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    EOF1 of the (a) winter seasonal-mean Z200, (b) transient eddy activity, (c) transient eddy forcing, and (d) Niño-3.4 SST index is correlated with PC1 of the Z200 (0.94), transient eddy activity (0.95), and transient eddy forcing (0.88). Contour interval is 10 m for Z200, 1 m for transient eddy activity, and 1 m s−2 for transient eddy forcing.

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    Potential predictability of winter seasonal-mean Z200 anomalies in the PNA region: (a) signal variance, (b) noise variance, (c) S–N ratio, and (d) perfect model correlation skill of Z200 for the period 1901–2004. Contour interval for signal variance is 5 × 102 m2, for noise variance is 10 × 102 m2, for the S–N ratio is 0.1, and for perfect model correlation skill is 0.2.

  • View in gallery

    Pattern correlation (black solid line) of Z200 over the PNA region (30°–75°N, 180°–60°W). The Niño-3.4 index (5°S–5°N, 170°–120°W) is separated into negative (blue bars, multiplied by −1) and positive index (red bars).

  • View in gallery

    El Niño vs La Niña: (a),(b) signal variance; (c),(d) noise variance; and (e),(f) S–N ratio of seasonal-mean Z200 anomalies in the PNA region. The contour interval for signal and noise variance is 10 × 102 m2. The shaded contour interval for the S–N ratio is ≥0.5 and statistically significant at the 95% level.

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    El Niño vs La Niña: (a),(b) signal variance and (c),(d) noise variance of transient eddy activity in the PNA region. The contour interval for the signal and noise variance is 20 m2.

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    Anomalous transient eddy activity composites during (a) ENSO, (b) El Niño, and (c) La Niña years. The contour interval is 2 m.

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    Signal and noise variance of Z200 (102 m2) over the PNA region (30°–75°N, 180°–60°W); S–N ratio and Niño-3.4 index (°C) are shown on the secondary axis for (a) El Niño and (b) La Niña years.

  • View in gallery

    Transient eddy feedback (TF) intensity (m2 s−2); the signal variance of Z200 (×20 m2) over the PNA region (30°–75°N, 180°–60°W); the Niño-3.4 index (°C) is shown on the secondary axis for (a) El Niño and (b) La Niña years. The zero line is drawn with respect to TF intensity.

  • View in gallery

    El Niño vs La Niña at 2°C conditions: (a),(b) signal variance and (c),(d) noise variance of Z200 over the PNA region. The contour interval for the signal is 20 × 102 m2 and for the noise is 10 × 102 m2.

  • View in gallery

    (a) Signal amplitude (m), (b) noise amplitude (m), and (c) S–N of Z200 over the PNA region (30°–75°N, 180°–60°W) for El Niño and La Niña conditions.

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    (a) Signal and (b) noise amplitude (mm day−1) of tropical precipitation forcing in the tropical Pacific region (10°S–10°N, 150°E–80°W) for El Niño and La Niña conditions.

  • View in gallery

    (a) Signal and (b) noise amplitude (m s−2) of transient eddy forcing over the PNA region (30°–75°N, 180°–60°W) for El Niño and La Niña conditions.

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Contribution of Synoptic Transients to the Potential Predictability of PNA Circulation Anomalies: El Niño versus La Niña

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  • 1 Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
  • | 2 Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia, and School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
  • | 3 Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
  • | 4 Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia, and Earth System Physics Section, International Centre for Theoretical Physics, Trieste, Italy
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Abstract

The potential predictability (PP) of seasonal-mean 200-hPa geopotential height (Z200) anomalies in the Pacific–North American (PNA) region is examined for El Niño and La Niña separately by using 50 ensemble members of twentieth-century AGCM simulations. Observed sea surface temperature (SST) is prescribed for the period 1870–2009, and 14 El Niño and La Niña years after 1900 are selected for the present study. The domain-averaged value of PP for Z200 in the PNA region, as measured by the signal-to-noise ratio, for El Niño is about 60% larger than that of La Niña. Such a large PP is mainly due to a larger signal and partly to less noise during El Niño compared to that during La Niña . The transient eddy feedback to the PNA circulation anomalies is stronger during El Niño events (about 50%) than that during La Niña, and this difference in the transients contributes significantly to the different Z200 signals in the PNA region. The noise variance of the transients during El Niño is about 17% smaller than during La Niña, and thus transients play an important role in the reduction of Z200 noise during El Niño. Idealized experiments with the same spatial pattern but different signs of SST anomalies confirm the results mentioned above. Moreover, these experiments with several different amplitudes of positive and negative phases of tropical Pacific SST anomalies show that signals of Z200 and transients are proportional to precipitation anomalies in the tropical Pacific, and noises of Z200 for El Niño cases are somewhat smaller than the corresponding values of La Niña.

Corresponding author address: M. Adnan Abid, Center of Excellence for Climate Change Research (CECCR)/Department of Meteorology, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia. E-mail: mabid@kau.edu.sa

Abstract

The potential predictability (PP) of seasonal-mean 200-hPa geopotential height (Z200) anomalies in the Pacific–North American (PNA) region is examined for El Niño and La Niña separately by using 50 ensemble members of twentieth-century AGCM simulations. Observed sea surface temperature (SST) is prescribed for the period 1870–2009, and 14 El Niño and La Niña years after 1900 are selected for the present study. The domain-averaged value of PP for Z200 in the PNA region, as measured by the signal-to-noise ratio, for El Niño is about 60% larger than that of La Niña. Such a large PP is mainly due to a larger signal and partly to less noise during El Niño compared to that during La Niña . The transient eddy feedback to the PNA circulation anomalies is stronger during El Niño events (about 50%) than that during La Niña, and this difference in the transients contributes significantly to the different Z200 signals in the PNA region. The noise variance of the transients during El Niño is about 17% smaller than during La Niña, and thus transients play an important role in the reduction of Z200 noise during El Niño. Idealized experiments with the same spatial pattern but different signs of SST anomalies confirm the results mentioned above. Moreover, these experiments with several different amplitudes of positive and negative phases of tropical Pacific SST anomalies show that signals of Z200 and transients are proportional to precipitation anomalies in the tropical Pacific, and noises of Z200 for El Niño cases are somewhat smaller than the corresponding values of La Niña.

