1. Introduction
Northeast Asia (NEA), including northern China, the Korean Peninsula, and Japan, is one of the most rapidly developing regions with more than hundreds of millions of people (e.g., Lu 2005; Wu et al. 2010; Chen et al. 2015). The atmospheric circulation in NEA is an essential member of the Northern Hemisphere summer circulation (e.g., Wu and Jiao 2017; Zhou et al. 2020). El Niño–Southern Oscillation (ENSO), as the strongest interannual variability signal, has been realized to be a good predictor for the anomalies of precipitation and circulation in summer NEA and even the whole of East Asia due to the remarkable impacts of ENSO on the East Asia summer climate (e.g., Zhang et al. 1996; Wang et al. 2000; Wu and Wang 2002; Wang et al. 2008; Yim et al. 2008b; Wen et al. 2015; Liu et al. 2018; Wei et al. 2020; Zhou et al. 2020; Tang et al. 2022).
Previous studies suggested that ENSO and the NEA summer circulation are linked through three pathways. One is the anomalous convection over the western North Pacific (WNP) and associated with the meridional Rossby wave train at the lower-level troposphere along the East Asian coast, known as the East Asia–Pacific/Pacific–Japan (EAP/PJ) pattern (Huang and Li 1987; Nitta 1987; Wang et al. 2001; Wu and Wang 2002; Kosaka and Nakamura 2006; Lee et al. 2006; Kosaka and Nakamura 2010; Chen and Lu 2014). Huang et al. (2007) summarized the role of the EAP/PJ teleconnection arising from the thermal forcing over the WNP in forming the tripolar distribution of summer precipitation in eastern China, with severe floods in the Yangtze and Huaihe River valleys and prolonged droughts in North China. The pathways of the meridional teleconnection could also depend on the diversity of ENSO decaying (Jiang et al. 2019).
The second pathway linking ENSO and the NEA summer circulation is the summer South Asian high (SAH) located in the upper levels over the South Asian highland. El Niño (La Niña) can lead to a stronger (weaker) and more southerly (northerly) SAH through the Walker circulation or the charging process in the tropical Indian Ocean (e.g., Xie et al. 2009; Huang et al. 2011; Xue et al. 2015; Xue et al. 2018), and subsequently the summer SAH anomalies can affect the precipitation and circulation in NEA and East Asia. Huang and Qian (2004) revealed that an intensified summer SAH usually induces floods in the Yangtze River and droughts in North China, and Wei et al. (2014; 2015) emphasized that the northwestward (southeastward) shift of summer SAH often corresponds to more (less) rainfall in North China. A recent study from Xue et al. (2021) further demonstrated the distinct impact of SAH’s diversity on precipitation of the Yangtze River valley, North China, and Japan.
The third pathway connecting ENSO and the NEA summer climate is the extratropical wave train in the Northern Hemisphere excited by the Indian summer monsoon rainfall (ISMR). The reverse relationship between ENSO and ISMR has been identified since the beginning of the twentieth century (e.g., Pant and Parthasarathy 1981; Webster and Palmer 1997; Kawamura 1998; Kawamura et al. 2005; Feba et al. 2019). The heat released by the precipitation over India can trigger a Rossby wave train along the upper-level westerly jet stream extending from western Europe and India to the North Pacific, which can influence the NEA circulation through modifying the moisture transport (e.g., Wu 2016; R. H. Huang et al. 2017; Wu and Jiao 2017; Ha et al. 2018; Liu and Huang 2019; Son et al. 2021). This wave train pattern is called the circumglobal teleconnection (CGT) (Ding and Wang 2005; Ding et al. 2011), and the process from central Asia to the Pacific is also known as the Silk Road pattern (SRP) (Lu et al. 2002; Enomoto et al. 2003; Kosaka et al. 2009).
