1. Introduction
El Niño–Southern Oscillation (ENSO), the most prominent year-to-year climate fluctuation on the planet, is characterized by alternating warmer (El Niño) and colder (La Niña) sea surface temperature (SST) conditions along with a zonal atmospheric pressure swing across the tropical Pacific basin. Although its dynamics originate in the tropical Pacific, ENSO’s reach projects far beyond through atmospheric teleconnections that affect weather and climate patterns worldwide (e.g., van Loon and Madden 1981; Ropelewski and Halpert 1987; Trenberth and Caron 2000; McPhaden et al. 2006). For example, strong El Niño events are typically followed by massive summer monsoon flooding over the Yangtze River basin, home to about a third of the population in China (Zhang et al. 2016b). Hence, ENSO has been receiving wide attention from scientists and policymakers. In the second half of the twentieth century, great advances have been achieved in understanding the physical nature of ENSO (e.g., Bjerknes 1969; Wyrtki 1975; Rasmusson and Carpenter 1982; Zebiak and Cane 1987; Neelin et al. 1998; Wallace et al. 1998). The Bjerknes feedback loop between trade wind intensity, subsurface ocean temperature anomalies, and the zonal SST gradient in the Pacific acts as the key process amplifying initial SST and/or wind anomalies (Bjerknes 1969). However, this positive ocean–atmosphere feedback by itself does not allow for an oscillation with the observed phase transitions between ENSO warm and cold events. Subsequently, various conceptual theories were proposed to explain ENSO’s phase oscillatory behavior by invoking delayed negative feedback mechanisms (e.g., Schopf and Suarez 1988; Battisti and Hirst 1989; Jin 1997a,b; Picaut et al. 1997; Weisberg and Wang 1997). For example, the ENSO recharge oscillator theory highlights the importance of the equatorial Pacific heat content discharge/recharge process along the equator (Jin 1997a,b), which is supported by the observed equatorial thermocline depth evolution (Meinen and McPhaden 2000).
The ENSO phenomenon displays a pronounced seasonal synchronization with the annual cycle (AC), with SST anomalies on average decreasing during the beginning of the year and increasing during the second half of the year (Rasmusson and Carpenter 1982; Stein et al. 2011, 2014). This tight seasonal phase-locking indicates an inherent strong interaction between ENSO and the AC. Jin et al. (1994) suggested that the nonlinear resonance between ENSO and the AC could produce new time scales and could be responsible for the genesis of ENSO’s irregularity. Based on analyses of both observations and coupled general circulation model (CGCM) simulations, recent studies found that nonlinear atmospheric interactions between interannual ENSO variability and the Pacific warm pool AC gives rise to a new ENSO-dependent climate variability, which can be captured by the second empirical orthogonal function (EOF) mode of tropical Pacific surface wind anomalies (Stuecker et al. 2013, 2015a,b). This combination mode (denoted as C-mode) comprises a meridionally asymmetric atmospheric circulation pattern that includes the southward shift of central Pacific zonal wind anomalies during late winter and early spring, which is crucial for the seasonally modulated termination of ENSO events (e.g., Harrison 1987; Harrison and Vecchi 1999; Vecchi 2006; Ohba and Ueda 2009; McGregor et al. 2012; Stuecker et al. 2013). Furthermore, the C-mode causes characteristic interhemispheric sea level variability patterns in the Pacific (Widlansky et al. 2014) and is responsible for the genesis of the anomalous low-level western North Pacific (WNP) anticyclone (Stuecker et al. 2015b). The C-mode dynamics can explain the observed meridionally antisymmetric atmospheric response over the WNP region well (Stuecker et al. 2015b), with local air–sea coupling in the WNP (Wang et al. 2000) and the delayed Indian Ocean warming capacitor effect (Yang et al. 2009; Xie et al. 2009) amplifying the C-mode response in boreal summer following an El Niño event. This anomalous WNP circulation induces precipitation anomalies over East Asia via modulating low-level moisture transports (Zhang et al. 2016a). Therefore, the C-mode plays a crucial role in both directly shaping the ENSO life cycle and in further extending ENSO’s impacts toward the WNP as well as other extratropical regions.
