Abstract

This study attempts to assess the possible linkage between Ural–Siberian blocking and the East Asian winter monsoon (EAWM). During the boreal winter, the dominance of blocking thermally enhances cold advection downstream. The frequent occurrence of Ural–Siberian blocking potentially promotes a cold EAWM and vice versa. The seasonal blocking activity can be regarded as the combined effect of the Arctic Oscillation (AO) and the El Niño–Southern Oscillation (ENSO). Weakened (strengthened) meridional flow in the positive (negative) phase of the AO is unfavorable (favorable) for the formation of blocking highs. Because the AO shows a close relationship with the North Atlantic Oscillation (NAO), its teleconnection with Ural–Siberian blocking may exist in the form of an eastward-propagating wave train. Be that as it may, the wave train signal across East Asia may be disturbed by the external effect of a strong ENSO event, which probably enhances (weakens) the westerlies near Siberia in its warm (cold) phase. Consequently, the blocking–EAWM relationship is stronger (weaker) when the AO and ENSO are in phase (out of phase). If both AO and ENSO attain the positive (negative) phase, the Siberian high tends to be weaker (stronger) and the temperature tends to be higher (lower) in East Asia, with less (more) Ural–Siberian blocking. On the other hand, if they are out of phase, they are not strongly linked to the intensity of the Siberian high, and the blocking activity over Ural–Siberia is unclear.

1. Introduction

Atmospheric blocking (“blocking”) is made up of anticyclonic and cyclonic vortices. Its dominance over the extratropics characterizes a strong meridional-type flow (Rex 1950) and promotes the advection of polar air masses along its downstream cyclonic component. When a blocking high is located upstream adjacent to Siberia in the boreal winter, the enhanced northerly cold advection increases mass convergence in the upper troposphere, which in turn intensifies the surface Siberian high and triggers a cold air outbreak in East Asia (Ding 1990). This provides a fundamental linkage between the blocking and the East Asian cold surge. Takaya and Nakamura (2005b) studied the impact of blocking highs on cold surges by classifying them based on their origin in the Atlantic or Pacific. The Atlantic group includes the highs over the Eurasian continent, which is often regarded as a wave train signal propagating from the North Atlantic Ocean (e.g., Joung and Hitchman 1982). The Pacific group, on the other hand, refers to a quasi-stationary blocking high over the North Pacific, which may exert an influence on the East Asian trough and indirectly affect the cold surge pathways in East Asia. No matter how strong the blocking high is, the Siberian high undergoes substantial intensification only if there are preexisting cold anomalies (Takaya and Nakamura 2005a). Therefore, the Pacific group has a relatively less significant impact on the cold surges, and the focus of this study is on the blocking upstream of Siberia.

While strong blocking events were found to play a crucial role in reinforcing severe cold surges in East Asia (e.g., Joung and Hitchman 1982; Takaya and Nakamura 2005a; Lu and Chang 2009), the frequent occurrence of Ural–Siberian blocking was found to be the main contributor to two prolonged cold periods in the 2000s, which happened in February–March 2005 (Lu and Chang 2009) and January 2008 (Zhou et al. 2009). In particular, the 2008 episode combined with plentiful southwesterly moisture transported from the Bay of Bengal and a strong La Niña event, resulting in freezing rain and snowstorms that lasted for three weeks in southern China (Wen et al. 2009; Zhou et al. 2009). On the other hand, a lower frequency of Ural–Siberian blocking probably results in a warmer East Asian winter climate. Wu and Leung (2009) showed that the longest cold spell in Hong Kong, a coastal city in southern China, tends to be shorter in a winter without a Eurasian blocking event, such that the winter gets warmer. Moreover, the winter-mean intensity of the Siberian high has been found to be directly proportional to seasonal blocking activity over Ural–Siberia (L. Wang et al. 2010). As the Siberian high is a semipermanent feature of the East Asian winter monsoon (EAWM; e.g., Lau and Li 1984), a linkage apparently exists between Ural–Siberian blocking and the EAWM.

The EAWM is the most energetic global monsoon system, involving interaction between the extratropics of the East Asian continent and the tropics of the Pacific Ocean. Therefore, an accurate forecast of the EAWM should consider climate factors from both sources. On one hand, climate variability over the extratropical region can be captured by the Arctic Oscillation (AO; Thompson and Wallace 1998). On interannual time scales, Gong et al. (2001) showed that it is negatively correlated with the intensity of the Siberian high such that the AO may exert an impact on the EAWM (Gong and Ho 2002). After the climate shift in the middle 1970s, the stronger polar westerlies under the predominantly positive phase of the AO could have been a major factor responsible for the weakening trend of the Siberian high (Gong and Ho 2002). On the other hand, the El Niño–Southern Oscillation (ENSO) plays a major role in interannual climate variability over the tropical Pacific Ocean. The significant impact associated with a strong El Niño event in 1982/83 (Quiroz 1983) motivated extensive work focusing on its impact on global climate. Previous researchers have provided evidence for the tendency of the EAWM to be weak in El Niño years (e.g., Li 1990; Zhang et al. 1997). Li (1990) suggested that the Ferrel cell is activated by the active Hadley cell in El Niño years such that the strong westerlies near the surface inhibit the intensification of the Siberian high. Chen et al. (2000) further showed that the EAWM tends to be stronger (weaker) in La Niña (El Niño) years when tropical sea surface temperatures (SSTs) in the western North Pacific are higher (lower). As warmer (cooler) SSTs in La Niña (El Niño) years enhance (suppress) the convection that drives the meridional circulation in East Asia, the ENSO may remotely control the sinking motion over Siberia.

Whereas the EAWM is dynamically connected with blocking, it is teleconnected with the AO and ENSO. Thus, it raises the question of how the two climate factors exert an impact on the relationship between Ural–Siberian blocking and the EAWM. In addition, El Niño occurred more frequently in the positive phase of the Pacific decadal oscillation (PDO) from the middle 1970s to the 1990s (Mantua et al. 1997; X. Wang et al. 2009). What are the consequences of the increasing frequency of El Niño events and the positive phase of the AO?

This paper is organized as follows. Data and methods are presented in section 2. Next, the two major geopotential height modes over the Ural–Siberian region and their associated impacts on Ural–Siberian blocking and the EAWM are introduced in section 3. Further analyses consider the role of the AO and ENSO. Sections 4 and 5 make the comparison between different polarities of the AO and the ENSO, respectively, whereas section 6 concerns their combined effect. The results are discussed and summarized in section 7.

2. Data and methods

a. Data

The winter period covers the five consecutive months from November through March (NDJFM) for the 60 years from 1950 to 2009, where year 1950 represents the five months from November 1950 to March 1951, and so on. The 60 years of data were extracted from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) 2.5° latitude by 2.5° longitude gridded reanalysis datasets (Kalnay et al. 1996). The raw data include monthly fields of temperature T, sea level pressure (SLP), geopotential height Z, zonal wind U, and meridional wind V. In particular, daily fields of geopotential height at 500 hPa (Z500) were also extracted for detecting blocking events. Furthermore, the AO index and the ENSO index (SST anomalies over the Niño-3.4 region) were freely adopted from the Climate Prediction Center (CPC) website (http://www.cpc.ncep.noaa.gov/).