Because of the fluctuations from the multiple pathways, the relationship between ENSO and the NEA summer circulation is not stable, showing apparent interdecadal variations (e.g., Wang 2002; Wu and Wang 2002; Wu et al. 2010; Han et al. 2017). Wu and Wang (2002) reported an interdecadal variation between ENSO and East Asian summer monsoon around the late 1970s, which can be attributed to the reduced variability of ISMR associated with the weakened relationship between ENSO and ISMR (e.g., Kumar et al. 1999; Wang et al. 2012) and the weakened ISMR–NEA summer rainfall relationship during this period (Feng and Hu 2004; Li et al. 2015; Lin et al. 2017; Wu and Jiao 2017; Liu and Huang 2019). This means that the ISMR variability change may modify the ENSO–NEA circulation relationship. Recently, Yang and Huang (2021) reported a clear restoration relationship between ENSO and ISMR since 1999/2000 with an interdecadal shift in the evolutionary characteristics of the summer ENSO. In 1979–97, summer ENSOs often continued from the previous winter, whereas summer ENSOs more emerged from late spring during 2000–18; these are distinguished as continuing ENSOs and emerging ENSOs, respectively, in Yang and Huang (2021). In addition, some other climate systems in the Northern Hemisphere also experienced interdecadal transition after the 1999s (L’Heureux et al. 2013; England et al. 2014; Jo et al. 2015; Wu et al. 2016; Tao et al. 2017; Wang et al. 2017; Hu et al. 2018; He et al. 2020; Liu et al. 2020; Meehl et al. 2021; Song et al. 2021; Li et al. 2022), which serves as the climate background of the interannual variability and could also modulate the ENSO–NEA relationship. Therefore, it is unclear whether the ENSO–NEA summer circulation relationship has undergone an interdecadal shift after 2000, and if so whether the restored ENSO–ISMR relationship with the ISMR-excited CGT/SRP wave train is the key process.
In the present study, using observations and multiple model experiments, we reveal that the relationship between ENSO and the NEA summer circulation, mainly in the middle and upper-level troposphere, has strengthened since 1999/2000. The transition can be attributed to the active ENSO-related CGT/SRP wave train inspired by the enhanced ISMR–CGT/SRP relationship, which can be explained by the restored ENSO–ISMR relationship resulting from the interdecadal shift of ENSO evolution. This paper is organized as follows. The datasets and methods are described in section 2. The fact of interdecadal transition is verified in section 3. Then, section 4 investigates the role of ISMR-related CGT/SRP in connecting ENSO and the NEA circulation. In section 5, the phenomenon and mechanism of the interdecadal enhancement are further confirmed with model experiments. A summary and discussion are given in section 6.
2. Datasets and methods
This study uses the monthly air temperature, surface air temperature, geopotential height, and winds derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis with the spatial resolution of 2.5° for the periods of 1948–2019 (Kalnay et al. 1996). Five precipitation datasets and four sea surface temperature (SST) datasets are selected to verify the result’s reliability. The precipitation datasets are the monthly precipitation from all-Indian monthly rainfall from the Indian Institute of Tropical Meteorology (IITM) (Parthasarathy et al. 1994), the Global Precipitation Climatology Centre V2018 (GPCC) (Schneider et al. 2017), the Climate Research Unit TS V4.03 (CRU) (Harris et al. 2014), the Global Precipitation Climatology Project V2.3 (GPCP) (Adler et al. 2003), NOAA’s Precipitation Reconstruction over Land (PRECL) (Chen et al. 2002). The SST datasets are the Met Office Hadley Centre’s sea surface temperature (HadISST) (Rayner et al. 2003), the Extended Reconstructed SST dataset V5 (ERSST) (B. Huang et al. 2017), Centennial Observation-Based Estimates of SST V2 (COBESST2) (Hirahara et al. 2014), and Kaplan Extended V2 SST anomaly (hereafter simply Kaplan; Kaplan et al. 1998).
In this paper, the summer [June–September (JJAS)] Niño-3 index (averaged SST anomalies in 90°–150°W, 5°S–5°N) was used to represent summer ENSO. The summer ENSOs were classified into two types, continuing ENSOs and emerging ENSOs, based on their different evolutions from the previous boreal winter, following Yang and Huang (2021). The details of the classification method are introduced in the online supplemental material. The defined El Niño and La Niña cases and their classification are shown in Table 1. Based on the selected cases, the composited SST anomalies for the continuing ENSOs and emerging ENSOs are shown in Fig. S1 in the online supplemental material. The composited SST anomalies for the continuing ENSOs well reflect the sustained SST signals in the tropical Pacific and sufficient responses in other ocean basins, whereas the composites for the emerging ENSOs are more concentrated in the tropical Pacific with weak responses in other oceans. A CGT index (CGTI) following Ding and Wang (2005) was defined by the averaged geopotential height anomalies at 200 hPa in JJAS over the key area (35°–40°N, 60°–70°E) to represent the wave train.
The definitions of continuing and emerging ENSOs from HadISST based on 1948–2019.
The simulations from the Atmospheric Model Intercomparison Project (AMIP) participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) (Gates et al. 1999; Eyring et al. 2016) were analyzed to confirm the restoring relationship between ENSO and the NEA circulation after 1999/2000. A list of all the 45 models is given in Table 2. In addition, a 10-member Pacific Ocean–Global Atmosphere (POGA) pacemaker experiment for the period 1948–2014 was also analyzed to verify the role of the Pacific SSTAs, which was performed in the NOAA Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.1 (Kosaka and Xie 2016). All the model datasets were interpolated onto the same 2.5° × 2.5° grid as observations before analysis.
