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  • View in gallery

    Evolution of SST anomalies averaged over 2°S–2°N obtained through one-sided regression on the (left) positive and (right) negative Niño-3.4 index during DJF (0/1) for each CGCM and OBS. The shaded areas indicate where the regression is above the 95% significant level.

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    One-sided regressions of the SST (°C) and wind stress (N m−2) for the El Niño phase of the Niño-3.4 index during DJF (0/1) for (a) observations, (b) composite derived from high-transitivity models, and (c) composite derived from low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.2°C (−0.2°C). (d)–(f) As in (a)–(c), but for diabatic heating and precipitation for (second row) DJF (0/1), (third row) MAM (1), and (bottom row) JJA (1). The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than (d) 20 W m−2 (−20 W m−2) and (e),(f) 1 mm day−1 (−1 mm day−1).

  • View in gallery

    Time evolution of the zonal wind stress anomalies (N m−2) averaged over the equatorial band (130°E–170°W) of each CGCM for (a) four high-transitivity models and (b) five low-transitivity models from February (0) to February (2), based on one-sided regression for positive Niño-3.4 index during DJF (0/1).

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    Composited climatological annual mean of simulated SST (°C) for the (a) high-transitivity models and (b) low-transitivity models. (c),(d) As in (a),(b), but for precipitation (mm day−1). (e) Composited climatological annual mean of simulated zonal wind stress (N m−2) at the equator for the high- (solid) and low- (dashed) transitivity models.

  • View in gallery

    Scatter diagrams of the climatological annual-mean precipitation (mm day−1) of the WCP (5°S–5°N, 150°E–130°W) and the EPI of the warm-phase ENSO. Letters indicate model IDs shown in Table 1, and the asterisk is the observations.

  • View in gallery

    One-sided lag correlation between the SST over the IO for (top) DJF (0/1), (middle) MAM (1), and (bottom) JJA (1) and El Niño phase of the Niño-3.4 index during DJF (0/1) for (a) composite derived from high-transitivity models and (b) composite derived from low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) correlation coefficient is greater (smaller) than 0.3 (−0.3).

  • View in gallery

    As in Fig. 2, but for (a, d) observations, (b, e) composite derived from high-persistence models, and (c, f) composite derived from low-persistence models, based on one-sided regression for negative Niño-3.4 index during DJF(0/1).

  • View in gallery

    As in Fig. 3, but for (a) four high-persistence models and (b) four low-persistence models from February (0) to February (2), based on one-sided regression for negative Niño-3.4 index during DJF(0/1).

  • View in gallery

    Evolution of the composited OHC anomalies (°C) averaged over 2°S–2°N for (a) high-persistence models and (b) low-persistence models. The OHC anomalies of each CGCM are obtained through one-sided regression on the negative Niño-3.4 index during DJF (0/1).

  • View in gallery

    As in Fig. 9, but for (a) composited equatorial zonal wind stress anomalies (N m−2) and OHC tendency anomalies (°C) at (b) the equator and (c) the off equator derived from low-persistence models.

  • View in gallery

    One-sided regressions of the OHC (°C) and zonal stress (N m−2) for La Niña phase of the Niño-3.4 index during NDJ (0/1) for composite derived from (a) high- and (b) low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.2°C (−0.2 °C). (c),(d) As in (a),(b), but for zonal wind stress. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.005 N m−2 (−0.005 N m−2).

  • View in gallery

    (a) Seasonal evolutions of climatological precipitations over the WCP (Fig. 4c, solid box: 5°S–5°N, 150°E–160°W) for the four low-LP models (solid) and observations (dashed). The precipitation is plotted as the difference from the values during January. (b) Intermodal correlation between the EPI of the cold phase of each CGCM with the seasonal change in climatological precipitation from boreal winter to spring (March–April minus December–January).

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Simulation of Asymmetric ENSO Transition in WCRP CMIP3 Multimodel Experiments

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  • 1 Central Research Institute of Electric Power Industry, Chiba, Japan
  • 2 Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
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Abstract

Based on the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset, the relationships between the climatological states and transition processes of simulated ENSO are investigated. The air–sea coupled system of the observed ENSO can remain in the weak cold event for up to 2 yr, whereas those of the warm events tend to turn rapidly into a cold phase. Therefore, the authors separately investigate the simulated transition process of a warm-phase and a cold-phase ENSO in the CMIP3 models. Some of the models reproduce the features of the observed transition process of El Niño/La Niña, whereas most models fail to concurrently reproduce the process during both phases.

In the CMIP3 models, four climate models simulate well the rapid transition from El Niño to La Niña. The intensity of a rapid transition of El Niño is mainly related to the intensity of the simulated climatological precipitation over the western–central Pacific (WCP). The models that have strong WCP precipitation can simulate the rapid termination of the equatorial zonal wind in the WCP, which tends to result in the termination of El Niño phase. This relationship is not applicable for the La Niña transition phase. The simulation of La Niña persistency is related to the reflection of off-equatorial Rossby waves at the western boundary of the Pacific and the seasonal evolution of the climatological precipitation in the WCP. Differences in the transition processes between El Niño and La Niña events are fundamentally due to the nonlinear atmospheric (convective) response to SST, which originates from the distribution of climatological SST and its seasonal changes. The results of the present study indicate that a realistic simulation of the climatological state and its seasonality in the WCP are important to be able to simulate the observed transition process of the ENSO.

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

Abstract

Based on the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset, the relationships between the climatological states and transition processes of simulated ENSO are investigated. The air–sea coupled system of the observed ENSO can remain in the weak cold event for up to 2 yr, whereas those of the warm events tend to turn rapidly into a cold phase. Therefore, the authors separately investigate the simulated transition process of a warm-phase and a cold-phase ENSO in the CMIP3 models. Some of the models reproduce the features of the observed transition process of El Niño/La Niña, whereas most models fail to concurrently reproduce the process during both phases.

In the CMIP3 models, four climate models simulate well the rapid transition from El Niño to La Niña. The intensity of a rapid transition of El Niño is mainly related to the intensity of the simulated climatological precipitation over the western–central Pacific (WCP). The models that have strong WCP precipitation can simulate the rapid termination of the equatorial zonal wind in the WCP, which tends to result in the termination of El Niño phase. This relationship is not applicable for the La Niña transition phase. The simulation of La Niña persistency is related to the reflection of off-equatorial Rossby waves at the western boundary of the Pacific and the seasonal evolution of the climatological precipitation in the WCP. Differences in the transition processes between El Niño and La Niña events are fundamentally due to the nonlinear atmospheric (convective) response to SST, which originates from the distribution of climatological SST and its seasonal changes. The results of the present study indicate that a realistic simulation of the climatological state and its seasonality in the WCP are important to be able to simulate the observed transition process of the ENSO.

