1. Introduction
The summer North Atlantic Oscillation (SNAO) is one of the most prominent boreal teleconnection patterns. It is generally defined by the leading mode of sea level pressure (SLP) or lower-level geopotential height anomalies over the extratropical Atlantic, and manifests as a north–south seesaw pattern, with one center located over Greenland and the other over the subtropical North Atlantic (Portis et al. 2001; Hurrell and Folland 2002; Sun et al. 2008; Folland et al. 2009; Linderholm et al. 2011; Bollasina and Messori 2018; Hall and Hanna 2018; Hong et al. 2018a; and many others). The SNAO is mainly considered as a teleconnection pattern on the interannual time scale, but has considerable component of interdecadal variations (Sun et al. 2008, 2009; Sun and Wang 2012; Wang et al. 2016).
The SNAO can exert significant influence over the surrounding regions, particularly the North Atlantic and Europe. It is usually accompanied by the north–south shift of the upper-tropospheric westerly jet and storm track over the North Atlantic (Folland et al. 2009; Linderholm et al. 2011; Dong et al. 2013) and is closely related to a seesaw pattern of precipitation anomalies, with one center over northwestern Europe and the other over southern Europe (Folland et al. 2009; Allan and Zveryaev 2011; Linderholm et al. 2011; Bladé et al. 2012; Dong et al. 2013; Saeed et al. 2014; Cherenkova et al. 2020; Osborne et al. 2020). It is also documented to play an essential role in European summer heat waves (e.g., Della-Marta et al. 2007; Kueh and Lin 2020; Li et al. 2020).
The SNAO can extend its influences farther eastward, reaching East Asia. These influences include precipitation and temperature anomalies over East Asia (Yang and Zhang 2008; Linderholm et al. 2011; Jin and Guan 2017; Wang et al. 2018; Du et al. 2020; Liu et al. 2020; Li et al. 2021), South Asia (Goswami et al. 2006; Krishnamurthy and Krishnamurthy 2016; Sun and Ming 2019), and the Tibetan Plateau (Liu et al. 2015, 2021; Shang et al. 2021). The influences of the SNAO on the downstream regions were explained mainly through the zonal teleconnection wave patterns, among which the Silk Road pattern (SRP) is the most important (Yang and Zhang 2008; Linderholm et al. 2011; Jin and Guan 2017; Bollasina and Messori 2018; Shang et al. 2021).
The SRP is a wavelike pattern propagating eastward along the Asian westerly jet, and manifests as the leading mode of the upper-tropospheric meridional wind anomalies over the midlatitude Eurasian continent in summer (Lu et al. 2002; Enomoto et al. 2003; Yasui and Watanabe 2010; Chen and Huang 2012; Hong and Lu 2016; and many others). The SRP presents as alternate positive and negative meridional wind anomalies from around the Asian jet entrance, specifically the Caspian Sea, to East Asia, and it plays an important role in linking the circulation and climate anomalies between the upstream regions of the Asian jet, such as Europe, and the downstream regions including East Asia (Lu et al. 2002; Enomoto et al. 2003; Yasui and Watanabe 2010; Chen and Huang 2012; Hong et al. 2021). This midlatitude wavelike pattern is an important component of the circumglobal teleconnection pattern (CGT), which emphasizes the circumglobal feature (e.g., Ding and Wang 2005; Zhou et al. 2019, 2020). An important feature for the SRP is that the anomalies of its westernmost part (i.e., those around the Asian jet entrance, including the Caspian Sea) are demonstrated to be crucial for the SRP excitation (Enomoto et al. 2003; Sato and Takahashi 2006; Kosaka et al. 2009; Yasui and Watanabe 2010; Hong and Lu 2016). Disturbances entering this region can efficiently propagate eastward under the waveguide effect of the Asian westerly jet (Hoskins and Ambrizzi 1993).
It can be noticed from previous results that there are significant anomalies over the North Atlantic and Europe in association with the SRP, including circulation anomalies over the North Atlantic (e.g., Ding and Wang 2005; Saeed et al. 2014; Sun et al. 2019; Monerie et al. 2021) and precipitation anomalies over the northwestern Europe (e.g., Saeed et al. 2014; Wang et al. 2017). These SRP-related anomalies greatly resemble those associated with the SNAO. Some studies suggested a positive relationship between the SNAO and SRP (Saeed et al. 2014; Hong et al. 2017; Jin and Guan 2017). Furthermore, Hong et al. (2018a) revealed that this relationship is strong in late summer, but is absent in early summer. Therefore, in this study we will focus on the SNAO–SRP relationship in late summer.
