Abstract

Based on the Japanese 55-year reanalysis dataset, this study identifies 92 Europe–Siberia blocking high events (ESBs) over the 60 winters (November–March) from 1958/59 to 2017/18. According to the influence on the surface air temperature at 2 m over the middle and lower reaches of the Yangtze River, the ESBs are classified into three types: cold, neutral, and warm. Although cold-type ESBs are dominant, the number of warm-type ESBs is not negligible. The present study mainly focuses on the differences between cold-type and warm-type ESBs. Both the cold-type ESBs and the warm-type ESBs are characterized by height anomalies with a northwest–southeast-tilting dipole pattern over the Eurasian continent in the mid- and upper troposphere. However, the tilting dipole pattern of the warm type is located to the northwest of its cold-type counterpart, which reflects differences in the propagation of Rossby wave packets. The Siberian high is stronger in cold-type ESBs than in warm-type ESBs. The induced advection of the climatological mean air temperature by the anomalous meridional wind velocity in the lower troposphere accounts for the largest portion of the observed tendency of the air temperature for both ESB types. In addition, diabatic heating tends to counteract the local cooling tendency of air temperature over the Yangtze River region for the cold-type ESBs. Finally, cold-type ESBs are generally characterized by air parcels originating in the region to the north and northeast of the Tibetan Plateau, while warm-type ESBs are characterized by diverse trajectories.

1. Introduction

A blocking high is a large-scale anomalous atmospheric phenomenon that occurs over mid- and high latitudes. Because blocking highs are highly persistent (e.g., Knox and Hay 1984), they can cause extreme weather in the areas they directly or indirectly affect. Moreover, due to the complexity of their formation and maintenance, blocking highs pose a challenge for medium-range weather forecasts (Matsueda and Palmer 2018). Blocking highs can be detected throughout the year, but they are more persistent and more intense in the cold season (e.g., Tibaldi et al. 1994; Lupo and Smith 1995; Barriopedro et al. 2006).

Climatologically, in boreal winter, blocking highs tend to occur over the Euro-Atlantic region and the central and eastern Pacific (e.g., Dole and Gordon 1983; Tibaldi and Molteni 1990; Pelly and Hoskins 2003; Schwierz et al. 2004; Barriopedro et al. 2006). In addition, the frequency of blocking highs is also high around the Ural Mountains, which is usually considered the third most preferential region for the occurrence of blocking highs (Dole and Gordon 1983; Lupo and Smith 1995; Wiedenmann et al. 2002). In particular, blocking highs around the Ural Mountains have been regarded as important upstream precursors of severe cold surges in East Asia (Tao 1957; Takaya and Nakamura 2005b; Cheung et al. 2013). For example, the blocking frequency in the Ural Mountains in January 2008 exceeded the 95th percentile for the period 1950–2007 (Zhou et al. 2009), and the blocking highs were regarded as an important circulation contributor to the long-lasting and severe snow and freezing rain that affected southern China (Tao and Wei 2008; Wen et al. 2009; Bueh et al. 2011a). Thus, exploring the dynamical processes of blocking highs around the Ural Mountains can improve our understanding of the variability of the surface air temperature (SAT) anomalies over East Asia.

Cheung et al. (2013) showed the climatological aspects and evolutionary features of the Ural–Siberia blocking high in boreal winter via thermodynamic and geostrophic vorticity tendency equations. They noted that the horizontal advections of both vorticity and air temperature played fundamental roles in the generation of Ural–Siberia blocking highs. In their study, a Ural–Siberia blocking high refers to a blocking high centered in the interval (30°–100°E), which covers parts of Europe and the Ural Mountains. Based on winter-mean data, Cheung et al. (2012) implied that the 30°–100°E region is so broad that it can obscure the seasonal influences over East Asia of the blocking highs over the Ural Mountains and over eastern Europe. Moreover, Luo et al. (2016b) noted that blocking highs over different regions around the Ural Mountains can exert different influences on the temperature anomalies over Eurasia. Figure 7 in their paper illustrates that the Ural blocking highs are accompanied by 11-day mean SAT anomalies extending from midlatitude Eurasia southeastward to eastern China. Note that the 11-day mean SAT anomaly in Luo et al. (2016b) is averaged from 5 days preceding to 5 days after the peak day of the Ural blocking high. However, for eastern China or East Asia, the strongest cold anomalies generally occur over the decay stage of Ural blocking highs (Tao 1957; Takaya and Nakamura 2005b; Cheung et al. 2013). Moreover, cold surges originating from mid and high latitudes over Asia usually propagate southeastward along the northeastern slope of the Tibetan Plateau and can reach as far south as the middle and lower reaches of the Yangtze River or even the South China Sea (Tao 1957; Ding and Krishnamurti 1987). Therefore, exploring whether differences exist in the SAT anomalies over eastern China after the peak days of the blocking highs over the Ural–Siberia region, which constitutes the main issue of the present study, is a worthwhile endeavor.

For an improved understanding in this regard, the different underlying dynamical features of the different types of blocking highs should be identified. Many previous studies have shown that upstream quasi-stationary Rossby wave packets can facilitate the formation of persistent anticyclone anomalies (Nakamura 1994; Nakamura et al. 1997; Takaya and Nakamura 2005a; Cheung et al. 2013; Luo et al. 2016b). The present study will discuss, among other things, the differences in the propagation of the Rossby wave packets during the formation of different types of blocking highs over the Ural–Siberia region.

