Several mechanisms have been suggested for this asymmetric behavior of ENSO. These include the role of nonlinear vertical advection on El Niño‐La Niña asymmetry [ An and Jin , 2004 ; Jin et al ., 2003 ], which pointed out that when El Niño anomaly propagates eastward from the subsurface, the anomalously warm water is upwelled and intensifies the existing anomaly. Some studies argue that the horizontal advection enhances ENSO asymmetry during the developing stage, while vertical advection comes into action after the mature stage [ Kim and Cai , 2014 ; Su et al ., 2010 ]. Others highlighted the role of the nonlinear SST‐wind feedback [ Frauen and Dommenget , 2010 ; Philip and van Oldenborgh , 2009 ], biological feedback [ Park et al ., 2011 ], fundamental nonlinearity of the system [ Timmermann et al ., 2003 ], or stochastic forcing including westerly wind bursts [ Gebbie et al ., 2007 ], but there is no consensus, and this issue of what drives an El Niño into extreme continues to challenge scientist worldwide. Here we provide a new mechanism that highlights that the linear zonal advection and the migration of atmospheric convection coupled with ocean currents are the major processes that push El Niño to extremes.

Observational reanalysis. (a) Niño3 SSTa index. High and low skewness eras are indicated by red and blue colors, respectively. (b) Contribution of advections to extreme ENSO development. Dark (light) red bars represent the average contributions to El Niño peaks during the high (low) skewness era, while blue bars are for La Niña peaks. Contributions to La Niña peaks are multiplied by −1 for direct comparison. (c) Asymmetric contributions (sum of El Niño and La Niña) in the observation. Positive value means net positive contribution, i.e., stronger El Niño/weaker La Niña. Dark and light grey bars indicate high and low skewness era, respectively. Significant differences at the 95% confidence level (Student'stest) between the high and the low skewness eras (El Niño and La Niña) are indicated by top‐right–bottom‐left (top‐left–bottom‐right in Figure 1 b only) diagonal lines.

El Niño–Southern Oscillation (ENSO) is a dominant mode of natural climate variability spurring global‐scale weather‐related disasters [ Kerr , 1999 ]. The location, intensity, and scale of impact differ greatly according to its phase, namely, El Niño (warm) and La Niña (cool) [ Cai et al ., 2010 ; Frauen et al ., 2014 ; Hoerling et al ., 1997 ]. The amplitude of El Niño can grow far greater than that of the La Niña counterpart [ An and Jin , 2004 ], with a commensurately different impact. Understanding the cause of this amplitude asymmetry, in general, and the mechanism of extreme El Niño events in particular, has been a long‐standing issue [ An and Jin , 2004 ; Duan et al ., 2008 ; Frauen and Dommenget , 2010 ; Monahan and Dai , 2004 ; Su et al ., 2010 ; Yeo and Kim , 2014 ]. During the boreal winters of 1982–1983 and 1997–1998, the area‐averaged sea surface temperature anomaly (SSTa) over the Niño3 region (150°W–90°W, 5°S–5°N) reached up to +3°C and many parts of the world experienced exceptional weather extremes such as catastrophic floods, severe droughts, and devastating hurricanes [ McPhaden , 1999 ; Philander , 1983 ]. The extreme El Niño event of 1997–1998 alone caused an estimated loss of $50 billion and 23,000 fatalities worldwide [ Sponberg , 1999 ]. On the other hand, a negative SSTa has rarely reached amplitude of even −2°C over the 20th century (Figure 1 a).

The contribution of each advective heating term is averaged for the ENSO pitches (e.g., Figure 1 b). The contributions to La Niña development are multiplied by −1 so that one can directly compare with those to El Niño. The asymmetric contribution is defined as the sum of El Niño and La Niña contributions (e.g., Figure 1 c and Figures 2 b and 2 c).

The heat budgets are calculated over the Niño3 region and the top 50 m. The advective heating terms (, whereis three‐dimensional oceanic current andis oceanic temperature) are calculated in respect to the 31 year window climatology at each grid point and averaged within the box (cf. Figure S4 in the supporting information). To compare between different model simulations and the observations, each term is scaled by the models' standard deviation of Niño3 SSTa, giving the unit of mon. The three terms in each blanket represent the advection of mean temperature by anomalous (zonal, meridional, and vertical) currents, the advection of anomalous temperature by mean currents, and the nonlinear advection, respectively.

To target the final push that enhances extreme ENSO, we defined El Niño (La Niña) pitch by following two steps: First, we select extreme ENSO when Niño3 SSTa index is greater than the top (less than the bottom) 10% within the 31 year window. Here we utilized rank statistics, as the characteristics of Niño3 index in models are different, but the main results are unaffected if one uses standard deviations as the criteria. Second, we find the period that is still warming (cooling), i.e., ∂ Niño3/ ∂t ≥ 0 ( ∂ Niño3/ ∂t ≤ 0), among the selected events, i.e., the growth phases of these extreme events (Figure S3 in the supporting information).

