Abstract

Although the impact of the North Atlantic Oscillation (NAO), especially the antecedent NAO in winter and spring, on East Asian summer climate has been studied extensively, the possible connection from the summer NAO (SNAO) and then the Tibetan Plateau (TP) to East China summer rainfall remains unclear. This study reveals that on interannual time scales the SNAO is significantly correlated with the variations of East China summer rainfall and the thermal forcing of the TP provides an intermediate bridge effect in this Eurasian teleconnection. The SNAO primarily regulates the rainfall variability over the TP through large-scale wave trains and the TP rainfall anomalies in turn lead to a change in local diabatic heating, which excites Rossby waves to the downstream regions. To the northeast of the TP, an anomalous barotropic cyclone is formed in the nearly entire troposphere, generating low-level northerly flow anomalies over northern China. Meanwhile, the TP heating also induces low-level southerly flow anomalies over southern China. The anomalous northerly and southerly winds converge in the lower troposphere, enhancing the summer rainfall over central East China. Compared to the SNAO, the TP thermal forcing exerts a more direct impact on the variations of East China summer rainfall in the Eurasian teleconnection discussed.

1. Introduction

The North Atlantic Oscillation (NAO) is known as a large-scale seesaw in atmospheric mass between the subpolar low and the subtropical high over and around the North Atlantic Ocean (Chen and Hellström 1999). Previous studies have indicated that the NAO not only modulates the climate over the North Atlantic and its adjacent regions (Hurrell 1995; Chen and Hellström 1999), but also exerts a remote impact on the weather and climate over East Asia (e.g., Liu and Yin 2001; Fu and Zeng 2005; Gu et al. 2009; Wu et al. 2009; Jin and Guan 2017). Since the strongest NAO signals appear in winter, most previous studies have focused on the climate impact of wintertime NAO. Indeed, the summer NAO (SNAO) possesses a relatively smaller spatial extent, and is located farther northward with its southern node over northwestern Europe (Folland et al. 2009). However, it can also exert a strong influence on the Asian climate (Sun et al. 2008; Folland et al. 2009; Linderholm et al. 2011, 2013). For instance, Linderholm et al. (2011) indicated that the SNAO was strongly linked to the interannual variability of the East Asian summer monsoon (EASM). Particularly, it was significantly and positively (negatively) correlated with the rainfall over southeastern China (central East China). The authors proposed a possible mechanism: the changes in the position of North Atlantic storm tracks and transient eddy activity associated with the SNAO contributed to the downstream sea level pressure anomalies over northeastern East Asia. According to Sun and Wang (2012), the SNAO can produce an anomalous meridional dipole pattern of rainfall over central and northern East Asia through altering the stationary wave activity over the Eurasian continent.

The Tibetan Plateau (TP), with an average topography height of >4 km, lies between the North Atlantic–European region and East Asia. Much effort has been devoted onto identifying the various effects of the plateau on the weather and climate over many places of the world (e.g., Ye and Wu 1998; Kitoh 2004; Yanai and Wu 2006; Zhao et al. 2007; Xu et al. 2010; Wu et al. 2012). Particularly, in summer, the TP thermal forcing exerts a striking impact on the interannual variability of the EASM including East China rainfall (Wang et al. 2014; Hu and Duan 2015). The TP heating forces Rossby wave trains to the downstream region and results in anomalous eastward warm advection and synoptic disturbances, which consequently regulate the rainfall pattern over East China. In the meantime, local climate such as the summer moisture transport, rainfall, and diabatic heating over the TP is also modulated by the midlatitude westerlies (Curio et al. 2015; Lin et al. 2016) or the upstream SNAO (Liu et al. 2015; Wang et al. 2017). However, the interrelationship among the three elements (i.e., SNAO, TP, and EASM) has not been fully understood yet.

The objective of this study is to demonstrate that the TP thermal forcing provides an intermediate bridge effect in the teleconnection between the SNAO and the summer rainfall over East China. We will underline the associated physical mechanism in detail. The rest of this paper is organized as follows. Section 2 describes the data, methodology, and experiments applied in this study. The bridge effect of TP heating on Eurasian teleconnection is identified and discussed in section 3. Finally, a summary and further discussion are given in section 4.

