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  • View in gallery

    (a) Climatological mean and (b) standard deviation of December–February (DJF) mean meridional winds at 1000-hPa based on the period 1962–2011. The contour interval is 2 m s−1 in (a) and 0.2 m s−1 in (b). Shading is applied for highlighting purpose. The two boxes denote the regions that are used to define the N-index and the S-index.

  • View in gallery

    The (a) first and (b) second EOF mode, and the (c) first and (d) second rotated EOF mode, of DJF mean meridional wind anomalies at 1000 hPa for the period 1962–2011. The shading is applied for highlighting purpose. The two boxes denote the regions used to define the N-index and the S-index.

  • View in gallery

    The normalized interannual time series of the N-index (solid curve) and the S-index (dashed curve) for the period 1962–2011. The two horizontal lines represent the values of ±0.5.

  • View in gallery

    Anomalies of DJF (a),(b) SLP, (c),(d) 850-hPa wind, and (e),(f) SST obtained by regression on the (left) N-index and (right) S-index, based on the period 1962–2011. The contour interval is 0.5 hPa in (a) and (b) and 0.2°C in (e) and (f). The wind scale is shown on the top-right corner in (c) and (d). The shaded areas denote that the SLP [in (a) and (b)], either u-wind or υ-wind [in (c) and (d)], and SST [in (e) and (f)] anomalies are significant at the 95% confidence level.

  • View in gallery

    Composite maps of DJF SLP anomalies (hPa) for different cases listed in Table 1. The dashed contours denote that the anomalies are significant at the 95% confidence level according to the Student's t test.

  • View in gallery

    Composite maps of DJF 850-hPa wind anomalies (m s−1) for different cases listed in Table 1. The wind scale is shown on the top-right corner. The shaded areas denote that either u-wind or υ-wind anomalies are significant at the 95% confidence level according to the Student's t test.

  • View in gallery

    As in Fig. 5, but for land surface temperature anomalies (°C).

  • View in gallery

    As in Fig. 5, but for land precipitation anomalies (mm month−1).

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    As in Fig. 5, but for SST anomalies (°C).

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    Partial correlations between the snow cover frequency and the (a) N-index and (b) S-index based on the period 1967–2009.

  • View in gallery

    Partial correlations between the sea ice concentration and the (a) N-index and (b) S-index based on the period 1962–2011.

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Distinguishing Interannual Variations of the Northern and Southern Modes of the East Asian Winter Monsoon

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  • 1 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
  • | 3 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

The East Asian winter monsoon (EAWM)-related climate anomalies have shown large year-to-year variations in both the intensity and the meridional extent. The present study distinguishes the interannual variations of the low-latitude and mid- to high-latitude components of the EAWM to gain a better understanding of the characteristics and factors for the EAWM variability. Through composite analysis based on two indices representing the northern and southern components (modes) of the EAWM variability, the present study clearly reveals features unique to the northern and southern mode. The northern mode is associated with changes in the mid- to high-latitude circulation systems, including the Siberian high, the Aleutian low, the East Asian trough, and the East Asian westerly jet stream, whereas the southern mode is closely related to circulation changes over the global tropics, the North Atlantic, and North America. A strong northern mode is accompanied by positive, negative, and positive surface temperature anomalies in the Indochina Peninsula, midlatitude Asia, and northeast Russia, respectively. A strong southern mode features lower temperature over tropics and higher temperature over mid- to high-latitude Asia. While the southern mode is closely related to El Niño–Southern Oscillation (ENSO), the northern mode does not show an obvious relation to the tropical sea surface temperature (SST) change or to the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO) on the interannual time scale. Distinct snow cover and sea ice changes appear as responses to wind and surface temperature changes associated with the two modes and their effects on the EAWM variability need to be investigated in the future.

Corresponding author address: Renguang Wu, Fok Ying Tung Remote Sensing Science Building, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China. E-mail: renguang@cuhk.edu.hk

Abstract

The East Asian winter monsoon (EAWM)-related climate anomalies have shown large year-to-year variations in both the intensity and the meridional extent. The present study distinguishes the interannual variations of the low-latitude and mid- to high-latitude components of the EAWM to gain a better understanding of the characteristics and factors for the EAWM variability. Through composite analysis based on two indices representing the northern and southern components (modes) of the EAWM variability, the present study clearly reveals features unique to the northern and southern mode. The northern mode is associated with changes in the mid- to high-latitude circulation systems, including the Siberian high, the Aleutian low, the East Asian trough, and the East Asian westerly jet stream, whereas the southern mode is closely related to circulation changes over the global tropics, the North Atlantic, and North America. A strong northern mode is accompanied by positive, negative, and positive surface temperature anomalies in the Indochina Peninsula, midlatitude Asia, and northeast Russia, respectively. A strong southern mode features lower temperature over tropics and higher temperature over mid- to high-latitude Asia. While the southern mode is closely related to El Niño–Southern Oscillation (ENSO), the northern mode does not show an obvious relation to the tropical sea surface temperature (SST) change or to the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO) on the interannual time scale. Distinct snow cover and sea ice changes appear as responses to wind and surface temperature changes associated with the two modes and their effects on the EAWM variability need to be investigated in the future.

