1. Introduction
Climate variability on interdecadal scales has received considerable attention in the past decades. Policymakers and stakeholders would gain significantly from an extensive understanding of interdecadal climate variability because they would be able to formulate long-term plans for agriculture, water resources, environment, and infrastructure promptly. Observational evidence indicates a pronounced decadal–interdecadal variability in the East Asian summer rainfall (EASR) over the twentieth century (Nitta and Hu 1996; Wang et al. 2000; Chan and Zhou 2005; Zhou et al. 2006; Ding 2007). Several studies have revealed that the EASR underwent a striking transition around the late 1970s, featuring a southern flooding–northern drought pattern with more rainfall in the middle and lower reaches of the Yangtze River valley (YRV) and less rainfall in North China (Gong and Ho 2002; Yu et al. 2004; Wang et al. 2013). Concurrently, the East Asian summer monsoon (EASM) weakened in the late 1970s (Wang 2001; Jiang and Wang 2005; Wu et al. 2008). Since the late 1990s, the EASM has intensified (Zhu et al. 2014; Zhang 2015) and the onset has remarkably advanced (Xiang and Wang 2013); consequently, summer rainfall has decreased along the YRV’s middle and lower reaches but increased to the north (Si et al. 2009; Zhu et al. 2013; Xu et al. 2015).
There is considerable evidence suggesting that the interdecadal variability of EASR is closely associated with the Atlantic multidecadal variability (AMV) and atmospheric heat source over the Tibetan Plateau (TPHS). The AMV refers to a variability of the North Atlantic sea surface temperature (SST) with a period of about 60–80 years (Schlesinger and Ramankutty 1994; Kerr 2000), which is often thought to be driven by the Atlantic meridional overturning circulation (Delworth and Mann 2000; Hu et al. 2012). The AMV is also thought to be regulated by volcanic and anthropogenic aerosols during the twentieth century (Otterå et al. 2010; Booth et al. 2012; Qin et al. 2020). Many studies have presented evidence that the AMV can substantially modulate the climate variation in the Northern Hemisphere (Sutton and Hodson 2005; Zhang and Delworth 2006; Knight et al. 2006; Wang et al. 2009; Ting et al. 2011; Sun et al. 2015; Miao and Jiang 2021). In East Asia, a positive AMV was thought to enhance EASM through coupled ocean–atmosphere feedback in the western Pacific and Indian Oceans (Lu et al. 2006). A negative AMV (the cooling North Atlantic), such as that seen in the 1960s, significantly suppressed the EASM (Liu and Chiang 2012). The weakened monsoon circulation induced a long-lasting drought over the North China portion of the EASM region in the 1960s (Liu and Chiang 2012). Since the 1990s, the warming trend in the North Atlantic has caused increased hot temperature extremes over northern East Asia (Dong et al. 2016; Hong et al. 2017; Zhang et al. 2020). Moreover, beyond East Asia, the AMV affects many regions in the Northern Hemisphere through a circumglobal wave train pattern (Si and Ding 2016; Nicolì et al. 2020; Monerie et al. 2021). Recent studies have emphasized the synergistic impacts of the AMV and other oceans on the atmospheric circulation and climate anomalies over East Asia (Yang et al. 2017; Zhang et al. 2018; Huang et al. 2019).
The Tibetan Plateau (TP), with a mean elevation in excess of 4000 m, is located between the North Atlantic–European region and East Asia. The TP serves as an elevated heat source in boreal summer and plays a critical role in the downstream East Asian climate (Tao and Ding 1981; Luo and Yanai 1984; Ye and Wu 1998; Wang et al. 2008; Xu et al. 2010). Modeling study reveals the important role of the TP soil temperature in the lagged relationship between spring TPHS and EASR (Xu et al. 2022). The TPHS is correlated with the EASM and EASR, and a cooling TP since the late 1970s can cause above-normal summer rainfall in the middle and lower reaches of the YRV (Zhang et al. 2004; Wang et al. 2008; Ding et al. 2009). After the late 1990s, the TP has shown a notable warming trend, which intensified the EASM, thereby causing a northward migration of the summer rainfall belt over East Asia (Si and Ding 2013; Zhang et al. 2021). Moreover, the TPHS can cause significant climate fluctuations in other regions of the Northern Hemisphere, such as South Asia (Jiang and Ting 2017), west Asia, North Africa, and south Europe (Lu et al. 2018; Monerie et al. 2019; Sun et al. 2021; Wang et al. 2022), through the atmospheric teleconnection. Besides, the snow cover in the TP, through altering TPHS, exerts a modulating effect on the teleconnection between El Niño–Southern Oscillation and EASM (Wu et al. 2012). Recent studies additionally suggest that zonal teleconnections at midlatitude in the Northern Hemisphere also significantly affect the summer monsoon (Fan et al. 2022) and sensible heat flux (Zhu et al. 2019) over the TP. In particular, the positive phase of Pacific decadal oscillation since the 1980s weakens the sensible heat flux over the TP in the spring through a zonal wave train (Zhu et al. 2019).
