A Continuing Increase of the Impact of the Spring North Pacific Meridional Mode on the Following Winter El Niño and Southern Oscillation

Yuqiong Zheng aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

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Shangfeng Chen aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

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Wen Chen aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

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Bin Yu cClimate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

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Abstract

This study reveals that the impact of the spring North Pacific meridional mode (PMM) on the following-winter El Niño–Southern Oscillation (ENSO) shows a continuing increase in the past. A comparative analysis is conducted for the high- and low-correlation periods to understand the factors for the strengthened impact of the PMM. The spring PMM-related sea surface temperature (SST) and atmospheric anomalies over the subtropical northeastern Pacific propagate southwestward to the tropical central Pacific via wind–evaporation–SST feedback in the high-correlation period. The tropical SST and atmospheric anomalies further develop to an ENSO-like pattern via positive air–sea interaction. In the low-correlation period, SST and atmospheric anomalies over the subtropical northeastern Pacific related to the PMM cannot extend to the deep tropics. Therefore, the spring PMM has a weak impact on ENSO. The extent to which the PMM-related SST and atmospheric anomalies extend toward the tropics is related to the background flow. The stronger mean trade winds in the high-correlation period lead to an increase in the air–sea coupling strength over the subtropical northeastern Pacific. As such, the spring PMM-related SST and atmospheric anomalies can more efficiently propagate southwestward to the tropical Pacific and exert stronger impacts on the succeeding ENSO. In addition, the southward shifted intertropical convergence zone in the high-correlation period also favors the southward extension of the PMM-related SST anomalies to the tropics and contributes to a stronger PMM–ENSO relation. The variation and its formation mechanism of the spring PMM–winter ENSO relationship appear in both the observations and the long historical simulation of Earth system models.

Significance Statement

The North Pacific meridional mode (PMM) is the leading atmosphere–ocean coupling pattern over the subtropical northeastern Pacific after removing the ENSO variability, with maximum variance during boreal spring. Previous studies indicated that the PMM plays an important role in relaying the impact of the atmosphere–ocean forcings over the extratropics on the tropical ENSO. This study reveals that the impact of the spring PMM on the following winter ENSO shows a continuing increase in the past 70 years. The physical mechanisms for this strengthened impact are further examined. Results obtained in this study have important implications for improving the prediction of the tropical ENSO variability.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shangfeng Chen, chenshangfeng@mail.iap.ac.cn

Abstract

This study reveals that the impact of the spring North Pacific meridional mode (PMM) on the following-winter El Niño–Southern Oscillation (ENSO) shows a continuing increase in the past. A comparative analysis is conducted for the high- and low-correlation periods to understand the factors for the strengthened impact of the PMM. The spring PMM-related sea surface temperature (SST) and atmospheric anomalies over the subtropical northeastern Pacific propagate southwestward to the tropical central Pacific via wind–evaporation–SST feedback in the high-correlation period. The tropical SST and atmospheric anomalies further develop to an ENSO-like pattern via positive air–sea interaction. In the low-correlation period, SST and atmospheric anomalies over the subtropical northeastern Pacific related to the PMM cannot extend to the deep tropics. Therefore, the spring PMM has a weak impact on ENSO. The extent to which the PMM-related SST and atmospheric anomalies extend toward the tropics is related to the background flow. The stronger mean trade winds in the high-correlation period lead to an increase in the air–sea coupling strength over the subtropical northeastern Pacific. As such, the spring PMM-related SST and atmospheric anomalies can more efficiently propagate southwestward to the tropical Pacific and exert stronger impacts on the succeeding ENSO. In addition, the southward shifted intertropical convergence zone in the high-correlation period also favors the southward extension of the PMM-related SST anomalies to the tropics and contributes to a stronger PMM–ENSO relation. The variation and its formation mechanism of the spring PMM–winter ENSO relationship appear in both the observations and the long historical simulation of Earth system models.

Significance Statement

The North Pacific meridional mode (PMM) is the leading atmosphere–ocean coupling pattern over the subtropical northeastern Pacific after removing the ENSO variability, with maximum variance during boreal spring. Previous studies indicated that the PMM plays an important role in relaying the impact of the atmosphere–ocean forcings over the extratropics on the tropical ENSO. This study reveals that the impact of the spring PMM on the following winter ENSO shows a continuing increase in the past 70 years. The physical mechanisms for this strengthened impact are further examined. Results obtained in this study have important implications for improving the prediction of the tropical ENSO variability.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shangfeng Chen, chenshangfeng@mail.iap.ac.cn