Corresponding author address: M. Adnan Abid, Center of Excellence for Climate Change Research (CECCR)/Department of Meteorology, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia. E-mail: mabid@kau.edu.sa

1. Introduction

Seasonal-mean atmospheric anomalies in the Pacific–North American (PNA) region are closely associated with the El Niño–Southern Oscillation (ENSO) phenomenon (Horel and Wallace 1981; Wallace and Gutzler 1981). During ENSO, the upper-level divergence anomalies in the tropical Pacific associated with sea surface temperature (SST) and precipitation anomalies are the primary forcing for the PNA circulation anomalies (Hoskins and Karoly 1981; Held and Kang 1987). Not only this tropical forcing but also the seasonal-mean anomalies of transient eddy vorticity flux divergence are known to play a role as an important forcing for the PNA pattern (Held et al. 1989; Ting and Held 1990; Hoerling and Ting 1994). The anomalous transients in the downstream of the Pacific storm track associated with ENSO enhance the PNA circulation anomalies, forced directly by the tropical forcing (Jin et al. 2006). These state-dependent transients enhance the seasonal-mean predictability of the PNA circulation anomalies. However, the transients are also an important source of the noise for seasonal-mean anomalies and thus limit the seasonal-mean predictability in the extratropics (Rowell 1998; Kang and Shukla 2006). The present study separates the signal and noise parts of transient eddy forcing and investigates their roles in the potential predictability (PP) of seasonal-mean anomalies over the PNA region during ENSO. In particular, the difference in the PP for El Niño and La Niña is examined in terms of the differences in the signal and noise parts of transient anomalies for the two phases.

The PP is often assessed by the signal-to-noise (S–N) ratio of seasonal-mean anomalies obtained by ensemble simulations of an atmospheric general circulation model (AGCM) by prescribing SST anomalies. The signal consists of the variance of ensemble-mean anomalies, and the noise is obtained from the mean variance of ensemble deviations (Zwiers 1996; Rowell 1998). The difference in the PPs of PNA circulation anomalies for El Niño and La Niña during boreal winter has been a topic of several previous studies (Kumar and Hoerling 1997; Sardeshmukh et al. 2000; Chen 2004; Peng and Kumar 2005). Those previous studies have shown that the signal of seasonal-mean PNA circulation anomalies forced by a positive phase (El Niño) of ENSO SST anomalies is stronger, and thus the PP of the PNA anomalies is larger than during La Niña. One of the important reasons for the stronger PNA anomalies during El Niño is the stronger tropical precipitation anomalies compared to that during La Niña (Hoerling et al. 1997; Kang and Kug 2002). As mentioned above, the signal of PNA circulation anomalies is forced not only by the tropical forcing but also by the transient eddy forcing associated with the ENSO SST anomalies. The anomalous synoptic transient activity along the Pacific storm tracks, which is extended eastward to the jet exit region during El Niño years (Seager et al. 2010), produces the seasonal-mean transient eddy vorticity flux convergence anomalies that in turn force the local and downstream signals of seasonal-mean circulation anomalies (Held et al. 1989; Straus and Shukla 1997). Chen and Dool (1999) and Seager et al. (2010) showed that the anomalous transient activity in the jet exit region is more prominent during El Niño compared to that of La Niña, and thus the signal of transient eddy forcing is larger during El Niño compared to that of La Niña.

The transients consist of not only the state-dependent component mentioned above but also the stochastic component, which results in unpredictable noise and limits the seasonal-mean PP, particularly in the extratropics (Kang et al. 2004). A part of the noise of PNA circulation anomalies results from the noise component of tropical forcing, which is represented by variability of precipitation anomalies in the tropical Pacific for a given ENSO SST anomaly. Peng and Kumar (2005) showed that the internal variability of tropical rainfall anomalies during El Niño is larger than that of La Niña, but the internal variability of PNA circulation anomalies is smaller during El Niño compared to that of La Niña, indicating a larger role of extratropical transients on the internal variability (noise) of the PNA circulation anomalies. On the other hand, Sardeshmukh et al. (2000) showed with relatively large ensemble simulations for an El Niño and a La Niña case that the seasonal-mean noise of PNA circulation anomalies is larger during El Niño compared to that of La Niña and argued that the seasonal-mean predictability of PNA circulation anomalies is weaker during El Niño compared to that of La Niña. Several earlier studies, such as Kumar and Hoerling (1998) and Kumar et al. (2000), have demonstrated that the impact of SSTs on the noise component of seasonal-mean anomalies is small and thus the PP is largely dependent on the signal, which is proportional to the magnitude of SST anomalies and is larger during the positive phase of ENSO compared to that of the negative phase. The previous studies mentioned above have produced diverse results on the impact of SST on the noise part of the PNA circulation anomalies. The reason for the diversity may be due to the different models that they used and the different ENSO cases.

Model dependency of the seasonal-mean PP was studied by Kang and Shukla (2006) and Kang et al. (2011). They showed that the models they used have very different magnitudes of signal and noise, but the models with a larger signal tend to have larger noise, and the PPs of various models have relatively small differences, indicating that the signal and noise were somewhat related to each other and that the PP is not very dependent on the model. In addition, Kang et al. (2011) demonstrated using seven different European models that a major part of the signal and noise of the PNA circulation anomalies are all related to synoptic transients in the extratropical Pacific region. These studies indicate that the diverse conclusions on the PP, mentioned above, may be mainly due to the different ENSO cases [e.g., Sardeshmukh et al. (2000) used one El Niño and one La Niña case] and partly to different models. In the present study, we revisit the role of synoptic transients on the signal, noise, and PP of PNA circulation anomalies, particularly its differences for different phases of ENSO, using a sufficiently large sample of simulations (e.g., 50-member ensemble for 139 yr), including a number of El Niño and La Niña years. This study also performed several sets of large ensemble simulations with the same SST anomaly pattern but with different magnitude and different signs to clearly identify the dependency of the signal, noise, and PP of PNA circulation anomalies on the magnitude and phase of ENSO SST anomalies.