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

1. Introduction

The El Niño–Southern Oscillation (ENSO) phenomenon is associated with a quasi-periodic (3–7-yr time scale) warming (El Niño) and cooling (La Niña) of the tropical central–eastern Pacific (CEP) that influences the global climate. A number of investigators have suggested several conceptual theories to explain the self-sustained oscillation of the ENSO. Especially, the “delayed oscillator” (e.g., Schopf and Suarez 1988), “recharge oscillator” (e.g., Jin 1997), and “western Pacific (WP) oscillator” (e.g., Weisberg and Wang 1997) theories are three of the most influential ones, providing a comprehensive idea regarding the cyclic nature of the ENSO (Wang 2001). The phase transition of El Niño to La Niña takes place through a delayed negative feedback that originates from dynamic ocean adjustments and related atmospheric responses.

These mechanisms in the oscillator theories adequately reproduce the linear oscillation of the ENSO; in other words, it demonstrates that the termination of El Niño is consistent with a cyclic nature. However, some studies have pointed out that a kind of break in the ENSO cycle has been observed when La Niña goes to El Niño. The air–sea coupled system over the Pacific somehow remains in a weak La Niña state for a while (Kessler 2002; Nagura et al. 2008; McPhaden and Zhang 2009). Recent studies (Ohba and Ueda 2009a; Okumura and Deser 2010) have reported the asymmetry of the transition process of the ENSO through the nonlinear atmospheric response to the CEP sea surface temperature (SST) forcing (Hoerling et al. 1997, 2001; Kang and Kug 2002). In Fig. 1, observations (OBS) show the evidence that demonstrates the asymmetry of ENSO transition/duration obtained from one-sided regression analysis (for further information, see section 2c). El Niño events tend to turn into a La Niña during the following year (Fig. 1, OBS left), whereas La Niña events tend to remain in weak La Niña state (Fig. 1, OBS right). The asymmetry of transition/duration is new aspect of ENSO in addition to the asymmetry of amplitude (e.g., An et al. 2005) and spatial distribution (e.g., Kang and Kug 2002). The duration of La Niña can cause severe droughts, such as those that took place in central Asia from 1999 to 2001 (e.g., Hoerling and Kumar 2003; Ueda and Kawamura 2004). Therefore, the skill of seasonal climate forecasts for predicting the duration of La Niña with an air–sea coupled general circulation model (CGCM) is very important to the prediction of severe droughts.

Recent assessments for climate projections are summarized in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In this report, the future projections are based on 24 state-of-the-art CGCMs from many climate research centers and institutes worldwide that participate in the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al. 2007). However, following the IPCC AR4 Special Report on Emission Scenarios (SRES), future climate changes do not show any clear evolution of the ENSO properties (Meehl et al. 2007; Guilyardi et al. 2009). Part of the uncertainty is due to model systematic errors. Indeed, CGCMs still have errors in reproducing the observed characteristics of ENSO events (AchutaRao and Sperber 2002; An et al. 2005; van Oldenborgh et al. 2005; AchutaRao and Sperber 2006; Joseph and Nigam 2006; Guilyardi 2006; Capotondi et al. 2006; Yamaguchi and Noda 2006; Park 2008; Guilyardi et al. 2009; Lengaigne and Vecchi 2009). As presented in previous study (Neale et al. 2008), changes in the onset/termination processes of El Niño result in the change in the ENSO frequency. Therefore, the simulated transition processes of ENSO could be directly linked with its frequency. However, many CGCMs tend to have a strong cyclic nature; namely, La Niña tends to turn into El Niño, suggesting a weak nonlinearity of ENSO (Hannachi et al. 2003). Monahan and Dai (2004) also analyzed several CGCMs and found that the nonlinear structure in the ENSO simulated in CGCMs is highly model dependent. Many CGCMs fail to represent the spatial and temporal structure of the El Niño–La Niña asymmetry. Monahan and Dai (2004) also argued that the divergence in ENSO nonlinearity simulated in different CGCMs mainly results from difficulties in representing the mean state of the tropical Pacific.

Recently, several studies have pointed out the dominant role of the atmospheric component in setting the ENSO characteristics in state-of-the-art models (Guilyardi et al. 2004; Kim et al. 2008; Neale et al. 2008) in relation with the mean state of SST and precipitation distribution. The change in the simulated climatological mean state can influence the ENSO-related feedback processes and has the potential to modify the ENSO properties. However, the effect of the biases in the simulated climatological states on the ENSO simulation is not clear. To further improve ENSO in the CGCMs, it is important to compare the detailed physical processes between the CGCMs and observations and then assess the role of the simulated mean state over the tropical Pacific. Therefore, investigating the cause of ENSO discrepancy between observations and simulations in the CMIP3 CGCMs could contribute to the understanding of the natural climate, in relation to the asymmetry of ENSO.

In the present study, we identify the signature of the asymmetric transition process of ENSO in each of the CMIP3 climate models and investigate its relationship to the atmospheric climatological Pacific conditions in the models. The CGCMs, to some extent, can capture some aspects of the observed ENSO. In this paper, we will first consider the transition from El Niño to La Niña, and later we will consider the persistency of La Niña. Because of the asymmetry of the change after mature phase, these transitions are studied separately. The capture of mechanisms of these asymmetric phase transition may also provide a stringent test for coupled models. In addition, the present study emphasizes the importance of the asymmetric transition process of the ENSO, which is tied to the simulation of the (nonlinear) cyclic nature of the ENSO in CGCMs. This paper is organized as follows: section 2 contains a description of the data utilized in the present study. Section 3 examines the transition processes for El Niño and La Niña separately in the CGCMs. We discuss the result of analysis in section 4. Finally, we summarize our conclusions in section 5.