High and low skewness eras are identified when the normalized skewness of a 31 year window reaches maximum and minimum values in the historical run (Figures S1–S2 in the supporting information), respectively. The maximum and minimum values of normalized skewness are sought within 1900–2013 (whose center years of 31 year window are within 1915–1998) for the observational reanalysis (ERSSTv3b), 1885–2005 (center within 1900–1990) for historical runs, and 2006–2100 (center within 2021–2085) for RCP8.5 runs of CMIP5 models. The two 31 year time slots are referred to as high and low skewness eras, respectively.

Model results. (a) The range of normalized skewness of Niño3 index for ERSSTv3b and 22 CMIP5 historical runs. Current value of normalized skewness is indicated by “plus” sign in the observation. The solid horizontal line indicates the skewness criterion of 0.75, which is used to classify models as good (dark) or less‐skewed (light), for the further analysis. (b and c) Same as Figure 1 c but for CMIP5 models. Significant differences at the 95% confidence level (Student'stest) between the high and the low skewness eras (good and less‐skewed models) are indicated by top‐right–bottom‐left (top‐left–bottom‐right) diagonal lines.

To estimate and compare ENSO asymmetry between the observations and variety of climate models, we calculated normalized skewness [ An and Jin , 2004 ] of area‐averaged Niño3 SSTa index of a 31 year running window in the observational reanalysis (1900–2013) and historical simulations (1885–2005) of 22 CMIP5 models (cf. Figure 2 a). This calculation is accomplished by first removing 31 year running monthly climatology ( m ) from the area‐averaged SSTa. The skewness ( m 3 ) of SSTa index within the given window is then scaled by its variance ( m 2 ) to give normalized skewness ( s ) as s = m 3 / m 2 3/2 , where m n is the n th moment about its mean, i.e. , m n = ∑ ( x i − m ) n / N .

Extended Reconstructed Sea Surface Temperature version 3b (ERSSTv3b) [ Smith et al ., 2008 ] is used to calculate the observed skewness. Simple Ocean Data Assimilation version 2.2.4 (SODA2.2.4) [ Carton and Giese , 2008 ] is also utilized to estimate the heat budget and wind‐stress forcing. The analysis period for the observations is from 1900 to 2013 (2010 for SODA2.2.4). Here we loosely refer to the observational reanalysis products as the observation for the brevity. Rainfall variability is estimated from the Global Precipitation Climatology Project (GPCP) [ Adler et al ., 2003 ] data for the recent period (1979–2013). For climate model simulations, historical runs (r1i1p1) of 22 Coupled Model Intercomparison Project Phase 5 (CMIP5; Table S1 in the supporting information) model simulations that cover from 1885 to 2005 are used, while the future state is assessed for Representative Concentration Pathways scenario 8.5 (RCP8.5) [ Taylor et al ., 2011 ] experiment from 2006 to 2100. The list of models is given in Figure 1 a (Dark and light amber colors indicate models that are selected as “good” and “less‐skewed” models, respectively, which are described in the following section). All model outputs are monthly and preprocessed by linearly interpolating onto a 1°×1° regular grid.

3 Results and Discussion

The observed ENSO is positively skewed, and the present‐day observed value (Figure 2a) of normalized skewness (1983–2013; see Methods for details) is +0.9. A positive value means that El Niño grows greater in amplitude than La Niña, usually manifested as more extreme El Niño. We focus on the last push of warming (cooling) into extreme El Niño (La Niña) and pick cases of El Niño (La Niña) developing pitch of the top 10% ENSO. The 31 year running climatology is first removed from the Niño3 SST index, and the developing phases of top (bottom) 10% of the anomalies are selected. We then carry out a heat budget analysis over the top 50 m of the Niño3 region for these ENSO pitches during the high and low skewness eras to unravel the dynamics.

3.1 Observed Extreme El Niño In the observational reanalysis, several nonlinear dynamic heating terms suppress the growth of El Niño and La Niña during high skewness era (e.g., nonlinear zonal and meridional; Figure 1b), but make only a small contribution to the positive skewness during low skewness era. The nonlinear vertical advection has negative contribution to the skewness in both eras. During the developing stage of El Niño, the vertical component of nonlinear dynamic heating, i.e., −w ′ ∂T ′ z , downwells cold anomaly and suppresses the existing anomaly. By contrast, linear advection terms (especially , and , in terms of the asymmetric contribution (Figure 1c); note that the largest contribution comes from the zonal advection of mean temperature by anomalous currents) significantly promote extreme El Niño during the high skewness era. The linear zonal advective heating is the dominant term that makes the observed extreme El Niño during the high skewness era. These results are different from our previous understanding that extreme El Niño is generated by nonlinear heating, especially vertical advection, and demonstrate the importance of linear advection terms, zonal advection in particular, on the generation of extreme El Niño. As the number of realization of the observation is limited, we deploy state‐of‐the‐art climate models to confirm the importance of the zonal advection of mean temperature revealed in the observation. Although the overall model simulations of the 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical runs underestimate the present‐day observed skewness (i.e., less extreme El Niño), they show a comparable range of skewness variability on a multi‐decadal time scale (Figure 2a). Some models have a normalized skewness that is comparable to the observed, while the others fail—too weak or even negative. To examine the deciding factor of the ENSO asymmetry, we divided models into two groups (good and less‐skewed) according to their ability to simulate the observed skewness range.

