1. Introduction
The Hadley circulation (HC) features ascending motion in the tropics and descending motion in the subtropics (e.g., Glickman 2000), and it plays vital roles in balancing the atmospheric water vapor, mass, and energy budgets globally. Variations in the HC can affect tropical precipitation, tropical cyclone activity, and even the climate in the middle to high latitudes (e.g., Lindzen 1994; Chang 1995; Hou 1998; Held 2001; Trenberth and Stepaniak 2003; Doos and Nilsson 2011; Zhang and Wang 2013; Guo and Tan 2018a). Therefore, understanding the HC long-term change is of great importance. One of the most significant changes in the HC is its interannual variability, which has been discussed for decades (Oort and Yienger 1996; Quan et al. 2004; Tanaka et al. 2004; Ma and Li 2008; Nguyen et al. 2013).
A seminal study by Bjerknes (1966) revealed that a sudden warming of the equatorial sea surface temperature (SST) over the eastern Pacific could strengthen the HC intensity (HCI) in the boreal winter [December–February (DJF)]. The sudden warming of the equatorial eastern Pacific is actually induced by El Niño–Southern Oscillation (ENSO), which is the most prominent large-scale oceanic mode in the tropics on the interannual time scale. Subsequent studies suggested that ENSO exerts important impacts on the HCI in DJF (Oort and Yienger 1996; Wang 2002; Quan et al. 2004; Ma and Li 2008; Nguyen et al. 2013). During El Niño winter, the warm phase of ENSO, the Northern Hemisphere cell of the HC is strengthened, whereas during La Niña winter it is weakened (Oort and Yienger 1996). ENSO affects the HCI mainly by driving the anomalous HC structures, which is captured by the principal modes of the HC variability (e.g., Ma and Li 2008; Guo et al. 2016; Guo and Tan 2018b).
The HC interannual variability is dominated by two principal modes: the equatorial asymmetric mode (AM), which features a single cell across the equator, and the equatorial symmetric mode (SM), which features a pair of cells on the flanks of the equator (e.g., Ma and Li 2008; Guo et al. 2016; Guo and Tan 2018b). Previous studies have suggested that the two principal modes of the HC are affected by the change in the meridional gradients of the tropical SST anomaly (SSTA) (Feng et al. 2013; Guo et al. 2016). Because of the equatorial quasi-symmetric structure and the large meridional gradient, the ENSO SSTA is one of the most important drivers for the SM formation. However, recent studies by Guo and Tan (2018b,c) suggested that the ENSO SSTA can also drive the AM formation via the modulation of the western Pacific background SST during the boreal winter and early spring seasons. In these seasons, the western Pacific SST is warmer in the Southern Hemisphere than its counterpart in the Northern Hemisphere due to the annual cycle of the solar radiation (Vecchi and Harrison 2003; Spencer 2004; Lengaigne et al. 2006; McGregor et al. 2012; Stuecker et al. 2013, 2015a,b; Zhang et al. 2015). Thus, the southern part of the ENSO SSTA induces upward motion more easily because of a warmer background state in the Southern Hemisphere, which leads to the equatorially asymmetric structure in the HC anomalies, corresponding to the AM formation. Such an ENSO SSTA and background SST interaction was originally proposed to explain the southward shift of surface zonal wind during El Niño events (McPhaden et al. 2011; McGregor et al. 2012, 2013; Stuecker et al. 2013, 2015a,b; Zhang et al. 2015, 2016a,b).