2. Data

a. Multimodel database

Simulations analyzed in this paper come from models available via the IPCC AR4/CMIP3 database (Meehl et al. 2007). Analyzed data are basically obtained from the twentieth-century (20C3M) simulation of the CMIP3 data for 100 yr (1900–99). It is not an objective of this paper to compare the El Niño amplitude. Our target is on the asymmetric transition processes during relatively strong ENSO events. Hoerling et al. (2001) reported that the nonlinearity in atmospheric responses requires that CEP SST anomalies exceed one standard deviation of their interannual variability. Therefore, the asymmetry is strong during the strong ENSO events (Ohba and Ueda 2009a). Seven models with standard deviations of Niño-3.4 index less than one-half or more than twice the observed ones are excluded: Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1)-T47; CGCM3.1-T63; Goddard Institute for Space Studies Atmosphere–Ocean Model (GISS-AOM); Goddard Institute for Space Studies Model E-R (GISS-ER); Institute of Atmospheric Physics Flexible Global Ocean–Atmosphere–Land System Model gridpoint version 1.0 (IAP-FGOALS1); Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]; and MIROC3.2, medium-resolution version [MIROC3.2(medres)]. The list of selected models is given in Table 1, including the short names used in this paper. In this study, we focus on the interannual fluctuation of the CGCM systems without introducing added complexity of changes in radiative forcing. Therefore, we apply a high-pass filter to remove periodicities longer than 10 yr. The datasets are interpolated (2D linear interpolation) onto a common 2.5° longitude by 2.5° latitude grid. Monthly anomalies are calculated by removing a mean climatological cycle.

This study is focused primarily on the tropical Pacific, defined as 40°S–40°N, 120°E–60°W. We use the Niño-3.4 index, which is the area-averaged SST over the equatorial CEP (5°S–5°N, 170°–120°W) in each CGCM. As it is well known, the Niño-3.4 index adequately captures both the warm and cold ENSO events. We define months during the ENSO onset year as (0) and those during the succeeding year as (1).

b. Observational data

We used the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Simmons and Gibson 2000) as atmospheric data. These data cover the period from mid-1957 to mid-2002. Global monthly SST data are from the Extended Reconstructed Sea Surface Temperature, version 2 (ERSST.v2; Smith and Reynolds 2004). For the calculation of the interannual fluctuation of the SST and the reanalysis data, we also apply a high-pass filter to remove periodicities longer than 10 yr. To compare to simulated anomalous precipitation for a long period, we also use the monthly averaged vertically integrated apparent heat sources Q1 (Yanai et al. 1973) in place of precipitation; Q1 reflects the contributions from individual physical processes, such as radiative heating, cumulus convection, and turbulent processes. It is well known that convective heating contributes the most to the total Q1 beyond the radiative cooling and turbulent processes at the low level. The Q1 anomalies are obtained from the ERA-40 by use of classic Q1 and Q2 approach (Yanai et al. 1973). We also use the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) for climatological precipitation.

c. One-sided linear regression

We conducted one-sided linear regression (and correlation) analysis separately for positive and negative Niño-3.4 indices, as did Hoerling et al. (2001). The procedure involves calculating the linear relation between all occurrences of one sign of the index and the observational data. Anomalies used in this analysis are calculated with respect to means based on samples associated with a single sign of the SST index. The regressions thus indicate the change in a particular atmospheric and oceanic variable for a unit change of the warm or cold index. The one-sided regressions are estimated from roughly half of the record of the data, whereas the number of negative values slightly larger than positive in the observation because of the ENSO asymmetry. To increase statistical robustness as much as possible and simply investigate the general characteristic of simulated ENSO in the CMIP3 models, the regression and correlation coefficients are calculated from the 100-yr 20C3M simulations without a selection of years around strong El Niño events. We also conducted the analysis by selecting the ENSO events (exceeding a 0.5 standard deviation). Their results are mainly similar to those in the one-sided regression analysis without a selection of years. We also conducted the analysis by dividing the data into two periods (1900–50 and 1950–99). The correlation coefficients between the EPI index during 1900–99 and 1900–50 (or 1950–99) exceed 0.8, implying that the results presented in this study will not change significantly when we select the different 50 yr.

3. El Niño and La Niña transition process in CGCMs

In this section, an analysis of the temporal evolution of the anomalous atmospheric and oceanic component associated with El Niño and La Niña is discussed using the data of CGCMs. Figure 1 displays the evolution of the equatorial monthly-mean SST anomalies from January (0) to February (2), obtained through one-sided lag regressions to positive (El Niño) and negative (La Niña) December–February [DJF (0/1)] Niño-3.4 SST for each CGCM and observations. Observed El Niño and La Niña events tend to peak during boreal winter (Horii and Hanawa 2004). Therefore, we mainly focus on the seasonally phase-locked transition system from boreal winter to the subsequent season. It is noteworthy that the simulated ENSO in the CMIP3 CGCMs tend to peak during boreal fall and winter (Lengaigne and Vecchi 2009; Guilyardi et al. 2009), whereas most models underestimate the percentage of ENSO peaking during these seasons compared to observations. Some CGCMs fail to capture the peak of CEP SST anomalies.

Concurrent with the mature phase of the warm and cold events, substantial anomalies are presented over the CEP. A closer look at the OBS of Fig. 1 shows some conspicuous features. One of the most remarkable features is the difference in spatial structure. The location of the maximum SST anomaly during La Niña is shifted to the west by about 30° in the CEP relative to that of El Niño. It is particularly noteworthy that the El Niño–related warm SST anomalies rapidly turn into cold anomalies during the following year (Fig. 1, OBS left). In contrast, the negative event does not conclude with the significant opposite SST anomalies (Fig. 1, OBS right), suggesting that the cold events tend to remain cold SST anomalies. A less cyclic nature of La Niña termination (Kessler 2002) is seen. Thus, to some extent, the separate regression/correlation for the positive and negative index could capture the nature of ENSO asymmetries.

Among the CGCMs, the overall features of SST anomalies around the peak of the simulated ENSO are relatively similar to each other. As expected, the evolutions of the SST anomalies in few models show asymmetric responses to the Niño-3.4 index. Whereas most models display a nearly symmetric relationship between El Niño and La Niña [Commonwealth Scientific and Industrial Research Organisation Mark version 3.0 (CSIRO Mk3.0); Geophysical Fluid Dynamics Laboratory Climate Model version 2.0 (GFDL CM2.0); Hadley Centre Global Environmental Model version 1 (HadGEM1); Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM3.0); Meteorological Institute of the University of Bonn, ECHO-G Model (MIUBECHOG); ECHAM5/Max Planck Institute Ocean Model (MPI-OM); Meteorological Research Institute Coupled GCM version 2.3.2 (MRI-CGCM2.3.2); National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3); and NCAR Parallel Climate Model (PCM)], especially on the transition period, a few models [GFDL CM2.1; Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3 (CNRM-CM3); CSIRO Mk3.5; third climate configuration of the Met Office Unified Model (HadCM3); Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4; and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) relatively capture the asymmetry of transition as seen in the observational data (Ohba and Ueda 2009a; Okumura and Deser 2010).