However, even in late summer, the correlation coefficient between the indexes for the SNAO and SRP is moderate, although statistically significant, as will be shown in the present results. Therefore, it is expected that while the SNAO generally connects closely with the SRP, there should also be a considerable number of years when the SNAO does not correspond well to the SRP. Thus, a question arises: What are the main differences between these two categories? What features are there in the strong SNAO–SRP linkage years, and what is the underlying mechanism responsible for the linkage? These are the main issues of the present study, and answering these questions may help us better understand the relationship between the dominant circulation modes over the North Atlantic and Eurasian continent.
On the other hand, when investigating the interdecadal changes of Eurasian climate, recent studies suggested that they can be attributed to the interdecadal change of SNAO (Sun et al. 2008, 2009; Sun and Wang 2012; Wang et al. 2016). The SNAO was in a positive phase since the 1970s and turned to the negative phase around the year 2000 (Folland et al. 2009; Wang et al. 2016; Du et al. 2020). The SRP experienced a similar interdecadal variation (Lin et al. 2016; Hong et al. 2017; Wang et al. 2017; Sun et al. 2019). Then what is the relationship between the SNAO and SRP like on the interdecadal time scale? Is the underlying mechanism similar to that on the interannual time scale?
Focusing on above issues, this study investigates the relationship between the SNAO and SRP in late summer suggested by Hong et al. (2018a), on both the interannual and interdecadal time scales. For the former, we select strong SNAO years and divide them into two categories, according to whether the SNAO is closely connected to the SRP or not. Analysis of the first category can highlight the SNAO–SRP connection, and thus is helpful in understanding the possible physical mechanism for the SNAO–SRP relationship. In addition, comparison between the first and second categories can also be helpful in understanding the SNAO–SRP relationship, but from the opposite point of view. The remainder of this paper is organized as follows. The interannual results will be presented in section 3. In section 4, we attempt to find out the possible physical mechanism responsible for the correspondence between the SNAO and SRP. In section 5, we examine whether the features and mechanism suggested by interannual variability are valid or not for the interdecadal time scale. Conclusions and discussion are given finally in section 6.
2. Dataset and methods
All the monthly circulation variables used in this study are from the Japanese 55-Year Reanalysis (JRA-55; Kobayashi et al. 2015), with a 1.25° × 1.25° horizontal resolution. We used the monthly precipitation data of the National Oceanic and Atmospheric Administration’s (NOAA’s) Precipitation Reconstruction (PREC) dataset (Chen et al. 2002), which has a horizontal resolution of 2.5° × 2.5°. The data time period applied in this study is 1958–2019 for all datasets.
Previous studies demonstrated that for both SNAO (Folland et al. 2009; and many others afterward) and SRP (Hong et al. 2018a), the spatial and temporal behaviors are similar between July and August but differ notably from those in June. Hong et al. (2018a) further indicated that the SNAO–SRP relationship is significant only in late summer, and absent in June. Therefore, we use the July–August (JA) average as summer in this study. We also did analyses using datasets of only July or August, and obtained similar results (not shown).
Following Yasui and Watanabe (2010) and Hong et al. (2018a), we define the SRP as the leading empirical orthogonal function (EOF1) mode of the 200-hPa meridional wind (V200) anomalies within the domain 20°–60°N, 0°–150°E. A positive phase of SRP is indicated by the pattern with a positive anomalous center over the Caspian Sea. The SNAO is identified as the EOF1 of the SLP anomalies within the domain 30°–85°N, 70°W–30°E. Correspondingly, standardized time series of the first principal components are taken as the SRP index (SRPI) and SNAO index (SNAOI), respectively.
In this study, we will conduct analyses with respect to interannual and interdecadal components, separately. We applied a 9-yr Gaussian filtering method (Haddad and Akansu 1991) onto the original series, for both the indexes and the variables. The interdecadal components of the indexes are determined by the filtered results. For both the indexes and variables, their interannual components are regarded as the difference between the original series and the filtered sequence.