Although ENSO has a robust influence on the HC interannual variability, its influence is not unchangeable. Much evidence suggests that the connections between ENSO and its related climate variability, such as the Indian monsoon system, have interdecadal variations (Krishna Kumar et al. 1999; Torrence and Webster 1999; Krishnamurthy and Goswami 2000; Wang et al. 2008). Such variation in the connection between ENSO and the corresponding atmospheric circulation response is referred to as a “regime change” (e.g., An and Jin 2000; Fedorov and Philander 2000). Quan et al. (2004) pointed out that the boreal winter HC northern cell has a strengthening relationship with ENSO during the 1970s, which is a type of HC regime change. They inferred that the HC regime change was caused by a southward shift of the precipitation anomalies during the mid-1970s, because of a faster SST warming trend in the southeastern Pacific than in its counterpart north of the equator. Recently, however, Guo and Tan (2018b,c) suggested that the asymmetric HC anomalies associated with the ENSO SSTA result from the modulation of the western Pacific climatological SST rather than that over the eastern Pacific, because the western Pacific warm pool mean state is much warmer than that of the cold tongue region in the eastern Pacific. As the Pacific warm pool has had a significant warming trend since the 1950s (SanchezGomez et al. 2008; Cravatte et al. 2009; Heo et al. 2012; Park et al. 2012; Wang et al. 2016), how this warming is related to the regime change in the relationship between boreal winter HC and ENSO is an open question. In addition, HC variability is dominated by two principal modes (i.e., the AM and SM). How the connection between ENSO and the two principal modes changes is also unclear.
This study mainly focuses on the variation of the interannual relationship between the principal modes of the boreal winter HC interannual variability and ENSO, and also on the possible mechanisms for the HC regime change. The remainder of this paper is arranged as follows. Section 2 introduces the datasets and methods. Section 3 analyzes the variation of the interannual relationship between the HC principal modes and ENSO. Section 4 further investigates the underlying mechanisms. Section 5 gives the conclusions and discussion.
2. Datasets and methods
The monthly mean atmospheric variables used in this study are from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis (NCEP-1; Kalnay et al. 1996) from 1948 to 2014, which has a horizontal resolution of 2.5° × 2.5° in longitude and latitude with 17 vertical levels. The Twentieth Century Reanalysis (20CR), version 2, dataset is also employed to validate the results of NCEP-1. The 20CR dataset has a horizontal resolution of 2° × 2° in longitude and latitude with 24 vertical levels and covers the period from 1871 to 2012 (Compo et al. 2011). The monthly mean SST datasets are derived from the Extended Reconstructed SST (ERSST), version 3b, reanalysis (Smith et al. 2008) on a 2.0° × 2.0° latitude–longitude grid and the Hadley Center Sea Ice and SST dataset (HadISST) on a 1.0° × 1.0° latitude–longitude grid (Rayner et al. 2003). The precipitation dataset is provided by the National Oceanic and Atmospheric Administration precipitation reconstruction dataset (Chen et al. 2002) on a 2.5° × 2.5° latitude–longitude grid.
To depict the HC, the mass streamfunction (MSF) was employed, which is derived by vertically integrating the zonal mean meridional wind (e.g., Holton 1994; Oort and Yienger 1996). A positive (negative) value of the MSF denotes clockwise (anticlockwise) zonal mean meridional circulation. The HCI is defined as the maximum of the absolute value of the MSF between 0° and 30°N for the boreal winter HC. The ENSO variability is characterized by the DJF mean Niño-3.4 index, which is defined as the area-averaged SSTA over the region of 5°S–5°N, 170°–120°W. The principal modes of the interannual variability of the DJF HC are extracted by using the empirical orthogonal function (EOF) analysis method. We applied the EOF method on the year-to-year seasonal mean MSF anomalies during the boreal winter. The MSF anomalies include all latitudes, which means both the wintertime Northern Hemisphere cell and the Southern Hemisphere cell are included. The linear trends of all the variables, including the Niño-3.4 index and the HC-related indices, have been removed to avoid contamination by the long-term trend. As this study focuses on the interannual time scale, all the variables are also subjected to a 9-yr high-pass Lanczos filter (Duchon 1979). A two-tailed Student’s t test was applied to evaluate the statistical significance.