The area-averaged lag correlation coefficient between the DJF (0/1) and DJF (1/2) SST over the Niño-3.4 for both the warm and cold phases of ENSO is shown in Table 2 for each model. For comparison, we also present the results derived from ERSST for two periods (1950–2005 and 1975–2005). As estimated by the lag correlation, four or five models capture the transitivity (persistency) of strong El Niño (La Niña) events, as seen in the observations. It is noteworthy that the model having El Niño transitivity (ET) is inconsistent with the model having a La Niña persistency (LP). We use the coefficients as an index for the intensity of the ET and LP and denote it as the ENSO persistency index (EPI). The positive (negative) values of the EPI suggest that the models tend to have persistency (transitivity) after the mature phase of the ENSO event. Three models (GFDL1, HadC3, and CNRM) show relatively strong asymmetry, as seen in the observations. To compare the high- and low-ET/LP models, we pick up some (4 or 5) high- and low-ET/LP models that exceed the upper and lower 80% critical value of the Student’s t distribution. The selected high (low) ET/LP models have relatively strong (weak) transitivity/persistency, which is denoted by the boldface (italic) values in Table 2.

a. Simulation of El Niño transitivity

First, we present a composite of the one-sided lag regression of SST and precipitation with wind stress over the Pacific for the four high-ET (B, F, H, and M) and five low-ET (G, I, J, N, and Q) models (Fig. 2). Figures 2a–c show the regression of the SST during DJF (0/1) for the observations and composite of the high- and low-ET models, respectively.

In comparison with the observation (Fig. 2a), the simulated SST in the CMIP3 models during the mature phase of El Niño tends to have a westward extension and a thin equatorial signal (Figs. 2b,c). This feature is associated with a typical systematic error in the CGCMs (e.g., Meehl et al. 2001), which results in a westward shift of the ascending branch of the Walker circulation in the WP. Although this is a common bias in the models, the extension is particularly developed in low-ET models. The SST and wind stress anomalies in the low-ET model extend too far west into the Pacific (Fig. 2c).

To confirm the difference in the atmospheric responses between the high- and low-ET model, we also show the observed diabatic heating (Fig. 2d) and composite of the precipitation (Figs. 2e,f) with the surface wind vector for the high- and low-ET models during DJF (0/1), MAM (1), and JJA(1). The simulated precipitation anomalies during the DJF (0/1) have a large-scale structure, recognized as the east–west contrast between the WP and the central Pacific. Several differences between the high- and low-ET composite emerge; the first is that the location of the maximum precipitation anomaly in the low ET is shifted to the west in the equatorial Pacific (Fig. 2f) relative to that of the high ET (Fig. 2e). Associated with the westward shift in the anomalous precipitation, the spatial pattern of surface wind anomalies in the low-ET models is also more shifted to the west than that in the high-ET models. The second is the evolution of the anomalous wind and precipitation after their mature phase. The WP precipitation anomalies in the low-ET models persist during the decay phase. However, the spatial pattern of high ET is a northwest–southeast contrast (Fig. 2e), as is also evident in the observations (Fig. 2d) during DJF (0/1) to March–May [MAM; (1)]. This process is closely linked to the seasonal warming of the southern part of the off-equatorial Pacific from the boreal winter to spring (e.g., Vecchi 2006), which could be associated with the seasonal SST warming of the eastern part of the basin. The third is the generation of the anomalous equatorial WP easterly, which is attributed to the additional hasting process to the ENSO transition: namely, the WP oscillator (Wang et al. 1999). Although the high-ET models show the anomalous easterly over the WP after the mature to the decay phase, those in the low ET do not.

To seek more convincing evidence for the difference in the atmospheric response to the ENSO SST anomalies between the high- and low-ET models, we plotted a time series of the simulated equatorial wind stress anomalies derived from the positive side regression (Fig. 3). Near the end of the calendar year, there is a southward shift of the anomalous equatorial westerlies. Vecchi and Harrison (2006) and Vecchi (2006) showed that this feature is related to the seasonal motion of the warmest SST to south of the equator, which changes the convective anomalies from those centered on the equator to south of the equator, that displaces convective anomalies from the equator southward and causes thermocline shallowing. Recent studies (e.g., Kug and Kang 2006; Ohba and Ueda 2007) additionally pointed out the importance of the equatorial WP wind for the subsequent ENSO turnabout. Following the rapid change in the equatorial wind stress anomalies, the thermocline in the eastern Pacific turns into La Niña condition rapidly (Ohba and Ueda 2009a). Therefore, it will be useful to look at the timing of the change in the equatorial zonal wind anomalies.

As pointed out by Okumura and Deser (2010), the transition of El Niño event is very sensitive to the wind stress variations in the western Pacific. In Fig. 3, we show the time evolution of the zonal wind stress anomalies for the high- and low-ET models, zonally averaged on the equatorial WP (130°E–170°W). Following the onset of positive ENSO SST anomalies, the anomalous westerlies gradually increase. In the high-ET models (Fig. 3a), the maximum westerly anomalies appear during the boreal fall (0) and then rapidly decrease during the mature phase. During the subsequent seasons, the simulated wind anomalies turn into easterlies. The rapid reduction of the zonal wind can be accounted for by the expansion of the anomalous easterlies over the WP (Fig. 2e), which acts as a counterbalance to the westerlies over the CEP. In contrast with the high-ET models, the zonal wind stress anomalies in low-ET models decrease gradually from the boreal fall and winter (0) to the spring of the subsequent year. Equatorial westerlies, or only very weak wind anomalies, remain for the subsequent season.