3. Interannual relationship between the SNAO and SRP
Figure 1 shows the SLP and V200 anomalies regressed onto the standardized SNAOI and SRPI, respectively. There is a great resemblance between the SNAO- and SRP-related anomalies with respect to both the SLP and V200 (cf. Figs. 1a and 1b, and Figs. 1c and 1d). Both the SNAO- and SRP-related SLP anomalies (Figs. 1a,b) show a northwest–southeast seesaw pattern, with the northern pole over Greenland and the southern pole centered at the North Sea. The V200 anomalies associated with these two indexes (Figs. 1c,d) both show a wavelike pattern, which manifests as alternate southerly and northerly anomalies from the North Atlantic to East Asia. These similarities can be verified by the pattern correlation coefficients between the SNAO- and the SRP-related distributions, which is 0.92 for SLP anomalies within the domain 30°–85°N, 70°W–30°E (Figs. 1a,b; the domain is shown in Fig. 1a) and 0.68 for V200 anomalies within the domain 20°–60°N, 0°–150°E (Figs. 1c,d; the domain is shown in Fig. 1d). All these results indicate that the SNAO- and SRP-related anomalies present high similarities to each other.
The (a),(b) SLP (contours; hPa) and (c),(d) 200-hPa meridional wind anomalies (contours; m s−1) regressed onto the standardized (top) SNAOI and (bottom) SRPI. Solid and dashed contours delineate positive and negative anomalies, respectively. Contour intervals of all the panel plots are 0.5, and zero contours are omitted. Vectors in (c) and (d) indicate wave activity flux (WAF) related to the SNAO and SRP, respectively. Shading indicates anomalies significant at the 0.05 level, based on the Student’s t test. Thick lines in (c) and (d) indicate the climatological jet axis. The two boxes in (a) and (d) indicate the domain used to define the SNAO and SRP, respectively.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
On the other hand, there are some distinctions that cannot be ignored between the SNAO- and SRP-related distributions. The SNAO-related SLP anomalies (Fig. 1a) are much stronger and present much broader zonal spatial scope than the SRP-related ones (Fig. 1b). The positive SLP anomaly center around the North Sea presents a southwestward extension in Fig. 1a but is locally distributed in Fig. 1b. In the upper troposphere, the wavelike pattern, particularly the part along the Asian jet, is much weaker for the SNAO-related anomalies (Fig. 1c) than the SRP-related ones (Fig. 1d). The wave propagation, which is reflected by the WAF, is most active from Greenland to the Caspian Sea for the SNAO-related wave pattern (Fig. 1c), whereas it is active from the North Sea to the Caspian Sea and penetrates farther eastward along the Asian jet for the SRP-related wave pattern (Fig. 1d). Besides, the wavelike patterns along the upstream of the Asian jet do not exactly coincide between Figs. 1c and 1d. Rather, there is a slight phase difference of about 10° in longitudes between them, which may result in the relatively smaller pattern correlation coefficient of V200 anomalies (0.68) than that of SLP anomalies (0.92). These distinctions are reflected by the weak temporal correlation coefficient between the SNAOI and SRPI, which is only 0.31, despite being statistically significant at the 0.05 level. The relatively small coefficient suggests a considerable number of years in which the SNAO does not correspond to the SRP.
Figure 2a compares the time series of the SNAOI and SRPI. There is a general consistency between the SNAOI and SRPI: a positive SNAOI is more accompanied by a positive SRPI, and vice versa. There are 37 years in total when the SNAOI and SRPI are of the same sign, more than those (25 years) of opposite signs. On the other hand, both indexes show a considerable interdecadal variation. Their interdecadal components explain 28.1% (for the SNAO) and 31.8% (for the SRP) of the total variance, respectively. The two interdecadal sequences both tend to be in a positive phase from the late 1960s to the late 1990s, and turn into a negative phase from around 2005. These consistencies suggest a positive relationship between the interdecadal components of the SNAO and SRP. In view of the considerable and generally consistent interdecadal components of the two indexes, the following analyses will be based on the interdecadal and interannual results, respectively. The interdecadal results will be based on the differences between two periods, 1970–97 and 2005–19, when the interdecadal sequence is in the positive and negative phases, respectively, for both the SNAO and SRP.
Time series of (a) the original (bars) and interdecadal (lines) and (b) the interannual sequences of the SNAOI and SRPI.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
Figure 2b shows the standardized time series of the interannual components for the SNAOI and SRPI (hereafter SNAOIIA and SRPIIA, respectively). There is generally an in-phase correspondence between them, although this correspondence seems to be relatively weak: There are 33 years when the SNAOIIA and SRPIIA are of same sign, slightly more than the 29 years of opposite signs. The relationship between the interannual SNAO and SRP can be better illustrated by the scatterplot between the standardized SNAOIIA and SRPIIA in Fig. 3. The SRPIIA generally increases with SNAOIIA, indicating a positive relationship between them. However, the correlation coefficient between the two indices is only 0.22, being insignificant and even smaller than that between their raw sequences (0.31), showing that there are a considerable number of years when the SNAO and SRP do not correspond to each other on the interannual time scale. Therefore, the following analysis will be based on a systematic comparison between the corresponding and non-corresponding cases, hereafter referred to as the strongly linked (SL) category and weakly linked (WL) category, respectively. The SL category delineates the years when both the SNAOIIA and SRPIIA are strong (greater than 0.5 or smaller than −0.5) and feature the same phase, indicating that the strong SNAO connects closely to the SRP. The WL category indicates strong SNAO (absolute values of SNAOIIA greater than 0.5) but weak SRP (absolute values of SRPIIA smaller than 0.5), indicating that even the strong SNAO does not correspond well to the SRP. In the following interannual results (Figs. 4–8), we show only the differences between the positive and negative SNAOIIA years for brevity.