3. Relationship between the boreal winter HC principal modes and ENSO
a. Principal modes of the interannual variability of the boreal winter HC
The first two principal modes of the interannual variability of the DJF HC are shown in Fig. 1. EOF-1 shows a single cell across the equator with ascending and descending branches on the flanks of the equator (contours in Fig. 1a). EOF-2 shows a pair of cells located on the flanks of the equator with a common ascending branch on the equator (contours in Fig. 1b). The two principal modes show distinct spatial structures: EOF-1 is asymmetric about the equator, while EOF-2 is quasi-symmetric about the equator. Hence, EOF-1 and EOF-2 are referred to as AM and SM, respectively. Both the AM and SM show positive values around the core of the northern cell of the DJF climatological HC (shading in Figs. 1a,b), which indicates that both EOFs can strengthen the HCI in their positive phases. The principal components (PCs) of EOF-1 and EOF-2 show pronounced interannual variability, which has a good in-phase relationship with the Niño-3.4 index (Figs. 1c,d). The correlation coefficient between PC-1 and the Niño-3.4 index is 0.42 (p < 0.001), and that between PC-2 and the Niño-3.4 index is 0.75 (p < 0.001). This indicates that the formations of the AM and SM are both closely related to ENSO. This is because the background SST peaks on the equator in December and off the equator to the Southern Hemisphere in January and February due to the annual cycle of the solar radiation. Thus, ENSO SSTA-induced HC anomalies tend to exhibit a symmetric structure in December but an asymmetric structure in January and February because of the modulation by the background SST. In seasonal means, both the AM and SM are related to ENSO forcing. More detailed physical analysis can be seen in Guo and Tan (2018b).
The (a) EOF-1 and (b) EOF-2 modes of the year-to-year variability of the DJF MSF based on the period 1948–2014. Explained variances of the two principal modes are denoted on the top-right corner of each plot. The contour intervals in (a) and (b) are 0.03, and the solid (dashed) contours denote positive (negative) values; shading denotes the climatological MSF in DJF (1.0 × 1010 kg s−1). The corresponding standardized PCs of the (c) EOF-1 and (d) EOF-2 modes (blue lines); the red lines are the Niño-3.4 index.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
To determine the horizontal character of the circulation anomalies associated with the AM and SM, Fig. 2 shows the anomalies of the vertical shear of the meridional divergent wind between 200 and 850 hPa (VSH), the vertical velocity at 500 hPa, the SSTA, and the divergence and horizontal wind at 850 hPa regressed upon PC-1 and PC-2, respectively. The VSH is a proxy for the regional HC (e.g., Quan et al. 2004; Sun and Zhou 2014), and it can be seen that the main contribution to the two modes is from the central Pacific (Figs. 2a,b). Over the central Pacific, the positive VSH anomalies associated with the AM extend to the Southern Hemisphere by about 5° in latitude (Fig. 2a). Consistently, there is also a southward-extended upward motion at 500 hPa (Figs. 2e) and southward-located convergence at 850 hPa (Figs. 2e), which indicates the southward located upward branch of the AM. The surface wind associated with the AM has an obvious southward shift off the equator (Fig. 2e), which resembles the surface wind anomalies related to the ENSO combination mode. The ENSO combination mode features off-equatorially located surface westerly wind anomalies in the Southern Hemisphere, which could be depicted by the EOF-2 of the tropical Pacific surface wind anomalies. This results from the interaction between southward shift of the background SST and the ENSO SSTA in late winter and early spring seasons (McPhaden et al. 2011; McGregor et al. 2012; Stuecker et al. 2013; Zhang et al. 2015). Because ENSO drives the AM under the modulation of the background SST over the South Pacific convergence zone (Guo and Tan 2018b,c) and drives the ENSO combination mode in the similar way, the resemblance between the AM-associated surface wind anomalies and that of the ENSO combination mode indicates that the AM–ENSO relationship is closely related to the modulation of the background SST over the South Pacific convergence zone. In contrast, positive and negative VSH anomalies are symmetric about the equator over the central Pacific for the SM (Fig. 2b). The SM corresponds to strong upward motion and low-level convergence on the equator (Figs. 2d,f), indicating an equatorially located upward branch of the SM (Fig. 1b). As shown in Fig. 2f, the surface wind anomalies associated with the SM converge to the equator, showing an equatorial symmetric structure that resembles the classical wind pattern associated with the ENSO SSTA (Zhang et al. 2015).
