J. Meteor. Res.  2019, Vol. 33 Issue (5): 810-825 PDF
http://dx.doi.org/10.1007/s13351-019-9023-z
The Chinese Meteorological Society
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#### Article Information

GAO, Ni, Cholaw BUEH, Zuowei XIE, et al., 2019.
A Novel Identification of the Polar/Eurasia Pattern and Its Weather Impact in May. 2019.
J. Meteor. Res., 33(5): 810-825
http://dx.doi.org/10.1007/s13351-019-9023-z

### Article History

in final form June 6, 2019
A Novel Identification of the Polar/Eurasia Pattern and Its Weather Impact in May
Ni GAO1, Cholaw BUEH2, Zuowei XIE2, Yuanfa GONG1
1. College of Atmosphere Science, Chengdu University of Information Technology, Chengdu 610225;
2. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
ABSTRACT: The Polar/Eurasia (POL) pattern was previously identified based on the empirical orthogonal function method and monthly mean data, in which the positive and negative phases are anti-symmetric in spatial distribution. This paper identifies the positive (POL+) and negative (POL) phases of the POL pattern through applying a novel approach, i.e., self-organizing maps, to daily 500-hPa geopotential height fields in May over 1948–2017. The POL+, POL1, and POL2 patterns defined by this method represent actual physical modes. The POL+ pattern features a wave train from the northeastern Atlantic/northern Europe via the subarctic regions of Eurasia to Lake Baikal. The POL1 pattern is characterized by a planetary-scale dipole pattern with a positive anomaly band over subarctic Eurasia and a negative anomaly band from central Asia to the Sea of Okhotsk. The anomaly centers of the POL2 pattern are basically anti-symmetrical tothose of the POL+ pattern. The POL+ pattern increases the blocking frequency over the northeastern Atlantic/northern Europe and northeastern Asia, where high-frequency transient eddies are highly recurrent in the north. Accordingly, precipitation increases apparently in the subarctic Asian continent and western Siberia, and decreases around Europe and Lake Baikal. A mimic wave train is also observed in the surface air temperature anomaly field. During the POL1 period, the blocking frequency is abnormally high over Eurasia, whereas high-frequency transient eddies are apparently suppressed over northern Eurasia. Correspondingly, significant precipitation deficits are observed in northern Eurasia. The POL1 pattern also causes a remarkable temperature increase in the subarctic seas of Eurasia and a considerable temperature drop in the midlatitude Asian continent. As the POL2 pattern prevails, the blocking frequency decreases over the North Atlantic/Europe but strengthens over the Asian continent. The POL2 pattern also causes wavelike anomalies of precipitation and surface air temperature over northern Eurasia.
Key words: Polar/Eurasia (POL) pattern     self-organizing maps     blocking     transient eddy
1 Introduction

Atmospheric teleconnection patterns are recurrent and persistent modes of low-frequency variability, and each has a preferred geographical circulation center with a dipolar or wave-train structure (Wallace and Gutzler, 1981; Hurrell, 1996). The impacts of teleconnection patterns on the weather and climate of the Northern Hemisphere have been extensively studied and emphasized (Bjerknes, 1969; Wallace and Gutzler, 1981; Barnston and Livezey, 1987; Hurrell, 1996; Tan and Chen, 2014). Among a number of teleconnection patterns, the Polar/Eurasia (POL) pattern is closely associated with the connection between the Arctic polar vortex activity and the midlati-tude circulation over the Asian continent (Barnston and Livezey, 1987). The POL pattern was first named the northern Asian (NA) pattern (e.g., Esbensen, 1984; Barnston and Livezey, 1987). However, this pattern was later formally documented by the Climate Prediction Center (CPC) of NOAA, U.S. and renamed the POL pattern according to the geographical locations of certain centers of action. The POL pattern in cold seasons consists of two centers of action: one around the Kara Sea and the other over midlatitude East Asia (Barnston and Livezey, 1987). However, in summer, the POL pattern has three centers of action with an additional anomaly center over midlatitude Europe (Barnston and Livezey, 1987).

In previous studies, the POL pattern was primarily defined by using the empirical orthogonal function (EOF) method. By applying the rotated EOF analysis to the 500-hPa geopotential height (Z500) field, Horel (1981) identified a number of teleconnection patterns in the Northern Hemisphere, in which the fourth (seventh) EOF mode in winter (summer) resembles the POL pattern. Analysis of one-point correlation map of the monthly geopotential height field at 700 hPa in winter also finds a teleconnection pattern mimicking the POL pattern (Esbensen, 1984). Hsu and Wallace (1985) applied rotated EOF analysis to the wintertime 5-day mean sea-level pressure field over the Northern Hemisphere and noted that the corresponding Z500 anomaly field for the second EOF mode resembles the POL pattern, which they called the Siberian pattern. The identification of the POL pattern (Barnston and Livezey, 1987) is also based on a rotated EOF analysis applied to the monthly mean geopotential height field at 700 hPa over the Northern Hemisphere.

Balling and Goodrich (2011) suggested that the POL pattern is closely associated with the North Atlantic Oscillation (NAO), and both patterns are linked to changes in the polar vortex intensity. Under the global warming background, the warming trend in the Arctic region is the most pronounced (Bekryaev et al., 2010). Correspondingly, the summer polar vortex has gradually contracted since the 1970s (Frauenfeld and Davis, 2003; Angell, 2006; Piao et al., 2018). Supposedly, against the global warming trend, the summer POL pattern is likely to occur more frequently in its negative phase than in its positive phase. It has been recognized that the POL pattern exhibits considerable variations on different timescales and in different seasons.

Recent studies have revealed that the POL pattern is closely related to summer precipitation anomalies in the northern part of Asia (Lin, 2014; Bueh et al., 2016; Lin and Wang, 2016; Ye et al., 2016). Bueh et al. (2016) demonstrated that the subtle collocation of the POL pattern and the circulation pattern typical of the anomalous Indian summer monsoon is essential to summer rainfall in northern China. Specifically, a negative POL pattern and evident Indian summer monsoon circulation work in concert to strengthen water vapor transport towards northern China, resulting in abundant rainfall there. In contrast, a positive POL pattern and weak Indian summer monsoon circulation significantly decrease rainfall in northern China. On the other hand, Lin (2014) and Lin and Wang (2016) found that the positive (negative) POL pattern strengthens (weakens) the northeastern Asian low pressure system, hence resulting in rainfall increase (decrease) in northeastern Asia.

The aforementioned studies mainly discussed the simultaneous relationship between the POL pattern and summer precipitation in East Asia. In fact, through a bridging effect of the POL pattern, some teleconnection patterns in late spring are also linked with summer precipitation in East Asia. Gong and Ho (2003) found that the Arctic oscillation (AO) or NAO in May has a significant negative correlation with the subsequent summer monsoon precipitation in East Asia. Piao et al. (2018) further verified that the NAO pattern in May is preferentially transformed into the POL pattern in June and July, thus modulating June and July precipitation in Asian inland plateaus. This issue is naturally followed by the following question: what is the simultaneous climatic impact of the POL pattern itself in May? Chen et al. (2013) defined a Lake Baikal ridge index in May to characterize the anomalous circulation around Lake Baikal. They found that in May, the anomalous circulation pattern around Lake Baikal significantly affects not only temperatures in northern China but also precipitation in the middle and lower reaches of the Yangtze River and southwestern China. Notably, the circulation patterns corresponding to the positive and negative Lake Baikal ridge indices resemble the positive and negative phases of the POL pattern (Barnston and Livezey, 1987). Thus, the POL pattern in May may affect the temperature and precipitation in East Asia, an effect that deserves in-depth investigation. Moreover, since Yeh et al. (1958) reported the phenomenon of so-called “abrupt circulation change in East Asia in June,” researchers have recognized that May is a critical period in the spring-to-summer transition process in East Asia because the pre-warming process in May serves as a precondition for establishment of summer climate. As will be addressed later, the POL pattern in May indeed has a significant impact on the surface air temperature in East Asia. Therefore, understanding of the POL pattern in May as well as its weather impacts will shed light on the study of the spring-to-summer transition in East Asia.