Mathematically, the C-mode time series can be well reconstructed by a product of the Niño-3.4 index and the Pacific warm pool SST AC. The resulting C-mode time series exhibits characteristics near-annual frequencies at periods of ~10 and ~15 months (Stuecker et al. 2013, 2015b). Therefore, it is compelling to infer that the C-mode magnitude is closely connected to ENSO’s characteristics, especially ENSO amplitude. Recent studies have shown that the C-mode occurs most prominently during strong El Niño events but is less pronounced during weak El Niño and La Niña events (McGregor et al. 2013; Stuecker et al. 2013). Large amplitude El Niño events generally give rise to a well-organized WNP anticyclone and thus result in a strong connection between ENSO and the East Asian monsoon (Wang et al. 2008; Xie et al. 2010). In addition, the atmospheric circulation pattern resulting from this ENSO/AC interaction also exhibits a high dependence on the zonal structure of ENSO SST anomalies. A strong meridionally asymmetric atmospheric response (i.e., strong C-mode) appears predominantly during traditional eastern Pacific (EP) El Niño events rather than during central Pacific (CP) El Niño events (McGregor et al. 2013; Zhang et al. 2015). Importantly, both ENSO intensity and its zonal structure exhibit significant decadal variability in observations. Past ENSO activity is characterized by both periods of relative quiescence, when El Niño and La Niña events tend to be weak, and active periods when ENSO events are strong and punctuated with the occasional occurrence of extreme El Niño events (e.g., Dong et al. 2006; Lübbecke and McPhaden 2014). Significant regime changes in ENSO patterns can also be detected on decadal time scales, with El Niño events being more frequently observed in the CP after the late 1990s (e.g., Yeh et al. 2009; Xiang et al. 2013; Ren et al. 2013; Zhang et al. 2014; Hu and Fedorov 2018). Hence, one scientific issue remains to be answered, namely whether ENSO/AC interactions vary on decadal and longer time scales. If so, which external forcing is responsible for this variation?
Although previous studies have well illustrated the C-mode dynamics on seasonal-to-interannual time scales (Stuecker et al. 2013, 2015b), whether ENSO/AC interaction exhibits long-term variations has not been sufficiently elucidated. Here we focus on decadal variations of the ENSO/C-mode relationship and demonstrate that this nonstationarity can be largely attributed to ENSO property changes in response to AMO phase shifts. In the remainder of this paper, section 2 describes the utilized datasets and methodologies. The changing character of ENSO/AC nonlinear interactions due to a decadal modulation effect by the AMO is illustrated in section 3. Next, in section 4 we explore the possible mechanisms that can explain this modulation based on both observations and a CGCM experiment. The major conclusions are summarized and discussed in section 5.
2. Data and methodology
a. Datasets
The monthly datasets (1948–2018) used in this work include global SST from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST analysis, version 3 (ERSST; Smith et al. 2008). Atmospheric circulation is characterized by monthly surface (10 m) wind, which is obtained from the National Centers for the Environmental Prediction–National Center for the Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). The precipitation anomalies are investigated using both the NOAA precipitation reconstruction dataset (PREC) (Chen et al. 2002) and the station precipitation dataset provided by the China Meteorological Administration (CMA). The horizontal spatial resolution is 2° × 2° for the SST dataset and 2.5° × 2.5° for the precipitation and atmospheric circulation datasets.
b. Methodology
Besides simple linear statistical tools such as composite and linear regression analyses, we use a partial correlation technique (e.g., Yamagata et al. 2004; Ashok et al. 2007) to delineate the partial relationship between two variables while excluding potential influences arising from an additional independent variable. Anomalies for all fields are computed as the deviation from the long-term climatological mean (1948–2018). The linear trend was removed to avoid possible influences associated with the long-term trend. We employ several climatic indices to facilitate our analyses. The Niño-3.4 index, which is defined as the area-averaged SST anomalies in the Niño-3.4 region (5°S–5°N, 120°–170°W), is used to quantify ENSO intensity. Following conventions, we utilize a threshold of ±0.5 standard deviations of the Niño-3.4 index during the DJF season to define ENSO events. With this method, 22 El Niño and 26 La Niña events are identified (Table 1). The boreal winter of 1948, for example, refers to December 1948–February 1949. The calendar months labeled with (0) and (1) denote the months in ENSO developing and decaying year, respectively. The AMO index is calculated as the area-averaged SST anomalies within the domain of 0°–60°N and 0°–80°W (Trenberth and Shea 2006). In this study, the ENSO decaying spring [MAM(1)] is our focus since the C-mode generally has its peak and exhibits the strongest decadal variations during this season (see Fig. 3). All the above indices are normalized before our analysis. All statistical significance tests were performed based on the two-tailed Student’s t test.