Anomalies of the (a),(b) VSH (m s−1), (c),(d) vertical velocity at 500 hPa (shading; 10−2 Pa s−1) and SSTA (contours; K), and (e),(f) 850-hPa divergence (shading; 10−7 s−1) and surface wind (vectors; m s−1) regressed on (left) PC-1 and (right) PC-2. The stippled areas indicate values exceeding the 95% confidence level. The vectors in (e) and (f) only show the wind anomalies exceeding the 95% confidence level. The black dashed lines denote the equator. Contour intervals for (c) and (d) are 0.1 K. Solid thick contours in (a)–(d) denote the values of zero.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
Additionally, we also find that PC-2 corresponds to a stronger and more westward extended ENSO-like SSTA than that of PC-1 (Figs. 2c,d). Because the warm background SST over the western Pacific is more westward retreated in December (Guo and Tan 2018b), the SM-related convection and SSTA are more westward shifted (Fig. 2d), whereas the warm background SST in February moves to the Southern Hemisphere as indicated by a more eastward extended South Pacific convergence zone, and thus the convection shifts into the Southern Hemisphere and the SSTA is located more eastward (Fig. 2c).
b. Strengthened AM–ENSO interannual relationship
Quan et al. (2004) suggested that the relationship between the HC northern cell and ENSO was strengthened during the 1970s, called the HC regime change. To investigate the change of the relationship between the HC and ENSO, a 21-yr sliding correlation was calculated between the Niño-3.4 index and the first two PCs of the HC anomalies (Fig. 3). PC-2 has a steady and statistically significant correlation with the Niño-3.4 index, with correlation coefficients above 0.65 (p < 0.01). In contrast, the correlation between PC-1 and the Niño-3.4 index experienced a significant strengthening around the mid-1970s. Before 1976, correlation coefficients between PC-1 and the Niño-3.4 index are relatively small and statistically insignificant, whereas after 1979 the correlation coefficients are above 0.43 (p < 0.05). This indicates that ENSO exerted stronger control on the AM after the mid-1970s. The sliding correlation was also calculated with time windows of 15, 19, and 23 years to test the sensitivity of the correlation to the length of time windows. The conclusion remains unchanged, but with a slight difference in the specific year when the correlation coefficients between PC-1 and the Niño-3.4 index reach to the 95% significance level. These results suggest that the HC regime change associated with ENSO may be attributed to the strengthened AM–ENSO interannual relationship. This will be further verified in the following section.
The 21-yr sliding correlations of the Niño-3.4 index with PC-1 (red dotted line) and PC-2 (black dotted line). The blue dashed line indicates the 95% confidence level.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
The SM has a steady statistical significant relationship with ENSO, which has been discussed in previous studies (e.g., Ma and Li 2008; Feng and Li 2013). However, the cause of the strengthened AM–ENSO relationship is still unclear. We mainly focus on the cause of the strengthened AM–ENSO relationship in the following analysis. To do this, we compare the ENSO-related atmospheric circulation anomalies between the insignificant and significant correlation periods, which are 1948–76 and 1979–2014, respectively. The years 1977 and 1978 were excluded because they show unstable correlations. The conclusion does not change when these two years are included.