Furthermore, many studies have shown that teleconnection patterns (Horel 1981; Wallace and Gutzler 1981; Barnston and Livezey 1987) are evident in both the evolution of blocking highs over the Eurasian continent and the SAT anomalies over East Asia. Luo et al. (2016b) showed that the number of Ural blocking highs that are preceded by a positive North Atlantic Oscillation accounts for nearly 60% of all Ural blocking highs. In addition, the Scandinavian pattern, another important wintertime teleconnection pattern, generally precedes the formation of blocking highs over Europe (Tyrlis and Hoskins 2008). The Scandinavian pattern also emerged as a precursory circulation pattern for the extremely persistent cold weather over southern China in January 2008 (Bueh et al. 2011a; Zhou et al. 2009). Takaya and Nakamura (2005b) pointed out that there are two origins of the amplification of the Siberian high (i.e., the “Atlantic origin” and the “Pacific origin”). The “Atlantic origin” might be associated with the Eurasian pattern (Wang and Zhang 2015), while the “Pacific origin” might be associated with the west Pacific pattern. Although these studies hint that blocking highs are associated with teleconnection patterns, a significance test for their relationships is lacking. Thus, the present study discusses the relationship between the blocking highs and several teleconnection patterns that have primary anomaly centers over or around the Eurasian continent.

To explore the abovementioned issues, the remainder of this paper is organized as follows: section 2 introduces the data and methods employed to detect the blocking highs around the Ural Mountains and diagnose their dynamical features; section 3 describes the method used to classify those blocking highs and shows some statistical results; section 4 presents a comparison between the different evolution and mechanisms of the two types of blocking highs, while section 5 provides the main conclusions of this study and further discussion.

2. Data and methods

a. Data

The 6-hourly fields from the Japanese 55-year reanalysis (JRA-55) project conducted by the Japan Meteorological Agency from 1958 to 2018 (Kobayashi et al. 2015) are mainly analyzed in this study. Daily mean fields are obtained from these 6-hourly fields. This study limits the analysis to 60 years of the extended boreal winter period (i.e., from November to March of the following year). The horizontal resolution of this dataset is 1.25° × 1.25°.

This study uses meteorological variables on isobaric surfaces, including the geopotential height, wind velocity, and air temperature. To measure the influence of different types of blocking highs on the surface weather, the SAT, sea level pressure (SLP), and surface pressure are also utilized. The monthly geopotential height at 500 hPa (Z500) is also used to obtain the teleconnection patterns. All of the abovementioned variables are reanalysis fields based on the assimilation of observational data. In addition, the surface geopotential is used to represent large orographic features.

The diabatic heating rate at 950 hPa is also used to identify the formation of near-surface air temperature anomalies over the Yangtze River region. In JRA-55, the diabatic heating rate is composed of the large-scale condensation heating rate, convective heating rate, vertical diffusion heating rate, solar radiative heating rate and longwave radiative heating rate. The large-scale condensation heating rate represent the heating effect by large-scale forced uplift, while the convective heating rate represent the heating effect by cumulus convection. The vertical diffusion heating rate represents the contribution from the turbulent transport of heat in the planetary boundary layer. The longwave radiative heating rate and the solar heating rate are the two diabatic heating fields associated with radiation. In contrast to the abovementioned reanalysis fields, the five diabatic heating fields are diagnostic fields. The parameterization methods for deriving the five diabatic heating rates are described by Kobayashi et al. (2015), and more details can be found in an online document (https://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm).

b. Methods

1) Data processing

The methods utilized to obtain the anomaly field are the same as those employed in Nakamura et al. (1997). The local anomaly of a given variable on a particular day is defined as its departure from the local value of the climatological mean annual cycle for the corresponding calendar date. The climatological mean annual cycle is defined as the 31-day running mean of the 60-yr climatological mean daily fields. The purpose of applying the 31-day running mean to the 60-yr climatological mean daily fields is to further minimize the day-to-day variability.

2) Definition of blocking high

The blocking identification method of Tibaldi and Molteni (1990) is used to detect blocking highs around the Ural Mountains. First, the instantaneous local blocking index is calculated at every longitude:

 
{GHGN=Z(ϕn)Z(ϕ0)ϕnϕ0GHGS=Z(ϕ0)Z(ϕs)ϕ0ϕs,
(1)

where Z is the daily geopotential height at 500 hPa, GHGN refers to the meridional gradients to the north of a chosen reference latitude ϕ0, while GHGS refers to the meridional gradients to the south. Here, ϕn = 80°N + Δ, ϕ0 = 60°N + Δ, and ϕs = 40°N + Δ, where Δ is a variable whose value is set from −5° to +5° with an interval of 1.25° instead of Δ = 0°, ±4° in the original index because the JRA data has a horizontal resolution of 1.25°. An instantaneous local blocking is considered to occur if GHGN < −10 m degree−1 and GHGS > 0 for at least one value of Δ. Then, a blocking high event is considered to occur around the Ural Mountains if the instantaneous local blocking occurs over at least 15 consecutive degrees longitude within the longitudinal sector from 30° to 100°E and persists for at least 4 days. The region from 30° to 100°E is approximately centered on the longitude (60°E) of the Ural Mountains. The longitudinal sector with consecutive blocking occurrence is referred to as the blocking region in which the primary anticyclonic anomaly center is searched.

As proposed by Barriopedro et al. (2010), a blocking high flow is characterized not only by a gradient-reversed configuration, which is the basic idea for defining a blocking flow in Tibaldi and Molteni (1990) and Pelly and Hoskins (2003), but also by persistent height anomalies (Dole and Gordon 1983). Accordingly, we include an additional constraint requiring the amplitude of the geopotential height anomaly at 500 hPa at the primary anomaly center to exceed 5 gpdm. For a particular blocking high event on a particular day, the location of the primary anomaly center is identified as the grid point with the maximum height anomaly within the blocking region bounded by 55° and 80°N. Accordingly, all of these blocking highs are referred to as Europe–Siberia blocking highs (ESBs). We choose to define the ESB peak day as the day with the largest anomaly height in the primary anomaly center. In sections 3 and 4, the peak day is regarded as the reference day for the composite analysis. Moreover, day 0 refers to the peak day, and day N (−N) refers to N days after (before) the peak day.