Currently, there are two perspectives on the nature of teleconnection patterns. In the conventional view, teleconnection patterns are regarded as discrete types of standing wave oscillations with geographically fixed centers of action (e.g., Wallace and Gutzler, 1981). Alternatively, a few studies suggest a continuum of teleconnection patterns, rather than a small number of discrete regimes (Kushnir and Wallace, 1989; Franzke and Feldstein, 2005; Rousi et al., 2015; Yuan et al., 2015). The identification of teleconnection patterns with the EOF method, such as the POL pattern (Barnston and Livezey, 1987), matches with the conventional discrete perspective. Due to the orthogonality nature of EOF modes, the teleconnection patterns identified through the EOF method may represent nonphysical modes. Considering this, the current study applies the self-organizing maps (SOM) method proposed by Kohonen (1990, 1997) to define the POL pattern. One advantage of this method is that it can visualize the continuum of representative patterns in their topological ordering. The SOM method can be applied to unsupervised learning processes and used to classify a set of data without the influence of subjective factors, thereby eliminating subjective interference in most clustering methods (e.g., Kohonen, 1997; Liu et al., 2006; Johnson et al., 2008; Yuan et al., 2015). Bao and Wallace (2015) analyzed Z500 data using the SOM method and found that the classification results have more nonlinear, independent characteristics compared with those obtained from hierarchical cluster analysis (Ward, 1963). Sheridan and Lee (2011) proposed that the SOM can be applied to weather and climate analyses, and the resultant spatial patterns can represent actual physical modes, including typical teleconnection patterns and transition patterns, thereby reflecting the continuum characteristics among representative teleconnection patterns (Rousi et al., 2015). To date, the SOM method has been extensively applied in the study of teleconnection patterns (Cavazos, 1999; Reusch et al., 2007; Johnson and Feldstein, 2010; Johnson, 2013; Lee and Feldstein, 2013; Feldstein and Lee, 2014).

Historically, the POL pattern was mostly defined with the EOF method, and the circulation patterns of its positive and negative phases are linearly anti-symmetrical to each other. Thus, the obtained pattern could not be guaranteed to represent a physical mode. On the other hand, the previous identification of the POL pattern was primarily based on monthly or seasonal mean data, thus reducing intraseasonal signals to a certain degree. In the present study, based on the daily Z500 data, we redefine the POL pattern with the SOM method and analyze the characteristics of its different phases. The relations of the POL pattern with blocking activities and transient high-frequency eddies over the Eurasian continent are documented, and the impacts of the POL pattern on surface air temperature and precipitation in the Eurasian continent are also analyzed. This paper is organized as follows. Section 2 presents the data and methods. Section 3 defines the POL pattern and describes the validation process. Section 4 discusses the relations of the POL pattern with blocking activities and high-frequency transient waves. Section 5 discusses the impacts of the POL pattern on surface air temperature and precipitation in the Eurasian continent. Section 6 includes a summary and discussion.

2 Data and methods

The daily and monthly mean data used in this study come from the NCEP−NCAR reanalysis (Kalnay et al., 1996) for May from 1948 to 2017. The geopotential height and zonal wind are archived on a 2.5° × 2.5° horizontal grid at 17 vertical pressure levels from 1000 to 10 hPa. Data of the surface air temperature at 2 m (T2m) are available on a T42 Gaussian grid with 192 × 94 points. We also use the gridded daily precipitation data on a 0.5° × 0.5° grid over Eurasia (15°S−84°N, 15°E−165°W) for 1951−2007 from the Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of Water Resources Project by the Research Institute for Humanity and Nature (RIHN) and the state-of-the-art daily precipitation datasets on high-resolution grids for Asia developed by the Meteorologi-cal Research Institute of Japan Meteorological Agency (MRI/JMA;Yatagai, 2012). Finally, this study adopts the monthly teleconnection pattern indices in May, which are provided for 1950−2017 by the CPC (http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml). The indices include the NAO index, Scandinavian pattern (SCA) index, East Atlantic pattern (EA) index, East Atlantic/West Russia pattern (EAWR) index, and POL index.

In this study, the POL pattern is defined in May by using the SOM method based on the daily Z500 field for the study area (40°–80°N, 60°W–180°). The following pre-processing procedure is undertaken before the SOM classification. (1) In each year, the daily area-mean (40°–80°N, 60°W–180°) values of Z500 are calculated for 1–31 May, constituting a time series of 31 days. Then, the seasonal trend of this area-mean time series is calculated. (2) An “anomaly” field (referred to as ZA500 field) is obtained by subtracting the seasonal trend of the area-mean time series from the original daily Z500 fields for 1–31 May of that year. The daily ZA500 fields with a sample length of 2170 days (70 × 31 days) are taken as input samples for the SOM analysis. In the pre-processing steps, the purpose of removing the area-mean trend is to highlight the spatial heterogeneity of the input field, eliminate the long-term climate trend associated with global warming, and retain consistent signals of daily “anomaly” fields. In addition, for convenience of discussion, a climatological mean ZA500 field in May is also defined and calculated in a similar manner as the daily ZA500 field. Specifically, the climatological mean ZA500 field in May is obtained by first subtracting the area-mean Z500 quantity from the monthly mean Z500 field in each year and then averaging the results over 70 yr (1948–2017).

In the SOM classification process, the larger the cluster number (NC), the smaller the differences among members of each cluster and the average distances with respect to all pairs of clusters. However, in practice, NC should not be excessively large. Instead, NC should be small enough to allow for the classification results to be practically meaningful. Following the method of Lee et al. (2017), we determine the optimal NC through the following steps: (1) all the classification results with 2 ≤ Nc ≤ 20 are obtained through repeating the SOM classification process, and (2) the mean pattern correlation coefficient (R) is calculated for NC clusters, as noted in Lee at al. (2017). A large NC corresponds to a high R, and vice versa (Fig. 1a). An optimal NC can be searched if (1) R is as high as possible to ensure the overall similarity among members of each cluster, and (2) the average distance (D) with respect to all pairs of clusters is as large as possible to ensure significant differences among all clusters. D can be calculated based on the Ward’s distance between each pair of clusters (Ward, 1963; Xu et al., 2013). A large NC corresponds to a low D, and vice versa (Fig. 1b). The details of the determination of an optimal NC will be exemplified in Section 3.1.

 Figure 1 Variation of (a) the average pattern correlation coefficient (R) between each member and the corresponding centroid of clusters, (b) the average distance between each pair of clusters (Ward’s distance; D), and their corresponding slopes with the number of nodes (NC) in the SOM neural network.

In this study, the high-frequency transient eddy kinetic energy (EKE) anomalies at 300 hPa are used to characterize the variability of major storm tracks and the intensity of transient synoptic wave processes. The EKE is calculated with a high-pass filter with an 8-day cutoff period based on daily 300-hPa horizontal wind data (Pelly and Hoskins, 2003; Lehmann et al., 2014; Xie and Bueh, 2017a). The daily EKE can be expressed as follows:

 ${\rm{EKE}} = {{({u'^{2}} + {v'^{2}})} /2},$ (1)

where u' and v' are 8-day high pass-filtered 300-hPa zonal and meridional winds, respectively.