2. Data, methodology, and experimental design

a. Data

Monthly atmospheric data are employed from the long-term Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015), which has a horizontal resolution of 1.25° × 1.25°. This dataset extends from 1000 to 1 hPa with 37 vertical pressure levels. In addition to its long time period, we choose JRA-55 also because its atmospheric circulation and diabatic heating fields agree well with those derived from station and satellite observations on the interannual time scale over the TP and adjacent regions (Hu and Duan 2015). Monthly rainfall data are extracted from the Global Precipitation Climatology Centre (GPCC) with a horizontal resolution of 1.0° × 1.0° (Schneider et al. 2014). The GPCC, a widely used rainfall dataset over land, is based on quality-controlled rain gauge data drawn from 67 200 stations worldwide that had data of 10 or more years. The monthly mean sea surface temperature (SST) from the Met Office’s Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003), with a horizontal resolution of 1.0° × 1.0°, is used to calculate the El Niño–Southern Oscillation (ENSO) index.

b. Methodology

An empirical orthogonal function (EOF) covariance analysis is used to explore the dominant spatial modes (i.e., patterns) of variability and how they change with time [time series of principal components (PCs)]. The monthly SNAO index is calculated following the definition of Folland et al. (2009), which is represented by the first principal component (PC1) time series corresponding to the leading EOF mode of mean sea level pressure over the extratropical North Atlantic–European sector (25°–70°N, 70°W–50°E) using the JRA-55 data. It should be noted that the EOF analysis over this domain mainly reproduces the southern part of the full SNAO pattern (Linderholm et al. 2011). Thus, when the southern node is higher (lower) than average pressure, the value of SNAO index is positive (negative). In the current study, we focus on the boreal peak summer for the period of 1958–2014. Following Linderholm et al. (2011), the peak summer mean of each variable is calculated using the data for July and August only. June is excluded from the analysis since the temporal behaviors of both the SNAO and TP rainfall in June are very different from those in July and August (Folland et al. 2009; Jiang et al. 2016). For example, the TP rainfall in peak summer (July–August) is always influenced by large-scale circulation anomalies, while the rainfall in June is affected by both the local antecedent snow cover and the onset of local rainy season (Zhao et al. 2007; Jiang et al. 2016). All analyses are performed on the 8-yr high-pass-filtered time series, as the interannual time scale is emphasized.

A partial correlation analysis (Wilks 1995, 233–237; Linderholm et al. 2011; Hu and Duan 2015) is applied to compare the relative contributions of the SNAO and the TP thermal forcing on the rainfall and atmospheric circulation over East Asia. This method involves the correlation between two variables while eliminating the influence of a third variable. Thus, in this case we can show the relationships between SNAO and East Asian summer climate (rainfall or atmospheric circulation), which are statistically independent from the TP impact:

 
formula

where indicates the partial correlation coefficient between A (e.g., rainfall) and B (SNAO), which is independent from C (TP heating). The terms , , and are the correlation coefficients between A and B, A and C, and B and C, respectively.

A partial regression analysis is also employed to distinguish the effects of SNAO and TP thermal forcing. The East Asian climate variable (rainfall or atmospheric circulation) is regressed onto the two factors, and the standard regression coefficients can be explained as their relative contributions. The standard regression coefficients are calculated as follows. We set x1 as the SNAO index, x2 as TP heating, and y as East Asian climate variable, and obtain their corresponding regression coefficients βi using a linear least squares method: . The standard regression coefficients bi are then calculated by , where and are the standard deviations of the time series of xi and y, respectively.