3) Significance test

The statistical significance of the ESB composite anomalies is tested based on two-tailed Student’s t tests at each grid point. As noted by Wilks (2016), the false discovery rate (FDR) should be controlled in multiple hypothesis tests in the case of overoptimistic significance results. Following Wilks (2016), the threshold value for the significance level, pFDR*, is determined based on the distribution of ascending sorted p values:

 
pFDR*=maxi=1,,N[p(i)(i/N)αFDR],
(2)

where p(i) is the ith smallest p value of all p values evaluated at each grid point of a composite map, N is the total number of grid points, and the control level for the FDR, αFDR, is set to 0.05 throughout our study. After applying the FDR procedure, the proportion of falsely rejected null hypotheses is effectively controlled. In the following, the pFDR* value is indicated in the relevant figure captions.

4) Thermodynamic energy equation at the lower troposphere

The thermodynamic energy equation [Holton 2004, their Eq. (2.42)] is employed to diagnose the formation of air temperature anomalies in the lower troposphere over the Yangtze River region. Every term can be represented as the sum of its local anomaly and the climatological mean. After removing the terms describing the climatological mean state, Eq. (3) gives the tendency equation for the anomalous air temperature T′:

 
Ttobs=(uTx)x_adv(υTx)y_adv(ωTp)+RCpp(Tω)z_mot+QCpdia,
(3)

where prime indicates the local anomaly. The terms u, υ, and ω represent the velocity on isobaric surfaces. The units of u and υ are m s−1, and the unit of ω is Pa s−1. Cp is the specific heat at constant pressure, R is the gas constant of dry air, and Q is the rate of diabatic heating per unit mass. The terms of Eq. (3) from left to right represent (i) the observed tendency of the air temperature anomaly, (ii) the advection in the zonal direction and (iii) that in the meridional direction, (iv) the contribution from the vertical motions, and (v) the diabatic heating; these terms are denoted obs, x_adv, y_adv, z_mot, and dia, respectively, in the corresponding analyses in section 4b. Note that the 6-hourly fields are used to evaluate each term in Eq. (3). To be consistent with the analysis in sections 4a and 4d, which are based on daily mean fields, the obtained 6-hourly terms in Eq. (3) are finally daily averaged.

Note that the discretized thermodynamic equation of Eq. (3) is not closed due to various errors in the budget analysis (including temporal and spatial discretization errors, imperfect descriptions of both the real state of the atmosphere and the real diabatic heating fields by the JRA-55 datasets, etc.). To evaluate the uncertainty of our analysis due to the errors, the residual term is also calculated as the difference between the “obs” term on the left-hand side of Eq. (3) and the sum of the four terms on the right-hand side of Eq. (3). Thus, the residual term includes the sum of the various errors. In section 4b, the potential influences of the errors are discussed.

5) Trajectory analysis

The backward trajectories of air parcels in the lower troposphere over the Yangtze River region are analyzed with the tool developed by Wernli and Davies (1997). The method can be expressed using the following equation:

 
{r*n1=rnU(rn)ΔtU*=[U(rn)+U(r*n1)]/2,
(4)

where rn represents the three-dimensional location vector of an air parcel at the nth time step, r*n1 represents the potential location vector at the previous time step, U represents the three-dimensional wind velocity at the regular longitude/latitude grids, and U* represents the adjusted mean wind. The wind velocity at a particular grid between the regular grids, which is denoted by U(r*n1), is obtained by bilinear interpolation in both the horizontal and vertical directions. The 6-hourly fields are used to calculate the trajectories of air parcels, and Δt is accordingly set to 21 600 s. The location vector at the (n − 1)th time step is finally obtained by 100 iterations performed on Eq. (4). The backward tracing is terminated if the air parcel is found to exist underground, which is determined when the value of the pressure surface at which the air parcel is located is larger than the local surface pressure.

Following the method of Zschenderlein et al. (2018), we also analyze the evolution of the temperature and the potential temperature of a particular air parcel along its trajectory. If the potential temperature varies slightly while the temperature varies considerably, the adiabatic process is inferred to be dominant during the movement of the air parcel. In contrast, the diabatic process is inferred to dominate if the potential temperature varies greatly.

6) Wave-activity flux

The wave-activity flux formulation derived by Takaya and Nakamura (1997, 2001) is used. The flux is independent of the wave phase without any temporal or zonal averaging and is parallel to the local group velocity of stationary Rossby wave packets in the Wentzel–Kramers–Brillouin sense. Thus, this flux is suitable for diagnosing the propagation of the low-frequency Rossby wave packets associated with ESBs on a zonally varying basic flow. The flux on the isobaric surface is expressed as follows:

 
W=p2000|U|([u(υ2ψυx)+υ(uυ+ψux)]i+[u(uυ+ψux)+υ(u2+ψuy)]j+{f0RaN2H0[u(υTψTx)+υ(uTψTy)]}k),
(5)

where the prime symbol denotes a low-frequency perturbation and U = (u, υ) is the horizontal basic flow velocity. The low-frequency circulation is obtained by applying the Lanczos filter with a cutoff frequency of 8 days. In this study, the low-frequency perturbations in Eq. (5) are obtained by the composites of the daily low-frequency anomalies associated with ESBs, while the basic flow U is represented by the climatological mean annual cycle, which has been described in section 2b(1). The subscripts indicate partial differentiation, whereas the other notations in Eq. (5) are standard.

