During boreal winters, cold air outbreaks (CAOs) often hit China and have a great impact on the economy, health, transportation, and other functions. It was also reported that the extremely cold temperature would increase the risk of ischemic heart disease mortality (Guo et al., 2013). The CAOs are characterized by a distinct sharp drop in temperature and an intense northerly wind from the Siberian region (Wang and Ding, 2006). Sometimes a CAO brings snowstorm, heavy rainfall, glaze, and frost events. In January 2008, southern China suffered from persistent cold air temperature and heavy rainfall at historic record levels since 1950 (the so-called “0801” event, see Tao and Wei, 2008; Hui, 2009; Wen et al., 2009; Zhou et al., 2009; Bueh et al., 2011; Nath et al., 2014). From 11 December 2009 to 20 January 2010, northern China also experienced prolonged cold weather. In recent years, CAOs have received increasing attention from both scientists and the government.

Two typical types of CAOs have been recognized on the basis of their spatial extent and duration, the ordinary cold wave events (CWEs) and the extensive and persistent extreme cold events (EPECEs), according to time of the CAOs (Peng and Bueh, 2011, 2012). Peng and Bueh (2011, 2012) noted that, compared with the CWEs, EPECEs are characterized by cold surface air temperature (SAT) ano-malies with a relatively large spatial extent and a longer persistence. Taking the “0801” event as an example, a typical EPECE that persisted for about one month, a cold SAT covered most areas of China and produced severe icing conditions over central and southern China (Zhou et al., 2009; Bueh et al., 2011; Peng and Bueh, 2012). Peng and Bueh (2012) revealed that most EPECEs begin as a CWE. However, they found that, unlike the CWEs, the EPECEs are preceded by a southwest–northeast-oriented ridge over the European/Barents Sea regions.

On the intraseasonal timescale, the CAOs over East Asia are closely related to the blocking circulations over the Eurasian continent. Tao (1959) summarized that three major routes of CAOs over East Asia are accompanied by three different blocking circulations. Ye et al. (1962) proposed that the breakdown of the blocking circulation over the Ural Mountains is the main contributor to the CWEs over East Asia. Shi and Zhi (2007) investigated the blocking activity over the Eurasian continent and found three key regions where blocking occurs, namely, the Ural Mountains, Lake Baikal, and the Sea of Okhotsk. Martius et al. (2009) found that blocking was a precursor to stratospheric sudden warming (SSW). Shaw and Perlwitz (2013) and Nath et al. (2016) suggested that the planetary wave reflection from the stratosphere during SSW has significant impact on CAOs. Ji et al. (2008) and Li et al. (2010) concluded that blocking circulations over these three key regions are related to CAOs over different regions, consistent with the conclusion of Tao (1959). In particular, they emphasized the crucial importance of blocking over the Ural Mountains on the CAOs over China. It was noted in the “0801” event that the recurrence of blocking circulation over the Ural Mountains facilitated a long-lasting cold background condition, causing four precipitation events with icy rain and snowfall over southern China (Tao and Wei, 2008; Zhou et al., 2009; Nath and Chen, 2016). Zhang et al. (2008) also noted that during the “0801” event, the blocking circulation over the Ural Mountains was located to the north of its climatological position and maintained strong amplitude. Bueh et al. (2011) suggested that an anticyclonic anomaly over Scandinavia/western Russia with an eastward extension played an important role in the long-lasting cold temperature in China during the “0801” event. Nath and Chen (2016) concluded that the reflection of wavenumber 1 from the stratosphere down to the troposphere played a nonnegligible role for the blocking activity over the Urals–Siberian region and thereby favoring the “0801” event. Prior studies suggest that the blocking behaviors are quite different for the CWEs and the EPECEs. However, such a difference has thus far not yet been clarified in depth.