On the interannual time scale, the variability of Asian summer monsoon is strongly related to ENSO (Webster and Yang 1992). In this study, partial correlation and partial regression analyses are also applied to remove the possible influence of ENSO before examining the interrelationship among SNAO, TP, and East China rainfall. The ENSO index is represented by the averaged SST anomaly in the Niño-3.4 region (5°S–5°N, 170°–120°W). In addition, the wave activity flux derived following Takaya and Nakamura (2001) is used to reveal the potential mechanism for the remote impact of the SNAO on downstream regions. This flux can be used as a diagnostic tool for the generation, propagation, and absorption of quasigeostrophic wave packets on a zonally varying basic flow. The Student’s t test is applied to assess the statistical significance of the results such as the coefficients of partial correlation and partial regression and the differences between model sensitivity experiments. Moreover, it should be noted that all correlation significances allow for serial correlation (autocorrelation) in both series.

c. Experimental design

To verify the direct impact of TP heating anomaly on East China summer rainfall, we also conduct several model sensitivity experiments. The atmospheric general circulation model (AGCM) applied is the Finite-volume Atmospheric Model (FAMIL) developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics of the Chinese Academy of Sciences (Zhou et al. 2015). For the model’s physical parameterizations, readers can refer to the introduction provided by Hu and Duan (2015). We choose the horizontal resolution of C48 (1.875° × 1.875°) and 32 vertical levels with the top level at about 2.16 hPa. With these model parameters, the FAMIL has been successfully used in studying the Asian monsoon and the climate effect of TP thermal forcing (e.g., Duan et al. 2013; Hu and Duan 2015; Liu and Duan 2018).

Three AGCM experiments are conducted using the FAMIL. The first is a control run (named CTL) in which climatological means of monthly SST and sea ice with seasonal evolutions from the HadISST data are prescribed as the lower boundary. Other external forcing fields such as aerosol, ozone, CO2, etc., are set at their climatological values. In the other two sensitivity experiments, the external forcings and other settings are the same as in CTL, but a heating anomaly is added (positive TP heating run named TPHp) or removed (negative TP heating run named TPHn) over the southeastern TP region (25°–35°N, 85°–105°E) in peak summer (July–August). Since the condensational latent heating associated with rainfall dominates the total heating over the TP in summer, only the latent heating anomaly is considered in the sensitivity experiments. Each experiment is integrated over 21 years, and the mean values for the last 20 years are analyzed. More detailed information about the experiments will be further depicted in section 3c.

3. Results

a. Teleconnection between SNAO and East China rainfall

The teleconnection pattern over the North Atlantic–European region and East Asia is first revisited using the JRA-55 and GPCC datasets for the period of 1958–2014. Linderholm et al. (2011) have indicated that SNAO signals can propagate to East China in about one week after a maximum (minimum) event, which was defined as the daily SNAO index above (below) than +1 (−1) standard deviation (SD). Figure 1a shows the simultaneous regressions of summer rainfall and 850-hPa wind against the negative SNAO time series (the SNAO index multiplied by −1.0). Apparently negative rainfall anomalies occur in southeastern China near the coast (south of 26°N) and positive anomalies appear from the Yangtze River basin to South Korea and Japan. These features well match the previously reported spatial correlation between SNAO and East Asian summer rainfall (Linderholm et al. 2011). Moreover, such a spatial pattern of rainfall anomalies over East Asia is similar with the leading mode (EOF1) of rainfall variation associated with the interannual variability of the EASM (Wang et al. 2008b). In the corresponding regression field of low-level circulation, an anomalous cyclone is formed over the north of East Asia, while an anticyclonic response around the south of East Asia (with a center over the South China Sea) enhances the southerly wind anomalies in southeastern China. As a result, the cold and dry airflow from the mid- to high latitudes converges with the anomalous southerlies along the Yangtze River basin. In particular, the regression of moisture transport in Fig. 1b clearly shows that the low-level southwesterly flow anomalies bring more water vapor northward, giving rise to a distinct moisture convergence belt across central East China (along the Yangtze River basin).

Fig. 1.

(a) Patterns of regression of summer rainfall (shading, unit: mm day−1) and 850-hPa winds (vectors, unit: m s−1) upon the negative SNAO time series. (b) As in (a), but for the vertically integrated (surface–100 hPa) moisture transport (vectors, unit: kg m−1 s−1) and its divergence (shading, unit: 10−6 kg m−2 s−1). The impacts of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote the significant values exceeding the 90% confidence level for rainfall and moisture divergence, and black vectors indicate the significant values exceeding the 90% confidence level for winds. The bold gray curves denote the topographic height of 1500 m, and the red curve represents the Yangtze River over the central China. The red rectangular box (25°–35°N, 85°–105°E) denotes the averaging area for the TP rainfall index.