The independent effects of the AMV and TPHS on the EASR have been studied extensively in the past; however, no attention has been given to their synergistic impact. Moreover, the relationship between the AMV and TPHS also remains unclear. Hence, we aim to address the relationship between the AMV and TPHS and the role of TP in the impact of AMV on the interdecadal variation of EASR. The rest of this article is organized as follows: Section 2 introduces the datasets, methods, the model, as well as the experiments. A distinctive relationship between the AMV and TPHS is presented in section 3. In section 4, we analyze the role of TP in the impact of AMV on the interdecadal variation of EASR. Finally, a summary is presented in section 5.
2. Data, methods, model, and experiments
a. Data
The observational and reanalysis data used in this study and their sources are as follows: 1) the monthly mean SST data from the Hadley Centre HadISST1.1 dataset on a 1° × 1° grid (Rayner et al. 2003); 2) the monthly mean land precipitation data from the Climatic Research Unit Time Series 3.26 (CRU-TS_3.26), with a horizontal resolution of 0.5° × 0.5° (Harris et al. 2014); and 3) the atmospheric pressure, sensible heat flux, and net radiation-flux data from Twentieth-Century Reanalysis (20CR), version 2, dataset of the National Oceanic and Atmospheric Administration for the period 1901–2012 (Compo et al. 2011) and the NCEP–NCAR reanalysis for the period 1949–2021 (Kalnay et al. 1996). We only focus on the summer [June–July–August (JJA)] season.
b. Method
The AMV index is defined as the linear detrended area-weighted average of the JJA mean North Atlantic (80°–0°W, 0°–60°N) SST anomalies.
To depict the TPHS intensity, we define the linear detrended vertically integrated Q1 averaged over the TP as the TPHS index.
In addition, the singular vector decomposition (SVD) method is used to acquire the most leading coupled mode between the North Atlantic SST and TPHS (Bretherton et al. 1992). The two-tailed Student’s t test is used to measure the significance of the composite analysis results. According to the test, the null hypothesis H0 assumes that the difference between two sample means is zero (supposing that the two samples with Gaussian distribution are independent); conversely, the alternative hypothesis H1 assumes that the difference is not zero. To obtain interdecadal variability of the AMV and TPHS indices, we filter out the variability shorter than 13 years by applying a low-pass Lanczos filter. The statistical significance of the regression and correlation analysis is evaluated by the Monte Carlo method (Livezey and Chen 1983).
c. Model and experiments
A 10-member ensemble of North Atlantic pacemaker simulations performed by the NCAR CESM1 model (Hurrell et al. 2013) is analyzed in this study. The pacemaker simulation’s fully restored time-evolving SST anomalies in the North Atlantic (5°–55°N, with two buffer zones along 0°–5°N and 55°–60°N) are nudged to observations. In this way, the observed variation of SSTs in the North Atlantic is maintained in each simulation, with the rest of the model’s coupled climate system free to evolve. The simulations are branched from 1920 to 2013. Several studies have suggested that the pacemaker simulations can capture the atmospheric response to the observed North Atlantic SST forcing (Yang et al. 2020; Meehl et al. 2021).
3. Distinctive relationship between the AMV and TPHS
a. Modulation effects of the AMV on the TPHS
The time series of the TPHS and AMV indices since the 1900s are shown in Fig. 1, both of which display significant interdecadal variabilities. The two indices are almost in phase during the entire period, with positive phases from the late 1990s to the 2000s and from the mid-1920s to the late 1960s, but negative ones from the late 1960s to the late 1990s and from the late 1910s to the mid-1920s. It appears that the TPHS has a close relationship with the AMV, and their correlation coefficient is 0.70, reaching the 95% confidence level. In addition, we extend the TPHS index to year 2021 by using the NCEP–NCAR reanalysis data. As seen in Fig. 2, the two indices are also in phase from 1949 to 2021, further confirming their in-phase relationship on the interdecadal scale.