7) Teleconnection pattern indices

The teleconnection patterns are first obtained through rotated principal component analysis as in Barnston and Livezey (1987) but with minor modifications. A brief description is provided here. First, the 10 leading unrotated empirical orthogonal functions are determined from the standardized monthly Z500 height anomalies in the 20°–90°N region with a temporal sequence of 60 years × 5 months/year = 300 months. Then, the 10 leading rotated modes are obtained by applying the Kaiser varimax rotation to the 10 unrotated modes. Excluding a spurious mode that has not been documented by previous studies and seems to have no apparent physical meaning, nine evident teleconnection patterns are recognized: North Atlantic Oscillation, polar/Eurasia [or northern Asia pattern in Barnston and Livezey (1987)], west Pacific, Pacific/North American, east Atlantic/west Russia, east Atlantic, tropical/Northern Hemisphere, east Pacific–North Pacific, and Scandinavian patterns. The Scandinavian pattern was referred to as the Eurasia-1 pattern, and the east Atlantic/west Russia pattern was referred to as the Eurasia-2 pattern by Barnston and Livezey (1987). The present study will mainly discuss the following six patterns, North Atlantic Oscillation, polar/Eurasia, west Pacific, east Atlantic/west Russia, east Atlantic, and Scandinavian patterns, all of which have their primary centers of action around the Eurasian continent.

The daily indices of the six teleconnection patterns are obtained by projecting the standardized daily Z500 anomalies to the teleconnection patterns, which is similar to the method used by Baldwin and Dunkerton (2001) to calculate the daily Arctic Oscillation index (although the nonstandardized anomalies are utilized there). The daily index for each of the six teleconnection patterns is further normalized locally by its standard deviation over 31 days per year × 60 years = 1860 days, where the annual 31-day sequence is centered on that calendar day.

3. Classification of ESBs

According to the detection method described in section 2b(2), 92 ESBs are detected during the 60 boreal winters. The 7-day averaged composite results of the 92 ESBs are shown in Fig. 1. The height anomalies at 500 hPa are averaged from day −3 to day 3 (Fig. 1a). Significant anticyclonic height anomalies occur over northwestern Eurasia with a primary center at approximately (65°N, 55°E), while significant cyclonic height anomalies are mainly located to the southeast and southwest of the anticyclonic height anomalies. This spatial pattern is similar to that of the Ural–Siberia blocking highs on the peak day in Cheung et al. (2013). To show the influence of ESBs on the SAT during their decay stage, the composite SAT anomalies (Fig. 1b) are averaged from day 1 to day 7. Warm SAT anomalies occur over the polar region, while cold anomalies occur over the middle latitudes of Eurasia; this phenomenon is sometimes referred to as the “warm Arctic–cold continent (Eurasia)” pattern (Overland et al. 2011; Luo et al. 2016a). This temperature pattern describes the out-of-phase relationship between the SAT anomalies over the Arctic and those over the midlatitude Eurasian continent. Significant midlatitude negative SAT anomalies also extend southeastward along the northeastern slope of the Tibetan Plateau, reaching as far as the Yangtze River region [(25°–32.5°N, 106.25°–118.75°E), green rectangle in Fig. 1b]. Therefore, the overall relationships between the upstream blocking highs and the SAT over the Yangtze River region are basically consistent with those observed in previous studies (Tao 1957; Takaya and Nakamura 2005b; Cheung et al. 2013).

To further explore the subsequent influence of ESBs, Fig. 2 shows the SAT anomalies averaged over the area of the Yangtze River after the peak days of the 92 ESBs. Evidently, many ESBs are followed by negative SAT anomalies, which is consistent with both the results of early studies on ESBs (Tao 1957; Takaya and Nakamura 2005b) and the results shown in Fig. 1. However, for some of the ESBs, the Yangtze River region is dominated by positive SAT anomalies. This phenomenon inspires us to classify the ESBs according to the mean values of the SAT anomalies over the Yangtze River region after the peak days of the ESBs.

Here, the area-averaged SAT anomalies are averaged from day 3 to day 4, during which the SAT anomalies display a relatively large spread. An ESB event is regarded as a warm type if the 2-day mean SAT anomaly is higher than 1°C or a cold type if the 2-day mean SAT anomaly is lower than −1°C. The remaining ESBs are regarded as the neutral type. Finally, the 92 ESBs are classified into 54 cold-type events, 14 neutral-type events and 24 warm-type events. Clearly, the cold type is dominant, but the number of warm-type events is not negligible, accounting for approximately 26% of all ESBs. The difference in the composite SAT anomalies between the two types of ESBs (shown by the thick lines in Fig. 2) is most evident from day 3 to day 4, which is consistent with our choice of this particular 2-day period to classify the ESBs.

With regard to the ESBs of the same type, evident case-to-case variability occurs in the SAT anomalies over the Yangtze River region (Fig. 2). This case-to-case variability in the SAT can be explained by the distinct anomalies of the circulation features associated with, for example, the Siberian high, the East Asia trough, and/or the subtropical jet. In addition, the variability in the shape and magnitude of ESBs can contribute to the SAT variability as well. A discussion of the case-to-case variability of the SAT anomalies is beyond the scope of the present study.

Figure 3 shows the spatial distribution of the anomaly centers at the peak days for the three types of ESBs. The thick red, black and blue circles with crosses indicate the mean positions of the 54 cold-type, 14 neutral-type, and 24 warm-type ESBs, respectively. Clearly, the warm-type ESBs tend to occur to the northwest of the cold-type ESBs, while the neutral-type ESBs tend to occur in between. In the following section, a comparison analysis between the cold and warm types of ESBs is performed.

4. ESB comparison analysis

a. SLP and SAT anomalies

The Siberian high represents the dominant weather system for the Eurasian continent near the surface in boreal winter, and it generally induces cold air outbreaks southeastward along the leeward slopes of the Tibetan Plateau (Tao 1957; Ding 1990). Climatologically, the northerlies associated with the Siberian high prevail across the region to the south of 40°N over East Asia and the Yangtze River region is subject to the influence of the anticyclonic high system (Fig. 4).