Fig. 1.

(a) Patterns of regression of summer rainfall (shading, unit: mm day−1) and 850-hPa winds (vectors, unit: m s−1) upon the negative SNAO time series. (b) As in (a), but for the vertically integrated (surface–100 hPa) moisture transport (vectors, unit: kg m−1 s−1) and its divergence (shading, unit: 10−6 kg m−2 s−1). The impacts of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote the significant values exceeding the 90% confidence level for rainfall and moisture divergence, and black vectors indicate the significant values exceeding the 90% confidence level for winds. The bold gray curves denote the topographic height of 1500 m, and the red curve represents the Yangtze River over the central China. The red rectangular box (25°–35°N, 85°–105°E) denotes the averaging area for the TP rainfall index.

Based on the GPCC rainfall data, we calculate the PC1 time series of summer rainfall over East China (20°–42°N, 106°–122°E), reflecting the interannual variability of the leading spatial mode of rainfall (explaining 21.0% of the total variance). The correlation coefficient between SNAO and the PC1 of East China rainfall is −0.40 (see Table 1), significantly exceeding the 99% confidence level, which indicates an evident Eurasian teleconnection.

Table 1.

Coefficients of interannual correlation between the first principal component (PC1) of East China summer rainfall (EC rainfall) and the SNAO index for the period of 1958–2014, as well as between the PC1 and the Tibetan Plateau rainfall (TPR) index. Shown are also their corresponding partial correlation coefficients with the impact of TPR or SNAO excluded. Values with “*” are statistically significant at the 99% confidence levels.

Coefficients of interannual correlation between the first principal component (PC1) of East China summer rainfall (EC rainfall) and the SNAO index for the period of 1958–2014, as well as between the PC1 and the Tibetan Plateau rainfall (TPR) index. Shown are also their corresponding partial correlation coefficients with the impact of TPR or SNAO excluded. Values with “*” are statistically significant at the 99% confidence levels.
Coefficients of interannual correlation between the first principal component (PC1) of East China summer rainfall (EC rainfall) and the SNAO index for the period of 1958–2014, as well as between the PC1 and the Tibetan Plateau rainfall (TPR) index. Shown are also their corresponding partial correlation coefficients with the impact of TPR or SNAO excluded. Values with “*” are statistically significant at the 99% confidence levels.

b. Effect of TP thermal forcing on the Eurasian teleconnection

The analyses in section 3a have identified that the SNAO is significantly correlated with the variations of East China rainfall. It is also worth noting that both the rainfall and the moisture convergence over southeastern TP are enhanced with the negative SNAO time series (Fig. 1). Here, we define a TP rainfall index, which is the area-averaged summer (July–August) rainfall over southeastern TP (25°–35°N, 85°–105°E) where the topography is above 1500 m. On the interannual time scale, the coefficient of correlation between SNAO and TP rainfall index is −0.58, which remains almost unchanged (−0.57) after removing the effect of ENSO (Fig. 2a). As pointed out by Watanabe (2004), the NAO is tied to East Asian climate variability by stationary Rossby waves. The regression of 200-hPa streamfunction upon the negative SNAO time series (Fig. 2b) shows evident wave trains extending from the North Atlantic and Europe to the downstream regions. This result matches with the wave train pattern represented by the interannual correlation between SNAO and 300-hPa geopotential height [Fig. 1b in Linderholm et al. (2011)]. Apparent divergence of wave activity flux appears at the negative anomaly center of streamfunction (roughly over 50°–65°N, 30°W–5°E in Fig. 2b), corresponding to the southern node of the SNAO, which indicates a wave source region. The wave activity flux diverges southeastward from the source region, and converges around west of the TP. As a result, a positive streamfunction anomaly is formed at upper levels over the southern TP and northern India (Fig. 2b). Climatologically, a lower-level cyclonic circulation and an upper-level anticyclonic circulation occur over the TP as a result of the thermal forcing in summer (Duan and Wu 2005). As seen in the vertical cross sections of relative vorticity and vertical velocity anomalies from Fig. 2c, over the TP, deep negative (shallow positive) anomalies of relative vorticity appear in the upper (lower) layer under the negative SNAO conditions, along with the significant upward motion. It is thus suggested that a negative SNAO can enhance the vertical baroclinic structure and the pumping effect of the TP, and vice versa. Such a circulation structure leads to an increase in TP summer rainfall (Fig. 1a). In addition, equivalent barotropic structures of large-scale wave trains represented by the strong and deep vorticity anomalies appear over the upstream regions of the TP, along with the significant eastward propagation of wave activity fluxes.