1. Introduction

El Niño–Southern Oscillation (ENSO) is the most important air–sea coupling system in the tropics on the interannual time scale (Bjerknes 1969; Alexander et al. 2002). ENSO events can considerably adjust atmospheric and oceanic circulation patterns over the majority of the world via teleconnections and contribute notably to the occurrences of extreme weather and climate events (Gray 1984; Wang et al. 2000; Alexander and Scott 2002; Chiang and Sobel 2002; Wu et al. 2003; Huang et al. 2004; Chan 2005; Graf and Zanchettin 2012; Zhang et al. 2012; Cheung et al. 2012; Chen et al. 2019; J. P. Chen et al. 2020; Hu et al. 2020). In addition, ENSO is recognized as the primary source of short-term climate prediction over tropical regions, East Asia, and North America (Yu and Zwiers 2007; Jin and Kirtman 2010; Yang et al. 2018; Tan et al. 2019). Therefore, uncovering the factors leading to the occurrence of ENSO events and improving the prediction skill of ENSO are of critical importance. Many studies have pointed out that ENSO can be considered as a self-sustaining oscillation system (Schopf and Suarez 1988; Battisti and Hirst 1989; Jin 1997; Wang 2001; Ren et al. 2016). Particularly, the air–sea interaction and ocean dynamic processes in the tropical Pacific are suggested to play an important role in the occurrence, development, and phase transition of ENSO events (Bjerknes 1969; Schopf and Suarez 1988).

Over the past two decades, extratropical forcings of ENSO have received more and more attention (Nakamura et al. 2006; Kim et al. 2009; Di Lorenzo et al. 2010; Wang et al. 2011; Yu et al. 2012; Chen et al. 2014, 2017; Ding et al. 2015; Zheng and Yu 2017; Su et al. 2018; Lai et al. 2018; Zhong et al. 2019). For example, Vimont et al. (2001, 2003) suggested that the wintertime North Pacific Oscillation (NPO), the second empirical orthogonal function (EOF) mode of sea level pressure (SLP) anomalies over North Pacific (Park et al. 2013; Song et al. 2016; Chen and Song 2018; Wang et al. 2019b), plays a crucial role in the occurrence of ENSO events in the following winter via the seasonal footprinting mechanism and the trade wind charging mechanism (Anderson et al. 2013; Anderson and Perez 2015).

The Pacific meridional mode (PMM), the leading mode of atmosphere and ocean coupling variability over the subtropical northeastern Pacific after removing the ENSO variability, is a key system to relay the impact of extratropical atmospheric variability on tropical ENSO (Chiang and Vimont 2004; Larson and Kirtman 2014; Lin et al. 2015; Min et al. 2017; Zheng et al. 2021; Fan et al. 2021). The strong impact of the spring PMM on the subsequent winter ENSO can be found both in the observational analysis and numerical simulations (Zhang et al. 2009; Larson and Kirtman 2013, 2014; Lin et al. 2015). Chang et al. (2007) suggested that positive spring PMM events precede more than 70% of winter El Niño events. Larson and Kirtman (2013) performed a high-resolution numerical experiment and found that the PMM acts as an ENSO trigger with a lead time of 7–9 months. Lin et al. (2015) found that the PMM is well captured in most CMIP5 models and has a closer linkage with the central Pacific (CP)-type ENSO (Ashok et al. 2007; Yu and Kim 2011; Yu et al. 2012; Yeh et al. 2015; S. F. Chen et al. 2020) than the eastern Pacific (EP)-type ENSO. Lu et al. (2017) and Lu and Liu (2018) pointed out that the predictability of ENSO could be increased significantly if incorporating the information of PMM. The wind–evaporation–SST (WES) feedback mechanism is the key process in connecting PMM to ENSO (Xie and Philander 1994; Yu et al. 2010; Lin et al. 2015; Amaya 2019). In particular, the PMM-related SST and zonal wind anomalies over the subtropical northeastern Pacific extend southwestward to the equatorial central Pacific via the WES feedback (Xie and Philander 1994), which further contributes to the occurrence of CP ENSO events (Wang and Wang 2013; Wang et al. 2018, 2019a; Chen et al. 2021). Moreover, the surface wind stress curl anomalies over the subtropics in association with the PMM could adjust the subsurface ocean heat content over the tropical Pacific via modulating meridional ocean heat transport, which also plays a role in the following winter ENSO occurrence (Anderson et al. 2013; Anderson and Perez 2015).

The intensity of the spring PMM displays notable interdecadal variations (Yu et al. 2015; Yeh et al. 2015, 2018; Yu and Fang 2018). Yu et al. (2015) suggested that the strength of the PMM drops briefly around 1960, 1976, and 1993, showing a stepwise increase, starting from a relatively weak state, and then jumping to a strong state in the early 1990s. They suggested that the increase in the PMM strength after the early 1990s may be due to a phase transition of Atlantic multidecadal oscillation (AMO) and may have an important contribution to the recent increased emergence of the CP-type ENSO events. Yeh et al. (2015) demonstrated that the location of the maximum SST anomalies associated with the PMM shows a southward shift since the 1990s. Although these previous studies imply an enhancement of the impact of the spring PMM on the following winter ENSO after the early 1990s (Yu et al. 2015; Yeh et al. 2015), the detailed characteristic and particularly the mechanism of the interdecadal change of the spring PMM–winter ENSO connection remain to be explored. Our present study indicates that the relationship between the spring PMM and the following winter ENSO shows a steady increase from 1948 to 2021. The factors responsible for this interdecadal enhancement are further examined. Investigating interdecadal change of the spring PMM–winter ENSO relation would have important implications for improving the prediction of ENSO.