The unrealistic features in the low-ET models reported above could be accompanied by several deficiencies in the simulated climatology to some extent. To discuss the link between model climatology and the simulated ENSO, we show the composited climatological SST, precipitation, and equatorial zonal wind stress for high- and low-ET CGCMs (Fig. 4). As presented in Figs. 4b,d, the low-ET models suffer from a cold tongue problem. The simulated climatological rainfall over the equatorial WCP is much more reduced (Fig. 4d) than that in the high-ET models (Fig. 4c). This is a consequence of the simulated strong cold tongue extending too far west, pushing westward the ascending branch of the Walker circulation with the enhanced equatorial surface easterlies in the WP (Fig. 4e).

There is a possible linkage between the transitivity of El Niño and intensity of the climatological WP precipitation. Figure 5 shows an intermodel relationship between the annual-mean precipitation rate averaged over the equatorial WCP (Fig. 4c, solid box: 5°S–5°N, 150°E–160°W) and the EPI of the warm phase (Fig. 5). The correlation coefficient is −0.69. The simulated precipitation over the equatorial WCP in the CGCMs is less than in observations, as already pointed out in previous studies (e.g., Lin 2007; Ose and Arakawa 2009). Although other factors may also affect the simulation of the El Niño transition in the studies mentioned above, Fig. 5 implies that the bias over the equatorial WCP affects the intensity of the transition process of El Niño; namely, weaker WCP precipitation tends to result in the weaker transitivity of the simulated El Niño in the CGCMs.

The argument presented above links the direct atmospheric response to El Niño in the WP from boreal winter to spring to the simulated climatological state of CGCMs (Wang et al. 2000; Wang and Zhang 2002). The question of how the subsequent equatorial easterly anomalies during the decay phase of the ENSO (Fig. 3a) appear needs to be addressed. In addition to the direct response to the El Niño–related SST anomalies, external forcing could also contribute to the enhancement of easterly anomalies over the equatorial WP. Recently, several studies recognized the importance of the Indian Ocean (IO) feedback on the ENSO transition (e.g., Annamalai et al. 2005; Kug and Kang 2006; Ohba and Ueda 2007) via the generation of IO basin-wide warming (e.g., Klein et al. 1999) in response to the El Niño forcing. These studies have shown that the IO basin-wide warming during El Niño mature to decay phase can influence the tropical WP wind variability. According to them, the basin-wide warming during the boreal winter strengthens the surface easterlies over the equatorial WP, which induces an advanced transition to La Niña. In Fig. 6, we show a composite of the correlation coefficient between IO SST and DJF (0/1) Niño-3.4 SST for high- and low-ET models. It is clearly evident that the high-ET models have a strong interbasin coupling between the IO and Pacific, whereas the low-ET models do not. The correlation coefficient between the EPI of the warm phase and the area-averaged one-sided correlation coefficients of SST over the IO (30°S–30°N, 40°–120°E) is −0.61. The result presented here suggests that the simulation of the IO basin-wide warming could contribute to the transition process of El Niño in the CGCMs in addition to the climatological condition of the WCP.

b. Simulation of La Niña persistency

The above section presented the relationship between the ET and climatological precipitation over the WP in the CGCMs. However the correlation coefficient between EPI during the cold phase and precipitation rate averaged in the WP is very weak (0.16), suggesting that La Niña persistency is regulated by another factor. To seek another regulation factor of La Niña persistence, we conducted a composite analysis of the cold phase for the four high-LP (B, D, F, and H) and four low-LP models (C, M, P, and Q). Figure 7 shows the observations and composite of the high- and low-LP models of SST during DJF (0/1) (Figs. 7a–c, respectively) and precipitation with the surface wind vector during DJF (0/1), MAM (1), and JJA (1) (Figs. 7d–f, respectively).

In comparison with the observation (Fig. 7a), the simulated SST anomalies in the CMIP3 models during the mature phase of La Niña tend to have a westward extension and a thin equatorial signal (Figs. 7b,c), as seen in the simulated El Niño (Fig. 2). The location of the simulated precipitation anomalies in the models is shifted to the west in the WP (Figs. 7e,f) relative to those of observation (Fig. 7d). These are a common bias in the models. Although the spatial pattern of the SST, precipitation, and wind anomalies during DJF (0/1) are relatively similar to each other (Figs. 7b,c), they have some differences during the following season. The precipitation and wind stress anomalies over the equatorial WCP in the low-LP models (Fig. 7f) are reduced by more than half during MAM (1), and La Niña then terminates during JJA (1). However, the high-LP models retain the anomalies for these seasons (Fig. 7e).

In Fig. 8, we show the time evolution of the zonal wind stress anomalies for the high-LP and low-LP models, zonally averaged on the equatorial WP region. Following the onset of La Niña, the easterly wind stress anomalies gradually increase. In the high-LP models (Fig. 8a), maximum anomalous easterlies appear during the boreal late winter (0/1) to spring (1). The anomalies during the following season retain relatively negative values (easterlies); therefore, the following winter (1) shows weak easterlies. The persistent easterly anomalies can counteract the transition process of ENSO. In comparison with the zonal wind stress anomalies in the high-LP models, those in the low-LP models decrease rapidly from the boreal fall (0) and winter (0) to the subsequent spring (1). The equatorial easterlies get closer to zero and then turn into westerly anomalies during the subsequent seasons.

The zonal wind stress anomalies associated with the anomalous surface wind directly displace the thermocline vertically via Ekman pumping. The signal of thermocline variations rapidly propagates eastward and then changes the oceanic vertical structure in the equatorial Pacific. Thus, we will compare the observed equatorial thermocline behavior between high and low LP, especially around the mature phase of La Niña. Displayed in Fig. 9 is the evolution of the equatorial monthly-mean ocean heat content (OHC) anomalies of the composited one-sided regressions for high- and low-LP models. During the mature to the decay phase of a cold ENSO event, seasonally fixed cold anomalies of OHC take place along the equatorial band (Figs. 9a,b). The modulation of OHC describes the most important dynamical feature in both wave transition-type oscillators (delayed oscillator/WP oscillator) and recharge oscillator paradigms. In agreement with the theories, the OHC shown in Fig. 9 not only serves as the key positive feedback that overcomes a damping effect but is also one of the primary factors responsible for the phase turnaround from cold to warm.