For standardizing the raw data, the following normalizing procedure is performed. Normalizing daily data for a calendar day x uses the 11-day samples from day x − 5 to x + 5 for the entire 60-yr study period (i.e., 660 data points), while normalizing monthly data for a particular month uses all 60 samples from the same month (i.e., 60 data points). The raw data from 29 February of leap years are excluded in the normalization procedure. The normalized value of each of these days is taken as the mean of the values for 28 February and 1 March.

b. Detection of atmospheric blocking

The blocking event detection algorithm basically follows the one adopted by Barriopedro et al. (2006, hereafter B06), which can be viewed as a modified version of Tibaldi and Molteni (1990). It begins with identifying the longitude that potentially characterizes the blocking-type circulation by applying the following zonal index equations to the daily field of Z500:

 
formula

where φN and φS represent the reference latitude at the north and the south;

 
formula
 
formula

where φN = 80°N + Δ, φ0 = 60°N + Δ, φS = 40°N + Δ, and Δ = −5°, −2.5°, 0°, 2.5°, and 5°.

Three reference latitudes are selected along each longitude based on previous climatological studies. The central point φ0 is chosen as 60°N (Treidl et al. 1981), while the northern and the southern points are set at 80° and 40°N, respectively (Austin 1980; Tibaldi and Molteni 1990). Since the geographic location of the blocking anticyclone varies with the season, a fluctuation of 5° of latitude is accepted (B06), which is indicated by Δ. A longitude is said to be blocked (i.e., blocking longitude) when Eqs. (1) and (2) are simultaneously satisfied by at least one of the five referencing latitude pairs.

As in B06’s study, a blocking region has to be made up of at least five consecutive blocking longitudes (i.e., 12.5° of longitude), except that a nonblocking longitude is not accepted in between. Once a new blocking region is identified, it marks the beginning of an event. Following that, the algorithm looks for the blocking region belonging to the same event. If either (i) any longitude constituting the blocking region on calendar day x overlaps with that on the previous day x − 1 or (ii) the centers between the blocking regions on the two days are closer than 25°, then the event is considered persisting between the two consecutive days (day x − 1 to day x). If neither of the two conditions (i) or (ii) is satisfied on day x, the event is said to be terminated on day x − 1. After the detection procedures, only those events with a duration of at least 4 days are retained in this study, which is designated by the characteristic time of the blocking region (Pelly and Hoskins 2003).

c. Study region of blocking

The scope of this study is confined to the blocking events upstream of Siberia, where the downstream cyclonic vortex of the blocking may exert a direct impact on the climatological region of the Siberian high (40°–65°N, 80°–120°E). The region is selected based on the average blocking high movement deduced from Fig. 1, which shows three local maxima over the Eurasian continent. The first two are located in the eastern Atlantic Ocean and the European continent over the Euro–Atlantic sector, while the third is over the Ural–Siberian region, which matches the secondary peak of blocking frequency near the Ural Mountains identified by others, albeit in summer (e.g., Tibaldi et al. 1994; Diao et al. 2006). As a result, the area of interest is narrowed to 30°–100°E. This region encompasses the regions considered by previous studies focusing on the impact of blocking on the East Asian winter climate—for example, 40°–80°E in Wu and Leung (2009) and 30°–90°E in L. Wang et al. (2010). This region also coincides with the extraordinary peak of blocking frequency in January 2008 identified by Zhou et al. (2009).

Fig. 1.

Longitudinal frequency distributions (%) of winter-mean onset (solid) and decaying (dashed) longitudes of blocking highs.

Fig. 1.

Longitudinal frequency distributions (%) of winter-mean onset (solid) and decaying (dashed) longitudes of blocking highs.

d. EAWM indices

Surface air temperature is indicative of the coldness of a region. Because the temperature variations are dissimilar between the northern and southern portions of East Asia on interannual time scales (B. Wang et al. 2010), there is no unique EAWM index able to capture the temperature variability in the entire EAWM region (Wang and Chen 2010a). It is therefore necessary to use at least two indices (Wu et al. 2006; B. Wang et al. 2010). On one hand, the strength of the EAWM can be measured by the Siberian high index (SHI), which is defined as the area-averaged SLP over 40°–65°N and 80°–120°E (Panagiotopoulos et al. 2005; L. Wang et al. 2010). On the other hand, the two temperature modes in the EAWM identified by B. Wang et al. (2010), which are taken as the first two leading modes of the empirical orthogonal function (EOF) applied to the covariance matrix of 2-m air temperature over 0°–60°N and 100°–140°E, are applicable for the purpose of this study. The two modes are illustrated in Fig. 2, where the first mode [temperature mode 1 (TM1)] has a center of action over northeast Asia and the second mode [temperature mode 2 (TM2)] has a center of action over China. For convenience, the sign of the TM2 is reversed such that the positive (negative) phase of the two modes represents higher (lower) temperatures in the northern and southern portions of the domain, respectively. In addition, it should be emphasized that the signs to the north and the south of the TM2 are opposite (Fig. 2b).

Fig. 2.

The (a) first and (b) second eigenvectors (EOF1 and EOF2) of winter-mean 2-m air temperature in the EAWM, with the explained variance shown at the top right. They are referred to as TM1 and TM2.

Fig. 2.

The (a) first and (b) second eigenvectors (EOF1 and EOF2) of winter-mean 2-m air temperature in the EAWM, with the explained variance shown at the top right. They are referred to as TM1 and TM2.

3. Recurring blocking high centers over Ural–Siberia

An EOF analysis is first performed on the covariance matrix made up of the winter-mean Z500 with a domain enclosing 40°–80°N and 30°–100°E. The first 10 EOFs are then extracted (EOF1–10). In particular, the first two modes (EOF1–2) are well separated from the remaining eight modes. They account for 67.2% of the total variance by displaying different orientations of an anomalous ridge–trough couplet (Figs. 3a,b). In each winter, one dominant EOF mode stands out that contains the largest value in magnitude among the 10 principal components (PCs) of all extracted EOFs in that winter. Table 1 shows that 44 out of 60 winters demonstrate dominance in the first two EOF modes. The table further divides each mode into two groups based on the polarity of the dominant EOF. As shown in Fig. 4, it is noteworthy that blocking frequency anomalies are significantly different over the central (western and eastern) portion of the Ural–Siberian region when the EOF1 (EOF2) mode predominates. It is suggested that the dominance of the two modes represents the recurrence of blocking highs over at most three geographic locations in the study region. As deduced by Figs. 5 and 6, however, their dominance should represent a large discrepancy in blocking activity over the Ural Mountains and eastern Europe, respectively. The impacts arising from the persistence of blocking over these locations on the EAWM are discussed as follows.

Fig. 3.

The first and second eigenvectors (EOF1 and EOF2) of the Z500 in the Ural–Siberian region. Their explained variance is indicated at the top right.

Fig. 3.

The first and second eigenvectors (EOF1 and EOF2) of the Z500 in the Ural–Siberian region. Their explained variance is indicated at the top right.

Table 1.

List of years for the period 1950–2009 showing dominance in the EOF1 and EOF2 modes.

List of years for the period 1950–2009 showing dominance in the EOF1 and EOF2 modes.
List of years for the period 1950–2009 showing dominance in the EOF1 and EOF2 modes.
Fig. 4.