The remainder of this paper is organized as follows. In section 2, we introduce the data and methodology. Section 3 examines interdecadal change in the relationship between the spring PMM and the following winter ENSO. Section 4 explores the physical processes for the interdecadal change. Conclusions and discussion are presented in section 5.

2. Data and methods

The monthly SST data are derived from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST version 5b (ERSSTv5b), which has a horizontal resolution of 2° × 2° spanning from January 1854 to the present (Huang et al. 2017). The monthly surface winds, precipitation, and sea level pressure are obtained from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996), starting from January 1948. In addition, we employ the historical simulation outputs of the Community Integrated Earth System Model (CIESM; Lin et al. 2020) and the Model for Interdisciplinary Research on Climate, version 6 (MIROC6; Tatebe et al. 2019), which participated in CMIP6 to confirm the observational results. Linear trends of all data have been removed to focus on the interannual variations.

We use the Niño-3.4 SST index to characterize the ENSO variability, which is defined as the area-averaged SST anomalies over 5°S–5°N, 120°–170°W. The PMM is represented by the first singular value decomposition mode [SVD; also called maximum covariance analysis (MCA); Bretherton et al. 1992] of SST and horizontal components of surface winds over the subtropical eastern Pacific (Chiang and Vimont 2004; Chang et al. 2007). The PMM index is defined as the expansion coefficient time series of SST corresponding to the first SVD mode. Following Chang et al. (2007), the cold tongue SST index (SST anomalies averaged over 6°S–6°N, 180°–90°W) was linearly removed prior to the SVD analysis to ensure the independence from the ENSO cycle. The spring PMM index refers to the PMM SST index averaged from February to April, since February–April is the time with the largest PMM SST variability (Chang et al. 2007; Zheng et al. 2021).

To measure the significance level of the difference between two correlation coefficients, the Fisher’s rz transformation is used here. The correlation coefficients (r1 and r2) are subjected to the Fisher transform (S. F. Chen et al. 2020):
z1=0.5×ln(1+r11r1),
z2=0.5×ln(1+r21r2).
The test statistic (u), which fits the normal distribution, is shown by the following:
u=z1z21N13+1N23,
where N1 and N2 represent the r1 and r2 related sample sizes, respectively. Based on the two-tailed Student’s t test, the statistical significance level can be obtained.

3. Interdecadal shift in the spring PMM–winter ENSO connection

The spatial structure of the PMM is displayed in Fig. 1a, which shows regression maps of the SST and surface wind anomalies onto the PMM index (Fig. 1b). During the positive phase of the PMM, marked positive SST anomalies appear off the west coast of North America extending southwestward to the tropical central Pacific, together with cold SST anomalies in the tropical eastern Pacific, forming a meridional SST gradient (Chiang and Vimont 2004; Chang et al. 2007; Amaya 2019). Correspondingly, pronounced low-level southwesterly wind anomalies appear over the subtropical North Pacific, accompanied by easterly wind anomalies over the tropical eastern Pacific.

Fig. 1.
Fig. 1.

(a) Regression maps of SST (°C) and surface wind (m s−1) anomalies onto the normalized PMM index. (b) Time series of normalized monthly PMM index from 1948 to 2021. (c)–(f) Regression maps of SST (shading; °C) and surface wind (vector; m s−1) anomalies in (c) MAM(0), (d) JJA(0), (e) SON(0), and (f) D(0)JF(1) [where “0” and “1” represent the years during and after the spring PMM, respectively] onto the spring PMM index over 1948–2021. SST anomalies that are not significant at the 95% confidence level are not shown. Surface wind anomalies less than 0.07 m s−1 in both directions are not shown.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

To provide a background to understand the physical process for the spring PMM to trigger the succeeding winter ENSO, the seasonal evolutions of SST and surface wind anomalies from spring to following winter regressed onto the spring PMM index are shown in Figs. 1c–f. During the positive phase of the spring PMM, the anomalous southwesterly winds and SST warming over the subtropical northeastern Pacific extend southwestward to the tropical central Pacific via the WES feedback mechanism (Fig. 1c). Subsequently, the resultant westerly wind anomalies over the tropical western-central Pacific result in SST warming in the tropical central-eastern Pacific in the following summer (Fig. 1d) via stimulating eastward propagating equatorial Kelvin waves (McPhaden and Yu 1999; Thompson and Battisti 2000, 2001; Huang et al. 2001; Philander and Fedorov 2003; Lengaigne et al. 2004; Chen et al. 2015). Finally, the tropical SST, precipitation, and atmospheric anomalies develop to an El Niño–like pattern in the following winter via the Bjerknes-like positive air–sea feedback mechanism (Figs. 1e,f). The physical processes for the impact of the negative spring PMM on the following winter La Niña are similar but with opposite conditions (Chang et al. 2007; Amaya 2019).