Along the equatorial Pacific, strong negative anomalies associated with the shallow thermocline are found especially on the developing and mature phase of La Niña (Fig. 9). The evolution of the composited OHC anomalies during the onset year in the low-LP models is similar to that in the high-LP models. A closer look at Figs. 9a,b shows that temporal progress of the OHC anomalies are considerably different after the mature phase of La Niña between the high- and low-LP models. In the high-LP models (Fig. 9a), the OHC anomalies gradually decrease during the following season and are reenhanced during the DJF (1/2). Thus, cooling via thermocline feedback continues until the following year (1), which is similar to the observations (Ohba and Ueda 2009a). In the low-LP models (Fig. 9b), the turnabout of the OHC anomalies is recognized during the DJF (0/1), namely, during its mature phase. The ENSO transition of subsurface condition from the La Niña to the El Niño phase is established during the following year. It is conceivable that the difference in OHC could be one of the most important points for the different persistency of the La Niña.

To describe phase transition in relation to variability in the wind forcing, we also plot a longitude–time section of the composite of zonal wind stress anomalies (Fig. 10a) and the time rate of the change in OHC near the equator (Fig. 10b) for the low-LP models. A precursor to the ENSO phase transition is the buildup of heat content in the WP (Weisberg and Wang 1997; Guilyardi et al. 2003), which is attributed to equatorial wind changes (Wang et al. 1999; Harrison and Vecchi 1999; Vecchi and Harrison 2003). During the developing stage of La Niña (from June through October), strong anomalous surface easterlies characterize the atmospheric response to the equatorial Pacific cooling. In contrast, the anomalous WP easterlies are reduced rapidly during the mature to decay phase of La Niña (Fig. 10a). The rapid change of the equatorial wind can generate eastward-propagating thermocline deepening, leading to the turnaround of the SST tendency in the CEP. In the low-LP models, the signal of negative OHC anomalies propagates eastward from WP at a speed of about 40°–50° of longitude per month, roughly the propagation speed of the first and second baroclinic Kelvin modes (e.g., Boulanger et al. 2003). Consequently, the thermocline slope reduces and the CEP returns to warm conditions (Fig. 10b).

The model biases that generate the difference need to be examined. In the low-LP models, at least two processes can be considered here as a cause of the rapid reduction of La Niña. One of them is a case where the reduction of SST anomalies precedes to reduction of convective anomalies due to negative feedback processes such as delayed oscillator. It seems that it is mainly related to the transition mechanism within the ocean. Another one is a case where the reduction of convective anomalies precedes to reduction of SST anomalies. This process could be related to seasonal change of atmospheric condition. Weakened climatological convective activity could lead to the reduction of convective activity anomalies.

As for the former, one possible cause is the reflection of the off-equatorial Rossby waves on a western coast boundary. The reflection of the downwelling Rossby waves could generate the downwelling Kelvin waves that could contribute to the attenuation of La Niña. The Rossby waves are directly forced by the anomalous wind stress curl via Ekman pumping. In view of this, the OHC and zonal wind stress during the mature phase [November–January (NDJ)] of La Niña are presented in Fig. 11. In the low-LP models (Fig. 11b), the relatively strong off-equatorial Rossby waves responses (positive OHC anomalies) are found, corresponding to the relatively strong positive meridional gradient of the zonal wind stress (Fig. 11d) from equator to off equator (near 10°N). In contrast, the wind stress curl in the high-LP models is significantly weak and then result in the weakened generation of Rossby waves. The correlation coefficient between the anomalous OHC off-equatorial WP during November–December (3°–7°N, 120°–160°E) and EPI index of La Niña is 0.52. To show more convincing evidence, we also plot the OHC tendency anomalies along the latitudinal belt of 5°–9°N (Fig. 10c). We can found a relatively rapid decrease in the anomalies in the off-equatorial WP (Fig. 10c), concurrently with the generation of equatorial downwelling Kelvin waves (Fig. 10b). Combination of Figs. 10b,c represents the reflection of Rossby waves at the western boundary.

In addition to the reflection of Rossby waves, the other possible cause is the seasonal change in the atmospheric sensitivity to the SST anomalies. As indicated in Graham and Barnett (1987), SST–precipitation is positively correlated, but the relationship is highly nonlinear. This relationship functions in a different way during El Niño and La Niña. This is because the anomalous convection due to warm SST anomalies during El Niño can provide additional heating regardless of the basic-state convection field, provided the anomalous convection can be larger than in the basic state. On the other hand, an anomalous reduced precipitation due to cold SST anomalies during La Niña can produce additional negative precipitation anomalies only in the region where the basic-state convection exists. Therefore, attenuation of climatological precipitation could result in reduction of anomalous precipitation, especially in strong La Niña. In the observations, climatological precipitation in the WCP is relatively strong and its seasonal change is much weaker than in the eastern Pacific; namely, the WCP is in a relatively stable condition. This plays in favor of the duration of La Niña (Ohba and Ueda 2009a) through the persistency of enhanced Bjerknes feedback. However, a majority of the CGCMs reproduces precipitation over the equatorial central Pacific less than in the observations, as already pointed out by Lin (2007). The precipitation anomalies for the composited high- and low-LP models (Figs. 7e,f) show a horseshoe shape that is attributed to weak precipitation in the equatorial central Pacific. As presented in previous studies (e.g., Vecchi 2006), change in near-equatorial precipitation could lead to the termination of ENSO. Therefore, in a majority of the CGCMs, seasonal evolution of climatological precipitation in the equatorial Pacific may affect the annual cycle of La Niña (i.e., persistency of La Niña). Reduced intensity of the climatological precipitation could result in a decrease in the atmospheric response to underlying SST anomalies.

Figure 12a represents the seasonal evolution of climatological precipitation over the WCP (Fig. 4c, solid box: 5°S–5°N, 150°E–160°W) for the four low-LP models (solid) and the observations (dashed). In the observations, climatological rainfall in the equatorial WCP tends to peak during the boreal spring, as it is well known. Unfortunately, those of the low-LP models tend to peak during early boreal winter and are reduced by about 30%–70% during late winter to spring. We also show the spatial pattern of the correlation coefficient between the difference of climatological precipitation from DJF to MAM and the EPI of the cold phase on each CGCM (Fig. 12b). Relatively high correlation is found, especially over the central Pacific. These results suggest that the biases in the seasonality of the climatological precipitation field could be one of possible reasons leading to the persistency of La Niña.