Synoptic transient eddies play a crucial role in the formation and maintenance of the blocking circulation. Shutts (1983) demonstrated that a transient eddy contributes to the formation of blocking circulation through the cascade of enstrophy. Nakamura and Wallace (1993) found that the amplification of blocking is preceded by an abnormal recurrence of transient eddies upstream, which feed the blocking with low potential vorticity (PV). Zhi (1993) investigated the role of low- and high-frequency eddies in the formation and maintenance of a typical blocking event. It was noted that the formation and maintenance of blocking are due primarily to low-frequency eddies, with secondary contributions from high-frequency eddies. Nakamura et al. (1997) quantitatively evaluated the contribution of transient eddy feedback forcing to the blocking over North Pacific and North Atlantic and emphasized that the amplification of the North Pacific blocking is mainly due to transient eddy feedback forcing. Several studies recently revealed that a transient eddy can break down cyclonically or anticyclonically and contributes considerably to the formation and maintenance of blocking over North Atlantic and western Europe (Woollings et al., 2008; Michel and Rivière, 2011). However, it is still unclear which type of synoptic Rossby wave breaking (RWB) is responsible for the blocking activity of the CWEs or EPECEs.

Adopting this perspective, this study investigated, on the intraseasonal timescale, the different blocking behaviors of the CWEs and EPECEs on the basis of three types of blocking indices and tried to further clarify their intrinsic features in terms of the synoptic RWB analysis. Section 2 describes the data and methods used in the study. In Section 3, we discuss blocking activities for the CWEs and EPECEs. Section 4 presents the causative factors in distinguishing the blocking behaviors during the CWEs and EPECEs. A summary and discussion are provided in Section 5.

In this study, we used the daily-mean gridded data from the NCEP/NCAR reanalysis (Kalnay et al., 1996) during the winters (November 1–March 31) from 1951 to 2011. The variables derived here were the zonal (u) and meridional (v) winds, and geopotential height (z). These data were on a grid with a horizontal resolution of 2.5° × 2.5° and 17 vertical pressure levels. We used the observed daily surface air temperature from 756 stations in China, which were archived by the China Meteorological Administration.

For the CWEs and the EPECEs, we adopt the same cases as in Peng and Bueh (2011, 2012), which are listed in Tables 1 and 2. The CWEs, characterized by sharp SAT drops, are defined on the basis of the difference between the maximum and the minimum daily mean SATs during an event and the daily mean SAT anomaly relative to its climatological mean (1951–2011). For details of the definition of the CWEs, readers are referred to Peng and Bueh (2012). As in that study, we select a total of 25 CWEs from all of the identified CWEs according to the following criteria: 1) the 500-hPa geopotential height (Z500) anomaly relative to the long-term daily climatology of 1950–2005 averaged over the Ural Mountains (40°–60°N, 40°–70°E) during an event was greater than 100 m because the blocking circulation over the Ural Mountains is a typical feature of the CAOs in China; 2) the total number of stations in China during a CWE is as large as possible; and 3) to distinguish the features between the ordinary CWEs and the EPECEs, the selected CWEs do not overlap with the EPECEs.

An EPECE was determined when 8 consecutive days occurred with the number of extreme cold temperature stations exceeding 10% of the total number of stations (Peng and Bueh, 2011). After they were identified, the EPECEs were then categorized by applying agglomerative hierarchical clustering to the SAT anomalies at 756 National Standard Stations in China. Initially, a set of “objects” (e.g., 756 stations of SAT anomalies) was defined and each object was considered as a cluster. Then, the Euclidian distance was computed between each cluster, and the clusters merged iteratively until all of the objects had been aggregated into a single group on the basis of the closest Euclidian distance. At the same time, the maximum intra-cluster distances and minimum inter-cluster distances were calculated. The number of groups was determined when these two distances became invariant simultaneously. Following this procedure, all of the EPECEs were classified into five groups. The EPECEs chosen in the present study were members of the largest group among the five groups. Readers are referred to Peng and Bueh (2011, 2012) for more details.

Given the wide variety of blocking identifications, we employ three blocking indices to investigate the blocking activity during CAOs to obtain reliable results. The first is the conventional blocking index defined by Tibaldi and Molteni (1990) (referred to as the TM90 index), which is based on the fact that blocking occurs where the meridional Z500 gradients are locally reversed around 50°N. Initially, the 8-day low-pass filter (Nakamura et al., 1997) is applied to the Z500 field to identify blockings that are quasi-stationary. For each longitude, the southern and northern Z500 gradients (GHGS and GHGN) are computed as follows:

where $\phi$ _n = 80°N+Δ, $\phi$ _o = 60°N+Δ, $\phi$ _s = 40°N+Δ, Δ= –5°, 0°, or 5°. A given longitude is considered as “blocked” and is designated as 1 at a given time when the following criteria are satisfied: GHGS > 0 and GHGN < –10. The second index is developed by Pelly and Hoskins (2003) (referred to as the PH03 index), who defined a blocking where the meridional potential temperature gradient on the PV surface near the tropopause (e.g., 2 PVU; 1 PVU = 10^–6 K kg^–1 m² s^–1) is locally reversed. The PH03 index procedure consists of four steps: 1) The Ertel PV was calculated per ${\rm PV} \!=\! - g\left( {f \!+\! {ζ _\theta }} \right)$ $\left( {\partial p/\partial \theta } \right)$ (Hoskins et al., 1985), where g is gravity, f is the Coriolis frequency, ζ_θ is the relative vorticity on the isentropic surface, p is the pressure level, and θ is the potential temperature. Then the potential temperature on a 2-PVU surface was linearly retrieved from the resultant isentropic PV fields. 2) The blocking index at longitude λ_o was defined as $B \!\!=\!\! \displaystyle\frac{2}{{\Delta \phi }}\!\!\int_{{\phi _{\rm o}}}^{{\phi _{\rm o}} + \Delta \phi /2} {\theta {\rm d}\phi } \!-\!\! \frac{2}{{\Delta \phi }}\!\!\int_{{\phi _{\rm o}} + \Delta \phi /2}^{{\phi _{\rm o}}} {\theta {\rm d}\phi } $ , where ϕ_o is the reference latitude around the maximum latitude of the climatological annual mean transient eddy kinetic energy at 300 hPa. Local instantaneous blocking at λ_o is defined if B is positive. 3) Local instantaneous blocking is considered as large-scale blocking if B is positive for at least 15° of longitude. 4) The PH03 index at a particular longitude is specified as 1 when a large-scale blocking occurs within 10° of longitude of that point for at least 4 consecutive days.

In this study, we also employ the two-dimensional blocking indicator proposed by Small et al. (2014) based on the modified PV anomaly proposed by Schwierz et al. (2004) (referred to as the mAPV index). The index considers a blocking event as a negative PV anomaly underneath a raised tropopause (Schwierz et al., 2004). The identification procedure consists of three steps: 1) The PV within 500–150 hPa is averaged vertically and then is subtracted by its zonal mean to retain the vertical mean PV anomaly. 2) Every closed mAPV anomaly is identified when a PV anomaly with above –1.0 PVU crossing the region from 40° to 75°N and its bounding rectangle is retained. 3) A blocking event is determined when there are 5 consecutive days with the rectangles above 10⁶ km² overlapped by no less than 15° × 15°. This index is capable of identifying blocking of both wave breaking and isolated high. Readers are referred to Bueh and Xie (2015) and Xie and Bueh (2015) for more details regarding these three blocking indices.

To investigate synoptic cyclonic and anticyclonic RWB events, we adopt the wave-breaking detection method proposed by Rivière et al. (2010). The algorithm aims to retrieve RWB days having a meridional overturning of the PV contour and identify a synoptic RWB event persisting less than 6 consecutive RWB days. The detection method for a synoptic RWB event consists of four steps as in Bueh and Xie (2015). First, we extract each PV contour at 0.5-PVU intervals over all PV values at 330 K and retain the PV contours that are circumpolar. Second, a RWB day is identified when the PV contour has meridional overturning. Third, we divide the RWB into the anticyclonic RWB (AWB) and cyclonic RWB (CWB) according to the PV contour reversing southward and northward. To determine the AWB and CWB, we compare the latitudes of the points having the same longitude along the reversal part, i.e., the previous point (P_b) and the first point P_i of the wave breaking part (Fig. 1). An AWB (CWB) occurred when the latitude of P_b was greater (smaller) than P_i. Finally, we simply defined an AWB index and a CWB index to represent the AWB and CWB features. The AWB (CWB) index was set at 1 for each grid point that was considered to an AWB (CWB) persisting for fewer than 6 consecutive days; otherwise the index was set at 0.