Fig. 2.

(a) Time series of normalized SNAO (black) and rainfall over the southeastern TP (red) during the period of 1958–2014 using the filtered data to highlight the interannual variability; R is the correlation coefficient between the two curves, while PR is the partial correlation coefficient with the influence of Niño-3.4 SST excluded. (b) Patterns of regression of 200-hPa streamfunction (shading, unit: 105 m2 s−1) and wave activity flux (vectors are omitted where the zonal wind less than 1 m s−1, unit: m2 s−2) against the negative SNAO index, and bold gray curves denote the topographic height of 1500 m. (c) Vertical cross section of regressions of relative vorticity (shading, unit: 10−6 s−1), wave activity flux (vectors, unit: m2 s−2) and vertical velocity (contours with intervals of 0.2, unit: 10−2 Pa s−1) against the negative SNAO time series along the wave train [A–B–C–D, shown in (b) (dashed purple line)], and the gray shading represents the topography. In (b) and (c), the impacts of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote that the statistical significance of streamfunction in (b) or relative vorticity in (c) exceeds the 90% confidence level.

Fig. 2.

(a) Time series of normalized SNAO (black) and rainfall over the southeastern TP (red) during the period of 1958–2014 using the filtered data to highlight the interannual variability; R is the correlation coefficient between the two curves, while PR is the partial correlation coefficient with the influence of Niño-3.4 SST excluded. (b) Patterns of regression of 200-hPa streamfunction (shading, unit: 105 m2 s−1) and wave activity flux (vectors are omitted where the zonal wind less than 1 m s−1, unit: m2 s−2) against the negative SNAO index, and bold gray curves denote the topographic height of 1500 m. (c) Vertical cross section of regressions of relative vorticity (shading, unit: 10−6 s−1), wave activity flux (vectors, unit: m2 s−2) and vertical velocity (contours with intervals of 0.2, unit: 10−2 Pa s−1) against the negative SNAO time series along the wave train [A–B–C–D, shown in (b) (dashed purple line)], and the gray shading represents the topography. In (b) and (c), the impacts of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote that the statistical significance of streamfunction in (b) or relative vorticity in (c) exceeds the 90% confidence level.

Previous studies have indicated that the atmospheric latent heating associated with rainfall dominates the total diabatic heating over the TP in summer (e.g., Ye and Gao 1979; Hu and Duan 2015). Here we prove that the interannual variability of local diabatic heating (based on the JRA-55) is highly correlated with the GPCC summer rainfall over the southeastern TP during 1958–2014 (with a correlation of 0.88). It is thus concluded that the SNAO can regulate TP summer rainfall through a large-scale wave train. In other words, the SNAO can also directly modulate the interannual variability of the TP thermal effect.

As introduced in section 1, the TP thermal forcing exerts a significant impact on the interannual variability of EASM including East China rainfall (Wang et al. 2014; Hu and Duan 2015). Thus, the TP rainfall index is used here as a measure of TP thermal forcing to testify its relationship with the climate over East China.

Figure 3a shows the regressions of summer rainfall and 850-hPa wind against the TP rainfall index. The responses of rainfall and atmospheric circulation over East Asia are very similar with the relationship between the SNAO and EASM shown in Fig. 1a. An anomalous cyclone is formed over the north of East Asia, while an anticyclonic response appears around southern East Asia. Subsequently, positive summer rainfall anomaly occurs over central East China (along the Yangtze River basin, Fig. 3a). The interannual correlation between the TP rainfall index and the PC1 of East China summer rainfall is 0.53 (see Table 1), significantly exceeding the 99% confidence level, also suggesting a close relationship between TP thermal forcing and the summer rainfall pattern over East China.

