.

Second, the “zonal distance” of the diabatic heating/cooling between the IO and Pacific differs between El Niño and La Niña, as reported by Okumura et al. (2011). Compared with El Niño, the Pacific precipitation anomalies during La Niña are displaced westward by 10°–40° in longitude and the large equatorial easterly wind anomalies extend farther into the WP throughout the developing and mature phases (e.g., Ohba and Ueda 2009a). This zonal shift in convection anomalies can change the sensitivity of the WP wind to the IO SST anomalies. From a linear theory, the intensity of the atmospheric response to diabatic heating or cooling depends on the zonal distance between the heat sources or sinks (e.g., Gill 1980). To illustrate the importance of the zonal distance in the convections, we use a linear baroclinic model (LBM) in which equations are linearized about the observed DJF mean state obtained from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005). The detailed description of the LBM is found in Watanabe and Kimoto (2000). We use a version with T42 resolution in the horizontal and 20 sigma levels in the vertical. The model is forced by prescribed diabatic heating. In the vertical, heating is confined between 900 and 100 hPa, with a peak at 500 hPa, to mimic the condensational heat release in deep convection. The horizontal distribution of the heating (K day−1) at 500 hPa and the steady low-level wind response are illustrated in Fig. 10a. Although our experimental design follows Annamalai et al. (2005), additional heating anomalies exist over the CEP. We change the peak longitude of the positive Pacific heating that mimics the ENSO-related diabatic heating anomaly.

Fig. 10.
Fig. 10.

(a) The imposed heating rate (K day−1; contours and color shading) at 500 hPa for the linear atmospheric model and 850-hPa wind response (m s−1; vectors) to the heating. (b) Scatterplot between the center longitudes of the Pacific heating vs zonal wind anomaly at the equatorial WP derived from the linear atmospheric model (solid line). Five strong El Niño (La Niña) observed events are overlain by red (blue) circles based on NOAA interpolated OLR and NCEP–NCAR reanalysis. The dashed line represents the wind anomaly when the LBM is forced by the IO heating only.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

Figure 10b shows a scatter diagram of the zonal wind response against the peak (center) longitude of the heating derived from the LBM experiment. The WP easterlies relative to the IO warming are observed only when the center longitude of the CEP heating strays east of 160°W. In addition, we also plot the observed relationship between the center longitude as determined using anomalous outgoing longwave radiation [OLR; National Oceanic and Atmospheric Administration (NOAA) interpolated OLR for 1975–2010; Liebmann and Smith 1996] and the surface zonal wind anomaly at the equatorial WP for five strong El Niño (red) and La Niña (blue) events during DJF0/+1. The LBM responses to the heating located at various longitudes well capture the observed relationship. The close proximities of the diabatic cooling anomalies over the tropical IO and Pacific during La Niña cause destructive interference that weaken the magnitude of the negative WP zonal wind anomalies in response to the IO cooling. As presented here, the sensitivity of the WP wind to the IO heating (Fig. 10, dashed line) can be potentially adjusted by the Pacific basin internal process: that is, the difference in the zonal distance in convection between the warm and cold phases. Thus, we conclude that collaboration between the IO feedback and Pacific internal process is considered to be an important factor contributing to the asymmetry in the Indo-Pacific interbasin coupling.

The westward shift of the anomalous atmospheric responses during the negative phase of ENSO is mainly due to the zonal asymmetry of the climatological SST, as reported by Hoerling et al. (1997). Indeed, an AGCM forced with perfectly symmetric positive and negative CEP SST anomalies successfully simulates nonlinear atmospheric responses that closely resemble the observed patterns (Okumura et al. 2011). Thus, one of the fundamental causes of the ENSO asymmetry is the nonlinear atmospheric response to a direct impact of the CEP SST forcing (Ohba and Ueda 2009a). The averaged absolute values of the sum of the DJF+1/2 Niño-3.4 indexes during El Niño and La Niña in the forecast CIO and NIO is about 3.2° and 1.7°C, respectively. Therefore, our CGCM experiments estimate that approximately 50% of the ENSO asymmetry originates from the direct nonlinear atmospheric response to the CEP SST forcing, whereas the rest will be attributed to the phase-dependent nature of the Indo-Pacific interbasin coupling.