4. Physical mechanisms in the strengthened AM–ENSO relationship and its influence on HC intensity
a. Difference in ENSO-related atmospheric circulation anomalies between 1948–76 and 1979–2014
To understand why the AM–ENSO interannual relationship is strengthened after the mid-1970s, Fig. 4 shows the anomalies of VSH, vertical velocity at 500 hPa, SSTA, and the divergence at 850 hPa regressed on the Niño-3.4 index based on the periods 1948–76 and 1979–2014, respectively. During 1948–76, the VSH anomalies show negative values over the central Pacific and the Maritime Continent (Fig. 4a). The vertical velocity anomalies are weak and show a zonal dipole pattern (Fig. 4c), indicating an anomalous Pacific Walker circulation. There is also strong convergence at 850 hPa over the Pacific equatorial region (Fig. 4e), which obviously tends to induce an upward branch located on the equator. In contrast, during 1979–2014, the VSH shows positive anomalies over the central northern Pacific, which extends to the Southern Hemisphere by about 5° in latitude (Fig. 4b). There are also upward motion anomalies at 500 hPa and low-level convergence anomalies at 850 hPa over the central Pacific (Figs. 4d,f), which are southward shifted by about 5° in latitude. The anomalous circulation patterns during 1979–2014 (Figs. 4b,d,f) resemble that of the AM (Figs. 2a,c,e), implying that the strengthened AM–ENSO relationship possibly depends on how ENSO drives these circulation patterns (Figs. 4b,d,f). Additionally, SSTA during the period 1979–2014 is obvious larger than that in the period 1948–76 over the equatorial region (Figs. 4c,d), which indicates an increased ENSO amplitude in the latter period.
Anomalies of the (a),(b) VSH (m s−1), (c),(d) vertical velocity at 500 hPa (shading; 10−2 Pa s−1) and SSTA (contours; K), and (e),(f) 850-hPa divergence (shading; 10−7 s−1) regressed on the Niño-3.4 index for (left) 1948–76 and (right) 1979–2014. Contour intervals for (c) and (d) are 0.1 K. The stippled areas indicate values exceeding the 95% confidence level. The black dashed lines denote the equator. The solid thick contours in (a)–(d) denote the values of zero.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
To further answer how ENSO drives the different circulation patterns during the two periods, Fig. 5 shows the anomalous precipitation and low-level wind at 850 hPa regressed on the Niño-3.4 index during the two periods, respectively. During 1948–76, there are positive precipitation anomalies over the central Pacific centered around 5°S and weak surface wind anomalies in the Southern Hemisphere around 150°W. However, during 1979–2014, the precipitation anomalies are much stronger than those during the former period and they are concentrated in the central Pacific (160°E–110°W), with the maximum anomaly located around 5°S. The off-equatorial precipitation anomalies lead to a Gill-type low-level circulation pattern (Gill 1980), that is, a cyclonic Rossby wave response over the west of the precipitation center with strong cross-equatorial wind (Fig. 5b), which corresponds to the AM. Both the AM and the ENSO combination mode are associated with southward-shifted low-level wind anomalies (Stuecker et al. 2013, 2015a,b; Zhang et al. 2015, 2016a,b; Guo and Tan 2018b,c), which is caused by combined effect of the western Pacific background SST and ENSO SSTA. Therefore, the physical mechanisms for the strengthened AM–ENSO relationship may involve the changes in both the western Pacific background SST and the ENSO SSTA during the two periods.
Anomalies of precipitation (shading; mm day−1) and surface wind (vectors; m s−1) regressed on the Niño-3.4 index for (a) 1948–76 and (b) 1979–2014. The stippled areas indicate values exceeding the 95% confidence level. The vectors only show the wind anomalies exceeding the 95% confidence level. The blue dashed lines denote the equator.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
b. Physical mechanisms for the strengthened AM–ENSO interannual relationship
Based on the above results, an increase of either the background SST or the ENSO magnitude could strengthen the AM–ENSO interannual relationship. To investigate how the background SST changed during the past decades, Fig. 6 shows the difference in the climatological SST between 1948–76 and 1979–2014, based on the ERSST and HadISST datasets, respectively. It can be seen that the tropical Pacific shows an overall warming trend except for the Niño-3.4 region (Figs. 6a,b), which is consistent with previous studies (Quan et al. 2004; Parker et al. 2007; Deser et al. 2010). The South Pacific convergence zone is the key region where the background SST and the ENSO SSTA combine to drive the AM formation (Guo and Tan 2018b,c). The SST over this region has statistically significant warming trend during the past several decades (Fig. 6c). The warming trends are 0.09 K decade−1 (p = 0.002) and 0.07 K decade−1 (p = 0.011) for ERSST and HadISST, respectively.