Next, we turn our attention to examine interdecadal change of the relationship between the spring PMM and the following winter ENSO. Different lengths of moving correlations all suggest that the relationship between the spring PMM and the following winter ENSO shows a steady increase from 1948 to 2021 (Fig. 2). The intensified impact of the PMM on the ENSO may play a role in contributing to the increased occurrence frequency of the central Pacific ENSO events during several recent decades. To examine the strengthened impact of the spring PMM, we select two periods with the lowest and highest correlations according to the 29-yr moving correlation. From Fig. 2, the spring PMM index has the lowest and highest correlations with the following winter Niño-3.4 SST index during 1952–80 (the central year is 1966) and 1990–2018 (the central year is 2004), respectively, for the 29-yr moving window. Particularly, the correlation coefficient between the spring PMM index and the winter Niño-3.4 SST index is only 0.16 over 1952–80. By contrast, the spring PMM index has a strong correlation with the winter Niño-3.4 SST index over 1990–2018, with a correlation coefficient of 0.59, significant at the 99.9% confidence level. The difference of the correlation coefficients between the low-correlation and high-correlation periods exceeds the 95% confidence level according to the Fisher’s rz transformation. Eight of 18 spring PMM events are followed by ENSO events in the subsequent winter in the low-correlation period (Table 1). This suggests that the occurrence ratio of the ENSO following the spring PMM is about 44% in the low-correlation period. By contrast, in the high-correlation period, the corresponding occurrence ratio of ENSO reaches 57% (12 of 21), larger than that in the low-correlation period (Table 1).

Fig. 2.
Fig. 2.

The 15-yr (blue line), 17-yr (green line), 21-yr (yellow line), 25-yr (orange line), and 29-yr (red line) running correlations between the spring PMM index and the following winter Niño-3.4 index. Horizontal lines denote the correlation significant at the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Table 1

Lists of the positive and negative spring PMM years. Here positive (negative) spring PMM years are identified when the normalized spring PMM index is larger than 0.5 (less than −0.5). Years in bold indicate positive (negative) spring PMM years are followed by El Niño (La Niña) event in the following winter.

Table 1

Previous studies indicated that there may exist an asymmetric impact of the spring PMM on the following winter ENSO (Fang and Yu 2020; Fan et al. 2021; Zheng et al. 2021). We have examined the occurrence frequency of the winter El Niño (La Niña) events followed by positive (negative) spring PMM during the high- and low-correlation periods, respectively. It is shown that the probability of El Niño (La Niña) occurring in winter following positive (negative) spring PMM increases from 40% (50%) in the low-correlation period to 60% (55%) in the high-correlation period. This suggests that the increase of positive PMM to El Niño is greater than negative PMM to La Niña.

Seasonal evolutions of SST anomalies from the simultaneous spring to the following winter regressed upon the spring PMM index for the low-correlation and high-correlation periods are shown in Fig. 3. The corresponding evolutions of precipitation and surface wind anomalies are shown in Fig. 4. In the high-correlation period (Figs. 3e–h and 4e–h), the evolutions of the spring PMM related air–sea anomalies are highly similar to those shown in Figs. 1c–f. In spring, the SST field is characterized by significant warming in the subtropical northeastern Pacific extending southwestward to the equatorial central Pacific (Fig. 3e). Formation of this SST warming (Fig. 3e) is attributed to the decreased upward surface latent heat flux induced by the southwesterly wind anomalies (Fig. 4e), which oppose the climatological northeasterly trade winds. The marked spring SST warming in the tropical and subtropical North Pacific is accompanied by enhanced atmospheric heating (indicated by positive precipitation anomalies) (Fig. 4e), which favors the occurrence of westerly wind anomalies over the equatorial western Pacific via a Gill response (Fig. 4e). The anomalous westerly winds in the equatorial western Pacific play an important role in the generation of SST warming in the tropical central-eastern Pacific in the following summer via stimulating equatorial warm Kelvin waves (Fig. 3f) (Lengaigne et al. 2004). Subsequently, the SST warming, westerly wind anomalies and positive precipitation anomalies over the tropical Pacific maintain and develop to an El Niño event in the succeeding winter via the positive air–sea feedback in the tropics (Figs. 3f–h and 4f–h).

Fig. 3.
Fig. 3.

Regression maps of SST anomalies (shading; °C) in (a),(e) MAM(0), (b),(f) JJA(0), (c),(g) SON(0), and (d),(h) D(0)JF(1) onto the normalized spring PMM index for periods of (left) 1952–80 and (right) 1990–2018. Dotted areas indicate SST anomalies exceeding the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for precipitation (shading; mm day−1) and surface wind (vector; m s−1) anomalies. Dotted areas indicate precipitation anomalies exceeding 95% confidence level. Surface wind anomalies in zonal direction that are not significant at the 95% confidence level are not shown.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

By contrast, SST, precipitation and wind anomalies in the low-correlation period (Figs. 3a–d and 4a–d) are much weaker compared to those in the high-correlation period (Figs. 3e–h and 4e–h). In the low-correlation period, the marked SST warming and associated positive precipitation anomalies are mainly confined to the subtropical northeastern Pacific, with relatively weak amplitudes over the equatorial central Pacific in spring (Figs. 3a and 4a). Accordingly, the westerly wind anomalies in the equatorial western Pacific are much weaker (Fig. 4a) compared with their counterpart in the high-correlation period (Fig. 4e). Therefore, the weak spring westerly wind anomalies cannot exert clear impacts on the following winter ENSO (Figs. 3b–d).