b. Summary

Warm and cold phases of ENSO exhibit significant asymmetry in their transition and duration such that El Niño tends to turn rapidly into La Niña after the mature phase, whereas La Niña tends to persist for up to 2 yr. Several studies have indicated that the IO warming during El Niño assists in the phase transition (e.g., Kug and Kang 2006; Ohba and Ueda 2007) through modulation of the WP surface wind (e.g., Watanabe and Jin 2003; Annamalai et al. 2005). However, the effect of IO cooling on La Niña after the mature phase is not well identified because very few climate models accurately reproduce the La Niña duration (Ohba et al. 2010). In this study, a state-of-the-art CGCM that effectively captures the ENSO asymmetry is used to investigate the possible role of IO SST anomalies on the transition/duration asymmetry. To assess the asymmetric IO feedback of the ongoing ENSO evolution, identical-twin forecast experiments are conducted using two different air–sea coupling strategies. In the decoupled-IO framework, the evolution of the Niño-3.4 index during El Niño is significantly incoherent during and after the mature phase as compared to that in Ctrl. The identical-twin forecast experiments show that the prediction skill of the ENSO warm phase is extended by approximately one year and 8 months in the presence and absence of IO coupling, respectively. However, such a difference in duration was not detected in the cold phase. The effect of IO coupling on the predictability of ENSO evolution is significantly weaker in the decay phase of La Niña. These results imply that the ENSO asymmetry is enhanced by the Indo-Pacific interbasin coupling, and nearly 50% of this asymmetry could be attributed to the asymmetric IO feedback.

The phase-dependent nature of the Indo-Pacific interbasin coupling is illustrated schematically in Fig. 11. The coupled IO during the mature phase enhances the surface easterly anomalies over the equatorial WP, as shown in Fig. 11 (top left), and hence induces an advanced transition from El Niño to La Niña through the generation of additional negative oceanic Kelvin waves (Fig. 11, top right). However, this mechanism can be much weaker for the cold IO SST anomalies present during La Niña (Fig. 11, bottom left). Thus, the CEP cooling continues through the following year (Fig. 11, bottom right).

Fig. 11.
Fig. 11.

Schematic diagram illustrating the asymmetric role of the IO in the transition of (top) El Niño and (bottom) La Niña after their mature phases. Red (black) shading indicates positive (negative) SST and subsurface temperature anomalies. White outlined arrows represent anomalous surface wind. Clouds (larger downward arrows) indicate enhanced (reduced) convective activity.

Citation: Journal of Climate 25, 9; 10.1175/JCLI-D-11-00409.1

Previous studies have shown that zonal wind stress over the equatorial WP is an important element for ENSO transition (e.g., Weisberg and Wang 1997), and therefore the spring prediction barrier is essentially caused by deficiencies in the WP equatorial zonal wind in the forecast models (e.g., Wu et al. 2009). Our experiment indicates that an improvement in the seasonal forecasting of the IO SST anomalies may ultimately lead to more skillful ENSO prediction after the mature phase. By improving the SST response of the IO in the El Niño phase, we can expect forecasts to overcome the spring prediction barrier in some extent. In addition, forecasts during the El Niño decay phase show faster skill deterioration than those in La Niña (Jin and Kinter 2009). The results of our forecast experiments suggest that the asymmetry of the spring barrier is attributed to the phase-dependent nature of the Indo-Pacific interbasin coupling.

Because the present study is based on the result of only one model simulation, a more comprehensive analysis of the Coupled Model Intercomparison Project phase 5 (CMIP5) models should be performed in the future. This additional testing will allow us to promote a better understanding of the ENSO asymmetry. Given the more frequent occurrence of the ENSO-related IO warming in recent decades (e.g., Xie et al. 2010), probably aided by the weakened Walker circulation (Vecchi et al. 2006) and ENSO-like decadal variability in the Pacific (e.g., Luo and Yamagata 2001), it is conceivable that the intensified IO warming is an important factor in the long-term variability of ENSO behavior. This may have implications for our future projection of the Indo-Pacific climate variations in a changing climate.

Acknowledgments

This work was supported by the Innovative Program of Climate Change Projection for the 21st Century (“Kakushin” program) from MEXT, Japan.

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