DJF mean SST difference between 1979–2014 and 1948–76 based on the (a) ERSST and (b) HadISST datasets. The stippled areas indicate values exceeding the 95% confidence level. (c) Time series of the mean SST averaged over the blue box (10°S–0°, 160°E–170°W) based on the ERSST (blue line) and HadISST (red line) datasets. The blue and red dashed lines denote the trend lines based on ERSST and HadISST datasets, respectively.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
Another factor affecting the AM–ENSO interannual relationship is the ENSO amplitude. Figure 7 shows the 21-yr sliding standard deviation of the Niño-3.4 index for ERSST and HadISST, respectively. There is an obvious intensification of the standard deviation of the Niño-3.4 index around the 1970. Before the 1970, the Niño-3.4 index has relatively small variability, indicating that the ENSO amplitude is relatively small. After 1970, however, there is a perturbation in the standard deviation of the Niño-3.4 index and it is steadily above 0.8, indicating a larger ENSO amplitude than before.
The 21-yr sliding standard deviations of the Niño-3.4 index for ERSST (red dotted line) and HadISST (black dotted line) datasets during 1948–2014.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
To further understand how the background SST over the western Pacific and the ENSO SSTA strengthens the AM–ENSO interannual relationship, Fig. 8 shows the monthly evolution of the zonally averaged climatological SST, SSTA, and 850-hPa divergence anomalies regressed on the Niño-3.4 index during 1948–76 and 1979–2014, respectively. For both periods, the SSTA maximums are steadily located at the equator throughout the year (indicated by black contours) because of the meridionally symmetric structure of ENSO pattern, while the maximums of the climatological SST (indicated by shading) move back and forth around the equator due to the annual cycle of the solar radiation. As the background SST in the Southern Hemisphere is warmer than that in the Northern Hemisphere in DJF, the symmetric ENSO SSTA drives asymmetric HC anomalies in this season (Guo and Tan 2018b,c). For 1979–2014, the low-level divergence anomalies regressed on the Niño-3.4 index are much stronger in the Southern Hemisphere (indicated by the blue box in Fig. 8b) than in the Northern Hemisphere, implying an equatorially asymmetric HC anomaly. In addition, the evolution of divergence anomalies also shows sinusoidal variation similar to that of the climatological SST, even though the divergence anomalies are related to the equatorially symmetric SSTA. These results indicate that the climatological SST has a strong modulation effect on the ENSO-driven HC asymmetric anomalies. In contrast, during 1948–76, the climatological SST in DJF is weaker than that during 1979–2014. The 850-hPa divergence anomalies are quasi-symmetric about the equator, which may be caused by the weak background SST during this period (Fig. 8a). As a result, the AM is weak before the mid-1970s and intensified afterward (red dotted line in Fig. 9a), which leads to a strengthened AM–ENSO relationship. We also note that both the 21-yr sliding standard deviation of the Niño-3.4 index and the 21-yr sliding correlation between PC-2 and the Niño-3.4 index show decreases around 1970, 1986, and 1996, which implies that the changes in ENSO amplitude also affect the SM–ENSO relationship (Figs. 3 and 7). Given that the AM is closely related to the HCI (Table 1), the following section discusses the possible influence of the AM–ENSO relationship on the HCI as well as its relationship with ENSO.
Annual evolutions of the zonal and climatological mean SST (shading; K), the Niño-3.4 index regressed SSTA (black contours; K), and the 850-hPa divergence anomalies (white contours; 10−7 s−1) during (a) 1948–76 and (b) 1979–2014. The blue boxes indicate the DJF season within the latitude band of 0°–10°S.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
(a) The 21-yr sliding standard deviation of PC-1 (red dotted line) and HCI (black dotted line). (b) 21-yr sliding correlation of HCI with PC-1 (red dotted line) and the Niño-3.4 index (black dotted line). Blue dashed line in (b) denotes the 95% confidence level.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
Correlation between the PCs and the HCI and Niño-3.4 index during 1948–76, 1979–2014, and 1948–2014. The bold values indicate that the correlation coefficients exceed the 95% confidence level.