List of the 45 CMIP6 AMIP models used in this study, of which 30 models that passed the selected criterion are in bold.
The annual cycle and the linear trend of observation and model outputs were removed. All the significant tests were based on a two-sided Student’s t test, with a 0.05 (5%) critical level of significance. The degrees of freedom were given by the number of years minus 2, since the autocorrelations of yearly datasets represented by seasonal mean are weak.
3. Interdecadal change between ENSO and NEA circulation
Figure 1 displays the regression of JJAS mid- to high-latitude circulation fields onto the simultaneous Niño-3 index during 1948–2019. An obvious global zonal wave train is noticed in the 200-hPa geopotential height anomalies (H200) at midlatitudes, with five prominent centers located at the North Atlantic, North Africa and central Asia, Northeast Asia, the central North Pacific, and the eastern North Pacific (Fig. 1a). A similar but weaker pattern can be observed in the 500-hPa geopotential height anomalies (H500) and the 500-hPa air temperature anomalies (T500) in the middle-level troposphere (Figs. 1b,d), especially in NEA. However, ENSO-related 850-hPa geopotential height anomalies (H850) exhibit relatively independent and weak centers in the North Atlantic, regions around the Arabian Peninsula, and the North Pacific, without apparent wave trains (Fig. 1c). The globally extratropical circulation in the middle to upper troposphere with a distinct center in NEA driven by ENSO is generally consistent with results in previous studies (Ding et al. 2011). A rectangular region [denoted by the solid black boxes (30°–50°N, 110°–140°E) in Fig. 1] was selected to represent NEA in the following analyses.
Regression of JJAS (a) H200, (b) H500, (c) H850, and (d) T500 onto JJAS Niño-3 index during 1948–2019. Hatching denotes statistical significance exceeding the 95% confidence level. Black boxes (30°–50°N, 110°–140°E) denote the region of NEA.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
To explore the evolution of the ENSO–NEA circulation relationship in a more extended period, Fig. 2 shows the 19-yr sliding correlation between the JJAS Niño-3 index and the JJAS circulation anomalies at different heights over NEA. The negative correlation between ENSO and NEA H200 is not stable, showing apparent interdecadal transition (Fig. 2a). The ENSO–NEA H200 relationship is weakened from the early 1970s to the middle 1980s, then keeps a relatively stable period afterward, but experiences an interdecadal enhancement after 1999/2000, with correlation coefficients even reaching −0.8. The enhanced relationship does not depend on the selection of the Niño-3 index or sliding windows (results not shown) and is confirmed by multiple SST datasets. Figures 2b and 2d show the sliding correlation of ENSO with NEA H500 and T500 to further analyze the vertical characteristics of this enhanced relationship, which also presents a negative correlation and exhibits a similar interdecadal shift. However, unlike the middle to upper circulation, the sliding correlation between ENSO and NEA H850 is weak and fluctuates around −0.2 without apparent interdecadal shift (Fig. 2c), consistent with the result in Fig. 1c.
The 19-yr sliding correlation between the JJAS Niño-3 index and the regional average of (a) H200, (b) H500, (c) H850, and (d) T500 over NEA (30°–50°N, 110°–140°E) based on the HadISST, ERSST, COBESST, and Kaplan datasets. The dashed lines denote the 95% confidence level, and the purple line indicates 1999.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Based on the sliding correlation between ENSO and the NEA circulation in Fig. 2, we selected two subperiods, 1980–99 and 2000–19, with weak and strong negative correlation, respectively. Figure 3 shows the regression of atmospheric circulation onto JJAS Niño-3 index during the two epochs. During 1980–99, the ENSO-related H200 exhibits a weaker zonally teleconnection wave train over the entire Northern Hemisphere (Fig. 3a), with weaker centers located in northern Africa, Northeast Asia, the central North Pacific, the eastern North Pacific, and eastern Canada. The wave train is significant during 2000–19, intensifying along with the zonal band with remarkable changes in the North Atlantic, northern Africa and central Asia, Northeast Asia, the central North Pacific, the eastern North Pacific, and Central America. The zonal wave trains of H500 and T500 associated with ENSO (Figs. 3c,d,g,h) are not as pronounced as the results of H200 (Figs. 3a,b) but also show pronounced interdecadal shift, especially in NEA, consistent with results in Fig. 2. Unsurprisingly, the regression of H850 on ENSO does not show apparent zonal or meridional wave trains and interdecadal shift. Therefore, the interdecadal transition of the ENSO–NEA circulation relationship mainly appears in the middle and upper levels rather than the lower level (Figs. 2 and 3). Similar results can be found when the time periods of 1985–99 and 2000–14 were selected (not shown), indicating that the main conclusions are independent of the time period selection. In Fig. 3b, we can also find the significant anomalies in the North Pacific, implying a possible role in the interdecadal shift between ENSO and the North Pacific Oscillation (NPO) (Yeh and Kirtman 2004; Yeh et al. 2018). However, in a brief check (not shown) the variation of the ENSO–NPO relationship is not consistent with that of the ENSO–NEA H200 relationship (Fig. 2).