The interdecadal-filtered (a) AMV (°C) and (b) TPHS (W m−2) indices for 1901–2012, derived from the NOAA-20CR dataset. The dotted lines are ±0.25 standard deviation of the two indices. The blue and red bars denote the indices are below −0.25 and above +0.25 standard deviation, respectively.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
The (a) AMV (°C) and (b) TPHS (W m−2) indices for 1949–2021, derived from the NCEP–NCAR reanalysis dataset. The filling curve is the interdecadal-filtered values.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
We also examine the relationship between the TPHS and the interdecadal SST modes in the Pacific and Indian Oceans, respectively. Figure 3 shows the second leading mode of the empirical orthogonal function (EOF) analysis for the North Pacific and Indian Ocean SSTs. Their first EOF modes indicate a basinwide warming trend (figure not shown). The second EOF mode for the North Pacific and Indian Ocean SST variability corresponds to the Pacific decadal oscillation (PDO) and Indian Ocean dipole (IOD) mode, respectively (Fig. 3). The correlation coefficients between the TPHS index and the PDO and IOD indices are −0.28 and 0.35, respectively, and much lower than that between the TPHS and AMV indices (0.70). This result shows that the TPHS highly correlates with the AMV rather than the PDO and IOD in summer. Hence, this comparison further confirms that the interdecadal variation of the summer TPHS is significantly modulated by the AMV.
(a) Spatial pattern and (b) corresponding time series of second-leading EOF mode for the low-pass-filtered North Pacific (0°–70°N, 110°E–100°W) SST. (c),(d) As in (a) and (b), but for the Indian Ocean (40°S–20°N, 50°–110°E).
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
The SVD method is used to objectively capture the coupled mode between the North Atlantic (80°W–0°, 0°–60°N) SST and TPHS on the interdecadal scale. Before the analysis, the linear trend within each grid in the SST and TPHS fields is removed. Figure 4 displays the leading SVD mode for the SST and TPHS fields, accounting for 66.5% of the total squared covariance. Hence, this mode represents the coupled relationship between the AMV and TPHS. The results show that on the interdecadal scale, for the North Atlantic SST pattern, there are positive anomalies in most domains with two maximum centers in the low latitudes and subpolar regions but negative over the coasts of northeastern North America and west Europe (Fig. 4a). Meanwhile, the heat source is above normal in most domains of the TP, except for scattered areas over the southern and northern flanks (Fig. 4b). A high correlation coefficient of 0.86 between the two corresponding time coefficients of the two fields shows a close relation between the AMV and TPHS (Fig. 4c). In addition, the field significant test is also used to verify the relationship between the time series of the first SVD (SVD1) for the North Atlantic SST field and TPHS field, based on the method of Livezey and Chen (1983): 43.9% of the entire region exceeds the 95% significance level. The test results show that in less than 1% of the cases, the proportion of the correlation coefficient exceeding the 95% confidence level is greater than 43.9% of the entire region. Hence, it is highly unlikely that the positive relationship between the AMV and TPHS is a chance occurrence.
Spatial patterns and correspondent time coefficients associated with the first SVD mode of the detrended and filtered summer SST in the North Atlantic Ocean and heat source over the TP. (a) Temporal correlation pattern for the first dominant coupled mode for the North Atlantic SST. (b) As in (a), but for the TPHS. (c) Standardized time series. The correlation coefficients exceeding 95% confidence level are stippled.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
Next, we investigate the mechanism for the remote modulation of the AMV on the TPHS, which can be distinguished from the regression pattern of 500-hPa geopotential height and wave-activity flux upon the AMV index (Fig. 5a). The AMV, as noted in a previous study (Si and Ding 2016), is associated with the climate fluctuation over the globe via a circumglobal Atlantic–Northern Hemisphere teleconnection. The teleconnection exhibits a distinct circumglobal zonal wave train pattern with seven centers of action situated along the northern midlatitude (Fig. 5a), which originates from the midlatitude North Atlantic, propagates northeastward to western Europe, then extends eastward through the Eurasian continent to the western North Pacific along the zonal westerly (Fig. 5a). The evidence of AMV-associated atmospheric teleconnection has also been identified in several modeling studies. By analyzing idealized AMV experiments, Nicolì et al. (2020) suggested that this AMV-induced circumglobal stationary wave train could affect vast portions of northern Eurasia. The propagation of the Rossby wave follows the subtropical jet stream and regulates the climate from Europe to eastern Asia, which is triggered by the North Atlantic SST and is associated with the AMV (Monerie et al. 2021). Ruprich-Robert et al. (2017) distinguished different roles of the tropical and subpolar portions of the AMV in modulating the circumglobal stationary wave train. Since the nineteenth century, amplitude of the AMV has experienced an increasing trend under the anthropogenic forcings, which will intensify this zonal teleconnection (Moore et al. 2017).