El Niño and La Niña events for the 1948–2017 period.
c. Coupled model experiment
Due to the relatively short observational period, a CGCM experiment is conducted with the Geophysical Fluid Dynamics Laboratory (GFDL) CM2M (Galbraith et al. 2011) model to test our observational hypotheses. The atmospheric component of the model has a ~3.5° × 3° spatial resolution with 24 vertical levels. The oceanic component has nominal 3° horizontal resolution increasing to 0.6° latitudinal resolution at the equator and 28 vertical levels including five in the upper 50 m. In the experiment, the North Atlantic SSTs are relaxed to the monthly climatology plus a 50-yr sinusoidally varying AMO anomaly with a 2-day relaxation time (Levine et al. 2017). The AMO anomaly is calculated by regressing the SST anomalies upon the AMO index. Outside of the prescribed region the SSTs can evolve freely. The AMO-forced experiments are integrated for 500 years, yielding nine complete AMO cycles because of a phase shift. We compute the anomalies by subtracting the long-term monthly mean from the simulation itself.
3. Decadal variations of ENSO combination mode amplitude
(a),(b) The two leading EOF spatial patterns and (c) their corresponding normalized time series (PC) of surface wind anomalies (m s−1) over the tropical Pacific for the 1948–2017 period. The EOF spatial patterns are obtained by regression of the normalized PCs on the surface wind anomalies. The theoretical PC2 is computed as the product of the Niño-3.4 index and the annual cycle (N3.4 × annual cycle) and is shown together with the correlation coefficients (R) between the original and theoretical PC2 in (c). Different red and blue shadings in (a) and (b) indicate westerly and easterly anomalies that exceed the 90%, 95%, and 99% confidence levels, respectively.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
The C-mode is intrinsically associated with ENSO variability and the Pacific warm pool AC (Stuecker et al. 2013). During an El Niño event, while the Niño-3.4 typically reaches its peak in the boreal winter season, the PC2 remains negative until October of the El Niño developing year and then reverses its sign abruptly, generally lagging the Niño-3.4 index by about 2 months and reaching its peak value during the following late winter and early spring season. An approximately opposite picture is observed during La Niña events (Fig. 2). It can be inferred that the PC2 is highly related to Niño-3.4 (i.e., PC2 being the amplitude modulated ENSO signal by the AC), which is especially evident during the winter and spring seasons. To inspect possible decadal variations of the C-mode response, in Fig. 3 we display the 15-yr running correlation between the DJF Niño-3.4 index and PC2 in different months. During the ENSO mature winter, little decadal variability can be detected in the relationship between Niño-3.4 and the C-mode. However, prominent decadal nonstationary behavior of the relationship is detected during the ENSO decaying spring season. The Niño-3.4 index is positively correlated with the MAM PC2 from the mid-1960s to mid-1990s, with an El Niño event being accompanied by a positive C-mode index. In contrast, no statistically significant relationship between them can be observed during other periods. Here, a sliding 15-yr window is used to detect the Niño-3.4/C-mode relationship on decadal time scales. The qualitative conclusions remain unchanged if we utilize other moving windows, such as 13 and 17 years (not shown). Considering that boreal spring is the season when the C-mode response is generally strong and also exhibits considerable decadal variations, we focus the investigation on this season hereafter.
Composite monthly evolutions of the normalized Niño-3.4 and PC2 indices for El Niño and La Niña events during the 1948–2017 period. Small and big dots indicate values exceeding the 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
15-yr running correlation coefficients between the DJF Niño-3.4 index and the PC2 time series as a function of year (x axis) and the PC2 calendar month after the ENSO peak (y axis) during the 1948–2017 period. Year in the x axis denotes the middle year of each 15-yr window. Correlation coefficients exceeding the 95% significance level are marked with black dots.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
To further illustrate the decadal variability of the ENSO/C-mode linkage, we in Fig. 4 show the 15-yr running correlation between the DJF Niño-3.4 and MAM PC2 (red solid line) from 1948 to 2018. It is clear that the Niño-3.4/PC2 relationship exhibits significant decadal variations with statistically significant correlations at the 99% confidence level occurring primarily during the period from the mid-1960s to mid-1990s. As a key component of the C-mode, the relationship of the WNP anticyclonic low-level circulation anomalies with ENSO is also examined. Here, a WNP anticyclone index (WNPACI) is defined as the 850-hPa streamfunction averaged over the region 5°–25°N and 90°–150°E. It can be seen that the 15-yr running correlation between the MAM WNPACI and the previous DJF Niño-3.4 index (black solid line in Fig. 4) displays a similar feature with that of the C-mode on decadal time scales.