Regression of JJAS (a),(b) H200, (c),(d) H500, (e),(f) H850, and (g),(h) T500 onto JJAS Niño-3 index during 1980–99 and 2000–19. Hatching denotes statistical significance exceeding the 95% confidence level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Both the sliding correlation and associated spatial correlation during different time periods (Figs. 2c and 3e,f) illustrate that the interdecadal transition of the ENSO–NEA circulation relationship mainly appears in the middle and upper levels rather than the lower level, although the lower-level circulation is an important part of NEA summer circulation and has an intensive connection with the upper-level circulation (Lu et al. 2002; Ding and Wang 2005; Kosaka and Nakamura 2006; Xu et al. 2019). This result implies that the meridional EAP/PJ wave train, which has more apparent signals in the middle to lower levels, could not be the key process leading to the interdecadal transition, although some recent studies have reported an interdecadal shift of EAP/PJ teleconnection around 2000s (Xu et al. 2019; Li and Lu 2020; Wang et al. 2020; Sun et al. 2021). In contrast, the zonal wave train in the upper level should be the key process in the interdecadal transition of ENSO–NEA relationship. The weaker zonal wave train in the lower level is due to the effect of topography and other land surface disturbances on the midlatitude wave train (e.g., Held et al. 1985; Lu et al. 2002; Sandu et al. 2019).
Lu et al. (2002) proposed that the meridional winds at 200 hPa (V200) can better reflect the characteristics of the zonal wave train due to its independence from the westerly jet stream. The regression of V200 against Niño-3 index (Fig. 4) manifests the enhancement between ENSO and 200-hPa circulations after 1999/2000, especially in NEA. Remarkably enhanced centers of the wave train from V200 could be observed in the North Atlantic, North America, and Asia during 2000–19 compared to 1980–99, although the wave train from V200 is not as clear as ENSO-related H200 for the whole Northern Hemisphere. We also analyzed the regression of zonal wind at 200 hPa (Fig. S2), which also shows a zonal wave train consistent well with the results in geopotential height and meridional wind. Ding and Wang (2005) stated that the wave train is zonally propagating through the whole Northern Hemisphere as the CGT pattern, whereas the SRP is concentrated in the Eurasian region as a part of the CGT (Hong and Lu 2016; Zhou et al. 2019). Given the similarity between the two wave trains in the Eurasian region, we did not distinguish CGT and SRP here.
Regression of JJAS V200 onto JJAS Niño-3 index during (a) 1980–99 and (b) 2000–19. Hatching denotes statistical significance exceeding the 95% confidence level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Corresponding to the distinct interdecadal shift of ENSO-related middle- and upper-level circulation, ENSO’s impact on precipitation and surface air temperature over NEA also experiences a significant shift around 1999/2000. As shown in Figs. 5a and 5b, the response of precipitation to ENSO is enhanced remarkably over East Asia after 1999/2000, with the negative anomalies of precipitation shifting from Shanxi Province to the Shandong and Korean Peninsulas. In addition to precipitation, the surface air temperature associated with ENSO displays remarkable differences around 1999/2000 in Figs. 5c and 5d. The surface air temperature of NEA is almost uncorrelated with ENSO during 1980–99, but exhibits a significant negative correlation after 1999/2000 from eastern Mongolia to Japan. The interdecadal shift in surface air temperature is consistent with the results in Fig. 3. The above results are in good agreement with the effects of ENSO on the NEA precipitation and temperature extensively studied by previous researchers (Wu et al. 2010; Chen and Lu 2014; Han et al. 2017), and further emphasize the influence of restoring ENSO–NEA circulation on precipitation and surface temperature after 1999/2000.