c. Possible influence on HCI and its connection to ENSO
The climate impacts exerted by the HC are closely related to its intensity, that is, the HCI (e.g., Chang 1995). The HCI is related to both the AM (r = 0.62) and the SM (r = 0.47) during the whole period (Table 1). The AM is not correlated with the HCI during the former period (r = −0.1), but it explains ~72.3% of the HCI variance during the latter period (r = 0.85). In comparison, the SM explains ~20%–23% of the HCI variance for the three periods (Table 1). These results indicate that, in the former period, the SM is the main contributor to HCI although the contribution is not large (~23%). In the latter period, both the AM and SM contribute to the HCI variance (explaining over 90% of the variance), and the AM contribution is more than 3 times larger than that of the SM. These results also imply that the HCI–ENSO relationship is connected to the SM in the former period, but it is dominated by the AM–ENSO relationship in the latter period.
Therefore, the HCI is weak before the mid-1970s and significantly intensified afterward, in good agreement with PC-1 (Fig. 9a). However, this relationship is not stable. Figure 9b shows the 21-yr sliding correlation coefficients of the HCI with PC-1. The HCI and PC-1 have a weak connection before the mid-1970s, but it strengthened during the 1970s (red dotted line in Fig. 9b). This means that the AM has a larger contribution to the HCI variation in the latter period than that of the former period (Table 1). Previous studies suggested that HC could be significantly strengthened during the El Niño winter (e.g., Bjerknes 1966; Oort and Yienger 1996; Wang 2002; Quan et al. 2004). However, the HCI actually has a weak connection with ENSO before the 1970s (black dotted line in Fig. 9b), similar to the AM–ENSO relationship (red dotted line in Fig. 3). The difference is that the transition timing for the strengthened HCI–ENSO relationship is several years earlier than that of the AM–ENSO relationship. This is because that the HCI is also related to the SM, with a correlation coefficient of 0.47 (Table 1), and the SM–ENSO relationship is steady and statistically significant (see Fig. 3 and Table 1).
5. Conclusions and discussion
The interannual relationship between the boreal winter HC principal modes and ENSO during 1948–2014 was investigated. It is found that the AM of the DJF HC shows a strengthened relationship with ENSO, and the correlation coefficients between PC-1 and the Niño-3.4 index are statistically insignificant during 1948–76 and significant during 1979–2014. In contrast, the SM of the DJF HC has a steady and statistically significant correlation with the Niño-3.4 index. The strengthened AM–ENSO interannual relationship also leads to a strengthened HCI–ENSO interannual relationship due to the close relationship between the AM and HCI on the interannual time scale. The regime change in the relationship between boreal winter HC and ENSO is mainly due to the strengthened AM–ENSO interannual relationship. Further analysis suggests that the strengthened AM–ENSO interannual relationship is caused by the combined effect of the long-term warming trend of the western Pacific background SST and increased ENSO amplitude after the 1970s, which caused much stronger upward motion over the South Pacific convergence zone.
As the study period covers the last several decades, the results may be biased by some factors, such as the outdated dataset, the refining of spatial resolution, and introducing new satellite observations in the 1970s. To validate the strengthened AM–ENSO relationship, we also calculated the AM and SM indices with the definition by Guo and Tan (2018c) based on NCEP-1 and another commonly used reanalysis dataset, the 20CR. Both the strengthened AM–ENSO relationship and the stable SM–ENSO relationship can well be reproduced by the two datasets, and 20CR has only a slight decrease of correlation coefficients compared with that of NCEP-1 during 1979–2014 (Fig. 10). Additionally, when forced only by the observed SST, an atmospheric general circulation model can reproduce the strengthened relationship between HC strength and ENSO (Quan et al. 2004), which means that the change of the observed SST can individually drive such a relationship without introducing new observation data or refining the spatial resolution. Thus, the results are reliable, albeit with a possibly quantitative influence from the abovementioned factors.