We define blocking events with the method proposed by Pelly and Hoskins (2003). From a potential vorticity (PV) perspective, Pelly and Hoskins (2003) defined a one-dimensional blocking high index B (also called PH03 index) according to the reversal of the potential temperature (θ) at dynamic tropopause. The dynamic tropopause is defined as the 2 PVU (1 PVU = 10−6 m2 s−1 K kg−1) surface. In this method, a daily B index at every grid (λ0, φ0) along the latitude of the maximum climatological mean EKE is calculated as

 $B = {2 / \Delta }\varphi \int_{{\varphi _0}}^{{\varphi _0} + {{\Delta \varphi } / 2}} {\theta {\rm d}\varphi } - {2 / \Delta }\varphi \int_{{\varphi _0} - {{\Delta \varphi } / 2}}^{{\varphi _0}} {\theta {\rm d}\varphi },$ (2)

where Δφ is set to 30°. The B index at a particular longitude is designated as 1 if B is positive for at least 15° of longitude, and there are grid points with positive B values within 10° of longitude of the particular longitude for at least 4 consecutive days; otherwise, it is specified as 0. Obviously, the B index represents a large-scale meridional gradient of θ.

3 Identification of the POL pattern 3.1 SOM classification

Based on the method of determining the optimalNC as described in Section 2, all classification results with 2 ≤ Nc ≤ 20 are obtained through repeating the SOM classification process with the same daily ZA500 field input to 1 × NC grid maps. As shown in Fig. 1, as NC increases, R gradually increases (Fig. 1a), and D gradually decreases (Fig. 1b). Notably, as NC increases to a large number, the absolute change rates of both R and D tend to be stabilized. To illustrate the change rates of R and D, the slopes of R and D curves are also drawn in Fig. 1. As NC increases from 2 to 7, R increases gradually. However, as NC turns greater than 7, R reaches a relatively stable value, as indicated by the slope of R. A similar variation is also observed for D. These facts suggest that daily ZA500 fields can be categorized into at least 7 clusters, allowing for sufficient intra-cluster similarities and inter-cluster differences, thereby meeting the classification requirements. To highlight the spatial continuum of representative patterns in the topological ordering, we adopt a 3 × 3 SOM grid, rather than a 1 × 7 SOM grid. The 3 × 3 SOM patterns incorporate the 7 SOM patterns with pattern correlation coefficients from 0.66 to 0.98.

Figure 2 shows nine SOM patterns over 40°–80°N, 60°W–180° in May over 1948–2017. The number of occurrence days of each SOM pattern is indicated in brackets. As explained by Kohonen (1990, 1997), in the SOM neural network, similar SOM patterns are arranged on neighboring nodes, whereas dissimilar patterns are separated on distant nodes. Thus, differences among SOM patterns can be indicated by distances among nodes. For convenience, the nth SOM pattern is referred to as SOMn (e.g., the first and second SOM patterns are referred to as SOM1 and SOM2, respectively). To facilitate comparison between each SOM pattern and the corresponding climatological mean field, the climatological mean ZA500 field in May is shown in Fig. 3. Because the area-mean value is subtracted from the ZA500 field, the zero contour lines (the thick blue and green solid lines in Fig. 2 and the thick blue solid line in Fig. 3) can approximately reflect the alignment and shape of the polar vortex. As demonstrated in Fig. 2, significant differences among the nine SOM patterns are primarily reflected in the polar vortex activity and the associated wavelike circulation over middle and high latitudes. In both the SOM1 and SOM5 patterns, the polar vortex is active over the subarctic region of the Eurasian continent. However, the polar vortex activities differ between these two SOM patterns. Specifically, the polar vortex apparently intrudes towards northern Europe in the SOM1 pattern, whereas it mainly invades southward to the Asian continent in the SOM5 pattern (Figs. 2a, 2e, 3). The polar vortex activity over the subarctic region of the Eurasian continent is relatively weak in both the SOM7 and SOM9 patterns. However, the mid- and high-latitude wavelike circulations differ significantly between these two SOM patterns (Figs. 2g, 2i, 3). In particular, the SOM5, SOM7, and SOM9 patterns are related to anomalous polar vortex activities over the subarctic Asia; therefore they may represent the POL pattern. In the SOM3 and SOM4 patterns (Figs. 2c, d), the region of Iceland/Greenland is controlled by a planetary ridge and trough, respectively. Thus, these two patterns may be related to NAO. The SOM2, SOM6, and SOM8 patterns are characterized by three distinct wavelike circulations over the North Atlantic/Europe (Figs. 2b, f, h).

 Figure 2 (a–i) Composite ZA500 fields of nine SOM patterns (SOM1–SOM9) over 40°–80°N, 60°W–180° in May during 1948–2017, with the number of occurrence days shown in the bracket. The ZA500 is constructed by removing the seasonal trend of area-mean (40°–80°N, 60°W–180°) Z500 in each year, representing an anomaly with respect to the area-mean (see text for details). The contour interval is 30 m. The thick blue line represents the zero line; the red solid line and blue dashed line represent positive and negative values, respectively; and the green line represents the zero line in May of the climatological mean ZA500 field.
 Figure 3 As in Fig. 2, but for the climatological mean ZA500 field in May over 1948–2017.

For comparison with conventional teleconnection patterns, Fig. 4 shows the composite Z500 anomaly fields corresponding to the nine SOM patterns in May over 1948–2017. Each composite field is calculated based on all members of the corresponding SOM pattern (the sample number of each cluster is indicated in brackets). For convenience, such composite patterns are referred to as the first to ninth circulation patterns (C1–C9) in sequence. Here, Z500 anomalies are calculated relative to the climatological mean Z500 field and differ from the input “anomalies” of the SOM analysis (i.e., ZA500). To eliminate the interdecadal variation and climate trend, a dynamic climatological mean state is used in this study. Following the CPC’s new method of calculating the Oceanic Niño index (L’Heureux et al., 2013), we assign a fixed 30-yr average to each 5-yr period centered on the first year of that 5-yr period as the climatological mean. For example, anomalies in the period 1956–1960 are relative to the climatological mean of the period 1941–1970, anomalies in the period 1961–1965 are relative to the climatological mean of the period 1946–1975, and so on. In total, eight fixed 30-yr climatological mean states are applied in the current study (see Table 1).

 Figure 4 (a–i) Composite fields of Z500 anomalies (shading, m) of the nine circulation patterns (C1–C9), with respect to the climatological mean Z500 field in May over 1948–2017 (see text for details). The value in each bracket denotes the sample number of the corresponding SOM pattern shown in Fig. 2; only composite anomalies in the 95% confidence interval are shaded; and the lowest point in each panel is drawn at 30°N, 90°E.
Table 1 Information on the 30-yr climatological mean period centered on the first year of each 5-yr period (or equivalent)
 5-yr 1948–1970 1971–1975 1976–1980 1981–1985 1986–1990 1991–1995 1996–2000 2001–2017 30-yr 1948–1980 1956–1985 1961–1990 1966–1995 1971–2000 1976–2005 1981–2010 1986–2017

Because there is a one-to-one correspondence between Figs. 2 and 4, the C1–C9 patterns in Fig. 4 characterize the nine prevalent physical modes in May over North Atlantic/Eurasia. The patterns clearly reflect significantly different polar vortex activities and wave train-like circulation anomalies. Of the nine patterns, the C5, C7, and C9 patterns are similar to the conventional POL pattern, and all reflect anomalous polar vortex activities over the subarctic Asia. In the following section, we identify the POL pattern by analyzing the C1–C9 patterns.

3.2 The POL pattern

We recognize the attributes of C1–C9 patterns by analyzing their spatial and temporal correlations with the conventional teleconnection patterns defined by the CPC. It should be noted that conventional EOF analysis embodies linear characteristics, whereas SOM analysis reflects nonlinear characteristics. Therefore, differences would undoubtedly be identified between the circulation patterns obtained by the SOM method and those defined by the CPC.