Fig. 3.

(a) Patterns of regressions of summer rainfall (shading, unit: mm day−1) and 850-hPa winds (vectors, unit: m s−1) against the TP rainfall index. (b) As in (a), but for the partial regression patterns with the impact of SNAO excluded. (c) As in Fig. 1a, but for the partial regression patterns with the impact of TP excluded. The effects of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote the statistical significance of rainfall exceeds the 90% confidence level, black vectors indicate the winds exceeding 90% confidence level. The bold gray curves denote the topographic height of 1500 m. The red curve represents the Yangtze River over the central China.

Fig. 3.

(a) Patterns of regressions of summer rainfall (shading, unit: mm day−1) and 850-hPa winds (vectors, unit: m s−1) against the TP rainfall index. (b) As in (a), but for the partial regression patterns with the impact of SNAO excluded. (c) As in Fig. 1a, but for the partial regression patterns with the impact of TP excluded. The effects of Niño-3.4 SST were excluded before these regression analyses conducted. Dotted regions denote the statistical significance of rainfall exceeds the 90% confidence level, black vectors indicate the winds exceeding 90% confidence level. The bold gray curves denote the topographic height of 1500 m. The red curve represents the Yangtze River over the central China.

Figures 3a and 1a identify that both TP thermal forcing and SNAO exert a similar influence on the interannual variability of East China summer rainfall and the associated atmospheric circulations, and concurrently the SNAO can regulate the variation of TP rainfall (viz., the TP thermal forcing).

We also observe another interesting feature from Figs. 2b and 2c: the disturbance of SNAO with southeastward wave activity flux reaches the TP before exerting its effect on the climate in East China or East Asia (east of 105°E). The wave train represented by the streamfunction anomalies also shows a roughly northwest–southeast orientation. Given that the TP lies between the North Atlantic–European region and East China, a question arises: Does the TP play an intermediate bridge effect in the teleconnection between the SNAO and East China summer rainfall?

Consequently, we apply a partial regression analysis to better understand the relative relationships of SNAO and TP thermal forcing with the variations of East China climate. When the impact of SNAO is removed, the regressions of East Asian rainfall and low-level wind upon the TP rainfall index (Fig. 3b) show little change from the original result (Fig. 3a), suggesting that the relationship between the TP and East China summer rainfall is not obviously disturbed by the effect of SNAO. However, when the influence of TP thermal forcing is removed, the partial regressions of rainfall and atmospheric circulation upon the SNAO index (Fig. 3c) are significantly different from the original result (Fig. 1a). In particular, the anomalous cyclone (anticyclone) in northern (southern) East Asia disappears completely. Meanwhile, there are no uniform positive rainfall anomalies across the whole central East China (Fig. 3c). After excluding the TP forcing, the interannual partial correlation between the SNAO and the PC1 of East China rainfall is reduced in magnitude from −0.40 to −0.08 (see Table 1). Therefore, the TP does exert an intermediate bridge effect in the Eurasian teleconnection, in which the SNAO primarily regulates the interannual variability of summer precipitation and diabatic heating over the TP and the changed TP heating in turn influences the East China summer rainfall.

c. Direct impact of the TP thermal forcing on East China summer rainfall

In this section, three experiments are conducted to show the robustness of the direct impact of the TP thermal forcing on East China rainfall revealed from the statistical analyses in section 3b. Following Hu and Duan (2015), the summer latent heating anomaly employed in the sensitivity runs has almost the same magnitude as the natural interannual variability of the TP heating, which is quantified by the interannual SD of latent heating in the JRA-55 data during 1958–2014. In this study, anomalies with 2.5 SD latent heating are added into (subtracted from) the CTL diabatic heating output and served as the positive (negative) heating source in the TPHp (TPHn) run over the southeastern TP. Figure 4a shows the area-averaged heating profiles in the three runs over the experimental region (25°–35°N, 85°–105°E) in July and August. This magnitude of heating anomaly is realistic, since the anomalies of 2.5 SD can be observed in some years as seen from the JRA-55 data.