Longitudinal distribution of blocking frequency anomalies (days) over the Northern Hemisphere for the winters demonstrating dominance in the positive (black) and negative (gray) polarities of the (a) EOF1 and (b) EOF2 modes, where the error bars represent the σ deviation.

Fig. 4.

Longitudinal distribution of blocking frequency anomalies (days) over the Northern Hemisphere for the winters demonstrating dominance in the positive (black) and negative (gray) polarities of the (a) EOF1 and (b) EOF2 modes, where the error bars represent the σ deviation.

Fig. 5.

The Z500 anomaly in NDJFM for the years demonstrating dominance in (a) positive EOF1 mode, (b) negative EOF1 mode, (c) positive EOF2 mode, and (d) negative EOF2 mode. The shaded regions are significantly different from the 60-yr climatological mean at the 95% confidence level.

Fig. 5.

The Z500 anomaly in NDJFM for the years demonstrating dominance in (a) positive EOF1 mode, (b) negative EOF1 mode, (c) positive EOF2 mode, and (d) negative EOF2 mode. The shaded regions are significantly different from the 60-yr climatological mean at the 95% confidence level.

Fig. 6.

Surface air temperature anomaly (shading; °C) and 500-hPa wind anomaly (vector; m s−1) in the winters demonstrating dominance in (a) positive EOF1 mode, (b) negative EOF1 mode, (c) positive EOF2 mode, and (d) negative EOF2 mode. The air temperature enclosed by the dotted regions and the vector wind are significantly different from the 60-yr climatology of air temperature and either zonal wind or meridional wind component, respectively, at the 95% confidence level. Note that the Tibetan Plateau is shaded black.

Fig. 6.

Surface air temperature anomaly (shading; °C) and 500-hPa wind anomaly (vector; m s−1) in the winters demonstrating dominance in (a) positive EOF1 mode, (b) negative EOF1 mode, (c) positive EOF2 mode, and (d) negative EOF2 mode. The air temperature enclosed by the dotted regions and the vector wind are significantly different from the 60-yr climatology of air temperature and either zonal wind or meridional wind component, respectively, at the 95% confidence level. Note that the Tibetan Plateau is shaded black.

a. The Ural Mountains

The two polarities of the EOF1 mode show a large discrepancy in blocking frequency near the Ural Mountains (Fig. 4a). The more (less) frequent occurrence of blocking during the negative (positive) EOF1 coincides with an abnormally high (low) Z500 center near the Ural Mountains, as shown in Fig. 5a (Fig. 5b). The associated stronger (weaker) meridional flow along its downstream edge enhances (inhibits) cold advection toward Siberia. Correspondingly, more (less) intense cold air masses accumulate over western Siberia, giving rise to a more (less) intense Siberian high and a cooler (warmer) East Asia (Figs. 6a,b). This is evidenced by high correlations between the PC1 and both the TM1 and the SHI (Table 2). However, the PC1 forms a very weak relationship with the TM2. Even though the surface cold (warm) anomaly extends equatorward from western Siberia toward South China during the negative (positive) phase of EOF1, the surface air temperature over the southern part of East Asia is not significantly lower (higher) than normal. The statistical results suggest that blocking near the Ural Mountains, in general, has a direct impact on the northern part of East Asia, but whether the impact can be exerted on the southern part may depend on other factors determining the cold air pathway. In short, the impact of the EOF1 mode on the EAWM is analogous to the TM1.

Table 2.

Linear correlations between the two EOF modes and the EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.

Linear correlations between the two EOF modes and the EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.
Linear correlations between the two EOF modes and the EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.

b. Eastern Europe

The two phases of the EOF2 mode differ in blocking frequency primarily over the Euro–Atlantic sector across the European continent and the western side of the Ural-Siberian region (Fig. 4b). During its negative phase, there are two outstanding anomalous peaks over the climatological peak and eastern Europe. The abnormally high region at 500 hPa, however, is signified by a single center over 30°E only as shown in Fig. 5d, suggesting higher blocking activity over eastern Europe. Correspondingly, the northerly wind is stronger near the Ural Mountains and it brings much more intense cold air masses toward western Siberia over 45°–75°N, 60°–105°E (Fig. 6d). The cold anomaly region shifts westward with respect to the climatological Siberian high region and extends toward Asia in its zonal but not its meridional direction. On average, the temperature over only part of the northern region of East Asia is well below normal, but this still contributes to the strong linear relationship between the PC2 and the TM1, which is significant at the 95% confidence level. On the other hand, the temperature to the south of 30°N is close to normal, suggesting a low impact on the temperature anomaly in the southern part of East Asia. The significant correlation between the PC2 and the TM2 is noteworthy.

When the positive phase of EOF2 predominates, the blocking frequency is lower than normal over the Euro–Atlantic sector and slightly higher than normal over Siberia and the western North Pacific (black line of Fig. 4b). In Fig. 6c, an anomalous anticyclonic flow and pronounced warming can be observed over 60°–90°E, but it is not associated with stronger northerly cold advection downstream, suggesting that the meridional-type circulation is not significantly stronger there. Therefore, this group cannot be considered a recurring geographic location of blocking over Siberia. Rather, it is more representative of lower blocking activity over eastern Europe due to a low anomaly near 30°E (Fig. 5c) that brings anomalous southwesterly warm advection toward Siberia (Fig. 6c). Over the majority of East Asia, the surface air temperature is close to normal except for substantial cooling in the vicinity of Japan, which may be attributed to the deepening of the East Asian trough as inferred from Fig. 5c. In other words, cooling is not evidenced in the southern part of East Asia. Hence, both the predominant positive and negative phases of EOF2 are not related to the size of the surface temperature anomaly in the region captured by the TM2. Therefore, where does the linear relationship come from?

By simply regressing the two time series by the least squares fit, a linear relationship exceeding the 99% confidence level can be obtained and is shown in Fig. 7. Specifically, there is one year with an exceptionally negative value of the PC2 and a largely positive value of the TM2. If this point is removed from the regression, which is represented by the gray dotted line, the linear correlation is still significant at the 99% confidence level, albeit with a 6% decline in the explained variance. Thus, the results suggest that the predominant EOF2 mode (Fig. 3b) is capable of explaining part of the variance of the TM2 (Fig. 2b), but it may be due to the north–south thermal contrast rather than the temperature departure in the southern region. That is, the positive (negative) phase of EOF2 may be characterized by a cooler south (north) but a warmer north (south), as evidenced by the cold anomaly in the vicinity of Japan (western Siberia). This may be due to the incoherent driving force exerted on the EAWM by extratropical and tropical climate systems, which will be analyzed in section 6.

Fig. 7.

Scatterplot between standardized values of the PC2 and TM2 (dimensionless).

Fig. 7.

Scatterplot between standardized values of the PC2 and TM2 (dimensionless).

4. The impacts of the AO

In section 3, it was shown that the EOF1 mode is strongly related to the SHI and the TM1, whereas the EOF2 mode may represent a dipole temperature pattern of the EAWM. In addition to distinctive blocking activity near the Ural Mountains (eastern Europe) under the predominant EOF1 (EOF2) mode, it is suggested that the dominance of the two modes leads to two different blocking–EAWM relationships. Indeed, the Z500 patterns associated with the two EOF modes resemble a Rossby wave train signal coming from the North Atlantic Ocean (Fig. 5). The patterns are similar to the “North Atlantic–Ural–East Asia” teleconnection pattern depicted in Li et al. (2008), which may be explained by the North Atlantic Oscillation (NAO)—a major teleconnection pattern over the Northern Hemisphere (Hurrell 1995). In this study, another well-known teleconnection pattern (AO) is considered instead since it is more representative of the extratropical climate yet retains the characters of the NAO (Thompson and Wallace 1998; Deser 2000).