To facilitate comparisons, the POL pattern in May defined by the CPC (Barnston and Livezey, 1987) is shown in Fig. 5, which is obtained by regressing the Z500 anomaly fields in May against the time series of the CPC’s POL index in the same month over 1948–2017. As seen in Fig. 5, the conventional POL pattern in May is characterized by a meridional dipole pattern with an anomaly center over the subarctic region of the Eurasian continent and another anomaly center with an opposite sign over Lake Baikal.

 Figure 5 Z500 anomalies in May regressed against the CPC POL index in May over 1948–2017 with one standard deviation. The contour interval is 5 m. The solid (dash) lines represent positive (negative) anomalies, and the zero lines are omitted. Light (dark) shading marks the region where the anomalies are significant at the 90% (95%) confidence level. The lowest point is drawn at (30°N, 90°E).

The pattern correlation coefficients (Rs) of the C1–C9 patterns with the NAO, SCA, EA, EAWR, and POL patterns defined by the CPC are summarized in Table 2. The spatial correlations are calculated in the same domain (40°–87.5°N, 0°–357.5°E). The NAO, SCA, EA, and EAWR patterns are obtained by the same regression method of constructing the POL pattern in Fig. 5.

Table 2 Pattern correlation coefficients between the composite Z500 anomaly fields of the nine circulation patterns (C1–C9) and CPC teleconnection patterns
 NAO SCA EA EAWR POL C1 −0.28 −0.45 0.24 0.05 0.32 C2 −0.18 −0.48 −0.31 0.31 0.02 C3 −0.71 0.23 −0.34 0.20 −0.32 C4 0.69 −0.38 0.52 0.17 0.37 C5 0.07 0.20 −0.00 0.56 0.70 C6 −0.33 0.72 −0.19 −0.09 −0.17 C7 0.15 −0.42 0.17 −0.45 −0.46 C8 0.35 0.13 0.09 −0.59 0.10 C9 0.16 0.28 −0.14 −0.55 −0.75 Note: CPC teleconnection patterns are obtained by regressing the Z500 anomalies against the standardized CPC teleconnection pattern indices in May over the domain of 40°–87.5°N, 0°–357.5°E. The bold numbers signify that the absolute values of the correlation coefficients exceed 0.65.

Meanwhile, the projection indices of the C1–C9 patterns are calculated by projecting the Z500 anomaly fields in May over 1948–2017 onto the C1–C9 patterns using the following formula (Goss et al., 2016; Lee et al., 2017):

 ${P_{(t)}} = \frac{{\sum\nolimits_i {\sum\nolimits_j {{\varPhi '}({\lambda _i},{\varphi _j},t){\varPhi ^ * }({\lambda _i},{\varphi _j})\cos {\varphi _j}} } }}{{\sum\nolimits_i {\sum\nolimits_j {{{[{\varPhi ^ * }({\lambda _i},{\varphi _j})]}^2}\cos {\varphi _j}} } }},$ (3)

where λi (0° ≤ λi ≤ 357.5°E) is the longitude and φj (40°N ≤ φj ≤ 87.5°N) is the latitude for grid point (i, j); t is time; and Φ*(λi, φj) represents the Z500 anomaly field in each of the C1–C9 patterns (Fig. 4) and Φ′(λi, φj, t) is the original Z500 anomaly field in May of each year (relative to the dynamic climatological mean state; Table 1). The temporal correlations (Rt) of the P(t) with the NAO, SCA, EA, EAWR, and POL indices (provided by the CPC) are summarized in Table 3.

Table 3 Correlation coefficients between the projection indices of the nine circulation patterns (C1–C9) and the CPC teleconnection pattern indices
 NAO SCA EA EAWR POL C1 −0.43* −0.49* 0.27 0.09 0.46* C2 −0.26 −0.47* −0.41* 0.44* 0.06 C3 −0.73* 0.20 −0.29 0.19 −0.30 C4 0.70* −0.29 0.41* 0.16 0.35* C5 0.09 0.17 −0.02 0.58* 0.75* C6 −0.48* 0.69* −0.17 −0.14 −0.24 C7 0.24 −0.40* 0.16 −0.54* −0.58* C8 0.44* 0.09 0.11 −0.67* 0.10 C9 0.17 0.23 −0.11 −0.56* −0.78* Note: The symbol * marks values that are significant at the 99% confidence level, and the bold numbers signify that the absolute values of the correlation coefficients exceed 0.65.

As shown in Figs. 4, 5, the C5, C7, and C9 circulation patterns resemble the POL pattern in their spatial distributions. The negative anomaly center of the C5 pattern anchors over the northern Taimyr Peninsula, and its two positive anomaly centers are located over Lake Baikal and Northeast Atlantic/northern Europe, respectively. As displayed in Tables 2, 3, among the C1–C9 patterns, the C5 pattern has the highest Rt (0.75) and Rs (0.70) with the positive POL pattern. This finding suggests that the C5 pattern represents the positive phase of the POL pattern. For the C9 pattern, the subarctic region of the whole Eurasian continent is dominated by a positive anomaly band with its strongest center around the Barents Sea and by a negative anomaly band extending from Lake Balkhash to the Sea of Okhotsk. Among the C1–C9 patterns, the C9 pattern has the strongest negative Rt (–0.78) and Rs (–0.75) with the POL pattern (Tables 2, 3). This finding suggests that the C9 pattern represents the negative phase of the POL pattern. Regarding the C7 pattern, the positive anomaly zone is located over the subarctic Asia and extends eastward to the Bering Strait and Alaska, while its two negative anomaly centers are located over northeastern Atlantic/northern Europe and Lake Baikal, respectively. Among the C1–C9 patterns, the C7 pattern has the second strongest negative Rt (–0.58) and Rs (–0.46) with the POL pattern (Tables 2, 3). This finding suggests that the C7 pattern represents another negative phase of the POL pattern. Although both C7 and C9 patterns show the features of negative POL pattern, their horizontal structures differ considerably. In the SOM neural network (figure omitted), the distance between the C5 and C9 patterns is remarkably large, which is indicative of a significant difference between them. As representatives of the positive and negative phases of the POL pattern, the C5 and C9 patterns are not anti-symmetrical in horizontal spatial structure. The C5 circulation pattern exhibits a notable wave train-like distribution, whereas the C9 pattern is primarily characterized by a meridional dipole structure. Interestingly, the C5 and C7 patterns are anti-symmetrical to each other in horizontal spatial structure; however, the positions and intensities of their anomaly centers over North Atlantic and East Asia slightly differ. Hereafter, the C5, C9, and C7 patterns are referred to as POL+, POL1, and POL2 patterns, respectively.

As anticipated, the Rt (Rs) values of the C3 and C4 patterns with the conventional NAO pattern are –0.73 (–0.71) and 0.70 (0.69), respectively (Tables 2, 3). These findings suggest that the C3 and C4 patterns correspond to the negative and positive phases of the NAO pattern, respectively. In addition, the C4 circulation pattern is also positively correlated with the EA pattern. As indicated in Tables 2, 3, the C6 pattern corresponds to the positive phase of the SCA pattern and is negatively correlated with the NAO pattern. The C8 pattern corresponds to the negative phase of the EAWR pattern and is simultaneously positively correlated with the NAO pattern. The C1 and C2 patterns can be considered as combinations of three conventional teleconnection patterns (Tables 2, 3). The C1 pattern is a combination of the NAO, SCA, and POL patterns; while the C2 pattern combines the SCA, EA, and EAWR patterns. Of the nine circulation patterns identified in this study, six of them (i.e., C3, C4, C5, C6, C8, and C9) notably correspond to different phases of the NAO, POL, SCA, and EAWR patterns defined by Barnston and Livezey (1987). However, a one-to-one correspondence does not exist between the C1–C9 patterns identified in this study and the conventional teleconnection patterns.