Fig. 4.

(a) Vertical profiles of condensational heating (unit: K day−1) over the southeastern TP [see the red rectangular box in (b) or (c)] in different experiments. (b) Difference in 200-hPa winds (unit: m s−1) between the positive (TPHp) and negative (TPHn) TP heating experiments. (c) As in (b), but for 850-hPa winds and rainfall (shading, unit: mm day−1). Black vectors in (b) and (c) indicate the winds exceeding the 90% confidence level, and dotted regions in (c) denote the statistical significance of rainfall above the 90% confidence level. The bold gray curves in (b) and (c) denote the topographic height of 1500 m.

Fig. 4.

(a) Vertical profiles of condensational heating (unit: K day−1) over the southeastern TP [see the red rectangular box in (b) or (c)] in different experiments. (b) Difference in 200-hPa winds (unit: m s−1) between the positive (TPHp) and negative (TPHn) TP heating experiments. (c) As in (b), but for 850-hPa winds and rainfall (shading, unit: mm day−1). Black vectors in (b) and (c) indicate the winds exceeding the 90% confidence level, and dotted regions in (c) denote the statistical significance of rainfall above the 90% confidence level. The bold gray curves in (b) and (c) denote the topographic height of 1500 m.

The differences between the sensitivity experiments with positive (TPHp) and negative (TPHn) heating anomalies are used to demonstrate the direct influences of TP thermal forcing on East Asian rainfall and atmospheric circulation (Figs. 4b and 4c). Stronger heating over the TP can substantially enhance the South Asian high (Fig. 4b). To the northeast of the TP, a cyclonic anomaly exists from 850 to 200 hPa, suggesting a barotropic structure response in the troposphere. This cyclonic anomaly can be viewed as part of a Rossby wave train originating from the anticyclonic anomaly over the TP as a result of its thermal forcing (Wang et al. 2008a; Wang et al. 2014). The barotropic cyclone response to the northeastern TP further generates a low-level northerly flow anomaly over northern China (Fig. 4c). Meanwhile, the TP heating also induces an anomaly of southerly flow at 850 hPa over southern China. Subsequently, the anomalous northerly and southerly winds converge over East China in the lower troposphere, increasing the summer rainfall along the convergence belt (Fig. 4c). Moreover, Fig. 4c shows that the TP heating can suppress the rainfall over northern India and the Bay of Bengal, which matches with the previously reported results (Wu et al. 2012; Wang et al. 2016; Jiang and Ting 2017). Apparently, the responses of atmospheric circulation and rainfall to the TP heating in the sensitivity experiments are similar to the regressions against the TP rainfall index in observations (Figs. 3a and 3b). It is also noted that the simulated convergence belt (Fig. 4c) is located farther north compared to the observed (Figs. 3a and 3b). The reason for this discrepancy could be that the FAMIL inherently simulates a stronger EASM than observed, which leads to a more northward location of the climatological rainfall belt over East China (Hu and Duan 2015). Nevertheless, the results from the sensitivity experiments demonstrate the direct and significant impact of TP thermal forcing on the summer rainfall pattern over East China and support the observed bridge effect of the TP heating on the Eurasian teleconnection as shown in section 3b.

4. Summary and discussion

Based on both statistical analyses and model sensitivity experiments, the interannual relationships among the SNAO, the TP, and East China climate have been investigated. Apparently, there exists a significant Eurasian teleconnection between the SNAO and East China summer rainfall. Negative SNAO events (SNAO index <−0.8 SD) tend to give rise to dry conditions over southeastern China and wet conditions over central East China (along the Yangtze River basin), and vice versa. We have demonstrated that the TP thermal forcing exerts an intermediate bridge effect in this Eurasian teleconnection.