During the positive (negative) phase of the AO, its signature over the North Atlantic Ocean is characterized by a stronger (weaker) seesaw pressure pattern, with a stronger (weaker) subtropical ridge giving rise to an anomalous high (low) near the Iberian Peninsula (Thompson et al. 2000). A Rossby wave train may be generated there that propagates eastward across Eurasia. As a consequence, one of the Z500 patterns in Fig. 5 may appear to be deduced from the statistically significant correlations between the AO and both the PC1 and the PC2 listed in Table 2. Their impacts on Ural–Siberian blocking can be determined by comparing the blocking frequency anomalies between the two phases of the AO, as shown in Fig. 8. Whereas the largest difference occurs in the North Atlantic Ocean, where the AO shows the strongest signature, the difference also exceeds the 90% confidence level between 50° and 62.5°E, with a peak near the Ural Mountains. This suggests that the wave train signal is more likely to resemble the EOF1 mode with a center of action near 60°E (Fig. 3a).

Fig. 8.

(top) Longitudinal distribution of blocking frequency anomalies in the Northern Hemisphere under the positive phase of the AO (solid gray) and the negative phase of the AO (solid black), where the error bar represents the σ deviation (days); (bottom) confidence level (%) for the difference in blocking frequency between the two polarities of the AO in the Student’s t test.

Fig. 8.

(top) Longitudinal distribution of blocking frequency anomalies in the Northern Hemisphere under the positive phase of the AO (solid gray) and the negative phase of the AO (solid black), where the error bar represents the σ deviation (days); (bottom) confidence level (%) for the difference in blocking frequency between the two polarities of the AO in the Student’s t test.

On the other hand, the AO is a measure of the strength of the polar vortex. A larger amount of wave activity flux propagates upward from the lower troposphere such that the polar vortex gets warmer and weaker during its negative phase, and vice versa (Chen et al. 2005; Chen and Kang 2006). Accordingly, the weaker (stronger) westerlies circulating the polar region favor (inhibit) the advection of polar air southward, resulting in a cooler (warmer) Eurasia (Hurrell 1995; Thompson and Wallace 1998) and a stronger (weaker) Siberian high (Gong et al. 2001). These impacts are evidenced by the strong correlations between the AO and both the SHI and the TM1 (Table 3), which form the basic linkage between the AO and the EAWM.

Table 3.

Linear correlations between the AO/ENSO and the two EOF modes and EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.

Linear correlations between the AO/ENSO and the two EOF modes and EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.
Linear correlations between the AO/ENSO and the two EOF modes and EAWM indices, where the bold (italic) values exceed the 99% (95%) confidence level.

5. The impacts of the ENSO

Much extensive work has been done on the relationship between the ENSO and the strength of EAWM (e.g., Li 1990; Zhang et al. 1997; Chan and Li 2004; Zhou et al. 2007). In Table 3, it is noticeable that the ENSO shows a substantial linkage only with the TM2. It appears that the correlations between the ENSO and any factors with the center of action over the extratropics are very weak. Indeed, the impacts of the ENSO on Ural–Siberian blocking and the EAWM are evidenced by the tendencies of the PC1 and the SHI, as implied by Figs. 9 and 10. During warm ENSO events (where the anomaly of the ENSO index is greater than 1.0°C), the PC1 tends to be positive and the SHI tends to be negative. These results agree with previous findings that the EAWM tends to be weak in warm ENSO years (e.g., Li 1990; Chen et al. 2000). Furthermore, an anomalous anticyclonic flow in the lower troposphere was found over the Philippine Sea, and its northward propagation would weaken the climatological cyclonic flow over East Asia such that the EAWM would tend to be weaker (Wang et al. 2000). The surface westerlies near Siberia become stronger such that the cold air lacks the tendency to intrude southward (Li 1990). Among the nine warm ENSO years, there is only one exception (2009/10) with a largely negative PC1 and a strong Siberian high (Figs. 9 and 10) contributed by the extremely negative AO in December 2009 (Wang and Chen 2010b).

Fig. 9.

Scatterplot between the ENSO index and standardized values of the PC1, where the ENSO indices >1.0°C (<−1.0°C) are enclosed by the black (gray) dashed line.

Fig. 9.

Scatterplot between the ENSO index and standardized values of the PC1, where the ENSO indices >1.0°C (<−1.0°C) are enclosed by the black (gray) dashed line.

Fig. 10.

As in Fig. 9, but for the scatterplot between the ENSO index and SHI.

Fig. 10.

As in Fig. 9, but for the scatterplot between the ENSO index and SHI.

The impact of cold ENSO years on the extratropics is not as significant as that of the warm years. There are no very clear tendencies of the PC1 and the SHI, as seen in Figs. 9 and 10. Moreover, the likelihood of blocking can be deduced by Fig. 11, which illustrates blocking frequency anomalies in warm and cold ENSO years, in which the anomaly of the ENSO index is greater than 1.0°C and less than −1.0°C, respectively. The difference between the two groups exceeds the 90% confidence level between 65° and 152.5°E. However, the frequencies in the entire region are slightly above normal in cold years, which is in contrast to a remarkable decrease in warm years. Therefore, it is suggested that the extratropical circulation over East Asia is more sensitive to the AO. It may be modulated by the ENSO if the ENSO signal is strong enough to induce an anomalous circulation over the western North Pacific to propagate toward East Asia. Comparatively, warm ENSO events are stronger than cold events such that their impacts on both blocking and the EAWM are more pronounced.

Fig. 11.

(top) Longitudinal distribution of blocking frequency anomalies in the Northern Hemisphere during warm ENSO winters (solid gray) and cold ENSO winters (solid black), where the error bars indicate the σ deviation; (bottom) confidence level (%) for the difference in blocking frequency between the two polarities of the ENSO in the Student’s t test.

Fig. 11.

(top) Longitudinal distribution of blocking frequency anomalies in the Northern Hemisphere during warm ENSO winters (solid gray) and cold ENSO winters (solid black), where the error bars indicate the σ deviation; (bottom) confidence level (%) for the difference in blocking frequency between the two polarities of the ENSO in the Student’s t test.

6. The combined effect of the AO and ENSO

The results in the previous two sections reveal the possible impacts of the AO and ENSO on the occurrence of Ural–Siberian blocking and the strength of the EAWM. Since the EAWM interacts with both extratropical and tropical regions, it is affected by the combination of the two climate factors on the blocking–EAWM relationship and whether they are in phase or out of phase.