The AMO index (9-yr running average; blue bars) and the 15-yr running correlation for the DJF Niño-3.4 index with the subsequent MAM PC2 (red solid curve), the PREC_PC1 [the leading PC of precipitation anomalies in East China (15°–55°N, 100°–135°E); green solid curve], and the anomalous WNP anticyclone index (WNPACI; black solid curve) during the 1948–2017 period. The corresponding dashed lines indicate the 99% significance levels for the correlation coefficients.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
It is well known that during an El Niño mature winter and decaying spring the anomalous WNP anticyclone enhances moisture transport to East Asia and thereby leads to more precipitation over southern China (e.g., Wang et al. 2000; Zhang et al. 2017). This is confirmed by Figs. 5a and 5b, which show regression maps of the spring precipitation anomalies over China onto the preceding DJF Niño-3.4 and simultaneous PC2 indices, respectively. Considering that DJF Niño-3.4 and MAM PC2 are highly correlated, partial regression patterns of the precipitation anomalies are displayed in Figs. 5c and 5d to explore the independent influences from Niño-3.4 and PC2, respectively. We can see a clear statistically significant positive precipitation response to the C-mode over southeastern China (Fig. 5d), but no statistically significant signal coming from the Niño-3.4 index (Fig. 5c). These results suggest that the ENSO SST forcing itself is not able to produce significant precipitation anomalies over southern China. Instead, ENSO’s impact is conveyed to East Asia mainly through the C-mode capturing ENSO/AC interaction, which is consistent with previous studies (Zhang et al. 2016a). Since the C-mode response to ENSO shows variable strength on decadal time scales, we expect that the related spring precipitation anomalies over southern China should also display similar variations. Because the leading EOF captures monopole precipitation anomalies over southern China (You and Jia 2018), we here utilize the leading PC of the East Asian (15°–55°N, 100°–135°E) spring precipitation anomalies (PREC_PC1) to measure the intensity of precipitation anomalies over southern China. A positive PREC_PC1 index indicates precipitation surpluses in this region. Next, we compute the 15-yr running correlation coefficients between the DJF Niño-3.4 and PREC_PC1 (green solid line in Fig. 4). Similar to the PC2 and WNPACI, the relationship between the PREC_PC1 and ENSO also exhibits prominent decadal variations with statistically significant correlations only occurring during the period from the mid-1960s to the mid-1990s. These results demonstrate that both the ENSO/C-mode relationship and ENSO-associated climate impacts over East Asia vary on decadal time scales.
Regressions of MAM precipitation anomalies (contours in mm day−1, from 5 to 20 by 5) in southern China onto the (a) preceding DJF Niño-3.4 index and (b) simultaneous PC2 index for the 1948–2017 period. (c),(d) As in (a),(b), but for partial regressions removing linearly the impact of simultaneous PC2 index and preceding DJF Niño-3.4 index, respectively. Shadings indicate regression coefficients exceeding the 90%, 95%, and 99% confidence levels, respectively.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
To explore which external forcing might be responsible for these decadal variations, we show in Fig. 4 also the 9-yr running averaged AMO index (blue bars), given that the AMO has been widely proposed as an important external forcing responsible for ENSO property changes and variations of ENSO-related anomalous atmospheric responses on decadal time scales (Dong et al. 2006; Yu et al. 2015; Levine et al. 2017; Geng et al. 2017, 2018). It can be seen that the AMO index exhibits a positive phase before the mid-1960s and after the mid-1990s, while showing a negative phase during the interim period, thus exhibiting an approximate inverse temporal evolution with the running correlations shown in the same figure. Prominent C-mode responses during the decaying spring can be detected primarily during the negative AMO phase but not during the positive phase. This correspondence motivates our hypothesis that the C-mode response to ENSO could be modulated by the AMO.
We then categorize the whole time period into two epochs according to the AMO phase and then display the relationship of ENSO with the C-mode during its decaying spring season for each epoch (Fig. 6). When the AMO is in its negative phase, nearly all ENSO events (20 out of 21; over 95%) are accompanied by a same-sign C-mode response in the decaying spring season, and the correlation coefficient for the period reaches 0.79 (exceeding the 99% confidence level). In contrast, only 18 out of 27 ENSO events are accompanied by a same-sign C-mode response during the positive AMO phase. The correlation coefficient is reduced to 0.41, which is not significant at the 99% confidence level. The average C-mode amplitude is also smaller than that during the negative AMO phase, suggesting that the ENSO/AC interaction is weakened and/or that it projects onto a different (e.g., more meridionally symmetric) pattern during the positive AMO phase. To further illustrate the AMO modulation effect on the ENSO/C-mode relationship, we categorize ENSO events into four types according to the AMO phase, that is, El Niño events during a positive AMO phase (AMO+/El Niño) and during a negative AMO phase (AMO−/El Niño), as well as La Niña events during a positive AMO phase (AMO+/La Niña) and during a negative AMO phase (AMO−/La Niña) (see Table 2).