4. Factors leading to the interdecadal change

Figure 5a shows a scatterplot of the spring PMM-related westerly wind anomalies over the tropical western Pacific against the spring PMM–winter ENSO correlation. The two variables in Fig. 5a are highly corrected with each other, with a correlation coefficient of 0.94. This suggests that in decades when spring PMM-related westerly wind anomalies over the tropical western Pacific are strong (weak), the impact of the spring PMM on the following winter ENSO is strong (weak). Formation of the spring PMM-related westerly wind anomalies over the tropical western Pacific is mainly due to the WES feedback mechanism involving the air–sea interaction over the tropical and subtropical North Pacific. The strength of the spring PMM-induced westerly wind anomalies over the tropical western Pacific is closely related to the spring PMM-generated positive SST anomalies over the tropical central Pacific (Fig. 5b). Hence, a more southward extension of the SST and precipitation anomalies from the subtropical North Pacific to the tropical central Pacific would favor stronger westerly wind anomalies over the tropical western Pacific (Fig. 5b), which further result in a larger impact on the following winter ENSO (Fig. 5a).

Fig. 5.
Fig. 5.

(a) Scatter diagram of the spring zonal wind anomalies averaged over 0°–10°N, 140°E–180° (green box in Fig. 4a) regressed upon the spring PMM index for the 29-yr moving window against the 29-yr running correlation between the spring PMM index and the following-winter Niño-3.4 SST index. (b) Scatter diagram of the spring PMM-related spring SST anomalies averaged over 5°S–5°N, 160°E–170°W against the spring zonal wind anomalies averaged over 0°–10°N, 140°E–180°.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

A comparison of the spring PMM-related anomalies between the high-correlation and low-correlation periods as well as the results of Fig. 5 suggests that the strength of the impact of the spring PMM on the following ENSO events highly depends on whether the PMM-related air–sea signals can propagate to the tropical central Pacific. The southwestward propagation of the spring PMM-related SST and atmospheric anomalies into the deep tropics (the region within 10°S–10°N) is mainly governed by the WES feedback mechanism (Chang et al. 2007; Yu et al. 2015; Lin et al. 2015; Zheng et al. 2021). Figure 6a shows a scatterplot of the PMM coupling strength against the spring PMM-related zonal wind anomalies averaged over 0°–10°N, 140°E–180°. Here, the PMM coupling strength is defined as the correlation coefficient between the PMM SST index (expansion coefficient time series of SST) and the simultaneous PMM wind index (expansion coefficient time series of surface winds) (Yu et al. 2015; Lin et al. 2015; Zheng et al. 2021). It shows that the spring PMM-induced westerly wind anomalies over the tropical western Pacific are strong (weak) when the PMM coupling strength is strong (weak) (Fig. 6a), which would contribute to a close spring PMM–winter ENSO connection. Hence, as expected, we can see a significant connection between the PMM coupling strength and the spring PMM–winter ENSO connection (Fig. 6b).

Fig. 6.
Fig. 6.

Scatter diagram of the PMM coupling strength (x coordinate) for the 29-yr running period against (a) the springtime zonal wind anomalies regressed onto the normalized spring PMM index averaged over 0°–10°N, 140°E–180° (y coordinate; m s−1) during the corresponding 29-yr running period, and against (b) the 29-yr running correlation coefficient between the springtime PMM index and following winter Niño-3.4 SST index (y coordinate).

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

In summary, the air–sea coupling strength over the subtropical northeastern Pacific related to the PMM plays an important role in modulating the efficiency of the impact of the spring PMM on the winter ENSO. When the air–sea coupling strength related to the PMM is strong, the spring PMM-related air–sea coupling signals would easily propagate southwestward to the tropical central Pacific, and lead to a strong impact on the winter ENSO. This result is consistent with Lin et al. (2015) and Zheng et al. (2021), who reported that the ability of the CMIP5 and CMIP6 climate models in simulating the spring PMM–winter ENSO relation is closely related to the performance of the models in capturing the air–sea coupling strength over the subtropical northeastern Pacific.

An immediate question following the above analysis is: What are plausible factors for the change of the air–sea coupling (i.e., WES feedback) strength over the subtropical northeastern Pacific? Previous studies have demonstrated that intensity of the WES feedback is sensitive to the background mean state (Vimont et al. 2009; Amaya 2019), including the climatological mean trade winds and the meridional location of the intertropical convergence zone (ITCZ) (Vimont et al. 2009; Vimont 2010; Zhang et al. 2014; Amaya 2019).