The major Ural–Siberian blocking pattern is determined by comparing the magnitude of the first two leading EOFs listed in Table 4. It is suggested that the EOF1 (EOF2) mode tends to stand out for the in-phase (out of phase) condition. Therefore, the in- and out-of-phase conditions are accompanied by different blocking–EAWM relationships, as revealed in section 3. More evidence concerning the combined effect of the AO and ENSO can be provided by the correlation analyses listed in Tables 5 and 6. When the AO and ENSO are of the same polarity, both are closely related to the PC1, the SHI, and the TM1, but are weakly correlated with the PC2 and the TM2 (Table 5). These relationships are comparable to those with only the AO given by Table 3. Since the correlation coefficient between each of these factors and the AO is of the same sign as that with the ENSO, it is suggested that the ENSO collaborates with the AO in constructing the blocking–EAWM relationship. When the AO and ENSO are of opposite polarity, on the other hand, both of them show significant correlations with the PC2 and both the TM1 and the TM2 (Table 6). However, the correlation between each of these factors and the AO is opposite in sign to that with the ENSO (Table 6). This may indicate that the AO and ENSO exert an incoherent forcing on the extratropical–tropical interaction over the East Asian continent. Hence, this may explain why the two climate factors form a weak linkage with the SHI (Table 6). Indeed, such a weak relationship is similar to Wu and Wang’s (2002) suggestion that the Siberian high is excluded from the relationship between the AO and the EAWM. In short, the blocking–EAWM relationship is stronger (weaker) under the in-phase (out of phase) condition.

Table 4.

Mean and standard error of the standardized values of the first two leading principal components under different combinations of the AO and ENSO.

Mean and standard error of the standardized values of the first two leading principal components under different combinations of the AO and ENSO.
Mean and standard error of the standardized values of the first two leading principal components under different combinations of the AO and ENSO.
Table 5.

Linear correlations between the AO/ENSO and the two leading EOF modes and EAWM indices when the AO and ENSO are in phase, where the bold and italic values exceed the 99% and 95% confidence levels, respectively. Note that the EOF1 is the dominant blocking pattern.

Linear correlations between the AO/ENSO and the two leading EOF modes and EAWM indices when the AO and ENSO are in phase, where the bold and italic values exceed the 99% and 95% confidence levels, respectively. Note that the EOF1 is the dominant blocking pattern.
Linear correlations between the AO/ENSO and the two leading EOF modes and EAWM indices when the AO and ENSO are in phase, where the bold and italic values exceed the 99% and 95% confidence levels, respectively. Note that the EOF1 is the dominant blocking pattern.
Table 6.

As in Table 5, but for the cases when the AO and ENSO are out of phase. The bold italic values exceed the 90% confidence level. Note that the EOF2 is the dominant blocking pattern.

As in Table 5, but for the cases when the AO and ENSO are out of phase. The bold italic values exceed the 90% confidence level. Note that the EOF2 is the dominant blocking pattern.
As in Table 5, but for the cases when the AO and ENSO are out of phase. The bold italic values exceed the 90% confidence level. Note that the EOF2 is the dominant blocking pattern.

The above results show that the ENSO may modulate the impact of the AO on the extratropical circulation over Eurasia and its interaction with the tropical region, which agrees with the findings in section 5. To further illustrate how the combined effect of the AO and ENSO establish the blocking–EAWM relationship, the analysis is restricted to those years in which an anomaly of the ENSO index exceeds 1.0°C in magnitude. The years belonging to each of the four combinations are listed in Table 7. Their composites are shown in Fig. 12 and their longitudinal distributions of blocking frequency anomalies are illustrated in Fig. 13.

Table 7.

List of strong ENSO years and their classification based on the polarity of the AO.

List of strong ENSO years and their classification based on the polarity of the AO.
List of strong ENSO years and their classification based on the polarity of the AO.
Fig. 12.

Composite maps of Z500 anomalies (contour interval: 10 m) and standardized anomalies of surface air temperature (shading; °C) under different combinations of the AO and ENSO.

Fig. 12.

Composite maps of Z500 anomalies (contour interval: 10 m) and standardized anomalies of surface air temperature (shading; °C) under different combinations of the AO and ENSO.

Fig. 13.

Longitudinal distribution of blocking frequency anomalies (days) in different combinations of the AO and ENSO, with a box enclosing the study region.

Fig. 13.

Longitudinal distribution of blocking frequency anomalies (days) in different combinations of the AO and ENSO, with a box enclosing the study region.

The prominent feature of the positive AO is a stronger Icelandic low and Azores high over the North Atlantic Ocean, characterizing predominant zonal-type circulation (Figs. 12a,b). As mentioned in section 4, such a flow pattern is not favorable for blocking formation over Ural–Siberia and is responsible for pronounced warming over Eurasia. If a warm ENSO event also takes place, there is an anomalous anticyclone extending poleward from the equatorial Pacific in the upper troposphere (Fig. 12a). This is accompanied by an anomalous southerly wind near the surface, resulting in a weaker monsoonal flow (Wu and Leung 2009). Together with the low anomaly over the Urals, the spatial pattern of the Z500 anomaly over the study region is a northwest–southeast-oriented dipole (Fig. 12a), which resembles the positive phase of EOF1 (Fig. 3a) with an overall low blocking frequency over Ural–Siberia (solid gray line in Fig. 13). Conversely, the prevailing northeasterly wind near the surface over the South China Sea is strengthened when associated with a cold ENSO event (Chen et al. 2000; Wu and Leung 2009; Wang et al. 2009a; Zhou et al. 2010). The stronger monsoonal flow in the southern region does not advance the warming equatorward such that the warming is localized to the extratropical region under the positive AO–cold ENSO condition (Fig. 12b). In comparison to the positive AO–warm ENSO condition, the anomalous anticyclone over East Asia does not tilt southeastward such that the Z500 anomaly pattern is like the positive phase of EOF2 (Fig. 3b). Blocking frequency is relatively larger in the study region (dashed gray line in Fig. 13), but it is still lower than in the cold ENSO condition, as shown in Fig. 11.

In the negative phase of the AO, cold air advances southward from the polar region (Figs. 12c,d). Over East Asia, the tendency of cold air to intrude equatorward depends on the meridional-type circulation accompanied by the external effect of a strong ENSO event. This is favored by a cold ENSO event, which enhances the upper-tropospheric mass convergence near Siberia and the near-surface northerly wind over East Asia (Chen et al. 2000). In Fig. 12d, the spatial pattern is composed of an anticyclonic anomaly over the Urals and a cyclonic anomaly over Siberia, which takes shape in the negative phase of EOF1 (Fig. 3a). This negative EOF1-like pattern is signified by the more frequent occurrence of Ural–Siberian blocking (solid black line in Fig. 13). During a warm ENSO event, on the other hand, the northward extension of an anomalous anticyclone from the equatorial Pacific inhibits the southward intrusion of cold air, resulting in a cooler north and a warmer south temperature pattern (Fig. 12c). The blocking signature is sharpest over the Atlantic sector (dashed black line in Fig. 13), with an anticyclonic anomaly over Greenland and a cyclonic anomaly to its south (Fig. 12c). In contrast, the blocking frequency is quite low over Eurasia; it is as low as with the positive AO–warm ENSO over Siberia. The infrequency of blocking may arise from the stronger midlatitude westerly of a more active Ferrel cell driven by a more active Hadley cell in warm ENSO years (Li 1990). Because the low does not extend southeastward, the anomalous Z500 pattern over Ural–Siberia as shown in Fig. 12c is analogous to the negative phase of EOF2 (Fig. 3b). Therefore, the blocking–EAWM relationship is more comparable to the EOF1 (EOF2) mode when the AO and ENSO are in phase (out of phase), but what are the possible physical mechanisms responsible for such a relationship?