Scatterplot of the MAM PC2 and preceding DJF Niño-3.4 indices during (a) positive and (b) negative AMO phases with the corresponding linear regression lines (black lines). Neutral conditions, El Niño events, and La Niña events, are indicated by hollow gray, red solid, and blue solid circles, respectively. The corresponding correlation coefficients for the two periods (R) are also displayed.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
Category of ENSO events for the 1948–2017 period according to the AMO phase.
The composite surface wind and precipitation anomalies over the tropical Pacific for the four cases are displayed in Fig. 7. The El Niño events concurrent with a positive AMO phase show a statistically significant atmospheric response only in few areas of the tropical Pacific. Neither the characteristic anomalous WNP anticyclone nor a meridional shear of anomalous zonal wind across the equator can be clearly identified. The surface wind anomalies for an AMO+/La Niña decaying spring somewhat produce a pattern with meridional shear of anomalous zonal wind and statistically significant precipitation anomalies in the equatorial Pacific, however, the characteristic WNP circulation pattern is also absent. In contrast, both El Niño and La Niña events in the AMO negative phase are associated with pronounced precipitation anomalies and a statistically significant C-mode-like wind patterns in the tropical Pacific (including both the WNP circulation pattern and the characteristic meridional shear of anomalous zonal wind in the central Pacific), further confirming the AMO modulation effect on the ENSO/C-mode relationship. We emphasize that (as for La Niña events) the meridional shear of anomalous zonal wind is stronger in an AMO positive phase, whereas the WNP circulation pattern is more pronounced in an AMO negative phase (Figs. 7c,d). This inconsistency is possibly associated with the atmospheric background change in the tropical Pacific due to AMO forcing. Atlantic warming could lead to an intensification of Walker circulation, with strengthened precipitation and easterly surface wind anomalies in the central-eastern tropical Pacific (not shown) (Kucharski et al. 2011; McGregor et al. 2014; Zanchettin et al. 2016). As a result, the tropical atmospheric anomalies are strengthened for AMO+/La Niña but weakened for AMO−/La Niña decaying spring seasons.
Composite MAM surface wind (vectors in m s−1) and precipitation (shadings in mm day−1) anomalies in the tropical Pacific during (a) AMO+/El Niño, (b) AMO−/El Niño, (c) AMO+/La Niña, and (d) AMO−/La Niña events for the 1948–2017 period. The wind and precipitation anomalies are only displayed when they are statistically significant at the 95% confidence level.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
4. Possible modulation mechanisms by the AMO
We now turn to investigate the possible mechanisms responsible for the AMO modulation of the C-mode response. Figure 8 shows the composite time evolution of the zonal mean SST anomaly in the tropical (6°S–6°N) Pacific during the ENSO life cycle for the above four cases. ENSO SST anomalies typically mature in December, but their composite magnitudes and spatial structures are different for the different AMO phases. The El Niño events occurring in a positive AMO phase tend to peak at ~1.0°C with the maximum SST anomaly located at ~140°W (Fig. 8a). In contrast, the maximum intensity for the AMO−/El Niño SST anomaly reaches ~1.4°C and is located at ~120°W, a larger amplitude and farther eastward shifted pattern compared with the AMO+/El Niño case (Fig. 8b). In regard to the La Niña events, although the zonal locations of the maximum SST anomaly center do not show an apparent change between the two cases (both located at ~140°W), we observe a pronounced magnitude difference for the SST anomalies. The average SST anomaly magnitude of the AMO−/La Niña events is larger than that of the AMO+/La Niña case. These results are consistent with various previous studies (e.g., Dong et al. 2006; Timmermann et al. 2007; Kang et al. 2014; Levine et al. 2017; Hu and Fedorov 2018; Park and Li 2019), which argue that a positive AMO phase tends to produce weaker ENSO variability. A positive AMO is conducive to an intensified Walker circulation and enhanced trade winds in the western and central tropical Pacific. This basic state change is associated with a deepened thermocline and reduced vertical stratification in the equatorial Pacific, which weakens the coupled instability through which ENSO events grow (Zebiak and Cane 1987; Jin et al. 2006), hence reducing ENSO amplitude (Dong et al. 2006; Dong and Sutton 2007; Timmermann et al. 2007; Li et al. 2016). According to Eq. (1), a weaker ENSO magnitude yields a weaker ENSO/AC interaction, and thus the C-mode response is modulated by the AMO via its impact on ENSO amplitude.