(a) Regression of the JJA 500-hPa geopotential height (shading; gpm) and wave-activity flux (vector; m2 s−2) on the AMV index. (b) Vertical cross section of the JJA heat source (shading; W kg−1), geopotential height (contour; gpm), and wave-activity flux (vector; m2 s−2) along the wave train labeled ABC shown in (a) (dashed green line). The regression coefficient of the geopotential height in (a) and heat source in (b) exceeding 95% confidence level are stippled. The gray shading denotes the topography.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
In the midtroposphere, anomalous low pressure dominates the northern South Asia–TP–East Asia sector, while anomalous high pressure appears over the surrounding oceans, such as the western North Pacific and Indian Ocean (Fig. 5a). This “land low and ocean high” pressure pattern reinforces the Asian summer monsoon (Fig. 6). Moreover, a positive phase of AMV leads to a warming over the TP–Asia sector and enhanced land–sea thermal contrast between the Asian continent and the surrounding oceans through this wave train, which also strengthens the Asian summer monsoon. The southwesterly vapor transport associated with the reinforced South Asian summer monsoon (SASM) and the southeasterly vapor transport associated with the reinforced EASM strengthen the vapor transport to the TP, together with the anomalous low pressure over the TP, enhancing the summer precipitation and latent heating over the TP (Fig. 6).
Regression of the JJA 850-hPa wind (vectors; m s−1) and rainfall (shading; mm month−1) on the AMV index. Regression coefficients of rainfall on the AMV index exceeding 95% confidence level are stippled. The letters “C” and “A” represent the cyclonic and anticyclonic anomalies, respectively.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
To further explore the teleconnection between the AMV and TPHS, we examine the vertical structure of the atmospheric teleconnection pattern (Fig. 5b). The four centers of action from the North Atlantic to the TP reach their maximum strength in the middle and upper troposphere, which are accompanied by prominent eastward dispersion of the Rossby wave originating from the North Atlantic Ocean. Notably, the teleconnection pattern exhibits an equivalent barotropic structure in the vertical over the upstream regions of the TP, while it exhibits a baroclinic structure over the TP, with an anomalous low pressure in the lower levels accompanied by an anomalous high pressure in the middle and upper levels. This suggests that the warming of the North Atlantic (the positive phase of the AMV) can strengthen the baroclinicity over the TP, and vice versa.
Moreover, the AMV exerts a significant remote influence on the atmospheric heat source Q1 from the North Atlantic to the TP through the wave train pattern. In the Atlantic–west Europe sector, the negative center of action and North Atlantic warming enhance the convection and latent heating release, causing positive heat-source anomalies there (Fig. 5b). The positive center of action corresponds to an outflow of moisture toward the center and decreases precipitation and latent heating release, causing negative heat-source anomalies over east Europe and central Asia. In addition, a strengthened baroclinicity increases summer rainfall and latent heating (Fig. 6), yielding positive TPHS (Fig. 5b).
b. Booster effect of TP on the influence of AMV on the TPHS
Notably, the positive heat-source anomalies over the TP are stronger than those over other regions along the same latitudinal cross section (Fig. 5b). The boost in the TPHS is mainly due to the orographic effect of TP on the ASM latent release. On the one hand, the strong southwesterly SASM airflow driven by the positive phase of AMV, as described above, starts to split into two branches when reaching the South Asian subcontinent (Fig. 6). One branch flows through northwest India toward the southwestern flank of the TP. The other branch flows through the Bay of Bengal toward the southeastern flank of the TP. On the other hand, the strong southeasterly EASM airflow flows through Southwest China toward the eastern flank of the TP. When the three branches of warm and moist monsoon airflow approach the TP, they converge and climb along the slopes, resulting in an enormous ascending motion. Consequently, heavy precipitation occurs over the TP, particularly over the southern and eastern flanks (Fig. 6). The heavy precipitation eventually generates massive latent heating released in convection and condensation over the TP, with three maximum centers over the southern and eastern flanks (Fig. 7). Hence, the TPHS driven by the AMV is stronger than that over other regions along the same latitude, due to the booster effect of the TP (Fig. 5b). To quantify these results, the latent heat flux over the above three regions is investigated by analyzing four scenarios. The four scenarios are selected based on the AMV index values (strong scenarios: greater or less than one standard deviation; moderate scenarios: between 0.25 and one standard deviation). In general, the latent heating decreases in east Europe while it increases in the North Atlantic–west Europe sector and TP along with the increasing of the AMV index (Fig. 8). In the three regions, the latent heating anomalies are much larger in the case of strong scenarios compared to those in the case of moderate scenarios. It is also noteworthy that, in the case of strong scenarios, the latent heat anomalies in the TP are about 2.3 times and 1.25 times higher than those in North Atlantic–west Europe sector and east Europe, respectively.