The scatterplot of the SNAOIIA and SRPIIA. Black dots and asterisks indicate cases for the strongly and weakly linked category, respectively, and gray dots indicate the neutral cases.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
Composite 200-hPa meridional wind anomalies (contours; m s−1) for the (a) strongly linked (SL) and (b) weakly linked (WL) category. Solid and dashed contours delineate positive and negative anomalies, respectively. Contour intervals are 1 m s−1, and zero contours are omitted. Shading indicates anomalies significant at the 0.05 level, based on the Student’s t test. Vectors delineate the wave activity flux (5 m2 s−2). Bold lines delineate the climatological jet axis, which is determined as the first derivative of the 200-hPa zonal winds being zero.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
As in Fig. 4, but for the SLP anomalies (contours; hPa). Contour intervals are 0.5 hPa. Boxes indicate the central domain for the northern and southern poles of the SNAO, respectively.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
Composite precipitation anomalies (shading; mm day−1) for the (a) strongly linked and (b) weakly linked category. Dotted areas indicate anomalies significant at the 0.05 level, based on the Student’s t test. Boxes indicate the domain to represent northwestern Europe.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
As in Fig. 4, but for the Rossby wave source anomalies (contours; 10−11 s−2). Contours of ±3, ±5, ±7, and ±9 × 10−11 s−2 are plotted. Boxes indicate the domain representing northwestern Europe.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
As in Fig. 7, but for the anomalies of the planetary vortex stretching term −fDχ.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
Cases of each category have been marked in Fig. 3 and additionally listed in Table 1. There are 14 and 15 cases for the SL and WL categories, respectively, comparable to each other. The total number of selected cases (29) is about half of the total of 62 years. We also tested other criteria when selecting cases (e.g., 0.7 or 0.8) and obtained similar results (not shown), suggesting the robustness of the present results. There are also some cases in the second and fourth quadrants in Fig. 3, which also contribute to the small correlation coefficient between the SNAOIIA and SRPIIA. These cases suggest an out-of-phase change between the SNAO and SRP that is beyond the scope of the present study, which focuses on the features and relevant mechanisms responsible for a strong positive SNAO–SRP linkage.
Cases of the strongly and weakly linked categories.
Figure 4 shows the composite V200 anomalies for the two categories. Here, the anomalies denote the difference between the positive and negative SNAOIIA cases, and their significance levels are determined based on Student’s t test (Wilks 2011). For the SL category (Fig. 4a), there are significant positive and negative anomalies alternately distributed along the Asian jet from around the Caspian Sea to East Asia, in agreement with the anomalies associated with the SRP (Fig. 1d). Pattern correlation for the V200 anomalies within 20°–60°N, 0°–150°E between Figs. 4a and 1d is as high as 0.86, verifying the significant SRP appearing for the SL category. In comparison, the anomalies along the Asian jet for the WL category (Fig. 4b) are very weak and even tend to be out of phase with those for the SL category (Fig. 4a). The contrasts between the SL and WL categories verify the validity of the classification here. On the other hand, there is a significant positive anomaly to the southeast of the Greenland and a negative anomaly around the Scandinavia for the SL category (Fig. 4a), consistent with both the SNAO- (Fig. 1c) and SRP-related (Fig. 1d) anomalies over the North Atlantic. However, no clear anomalies over the high-latitude North Atlantic exist for the WL category (Fig. 4b). These sharp contrasts are also illustrated by the WAF. The WAF for the SL category (Fig. 4a) synthesizes that related to the SNAO (i.e., from Greenland to the Caspian Sea, as in Fig. 1c) and that related to the SRP (i.e., from the North Sea to the Caspian Sea and then along the Asian jet, as in Fig. 1d), reflecting the strong SNAO–SRP linkage. In comparison, the WAF for the WL category appears chaotic (Fig. 4b).