Composite SST anomaly temporal evolution (shadings in °C) in the tropical (6°S–6°N) Pacific for (a) AMO+/El Niño, (b) AMO−/El Niño, (c) AMO+/La Niña, and (d) AMO−/La Niña events during the 1948–2017 period. Black dots and dashed vertical lines respectively denote the locations and longitudes of either maximum or minimum SST anomalies during the ENSO life cycle. Values exceeding the 95% confidence level are marked by small gray dots.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
In addition to ENSO magnitude, we can also observe prominent differences in the zonal structure of the ENSO SST anomaly in Fig. 8. While the La Niña SST longitudinal position is basically invariant with the AMO phase, El Niño events bear distinct zonal structures of the SST anomaly during different AMO phases. The SST anomalies for the AMO+/El Niño case are westward shifted compared with those of the AMO−/El Niño case. The maximum SST anomalies along the equator are respectively located at ~140°W and ~120°W for the AMO+/El Niño and AMO−/El Niño composites. This result is consistent with previous studies (Yu et al. 2015; Hu and Fedorov 2018; Park and Li 2019), which suggested a physical linkage between North Atlantic warming and the zonal structure of El Niño SST anomalies. In their arguments, a positive AMO leads to an intensification of the Pacific subtropical high and/or stronger-than-average background trade winds in both zonal and meridional components, which in turn favors the development of more CP El Niño events in the tropical Pacific. It is demonstrated that the longitudinal position of El Niño SST anomalies has notable impacts on the C-mode response. In contrast to EP El Niño events, the westward shifted SST anomalies during CP El Niño tend to excite more equatorially symmetric convection anomalies on both sides of the equator due to the high climatological SSTs in the warm pool region and the nonlinear SST–convection relationship, which in turn leads to a weaker meridionally asymmetric C-mode response (McGregor et al. 2013; Zhang et al. 2015). Inspired by these studies, we examine in Fig. 9 the possible effect of the zonal El Niño SST anomaly location on the C-mode response amplitude. A close linear interrelationship (R = 0.69, exceeding the 99% confidence level) is clearly detected, suggesting that the C-mode response during an El Niño decaying spring tends to be weaker as the El Niño SST anomaly is shifted more westward, which provides further support for our hypothesis. To summarize, these observational results indicate that the decadal variation of the ENSO/C-mode relationship is largely determined by the changes of both ENSO amplitude and El Niño SST zonal structure due to an AMO modulation.
Scatterplot of the MAM PC2 index and longitudinal position of the maximum SST anomalies in the tropical (5°S–5°N) Pacific during the winter and spring season (DJFMAM) for all El Niño events. The black line denotes the corresponding linear regression line and the correlation coefficient (R) is shown as an inset.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
Due to the inherent limitation of diagnosing AMO-related impacts from the relatively short observational record, we conducted an experiment with the GFDL CM2M to further test our hypothesis that the AMO modulates the ENSO/C-mode relationship on decadal time scales. Here, the GFDL CM2M is utilized since the model simulates a relatively realistic C-mode response both in its spatial wind structures and temporal variability (Ren et al. 2016; Stuecker et al. 2015b). The model has also been widely used to investigate multitemporal and multiple spatial scale interactions, as well as the impacts of the ENSO/AC nonlinear interactions (Levine et al. 2017, 2018). We emphasize however that the model inevitably suffers from biases similar to those of other climate models. For example, the temporal correlation of the simulated MAM PC2 with the preceding DJF Niño-3.4 index is 0.19, much weaker than the 0.65 value seen in observations. This bias may be subject to the westward shifted wind anomalies manifested in the C-mode spatial pattern (not shown) (Ren et al. 2016), which is associated with a weaker amplitude and more westward shift of ENSO SST anomalies in the current generation of climate models (e.g., Guilyardi et al. 2009; Bellenger et al. 2014; Van Oldenborgh et al. 2005).
As done in Fig. 4, we calculate the 15-yr running correlation between DJF Niño-3.4 and the following MAM PC2 based on the AMO-forced model simulation. The simulation has nine complete AMO cycles and thus nine ensembles for the running correlation can be obtained. The red solid line in Fig. 10 displays the composite running correlation. We can see that the simulated ENSO/C-mode relationship varies on decadal time scales. Although the AMO index and running correlation in the simulation do not agree as well with each other as seen in the observations, their linkage is qualitatively reproduced. During a negative AMO phase, ENSO tends to be accompanied by a same-sign C-mode response in its decaying spring season. In contrast, ENSO winters are generally followed by only weak C-mode-like atmospheric patterns in the subsequent spring during a positive AMO phase. Next, we calculate the East Asian PREC_PC1 during the spring season and its relationship with the DJF Niño-3.4 index in the AMO pacemaker simulations (Fig. 10). Resembling the ENSO/C-mode relationship, the MAM PREC_PC1 response to ENSO also displays biased but qualitatively similar pronounced decadal variations as seen in observations, with a negative AMO corresponding to an enhanced ENSO/PREC_PC1 relationship. These results can still be obtained by considering the spread among each 50-yr AMO cycle in the simulation.