In terms of the role of the mean trade winds, following Vimont (2010), changes in the surface latent heat flux caused by per unit variation of surface winds (dominated by its zonal wind component) can be written as follows:
a=LHu=LHuw¯2.
Here, a is the WES parameter, indicating the sensitivity of evaporation at the ocean surface to surface wind changes. LH represents surface latent heat flux, u is the surface zonal wind anomaly, and w¯ denotes the mean wind speed. The above formula indicates that the WES feedback is proportional to the background trade wind speed, as the stronger mean wind speed will lead to larger surface latent heat flux response to the same zonal wind anomalies (Czaja et al. 2002; Vimont 2010). Based on the above theory, the intensity of the trade winds should be stronger in the high-correlation period than that in the low-correlation period. The difference in the mean circulation between the high-correlation and low-correlation periods is shown in Figs. 7a and 7b. The mean trade winds over the subtropical North Pacific are indeed significantly stronger in the high-correlation period than the low-correlation period, which provide a favorable background for the air–sea coupling (Figs. 7a,b and 8a). Besides, Figs. 7c and 8b reveal that the subtropical high is also stronger in the high-correlation period, consistent with the strengthening of northeasterly trade winds over the subtropical North Pacific in the high-correlation period (Figs. 7a,b).
Fig. 7.
Fig. 7.

Difference in the climatological mean (a) surface zonal winds (m s−1), (b) surface meridional winds (m s−1), (c) sea level pressure (SLP; hPa), and (d) precipitation (mm day−1) between 1990–2018 and 1952–80. Dotted areas indicate that the differences exceed 95% significance level. (e) The precipitation climatology (mm day−1) for the period of 1990–2018 (contours) and 1952–80 (shading).

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Fig. 8.
Fig. 8.

Scatter diagram of the climatological mean (a) surface wind speed averaged over 0°–10°N and 130°E–120°W (x coordinate; m s−1), (b) SLP averaged over 5°–15°N, 130°E–120°W (x coordinate; hPa), and (c) latitude of ITCZ (x coordinate; °N) for the 29-yr running period against the springtime zonal wind anomalies regressed onto the normalized spring PMM index averaged over 0°–10°N, 140°E–180° (y coordinate; m s−1) for the corresponding 29-yr running period.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

In terms of the role of the ITCZ, previous studies indicated that the southward extension of the spring PMM-related SST and wind anomalies from the subtropical northeastern Pacific to the tropical Pacific is closely related to the meridional location of the ITCZ (Okajima et al. 2003; Watanabe et al. 2011; Ham and Kug 2012; Xiang et al. 2013; Zhang et al. 2014; Martinez-Villalobos and Vimont 2016). In particular, it was found that if the meridional location of ITCZ shifted southward, the air–sea signals associated with the spring PMM could be transmitted to the deep tropics more easily (Okajima et al. 2003; Zhang et al. 2014). This is mainly because the climatological southwesterly winds to the south of the mean ITCZ would suppress the efficiency of the WES feedback related to the spring PMM (Okajima et al. 2003; Zhang et al. 2014). Figure 7d displays the difference of the climatological mean precipitation in spring between the high-correlation and low-correlation periods, which reveals that the meridional position of ITCZ is more southward in the high-correlation period (Figs. 7d,e). Figure 8c shows a scatter diagram of the 29-yr running mean of the spring PMM-related zonal wind anomalies over the tropical western Pacific against the mean ITCZ latitudes (defined as the mean of latitudes with maximum mean precipitation within 120°E–120°W). From Fig. 8d, the spring PMM-related zonal wind anomalies over the tropical western Pacific are stronger with more southward shifted ITCZ. The above results indicate that the mean meridional location of the ITCZ plays a role in determining the propagation of PMM associated surface zonal wind anomalies into the tropical Pacific, which further modulate the spring PMM–winter ENSO relation.

Overall, the above analysis suggests that the mean trade winds and the meridional location of the ITCZ over the subtropical North Pacific strongly affect the propagation of the spring PMM-related air–sea coupling anomalies to the deep tropics, as well as the strength of the spring PMM–winter ENSO relation.

In the following, we employ the long historical simulation of Earth system models to further examine the results obtained from the observations. The selected model needs to meet two conditions. On the one hand, the model should be able to well simulate the spatial patterns of PMM and ENSO. On the other hand, the spring PMM–winter ENSO relationship in the model should have significant interdecadal variations. CIESM and MIROC6 meets the above two conditions.

As shown in Fig. 9, both CIESM and MIROC6 are able to well capture the PMM and ENSO. The positive phase of PMM is characterized by marked positive SST anomalies extending along the west coast of North America into the tropical central Pacific, accompanied with southwesterly wind anomalies over subtropical North Pacific (Figs. 9a,e). The warm phase of ENSO features pronounced positive SST anomalies in the tropical central-eastern Pacific and negative SST anomalies in the western North Pacific (Figs. 9b,f). Obviously, the observed spatial structures of the PMM and ENSO patterns can be reasonably reproduced by the CIESM and MIROC6. The phase-locking characteristics of the PMM and ENSO can also be well simulated by CIESM and MIROC6, with the strongest variation of the PMM occurring in spring and the strongest variation of ENSO appearing in winter (Figs. 9c,d,g,h). Therefore, it is reasonable to employ the long simulation of the CIESM and MIROC to further explore the interdecadal variation of the relationship between the spring PMM and the following winter ENSO.