Figure 5 shows the composite SLP anomalies for the two categories. There is a clear SNAO-like pattern for both categories. This pattern is most remarkable for its northern pole over Greenland. Besides the great resemblance in pattern, the intensities of the northern pole are also similar between the two categories, confirmed by their comparable values (−2.57 hPa for the SL category and −2.04 hPa for the WL category, both significant at the 0.01 level) of averaged SLP anomalies within the domain 65°–80°N, 75°–30°W (shown as boxes in Fig. 5). These similarities suggest that the northern pole of the SNAO may not play a role in determining a specific category.
The southern pole of the SNAO differs notably between the two categories. First, this southern pole centered over the North Sea and located over the northwest Europe for the SL category (Fig. 5a), similar to that related with the SRP (Fig. 1b). By contrast, for the WL category, this southern pole shows a southwestward extension and its major body is over the ocean (Fig. 5b), resembling the anomaly associated with the SNAO (Fig. 1a). Second, this southern pole appears much stronger for the SL category (Fig. 5a) than the WL category (Fig. 5b). The intensity of the former is about twice as much as the latter, which can be inferred by the domain-averaged SLP anomaly being 1.3 hPa for the SL category (Fig. 5a), significant at the 0.001 level and comparable with the domain-averaged standard deviation (1.6 hPa). However, this domain-averaged SLP anomaly for the WL category is only 0.7 hPa (Fig. 5b), not significant at the 0.05 level. Here the “domain” is selected as 50°–60°N, 10°W–35°E, representing the central part of the SNAO’s southern pole and shown in Fig. 5. The anomalies are statistically significant over most areas of this southern pole for the SL category (Fig. 5a), but only significant at the southern pole’s southeast corner for the WL category (Fig. 5b). Third, there is a significant negative center over the Caspian Sea for the SL category (Fig. 5a), in agreement with Fig. 1b, but this anomalous center is absent for the WL category (Fig. 5b).
The disparities of the SNAO southern pole (Fig. 5) are consistent with the contrasts of the V200 anomalies (Fig. 4), considering the general barotropic vertical distribution in both pressure and meridional wind anomalies (not shown). Specifically, stronger SLP anomaly centered over the North Sea for the SL category (Fig. 5a) corresponds to a significant positive V200 anomaly to its west (i.e., southeast of Greenland) and negative V200 anomaly to its east (i.e., around Scandinavia) (Fig. 4a). In addition, a significant positive SLP anomaly over the northwestern Europe and negative anomaly over the Caspian Sea are accompanied by a strong gradient in between (Fig. 5a), and are reflected by a significant V200 anomaly over the Caspian Sea in the upper troposphere (Fig. 4a), which is essential for the SRP. However, for the WL category, the SLP anomaly over the northwestern Europe is much weaker (Fig. 5b), and thus the V200 anomalies are also quite weak over the North Atlantic and the Caspian Sea (Fig. 4b). The present results indicate that the SNAO–SRP connection appears when the SNAO’s southern pole is strong and concentrated over northwestern Europe. This is partially in agreement with Hong et al. (2018a), who suggested that the SNAO with the southern pole located to the southeast of the northern counterpart can trigger the SRP in late summer.
Figure 6 shows the composite precipitation anomalies for the two categories. The anomalies are generally negative over northwestern Europe and positive over the Mediterranean Sea for both categories. This north–south seesaw pattern of the precipitation anomalies is consistent with the SNAO-related anomalies in previous studies (Folland et al. 2009; Allan and Zveryaev 2011; Linderholm et al. 2011; Bladé et al. 2012; Dong et al. 2013; Saeed et al. 2014; Cherenkova et al. 2020; Osborne et al. 2020). However, the intensities of these anomalies show great differences between the two categories. They are much greater and more significant for the SL category (Fig. 6a). Based on various previous results (e.g., Folland et al. 2009; Linderholm et al. 2011; Dong et al. 2013), these significant precipitation anomalies correspond well to the significant SNAO southern pole (Fig. 5a), whereas they seem to be quite weak for the WL category (Fig. 6b). The average precipitation anomaly within the domain 50°–60°N, 10°W–35°E, which approximately represents the northwestern Europe, is −0.42 mm day−1 for the SL category, significant at the 0.001 level and greater than the interannual standard deviation (0.32 mm day−1), about triple that for the WL category (−0.16 mm day−1), which cannot reach even the 0.05 confidence level.