Figure 5 shows the composite SLP anomalies, while Fig. 6 shows the composite SAT anomalies. For the cold-type ESBs, on day −2 (Fig. 5a), significant positive SLP anomalies appear over Eurasia with the primary center near the Ural Mountains. Because the center of the climatological mean Siberian high is located between Lake Balkhash and Lake Baikal (Fig. 4), the positive anomalies shown in Fig. 5a indicate that the Siberian high is amplified mainly in its northwestern portion. Correspondingly, a northwest–southeast-tilting dipole pattern of SAT anomalies emerges on day −2 with significant positive anomalies centered over the Barents Sea and significant negative anomalies centered over Lake Balkhash (Fig. 6a), resembling the “warm Arctic–cold Eurasian” pattern to some extent. Note that significant negative SAT anomalies cover the region around Lake Baikal. Takaya and Nakamura (2005b) revealed that the preexisting negative SAT anomalies around Lake Baikal contribute to the amplified Siberian high through their vertical interaction with upper-level wave train anomalies. The positive SLP anomalies are enhanced on day 0 (Fig. 5b). Correspondingly, the dipole SAT anomalies are enhanced, and the majority of Asia is influenced by significant negative SAT anomalies (Fig. 6b). On day 2, although the majority of the positive SLP anomalies still persist over the Ural Mountains, the central amplitude is weakened to approximately 18 hPa (Fig. 5c). The positive SLP anomalies gradually extend southeastward along the northeastern slope of the Tibetan Plateau until day 6 (Figs. 5c–e). Correspondingly, the negative SAT anomalies gradually propagate southeastward along the northeastern slope of the Tibetan Plateau and persistently influence the Yangtze River region (Figs. 6c–e). After day 6 (not shown), the positive SLP anomalies gradually weaken and become nonsignificant, and the negative SAT anomalies over the Yangtze River region gradually weaken accordingly.

In contrast, for the warm-type ESBs, the Siberian high displays different features. Positive SLP anomalies occur on day −2 (Fig. 5f) but are mainly limited to the region west of 75°E, with primary centers around the Scandinavian Peninsula, contributing to the formation of the significant positive SAT anomalies centered over the Barents–Kara Sea (Fig. 6f). Negative SLP anomalies emerge over the region east of 75°E and become significant on day 0 with two primary centers around Lake Baikal (Fig. 5g), reflecting the weakening of the Siberian high in the vicinity of its central portion. After day 0, these significant negative SLP anomalies gradually propagate southward and affect the Yangtze River region approximately from day 2 to day 4 (Figs. 5h,i). Consequently, the Yangtze River region is influenced by significant persistent positive SAT anomalies from approximately day 2 to day 4 (Figs. 6h,i), with a maximum central magnitude of approximately +4.5°C on day 4 (Fig. 6i). On day 6, neither the SLP anomaly nor the SAT anomaly over the Yangtze River region are significant (Figs. 5j and 6j).

Therefore, the cold-type ESBs are characterized by an anomalous Siberian high that mainly exhibits enhancement in its northwestern portion, while the warm-type ESBs are associated with an anomalous Siberian high that mainly shows weakening in its central portion. Both the positive and negative SLP anomalies associated with the anomalous Siberian high tend to propagate along the northeastern slope of the Tibetan Plateau. This result is consistent with the findings of Hsu (1987), who uncovered the anticyclonic propagation features of the SLP anomalies around the Tibetan Plateau based on lead-lag linear correlation analysis. In accordance with the evolution of the SLP anomalies, the SAT anomalies for both types of ESBs also propagate along the northeastern slope of the Tibetan Plateau.

b. Analysis on the formation of air temperature anomalies

To explore how the anomalous Siberian high induces the air temperature anomaly over the Yangtze River region, Figs. 7a and 7e show the contributions from each term on the right hand side of Eq. (3), based on the composite fields and area-averaging over the Yangtze River region. Since the influence of the Siberian high is mainly confined to the lower troposphere, the 950 hPa level is chosen as the reference level at which all terms in Eq. (3) are evaluated.

For the cold type (Fig. 7a), the tendency of the observed air temperature anomalies (thick black line) is negative overall before day 4, corresponding to the gradual enhancement of the negative SAT anomalies over the Yangtze River region (Figs. 6a–d). The tendency reaches the maximum on day 1 and becomes significant on day 1 and day 2. After day 4, the observed tendency becomes positive, indicating the weakening of the negative anomalies (Fig. 6e). After decomposing the observed tendency, we find that the anomalous meridional temperature advection −(υT/∂y)′ (thick dashed red line in Fig. 7a) is the primary contributor, which is significant after day −1. If −(υT/∂y)′ is further decomposed into (υ¯T/y+υT¯/y)(υT/yυT/y¯), where the bar represents the climatological mean, the contribution of −(υT/∂y)′ mainly comes from that of (υT¯/y) (thin dashed red line in Fig. 7a), which is significant after day 1. Thus, the anomalous meridional velocity υ′ associated with the anomalous Siberian high advects the climatological mean temperature southward, mainly explaining the formation of the negative temperature anomaly over the Yangtze River region for the cold-type ESBs. The diabatic heating term (solid blue line) is also significant after day −5 but mainly counteracts the cooling tendency over the Yangtze River region. The significant diabatic term mainly arises from longwave radiative heating and vertical diffusion heating (dashed blue line and dashed red line in Fig. 7b). The remaining adiabatic terms are marginal, although the zonal advection term is significant on day −3 (dashed yellow line) and the vertical motion term is significant on day −5 and from day −2 to day 2 (dashed sky blue line).

The warm-type ESBs show a similar situation but with the opposite signs (Fig. 7e). Specifically, the tendency of the observed air temperature anomalies (thick solid black line) becomes positive from day −3 to day 3. During this warming period, especially from day −2 to day 3, the term (υT¯/y) (thin dashed red line) is still the main contributor to the observed tendency of the air temperature, similar to its counterpart in the cold-type ESBs (Fig. 7a). Both the zonal advection (dashed yellow line) term and the vertical motions term (dashed sky blue line) are significantly negative from approximately day 3 to day 5, contributing to the observed cooling tendency. Due to their relatively small amplitudes, the terms associated with the vertical motions are marginal in both the cold-type ESBs (Fig. 7a) and the warm-type ESBs (Fig. 7e), and we can also infer that the katabatic winds from the Tibetan Plateau and the subsidence in plains are not the primary contributors to the formation of the temperature anomalies over the Yangtze River region. The diabatic heating term is not significant (solid blue line in Figs. 7e,f) in the warm-type ESBs, which is different from the situation in the cold-type ESBs. This an asymmetric feature in the diabatic heating term deserves future study.