3.3 Interannual and interdecadal variations of the POL pattern

Figure 6 shows the total number of occurrence days of POL+, POL1, and POL2 patterns in May over 1948–2017. On average, all these patterns (POL+, POL1, and POL2) occur on approximately three days in May. However, they all exhibit remarkable interannual variability. For example, the POL+ pattern prevailed for 27 days in 1990, the POL1 pattern occurred for 21 days in 1954, and the POL2 pattern occurred for 13 days in 2005. In contrast, the patterns are absent in some years. In addition, all the POL-like patterns exhibit a notable interdecadal variation. The POL+ pattern occurred frequently in the 1960s and during the mid-1980s to mid-2000s, but it occurred less frequently in other periods. On the interdecadal timescale, as shown in Fig. 6, the periods when the POL+ pattern occurs frequently basically correspond to the periods when the POL1 pattern occurs less frequently, and vice versa. The POL2 pattern occurred frequently during mid-1980s to mid-1900s and in the 2000s.

 Figure 6 The total number of occurrence days for annual mean (dashed line) and 5-yr running mean (solid line) in May over 1948–2017 for the (a) POL+, (b) POL1−, and (c) POL2− patterns. The dotted horizontal line represents the climatological mean number of corresponding POL days
4 Impacts of the POL pattern on weather systems

In the previous sections, we have defined the POL+, POL1, and POL2 patterns and discussed their spatial structures and temporal features. Cai and Mak (1990) found that planetary wave structures result in enhanced regional baroclinic zones and thus give rise to the North Pacific and North Atlantic storm tracks, exerting great influence on regional weather. The large value of the high-frequency transient EKE often characterizes storm track regions, such as the North Atlantic and North Pacific storm tracks (Hoskins and Hodges, 2002; Bengtsson et al., 2006; Xie et al., 2017). In addition, EKE anomalies can capture the enhancement or suppression of synoptic disturbances. On the other hand, blocking is recognized as an important midlatitude weather system, and its formation and maintenance significantly affect various regions of the Northern Hemisphere (Hoskins et al., 1985; Xie and Bueh, 2017a, b). It was also observed that blocking activity is closely related to storm activity (Pelly and Hoskins, 2003). Considering these findings, in this section, we analyze how the POL pattern, as a planetary wave background, affects the distributions of blocking and transient EKE over North Atlantic/Eurasia.

4.1 The POL pattern and blocking

Figure 7 shows the blocking frequencies as a function of longitude with respect to the climatological mean and the POL+ , POL1 , and POL2 patterns in May over 1948–2017. The blocking frequency is represented by the ratio of blocking occurrence days relative to the total days in May. As shown by the climatological mean blocking frequency (black curve), blocking occurs frequently (up to 23.8%) over North Atlantic/Europe, which is consistent with the findings of Pelly and Hoskins (2003). In May, the blocking frequency over East Asia is relatively low, which is reflected by an abrupt decrease in the black curve near 110°E. Compared with the winter situation, the blocking frequency over North Pacific is relatively low (up to 6%). In the following discussion, we mainly focus on blocking activity over North Atlantic and the Eurasian continent.

 Figure 7 The latitudinal distribution of the blocking frequency in May over 1948–2017, identified by using the PH03 index. The red, blue, and green lines represent the blocking frequency during the POL+, POL1–, and POL2– pattern periods, respectively; and the black line refers to the climatological mean blocking frequency.

As demonstrated in Fig. 7, when the POL+ pattern prevails, a significant increase in the blocking frequency is observed over North Atlantic/Europe, where there is a climatological mean area of high blocking frequency, and its maximum reaches 38%. This phenomenon is consistent with the presence of a positive height anomaly center over the Northeast Atlantic Ocean (Fig. 4e). In addition, consistent with the wavelike circulation of the POL+ pattern, blocking activity is suppressed over western Siberia but is strengthened over Northeast Asia. For the POL1 pattern, the Z500 anomaly field is characterized by a positive anomaly band in the north and a negative anomaly band in the south (Fig. 4i), resembling the circulations feature of the large-scale tilted ridges and troughs (Bueh et al., 2011a, b; Bueh and Xie, 2015). Correspondingly, blocking activity strengthens over the entirety of midlatitude Eurasia (Fig. 7). Corresponding to the POL1 pattern, the highest blocking frequency center appears near 30°E (up to 46%), which is consistent with the positive height anomaly center of the POL1 pattern over the Barents Sea (Fig. 4i). Additionally, blocking activity weakens significantly over most regions of North Atlantic and the northwestern Pacific (120ºE–180º). Over North Atlantic to East Asia, the blocking frequencies show an out-of-phase relationship between the POL+ pattern and POL2 pattern (Fig. 7).

When the POL2 pattern prevails, a significant weakening in the blocking activity is observed over North Atlantic/Europe, which is consistent with the presence of a negative height anomaly center over this area (Fig. 4g). In addition, consistent with the wavelike circulation of the POL2 pattern, blocking activity strengthens over the entire midlatitude Asia.

4.2 The POL pattern and related EKE anomalies

Figure 8 shows the climatological distribution of 300-hPa EKE and the composite EKE anomalies corresponding to the POL+, POL1, and POL2 patterns in May over 1948–2017. Climatologically (Fig. 8a), there are two maxima over North Atlantic and North Pacific that correspond to two well-known storm tracks (e.g., He and Black, 2016; Xie et al., 2017). Considering the POL+ pattern (Fig. 8b), a positive Z500 anomaly center occurs over the northeastern Atlantic/northern Europe, corresponding to highly frequent blocking activity in situ (Figs. 2e, 4e, 7). Due to such a positive anomaly center, high-frequency transient waves will move along its two sides, i.e., southeastward or northeastward. Consequently, the transient EKE reduces over southern and western Europe. Corresponding to the increased blocking activity over northeastern Asia (Fig. 7), the westerly movement in the northern flank of the blocking circulation is enhanced (Figs. 2e, 3), whereas the westerly movement in the southern flank is weakened. As such, the resultant transient EKE anomalies are positive poleward and negative equatorward of the westerly movement over northeastern Asia. Overall, a dipole pattern of EKE anomalies, namely “positive north, negative south,” occurs over both the eastern and western coasts of the Eurasian continent (Fig. 8b).

 Figure 8 (a) Climatological mean 300-hPa transient EKE (m2 s−2) in May over 1948–2017. The thick dashed lines denote the climatological mean EKE of 70 m2 s−2, and the contour interval is 10.0 m2 s-2. Composite 300-hPa transient EKE anomalies (contour) for the (b) POL+, (c) POL1–, and (d) POL2– patterns. The contour interval is 5.0 m2 s–2. The solid (dash) lines represent positive (negative) EKE anomalies, and the zero lines are omitted. Light (dark) shading marks the regions where the anomalies are significant at the 90% (95%) confidence level. The lowest point in each panel is drawn at 30°N, 90°E.

Given the increased blocking frequency during the POL1 pattern period (Figs. 4i, 7), the transient synoptic waves are remarkably suppressed over the whole subarctic Eurasia (Fig. 8c). In addition, the ridge over the Scandinavian Peninsula extending into the Arctic (Figs. 2i, 4i) steers transient synoptic eddies northward. Consequently, the transient EKE increases significantly over the northernmost North Atlantic, including northern Greenland, the Greenland Sea, the Norwegian Sea, and the Barents Sea (Fig. 8c).

It is interesting to compare the transient EKE anomalies for the POL2 pattern (Fig. 8d) with those for the POL+ and POL1 patterns (Figs. 8b, c). Over northeastern Atlantic and western/northern Europe, the meridional dipole anomalies in Fig. 8d are basically opposite in sign with those dipole anomalies in Fig. 8b. This finding is also true in eastern Siberia. To some degree, this reflects the reverse phase characteristics of these two patterns. On the other hand, the extensive suppression of the transient EKE over the Asian continent, primarily to the north of 50°N, is basically similar for the POL1 and POL2 patterns (Figs. 8c, d), reflecting their partially in-phase feature.