Regression of the JJA (a) sensible heat flux, (b) latent heat flux, (c) vertically integrated net radiation heat flux, and (d) atmospheric heat source (W m−2) over the TP on the AMV index. Regression coefficients exceeding the 95% confidence level are stippled.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
Area-averaged JJA latent heat-flux anomalies over the North Atlantic–west Europe region (35°–65°N, 60°W–0°), east Europe (40°–54°N, 5°–30°E; 54°–70°N, 30°–65°E), and Tibetan Plateau (23°–45°N, 65°–105°E) during different AMV phases.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
From Eq. (3), the vertically integrated atmospheric heat source comprises three components: sensible heat flux, vertically integrated latent heat flux, and net radiation heat flux. We estimate the three components from the complete atmospheric heat-source equation [Eq. (3)] to identify the contribution to changes in the TPHS associated with the AMV. As shown in Fig. 7, both sensible heat flux and net radiation heat flux make small and negative contributions to the enhancement of TPHS. The magnitude and distribution of the latent heat flux resemble those of the actual heat source anomalies over the TP associated with the AMV. Moreover, the high coefficient is observed between the filtered time-series latent heat flux over the TP and the TPHS index (r = 0.77, significant at the 95% confidence level) for the period 1901–2012. Hence, the latent heat flux due to convection and precipitation plays a predominant role in the enhancement of TPHS modulated by the AMV. Previous study has found that the latent heating associated with precipitation is the primary factor in the TPHS in summer (Ye and Gao 1979). The results also indicate that the positive phase of the AMV can enhance the latent heating over the TP, resulting in an enhanced heat source there through the zonal teleconnection pattern, and vice versa.
c. Model results
To further validate the modulation effect of the AMV on the TPHS, the North Atlantic pacemaker ensemble simulations are analyzed in this section. Because the SSTs over the North Atlantic are nudged toward the observed anomalies, the simulated AMV index by the North Atlantic pacemaker simulations matches well with the observed one during 1920–2013 (figure not shown). The years in which the ensemble mean AMV index is below −0.25 and above +0.25 standard deviation are set as the negative (43 years) and positive (39 years) phases of the AMV, respectively. The composite simulated North Atlantic SST anomalies and the heat-source anomalies over the TP during the periods of the positive and negative phases and their difference are shown in Fig. 9. When the AMV is in the negative phase, the North Atlantic SST anomalies show a meridional “cold–mild–cold” tripole pattern with a significant maximum value of negative SST anomalies located in the subpolar gyre region (Fig. 9a). As for the TPHS, except for the weak positive anomalies over the northwest part of the TP, most parts of the TP have negative anomalies with significant maximum values over the southern and eastern flanks (Fig. 9b). In general, the SST and heat-source anomalies when the AMV is in the positive phase are mirror images of the negative phase of AMV with opposite signs, indicating that the AMV, indeed, is the primary cause of these anomalies (Figs. 9c,d). The differences between the positive and negative phases of the AMV (Figs. 9e,f) confirm the modulation effect of the AMV on the TPHS.