Fig. 9.
Fig. 9.

(a) Regression maps of SST (°C) and surface wind (m s−1) anomalies onto the normalized PMM index over 1948–2014 in the historical simulation of CIESM. (b) Regression maps of SST (°C) anomalies onto the normalized Niño-3.4 SST index over 1850–2014 in the historical simulation of CIESM. SST anomalies that are not significant at the 95% confidence level are not shown in (a) or (b). (c) The monthly evolution of variance of PMM index. (d) The monthly evolution of variance of Niño-3.4 SST index. (e)–(h) As in (a)–(d), but for the historical simulation of MIROC6.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Figure 10 displays the 29-yr running correlations between the springtime PMM index and the succeeding winter Niño-3.4 SST index in the CIESM and MIROC6. The spring PMM–winter ENSO relationship has undergone notable interdecadal changes (Fig. 10). For the CIESM, spring PMM has a close relation with the following winter ENSO before the late 1920s and after the early 1990. By contrast, the spring PMM has a weak relation with the winter ENSO from the late 1920s to the early 1990s (Fig. 10a). For the MIROC6, spring PMM has a close relation with the following winter ENSO before the late 1870s and after the early 1910s (Fig. 10b). According to the 29-yr running correlation results, we choose the high-correlation period with significant spring PMM–winter ENSO correlation (a total of 82 years and 101 years for the CIESM and MIROC6, respectively), and the low-correlation periods with weak spring PMM–winter ENSO relation (a total of 54 years and 35 years for the CIESM and MIROC6, respectively) for comparative analysis.

Fig. 10.
Fig. 10.

(a) The 29-yr running correlations between the spring PMM index and the following winter Niño-3.4 SST index based on the historical simulation of CIESM. The dashed lines denote the correlation significant at the 95% confidence level. The red dots indicate the central years with the corresponding 29-yr running correlations significant at the 95% confidence level. The blue dots indicate the central years with the corresponding 29-yr running correlations not significant at the 95% confidence level. (b) As in (a), but for the historical simulation of MIROC6.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Figure 11 shows the seasonal evolutions of SST and surface wind anomalies regressed onto the spring PMM index from spring to the following winter for the low-correlation and high-correlation periods in the CIESM and MIROC6, respectively. Results obtained from the CIESM and MIROC6 are similar to the observed. In the high-correlation period (Figs. 11e–h and 11E–H), positive SST and southwesterly wind anomalies are coupled with each other over the subtropical northeastern Pacific and extend to the tropical central Pacific via the WES feedback. The strong westerly wind anomalies over the tropical western Pacific promote the occurrence of the following winter El Niño–like pattern. By contrast, in the low-correlation period (Figs. 11a–d and 11A–D), the SST warming and atmospheric anomalies in spring are mainly confined to the subtropical northeastern Pacific and cannot develop to an El Niño–like pattern in winter.

Fig. 11.
Fig. 11.

Regression maps of SST (shading; °C) and surface wind anomalies (vector; m s−1) in (a),(e) MAM(0), (b),(f) JJA(0), (c),(g) SON(0), and (d),(h) D(0)JF(1) onto the normalized spring PMM index over high-correlation period (the first column) and low-correlation period (the second column) in the simulation of CIESM. SST anomalies that are not significant at the 95% confidence level are not shown. (A)–(H) As in (a)–(h), but for the historical simulation of MIROC6.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Figure 12 displays the scatter diagram of the 29-yr running mean of the trade wind speed and SLP over the subtropical North Pacific as well as the mean latitude of ITCZ against the 29-yr running correlation coefficient between the spring PMM index and the following winter Niño-3.4 SST index in the CIESM and MIROC6. Similar to that in the observation (Fig. 8), results obtained from the CIESM and MIROC6 both confirm that connection of the spring PMM with the following winter ENSO is stronger in the decade when the subtropical trade winds and subtropical high pressure are stronger, as well as the mean ITCZ is located more southward. Therefore, the main mechanisms for the interdecadal variation of the spring PMM–winter ENSO relationship can be validated in the long simulation of coupling climate models.

Fig. 12.
Fig. 12.

Scatter diagram of the 29-yr running correlation coefficient between the springtime PMM index and following-winter Niño-3.4 SST index (y coordinate) against the climatological mean (a) surface wind speed averaged over 0°–10°N, 130°E–120°W (x coordinate; m s−1), (b) SLP averaged over 5°–15°N, 130°E–120°W (x coordinate; hPa), and (c) latitude of ITCZ (x coordinate; °N) for the corresponding 29-yr running period in the simulation of CIESM. (d)–(f) As in (a)–(c), but for the historical simulation of MIROC6.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

5. Conclusions and discussion

This study reveals that the influence of the spring PMM on the following winter ENSO shows a steady increase from 1948 to 2021. This suggests that the spring PMM plays a more important role in the occurrence of ENSO events during recent decades. We then explore the factors responsible for the interdecadal enhancement of the spring PMM–winter ENSO connection, which is schematically illustrated in Fig. 13. The enhanced impact of the spring PMM on the subsequent winter ENSO is mainly attributed to the spring PMM-related atmospheric anomalies that can extend from the subtropical northeastern Pacific to the tropical Pacific, especially the westerly wind anomaly in the later high-correlation period. The propagation of the spring PMM-related air–sea coupling anomalies into the deep tropics and its linkage with following ENSO events are governed by WES feedback progress, which has a stronger strength in the later high-correlation period.