As seen in Fig. 7e, the residual term seems to be not negligible for the warm-type ESBs, implying uncertainty in our budget analysis. As has been revealed, the meridional temperature advection terms, especially the term (υT¯/y), are the most important for the formation of the air temperature over the Yangtze River region. Thus, it is meaningful to check whether (υT¯/y) retains this importance if the errors are added. We assume that all the errors or the residual term are totally generated by (υT¯/y) while other terms are represented perfectly. Clearly, this approach artificially maximizes the errors caused by (υT¯/y) while eliminating the residual term. Our results show that, after having been adjusted by adding the residual term, the reduced (υT¯/y) still predominates over other terms, although it is significant only on day 1 (not shown). Thus, meridional temperature advection, especially (υT¯/y), is an important contributor to the formation of air temperature anomalies over the Yangtze River region for both types of ESBs.

To further check whether large case-to-case variability in (υT¯/y) occurs among the ESBs, Fig. 7c shows the SLP anomaly, and Fig. 7d shows (υT¯/y) at 950 hPa for each of the 54 cold-type ESBs. Clearly, the majority of the 54 cold-type ESBs are accompanied by a positive SLP anomaly over the Yangtze River region after day 2 and by a negative value of (υT¯/y) after day 1, which could be inferred from the significant results indicated by the red circles. A similar situation occurs for the warm-type ESBs but with the opposite sign from day 1 to day 3 (Figs. 7g,h).

Therefore, the formation of the SAT anomalies over the Yangtze River region is mainly induced by the meridional advection of the climatological mean air temperature by the meridional wind velocity anomaly associated with the anomalous Siberian high. In addition, diabatic heating tends to counteract the local cooling tendency of air temperature over the Yangtze River region for cold-type ESBs.

c. Backward trajectories

This section analyzes the differences in the trajectories of air parcels between the cold-type and warm-type ESBs, and qualitatively discusses whether diabatic or adiabatic processes are dominant during the movement of air parcels. Here, the 5-day backward trajectories are traced beginning on day 4 of each event at the reference grid point (28.75°N, 115°E) at the 950 hPa pressure level. Clearly, the grid point is close to the central grid point of the Yangtze River region. According to Eq. (4), the trajectory is derived from the total fields. Thus, in this section, we adopt a different point of view in exploring the formation of the SAT anomalies over the Yangtze River region from section 4b, which focuses on the local anomalous circulation.

As shown in Fig. 8a for the 54 cold-type ESBs, the air parcels usually originate from the northern and northeastern Tibetan Plateau, as indicated by the filled blue circles. The parcels generally propagate along the northeastern slope of the Tibetan Plateau before they arrive at the Yangtze River region, exhibiting anticyclonic trajectories. Such consistent features among these trajectories could be attributable to the enhanced Siberian high because East Asia is still directly influenced by the enhanced Siberian high, which could regulate the trajectories of the air parcels. The red line in Fig. 8a is the trajectory derived from the composite wind field associated with the 54 cold-type ESBs. Clearly, an anticyclonic trajectory is exhibited to the east of the Tibetan Plateau and corresponds to the representative trajectory for the cold-type ESBs.

For the 24 warm-type ESBs (Fig. 8b), in contrast, the air parcels generally originate from the mid and lower latitudes of East Asia and show diverse trajectories. Such diverse trajectories might be associated with the weakened Siberian high. Once the Siberian high is weakened over the Yangtze River region for the warm-type ESBs (Figs. 5g–i), the perturbations at mid and lower latitudes might be more influential around the Yangtze River region, which might induce the diverse trajectories of the air parcels. Therefore, the trajectory derived from the composite wind velocity field for the 24 warm-type ESBs (red line in Fig. 8b) cannot be regarded as the representative trajectory for the warm-type ESBs.

Figure 8c displays the air temperature-potential temperature diagram for the air parcels during their movement for the cold-type ESBs. Overall, the polylines that connect the origins (blue circles) to the reference points (blue asterisks) tend to extend more along the y axis than along the x axis. This pattern could be easily inferred from the trajectory derived from the composite field for the cold-type ESBs (red line in Fig. 8c). In other words, the changes in the air temperature tend to be larger than the changes in the potential temperature during the movement of the air parcels associated with the 54 cold-type ESBs. This result indicates that adiabatic processes are more important during the movement of air parcels associated with cold-type ESBs.

For the 24 warm-type ESBs, consistent with the diverse trajectories of the air parcels, the variations in the temperature and the potential temperature also show large case-to-case variability (Fig. 8d). Some cases are characterized by a primarily diabatic process, while others are characterized by a primarily adiabatic process. The thick red line in Fig. 8d, which is obtained from the composite field, can hardly be representative of the overall situation for warm-type ESBs.