Composite of the (a) ensemble mean JJA SST anomalies (°C) in the North Atlantic and (b) heat-source anomalies (W m−2) over the TP during the periods of the negative phase of AMV simulated by the North Atlantic pacemaker simulations. (c), (d) As in (a) and (b), respectively, but during the periods of the positive phase of AMV; (e),(f) as in (a) and (b), respectively, but for the differences between the periods of the positive and negative phases of AMV. The stippling indicates statistical significance at the 95% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
4. Synergistic influence of the AMV and TPHS on the EASR
The TPHS exerts a significant impact on the summer rainfall and atmospheric circulation over the TP and its downstream regions, including East Asia. Locally, the TP warming (the positive phase of the TPHS) significantly increases summer precipitation over the TP, particularly along its southern flank (Fig. 10a). In East Asia, the precipitation anomalies show a tripole pattern with two wet anomalies along the Yellow–Huaihe River valley to the Korean Peninsula and South China, while there is one dry anomaly along the YRV to southern Japan, which covers the prevailing mei-yu–baiu–changma frontal area (Fig. 10a). The mechanism of the TPHS influences on the EASM and rainfall anomalies is found to be through a distinct Rossby wave train. The wave train moves along the low-level SASM southwesterly that is excited in the southwest periphery of the TP (Figs. 10b,c). The development of the wave train involves a Rossby wave energy propagation in SASM regions (Fig. 10b). Because of the strong vertical shear in the southwesterly monsoon region, the Rossby wave energy propagation is trapped in the lower troposphere, which generates a stationary Rossby wave train (Wang and Xie 1996). The eastward propagation of this wave train induces an anticyclonic anomaly in the South China Sea (Fig. 10c) then propagates northward into East Asia. The anticyclonic anomaly over the South China Sea enhances the southwesterly along the south coast of China, increasing the warm and moist air float transport toward South China and summer rainfall there. In northeast Asia, the anomalous anticyclone converges the northward water vapor and southward cold air to the north of the YRV, enhancing the summer rainfall along the Yellow–Huaihe River valley to the Korean Peninsula and decreasing the summer rainfall along the lower reach of the YRV and southern Japan (Fig. 10a). Our results agree with a previous modeling study (Wang et al. 2008) that warming over the TP induces a distinct Rossby wave train propagated along the SASM monsoon southwesterly that is excited in the southwest periphery of the TP. This wave train propagates into East Asia and leads to an enhanced EASM. These analyses identify that the TPHS exerts a striking impact on the EASM on the interdecadal scale, with an enhanced TPHS causing a strong EASM, and vice versa.
Regression of the (a) JJA rainfall (mm month−1), (b) 850-hPa geopotential height (shading; gpm) and wave-activity flux (vectors where the zonal wind is greater than 0; m2 s−2), and (c) the 850-hPa wind (vectors; m s−1) and its divergence field (shading; 10−8 s−1) on the TPHS index. Regression coefficients exceeding the 95% confidence level are stippled in (a) and (b). The letters “A” and “C” in (c) represent the anticyclonic and cyclonic anomalies, respectively. The gray curve denotes the topographic boundary of TP.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
The analyses in section 3 have identified that the AMV significantly modulates the variations of TPSH. Notably, through the zonal teleconnection pattern, the AMV can also affect the climate over regions downstream of the TP. Over the East Asia–western North Pacific regions, the meridionally arranged wave pattern is similar to that associated with the TPHS (Fig. 6). The anomalous anticyclone over the South China Sea and cyclone to the north of the YRV enhance the EASM, resulting in the northward shift of the summer rain belt in East Asia with dry anomaly along the YRV to southern Japan but wet anomaly along the Yellow–Huaihe River valley to the Korean Peninsula (Fig. 6). These results agree with previous observational (Si and Ding 2016) and numerical (Liu and Chiang 2012) studies, suggesting that the AMV can cause interdecadal changes in the EASM and EASR through the circumglobal teleconnection. For the EASR, although the impact of the AMV is similar to that of the TPHS, there are still differences between them. Compared with the AMV, the TPHS exerts a stronger impact on the rainfall over South China but a weaker impact on the rainfall over the YRV (Figs. 6 and 10a). Moreover, we also perform the field significance test (Livezey and Chen 1983) on the relationship among the AMV, TPHS index, and the summer rainfall in the East Asian monsoon region (10°–45°N, 100°–130°E), respectively. It shows that 54.3% and 49.4% of the entire region exceed the 90% significance level, respectively. The test results suggest that the correlation between the AMV, TPHS index, and the summer rainfall over the East Asian monsoon region exceeds the field significance test at the 99% confidence level.
The influences of the AMV and TPHS on the East Asian climate have been investigated separately by most previous studies, with little attention on their synergistic effect. To address this issue on the interdecadal scale, composite JJA precipitation anomalies are constructed for four combinations of the AMV and TPHS indices below and above a ±0.25 standard deviation (Fig. 1). The four combinations are classified as follows: 1) positive AMV (+AMV) and positive TPHS (+TPHS), 2) negative AMV (−AMV) and negative TPHS (−TPHS), 3) +AMV and −TPHS, and 4) −AMV and +TPHS.