The 21-yr sliding correlations of the Niño-3.4 index with the AM (red dotted line) and SM (black dotted line) indices based on the (a) NCEP-1 and (b) 20CR reanalysis datasets. The AM index is defined as the averaged MSF anomalies within 10°S–10°N and 1000–100 hPa, and the SM index is defined as the difference of averaged MSF anomalies between 15°S–0° and 0°–15°N within 1000–100 hPa (Guo and Tan 2018c). The upper and lower blue dashed lines indicate the 95% and 90% confidence levels, respectively.
Citation: Journal of Climate 31, 23; 10.1175/JCLI-D-18-0306.1
It is also worth pointing out that the interdecadal Pacific oscillation had a significant phase shift in the late 1970s (e.g., England et al. 2014; Wills et al. 2018), which may have had a modulating effect on the HC–ENSO relationship. When combined with the ENSO variability and global warming signals, the decadal variability of the interdecadal Pacific oscillation may also influence Hadley circulation variability beyond the first two principal modes.
For the HC regime change, Quan et al. (2004) hypothesized that it is caused by faster SST warming in the southeastern Pacific than in its counterpart in the Northern Hemisphere, which shifts the precipitation anomalies to the Southern Hemisphere and enhances the northern cell of the HC in boreal winter. Our results present two new explanations for the HC regime change: 1) the strengthened HC–ENSO relationship is mainly related to the strengthened AM–ENSO relationship, and 2) the cause of the strengthened AM–ENSO (or HC–ENSO) relationship is due to the warming of the western Pacific (10°S–0°, 160°E–170°W) and the increased ENSO amplitude rather than being due to the southeastern Pacific warming as suggested by the previous study. The main reason is that the western Pacific warm pool region has a much warmer background SST than that over the southeastern Pacific cold tongue region, and the warm pool is also warmer in the Southern Hemisphere than its counterpart in the Northern Hemisphere in boreal winter. That is why the ENSO-driven AM is mainly related to the SSTA over the midwestern Pacific (Guo and Tan 2018b,c). Moreover, the shift of the low-level circulation in response to ENSO to the Southern Hemisphere in the midwestern Pacific is mainly caused by the interaction between the SST annual cycle and the ENSO SSTA (McPhaden et al. 2011; McGregor et al. 2012, 2013; Stuecker et al. 2013, 2015a,b; Zhang et al. 2015, 2016a,b). This means that the precipitation and circulation anomalies in response to the ENSO SSTA tend to occur over the western Pacific, because of the warmer background SST (Figs. 4d and 5). When the tropical Pacific experiences an overall warming (Fig. 6), it can be inferred that the western Pacific should have stronger control on the HC–ENSO relationship than the eastern Pacific.
ENSO also shows a regime change in the influence on other aspects of the global circulation during the 1970s (e.g., An and Jin 2000; Fedorov and Philander 2000). For instance, the ENSO influence on the summer extratropical temperature is weak before 1980 but becomes stronger afterward because of the precipitation pattern change associated with ENSO in the tropics (Sun et al. 2016); the preferred interannual occurrence of the North Atlantic Oscillation has a phase change (Hurrell 1995; Thompson et al. 2000); the relationship between the Indian monsoon and ENSO is weakened (Krishna Kumar et al. 1999; Torrence and Webster 1999; Krishnamurthy and Goswami 2000; Wang et al. 2008); and the onset process of ENSO has changed over the tropical Pacific Ocean (Wang 1995). Our results provide a new understanding for the HC regime change and also imply that the midwestern Pacific may be a key region responsible for the global circulation regime change associated with ENSO. It can be inferred that under a future warming scenario, the influence of ENSO on the global climate may be more severe than at present because of the highly warmed background SST of the western Pacific.
Acknowledgments
We thank the editor and three anonymous reviewers for their helpful comments and suggestions on the manuscript. This work was jointly supported by the National Key Research and Development Program of China under Grant 2017YFC1501601, the National Natural Science Foundation of China (41461164008 and 41705057), the China Postdoctoral Science Foundation funded project (2018M632282), the National Key Project for Basic Research (973 Project) under Grant 2015CB425803, and the Natural Science Foundation of Jiangsu Province (BK20170637).
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