Section 2 introduces the model used and the experiments performed. Section 3 examines the signal, noise, and S–N ratio of the seasonal-mean PNA circulation anomalies produced by large ensemble AGCM simulations with observed SSTs for 139 yr. Section 4 examines how the transient affects the PP of seasonal-mean anomalies over the PNA region during El Niño and La Niña years. In section 5, the SST sensitivity experiments carried out with several different amplitudes of idealized El Niño and La Niña conditions are discussed to clarify the results obtained in section 4. In addition, the differences in the PP of the inter–El Niño/La Niña years are also examined in section 5. Section 6 summarizes results with concluding remarks of the present study.

2. Model and experiments

The model used is an intermediate complexity AGCM developed at the Abdus Salam International Centre for Theoretical Physics (ICTP), which is referred to as the Simplified Parameterization, Primitive Equation Dynamics (SPEEDY) model. It is a comprehensive simplified global model with a spectral triangular truncation at wavenumber 30 (T30) and 8 vertical levels (Molteni 2003; Kucharski et al. 2006a, 2013). The model has various physical processes, such as shortwave and longwave radiation, large-scale condensation, convection, and surface fluxes of momentum, heat, and moisture, which are parameterized. A mass flux scheme used for cumulus convection depends upon the conditional instability, boundary layer fluxes are considered by stability-dependent bulk formulas, and a one-layer thermodynamic model is used to determine the land and sea ice temperature anomalies (Molteni 2003; Kucharski et al. 2006a, 2013). This model has been used for numerous studies, including studies on the ENSO–Indian summer monsoon teleconnection on interannual-to-decadal time scales (Kucharski et al. 2006b, 2007), the North Atlantic Oscillation (Kucharski and Molteni 2003), the impact of land–sea thermal contrast on the global climatic modes (Molteni et al. 2011), the ENSO-related global teleconnections in boreal winter (Bulić et al. 2012; Bulić and Branković 2007; Bracco et al. 2004), and the relationship between ENSO and South Asian winter rainfall (Yadav et al. 2010). It has also been used to study the long-term changes in the SST-related global PP for the twentieth century by performing a 139-yr simulation with a 100-member ensemble (Ehsan et al. 2013).

In the present study, two sets of experiments were performed. The first experiment is similar to the Climate of the Twentieth Century Project experiment using general circulation models (Folland et al. 2002). The model is forced with monthly varying SST data obtained from the Hadley Centre (HadISST) for the period of 1870–2009 (Rayner et al. 2003). The sea ice concentration is prescribed as seasonally varying climatology derived also from the HadISST dataset for the period 1979–2008. The CO2 concentration is fixed at the average concentration for the period 1979–2008. Furthermore, this model does not include the aerosol forcing (Bulić et al. 2012; Kucharski et al. 2013).

The PP can be examined by using a large number of ensemble simulations, and it has been suggested that ensembles should include more than 40 members (Sardeshmukh et al. 2000). Thus, 50-member ensemble simulations were performed with the same SST boundary condition but with slightly different initial conditions. Although the model was integrated for 139 yr, the present study used the simulated data for the period after 1900 by considering the poorer quality of SST data before 1900. The second set is the SST sensitivity experiments to examine the differences in the PPs produced by El Niño and La Niña SST anomalies of equal amplitude. The spatial distribution of the SST anomalies used in this experiment is obtained from the composite of 14 El Niño events that occurred after 1900 (see Table 1 for the El Niño years). The SST anomalies obtained are divided by the Niño-3.4 index of the SST anomalies (1.4°C). The SST anomaly is added only in the region of 10°S–10°N, 170°–90°W, and elsewhere the climatological varying SST is prescribed. Eleven different SST experiments, including a control run, were performed with the same spatial pattern of SST anomalies but with different Niño-3.4 amplitudes of ±0.5°, ±1°, ±2°, ±3°, and ±4°C.

Table 1.

List of the El Niño and the La Niña years used in the analysis for the period 1901–2004.

Table 1.

3. Model properties related to the potential predictability of PNA circulation anomalies

The model’s ability to reproduce basic ENSO-related extratropical anomalies and, in particular, PNA circulation anomalies (as measured by the 200-hPa height response to ENSO) has been analyzed, for example, in Ehsan et al. (2013) and Kucharski et al. (2013). The present section investigates the model ability of simulating the PNA circulation anomalies and the transient anomalies over the North Pacific and North America further, which are closely related to ENSO SST anomalies, and examines the PP of PNA circulation anomalies in terms of the S–N ratio of seasonal-mean anomalies. First, we examine the dominant modes of the signal (ensemble mean) of winter-mean circulation and transient anomalies in the PNA region. For this purpose, the empirical orthogonal function (EOF) analysis is applied to the ensemble-mean winter-mean 200-hPa geopotential height (Z200) and the transient eddy activity in the domain of 20.5°–83.5°N, 146°E–26°W for the period 1901–2004. The detrended seasonal-mean dataset is obtained by removing an 11-yr running mean and is used for all analyses in the present study. The transients are defined by applying a 2–10-day time filter to the time series of individual simulations for the entire 139 yr but analyzed after 1900. The transient eddy activity of each season is estimated by the root-mean-square (RMS) of the high-frequency component of the Z200 for the winter season (Kang et al. 2011). Figure 1a shows the first EOF eigenvector (EOF1) of seasonal-mean Z200 anomalies, which resembles the PNA mode obtained from observation (Horel and Wallace 1981, and many others) and models (Kang et al. 2011, and many others). Figure 1b shows the first EOF eigenvector for the transient eddy activity, the spatial distribution of which has a north–south seesaw pattern in the extratropical eastern North Pacific and eastern North America (Lau 1988; Kang et al. 2011). Both eigenvectors explain most of the total variances over the domain: 65.1% for the Z200 and 63.6% for the transient activity. The time series associated with the two eigenvectors [principal components (PCs)] and that of the Niño-3.4 index are shown in Fig. 1d. All those time series are very similar; the correlation between the Niño-3.4 index and the PC of Z200 anomalies is 0.94 and the correlation between the Niño-3.4 index and the PC of the transient eddy activity is 0.95, meaning that both EOFs are closely related to ENSO. As mentioned in the introduction, it is well known that the changes of transient activity induce the forcing that reinforces the seasonal-mean circulation anomalies over the PNA region. As in Kang et al. (2011), the transient eddy forcing term is calculated by the inverse Laplacian of the transient eddy vorticity flux divergence, which is defined as
e1
where f is the Coriolis parameter, g is gravity, ζ is the vorticity, v is the wind vector, ϕ is the geopotential, and where ζ′ and v′ are filtered for time scales of 2–10 days. The overbar denotes seasonal mean, and the superscript a denotes the seasonal-mean anomaly from the climatology. The EOF1 of the transient eddy forcing is shown in Fig. 1c, and the associated PC time series is shown in Fig. 1d. The spatial pattern of the eigenvector is similar to the PNA mode shown in Fig. 1a, indicating that the transient eddy forcing acts as an important local forcing for the circulation anomalies over the PNA region. As seen in Fig. 1d, the fact that all those time series associated with individual EOF eigenvectors are very similar indicates that those three EOFs are mutually related to the ENSO SST anomalies represented by the Niño-3.4 index. In particular, it is noted that the transient eddy forcing anomalies appear to reinforce the PNA circulation anomalies, which are partly forced by the ENSO SST anomalies. This transient eddy feedback to the PNA circulation anomalies will be discussed in the next section.
Fig. 1.
Fig. 1.