Regression of JJAS CRU precipitation anomalies onto JJAS Niño-3 index during (a) 1980–99 and (b) 2000–19. (c),(d) As in (a) and (b), but for the regression of surface air temperature anomalies. Hatching denotes statistical significance exceeding the 95% confidence level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
4. Possible mechanisms of the interdecadal shift
a. Potential bridges connecting the ENSO–NEA circulation
The ISMR was recognized as one of the important sources stimulating and modifying CGT/SRP (Wu and Wang 2002; Ding and Wang 2005; Ding et al. 2011; Chen and Huang 2012; Wang et al. 2012; Lin et al. 2017; Liu and Huang 2019; Liu et al. 2020; Beverley et al. 2021; Son et al. 2021). To illustrate the possible role of ISMR in the connection of ENSO and the CGT/SRP, Figs. 6a and 6b display the regression of H200 onto ISMR during the two epochs. The ISMR-related wave train is evidently stronger and more similar to the pattern of ENSO-related H200 during 2000–19 than during 1980–99. The spatial correlations between ENSO-related H200 (Fig. 3b) and ISMR-related wave train during 2000–19 (Fig. 6b) within the Northern Hemisphere or Euro-Asian region (10°–60°N, 20°W–140°E) both are stronger than −0.8, much higher than the result of −0.4 in the period of 1980–99 (Table 3). This result illustrates that the more active ENSO-related CGT/SRP wave train after 1999/2000 is closely correlated with the variability of ISMR. Table 4 further shows the correlation coefficients between ISMR index and CGT index during two periods. The correlation of ISMR–CGTI is enhanced after 1999/2000 (similar results can be seen for SRP index). This result reveals that the more pronounced ENSO-related wave train after 1999/2000 is associated with the enhanced ISMR–CGTI linkage.
Regression of JJAS H200 onto the regional average of JJAS ISMR during (a) 1980–99 and (b) 2000–19. Hatching denotes statistical significance exceeding the 95% confidence level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
The spatial correlation between Reg.(ISMR, H200) and Reg.(Niño-3, H200) (Reg. = regression) within the Northern Hemisphere (10°–60°N, 0°–360°) during 1980–99 and 2000–19. The results in parentheses are only considering the Euro-Asian region (10°–60°N, 20°W–140°E).
The correlation coefficients between CGTI and all Indian summer rainfall based on IITM, CRU, GPCC, PRECL, and GPCP during 1980–99 and 2000–19.
The bridging role of ISMR-related CGT/SRP in the interdecadal shift of the ENSO–NEA circulation relationship is further verified by analyzing the wave activity flux and Rossby wave source. As shown in Figs. 7a and 7b, the ENSO-related wave activity flux and divergence at 200 hPa have enhanced apparently in the Northern Hemisphere after 1999/2000, propagating from North Africa to North America through central Asia and Northeast Asia. The ISMR-related wave activity flux and divergence also show a stronger pattern over the Eurasian region during 2000–19 (Fig. 7d). Compared to the result during 1980–99 results, the ISMR-related wave activity flux coincides well with the ENSO-related results over the Eurasian region during 2000–19, suggesting that the ISMR-excited wave train acts as a bridge between ENSO and midlatitude connections.
(a),(b) The wave activity flux (WAF; vectors) and associated divergence (shading) based on the regression of JJAS H200 on Niño-3 during 1980–99 and 2000–19. (c),(d) As in (a) and (b), but based on the regression of H200 on ISMR. Vectors smaller than 20% of the reference arrow are not shown.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Moreover, we can also note that the WAF from the North Pacific to the North Atlantic Ocean is significantly enhanced (Figs. 7a,b), which implies a possible role of the North Atlantic. Actually, there are apparent interdecadal variation in ENSO-related SST anomalies over the North Atlantic. The meridional dipole of ENSO-related North Atlantic SST anomalies changed from north positive–south negative in 1980–99 to north negative–south positive in 2000–19. However, the NEA circulation anomalies driven by the North Atlantic SST anomalies in 2000–19 show a weaker wave train at midlatitudes over the Eurasian region with positive anomalies over NEA. This anomalous NEA circulation pattern differs from the enhanced negative ENSO–NEA H200 relationship after 1999/2000, indicating that the ENSO-related North Atlantic SST anomalies could not contribute to the enhanced ENSO–NEA H200 relationship after 1999/2000.
In addition to the wave activity flux, the ENSO-related Rossby wave source at 200 hPa is also enhanced after 1999/2000, and displays a cyclonic/anticyclonic wave train along with upper-level wave trains in Figs. 8a and 8b, consistent with the route of ISMR-excited wave train in the upper level as depicted in previous studies (Ding and Wang 2005; Liu et al. 2020; Son et al. 2021). The Rossby wave source is mainly contributed by the divergence term (Figs. 8c,d), while the advection of the vorticity by divergent flow exerts a weak contribution to the RWS (results not shown), consistent with Liu et al. (2020). The negative (positive) source anomaly along the upper-level wave train is due to the divergence (convergence) anomaly associated with the local Hadley circulation. The balanced relationship between divergent wind and Rossby wave source is in accordance with Shiozaki et al. (2021). The ENSO-related divergent wind is also enhanced after 1999/2000, further supporting the conclusion that ISMR and the associated CGT/SRP wave train are the key bridge through which ENSO affects the NEA H200 after 1999/2000.