The composite AMO index (black bar) and the 15-yr running correlation for the DJF Niño-3.4 index with 1) the subsequent MAM PC2 (red solid curve) and 2) the PREC_PC1 (the leading PC of MAM precipitation anomalies in East China (15°–55°N, 100°–135°E); green solid curve) for the AMO cycle based on the AMO-forced CGCM simulations. Because of the phase shift, the simulation has nine 50-yr AMO cycles, and the ensemble spreads among the running correlations are displayed by pink and green shadings for the positive and negative AMO phases, respectively.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
To uncover the possible reasons for this AMO modulation, the composite equatorial (4°S–4°N) thermocline depth and surface zonal wind difference for the positive and negative AMO phases are displayed in Fig. 11 based on the model simulation. Here, the depth of the thermocline is defined as the ocean layer for which the vertical thermal gradient is maximized (Castaño-Tierno et al. 2018). It can be seen that a positive AMO is accompanied by enhanced trade winds in the western and central tropical Pacific and an increased tilt of the equatorial Pacific thermocline, consistent with former studies (Dong et al. 2006; Li et al. 2016). This could weaken the coupled instability through which El Niño events grow (Zebiak and Cane 1987; Jin et al. 2006), hence reducing ENSO variance. Figure 12 displays model-simulated standard deviation of the equatorial (4°S–4°N) SST anomalies during the boreal winter season for the positive and negative AMO phases, respectively. We can see that the relatively weaker ENSO variability during the positive AMO phase is realistically captured by the AMO-forced experiment. Furthermore, compared with the negative AMO phase, ENSO-associated SST variability tends to be shifted more westward during the positive AMO phase. However, this westward shift is mainly contributed by El Niño rather than La Niña events. Compared with a negative AMO phase, the maximum equatorial SST anomaly for El Niño distinctly shifts to the west during a positive AMO phase, which is consistent with the observations (Figs. 8a,b). On the contrary, La Niña is characterized by an eastward shifted equatorial DJF SST anomaly during a positive AMO phase. Although a slight eastward shift of the La Niña SST anomaly can also be detected in observations (Figs. 8c,d), it is exaggerated in this CGCM experiment. This may be associated with model biases in the simulation of La Niña events. Nevertheless, a relatively weaker ENSO variability and westward shifted El Niño events during a positive AMO phase are realistically reproduced by the AMO-forced experiment. These agreements between observations and CGCM simulations increase our confidence for the aforementioned mechanisms explaining the decadal modulation of the ENSO/C-mode relationship by the AMO.
Composite thermocline depth (black bars in m) and the surface zonal wind (green curve in m s−1) difference in equatorial (4°S–4°N) Pacific between the positive and negative AMO phases based on the AMO-forced CGCM simulations.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
The standard deviation of the DJF SST anomalies in the equatorial (4°S–4°N) Pacific during the positive (red solid curve) and negative (blue solid curve) AMO phases based on the AMO-forced CGCM simulations. The vertical dashed lines denote the corresponding longitudinal positions of the maximum standard deviations for the positive and negative AMO phases.
Citation: Journal of Climate 33, 11; 10.1175/JCLI-D-19-0740.1
5. Conclusions and discussion
The present study demonstrates decadal variations of the ENSO/AC interactions due to AMO forcing based on both observations and a climate model experiment. The ENSO/AC interactions, which yield combination tones in atmospheric circulations, can be typically largely represented by the second EOF of the surface wind anomalies over the tropical Pacific (i.e., C-mode). An El Niño event is generally accompanied by a strong positive phase of the C-mode response during the boreal spring season after its peak phase. However, the ENSO/C-mode linkage exhibits pronounced decadal variations, which are associated with changes of the AMO phase. During a negative AMO phase, we find a statistically significant positive correlation (R = 0.79, 99% confidence level) between ENSO and C-mode, while this association is weakened for a positive AMO phase. The anomalous WNP anticyclone, which is a main atmospheric component of the C-mode, exhibits a similar decadal varying statistical relationship with the preceding winter Niño-3.4 index. Only during an AMO negative phase can we observe a pronounced anomalous WNP anticyclone in the El Niño decaying spring. As the key bridge linking ENSO and the East Asian monsoon, the anomalous WNP anticyclone enhances moisture transport to East Asia and thereby causes more precipitation in southern China. The leading PCs of the spring precipitation anomalies in southern China also exhibit a similar varying statistical relationship with the preceding winter tropical Pacific ENSO events on decadal time scales. They show a statistically significant positive correlation only during a negative AMO phase. Importantly, these atmospheric responses cannot be detected with statistical confidence during a positive AMO phase.