For +AMV, the precipitation anomalies over East Asia generally show a tripole pattern with wet anomalies along the Yellow–Huaihe River valley to the northern Korean Peninsula and South China but dry anomalies along the YRV (Figs. 11a,c). The wet anomalies over South China are enhanced during +TPHS (Fig. 11a) but weakened during −TPHS (Fig. 11c). Meanwhile, for −AMV, the precipitation anomalies over East Asia also show tripole patterns but are of opposite signs (Figs. 11b,d). The dry anomalies over South China are enhanced during −TPHS (Fig. 11b) but weakened during +TPHS (Fig. 11d). Under −AMV and −TPHS, the rainfall anomalies in East Asia are more reminiscent of the southern flooding–northern drought in the 1970s–90s, with excessive rainfall along the middle and lower basins of the YRV but deficient to the north of the YRV (Fig. 11b). Under +AMV and +TPHS, the anomalous pattern is more reminiscent of the northward shift of the summer East Asian rain belt after the late 1990s (Si et al. 2009; Si and Ding 2013; Zhu et al. 2013; Xu et al. 2015), with deficient rainfall in the middle and lower basins of the YRV and excessive rainfall to the north of the YRV (Fig. 11a).
Composite of the JJA rainfall anomalies (mm month−1) under the four combined phases of the AMV and TPHS: (a) +AMV and +TPHS, (b) −AMV and −TPHS, (c) +AMV and −TPHS, and (d) −AMV and +TPHS. The stippling indicates statistical significance at the 90% confidence level according to the Student’s t test.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
To explore the atmospheric process through which the AMV and TPHS synergistically impact the EASR, the composite atmospheric circulation anomalies under the four combinations of the AMV and TPHS phases are analyzed. For +AMV, the associated teleconnection wave train is strong during +TPHS (Fig. 12a) but weak during −TPHS (Fig. 12c). Under +AMV and +TPHS, the +TPHS corresponds to a warming over the TP, and +AMV also leads to a warming over the TP and Asia continent through the wave train (Si et al. 2023). Hence, the remote and local heating superimpose over the TP and the surrounding region, which reinforces the wave train over the TP and downstream over the Asia–Pacific region. The TPHS boosts the impacts of the AMV on the downstream climate, which jointly lead to a “high–low–high”-like pattern in the East Asia–western North Pacific regions (Fig. 12a). The anomalous high pressure over the western North Pacific intensifies the subtropical high there (WNPSH). In the lower troposphere, a meridional anomalous “anticyclone–cyclone–anticyclone”-like wave train extends from East Asia to the western North Pacific (Fig. 13a). The anomalous anticyclonic circulation over the western North Pacific and strong WNPSH significantly enhance the EASM, resulting in wet anomalies along the Yellow–Huaihe River valley to the northern Korean Peninsula and South China but dry anomalies along the YRV (Fig. 11a). Under +AMV and −TPHS, the heating over the TP is significantly suppressed, which weakens the wave train over the TP and downstream over the Asia–Pacific regions (Fig. 12c). The anomalous high pressures are located over the TP and western North Pacific, weakening the TPHS but enhancing the WNPSH. Although the EASM is slightly strong (Fig. 13c), the suppressed TPHS weakens the wet anomalies over South China (Fig. 11c).
The same as Fig. 11, but for the JJA 500-hPa geopotential height (shading; gpm) and wave-activity flux (vectors where the zonal wind is greater than 0; m2 s−2). The stippling indicates statistical significance at the 95% confidence level according to the Student’s t test. The symbols “−” and “+” represent the local anomalous low and high pressures, respectively.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
The same as Fig. 11, but for the JJA 850-hPa wind (vectors; m s−1) and their divergence fields (shading; 10−7 s−1).