(a),(b) Regression of 200-hPa Rossby wave source (RWS; shading) and 200-hPa divergent wind (vectors) onto Niño-3 during 1980–99 and 2000–19. (c),(d) Regression of 200-hPa RWS divergence term onto Niño-3 during 1980–99 and 2000–19. Vectors smaller than 20% of the reference arrow are not shown and hatching denotes statistical significance exceeding the 95% confidence level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
To examine the stability of the results, we performed similar analyses for 1960–79 as shown in Fig. S3. ENSO also can excite apparent wave trains at midlatitudes (Fig. S3a), showing a higher spatial correlation with ISMR-excited wave trains exceeding 0.9 (Fig. S3b). In the meantime, the ENSO-related wave activity flux, divergence and Rossby wave source at 200 hPa over the Eurasian region are also apparently enhanced compared with the result during 1980–99 (Figs. S3c,d). This result further verifies that the mechanism of the enhanced ENSO–NEA H200 relationship is modulated by the ISMR-excited wave train.
b. The impact of the interdecadal transition of ENSO’s evolution
Yang and Huang (2021) have highlighted that the interdecadal shift of ISMR–ENSO is associated with the shift of ENSO’s typical evolution after 1999/2000, from continuing ENSOs to emerging ENSOs after 1999/2000, which exerts tremendous impacts on the global system (Hu et al. 2020). To shed light on the impacts of ENSO’s evolutions on the CGT/SRP wave train and the ENSO–NEA H200 relationship, we selected 19 continuing ENSOs (6 El Niños and 13 La Niñas) and 15 emerging ENSOs (8 El Niños and 7 La Niñas) (Table 1) from the periods of 1948–2019 based on the definition in Yang and Huang (2021). The percentage of emerging ENSOs from HadISST increased from 20% during 1980–99 to 50% during 2000–19, verifying the interdecadal transition from continuing ENSOs to emerging ENSOs since 1999/2000.
Figure 9 presents the response of JJAS H200 during two flavors of ENSO evolution and two subperiods. The composited H200 is distinct between the continuing and emerging ENSO events (Figs. 9a–c). The wave train is significant with clear active centers in northern Africa/central Asia and in Northeast Asia in emerging ENSOs, but very weak in continuing ENSOs. Unsurprisingly, distinct geopotential heights over NEA could also be observed (Table 5), among which the average ENSO-related H200 over NEA in emerging ENSOs can reach −46.44 gpm and is stronger than −13.85 gpm of continuing ENSOs. In addition, the ENSO-related CGTI in emerging ENSOs also shows superiority and reaches −51.34 gpm compared to continuing ENSOs (Table 5). This result reveals that the flavors of ENSO’s evolution, with distinct CGT/SRP wave trains, are the dominant factor determining the strength of the ENSO–NEA circulation relationship.
Composite JJAS H200 for (a) continuing ENSOs, (b) emerging ENSOs, and (d) ENSOs in 1980–99, as well as (e) ENSOs in 2000–19 with El Niños minus La Niñas. (c) The results of (b) minus (a). (f) The results of (e) minus (d).
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
The composited CGTI (35°–40°N, 60°–70°E) and NEA H200 (30°–50°N, 110°–140°E) for different ENSO groups and periods (gpm).
In the composite with ENSOs divided by the two subperiods (Figs. 9d–f), the ENSO–H200 relationship is significant during 2000–19 as emerging ENSOs dominated, whereas it is weak during 1980–99 as continuing ENSOs dominated. The ENSO-related H200 over NEA shows a clear negative anomaly center during 2000–19 with the regional average of −39.53, much stronger than −17.74 in the period of 1980–99 (Table 5). Similar to the results of NEA, the ENSO-related CGTI increases to −31.94 after 1999/2000, suggesting that the enhanced ENSO-related CGT/SRP plays a crucial role in the recent two transitions of the ENSO–H200 relationship. Recently, ENSO asymmetry has received broad attention (Wu et al. 2021). To further explore the effect of ENSO asymmetry, the composited JJAS H200 for El Niños and La Niñas are shown in Fig. S4. Although the wave train of El Niños is more pronounced in midlatitudes compared to La Niñas, the wave train strength of emerging ENSO is much stronger than that of continuing ENSO in both El Niños and La Niñas, especially in NEA, which reinforces the conclusions of Fig. 9 from the perspective of ENSO asymmetry.