EOF1 of the (a) winter seasonal-mean Z200, (b) transient eddy activity, (c) transient eddy forcing, and (d) Niño-3.4 SST index is correlated with PC1 of the Z200 (0.94), transient eddy activity (0.95), and transient eddy forcing (0.88). Contour interval is 10 m for Z200, 1 m for transient eddy activity, and 1 m s−2 for transient eddy forcing.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

A comparison with the results from the multimodel (DEMETER) study of Kang et al. (2011; see Fig. 5 therein) shows that the performance of the SPEEDY model is reasonable compared to that of the much more complex and higher-resolution models of the DEMETER ensemble. In particular, the pattern of the first EOFs of the Z200 transient eddy activity are similar to the DEMETER models. In terms of amplitude, the transient eddy forcing of the SPEEDY model is between the strong and weak transient eddy forcing model categories (not shown here) defined in Kang et al. (2011). Several papers (e.g., Hertwig et al. 2014; Williamson et al. 1995) have suggested that the minimum resolution required to resolve transient eddy-mean state interactions is spectral triangular truncation at wavenumber 42 (T42) and above. Therefore, we have also made test simulation with a higher-resolution version of the SPEEDY model (T47). It was noted that the structure of the transient eddy activity and forcing was very similar to the T30 resolution version, even though the magnitude of the transient eddy activity was increased (not shown). Therefore, it is believed that the T30 resolution of this model can be used to separate the transient eddy feedbacks for El Niño and La Niña years with comparatively high confidence by performing large ensembles of simulations that better allow for the separation of the signal and noise and thus the examination of the PP.

Next we examine the PP of the PNA circulation anomalies by using the S–N ratio. Figures 2a and 2b show the signal and noise variances of seasonal-mean Z200 anomalies, respectively. The signal variance shown here is the variance of the ensemble mean of seasonal-mean anomalies, and the seasonal-mean anomalies are obtained by removing the climatology of individual runs from the seasonal means of individual runs. Large variance of the signal appears in the northern Pacific and northern Canada, locations that coincide with the centers of the PNA mode shown in Fig. 1a. The noise variance, shown in Fig. 2b, is obtained from the deviation of the seasonal-mean anomaly of the individual run from its ensemble mean. Large amplitude of the noise variance appears in northern Canada and Alaska. The S–N ratio as a measure of PP is shown in Fig. 2c. A large S–N ratio appears in the zonal belt south of the subtropics, because ENSO significantly influences the Z200 anomalies in the tropical belt. In the extratropics, on the other hand, relatively large PP appears only in limited regions of the North Pacific and Canada near the centers of the signal variance. The PP is also examined by using the perfect model correlation, which is obtained by the correlation at each grid point between one of the simulations assumed as the observation and the ensemble mean of the other N − 1 simulations. This procedure is repeated for N times, and then the perfect model correction is obtained by the average of the N numbers of correlations (Rowell 1998). As expected, the spatial pattern of the perfect model correlation shown in Fig. 2d is similar to that of the S–N ratio shown in Fig. 2c, particularly in the PNA region. The results shown above are consistent with those of the previous studies obtained with other models (Hoerling and Kumar 2002; Phelps et al. 2004), indicating that the present model, although relatively simple, is capable of reproducing the extratropical signal, noise, and PP.

Fig. 2.
Fig. 2.

Potential predictability of winter seasonal-mean Z200 anomalies in the PNA region: (a) signal variance, (b) noise variance, (c) S–N ratio, and (d) perfect model correlation skill of Z200 for the period 1901–2004. Contour interval for signal variance is 5 × 102 m2, for noise variance is 10 × 102 m2, for the S–N ratio is 0.1, and for perfect model correlation skill is 0.2.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

In the present experiment, the prediction signal of seasonal mean comes from the SST anomalies particularly in the tropical Pacific. The global PP of an individual year depends on the amplitude of the tropical Pacific SST anomalies. The interannual variation of the PP is examined by using the perfect model spatial pattern correlation shown in Fig. 3. The calculation procedure of Fig. 3 is similar to that of the perfect model correlation mentioned above, except that the spatial pattern correlation is obtained for each year in the present case. Figure 3 shows that the pattern correlation skill of Z200 over the PNA (30°–75°N, 180°–60°W) region varies interannually in the range between 0 and 0.74. The Niño-3.4 index is also shown in the figure with the negative index multiplied by −1 shown with blue bars. It appears that the correlation skill is approximately proportional to the magnitude of the Niño-3.4 index. Interestingly, the correlation skill during El Niño (mean value of the 14 El Niño events is 0.57) is slightly larger than that of La Niña (mean value of the 14 La Niña events is 0.46). The 14 years of El Niño and La Niña are shown in Table 1. The next section examines the differences in the PPs of El Niño and La Niña in more detail.