The schematic diagram shown in Fig. 5 clearly depicts the overall physical processes. The SNAO primarily regulates the interannual variability of summer precipitation over the southeastern TP. Under the negative SNAO background, the vertical baroclinic structure and the pumping effect of the TP are strengthened through the southeastward propagation of large-scale wave disturbance. Namely, a negative SNAO (SNAO index <−0.5 SD) tends to increase the TP rainfall, accompanied with enhanced local diabatic heating that subsequently excites Rossby waves propagating northeastward in the upper troposphere. To the northeast of TP, an anomalous barotropic cyclone is formed, which further generates low-level northerly anomalies over northern China. Meanwhile, the TP heating also generates low-level southerly anomalies over southern China. These anomalous northerly and southerly winds converge in the lower troposphere, increasing the summer rainfall across the whole of central East China.

Fig. 5.

Schematic diagram showing the overall structure of the TP’s bridge effect in the teleconnection between SNAO and East China summer rainfall.

Fig. 5.

Schematic diagram showing the overall structure of the TP’s bridge effect in the teleconnection between SNAO and East China summer rainfall.

It should be noted that the Eurasian wave train related to the SNAO in Fig. 5 looks like the circumglobal teleconnection (CGT) pattern proposed by Ding and Wang (2005). Both the wave patterns propagate eastwardly via the subtropical jet stream from the European region to the East Asian monsoon region. To clarify this point, we further calculate the CGT index in July–August. However, no strong relationship can be found between the SNAO and the CGT at the interannual time scale. Moreover, the result related to the SNAO shown in the current study is basically not influenced by the effect of the CGT based on the partial regression analyses (figure not shown).

In this study, we have emphasized the role of the TP thermal effect in the Eurasian teleconnection, encouraging a better understanding of the extratropical influence on East Asian climate, especially the summer rainfall over East China. The result obtained may be useful for improving the seasonal forecast of East Asian summer climate. On the other hand, Linderholm et al. (2011) mentioned that the spatial pattern of anomalous streamfunctions showed a quite asymmetric feature with respect to different SNAO phases (positive and negative) in association with wave activity flux. This point is worth further investigation into the potential asymmetric influence of the SNAO. In addition, the SNAO is significantly correlated with the South Asian summer monsoon on an interannaul time scale (Linderholm et al. 2011), while the TP thermal forcing strongly regulates the variations of the monsoon (Wu et al. 2012; Wang et al. 2016). Thus, the possible connection among the SNAO, the TP, and the South Asian summer monsoon should also be investigated in future studies.

AGCM experiments with prescribed climatological SST are also conducted in this study to demonstrate the robustness of the direct impact of TP heating on East China summer rainfall. However, to some extent the Eurasian teleconnection pattern between the SNAO and East Asian climate (rainfall and circulation) on the interannual time scale cannot be reproduced in these climatological experiments. In the ongoing work, it is necessary to evaluate the present phase 5 of the Coupled Model Intercomparison Project (CMIP5) or the forthcoming CMIP6 models, and identify a model that can simulate a reasonably consistent Eurasian teleconnetion pattern shown in the current study. Then, we can use the model to conduct similar TP heating experiments and investigate the potential changes of the Eurasian teleconnection in model experiments.

Finally, it is worth pointing out that we have just emphasized the features of an interannual time scale in the current work; however, the SNAO variation on an interdecadal time scale is also significant (Allan and Folland 2016). Furthermore, studies have also been under way to describe the SNAO variations on decadal to multicentury time scales (Linderholm and Folland 2017). How do the possible teleconnections look like among the SNAO, the TP, and the Asian summer monsoon on longer time scales? This is an interesting question that has not been investigated so far.

Acknowledgments

We are grateful to Editor Dr. Mathew Barlow and three reviewers who provided thoughtful comments and valuable suggestions on this paper. We also appreciate the effective discussions with Prof. Chris Folland from the Met Office Hadley Centre and Dr. Senfeng Liu from the Institute of Atmospheric Physics, Chinese Academy of Sciences. This work was supported jointly by the National Natural Science Foundation of China (Grants 91637208 and 41605038), the National Key Scientific Research Plan of China (Grant 2014CB953900), and the Natural Science Foundation of Guangdong Province (Grant 2015A030310224).

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Footnotes

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