4. Analysis of Rossby wave source for the interannual SNAO–SRP relationship
The Rossby wave source (RWS) is applied to interpret how the SNAO corresponds to a significant or an absent SRP.
From Fig. 7, the most prominent feature that distinguishes the SL and WL categories is the RWS anomaly over the northwestern Europe. For the SL category (Fig. 7a), this anomaly spans across the entire northwestern Europe and shows a statistical significance. By contrast, for the WL category (Fig. 7b) there are only some scattered positive anomalies around the United Kingdom. The averaged RWS anomaly within the domain 50°–60°N, 10°W–35°E is 2.8 × 10−11 s−2 for the SL category, significant at the 0.001 level and more than twice as much as for the WL category (1.1 × 10−11 s−2, which cannot reach the 0.1 confidence level). Particularly, the former is even greater than the interannual standard deviation (2.6 × 10−11 s−2). There are also some significant anomalies over the Mediterranean Sea and the Black Sea for both categories. The significant RWS anomalies over the northwestern Europe and the Mediterranean Sea are similar to those related to the SRP [Fig. 6 in Hong et al. (2018b)]. Nevertheless, only those over northwestern Europe differ much between the two categories, indicating that the RWS anomalies over this region are crucial in linking shapes of the SNAO pattern and the SNAO–SRP connection. The anomalies over the Mediterranean Sea, on the other hand, are mainly responsible for the excitation of the SRP, as shown in Hong et al. (2018b).
Figure 8 shows the composite results of the planetary vortex stretching term −fDχ. A calculation for each term of the RWS (not shown) indicates that this term contributes overwhelmingly most to the RWS. The other term—namely the advection of absolute vorticity by divergent flow, −Vχ ⋅ ∇(f + ζ)—turns out to be too small over the northwestern Europe and thus is ignored. This may be because the great distance of this region from the jet stream results in the small gradient of absolute vorticity (Lu and Kim 2004; Shimizu and de Albuquerque Cavalcanti 2011).
The dominant role of the planetary vortex stretching term −fDχ on RWS can be verified by the great similarities between distributions of the anomalous RWS and −fDχ, with respect to both pattern and intensity, and for both SL and WL categories (cf. Figs. 7a and 8a, and Figs. 7b and 8b). A significant positive anomaly covers almost the entirety of northwestern Europe for the SL category (Fig. 8a) but is quite weak for the WL category (Fig. 8b). Besides, the anomalies around the Mediterranean Sea seem to resemble each other between the two categories. The great similarities, both in pattern and intensity, between the planetary vortex stretching term −fDχ (Fig. 8) and RWS (Fig. 7) confirm the predominant role of the term −fDχ on the RWS. As the planetary vortex stretching term −fDχ is determined by the divergence Dχ, although it can also be modified by the planetary vorticity f, the anomalies of the term −fDχ actually reflect those of the divergence Dχ but with opposite signs.
There is a good coherence between the RWS and precipitation anomalies over northwestern Europe, as shown in Figs. 6 and 7. Both are strong and significant for the SL category (Figs. 6a and 7a), but much weaker for the WL category (Figs. 6b and 7b). These suggest that significant negative (positive) precipitation anomalies and the related upper-tropospheric convergence (divergence) anomalies over northwestern Europe are crucial in inducing the upper-tropospheric RWS anomalies and further triggering the downstream SRP, as for the SL category. By contrast, for the WL category, both the SNAO-related precipitation and RWS anomalies over northwestern Europe are weak, thus corresponding to the absence of the SRP. The crucial role of the precipitation anomalies over northwestern Europe in triggering the SRP can be verified by the correlation coefficient between the interannual SRPI and the domain-averaged precipitation anomalies within 50°–60°N, 10°W–35°E, which is 0.43 and significant at the 0.01 level. The connections between the SNAO-related precipitation anomalies and SRP can be interpreted as follows. The SNAO-related negative precipitation anomalies over northwestern Europe (Fig. 6a) are associated with the local anomalous descending (not shown) and upper-tropospheric wind convergence (reflected by Fig. 8a). This ind convergence anomaly (Dχ < 0) directly leads to positive vortex stretching anomaly (−fDχ > 0), which determines the positive RWS anomalies over the northwestern Europe. These RWS anomalies further induce circulation anomalies of surrounding areas, including significant meridional wind anomalies over the Caspian Sea, which are crucial for the SRP inspiration (Hong and Lu 2016). As a result, the SRP is triggered.