Figure 1 Schematic describing the detection method for (a) AWB and (b) CWB (from Bueh and Xie, 2015). Each contour represents a PV isoline from west to east. The thin solid (thick dashed) part of the contour indicates a poleward (reversing, i.e., wave breaking) PV gradient.Pi is the first point of wave breaking part and Pb is the previous point along the PV contour having the same longitude (thin dashed meridian line) of Pi. All of the points from Pi to Pf are recognized to be related to an AWB (CWB) if Pi lies south (north) to Pb.

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As is evident from Tables 1 and 2, in most cases, the duration of the CWEs centered around 5 days, while the duration of the EPECEs ranged from 8 to 29 days. Therefore, to give an intuitive impression of the difference between these two CAO events, we selected a typical CWE case with a duration of 5 days and a typical EPECE case lasting for 10 days to examine their cold air activities and influences on China. The CWE chosen here is listed in Table 1 as the fifteenth, which took place from 21 to 25 December 1983 with its peak day on 23 December. The EPECE is listed in Table 2 as the 21st event, which occurred from 1 to 10 November 1981 with its peak day on 7 November.

Figure 2 shows the SAT anomalies (relative to the long-term daily climatology of 1951–2011) for the CWE and the EPECE. Hereafter, for brevity, day N (–N) refers to the day N days after (before) the onset day of the CWE or the EPECE. For a typical CWE, on the peak day (Fig. 2a), a moderate cold SAT anomaly prevailed over China. On the final day of the CWE (Fig. 2c), i.e., two days later, the cold SAT anomaly had considerably weakened and was mainly over southeastern and northeastern China, rendering it a transient feature. Compared with the CWE, the cold SAT anomaly of the EPECE was much more intensive and persistent. On the peak day (Fig. 2b), the cold SAT anomaly was below –16 °C over northeastern China. On day 8 (Fig. 2d), i.e., 2 days after the peak day, the cold SAT anomaly, though weakened, still covered most areas of China. This result was quite consistent with the findings of Peng and Bueh (2012).

Figure 2 The surface air temperature anomaly (°C) relative to the long-term daily climatology from 1951 to 2011 for the CWE on (a) day 6 and (c) day 8, and for the EPECE on (b) day 2 and (d) day 4.

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Figure 3 shows the distribution of the climatological mean blocking frequency using three different indices: TM90, PH03, and mAPV. It is clear that there are mainly two blocking frequency maxima over North Atlantic and North Pacific and that blocking occurred more frequently in the winter than in the other seasons. This result is consistent with the findings of earlier studies (Tibaldi and Molteni, 1990; Pelly and Hoskins, 2003; Small et al., 2014). During winter, the blocking frequency based on the TM90 index showed bimodal feature. In contrast, the blocking occurrence indicated by the PH03 index was characterized by three peaks over North Atlantic/European sector, Northeast Asia, and central North Pacific (Fig. 3b). In addition, the peaks of the blocking frequency from the PH03 index were higher than those from the TM90 index over North Atlantic and the Eurasian continent (Fig. 3b). Pelly and Hoskins (2003) attributed this significant difference to the use of variable blocking reference latitudes. However, the TM90 index is widely used in both real-time blocking monitoring and scientific studies. In the two-dimensional distribution of the blocking frequency (Figs. 3c, d), the maximum center over North Atlantic extended eastward to Lake Baikal. Although blocking frequencies using the three indices showed a variation in the amplitude, the blocking occurrence maxima were basically distributed in consistent locations over the ocean basins. The climatology sensitivity during the periods 1979–2011 and 1951–78 was examined, and a small difference was seen among the blocking frequencies of three climatologies.

Figure 3 Climatological mean (1950–2011) blocking frequency against longitude identified by using the (a) TM90 index and (b) PH03 index. The dashed line shows the annual mean, and the solid line indicates the winter mean. (c) The annual mean and (d) winter mean of the climatological mean blocking frequency in terms of the mAPV index. The units are given in percentage of blocking days.

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An analog of Fig. 3 is the distribution of the standard deviation (Fig. 4), which shows that the distributions of the standard deviation maxima agree well with those of the climatological means. This result indicates that the maxima of the blocking frequencies is characterized by the highest interannual variability. However, differences among the amplitudes of the standard deviation maxima are smaller than those of the climatological mean. The values of the maxima for the standard deviations of the blocking frequency are relatively close.

Figure 4 As in Fig. 3, but for the standard deviation.