Fig. 3.
Fig. 3.

Pattern correlation (black solid line) of Z200 over the PNA region (30°–75°N, 180°–60°W). The Niño-3.4 index (5°S–5°N, 170°–120°W) is separated into negative (blue bars, multiplied by −1) and positive index (red bars).

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

4. Contribution of transients to the seasonal-mean potential predictability: El Niño versus La Niña

The PP over the PNA region is affected directly by the SST anomalies in the tropical Pacific and indirectly by the state-dependent transient eddy forcing associated with the SST anomalies. In the previous section, it was shown that the PP averaged for the 14 El Niño events is 24% larger than that of La Niña. This difference certainly comes from the differences in the tropical Pacific SST and precipitation anomalies for El Niño and La Niña, as shown by several previous studies, such as Hoerling et al. (1997) and Peng and Kumar (2005). The difference in the PPs is also likely due in part to the difference in the transient activities for the two phases of ENSO, which is a topic of the present study.

Before examining the transient properties for the two ENSO phases, the PPs over the PNA region for El Niño and La Niña are examined separately in terms of the S–N ratio. The signal and noise for El Niño are computed with the procedures as described in the previous section, except that the seasonal-mean anomaly data used here are only for the 14 El Niño winters, listed in Table 1. Also, the 14 La Niña winters, listed in Table 1, are used for the corresponding analysis of La Niña years. Figures 4a and 4b show the signal variances for the El Niño and La Niña cases, respectively. The locations of the signal variances for both cases are similar to each other. However, the amplitudes of the El Niño signal are larger than that of La Niña. The domain-average value of the El Niño signal for the region north of 30°N is 36% larger than that of La Niña. On the other hand, the noise variances of El Niño, shown in Fig. 4c, are smaller than those of La Niña, shown in Fig. 4d, in most of the region. The domain-average value of the noise for El Niño is 18% smaller than that of La Niña. As a result, the S–N ratios of El Niño are larger than that of La Niña in the entire domain, as seen in Figs. 4e and 4f. Considering the domain-averaged value over the PNA region, the S–N ratio of El Niño is about 60% larger than that of La Niña. The results also indicate that such a large difference between the two S–N ratios is not only from the signal difference but also the noise difference. Most previous studies ascribed the larger PP for El Niño to the larger signal compared with the corresponding values of La Niña, and they argued that the noise is not much changed for the different phases of ENSO. The present study is consistent with the previous results in terms of the signal and PP. In addition, the present study shows that the noise is reduced for El Niño with respect to that of La Niña and the difference of the noise significantly contributes to an additional increase of PP during El Niño compared to La Niña years.

Fig. 4.
Fig. 4.

El Niño vs La Niña: (a),(b) signal variance; (c),(d) noise variance; and (e),(f) S–N ratio of seasonal-mean Z200 anomalies in the PNA region. The contour interval for signal and noise variance is 10 × 102 m2. The shaded contour interval for the S–N ratio is ≥0.5 and statistically significant at the 95% level.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

Now we examine how the transient anomalies during ENSO affect the signal, noise, and PP of the seasonal-mean Z200 anomalies, shown in Fig. 4. For this purpose, we separate the transient activity anomalies for boreal winter into the signal and noise parts, which are estimated in terms of the ensemble mean and its deviation, respectively. Figures 5a and 5b show the signal variances of the transient eddy activity during El Niño and La Niña, respectively. In both figures, there are relatively large signal values located near the centers of the EOF1 of transient activity shown in Fig. 1b, but the magnitudes are different. The domain-averaged value of the signal variance for El Niño is 30% larger than that for La Niña. As discussed in the previous section, ENSO changes the transient activity, which in turn reinforces the seasonal-mean Z200 anomalies over the PNA region. The e-folding time scale of transients to the Z200 anomalies over the PNA domain is estimated as follows:
e2
where is the ensemble-mean seasonal-mean Z200 anomalies, and is the ensemble-mean transient eddy forcing [shown in Eq. (1)], which is considered as a signal part of transient eddy forcing. The amplitude of seasonal-mean Z200 anomalies normalized with projection of the two variables, as estimated by the area integral, is calculated for the PNA domain of 30°–75°N, 180°–60°W. Equation (2) estimates the strength of the transient eddy feedback to the seasonal-mean anomalies in the PNA region during ENSO years. The average value of the e-folding time scales for 14 El Niño events is about 15.5 days, and for La Niña is about 26 days, indicating that the transient eddy feedback to the PNA circulation anomalies during El Niño is stronger (by about 2 times) than that of La Niña.
Fig. 5.
Fig. 5.

El Niño vs La Niña: (a),(b) signal variance and (c),(d) noise variance of transient eddy activity in the PNA region. The contour interval for the signal and noise variance is 20 m2.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

The reason for the stronger transient eddy forcing in El Niño compared to La Niña cases is likely due to asymmetry in the transient eddy activity anomaly during the ENSO phases. Several earlier studies (e.g., Held et al. 1989; Chen and Dool 1999; Seager et al. 2010) have discussed this basic state difference during El Niño and La Niña years and the associated transient eddy activity. Indeed, Fig. 1b shows that the transient eddy activity is significantly modified in the model during ENSO years, and it is shifted southward (northward) in El Niño (La Niña) years. However, there is also an asymmetry between the El Niño and La Niña anomalous transient eddy activity. Figure 6 shows the anomalous transient eddy activity for the ENSO, El Niño, and La Niña composites (Figs. 6a–c). There is a stronger shift in the transient eddy activity (that goes along with a stronger shift in the 200-hPa jet) in El Niño compared to La Niña cases. It is likely that this stronger shift is related to the stronger circulation response in El Niño years (see Fig. 4). Since the transients have a positive feedback on the seasonal-mean Z200 anomalies during El Niño, it can be deduced that the stronger transient eddy activity shift is also responsible for the stronger transient eddy forcing in El Niño compared to La Niña years.