4. Discussion

Most CGCMs are unable to simulate the feature of both phases of the ENSO transition. Only three models (GFDL1, HadC3, and CNRM) capture both observed El Niño and La Niña transition processes. The nonlinear atmospheric response to the ENSO-related SST anomalies is an important factor to understand the nonlinear ENSO cycle. This study underlines the importance of the asymmetric transition processes tied to the simulation of the (nonlinear) cyclic nature of ENSO in CGCMs. In addition, our analysis reemphasizes the importance of the spatial distribution and seasonal cycle of climatological condition in the CGCMs, especially in the equatorial WCP. Realistic simulation of the WCP climatological precipitation is one of the most important points to reproduce the transition system of ENSO.

Observational evidence shows that convective activity increases sharply above a threshold SST of 27°C (Graham and Barnett 1987), whereas temperatures above 27°C have little effect on the enhancement of convection. Because of the nonlinear relationship between SST and convection, the bias in the climatological SST fundamentally alters the response of equatorial atmospheric conditions to the same SST anomalies, leading to a different equatorial wind stress response. It is conceivable that the atmospheric response to the anomalous SST during the mature to the decay phase of ENSO is principally due to how the ENSO-related SST anomalies modulate the location of the 27°C isotherm. Therefore, the distribution of climatological SST and its seasonal changes could be an important factor for a realistic simulation of the oscillation in air–sea coupled models.

The intensity of the cold tongue (climatological precipitation) over the equatorial WCP is relatively strong (weak) in the CMIP3 climate models, especially Bjerknes Centre for Climate Research–Bergen Climate Model version 2 (BCCR-BCM2.0), HadGem, INM, and NCARp. As presented in this study, these biases could contribute to the transition system of the simulated ENSO. One of the most plausible causes is the parameterization of deep convection in the CGCMs. Deep convection is the primary heat source driving the large-scale circulation through the release of latent heat and the vertical redistribution of heat, moisture, and momentum. The representation of deep convection is therefore central in defining both the climatological SST and precipitation via their dynamical and thermodynamical feedbacks. Several recent studies have documented the impact of a modified convection scheme on ENSO in CGCMs. For instance, Kim et al. (2008) and Neale et al. (2008) have shown that the inclusion of “convective momentum transport” increases the climatological precipitation over the equatorial Pacific through redistribution of the momentum vertically toward the surface. The enhanced precipitation over the WCP results in the eastward shift of the ENSO-related anomalies. Therefore, the inclusion of convective momentum transport on the parameterization could enhance the El Niño transition on the CGCMs.

The EPI of the cold phase spreads on both the positive and negative sides. The standard deviation of EPI during cold phases among the models is 1.5 times as strong as that during warm phases. The results imply that the simulation of the La Niña duration, as observed during the cold events of 1971–72, 1974–76, 1984–86, and 1999–2001, is more difficult than that of the El Niño transition system in the CGCMs.

In addition to the WCP climatological condition, the IO SST variations could also contribute to ENSO transition in CGCMs (e.g., Annamalai et al. 2005; Kug and Kang 2006; Ohba and Ueda 2007; Izumo et al. 2010). As shown in recent studies (e.g., Ohba and Ueda 2009b), the ENSO impacts IO SST variability through thermodynamic atmospheric forcing as well as ocean dynamics. The thermodynamic forcing results in basin-wide SST anomalies of the same sign over the tropical IO, a few months following the peak of the ENSO. The link between the IO and ENSO has increased for the last decades of the twentieth century (Xie et al. 2010). Some CGCMs simulated the basin-wide SST anomalies following an ENSO (Saji et al. 2006), but it is beyond the scope of this paper to explore the reasons of deficiencies in the IO response to ENSO. In addition to the amplitude of the simulated ENSO, the simulation of the coupling between the IO and Pacific in the CGCMs could be responsible for the response of the precipitation anomalies over the maritime continents to El Niño, as investigated by Ose and Arakawa (2009). Further research is needed on the strength of the interbasin coupling and its relationship to the decadal modulations of ENSO and global warming.

5. Summary

Using the method of one-sided regression/correlation analysis applied to WCRP CMIP3 multimodel datasets, we investigated air–sea coupled processes of El Niño and La Niña from the mature to the decay phase in terms of the simulation of their transitivity and persistence, respectively. Regarding the nonlinear response of tropical convection, the air–sea coupled system over the tropical region tends to facilitate the warm-to-cold ENSO transition more than the cold-to-warm phase. This observed asymmetric behavior witnessed in observation is reproduced only in a few CGCMs, implying that the simulation of the air–sea coupled system is relatively difficult.

The atmospheric response for the warm phase of the observed ENSO causes a rapid reduction of the equatorial zonal wind stress via a southward shift of the anomalous westerly and an enhancement of the WP easterlies in relation to the Philippines anticyclone. This process plays a significant role in accelerating the following ENSO transition through a displacement of the thermocline depth anomalies. During the warm phase, some CGCMs reproduce these observed features on the transitivity of the warm phase, with their atmospheric response to the SST anomalies. For these models, the spatial distributions of the simulated precipitation and wind stress anomalies are close to what is observed during the mature to decay phase of the ENSO. These high-ET CGCMs capture a rapid decrease of the equatorial westerly anomalies with the intrusion of the WP easterly anomalies during the transition period, whereas the low-ET CGCMs fail to simulate the mechanism. The difference in the transition process between the high and low ET is largely dependent on the intermodel variability of precipitation in the equatorial WCP in addition to the IO SST forcing. Reduced climatological precipitation, associated with the strong cold tongue bias in the equator, causes the westward shift in the center of the El Niño–related precipitation anomalies and then weakens the effect of the “WP oscillator” (e.g., Weisberg and Wang 1997) and “southward shift in convection” (Vecchi 2006) mechanisms. The transition process in the low-ET models depends on other factors, such as the “delayed oscillator” or the “recharge oscillator,” which has weak seasonal dependence.

However, the simulated intensity of the El Niño transitivity is not associated with those of the La Niña persistency. The La Niña persistency is also reproduced by only a few CGCMs. The anomalous equatorial easterlies on the mature phase of an observed cold event persist until the subsequent spring, which tends to counteract the turnabout from a cold event to a warm event. Most models fail to simulate the observed persistence of equatorial easterly anomalies over the WP after their mature phase, resulting into a premature end through the generation of oceanic downwelling Kelvin waves. We consider that there are two possible reasons. One may be due to the difference in the intensity of the off-equatorial Rossby waves in the WP that results in the rapid reduction of La Niña. Another reason for the decrease in the easterlies may be related to the seasonality of climatological precipitation over the equatorial Pacific: namely, a decrease in the simulated climatological precipitation from the boreal winter to spring. The decrease in climatological precipitation could contribute to the decrease in sensitivity of the precipitation anomalies to the underlying SST anomalies.