3.2 Simulated Extreme El Niño In general, most of the climate models successfully simulate the observed contribution of nonlinear terms; however, averaged across all models, the linear advection terms make little contribution to the simulated asymmetry, and most of the skewness originates from nonlinear terms (Figure 2). The average contribution of linear dynamic heating terms are smaller than those in the observational reanalysis, while the nonlinear dynamic heating terms are comparable to the observed values. Nevertheless, in the good models, the contribution of the linear terms to the asymmetry is considerable (Figure 2b), but the linear terms are substantially underestimated in the less‐skewed models (Figure 2c). In the heat budgets of the good models, the largest sources of asymmetry in the linear terms are the zonal advection of mean temperature by anomalous current (− ,) and thermocline feedback (− ), followed by Ekman pumping (− ) and meridional advection (− ). In particular, the zonal advection always promotes positive skewness in all good models during the high skewness era, but it is insignificant for less‐skewed models.

3.3 Zonal Advection The zonal advection is a dominant process for extreme El Niño in good models (Figures 3a–3f), as in the observational analysis. The zonal advection during El Niño pitch of high skewness era shows a highly nonnormal distribution, which is different from that during La Niña pitch. The zonal advection shows strong clustering at the extreme positive values (≥+0.5 mon−1 in the observation (42% of El Niño developing pitches) and ≥+0.3 mon−1 in the models (25%)) in the high skewness era. On the other hand, it is bounded by smaller values in La Niña developing pitch. Other linear terms do not show such a large difference between the high and the low skewness eras or between El Niño and La Niña developing pitches (Figures S5–S7 in the supporting information), further underscoring the importance of zonal advection. In the observational reanalysis, all other terms (e.g., thermocline feedback) are either small in variance or symmetrically distributed around its mean. The linear zonal advection term is a major source of asymmetry that boosts extreme El Niño, and the same is true for good models when compared between the high and the low skewness eras. The secondary sources of difference in the models are the thermocline feedback (Figures S8–S10 in the supporting information), Ekman pumping, and meridional advection. Figure 3 Open in figure viewer PowerPoint Zonal advection and convective response. Histogram of the zonal advection for (a and b) observation, (c and d) good models, and (e and f) less‐skewed models, respectively. Upper graphs show the contribution to El Niño peaks, and the lower graphs show the contribution to La Niña peaks. The dark and light bars indicate high and low skewness eras, respectively. The ratios of heating terms that are greater or less than critical values, i.e., ±0.5 mon−1 for observation and ±0.3 mon−1 for models, respectively, are shown at the corners. (g and h) Developing El Niño composite of precipitation anomalies for good and less‐skewed models for each 10 percentile (i.e., top 10%, 10%–20%, and 20%–30%). Significant differences at the 95% confidence level are shaded. (i) Difference of maximum precipitation‐anomaly longitudes between extreme (top 10%) and moderate (10%–40%) El Niño versus the maximum normalized skewness of the models' Niño3 SSTa. The solid line indicates the regression line, and the “plus” sign indicates the maximum observational value.

3.4 Migration of Atmospheric Response The generation of the abnormal zonal current that boosts extreme El Niño depends on many factors. Among those factors is the atmospheric variability. The maximum normalized skewness of a model is well correlated with the model's ability to simulate the longitudinal migration of the equatorial precipitation response during extreme El Niño development (Figures 3g–3i). In the observations and good models, the locations of enhanced convection in response to El Niño tend to move from the equatorial western Pacific to the central Pacific, as their intensity gets stronger (Figure 3g); however, those during La Niña do not change much with their strength (Figures S11 and S12 in the supporting information). Only the good models show such a huge migration of the center of action, and the migration is small for the less‐skewed models. The linear correlation coefficient between the longitudinal migration and maximum skewness of climate models is over 0.65 (Figure 3i), whose longitudinal migration is defined as the difference in longitudes of the maximum equatorial (5°S–5°N) precipitation anomaly between extreme (top 10% of Niño3 SSTa) and moderate (10%–40%) El Niño. When atmospheric convection moves from the western Pacific to the equatorial central and eastern Pacific where the ocean is more sensitive to atmospheric forcing (Figures S13 and S14 in the supporting information), it excites a stronger zonal current response [Frauen and Dommenget, 2010; Kang and Kug, 2002; Su et al., 2010; Zhang and Sun, 2014]. Stronger zonal current boosts developing El Niño anomaly by transporting warm water to the east, enhancing westerly winds, which, in turn, enhance zonal current anomalies. As a result, asymmetric longitudinal eastward migration of convection enhances positive skewness of ENSO. We point out that the historical runs of our good models coincide with the models that exhibit zonal South Pacific Convergence Zone shifts [Cai et al., 2012] and models that simulate high rainfall skewness, which show a doubling of extreme El Niño events in the RCP8.5 future simulations [Cai et al., 2014].