d. Low-frequency Rossby wave propagation

In the upper troposphere (300 hPa) on day −4 for the cold-type ESBs (Fig. 9a), significant negative height anomalies occur over western Europe, while positive height anomalies are anchored around the Kara Sea and Taymyr Peninsula. Indeed, this circulation pattern emerges on day −5, and no significant circulation anomaly can be observed farther upstream (not shown). The stationary Rossby wave packets from western Europe propagate northeastward to the Ural Mountains and are then reflected southeastward, after which they finally converge around Lake Baikal. Corresponding to the downstream dispersion of wave energy along the arc-shaped path, from day −2 (Fig. 9b) to day 0 (Fig. 9c), the cyclonic anomalies over western Europe gradually weaken, while both the anticyclonic anomalies centered over the Ural Mountains and the cyclonic anomalies to the southwest of Lake Baikal gradually strengthen. The anticyclonic anomalies centered over the Ural Mountains have the strongest amplitude among the wave train anomalies over the Eurasian continent. Some studies have emphasized the role of upstream cyclogenesis in the enhancement of blocking highs through the poleward advection of anticyclonic vorticity and warm air (Colucci 1985; Cheung et al. 2013); this finding is also implied here by the cyclonic anomalies over western Europe (Figs. 9a,b). On the other hand, from a potential vorticity-inversion perspective, the enhanced anticyclonic anomalies might also be associated with the local coupling between the upper-level anticyclonic anomalies and the negative SAT anomalies around Lake Baikal (Takaya and Nakamura 2005b). After day 0 (Figs. 9d,e), the height anomalies gradually weaken. However, the wave-activity fluxes mainly emanate from the anticyclonic anomalies over the Ural Mountains and converge over the region from central Asia to East Asia. Correspondingly, the cyclonic anomalies over midlatitude East Asia weaken at a much more gradual rate than the anticyclonic anomalies over the Ural Mountains (Fig. 9e). During the evolution of the cold-type ESBs, the dipole of geopotential height anomalies tilts over the Eurasian continent in the northwest–southeast direction, which is a typical feature of persistent cold events over China (Bueh et al. 2011a,b) and midlatitude Asia (Shi et al. 2019).

For the warm-type ESBs, on day −4 (Fig. 9f), significant cyclonic anomalies around southern Greenland can be traced back to as far as day −6 (not shown). Rossby wave packets emanate from these cyclonic anomalies and propagate eastward, together with the northward propagation of Rossby wave packets from Eastern Europe, contributing to the enhancement of the anticyclonic anomalies centered around the northern Scandinavian Peninsula and Barents Sea from day −4 (Fig. 9f) to day 0 (Fig. 9h). Therefore, this finding is basically consistent with the finding of Tyrlis and Hoskins (2008) that the onset of blocking highs over Europe is generally preceded by the existence of cyclonic anomalies to the west of Greenland. Although the zonal extents of the primary anticyclonic anomalies are comparable between the two types of ESBs on day 0 (i.e., 90°) the center of the primary anticyclonic height anomaly associated with the warm-type ESBs is located slightly northwest of its counterpart. Such differences in the locations of these primary anticyclonic anomaly centers for the two types of ESBs might be related to the different preexisting circulation anomalies located upstream and the different propagation patterns of the associated Rossby waves. After day 0 (Figs. 9h–j), the wave-activity fluxes begin to diverge around the Ural Mountains and propagate both eastward and southeastward. The convergence of wave-activity flux is favorable for the enhancement and maintenance of the downstream cyclonic anomalies around Lake Baikal and the Caspian Sea. Thus, consistent with the northwest displacement of the primary anticyclonic height anomaly of the warm-type ESBs, the cyclonic anomaly around Lake Baikal is also located to the northwest of its cold-type ESBs counterpart.

Joung and Hitchman (1982) and Takaya and Nakamura (2005a) revealed that cold air outbreaks or cold anomalies over East Asia are accompanied by wave trains across the Eurasian continent. The wave train anomalies mainly show a barotropic structure across most of Eurasia but become baroclinic when they approach the coast of East Asia. Inspired by their studies, we also analyze the vertical structures of the primary height anomalies over the Eurasian continent for both types of ESBs (not shown). Our results are basically consistent with those of early studies (Joung and Hitchman1982;,Takaya and Nakamura 2005a) insomuch that both types of ESBs generally demonstrate a barotropic structure across most of Eurasia, which pertains to the evident horizontal propagation of Rossby waves in the troposphere. Moreover, the baroclinic structure of the wave trains over East Asia is also evident for both the cold-type and warm-type ESBs, with the height anomalies tilting northwestward with height (not shown).

e. Relationships with teleconnection patterns

One of the notable circulation differences between the two types of ESBs is the locations of the primary geopotential height anomalies, and these discrepancies appear to be related to differences in the large-scale teleconnection patterns. Figure 10 shows the composite daily teleconnection pattern indices during the evolution of the ESBs. Evidently, the cold-type ESBs are closely associated with a significant positive index of the east Atlantic/west Russia pattern from day −10 to day 7 (Fig. 10a). The east Atlantic/west Russia pattern in its positive phase consists of one anticyclonic anomaly center around the Ural Mountains and two cyclonic anomaly centers around western Europe and northeast China (Barnston and Livezey 1987). During the evolution of the cold-type ESBs, the significant height anomalies associated with the cyclonic anomaly over western Europe before day −2 (Figs. 9a,b), the primary anticyclonic anomaly around the Ural Mountains (left column of Fig. 9), and the significant cyclonic anomalies around northeast China after day 2 (Figs. 9d,e) resemble the positive east Atlantic/west Russia pattern. The North Atlantic Oscillation (red line) does not exhibit consistent variation during the evolution of the cold-type ESBs, as was also hinted by Luo et al. (2016b).

In contrast, for the warm-type ESBs, both the east Atlantic/west Russia and Scandinavian patterns become relevant from approximately day −2 to day 5 (Fig. 10b). The positive east Atlantic/west Russia pattern generally has one primary anticyclonic anomaly center over the Scandinavian Peninsula and one primary cyclonic center around Lake Baikal (Barnston and Livezey 1987; Bueh and Nakamura 2007). For the warm-type ESBs, the primary anticyclonic height anomaly around the Scandinavian Peninsula extends zonally and gradually covers the Ural Mountains (Figs. 9f–j), and a cyclonic height anomaly forms around Lake Baikal after day −2 (Figs. 9h–j), together indicating a mixture of the east Atlantic/west Russia and east Atlantic/west Russia pattern patterns.