5. Verifying the mechanism based on CMIP6 and POGA simulations
To confirm the mechanism of the interdecadal shift of ENSO–NEA relationship, we analyzed the AMIP experiments in 45 CMIP6 models from 1979 to 2014 as well as 10-member POGA experiments. First, we evaluated the interdecadal variation of ENSO–NEA produced in the model experiments. Thirty CMIP6 models (bolded in Table 1) and five POGA experiment members were selected, in which the 3-yr average of the 15-yr sliding correlation series with a sliding center located in 1997–99 was weaker than that of 2005–07.
Figures 10a and 10b show the multimodel ensemble mean of sliding correlations between the JJAS Niño-3 index and NEA H200 in the AMIP simulation of the selected 30 CMIP6 models. Although the negative correlation of ENSO–NEA H200 is weak relative to the observation, the relationship has begun to recover and strengthen since 1999 in Figs. 10a and 10b, which is largely independent of the selection of the sliding window. Limited by the data length, we selected two unequal subperiods, 1980–99 and 2000–14, to compare the relationship of ENSO and NEA H200 for 45 CMIP6 models in Fig. 10c. The majority of models can simulate a stable negative correlation between ENSO and NEA H200, with 26 models showing a stronger relationship during 2000–14 than during 1980–99.
Time series of (a) 15- and (b) 19-yr sliding correlation between JJAS Niño-3 index and the regional average of JJAS H200 over NEA. The shading indicates the 10th and 90th percentiles of 45 CMIP6 models. The thick red line represents the multimodel ensemble mean for 30 selected models. The dashed lines denote the 95% confidence level, and the purple line indicates 1999. (c) Scatterplot of ENSO–NAE H200 correlation coefficients between the periods of 1980–99 and 2000–14 for the 45 CMIP6 models. The solid black line is the critical line where the correlation coefficients of the two periods are equal. The correlation coefficient of the model inside the blue dashed rectangular box during 2000–14 is smaller than the periods of 1980–99, and has the opposite relationship for the model outside the rectangular box.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
To further verify the bridging effect of the CGT/SRP wave train, Fig. 11 shows the regression results for the selected 30 models. Compared to 1980–99, the zonal wave train at midlatitudes is significantly enhanced during 2000–14 in Fig. 11b, showing six centers that closely match the distribution of ENSO-related H200 in the observations (Fig. 3b). Besides, ENSO-related H200 exhibits a spatial correlation as high as −0.80 with ISMR-related H200 during 2000–14, greater than that of −0.38 during 1980–99. Thus, the AMIP results confirm the mechanism that ENSO-related CGT/SRP played a decisive role in the ENSO–NEA H200 restoring relationship after 1999/2000.
The multimodel ensemble mean of selected 30 CMIP6 models for the regression of JJAS H200 onto JJAS Niño-3 index during (a) 1980–99 and (b) 2000–14.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Similar sliding correlation and regression analyses were performed with POGA experiments. The selected five POGA experiments successfully simulate the weakening of the ENSO–NEA H200 relationship since the 1970s and the strengthening after 1999 as the observation (Fig. 12a). Although the midlatitude wave train simulated in POGA experiments differs from that in the observation and AMIP experiments, the midlatitude wave train is stronger after 1999/2000, especially in NEA region. This result emphasizes that the Pacific SST variation, but not other factors, dominates the interdecadal shift of ENSO–NEA circulation relationship (Figs. 12b,c). In conclusion, these results from the AMIP and POGA experiments support our hypothesis that the ENSO-related CGT/SRP wave train acted as a critical process in the restored relationship of ENSO–NEA H200 around 1999/2000.
(a) The ensemble-mean sliding correlation between JJAS Niño-3 index and the regional average of H200 over NEA with 15-, 17-, and 19-yr sliding windows for 5 selected members of the GFDL CM2.1 POGA experiment. The dashed lines denote the 95% confidence level for correlation with 19 samples and the purple line indicates 1999. (b),(c) As in Figs. 11a and 11b, but for the ensemble mean based on 5 selected members of the POGA experiment during 1980–99 and 2000–14, respectively.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
6. Summary
In this study, we find that the negative relationship between ENSO and NEA circulation in the middle to upper troposphere experiences interdecadal transition, weakening from the early 1970s, remaining stable since the middle 1980s, and strengthening after 1999/2000, based on multiple reanalysis datasets and model experiments. This interdecadal transition of ENSO–NEA relationship is connected with five well-defined centers at midlatitudes, appearing as a CGT/SRP pattern. The enhanced relationship of ENSO–NEA H200 with the active ENSO-related CGT/SRP pattern is likely attributed to the enhanced ISMR–CGT/SRP linkage resulting from the restored ENSO–ISMR relationship, which is driven by an interdecadal shift of the typical ENSO’s evolution from continuing ENSOs to emerging ENSOs around 1999/2000 (Hu et al. 2020; Yang and Huang 2021).