7. Discussion and conclusions

a. Physical mechanisms of Ural–Siberian blocking

The interaction between a cyclone and the planetary wave is one of the main physical mechanisms responsible for the establishment and maintenance of blocking (Frederiksen 1982; Hansen and Chen 1982; Dole and Gordon 1983; Colucci 1985). In other words, blocking is a manifestation of quasi equilibrium between waves of different spatial scales, as proposed by Charney and DeVore (1979). Specifically, intense cyclogenesis is an essential element for the formation of blocking highs (e.g., Rex 1950; Egger et al. 1986; Dole 1986), which often follow the intensification of a surface cyclone (Colucci 1985; Tsou and Smith 1990; Alberta et al. 1991). The cyclone under intensification is a developing baroclinic wave with a thermal trough lagging behind a height trough (Holton 2004, his Fig 6.6). A thermal ridge ahead of the trough advects the subtropical warm air masses for the establishment of a warm ridge on its downstream side (Illari 1984; Mullen 1987). If the warm ridge is located upstream of the Siberian high, it tightens the zonal temperature gradient. The large thermal contrast may create a resonance with the topographic forcing of the Ural Mountains such that a blocking high may form according to the postulation of Shabbar et al. (2001). Therefore, the blocking high over Ural–Siberia may involve interaction between the planetary wave above the Ural Mountains and a developing cyclone upstream at midlatitudes.

b. Blocking–EAWM relationship under the combined effect of the AO and ENSO

Rogers and Thompson (1995) found that more cyclones are formed over Europe, usually over the Mediterranean Sea, and move eastward across the midlatitudes in a cold Siberian winter. In the positive phase of the AO, however, the cyclones tend to move northeastward over the polar region, passing through the Kara Sea (Thompson and Wallace 1998). The associated stronger westerly wind advects more warm marine air toward Siberia (Rogers and Thompson 1995; Thompson and Wallace 1998). Accordingly, there is less blocking in Ural–Siberia and the winter gets milder in the extratropical region of East Asia. On the other hand, the westerly wind also becomes stronger in the midlatitudes during warm ENSO years because of a stronger Ferrel cell (Li 1990). This anomalous circulation inhibits the interaction of the cyclone with the planetary wave. Thus, there is less blocking in warm ENSO years, independent of the polarity of the AO. Overall, the blocking–EAWM relationship is stronger (weaker) when the AO and ENSO are in phase (out of phase), where the external forcing by the AO and ENSO are coherent (incoherent) over Ural–Siberia. The impacts of the AO and ENSO on the blocking–EAWM relationship are summarized in Fig. 14.

Fig. 14.

A schematic diagram for the predictability of Ural–Siberian blocking and the EAWM taking the role of the AO and ENSO into consideration.

Fig. 14.

A schematic diagram for the predictability of Ural–Siberian blocking and the EAWM taking the role of the AO and ENSO into consideration.

A decadal climate shift took place around the mid-1970s that involved the AO and PDO changing from their predominant negative phase to a positive phase. Since there are more warm (cold) ENSO events during the positive (negative) phase of PDO (Mantua et al. 1997), it is likely there will be more years of positive (negative) AO together with a warm (cold) ENSO after (before) the climate shift. Therefore, the blocking–EAWM relationship is not likely to undergo interdecadal variations. However, the blocking–EAWM relationship may help to explain the decadal variations in the EAWM. If there are more warm ENSO events and the positive phase of the AO predominates, there would be a greater likelihood for weak Ural–Siberian blocking and a weak EAWM. As suggested by L. Wang et al. (2010), more wave activity flux is emitted from the Ural Mountains and propagates eastward toward East Asia rather than propagating upward toward the stratosphere. This probably reflects a stronger polar vortex with stronger westerlies near 55°N (Chen et al. 2003, 2005) that contribute to a higher frequency of warm winters in East Asia after the climate shift. Furthermore, the impact of cold surges is larger during the negative phase of the AO because of the presence of blocking (Park et al. 2011). In addition to a decreasing number of cold surges in recent decades (Hong et al. 2008; Zhai et al. 2008; Wang et al. 2009b), the EAWM has also become weaker (Wang et al. 2009b; Hung and Kao 2010), which may be a consequence of the smaller impact exerted by the cold surges because of less blocking under the predominant positive phase of the AO (Park et al. 2011).

c. Conclusions

An overview of the relationship between Ural–Siberian blocking and the EAWM has been introduced. The spatial feature of Ural–Siberian blocking is acquired by performing the EOF analysis on the winter-mean Z500. The EOF1 (EOF2) mode represents the recurrence of blocking highs over the Ural Mountains (eastern Europe), which shows a stronger (weaker) linkage with the SHI and TM1 and a weaker (stronger) linkage with the TM2. Concerning the combined effect of the AO and ENSO, the EOF1 (EOF2) mode dominates and the blocking–EAWM relationship is stronger (weaker) when the two factors are in phase (out of phase), which is summarized in Fig. 14. Since the AO may undergo sharp intraseasonal transitions because of tropospheric–stratospheric interaction (Baldwin and Dunkerton 1999), further work is needed to verify the blocking–EAWM relationship at subseasonal time scales. In fact, the downward-propagating signal from the stratosphere is probably an important precursor for cold surges in East Asia (Jeong et al. 2006; Wang and Chen 2010b). Apart from the AO and ENSO, other factors such as the snow cover over Eurasia and the sea surface temperature over the North Atlantic Ocean and Indian Ocean are also important for the prediction of the EAWM (B. Wang et al. 2010); these might also be considered in our future work.

Acknowledgments

The first author is a recipient of a research studentship provided by the City University of Hong Kong. The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 104410). In addition, the authors would like to express their gratitude for the constructive feedback provided by the editor, Michael Alexander, and three anonymous reviewers.