5 Impacts of the POL pattern on tempera-ture and precipitation

Figure 9 displays the composite T2m anomalies corresponding to the POL+, POL1, and POL2 patterns in May over 1948–2017, superimposed with the corresponding 850-hPa horizontal wind anomalies. The POL+ pattern is associated with a wavelike distribution of T2m anomalies from the northeastern Atlantic to northeastern Asia. Specifically, the wavelike temperature distribution is characterized by two positive anomaly centers over North Atlantic/western Europe and northeastern Asia, respectively, and a negative anomaly center straddling the Ural Mountains and the Kara Sea (Fig. 9a). Of note, the two positive temperature anomaly centers basically correspond to the area of an anomalous southerly at 850 hPa (Fig. 9a) and increased blocking frequency (Fig. 7). In particular, the positive temperature anomaly center over northeastern Asia is remarkably pronounced, with an amplitude of up to 3°C. This result is consistent with the large climatological meridional temperature gradient over northeastern Asia (figure omitted). The region around the Ural Mountains is dominated by northerly anomalies and thus features negative temperature anomalies. In addition, due to northerly anomalies, negative temperature anomalies are discernable in southwestern China.

 Figure 9 Composite T2m anomalies (contour, °C) during the (a) POL+, (b) POL1−, and (c) POL2− periods overlaid with 850-hP horizontal wind anomalies (arrows, m s−1) in May over 1948–2017. The contour interval is 0.5°C, and zero lines are omitted. Shading marks the regions where the values are significant at the 95% confidence level. The lowest point in each panel is drawn at 20°N, 90°E.

The POL2 pattern is also associated with a wavelike distribution of T2m anomalies from Greenland/northeastern Atlantic to northeastern Asia (Fig. 9c). T2m anomalies in Fig. 9c are basically anti-symmetric with those in Fig. 9a. Specifically, the wavelike temperature distribution is characterized by two negative anomaly centers over Greenland and northeastern Asia (down to −1°C), which is consistent with the northerly anomalies at 850 hPa in situ. Meanwhile, a meridionally elongated positive temperature anomaly center extends from the Ural Mountains to the Arctic Ocean, which is consistent with the local predominant southerly anomalies ( Fig. 9c).

Corresponding to the POL1 pattern, positive temperature anomalies are dominant in the subarctic seas of the Eurasian continent due to the prevailing southerly anomalies. The strongest positive temperature anomaly of up to 3°C is observed near the Barents Sea (Fig. 9b). On the other hand, the midlatitude Asian continent is dominated by remarkably cold temperatures. As mentioned previously, the POL1 pattern exhibits a circulation feature typical of large-scale tilted ridges and troughs (Bueh et al., 2011a, b; Bueh and Xie, 2015). Therefore, extensive cold air accumulates in midlatitude Asia, where a large-scale tilted trough entrenches. In addition, positive temperature anomalies are observed in southwestern and southern China, which is consistent with the findings of Chen et al. (2013).

Figure 10 shows the composite precipitation anomalies (in percentage) corresponding to the POL+, POL1, and POL2 patterns in May over 1951–2007. When the POL+ pattern is prevalent, precipitation decreases considerably in Europe and in the region around Lake Baikal (Fig. 10a). Particularly, precipitation decreases by 60% near Lake Baikal. In comparison, a significant increase in precipitation occurs from central Asia to subarctic Asia. A pronounced precipitation increase of up to 90% is observed near the Lena River. As stated previously, precipitation in mid- and high-latitude Eurasia is dependent upon the intensity and frequency of transient synoptic disturbances to a certain degree. As shown in Fig. 8b, a considerable transient EKE increase occurs over the subarctic regions of the Asian continent, which is consistent with the increase in precipitation in situ. Similarly, significant transient EKE decreases are observed over Europe and around Lake Baikal, which is consistent with the precipitation decrease in these regions.

 Figure 10 Composite precipitation anomalies in percentage (%) corresponding to the (a) POL+, (b) POL1–, and (c) POL2– patterns in May over 1951–2007. Stippling marks the regions where anomalies are significant at the 95% confidence level

As shown in Fig. 10b, in response to the POL1 pattern, the most remarkable precipitation anomalies are along the subarctic regions of the whole Eurasian continent, and precipitation is reduced by 20% to 70% in most of these regions. This finding is consistent with the broad transient EKE decrease over the northern portion (to the north of 50°N) of the Eurasian continent (Fig. 8c). The POL1 pattern is also related to the precipitation decrease in the Gobi Desert of the Mongolian Plateau (down by –65%) and the precipitation increase in the Huaihe River basin of China (up by 80%). Corresponding to the POL1 pattern, notable precipitation anomalies occur in the subtropical region. Specifically, above-normal precipitation occurs in North Africa, and below-normal precipitation occurs in the Arabian Peninsula to the Indochina region. However, precipitation anomalies in these regions could not be explained by the local transient EKE anomalies. The issue regarding the relationship between the POL1 pattern and the anomalous precipitation in the subtropical region is beyond the scope of this study, and we retain it for future investigations.

For the POL2 pattern (Fig. 10c), a pronounced precipitation deficit (down to 80%) is observed over central and eastern Siberia, while above-normal precipitation occurs in northern Europe. These precipitation anomalies are consistent with the transient EKE anomalies in situ (Fig. 8d). In addition, the POL2 pattern is also related to a significant precipitation increase in eastern China.

6 Conclusions and discussion

In this study, nine circulation patterns in the domain of North Atlantic/Eurasia in May are defined by applying the SOM method to the daily ZA500 field for the period 1948–2017. Then, the positive and negative phases of the POL pattern, namely, POL+, POL1, and POL2 patterns, are identified according to temporal correlations and spatial similarities between the nine circulation patterns and the several conventional teleconnection patterns defined by the CPC. The POL+ and POL1 patterns defined in this study do not exhibit an anti-symmetrical feature, in contrast to the anti-symmetrical positive and negative phases of the conventional POL pattern. In this study, the associations of POL+, POL1, and POL2 patterns with the blocking activity and high-frequency transient waves are analyzed, and the impacts of the POL+, POL1, and POL2 patterns on surface air temperature and precipitation in the Eurasian continent are revealed.

The POL+ pattern exhibits a wavelike circulation feature, with a negative height anomaly center over subarctic Asia and two positive height anomaly centers over northeastern Atlantic/northern Europe and around Lake Baikal, separately. The POL+ pattern occurred frequently in the 1960s and during mid-1980s to mid-2000s. Corresponding to this pattern, blocking activity increased over North Atlantic/Europe and northeastern Asia, whereas it decreased around the Ural Mountains. Simultaneously, the enhancement and suppression of high-frequency transient waves are also distributed in a wavelike pattern. However, the transient EKE anomalies with a dipole pattern of “positive north, negative south” are more remarkable on the western and eastern coasts of the Eurasian continent, near the North Atlantic and North Pacific storm tracks, compared with other inland regions. Consistent with the distributions of transient EKE anomalies, precipitation increases in subarctic Asia and West Siberia, while it decreases around Europe and Lake Baikal. The POL+ pattern also causes wavelike surface air temperature anomalies. A remarkable temperature increase occurs in northeastern Asia, while a significant temperature decrease is observed around the Ural Mountains.

The POL1 pattern is characterized by a positive height anomaly band over the whole subarctic Eurasia and a negative height anomaly band in midlatitude Asia from Lake Balkhash to the Sea of Okhotsk, thus exhibiting a planetary-scale meridional dipole structure. This pattern occurred mostly in the 1950s and 1970s. Corresponding to the POL1 pattern, blocking activity increased over the entire Eurasian continent. Due to the highly frequent blocking, the high-frequency transient waves are suppressed over the Eurasian continent to the north of 50°N. Correspondingly, significant precipitation deficits are observed in subarctic Eurasia. In addition, the POL1 pattern is also associated with precipitation decrease in the Gobi Desert and precipitation increase in the Huaihe River basin of China. When the POL1 pattern dominates, the subarctic seas of the Eurasian continent become remarkably warm, but the midlatitude Asian continent is affected by considerably low temperatures.