5. Conclusions and discussion

Based on JRA-55 reanalysis data, 54 cold-type ESBs and 24 warm-type ESBs are selected according to their influences on the SAT anomalies over the Yangtze River region. We find that warm-type ESBs tend to occur relatively northwestward of cold-type ESBs. As inferred from the anomalous SLP field, the Siberian high is weakened for warm-type ESBs and enhanced for cold-type ESBs over the Yangtze River region. The anomalous Siberian high can induce the advection of climatological mean air temperature by anomalous meridional wind velocities in the lower troposphere, which is the main contributor to the air temperature anomalies in both ESB types. In addition, diabatic heating tends to counteract the local air temperature cooling tendency over the Yangtze River region in the cold-type ESBs.

Noticeable differences are also observed in the mid and upper-level anomalous circulations between the two types of ESBs. The cold-type ESBs are characterized by height anomalies with a northwest–southeast-tilting dipole structure over the Eurasian continent on their peak day, which is consistent with the propagation of Rossby wave packets along an arc-shaped path over the Eurasian continent. Indeed, the typical features of the cold-type ESBs are generally consistent with the wave train that crosses the Eurasian continent in association with the Ural blocking highs (Takaya and Nakamura 2005a; Cheung et al. 2012; Luo et al. 2016b). The circulation anomalies of the cold-type ESBs resemble the east Atlantic/west Russia pattern in its positive phase. In contrast, the height anomalies of the warm-type ESBs over the Eurasian continent are located to the northwest of their cold-type ESB counterparts. In addition to the positive east Atlantic/west Russia teleconnection pattern, the Scandinavian teleconnection pattern also becomes significant during the evolution of the warm-type ESBs.

In addition to the anomaly fields, differences occur in the total fields between the two types of ESBs. In the cold-type ESBs, the air parcels in the lower troposphere over the Yangtze River region on day 4 generally originate from the regions to the north and northeast of the Tibetan Plateau, and an adiabatic process is dominant during the motion of the air parcels. In contrast, in the warm-type ESBs, the air parcels generally originate from the mid and low latitudes of East Asia and exhibit diverse trajectories.

We also test the sensitivity of the results to minor changes in the criteria for either defining an ESB or classifying the types of ESBs, as shown in section 2b(2), and check whether the thermodynamic features over the Yangtze River region, as revealed in sections 4b and 4c, change obviously if the spatial location of the reference grid point is shifted slightly. Specifically, for the identification of an ESB, we change the requirement for the minimum longitudinal extent of the instantaneous local blocking to 10° or 17.5°, the minimum duration to 5 days, or the minimum magnitude of the primary height anomaly to 15 gpdm on the peak day. The SAT anomalies are averaged over different periods (e.g., from day 2 to day 4 or from day 2 to day 5) based on classification of the ESBs. In the results, the typical features of the cold-type and warm-type ESBs do not change qualitatively, but the significance weakens when fewer cases are identified for the composite analysis (not shown). In addition, the results of the formation of the air temperature anomaly over the Yangtze River region still do not change qualitatively if Eq. (3) is calculated at 975 hPa or if other starting points [e.g., (32.5°N, 117.5°E), (25°N, 117.5°E), and (28.75°N, 110°E)] are used for tracking the backward trajectories.

Early studies recognized that migratory synoptic eddies play an important role in the maintenance of blocking highs (Green 1977; Illari and Marshall 1983; Shutts 1983; Colucci 1985; Holopainen and Fortelius 1987; Mullen 1987; Nakamura et al. 1997; Tsou and Smith 1990). Furthermore, Han et al. (2011) noted that warm sea surface temperature anomalies over the North Atlantic are favorable for strengthening the blocking highs over the Ural Mountains by modulating transient eddy activities. We have also evaluated the instantaneous baroclinic wave activity, as in Nakamura et al. (1997), and its feedback forcing through the convergence of the heat flux and vorticity flux, as in Lau and Holopainen (1984). Although the cold-type ESBs are associated with significantly enhanced transient eddy activities over the Kara Sea and reduced transient eddy activities around the Ural Mountains, the eddy forcing in the mid and upper troposphere is marginally significant for both types of ESBs (not shown), indicating high case-to-case variability. The evolution of blocking highs is generally associated with both high- and low-frequency dynamics (Nakamura et al. 1997). The former can be diagnosed by transient eddy feedback forcing, while the latter can be represented by low-frequency Rossby wave propagation. Therefore, the lack of significant signals of the transient eddy feedback forcing implies that low-frequency Rossby wave propagation plays an important role in both types of ESBs.

Wang et al. (2010) showed that the stationary wave propagation associated with Ural blocking highs differed considerably between the period from 1957 to 1976 and the period from 1977 to 2000. Furthermore, the frequency of Ural blocking highs showed an increasing trend after the 1990s (Barnes et al. 2014; Wang and Chen 2014). Therefore, it is worth analyzing the dynamics of blocking highs during periods with high and low Ural blocking frequencies. In addition, Arctic sea ice loss (Honda et al. 2009; Liu et al. 2012; Mori et al. 2014; Luo et al. 2016a) and cooling over the eastern Pacific (Han et al. 2016) might contribute to the formation of anticyclonic anomalies around the Ural Mountains, which could advect warm air masses poleward and cold air masses equatorward, thereby enhancing the warm Arctic–cold Eurasia pattern (Luo et al. 2016a). The role of external forcings in the long-term variations in both the different ESB types and the SAT over Eurasia also deserves a dedicated further investigation.

Acknowledgments

This work was supported jointly by the National Key R&D Program of China (Grant 2016YFA0600702), National Science-Technology Support Plan Projects (Grant 2015BAC03B03), the Chinese Natural Science Foundation (41575057), and the Funding of Jiangsu Innovation and Entrepreneurship Team. The National Center for Atmospheric Research NCAR Command Language (NCL) was used to perform the calculations and draw the plots.

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