REFERENCES

REFERENCES
Alberta
,
T. L.
,
S. J.
Colucci
, and
J. C.
Davenport
,
1991
:
Rapid 500-mb cyclogenesis and anticyclogenesis
.
Mon. Wea. Rev.
,
119
,
1186
1204
.
Austin
,
J. F.
,
1980
:
The blocking of middle latitude westerly wind by planetary waves
.
Quart. J. Roy. Meteor. Soc.
,
106
,
327
350
.
Baldwin
,
M. P.
, and
T. J.
Dunkerton
,
1999
:
Propagation of the Arctic Oscillation from the stratosphere to the troposphere
.
J. Geophys. Res.
,
104
(
D24
),
30 937
30 946
.
Barriopedro
,
D.
,
R.
Garcia-Herrera
,
A. R.
Lupo
, and
E.
Hernandez
,
2006
:
A climatology of Northern Hemisphere blocking
.
J. Climate
,
19
,
1042
1063
.
Chan
,
J. C. L.
, and
C.
Li
,
2004
:
The East Asian winter monsoon. East Asian Monsoon, C.-P. Chang, Ed., World Scientific Series on Meteorology of East Asia, Vol. 2, World Scientific Publishing, 54–106
.
Charney
,
J. G.
, and
J. G.
DeVore
,
1979
:
Multiple flow equilibria in the atmosphere and blocking
.
J. Atmos. Sci.
,
36
,
1205
1216
.
Chen
,
W.
, and
L.
Kang
,
2006
:
Linkage between the Arctic Oscillation and winter climate over East Asia on the interannual timescale: Roles of quasi-stationary planetary waves (in Chinese)
.
Chin. J. Atmos. Sci.
,
30
,
863
870
.
Chen
,
W.
,
H.-F.
Graf
, and
R.
Huang
,
2000
:
The interannual variability of East Asian winter monsoon and its relation to the summer monsoon
.
Adv. Atmos. Sci.
,
17
,
48
60
.
Chen
,
W.
,
M.
Takahashi
, and
H.-F.
Graf
,
2003
:
Interannual variations of stationary planetary wave activity in the northern winter troposphere and stratosphere and their relations to NAM and SST
.
J. Geophys. Res.
,
108
,
4797
,
doi:10.1029/2003JD003834
.
Chen
,
W.
,
S.
Yang
, and
R.-H.
Huang
,
2005
:
Relationship between stationary planetary wave activity and the East Asian winter monsoon
.
J. Geophys. Res.
,
110
,
D14110
,
doi:10.1029/2004JD005669
.
Colucci
,
S. J.
,
1985
:
Explosive cyclogenesis and large-scale circulation changes: Implications for atmospheric blocking
.
J. Atmos. Sci.
,
42
,
2701
2717
.
Deser
,
C.
,
2000
:
On the teleconnectivity of the “Arctic Oscillation.”
Geophys. Res. Lett.
,
27
,
779
782
.
Diao
,
Y.
,
J.
Li
, and
D.
Luo
,
2006
:
A new blocking index and its application: Blocking action in the Northern Hemisphere
.
J. Climate
,
19
,
4819
4839
.
Ding
,
Y.
,
1990
:
Buildup, air-mass transformation and propagating of Siberian high and its relations to cold surge in East Asia
.
Meteor. Atmos. Phys.
,
44
,
281
292
.
Dole
,
R. M.
,
1986
:
The life cycles of persistent anomalies and blocking over the North Pacific
.
Adv. Geophys.
,
29
,
31
70
.
Dole
,
R. M.
, and
N. D.
Gordon
,
1983
:
Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation: Geographical distribution and regional persistence characteristics
.
Mon. Wea. Rev.
,
111
,
1567
1586
.
Egger
,
J.
,
W.
Metz
, and
G.
Müller
,
1986
:
Forcing of planetary-scale blocking anticyclones by synoptic-scale eddies
.
Adv. Geophys.
,
29
,
183
198
.
Frederiksen
,
J. S.
,
1982
:
A unified three-dimensional instability theory of the onset of blocking and cyclogenesis
.
J. Atmos. Sci.
,
39
,
969
987
.
Gong
,
D.-Y.
, and
C.-H.
Ho
,
2002
:
The Siberian high and climate change over middle to high latitude Asia
.
Theor. Appl. Climatol.
,
72
,
1
9
.
Gong
,
D.-Y.
,
S.-W.
Wang
, and
J.-H.
Zhu
,
2001
:
East Asian winter monsoon and Arctic Oscillation
.
Geophys. Res. Lett.
,
28
,
2073
2076
.
Hansen
,
A. P.
, and
T.-C.
Chen
,
1982
:
A spectral energetic study of atmospheric blocking
.
Mon. Wea. Rev.
,
110
,
1146
1165
.
Holton
,
J. R.
,
2004
:
An Introduction to Dynamic Meteorology. 4th ed. Elsevier Academic Press, 535 pp
.
Hong
,
C.-C.
,
H.-H.
Hsu
,
H.-H.
Chia
, and
C.-Y.
Wu
,
2008
:
Decadal relationship between the North Atlantic Oscillation and cold surge frequency in Taiwan
.
Geophys. Res. Lett.
,
35
,
L24707
,
doi:10.1029/2008GL034766
.
Hung
,
C.
, and
P.
Kao
,
2010
:
Weakening of the winter monsoon and abrupt increase of winter rainfalls over northern Taiwan and southern China in the early 1980s
.
J. Climate
,
23
,
2357
2367
.
Hurrell
,
J. W.
,
1995
:
Decadal trends in North Atlantic Oscillation: Regional temperatures and precipitation
.
Science
,
269
,
676
679
.
Illari
,
L.
,
1984
:
A diagnostic study of the potential vorticity in a warm blocking anticyclone
.
J. Atmos. Sci.
,
41
,
3518
3526
.
Jeong
,
J.-H.
,
B.-M.
Kim
,
C.-H.
Ho
,
D.
Chen
, and
G.-H.
Lim
,
2006
:
Stratospheric origin of cold surge occurrence in East Asia
.
Geophys. Res. Lett.
,
33
,
L14710
,
doi:10.1029/2006GL026607
.
Joung
,
C.-H.
, and
M. H.
Hitchman
,
1982
:
On the role of successive downstream development in East Asian polar air outbreaks
.
Mon. Wea. Rev.
,
110
,
1224
1237
.
Kalnay
,
E.
, and
Coauthors
,
1996
:
The NCEP/NCAR 40-Year Reanalysis Project
.
Bull. Amer. Meteor. Soc.
,
77
,
437
471
.
Lau
,
K. M.
, and
M. T.
Li
,
1984
:
The monsoon of East Asia and its global associations—A survey
.
Bull. Amer. Meteor. Soc.
,
65
,
114
125
.
Li
,
C.
,
1990
:
Interaction between anomalous winter monsoon in East Asia and El Niño events
.
Adv. Atmos. Sci.
,
7
,
36
46
.
Li
,
J.
,
R.
Yu
, and
T.
Zhou
,
2008
:
Teleconnection between NAO and climate downstream of the Tibetan Plateau
.
J. Climate
,
21
,
4680
4689
.
Lu
,
M.-M.
, and
C.-P.
Chang
,
2009
:
Unusual late-season cold surges during the 2005 Asian winter monsoon: Roles of Atlantic blocking and the central Asian anticyclone
.
J. Climate
,
22
,
5205
5217
.
Mantua
,
N. J.
,
S. R.
Hare
,
Y.
Zhang
,
J. M.
Wallace
, and
R. C.
Francis
,
1997
:
A Pacific interdecadal climate oscillation with impacts on salmon production
.
Bull. Amer. Meteor. Soc.
,
78
,
1069
1079
.
Mullen
,
S. L.
,
1987
:
Transient eddy forcing of blocking flows
.
J. Atmos. Sci.
,
44
,
3
22
.
Panagiotopoulos
,
F.
,
M.
Shahgedanova
,
A.
Hannachi
, and