Unlike the POL1 pattern with a planetary-scale meridional dipole structure, the POL2 pattern exhibits a wavelike circulation feature, showing an anti-symmetric feature of the POL+ pattern. The POL2 pattern occurred frequently in the mid-1980s and in the 21st century. Corresponding to this pattern, blocking activity decreases over North Atlantic/Europe and increases over the Asian continent. The POL2 pattern corresponds to a pronounced precipitation deficit over central and eastern Siberia and above-normal precipitation in northern Europe. These precipitation anomalies are consistent with the transient EKE anomalies in situ. The POL2 pattern also causes wavelike surface air temperature anomalies with a remarkable temperature increase around the Ural Mountains.

Historically, the POL pattern was primarily defined by the EOF method applied to the monthly or seasonal mean variable field. However, the extracted modes are mutually orthogonal. Therefore, the physical meaning of each mode, which is obtained through the EOF method, could not be explained unambiguously. Although this problem is partially mitigated by the rotated EOF method, the obtained positive and negative POL patterns remain anti-symmetrical to each other. Given the nonlinear feature of the physical modes in atmospheric circulation, the SOM method preserves the nonlinearity in the actual circulation modes. Therefore, the POL patterns newly defined in this study can be regarded as physical models, and thus more truly reflect the intrinsic features of the positive and negative POL patterns. However, it is by no means appropriate that the asymmetry between the positive and negative phases of a physical mode could not be obtained through the EOF analysis. In fact, such an asymmetry could also be captured by the EOF-based composite analysis.

The SOM analysis in this study reveals that a specific phase of the POL pattern, as defined by Barnston and Livezey (1987), is not a unique pattern but a continuum that can be well approximated by more than one pattern. For example, the negative phase of the POL pattern may be approximated by two discrete patterns, such as the POL1 and POL2 patterns. However, it is important to note that the weather impacts of the POL1 and POL2 patterns are considerably different. Dai and Tan (2017) revealed that the wintertime AO has five discrete, representative AO-like patterns from the continuum perspective. They noted that these five AO-like patterns have subtle differences in their weather impacts, which is quite analogous to the results for the two negative POL patterns in this study.

The life cycles of the POL patterns (POL+, POL1, and POL2) in May all exhibit an intraseasonal timescale feature with e-folding timescales ranging from 4 to 7 days. Therefore, it is necessary to explore the dynamics involved in their intraseasonal evolution processes. On the intraseasonal timescale, the high-frequency eddy feedback and the low-frequency Rossby wave propagation have been recognized as playing important roles in the formation of teleconnection patterns (Nakamura et al., 1997; Bueh et al., 2011b; Dai and Tan, 2017). Thus, how do these dynamic processes form the POL patterns? Is the exact type of the POL patterns predictable on this timescale? These issues need future investigations.

As shown in Fig. 9, the POL+, POL1, and POL2 patterns in May are capable of altering the surface air temperature condition in Siberia. We hypothesize that the POL+ pattern is favorable for early summer establishment in northern Asia during late May to early June, and the POL1 and POL2 patterns would delay summer establishment in northern Asia. This issue needs further investigation in detail.

Acknowledgments. We thank the writers of NCARG Command Language (UCAR/NCAR/CISL/TDD 2017), which was used to plot the figures in this paper.