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Figure 5 shows the Hovmöller diagram of the blocking frequency and corresponding anomalies for the CWEs and EPECEs using the TM90 and PH03 indices. Because the Hovmöller diagram does not include geographic locations, we define the Ural Mountains, the Yenisei River valley, and Northeast Asia encompassing 40°–75°, 75°–110°, and 110°–150°E, respectively. At each longitude, the blocking occurrence frequency on day i for CWEs or EPECEs was defined as f(i) =n(i)/N, where N is total number of CWEs or EPECEs and n(i) is the number of CWEs or EPECEs associated with blocking on day i. Though the maximum blocking frequency centers for the CWEs and EPECEs agree with those of the winter mean (Figs. 5a, b), blocking is considerably more frequent over the Eurasian continent. Because the relative blocking frequencies are calculated on the basis of the CAOs, the result indicates that both the CWEs and EPECEs are closely related to blocking recurrence over the Eurasian continent. This result agrees well with prior findings (Ji et al., 2008; Tao and Wei, 2008; Zhou et al., 2009; Li et al., 2010). In those studies, the connections between the blockings and the CAOs are extracted on the basis of the blocking occurrence or an individual CAO. In contrast, in this study, the connection is obtained from more CAOs. As indicated by the TM90 index (Fig. 5a), for the CWEs, from days –6 to –3, weak abnormally high blocking frequencies are observed over the Ural Mountains and Northeast Asia. From days –2 to 0, the occurrence of blocking over the Ural Mountains increases and extendes eastward to west of the Yenisei River valley. However, blocking over the Eurasian continent decreases after day 1. In contrast, the blocking frequency of the EPECEs is relatively higher than that of the CWEs (Fig. 5b). From days –4 to 8, blocking increases over both the Ural Mountains and Lake Baikal, particularly around Lake Baikal. On the other hand, a moderately high blocking frequency over North Pacific extends westward to the Yakutsk/Okhotsk region and eventually coincides with the enhanced blocking frequency band over Lake Baikal.

Figure 5 Hovmöller diagrams of the blocking frequency and corresponding anomalies identified by using the TM90 index for (a) the CWEs and (b) the EPECEs. (c) and (d) as in (a) and (b), but showing the blocking frequency in terms of the PH03 index. The units are given in percentage of blocking days. The four vertical blue dashed lines indicate 40°, 75°, 110°, and 150°E and the two horizontal blue dashed lines signify days 0 and 7 of the CAO events.

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As observed in Fig. 5c, from days –2 to 3, a considerably high blocking frequency is identified over Lake Baikal with the PH03 index, extending eastward to that of the TM90 index. The blocking of the EPECEs using the PH03 index is considerably more recurrent than that of the CWEs (Fig. 5d), consistent with the above-mentioned different blocking frequencies based on TM90. From days –10 to –8, the maximum abnormally blocking frequency extends eastward from western Europe to Lake Baikal. From days –7 to –1, the blocking frequently occurs further over Lake Baikal. From days 0 to 7, e.g., during the EPECEs, the maximum blocking frequency with a positive anomaly remains over Lake Baikal and extends eastward to the Yakutsk/Okhotsk region.

As the TM90 and PH03 indices are characterized by a longitude distribution, the exact blocking location remains uncertain. Therefore, we present below the blocking frequency in terms of the two-dimensional blocking indicator defined by Small et al. (2014). As the time and location of blocking vary among events, we average the blocking frequency in 4 days for each event, taking days –12 to –9, –8 to –5, and so on, for example, and then combine the 4-day mean blocking frequency for the CWEs and the EPECEs. Figure 6 shows the blocking frequency in an intraseasonal evolutionary process for the CWEs and the EPECEs as indicated by the mAPV index. We first discuss the situation of the CWEs. From days –12 to –9 (Fig. 6a), three maximum blocking centers with moderate positive blocking frequency anomalies reside over North Atlantic, the Ural Mountains, and North Pacific. From days –8 to –1 (Figs. 6b, c), the blocking occurrence is further enhanced over the Ural Mountains. During days 0 to 3 (Fig. 6d), the blocking frequency over the Ural Mountains increases considerably and extends eastward to Lake Baikal. Afterwards (Figs. 6e, f), the blocking frequency over the Eurasian continent gradually decreases and retreats westward to the Ural Mountains in a northwest–southeast tilt.