Fig. 6.
Fig. 6.

Anomalous transient eddy activity composites during (a) ENSO, (b) El Niño, and (c) La Niña years. The contour interval is 2 m.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

The noise variances of transient eddy activity for El Niño and La Niña are shown in Figs. 5c and 5d. Spatial patterns of both figures have little difference. However, the transient noise is stronger during La Niña compared to that during El Niño; the domain-averaged value of El Niño is about 17% smaller than that of La Niña. The noise can originate not only from extratropical transients, but also from tropical precipitation variability. Peng and Kumar (2005) showed that the internal variability of tropical Pacific rainfall anomalies increases (decreases) for El Niño (La Niña) events, since the tropical precipitation variability is proportional to the SST magnitude. Therefore, the stronger noise of the seasonal-mean Z200 anomalies during La Niña compared to that of El Niño, shown in Fig. 4, cannot be explained by the tropical noise but is related to the stronger noise of extratropical transients during La Niña.

Instead of the mean properties of PP for El Niño and La Niña shown in the previous figures, the signals, noises, and PPs of Z200 anomalies for individual years are examined to investigate how those properties are related to the ENSO SST anomalies of individual years. Figure 7a shows the domain-averaged values of the signal and noise variances, S–N ratio over the PNA region (30°–75°N, 180°–60°W), and Niño-3.4 index for individual El Niño years, and Fig. 7b shows those for La Niña years, but the Niño-3.4 is multiplied by −1. The signal and noise variances, respectively, are obtained from the square of the ensemble-mean anomaly and the square mean of ensemble deviations of an individual year. The signal variances are closely related to the magnitude of the Niño-3.4 index, although the relationship is stronger for El Niño (correlation is 0.8) than for La Niña (correlation is 0.5). The noise variance, on the other hand, is not closely related to the Niño-3.4 index for both El Niño and La Niña, and the mean value for 14 El Niño events is slightly smaller than that of the La Niña events, as seen in the previous section. Since the noise variation is relatively small, the year-to-year variations of S–N ratio shown in both figures are closely related to the variations of signal variances. It is also noted that the average value of the S–N ratio for El Niño is 0.56 and for La Niña is 0.35, indicating about 60% higher PP during El Niño with respect to that during La Niña. It should be noted that the mean Niño-3.4 amplitude in El Niño years (1.4°C) is only slightly larger than that in La Niña years (1.3°C) and therefore cannot explain the differences in S–N ratios (even if both S–N ratios are normalized by the respective Niño-3.4 amplitudes, the S–N ratio is still about 48% larger for El Niño).

Fig. 7.
Fig. 7.

Signal and noise variance of Z200 (102 m2) over the PNA region (30°–75°N, 180°–60°W); S–N ratio and Niño-3.4 index (°C) are shown on the secondary axis for (a) El Niño and (b) La Niña years.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

It should be mentioned that a portion of the signal, which is not related to the tropical Pacific SST anomalies and the diabatic heating induced by them, is related to other sources, such as the extratropical SST anomalies and the state-dependent transients. Here we examine the year-to-year variations of the state-dependent transient eddy forcing as an important source for the variations of the signal variance. Figures 8a and 8b show the variations of the transient eddy feedback intensity estimated by the projection of the transient eddy forcing anomaly to the seasonal-mean Z200 anomalies [denominator of Eq. (2)] over the PNA region for El Niño and La Niña years, respectively, along with the variations of the signal variance and the Niño-3.4 index. For the El Niño case, the transient eddy feedback intensity is closely related to the Niño-3.4 index with a correlation of 0.84 and to the signal variance of the Z200 anomalies with a correlation of 0.76, indicating that the transient eddy forcing induced by El Niño SST anomalies reinforces the seasonal-mean Z200 anomalies in the PNA region in most El Niño years. For the El Niño years of the early twentieth century (1906–58), the magnitudes of the Niño-3.4 index are roughly similar, but the signal of Z200 anomalies shows relatively large variations. The large variation of the signal is clearly related to the large variation of the transient eddy forcing. This result indicates that the transient eddy feedback is not solely determined by the tropical Pacific SST anomalies but can be different for similar values of the Niño-3.4 index, and such differences in the transient eddy feedback produce significantly different signals of the PNA circulation anomalies. Overall, the signal part of the Z200 anomalies over the PNA region is determined by the combination of the SST and transient eddy forcing anomalies. For the La Niña case, the transient eddy feedback intensity is less correlated to the Niño-3.4 index, with a correlation of 0.37 compared to that of El Niño. This relatively poor correlation is a possible reason why the signal variance is not well related to the Niño-3.4 index during La Niña, as indicated in Fig. 7. In particular, for the La Niña years of the period 1904–71, the signal variance is poorly correlated to the Niño-3.4 magnitude but is well related to the transient eddy feedback intensity. Therefore, for those La Niña years, the transient eddy feedback seems to be a primary forcing for the signal of Z200 anomalies and is not controlled by the tropical Pacific SST anomalies. Further studies are needed to understand the factors that control the transient eddy feedback other than the ENSO SST anomalies.

Fig. 8.
Fig. 8.