References
 Angell, J. K., 2006: Changes in the 300-mb north circumpolar vortex, 1963–2001. J. Climate, 19, 2984–2994. DOI:10.1175/JCLI3778.1 Balling, R. C. Jr., and G. B. Goodrich, 2011: Interannual variations in the local spatial autocorrelation of tropospheric temperatures. Theor. Appl. Climatol., 103, 451–457. DOI:10.1007/s00704-010-0313-8 Bao, M., and J. M. Wallace, 2015: Cluster analysis of Northern Hemisphere wintertime 500-hPa flow regimes during 1920–2014. J. Atmos. Sci., 72, 3597–3608. DOI:10.1175/JAS-D-15-0001.1 Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126. DOI:10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2 Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev, 2010: Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. J. Climate, 23, 3888–3906. DOI:10.1175/2010JCLI3297.1 Bengtsson, L., K. I. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. J. Climate, 19, 3518–3543. DOI:10.1175/JCLI3815.1 Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172. DOI:10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2 Bueh, C., and Z. W. Xie, 2015: An objective technique for detecting large-scale tilted ridges and troughs and its application to an East Asian cold event. Mon. Wea. Rev., 143, 4765–4783. DOI:10.1175/MWR-D-14-00238.1 Bueh, C., X. Y. Fu, and Z. W. Xie, 2011a: Large-scale circulation features typical of wintertime extensive and persistent low temperature events in China. Atmos. Ocean. Sci. Lett., 4, 235–241. DOI:10.1080/16742834.2011.11446935 Bueh, C., N. Shi, and Z. W. Xie, 2011b: Large-scale circulation anomalies associated with persistent low temperature over Southern China in January 2008. Atmos. Sci. Lett., 12, 273–280. DOI:10.1002/asl.333 Bueh, C., Y. Li, D. W. Lin, et al., 2016: Interannual variability of summer rainfall over the northern part of China and the related circulation features. J. Meteor. Res., 30, 615–630. DOI:10.1007/s13351-016-5111-5 Cai, M., and M. Mak, 1990: Symbiotic relation between planetary and synoptic-scale waves. J. Atmos. Sci., 47, 2953–2968. DOI:10.1175/1520-0469(1990)047<2953:SRBPAS>2.0.CO;2 Cavazos, T., 1999: Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in Northeastern Mexico and Southeastern Texas. J. Climate, 12, 1506–1523. DOI:10.1175/1520-0442(1999)012<1506:LSCACT>2.0.CO;2 Chen, D., C. Bueh, and K. Y. Zhu, 2013: Interannual and interdecadal variabilities of circulation over Lake Baikal region in late spring and their association with temperature and precipitation over China. Chinese J. Atmos. Sci., 37, 1199–1209. DOI:10.3878/j.issn.1006-9895.2012.12155 Dai, P. X., and B. K. Tan, 2017: The nature of the Arctic Oscillation and diversity of the extreme surface weather anomalies it generates. J. Climate, 30, 5563–5584. DOI:10.1175/JCLI-D-16-0467.1 Esbensen, S. K., 1984: A comparison of intermonthly and interannual teleconnections in the 700 mb geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 112, 2016–2032. DOI:10.1175/1520-0493(1984)112<2016:ACOIAI>2.0.CO;2 Feldstein, S. B., and S. Lee, 2014: Intraseasonal and interdecadal jet shifts in the Northern Hemisphere: The role of warm pool tropical convection and sea ice. J. Climate, 27, 6497–6518. DOI:10.1175/JCLI-D-14-00057.1 Franzke, C., and S. B. Feldstein, 2005: The continuum and dynamics of Northern Hemisphere teleconnection patterns. J. Atmos. Sci., 62, 3250–3267. DOI:10.1175/JAS3536.1 Frauenfeld, O. W., and R. E. Davis, 2003: Northern Hemisphere circumpolar vortex trends and climate change implications. J. Geophys. Res. Atmos., 108, 4423. DOI:10.1029/2002JD002958 Gong, D. Y., and C. H. Ho, 2003: Arctic oscillation signals in the East Asian summer monsoon. J. Geophys. Res. Atmos., 108, 4066. DOI:10.1029/2002JD002193 Goss, M., S. B. Feldstein, and S. Lee, 2016: Stationary wave interference and its relation to tropical convection and Arctic warming. J. Climate, 29, 1369–1389. DOI:10.1175/JCLI-D-15-0267.1 He, J., and R. X. Black, 2016: Heat budget analysis of Northern Hemisphere high-latitude spring onset events. J. Geophys. Res. Atmos., 121, 10113–10137. DOI:10.1002/2015JD024681 Horel, J. D., 1981: A rotated principal component analysis of the interannual variability of the Northern Hemisphere 500 mb height field. Mon. Wea. Rev., 109, 2080–2092. DOI:10.1175/1520-0493(1981)109<2080:ARPCAO>2.0.CO;2 Hoskins, B. J., and K. I. Hodges, 2002: New perspectives on the Northern Hemisphere winter storm tracks. J. Atmos. Sci., 59, 1041–1061. DOI:10.1175/1520-0469(2002)059<1041:NPOTNH>2.0.CO;2 Hoskins, B. J., M. E. McIntyre, and A. W. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc., 111, 877–946. DOI:10.1256/smsqj.47001 Hsu, H. H., and J. M. Wallace, 1985: Vertical structure of wintertime teleconnection patterns. J. Atmos. Sci., 42, 1693–1710. DOI:10.1175/1520-0469(1985)042<1693:VSOWTP>2.0.CO;2 Hurrell, J. W., 1996: Influence of variations in extratropical wintertime teleconnections on Northern Hemisphere temperature. Geophys. Res. Lett, 23, 665–668. DOI:10.1029/96GL00459 Johnson, N. C., 2013: How many ENSO flavors can we distinguish?. J. Climate, 26, 4816–4827. DOI:10.1175/JCLI-D-12-00649.1 Johnson, N. C., and S. B. Feldstein, 2010: The continuum of North Pacific sea level pressure patterns: Intraseasonal, interannual, and interdecadal variability. J. Climate, 23, 851–867. DOI:10.1175/2009JCLI3099.1 Johnson, N. C., S. B. Feldstein, and B. Tremblay, 2008: The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organizing maps. J. Climate, 21, 6354–6371. DOI:10.1175/2008JCLI2380.1 Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472. DOI:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 Kohonen, T., 1990: The self-organizing map. Proc. IEEE, 78, 1464–1480. DOI:10.1109/5.58325 Kohonen, T., 1997: Self-Organizing Maps. Springer-Verlag, Berlin, Heidelberg, 426 pp. Kushnir, Y., and J. M. Wallace, 1989: Low-frequency variability in the Northern Hemisphere winter: Geographical distribution, structure and time-scale dependence. J. Atmos. Sci., 46, 3122–3143. DOI:10.1175/1520-0469(1989)046<3122:LFVITN>2.0.CO;2 Lee, M. H., S. Lee, H. J. Song, et al., 2017: The recent increase in the occurrence of a boreal summer teleconnection and its relationship with temperature extremes. J. Climate, 30, 7493–7504. DOI:10.1175/JCLI-D-16-0094.1 Lee, S., and S. B. Feldstein, 2013: Detecting ozone- and greenhouse gas-driven wind trends with observational data. Science, 339, 563–567. DOI:10.1126/science.1225154 Lehmann, J., D. Coumou, K. Frieler, et al., 2014: Future changes in extratropical storm tracks and baroclinicity under climate change. Environ. Res. Lett., 9, 084002. DOI:10.1088/1748-9326/9/8/084002 L’Heureux, M. L., D. C. Collins, and Z. Z. Hu, 2013: Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño–Southern Oscillation. Climate Dyn., 40, 1223–1236. DOI:10.1007/s00382-012-1331-2 Lin, Z. D., 2014: Intercomparison of the impacts of four summer teleconnections over Eurasia on East Asian rainfall. Adv. Atmos. Sci., 31, 1366–1376. DOI:10.1007/s00376-014-3171-y Lin, Z. D., and B. Wang, 2016: Northern East Asian low and its impact on the interannual variation of East Asian summer rainfall. Climate Dyn., 46, 83–97. DOI:10.1007/s00382-015-2570-9 Liu, Y. G., R. H. Weisberg, and C. N. K. Mooers, 2006: Performance evaluation of the self-organizing map for feature extraction. J. Geophys. Res. Oceans, 111, C05018. DOI:10.1029/2005JC003117 Nakamura, H., M. Nakamura, and J. L. Anderson, 1997: The role of high- and low-frequency dynamics in blocking formation. Mon. Wea. Rev., 125, 2074–2093. DOI:10.1175/1520-0493(1997)125<2074:TROHAL>2.0.CO;2 Pelly, J. L., and B. J. Hoskins, 2003: A new perspective on blocking. J. Atmos. Sci., 60, 743–755. DOI:10.1175/1520-0469(2003)060<0743:ANPOB>2.0.CO;2 Piao, J. L., W. Chen, S. F. Chen, et al., 2018: Intensified impact of North Atlantic Oscillation in May on subsequent July Asian inland plateau precipitation since the late 1970s. Int. J. Climatol., 38, 2605–2612. DOI:10.1002/joc.5332 Reusch, D. B., R. B. Alley, and B. C. Hewitson, 2007: North Atlantic climate variability from a self-organizing map perspective. J. Geophys. Res. Atmos., 112, D02104. DOI:10.1029/2006JD007460 Rousi, E., C. Anagnostopoulou, K. Tolika, et al., 2015: Representing teleconnection patterns over Europe: A comparison of SOM and PCA methods. Atmos. Res., 152, 123–137. DOI:10.1016/j.atmosres.2013.11.010 Sheridan, S. C., and C. C. Lee, 2011: The self-organizing map in synoptic climatological research. Prog. Phys. Geogr., 35, 109–119. DOI:10.1177/0309133310397582 Tan, B. K., and W. Chen, 2014: Progress in the study of the dynamics of extratropical atmospheric teleconnection patterns and their impacts on East Asian climate. J. Meteor. Res., 28, 780–802. DOI:10.1007/s13351-014-4041-3 Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784–812. DOI:10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2 Ward, J. H. Jr., 1963: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 58, 236–244. DOI:10.2307/2282967 Xie, Z., R. X. Black, and Y. Deng, 2017: Daily-scale planetary wave patterns and the modulation of cold season weather in the northern extratropics. J. Geophys. Res. Atmos., 122, 8383–8398. DOI:10.1002/2017JD026768 Xie, Z. W., and C. Bueh, 2017a: Cold vortex events over Northeast China associated with the Yakutsk–Okhotsk blocking. Int. J. Climatol., 37, 381–398. DOI:10.1002/joc.4711 Xie, Z. W., and C. Bueh, 2017b: Blocking features for two types of cold events in East Asia. J. Meteor. Res., 31, 309–320. DOI:10.1007/s13351-017-6076-8 Xu, G. D., Y. Zong, and Z. L. Yang, 2013: Applied Data Mining. CRC Press, Inc., Boca Raton, FL, USA, 284 pp. Yatagai, A., K. Kamiguchi, O. Arakawa, et al., 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93, 1401–1415. DOI:10.1175/BAMS-D-11-00122.1 Ye, H. C., E. J. Fetzer, A. Behrangi, et al., 2016: Increasing daily precipitation intensity associated with warmer air temperatures over northern Eurasia. J. Climate, 29, 623–636. DOI:10.1175/JCLI-D-14-00771.1 Yeh, T. C., S. Y. Dao, and M. T. Li, 1958: The abrupt change of circulation over Northern Hemisphere during June and October. Acta Meteor. Sinica, 29, 249–263. Yuan, J. C., B. K. Tan, S. B. Feldstein, et al., 2015: Wintertime North Pacific teleconnection patterns: Seasonal and interannual variability. J. Climate, 28, 8247–8263. DOI:10.1175/JCLI-D-14-00749.1