Figure 6 The blocking frequency (contours) and corresponding anomalies (shading) in the intraseasonal evolutionary process for the CWEs (left column) during (a) days –12 to –9, (b) days –8 to –5, (c) days –4 to –1, (d) days 0 to 3, (e) days 4 to 7, and (f) days 8 to 11. The units are given in percentage of blocking days. The contours are drawn for every 10%. (g)–(l) As in (a)–(f), but for the EPECEs.

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For the EPECEs, from days –12 to –9 (Fig. 6g), the blocking occurs unusually frequently over eastern North Atlantic and moderately frequently over the Ural Mountains. Correspondingly, positive blocking frequency anomalies are observed over these two regions. In contrast to the CWEs, from days –8 to –1 (Figs. 6h, i), the maximum blocking frequency over the Ural Mountains increases and extends northeastward to the Kara Sea in a southwest–northeast orientation. During days 0 to 3 (Fig. 6j), the maximum blocking frequency with a positive anomaly over the Ural Mountains is further amplified and extends eastward to 110°E. During days 4 to 11 (Figs. 6e, f), although the blocking occurrence over the Ural Mountains decreases, the abnormally high blocking frequency is maintained north of Lake Baikal in a southwest–northeast tilt. It is noteworthy that the southwest–northeast-oriented band of high blocking frequency indicates a pronounced barotropic development of geostropic disturbances, which favor the persistence of blocking circulations (Zeng, 1983).

In summary, both the CWEs and the EPECEs are preconditioned by an increased blocking frequency over midlatitude North Atlantic. The blocking frequency climatological mean over North Atlantic is approximately 30%, while on average, it increases to 40% and 50% during the CWE and EPECE. For the CWEs, the blocking occurs mainly over the Ural Mountains and thus is characterized by a regional distribution. In contrast, the abnormally high blocking frequency of the EPECEs gradually extends northeastward north of Lake Baikal with a southwest–northeast orientation and remains throughout the EPECEs. Consequently, the resulting cold SAT tends to be more extensive, intense, and persistent.

As presented in the introduction, the synoptic RWB plays an important role in the formation and maintenance of blocking. Although some blockings exhibit RWB features (Pelly and Hoskins, 2003), the blockings and the synoptic RWB differ on the timescale (Michel and Rivière, 2011). Compared with blockings, a quasi-stationary circulation, the synoptic RWB is a migrating synoptic circulation that only persists for less than 6 days (Nakamura et al., 1997; Pelly and Hoskins, 2003; Michel and Rivière, 2011). As there are two types of RWB, e.g., AWB and CWB, it still unclear that RWB is conducive to the blocking activity for the CWEs and EPECEs. This section discusses the synoptic RWB occurrence, emphasizing its contribution to the blocking circulation.

Figure 7 shows the climatological mean synoptic RWB frequency. It is clear that the maxima of the AWB frequencies are mainly situated downstream of the two storm tracks, while those of the CWB reside upstream of the storm tracks. This is consistent with the result of Rivière et al. (2010) and Michel and Rivière (2011). Notably, an AWB frequency band appears over the Eurasian continent with two maximum centers of 15% over Europe and to the south of Lake Baikal. Considering that the synoptic CWB occurs mainly upstream of two storm tracks, it hardly contributes to the blocking frequency over the Ural Mountains and Lake Baikal (figure omitted). Therefore, we mainly discuss the synoptic AWB occurrence in the following section.

Figure 7 Climatological winter mean synoptic RWB frequencies. The red line shows the anticyclonic RWB, while the blue line denotes the cyclonic RWB. The units are given in percentage of RWB days. The contours are drawn for every 5%.

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Figure 8 displays the synoptic AWB frequencies during the CWEs and EPECEs. As anticipated, the distribution of the synoptic AWB frequency agrees well with that of the blocking frequency. Here, we first discuss CWEs. During days –8 to –5 (Fig. 8a), a maximum AWB frequency center of 15% resides over the Black Sea. A corresponding moderately high AWB frequency anomaly is perceptible over the region. From days –4 to 3 (Figs. 8b, c), the maximum AWB frequency is enhanced and extends eastward to Lake Baikal, which is east of the maximum blocking frequency (Figs. 6c, d). This suggests that the AWB helps the blocking frequency to increase and expand eastward. In the subsequent periods (Figs. 8d, e), the maximum AWB frequency around Lake Baikal dramatically decreases to the climatological mean. Therefore, the abnormally high frequency of the synoptic AWB is a relatively short-lived feature, consistent with the signature of the CWEs (Fig. 2f).