Transient eddy feedback (TF) intensity (m2 s−2); the signal variance of Z200 (×20 m2) over the PNA region (30°–75°N, 180°–60°W); the Niño-3.4 index (°C) is shown on the secondary axis for (a) El Niño and (b) La Niña years. The zero line is drawn with respect to TF intensity.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

5. Idealized experiments

In the previous section, the inter–El Niño/La Niña differences of the PP-related statistics are shown to be large, and those differences are partly due to the differences in the SST anomaly patterns of individual ENSO years. Here, we remove the differences in the SST anomaly patterns for El Niño and La Niña and examine how the PNA predictability is different for the same spatial pattern but with different signs and magnitudes of SST anomalies for El Niño and La Niña. As described in section 2, 11 sets of 110 ensemble simulations, including a control run, are carried out with different SST anomalies with Niño-3.4 indices of −4°, −3°, −2°, −1°, −0.5°, +0.5°, 1°, 2°, 3°, and 4°C. Figure 9 shows the signal and noise variances of seasonal-mean Z200 anomalies for the Niño-3.4 indices of −2° and +2°C. Figure 9 confirms the results of Fig. 4, in that the signal is larger but the noise is smaller during El Niño compared to the corresponding values of La Niña. Now we examine how those differences depend on the amplitude of the ENSO SST anomaly. Figure 10 shows the area-averaged values of the signal (Fig. 10a), the noise amplitude (Fig. 10b), and the S–N ratio of Z200 (Fig. 10c) for the PNA domain of 30°–75°N, 180°–60°W for different amplitudes of Niño-3.4 indices. Note that the signal does not approach exactly zero as the Niño-3.4 amplitude goes to zero. This is because of small noise residuals in the ensemble means, which reduce with increasing ensemble size (only an infinite large ensemble has a zero noise residual). In the warm (El Niño) phase, the signal is clearly increasing much faster with increasing Niño-3.4 SST amplitude than for the cold (La Niña) phase. For 4°C, the warm phase signal is about 85% larger than that of the cold phase. Another interesting result is observed in the noise. For the warm phase, the noise is decreased slightly with increase of the SST anomalies, but it remains almost the same for the cold phase. As a result, the S–N ratio increases strongly with increasing SST anomalies during the warm compared to the cold phase. For the Niño-3.4 amplitude of 2°C, the signal is larger than the noise, indicating an S–N ratio greater than 1 for the El Niño case, but remains significantly below 1 for the La Niña case. The S–N ratio is almost doubled for the 4°C compared to that of 2°C during El Niño, while only a 44% increase is observed for the corresponding La Niña conditions. It is also noted that, for the cold phase, the differences of the S–N ratio from 0.5° to 4°C SST anomalies is not significant, and it remains less than 1 even for the stronger (4°C) colder conditions. However, for the warm phase, a significant change of about 4 times increase in the S–N ratio is noted from 0.5° to 4°C, and it reaches almost 2 times for the stronger (4°C) warm conditions. In comparison, the S–N ratio for the stronger (4°C) warm conditions is about double that of the corresponding colder conditions. Overall, this behavior is due to weaker signal and stronger noise during La Niña compared to the El Niño case.

Fig. 9.
Fig. 9.

El Niño vs La Niña at 2°C conditions: (a),(b) signal variance and (c),(d) noise variance of Z200 over the PNA region. The contour interval for the signal is 20 × 102 m2 and for the noise is 10 × 102 m2.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

Fig. 10.
Fig. 10.

(a) Signal amplitude (m), (b) noise amplitude (m), and (c) S–N of Z200 over the PNA region (30°–75°N, 180°–60°W) for El Niño and La Niña conditions.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

Those results noted above are related partly to the differences in the tropical precipitation responses to different SST anomalies. The signal and noise of tropical precipitation anomalies are shown in Figs. 11a and 11b for different amplitudes of El Niño and La Niña conditions, respectively. The calculation procedure for obtaining the signal and noise are the same as that in Fig. 10, but the domain of the area average is 10°S–10°N, 150°E–80°W. The signal of tropical precipitation anomalies is increased during El Niño with the increasing Niño-3.4 anomaly amplitude. The signal of tropical forcing (precipitation) for the Niño-3.4 index of +4°C is about 230% stronger than that of the Niño-3.4 index of +1°C. The precipitation signal is not changed much for different amplitudes of La Niña. The reason for such a difference in the precipitation signals for different phases of ENSO could be related to the climatological SST distribution in the tropical Pacific (Gadgil et al. 1984). The ENSO SST anomalies are located in the central and eastern tropical Pacific, where climatological mean SST is relatively cold and a dry condition prevails for normal years. During El Niño, the warm SST anomalies warm the local SST and result in increase of the local precipitation. The precipitation anomalies in the central Pacific are shown to be an important source of forcing for the PNA teleconnection (Held and Kang 1987). Therefore, the signal of Z200 anomalies in the PNA region would be proportionally increased by the increase of El Niño SST anomalies. However, during La Niña, the cold SST anomalies do not affect the precipitation in the central and eastern tropical Pacific much, since precipitation change should be small for a dryer condition in the dry region (Hoerling et al. 1997; Kang and Kug 2002). In contrast to the signal changes, the noise changes of tropical precipitation for different amplitudes of ENSO are small for El Niño, and the noise is even decreased for increase of the La Niña amplitude. The relatively large noise of precipitation anomalies during El Niño was also shown by Peng and Kumar (2005). The decrease of the noise for stronger La Niña cases could be expected because the mean tropical precipitation during a strong La Niña is relatively small, and the fluctuations with respective to the relatively cold mean state should be small. It is hard to link those noise characteristics of tropical precipitation to the noise characteristics of Z200 anomalies shown in Fig. 10b. Therefore, the noise of PNA circulation anomalies appears to be more controlled by the noise originating from extratropical transients compared to the noise originating from the tropical Pacific.

Fig. 11.
Fig. 11.