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
For −AMV, the associated teleconnection pattern shifts to its opposite sign (Figs. 12b,d). Similarly, the teleconnection wave train is strong during −TPHS (Fig. 12b) but weak during +TPHS (Fig. 12d). Under −AMV and −TPHS, the wave train is also reinforced over the TP and downstream over the Asia–Pacific regions but becomes roughly opposite in sign to that under +AMO and +TPHS. The strong wave train reaches the Asia–Pacific regions, which leads to a “low–high–low”-like pattern in the East Asia–western North Pacific sector (Fig. 12b). Their synergistic impacts significantly weaken the WNPSH and EASM (Fig. 13b), causing more rainfall over the YRV, Korean Peninsula, and southern Japan but less over the Yellow–Huaihe River valley and Northeast China (Fig. 11b). Under −AMV and +TPHS, the wave train weakens, and the anomalous low pressure over the TP intensifies the latent heating and heat source there (Fig. 12d). The low pressure anomaly over the western North Pacific corresponds to a weakened WNPSH, which weakens the EASM (Fig. 13d). Moreover, the enhanced TPHS weakens the dry anomalies over South China (Fig. 11d).
5. Summary
Previous studies have shown that the EASR exhibits prominent interdecadal variability. Although the key roles of the AMV and TPHS in the interdecadal variability of the EASR have been identified in many previous studies, the studies on the relationship between the AMV and TPHS, and the role of TP in the impact of AMV on the EASR are inadequate.
In this study, we reveal a distinctive relationship between the AMV and TPHS, which is illustrated by the schematic diagram in Fig. 14. Under +AMV, the North Atlantic warming induces a zonal teleconnection wave train originating from the North Atlantic through Eurasia and extending to the North Pacific. This teleconnection pattern exhibits an equivalent barotropic structure in the vertical over the upstream regions of the TP and a baroclinic structure over the TP. The reinforced baroclinicity increases summer rainfall and latent heating over the TP, yielding an enhanced TPHS. Moreover, the +AMV enhances SASM and EASM. The enhanced warm and moist monsoon airflows approach the TP, converge, and climb up along its southern and eastern flanks and result in enormous latent heating release in convection and condensation over the TP. Eventually, heat-source anomalies over the TP are stronger than those over other regions along the same latitude, due to the orographic effect of the huge plateau. Hence, the AMV exerts a remote effect on the TPHS through the zonal teleconnection wave train, whereas the TP serves as a booster of this effect.
Schematic diagram showing the distinct relationship between the AMV and TPHS, and the booster effect of the TP on the influence of AMV on the EASR.
Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0472.1
The +AMV can enhance the EASM via the zonal teleconnection pattern, yielding a tripole rainfall pattern with one dry anomaly along the YRV to southern Japan but two wet anomalies over the Yellow–Huaihe River valley–Korean Peninsula regions and South China, and vice versa. The enhanced TPHS modulated by +AMV intensifies the EASM via a meridional teleconnection wave pattern, yielding a tripole rainfall pattern, which is similar to that caused by the +AMV. Meanwhile, the TPHS exerts a stronger impact on the summer rainfall over South China but a weaker impact on that over the YRV compared with the AMV.
When the AMV and TPHS are in phase, the zonal wave train originating from the North Atlantic is strong and propagates eastward to the Asia–Pacific regions, significantly modulating not only the TPHS but also the downstream EASM. Notably, the TP boosts this remote impact from the AMV. Positive phases of the AMV and TPHS jointly result in a significantly strong EASM through the effect of superimposition. This situation is more reminiscent of the northward shift of the East Asian rain belt since the late 1990s. When the reversals in phases of the AMV and TPHS occur, that is, −AMV and −TPHS, there is a significantly weak EASM, which is more reminiscent of the southern flooding–northern drought in the 1970s–90s in East Asia. Meanwhile, when the AMV and TPHS are out of phase, the zonal teleconnection wave train is weak, particularly over Asia and its surrounding regions, contributing to a slightly weak or strong EASM through the effects of cancelation.
Acknowledgments.
The authors thank the three anonymous reviewers for their constructive comments and for significantly improving the manuscript. Our research is jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA20100304), the National Natural Science Foundation of China (Grant 41875104), and the Second Tibetan Plateau Scientific Expedition and Research Program of China (Grant 2019QZKK0208).
Data availability statement.
All data used in this study are publicly available. The HadISST1.1 SST data are openly available at Met Office Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadisst/). The CRU-TS_3.26 precipitation data are openly available at Climatic Research Unit (CRU) at the University of East Anglia (https://research-portal.uea.ac.uk). The NOAA-20CR and NCEP/NCAR reanalysis data are openly available at National Oceanic and Atmospheric Administration (https://psl.noaa.gov/data/index.html). The CESM1 North Atlantic pacemaker simulations are accessible via the National Center for Atmospheric Research (NCAR) Climate Data Gateway (https://www.earthsystemgrid.org/).
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