Figure 8 Synoptic anticyclonic RWB frequency (contour) and corresponding anomalies (shading) in the intraseasonal evolutionary process of the CWEs (left column) during (a) days –8 to –5, (b) days –4 to –1, (c) days 0 to 3, (d) days 4 to 7, and (e) days 8 to 11. The units are given in percentage of RWB days. The contours are drawn for every 5%. (f) – (j) As in (a) – (e), but for the EPECEs.

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Compared with the CWEs, the synoptic AWBs occur more frequently and are more northward throughout the EPECEs (Figs. 7f–j). During days –8 to –5 (Fig. 7f), the broad synoptic AWB band over the Eurasian continent extends northeastward to the Kara Sea. Correspondingly, a positive AWB anomaly center of 8% dominates over the sub-Arctic region, which favors the recurrence of blocking over the West Siberian Plain. From days –4 to 3 (Figs. 7g, h), a maximum synoptic AWB frequency of 15% appears north of Lake Balkhash. A corresponding high synoptic AWB frequency anomaly extends from Lake Balkhash to Laptev Sea in a southwest–northeast orientation. Compared with the distribution of the blocking frequency of the mAPV index (Figs. 5h, i), the maximum center of the synoptic AWB frequency lays to the east of the maximum blocking frequency center. It can be inferred that the increased synoptic AWB frequency is favorable for the persistence of blockings over the Eurasian continent and draws them eastward. In contrast to the CWEs, from days 4 to 7 (Figs. 7e, f), the synoptic AWB frequencies dramatically increases around Lake Baikal, reaching 20% with a corresponding anomaly of 10%. During days 8–11 (Fig. 6g), although the synoptic AWB frequency around Lake Baikal decreases, the synoptic AWB frequency anomaly maintains a positive value over the Eurasian continent.

In this study, we investigate the blocking activities for the CWEs and EPECEs on the basis of three types of blocking indices and their causative factors in terms of the synoptic RWB on intraseasonal timescales during the winters of 1951–2011. Both the CWEs and EPECEs are preceded by an increased blocking frequency over midlatitudes of North Atlantic. For the CWEs, the blocking frequency mainly occurs around the Ural Mountains and exhibits regional features. In contrast, the blocking frequency of the EPECEs gradually extends eastward from the Ural Mountains to Lake Baikal in a southwest–northeast orientation. In response, the cold SAT inclines to spread as a large-scale extension over the region east of the Ural Mountains with a greater amplitude.

Both in the CWEs and the EPECEs, the abnormally high blocking frequency over the Eurasian continent is induced by the increased synoptic AWB. The synoptic AWB is abnormally recurrent over the Black Sea and extends eastward to Lake Baikal, which favors the recurrence and eastward extension of the blockings. For the CWEs, the synoptic AWB is a regional and short-lived feature, and thus, the blockings are confined over the Ural Mountains. In contrast, the synoptic AWB of the EPECEs is more recurrent and extends farther eastward. The abnormally high synoptic AWB frequency anomaly persists throughout the EPECEs in a southwest–northeast orientation.

This study mainly focused on the blocking activity associated with CAOs, which are a portion of the blocking events over the Eurasian continent. We will subsequently investigate blocking events with eastward extensions and their influence on the weather over China. Due to space limitation, this study does not provide additional dynamic aspects about the eastward extension of blocking circulations and the role that the RWB plays in the stratospheric anomalies’ downward propagation.

Acknowledgments . The authors appreciate the two anonymous reviewers for their constructive and helpful suggestions. We are grateful to Dr. Jingbei Peng for providing the information of CAOs. We also thank the writers of the NCAR Command Language, version 6.4.0 (UCAR/NCAR/CISL/TDD at http://dx.doi.org/10.5065/D6WD3XH5), which was adopted to plot the figures in this paper.