Previous studies showed that changes in the simulation of intraseasonal variability (e.g., Neale et al. 2008) and the vertical mean state of the thermocline (e.g., Meehl et al. 2001) can lead to changes in the triggering and amplification of ENSO. In addition to them, the present study shows the importance of the climatological surface–atmospheric condition over the Pacific. Improvement of the WCP precipitation could contribute not only to the magnitude and length of an ENSO (e.g., Capotondi et al. 2006) but also to the transitivity/persistency.

One caveat to our approach is that we have given attention primarily to the mean state while ignoring other some factors. In fact, the ENSO system can be affected by numerous mechanisms not fully considered here, such as stochastic forcing or external influence. Further investigations for the components (i.e., stochastic forcing, interactive feedback from the other basin, and a climatological atmospheric and thermocline state) are needed to understand the regulation of the transition system of ENSO in CGCMs.

Acknowledgments

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the WCRP Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multimodel dataset. Support of this dataset was provided by the Office of Science, the U.S. Department of Energy. We express special thanks to Drs. Y. M. Okumura, A. Capotondi, M. Lengaigne, S.-I. An, S.-W. Yeh, J.-Y. Yu, T. Mochizuki, B. Taguchi, J. Tsutsui, Y. Yoshida, and C. Deser for their helpful suggestions and discussions. This study was partially supported by the Global Environmental Research Fund (S-5-2) of the Ministry of the Environment, Japan. The availability of the WCRP CMIP3 multimodel dataset was made possible for the S-5-2 community by the Data Integration and Analysis System (DIAS).

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Fig. 1.
Fig. 1.

Evolution of SST anomalies averaged over 2°S–2°N obtained through one-sided regression on the (left) positive and (right) negative Niño-3.4 index during DJF (0/1) for each CGCM and OBS. The shaded areas indicate where the regression is above the 95% significant level.

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 2.
Fig. 2.

One-sided regressions of the SST (°C) and wind stress (N m−2) for the El Niño phase of the Niño-3.4 index during DJF (0/1) for (a) observations, (b) composite derived from high-transitivity models, and (c) composite derived from low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.2°C (−0.2°C). (d)–(f) As in (a)–(c), but for diabatic heating and precipitation for (second row) DJF (0/1), (third row) MAM (1), and (bottom row) JJA (1). The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than (d) 20 W m−2 (−20 W m−2) and (e),(f) 1 mm day−1 (−1 mm day−1).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 3.
Fig. 3.

Time evolution of the zonal wind stress anomalies (N m−2) averaged over the equatorial band (130°E–170°W) of each CGCM for (a) four high-transitivity models and (b) five low-transitivity models from February (0) to February (2), based on one-sided regression for positive Niño-3.4 index during DJF (0/1).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 4.
Fig. 4.

Composited climatological annual mean of simulated SST (°C) for the (a) high-transitivity models and (b) low-transitivity models. (c),(d) As in (a),(b), but for precipitation (mm day−1). (e) Composited climatological annual mean of simulated zonal wind stress (N m−2) at the equator for the high- (solid) and low- (dashed) transitivity models.

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 5.
Fig. 5.

Scatter diagrams of the climatological annual-mean precipitation (mm day−1) of the WCP (5°S–5°N, 150°E–130°W) and the EPI of the warm-phase ENSO. Letters indicate model IDs shown in Table 1, and the asterisk is the observations.

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 6.
Fig. 6.

One-sided lag correlation between the SST over the IO for (top) DJF (0/1), (middle) MAM (1), and (bottom) JJA (1) and El Niño phase of the Niño-3.4 index during DJF (0/1) for (a) composite derived from high-transitivity models and (b) composite derived from low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) correlation coefficient is greater (smaller) than 0.3 (−0.3).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 7.
Fig. 7.

As in Fig. 2, but for (a, d) observations, (b, e) composite derived from high-persistence models, and (c, f) composite derived from low-persistence models, based on one-sided regression for negative Niño-3.4 index during DJF(0/1).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 8.
Fig. 8.

As in Fig. 3, but for (a) four high-persistence models and (b) four low-persistence models from February (0) to February (2), based on one-sided regression for negative Niño-3.4 index during DJF(0/1).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 9.
Fig. 9.

Evolution of the composited OHC anomalies (°C) averaged over 2°S–2°N for (a) high-persistence models and (b) low-persistence models. The OHC anomalies of each CGCM are obtained through one-sided regression on the negative Niño-3.4 index during DJF (0/1).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 10.
Fig. 10.

As in Fig. 9, but for (a) composited equatorial zonal wind stress anomalies (N m−2) and OHC tendency anomalies (°C) at (b) the equator and (c) the off equator derived from low-persistence models.

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 11.
Fig. 11.

One-sided regressions of the OHC (°C) and zonal stress (N m−2) for La Niña phase of the Niño-3.4 index during NDJ (0/1) for composite derived from (a) high- and (b) low-transitivity models. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.2°C (−0.2 °C). (c),(d) As in (a),(b), but for zonal wind stress. The light- (dark-) shaded areas indicate where the positive (negative) regression coefficient is greater (smaller) than 0.005 N m−2 (−0.005 N m−2).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Fig. 12.
Fig. 12.

(a) Seasonal evolutions of climatological precipitations over the WCP (Fig. 4c, solid box: 5°S–5°N, 150°E–160°W) for the four low-LP models (solid) and observations (dashed). The precipitation is plotted as the difference from the values during January. (b) Intermodal correlation between the EPI of the cold phase of each CGCM with the seasonal change in climatological precipitation from boreal winter to spring (March–April minus December–January).

Citation: Journal of Climate 23, 22; 10.1175/2010JCLI3608.1

Table 1.

List of CGCMs used in the present study.

Table 1.
Table 2.

List of EPI of the El Niño and La Niña phase in each CGCM. The indexes are derived from the one-sided lag correlation analysis between the SST during the DJF (0/1) and DJF (1/2). Boldface (italic) values imply the high- (low-) ET/LP models.

Table 2.
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