Fig. 13.
Fig. 13.

Schematic diagram depicting the processes for the enhanced influence of the spring PMM on the following winter ENSO.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0190.1

Our research reveals the important role of the background state (i.e., trade wind and meridional location of ITCZ) in modulating the WES feedback strength and the spring PMM–winter ENSO relation. On one hand, in the later high-correlation period, with the strengthening of the background trade wind, the response of SST to the atmospheric anomaly is enhanced, leading to a strengthening of the air–sea interaction. On the other hand, as the region with the largest mean precipitation, the ITCZ indicates a sensitive area of atmospheric circulation response to SST anomalies. Hence, the spring PMM related air–sea coupling anomalies can be transmitted to the deep tropics more easily as the meridional location of ITCZ is shifted southward in the later high-correlation period. The mechanism of the interdecadal change of the spring PMM–winter ENSO relationship can be found in the long historical simulation of coupling models.

Previous studies have demonstrated that the spring SST anomalies in the northern tropical Atlantic (NTA) and the late-summer SST anomalies in the Atlantic warm pool (AWP) have a significant impact on the following ENSO events (Ham et al. 2013; Park et al. 2018; Wang 2019; Wang et al. 2021). We found that the correlations of the spring PMM index with the spring NTA SST index and the late-summer AWP SST index both weaken significantly after the early 1970s (not shown). In addition, spring and summer SST anomalies in association with the spring PMM are weak in most North Atlantic both during the high- and low- correlation periods. These suggest that the continuing strengthened impact of the spring PMM on ENSO is not likely due to the impact of the North Atlantic SST anomalies.

Furthermore, previous studies have revealed a close relation of the Pacific decadal oscillation (PDO) and NPO with the PMM (Liguori and Di Lorenzo 2018; Amaya 2019). For example, Liguori and Di Lorenzo (2018) indicated that the relationship between the PDO and the PMM increases significantly under the anthropogenic forcing. Yu et al. (2012) reported a change in the connection between the tropical central Pacific SST and the NPO-like extratropical atmospheric variability around the 1990. One may ask whether the PDO and change in the NPO variability contribute to change in the spring PMM–winter ENSO relationship. The PDO has a phase transition around the late 1970s (from negative to positive phase) and early 2000s (from positive to negative phase) (not shown; Mantua et al. 1997). However, the connection between the spring PMM and the winter ENSO shows a steady increase in the past. In addition, the correlation coefficients between the spring PMM index with the winter Niño-3.4 SST index during the positive and negative PDO phases are 0.41 and 0.35, respectively, both significant at the 95% confidence level. This suggests that the PDO does not have an obvious modulation on the impact of the spring PMM on the following winter ENSO. In addition, we have calculated the 29-yr running variance of the winter NPO index (not shown). We found that the amplitude of the NPO index decreased obviously since the late 1970s. Actually, a recent study reported that the impact of the winter NPO on the following winter ENSO shows an interdecadal decrease after the mid-1990s (Park et al. 2021, their Fig. 8), which is not consistent with a continuing increase of the impact of the spring PMM on the following winter ENSO. We have also examined the spatial pattern of the winter NPO for the high- and low-correlation periods. The spatial pattern of the winter NPO is defined as the second EOF mode of winter SLP anomalies over the North Pacific. We found that the spatial patterns of the winter NPO are similar during the two periods (not shown). The intensity of the southern lobe of the winter NPO in the high-correlation period is even slightly weaker than that in the low-correlation period (not shown). Thus, these evidences suggest that the change in the spring PMM–ENSO relation is not likely related to the change in the NPO variability.

A recent study indicated that the global warming would enhance the impact of the spring PMM on the following winter ENSO (Jia et al. 2021; Fan et al. 2022). This suggests that the enhancement of the spring PMM on the winter ENSO during recent decades may be related to the combined influences of both external forcing and internal climate variability. The relative and combined roles of the global warming and interdecadal climate variability modes in the interdecadal variation of the spring PMM–winter ENSO relationship should be further investigated.

Acknowledgments.

We thank the editor Dr. Y. M. Okumura, and three anonymous reviewers for their constructive suggestions, which helped to improve the paper. This study was supported jointly by the National Natural Science Foundation of China (Grants 42175039, 41961144025, and 41721004), and the Jiangsu Collaborative Innovation Center for Climate Change.

Data availability statement.

The NCEP–NCAR reanalysis data are obtained from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The SST data are derived from https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The surface winds, precipitation, and SLP data are extracted from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The historical simulation data of CIESM and MIROC6 are obtained from https://esgf-node.llnl.gov/search/cmip6/.

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