5. Interdecadal differences between the periods of 1970–97 and 2005–19
In this section, we will investigate features for the SNAO–SRP correspondence on the interdecadal time scale, and examine whether the mechanism is consistent with that on the interannual time scale. Analyses of this section are based on the differences between two periods: 1970–97 and 2005–19, which are selected according to the close phase shifts of the interdecadal sequences between the two indexes, as shown in Fig. 2a. The anomalous distributions for a separate period are consistent with those for the other period but with opposite phases (not shown). Therefore, we show only differences of the two periods for brevity.
Figure 9 shows the interdecadal differences of SLP and V200. The SLP (Fig. 9a) shows a significant SNAO-like pattern, with the northern pole over Greenland and the southern pole concentrated over the North Sea. On the other hand, there is a clear wavelike pattern, manifested by alternate positive and negative V200 anomalies in the midlatitudes from the Caspian Sea to East Asia (Fig. 9b), resembling that associated with the SRP (Fig. 1d). Besides, there is a significant positive anomaly over the southeast of Greenland and a negative anomaly over the North Sea (Fig. 9b), consistent with the significant SLP anomaly here (Fig. 9a). The WAF is active from Greenland and throughout midlatitude Eurasia (Fig. 9a), also suggesting the connection between the SNAO and SRP. Distributions of interdecadal differences for both the SLP and V200 (Fig. 9) are consistent with the anomalies for the interannual SL category (Figs. 4a and 5a), indicating similar circulation characteristics for the strong SNAO–SRP linkage between the interdecadal and interannual time scales.
Differences of (a) the SLP anomalies (contours; hPa) and (b) 200-hPa meridional wind anomalies (contours; m s−1) between the periods 1970–97 and 2005–19. Solid and dashed contours delineate positive and negative anomalies, respectively. Contour intervals are 0.3 hPa in (a) and 0.5 m s−1 in (b), and all the zero contours are omitted. Shading indicates anomalies significant at the 0.05 level based on Student’s t test. Vectors in (b) are the WAF related to the interdecadal differences.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
Figure 10 shows the interdecadal differences in precipitation and RWS. The precipitation (Fig. 10a) presents generally a north–south seesaw pattern over Europe, with a decrease over northwestern Europe but an increase over southern Europe. On the other hand, there are significant positive RWS differences over the northwestern Europe (Fig. 10b), and the RWS differences are contributed mainly by the vortex stretching term −fDχ (Fig. 10c). These anomalies of northwestern Europe (Figs. 10b,c) are consistent with those of the interdecadal precipitation difference (Fig. 10a), which are closely related to the anomalies of the SNAO southern pole (Fig. 9a). Besides, there are some significant positive and negative RWS anomalies over the Mediterranean Sea and the Black Sea (Fig. 10b), similar to those related to the SRP (Hong et al. 2018b). These anomalies, together with those of northwestern Europe, confirm the SNAO–SRP linkage on the interdecadal time scale. The decadal differences for both the precipitation and RWS (Fig. 10) are in agreement with the anomalies for the interannual SL category (Figs. 6a and 7a), although the decadal anomalies are weaker and show smaller regions with significant differences.
As in Fig. 9, but for (a) precipitation anomalies (shading; mm day−1), (b) the RWS anomalies (contours; 10−11 s−2), and (c) the term −fDχ (contours; 10−11 s−2). Contour intervals in (b) and (c) are ±3, ±5, ±7, and ±9 × 10−11 s−2; solid and dashed contours delineate positive and negative anomalies, respectively. Zero contours in all the panels are omitted. Shading in (b) and (c) and dots in (a) indicate anomalies significant at the 0.05 level, based on Student’s t test.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
These results suggest that the underlying mechanism for the strong SNAO-SRP linkage on the interdecadal time scale is similar to that on the interannual time scale. That is, the interdecadal variation of the SNAO corresponds to interdecadal negative (positive) precipitation anomalies over northwestern Europe, and induces positive (negative) RWS anomalies through upper-tropospheric wind convergence (divergence) anomalies. These RWS anomalies of northwestern Europe, like the disturbances, induce circulation anomalies of surrounding regions, including the meridional wind anomalies over the Caspian Sea, and resultantly trigger the downstream SRP. The mechanisms are summarized in Fig. 11.
Schematic plot for the mechanisms of the (a) strongly and (b) weakly linked categories. Solid (dashed) contours delineate positive (negative) geopotential height/sea level pressure anomalies. Colored shading of bottom and top panels indicates precipitation and RWS anomalies, respectively, and gray shading indicates the climatological jet stream. Vectors indicate the wind anomalies.
Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-21-0833.1
6. Conclusions and discussion
The summer North Atlantic Oscillation (SNAO) affects profoundly the interannual variability of Asian climate and this influence is generally explained through the Silk Road pattern (SRP), which is a teleconnection pattern along the Asian westerly jet. The correlation coefficient between July–August averaged SNAO and SRP is 0.31 during 1958–2019, statistically significant but moderate. Therefore, we classify the SNAO into two categories, a strongly linked category and a weakly linked category, according to whether the SNAO corresponds to the SRP or not, to figure out what features are crucial for the SNAO to efficiently trigger the downstream SRP.
The results show that the most prominent feature distinguishing the strongly linked and weakly linked categories is the southern pole of the SNAO. For the SNAO related closely to the SRP, the southern pole of the SNAO is centered and concentrated over the North Sea and is significant over all of northwestern Europe, whereas the southern pole shows a southwestward extension and quite weak intensity over northwestern Europe for the weakly linked category. Correspondingly, there are significant negative (positive) precipitation anomalies over the northwestern Europe for the strongly linked category, inducing significant positive (negative) local upper-tropospheric Rossby wave source anomalies and circulation anomalies over surrounding regions. These circulation anomalies lead to the ones over the Caspian Sea, which are crucial for excitation of the wave pattern along the Asian westerly jet, and thus the SRP is triggered. However, the SNAO-related precipitation and Rossby wave source anomalies for the weakly linked category are weak over northwestern Europe, corresponding to the absence of the SRP. The characteristics and the underlying mechanism for the strong SNAO–SRP linkage on the interdecadal time scale are consistent with those for the interannual strongly linked category.
The results indicate that the pattern of the SNAO’s southern pole plays a crucial role in determining whether the SNAO can trigger the SRP or not. Actually, the southern pole exhibits a stronger diversity in comparison with its northern counterpart, as has been documented by previous studies both for summer (Sun et al. 2008, 2009; Sun and Wang 2012; Hong et al. 2018a) and winter (Watanabe 2004; Zuo et al. 2015; Qiao et al. 2021). For instance, the southern pole of the NAO is located more eastward in late summer than early summer (Hong et al. 2018a) and has an eastward extension in late winter compared with early winter (Watanabe 2004; Zuo et al. 2015; Qiao et al. 2021). However, the reasons for strong diversity of the southern pole remain unclear. Whether internal atmospheric processes or atmosphere–ocean interaction affects the shapes of southern pole on both the interannual and decadal time scales is an open question. Considering that the different impacts of the NAO on climate in Europe and Asia are caused by the shape of the southern pole (e.g., Watanabe 2004; Sun et al. 2008), the underlying mechanisms for the diversity in the NAO’s southern pole need further investigation.
Despite the consistent features and mechanism between July and August for the strong SNAO–SRP linkage, the relationship between the SNAO and SRP differs significantly for the two months, which can be reflected by the wave activity flux. For the strongly linked category, the SRP origins from the North Sea in August, similar to the July–August averaged result as in this study, but also from North Africa in July (not shown), agreeing well with Kosaka et al. (2009). These on one hand suggest that the strong SNAO–SRP linkage for the July–August average is contributed more by the August results, and on the other hand suggest the existence of other factors possibly influencing the SNAO–SRP relation. For instance, the wave origin from the North Atlantic in July may be related to the rainfall anomalies of this region (Lu et al. 2002). In addition, anomalies including the Indian summer rainfall (Ding and Wang 2005; Wei et al. 2015; Zhang et al. 2018) and the SSTs of the North Atlantic (Goswami et al. 2006; Dong et al. 2013; Hong et al. 2017) can affect both the SNAO and SRP. Do these factors can play a role in the SNAO–SRP relation, and what are the underlying mechanisms? These issues should also be addressed.
Acknowledgments.
We thank the editor and three anonymous reviewers, who helped greatly in improving the manuscript. We also thank Dr. Sining Ling from the Institute of Atmospheric Physics for her help in beautifying the schematic plot. This work was jointly supported by the National Natural Science Foundation of China (Grants 42175036 and 42175039), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant 2019QZKK0102), and the National Key Research and Development Program of Ministry of Science and Technology of China (Grant 2018YFA0606403).
Data availability statement.
The circulation data used in this study are openly available from the Japanese 55-Year Reanalysis at https://rda.ucar.edu/datasets/ds628.1/#! access as described in Kobayashi et al. (2015). Precipitation data from the National Oceanic and Atmospheric Administration’s Precipitation Reconstruction dataset are openly available at https://psl.noaa.gov/data/gridded/data.prec.html as cited in Chen et al. (2002).
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