D. B.
Stephenson
,
2005
:
Observed trends and teleconnections of the Siberian high: A recently declining center of action
.
J. Climate
,
18
,
1411
1422
.
Park
,
T.-W.
,
C.-H.
Ho
, and
S.
Yang
,
2011
:
Relationship between the Arctic Oscillation and cold surges over East Asia
.
J. Climate
,
24
,
68
83
.
Pelly
,
J. L.
, and
B. J.
Hoskins
,
2003
:
A new perspective on blocking
.
J. Atmos. Sci.
,
60
,
743
755
.
Quiroz
,
R. S.
,
1983
:
The climate of the “El Niño” winter of 1982–83—A season of extraordinary climatic anomalies
.
Mon. Wea. Rev.
,
111
,
1685
1706
.
Rex
,
D. F.
,
1950
:
Blocking action in the middle troposphere and its effect upon regional climate. Part II: The climatology of blocking action
.
Tellus
,
2
,
275
301
.
Rogers
,
J. C.
, and
E.
Thompson
,
1995
:
Atlantic Arctic cyclones and the mild Siberian winters of the 1980s
.
Geophys. Res. Lett.
,
22
,
799
802
.
Shabbar
,
A.
,
J.
Huang
, and
K.
Higuchi
,
2001
:
The relationship between the wintertime North Atlantic Oscillation and blocking episodes in the North Atlantic
.
Int. J. Climatol.
,
21
,
355
369
.
Takaya
,
K.
, and
H.
Nakamura
,
2005a
:
Mechanisms of intraseasonal amplification of the cold Siberian high
.
J. Atmos. Sci.
,
62
,
4423
4440
.
Takaya
,
K.
, and
H.
Nakamura
,
2005b
:
Geographical dependence of upper-level blocking formation associated with intraseasonal amplification of the Siberian high
.
J. Atmos. Sci.
,
62
,
4441
4449
.
Thompson
,
D. W. J.
, and
J. M.
Wallace
,
1998
:
The Arctic Oscillation signature in the wintertime geopotential height and temperature fields
.
Geophys. Res. Lett.
,
25
,
1297
1300
.
Thompson
,
D. W. J.
,
J. M.
Wallace
, and
G. C.
Hegerl
,
2000
:
Annular modes in the extratropical circulation. Part II: Trends
.
J. Climate
,
13
,
1018
1036
.
Tibaldi
,
S.
, and
F.
Molteni
,
1990
:
On the operational predictability of blocking
.
Tellus
,
42A
,
343
365
.
Tibaldi
,
S.
,
E.
Tosi
,
A.
Navarra
, and
L.
Pedulli
,
1994
:
Northern and Southern Hemisphere seasonal variability of blocking frequency and predictability
.
Mon. Wea. Rev.
,
122
,
1971
2003
.
Treidl
,
R. A.
,
E. C.
Birch
, and
P.
Sajecki
,
1981
:
Blocking action in the Northern Hemisphere: A climatological study
.
Atmos.–Ocean
,
19
,
1
23
.
Tsou
,
C.-H.
, and
P. J.
Smith
,
1990
:
The role of synoptic/planetary-scale interactions during the development of a blocking anticyclone
.
Tellus
,
42A
,
174
193
.
Wang
,
B.
,
R.
Wu
, and
X.
Fu
,
2000
:
Pacific–East Asia teleconnection: How does ENSO affect East Asian climate?
J. Climate
,
13
,
1517
1536
.
Wang
,
B.
,
Z.
Wu
,
C.-P.
Chang
,
J.
Liu
,
J.
Li
, and
T.
Zhou
,
2010
:
Another look at interannual-to-interdecadal variations of the East Asian winter monsoon: The northern and southern temperature modes
.
J. Climate
,
23
,
1495
1512
.
Wang
,
L.
, and
W.
Chen
,
2010a
:
How well do existing indices measure the strength of the East Asian winter monsoon?
Adv. Atmos. Sci.
,
27
,
855
870
.
Wang
,
L.
, and
W.
Chen
,
2010b
:
Downward Arctic Oscillation signal associated with moderate weak stratospheric polar vortex and the cold December 2009
.
Geophys. Res. Lett.
,
37
,
L09707
,
doi:10.1029/2010GL042659
.
Wang
,
L.
,
W.
Chen
,
W.
Zhou
, and
R. H.
Huang
,
2009a
:
Interannual variations of East Asian trough axis at 500 hPa and its association with the East Asian winter monsoon pathway
.
J. Climate
,
22
,
600
614
.
Wang
,
L.
,
R.
Huang
,
L.
Gu
,
W.
Chen
, and
L.
Kang
,
2009b
:
Interdecadal variations of the East Asian winter monsoon and their association with quasi-stationary planetary wave activity
.
J. Climate
,
22
,
4860
4872
.
Wang
,
L.
,
W.
Chen
,
W.
Zhou
,
J. C. L.
Chan
,
D.
Barriopedro
, and
R.
Huang
,
2010
:
Effect of the climate shift around mid 1970s on the relationship between wintertime Ural blocking circulation and East Asian climate
.
Int. J. Climatol.
,
30
,
135
158
,
doi:10.1002/joc.1876
.
Wang
,
X.
,
D.
Wang
, and
W.
Zhou
,
2009
:
Decadal variability of twentieth-century El Niño and La Niña occurrence from observations and IPCC AR4 coupled models
.
Geophys. Res. Lett.
,
36
,
L11701
,
doi:10.1029/2009GL037929
.
Wen
,
M.
,
S.
Yang
,
A.
Kumar
, and
P.
Zhang
,
2009
:
An analysis of the large-scale climate anomalies associated with the snowstorms affecting China in January 2008
.
Mon. Wea. Rev.
,
137
,
1111
1131
.
Wu
,
B.
, and
J.
Wang
,
2002
:
Winter Arctic Oscillation, Siberian High and East Asian winter monsoon
.
Geophys. Res. Lett.
,
29
,
1897
,
doi:10.1029/2002GL015373
.
Wu
,
B.
,
R.
Zhang
, and
R.
D’Arrigo
,
2006
:
Distinct modes of the East Asian winter monsoon
.
Mon. Wea. Rev.
,
134
,
2165
2178
.
Wu
,
M. C.
, and
W. H.
Leung
,
2009
:
Effect of ENSO on the Hong Kong winter season
.
Atmos. Sci. Lett.
,
10
,
94
101
.
Zhai
,
P.
,
Z.
Yan
, and
X.
Zou
,
2008
:
Climate extremes and climate-related disasters in China. Regional Climate Studies of China, C. Fu et al., Eds., Springer-Verlag, 313–339
.
Zhang
,
Y.
,
K. R.
Sperber
, and
J. S.
Boyle
,
1997
:
Climatology and interannual variation of East Asian winter monsoon: Result from the 1979–95 NCEP/NCAR reanalysis
.
Mon. Wea. Rev.
,
125
,
2605
2619
.
Zhou
,
L. T.
,
F.
Tam
,
W.
Zhou
, and
J. C. L.
Chan
,
2010
:
Influence of South China Sea SST and the ENSO on winter rainfall over South China
.
Adv. Atmos. Sci.
,
27
,
832
844
,
doi:10.1007/s00376-009-9102-7
.
Zhou
,
W.
,
X.
Wang
,
T. J.
Zhou
,
C.
Li
, and
J. C. L.
Chan
,
2007
:
Interdecadal variability of the relationship between the East Asian winter monsoon and ENSO
.
Meteor. Atmos. Phys.
,
98
,
283
293
,
doi:10.1007/s00703-007-0263-6
.
Zhou
,
W.
,
J. C. L.
Chan
,
W.
Chen
,
J.
Ling
,
J. G.
Pinto
, and
Y.
Shao
,
2009
:
Synoptic-scale controls of persistent low temperature and icy weather over southern China in January 2008
.
Mon. Wea. Rev.
,
137
,
3978
3991
.