This mechanism is summarized in Fig. 13. During 1980–99 when the continuing ENSOs dominated, the weakened ENSO–ISMR relationship impairs the ISMR–CGT/SRP linkage, which in turn excites an inactive upper-level ENSO-related CGT/SRP wave train, and finally leads to a weakened connection between ENSO and H200 in NEA. In contrast, the restored ENSO–ISMR relationship during 2000–19 with more frequent emerging ENSOs reinforces the ISMR–CGT/SRP relationship, leading to an apparent upper-level ENSO-related CGT/SRP wave train. Therefore, the response of the NEA circulation to ENSO has been enhanced. This mechanism is well simulated in the AMIP and POGA experiments.
The schematic diagram for the interdecadal transition between ENSO and NEA H200 since 1999/2000. Orange lines show the teleconnection pathway generated from Indian diabatic heat forcing. Green lines show the ENSO-related circumglobal teleconnection in the Northern Hemisphere.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-22-0195.1
Although the results of POGA experiments further support that the tropical Pacific SST variations are the dominant factor for the ENSO–NEA circulation relationship, the POGA experiments cannot distinguish the potential roles of the interdecadal shift of ENSO evolution and internal climate variabilities, specifically the Pacific decadal oscillation (PDO) (Mantua and Hare 2002; Dong and Dai 2015; Henley et al. 2015). Although the PDO has undergone an interdecadal shift over the past decades, the timing and frequency of the interdecadal transition are not consistent with the ENSO–NEA H200 relationship (results not shown). More studies on the attribution of the interdecadal shift of ENSO evolution in future could help to understand the potential role of internal climate variability in the interdecadal shift of ENSO’s impact.
Corresponding to the interdecadal transition of ENSO–NEA H200, we also can find the interdecadal transition of the relationship between ENSO and the local precipitation and surface air temperature in the NEA after 1999/2000 (Fig. 5). This result implies that the middle and upper-level circulation could be an important route modifying the relationship between ENSO and East Asian summer monsoon. Previous studies have reported a regime shift in the evolution of the East Asian summer monsoon (e.g., Zhang 2015; Wu et al. 2016; Tao et al. 2017; Zhang et al. 2017; Wang 2021; Xie et al. 2021) and ENSO–East Asian summer monsoon relationship (e.g., Wu and Wang 2002; Wang et al. 2008; Yim et al. 2008a; Shi and Wang 2018; Sun et al. 2021). The apparent interdecadal shifts happening around 2000s in some climate systems can also have great impacts on the East Asian summer monsoon, such as ENSO (e.g., Hu et al. 2020), the EAP/PJ pattern (Xu et al. 2019), the North Atlantic SST, and the western Pacific subtropical high (Huang et al. 2020). It is unclear how the interdecadal shift of these climate systems together with the ENSO–NEA circulation relationship shift reported here influences the relationship between ENSO and East Asian summer monsoon. The present study provides a perspective from the upper-level troposphere circulation, which is believed to be helpful for a deeper comprehension of the variability of the East Asian summer monsoon and its interdecadal shift.
Acknowledgments.
This work was supported by the National Key R&D Program of China (2019YFA0606703), the National Natural Science Foundation of China (Grant 41975116), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y202025). The authors wish to acknowledge Dr. Yu Kosaka for providing POGA experiments. We thank three anonymous reviewers for their valuable comments that helped to improve the manuscript.
Data availability statement.
The NCEP–NCAR reanalysis, GPCP, PRECL, GPCC, ERSST V5, COBESST2, and Kaplan datasets are freely available at https://psl.noaa.gov/data/gridded/. The IITM precipitation is available from the Indian Institute of Tropical Meteorology at https://tropmet.res.in/static_pages.php?page_id=53. The CRU V4.03 precipitation is available at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/cruts.1905011326.v4.03/pre/. The HadISST data are available at https://www.metoffice.gov.uk/hadobs/hadisst/. The 45 AMIP models of CMIP6 can be downloaded at https://esgf-node.llnl.gov/search/cmip6/. The analysis scripts are available upon request from the corresponding author.
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