We suggest that these decadal variations of the ENSO/C-mode relationship are mainly attributable to ENSO magnitude and El Niño zonal structure changes due to an AMO modulation. On one hand, during the negative AMO phase, both El Niño and La Niña events tend to be stronger than those during a positive AMO phase. The possible mechanism can be summarized as an “atmospheric bridge–thermocline feedback.” It includes a reduction in trade wind strength in the western and central tropical Pacific and a shallower thermocline in the equatorial Pacific due to a multidecadal cooing of the North Atlantic. These atmospheric and oceanic responses enhance the vertical stratification in the equatorial Pacific, which could lead to an intensification of the thermocline feedback and thus result in a strengthening of ENSO activity (Dong et al. 2006; Dong and Sutton 2007; Zhang and Delworth 2005; Timmermann et al. 2007; Kang et al. 2014). According to the theoretical expression of the C-mode [Eq. (1)], larger ENSO amplitude inevitably yields stronger ENSO/AC interactions and an enhanced C-mode index. On the other hand, although the longitudinal location of the La Niña SST anomaly center seems to be invariant with the AMO phase, El Niño bears distinct zonal structures of its SST anomaly during different AMO phases. El Niño events concurrent with a positive AMO phase tend to mature with a westward-shifted SST anomaly in the tropical Pacific (relative to El Niño events during a negative AMO phase). This might be associated with an intensified subtropical Pacific high and/or stronger-than-average background trade winds induced by the anomalously warm North Atlantic via the atmospheric bridge, which tends to favor a westward shift of ENSO SST anomalies (Yu et al. 2015; Hu and Fedorov 2018). These westward-shifted SST anomalies in turn tend to excite relatively symmetric convection anomalies on both sides of the equator in the central Pacific (Zhang et al. 2015). As a consequence, the meridional asymmetric C-mode wind response is significantly reduced. The AMO-forced pacemaker CGCM experiment is capable of realistically reproducing this AMO-dependent decadal varying C-mode response during the ENSO decaying boreal spring season, which strengthens our confidence in the hypothesis of a decadal ENSO/C-mode relationship modulation by the AMO. However, given that different climate models exhibit different ENSO characteristics (and potentially different responses to an AMO forcing), we emphasize that it would be valuable to conduct similar experiments with other models in the future to test the robustness of the proposed mechanism.
Our study indicates that it is not sufficient to only focus on near-annual to interannual time scales when assessing ENSO and its interactions with the AC. We showed that decadal variability associated with the AMO can have important implications for both seasonally modulated intrinsic ENSO dynamics and their climate impacts. In fact, this AMO modulation on ENSO properties suggests a pronounced influence from the Atlantic onto the Pacific via pantropical climate interactions, which are highlighted by many recent studies (e.g., Kug and Kang 2006; Ham et al. 2013; Wang et al. 2017; Cai et al. 2019). In addition, ENSO property and C-mode changes could also be associated with other forcings, such as the Pacific decadal oscillation (PDO) (e.g., Yeh and Kirtman 2005; Verdon and Franks 2006; Kravtsov 2012; Chung and Li 2013; Lin et al. 2018) or changes in greenhouse gases and/or aerosols (e.g., Emile-Geay et al. 2008; Yeh et al. 2009; Cai et al. 2014, 2015). Research on C-mode dynamics and impacts are still at an early stage, with nonlinear processes in the ocean–atmosphere coupled system, their multiscale interactions, and their climate impacts being important targets for further investigations.
Acknowledgments
We thank the Editor in Chief and three anonymous reviewers, for their feedback that significantly improved the paper. This work was jointly supported by the National Key Research and Development Program on Monitoring, Early Warning and Prevention of Major Natural Disaster (2018YFC1506002), the National Natural Science Foundation of China (41905073), the General Program of Natural Science Foundation of Jiangsu Higher Education Institutions (19KJB170007), and the Startup Foundation for Introducing Talent of NUIST (2018r066). This paper is funded by SOEST contribution 10917 and IPRC contribution 1432.
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