(a) Signal and (b) noise amplitude (mm day−1) of tropical precipitation forcing in the tropical Pacific region (10°S–10°N, 150°E–80°W) for El Niño and La Niña conditions.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

Figures 12a and 12b show, respectively, the signal and noise parts of the transient eddy forcing area averaged over the PNA domain of 30°–75°N, 180°–60°W for different amplitudes of El Niño and La Niña SST anomalies. The transient eddy forcing signal is increasing faster with larger Niño-3.4 amplitude for El Niño than for La Niña conditions. The transient signal at 4°C is about 93% larger during El Niño compared to La Niña conditions. The results of Figs. 11a and 12a, along with Fig. 10a for the signal part, indicate that the transient eddy forcing is sensitively responding to the tropical precipitation associated with the SST anomalies during El Niño, and the transient eddy forcing reinforces the seasonal-mean Z200 anomaly signal in the PNA region. During La Niña, the increase of the transient signal is weaker compared to that of El Niño for different amplitudes of the Niño-3.4 index, as also seen in the precipitation signals shown in Fig. 11a. Therefore, the signal parts of transient eddy forcing and Z200 anomalies are closely linked to the tropical precipitation anomalies for both El Niño and La Niña. On the other hand, the noise of transient eddy forcing shown in Fig. 12b is slightly higher during La Niña compared to El Niño, but not much change is observed for different amplitudes. The decrease of seasonal-mean Z200 anomalies noise cannot be explained easily either by the noise of tropical precipitation or by the noise of transient eddy forcing. This result may indicate that extratropical noises other than the synoptic transients may exist as a source of PNA seasonal-mean Z200 anomalies noise. Potential sources may be the noise with intraseasonal time scales and the noise associated with the regional barotropic instability in the PNA region, the existence of which was demonstrated by Simmons et al. (1983) and Branstator (1985). But it cannot be ruled out that a certain (probably large) portion of the transient eddy forcing affects the noise of seasonal-mean Z200 anomalies in the PNA region.

Fig. 12.
Fig. 12.

(a) Signal and (b) noise amplitude (m s−2) of transient eddy forcing over the PNA region (30°–75°N, 180°–60°W) for El Niño and La Niña conditions.

Citation: Journal of Climate 28, 21; 10.1175/JCLI-D-14-00497.1

6. Summary and concluding remarks

The PP of PNA circulation anomalies is examined for El Niño and La Niña separately by using 50 ensemble members of twentieth-century AGCM simulations. For each ensemble member of the simulations, the observed SST is prescribed for the 139-yr period 1870–2009, but the data period used in the present study is from 104 yr after 1900. An equal number (i.e., 14) of El Niño and La Niña years are selected and used in the present study. The PP, as measured by the S–N ratio during El Niño, is larger than that of La Niña. The domain-averaged value of PP for the PNA region for the 14 El Niño years is about 60% larger than that of La Niña. Such a larger PP during El Niño is mainly due to larger signal and partly to less noise during El Niño compared to that during La Niña. The transient eddy feedback to the PNA circulation anomalies is stronger (about 2 times) during El Niño than during La Niña, and this difference in the transients for the two phases significantly contributes to make the difference in the signals of seasonal-mean Z200 anomalies in the PNA region. However, the noise variance of the transients during El Niño is about 17% smaller than during La Niña; thus, the transients play an important role in reduction of the noise of seasonal-mean Z200 anomalies during El Niño. Idealized experiments with the same spatial pattern but different signs of ENSO SST anomalies confirm the results mentioned above. Moreover, these experiments also show that the signals of seasonal-mean Z200 anomalies and transients are proportional to the precipitation anomalies in the tropical Pacific, and larger signals during El Niño are related to larger precipitation anomalies during the same period compared to those of La Niña. On the other hand, the noise is not significantly changed for different amplitudes of SST anomalies for both El Niño and La Niña cases, although the noise of seasonal-mean Z200 anomalies for El Niño cases is somewhat smaller than the corresponding values of La Niña. It is also shown that the noise of Z200 anomalies is not dependent on the tropical precipitation noise and not fully controlled by the synoptic transients. Other possible mechanisms for the noise of seasonal-mean Z200 anomalies suggested are regional barotropic instabilities and intraseasonal variability.

Previous studies have demonstrated that the PP of Z200 during El Niño is larger than that of La Niña, and this larger PP is due to the stronger signal during El Niño compared to that of La Niña and roughly the same noise for both El Niño and La Niña years (Kumar et al. 2000; Schubert et al. 2001; Peng and Kumar 2005, etc.). However, these previous studies have reached those conclusions based on limited periods (20–30 yr) of simulations or based on case studies. One of the values of the present study is that the statistics generated and analyzed are of 104-yr simulations of 50 ensemble members, which is much bigger than those of previous studies and is enough to confirm the previous findings. Moreover, the present study clearly demonstrates with not only observed SST experiments but also idealized SST experiments that the noise of seasonal-mean Z200 anomalies is reduced during El Niño compared to that during La Niña. Also demonstrated in the present study is that the signal of seasonal-mean Z200 anomalies in the PNA region is primarily controlled by the precipitation anomalies in the tropical Pacific associated with ENSO, which are much larger during El Niño than during La Niña. The larger precipitation anomalies during El Niño are not simply due to the SST anomalies, which are larger during El Niño than during La Niña, but are also due to a nonlinear character of precipitation responses to different phases of ENSO SST anomalies. The tropical precipitation anomalies modify the synoptic transient activity in the extratropical Pacific. The anomalous transient activity produces the transient eddy forcing anomalies that feed back to enhance the PNA circulation anomalies. It is found that the feedback intensity of transients during El Niño is about 2 times stronger than that of La Niña. Therefore, the stronger signal of seasonal-mean Z200 anomalies during El Niño is a combined effect of the direct response to larger precipitation anomalies in the tropical Pacific and the stronger feedback response to the transient eddy forcing compared to during La Niña.

Finally, it is mentioned that the PP depends on the model used, although Kang et al. (2011) have demonstrated that the PPs of seven European models are similar, since the signal and noise are proportionally changed for different models. Therefore, it is expected that different models produce similar results to those of the present study. However, further studies based on a multimodel framework may be valuable to confirm and to better understand details of the present results.

Acknowledgments

The authors thank two anonymous reviewers and an editor for their constructive suggestions and comments, which has significantly improved the manuscript. The authors thank the Center of Excellence for Climate Change Research (CECCR), Department of Meteorology and Deanship of Graduate Studies (DGS), King Abdulaziz University (KAU), Jeddah, Saudi Arabia, for providing the computational resources and support to conduct this research. We would also like to thank the Earth System Physics (ESP) section of the International Centre for Theoretical Physics (ICTP) for providing the SPEEDY model.

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