Corresponding author address: Renguang Wu, Fok Ying Tung Remote Sensing Science Building, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China. E-mail: renguang@cuhk.edu.hk

1. Introduction

The East Asian winter monsoon (EAWM) is an important climate system in the Northern Hemisphere during boreal winter. One feature unique to the EAWM is that the climatological northerly winds have a large meridional coverage, extending from the midlatitude East Asia all the way to the equatorial South China Sea (Zhang et al. 1997; Chen et al. 2000; Wang et al. 2010). This provides a way of directly linking the climate variability over the Eurasian continent to the anomalous tropical heating over the Maritime Continent, and thus the EAWM plays an important role in the tropics–extratropics interactions (Chang and Lau 1980, 1982; Lau and Chang 1987). As such, the variability of the EAWM exerts large influences not only on winter climate over the Asian and Pacific regions, but also on tropical heating and winter climate as far as North America (e.g., Lau and Chang 1987).

Because of the large socioeconomic impacts of the EAWM, there have been numerous studies of the EAWM variability and its factors. For this purpose, many indices have been proposed to measure the EAWM variability (e.g., Guo 1994; Sun and Sun 1995; Shi 1996; Sun and Li 1997; Ji et al. 1997; Cui and Sun 1999; Chen et al. 2000; Hu et al. 2000; Jhun and Lee 2004; Wang and Jiang 2004; Li and Yang 2010). These indices differ in the variable adopted and the domain used in the definition of the index. Wang (2007) and Wang and Chen (2010) provided reviews of the existing EAWM indices and their characteristics. Both consistencies and discrepancies have been noted in the circulation features among these indices and the associated winter climate anomalies over East Asia. The discrepancies arise partly from the difference in the domain used in defining the indices. In relation to the large meridional extent of the EAWM, the climate anomaly associated with the EAWM may not be consistent between the mid- to high latitudes and the low latitudes of East Asia. As such, the indices defined based on a domain at higher latitudes may show features different from those based on a domain at lower latitudes. This indicates that the meridional coverage of anomalous features associated with the EAWM variability may vary from year to year. As such, a single index may not be able to fully describe the features of the EAWM variability (e.g., Wu et al. 2006; Wang et al. 2010; Liu et al. 2012). Liu et al. (2012, 2013) showed that the low-latitude EAWM and the mid- to high-latitude EAWM are associated with different circulation systems and thus they should be investigated separately. Liu et al. (2013) indicated that the low-latitude EAWM variability is related to anomalous circulation over the South China Sea and around the Philippines at the lower troposphere and to the intensity of the subtropical westerly jet at the upper troposphere, whereas the mid- to high-latitude EAWM variability is linked to the activity of blocking high around the Lake of Baikal at the lower troposphere and to the location of the northern boundary of the subtropical westerly jet at the upper troposphere. He and Wang (2013) defined a consolidated index for the EAWM variability based on several individual indices with the purpose of combining the different features represented by these indices.

Previous studies have unraveled various factors for the EAWM variability. These factors include El Niño–Southern Oscillation (ENSO) (e.g., Tomita and Yasunari 1996; Zhang et al. 1996; Chen et al. 2000; Wang et al. 2000; He et al. 2008; Chen et al. 2013), the Arctic Oscillation (AO) (e.g., Wu and Huang 1999; Gong et al. 2001; Wu and Wang 2002; Chen et al. 2013), the snow cover over the Eurasian continent (e.g., Watanabe and Nitta 1999; Gong et al. 2003; Jhun and Lee 2004; Wang et al. 2010), and the Siberian high (e.g., Ding, 1990; Zhang et al. 1997; Wu et al. 2006). However, the relationship of the EAWM with ENSO and the AO shows variations depending on which index is used (Wang and Chen 2010). The low-latitude EAWM is more closely related to ENSO (e.g., Wang et al. 2010) and the mid- to high-latitude EAWM has a close relationship with the AO (e.g., He and Wang 2013). Given the differences in the features of the EAWM variability and in the factors, it is necessary to use more than one index for a better understanding of the EAWM variability and its factors (e.g., Wu et al. 2006; Wang et al. 2010; Liu et al. 2012). Kang et al. (2009) first identified two leading modes of wintertime surface air temperature in China. Wang et al. (2010) suggested that there are a northern mode and a southern mode for wintertime surface air temperature variability in East Asia. Liu et al. (2012, 2013) defined two wind indices for the EAWM based on an empirical orthogonal function (EOF) analysis of 1000-hPa meridional winds: the low-latitude EAWM (denoted EAWM-L) index and the mid- to high-latitude EAWM (EAWM-M) index. Liu et al. (2013) showed that the low-latitude EAWM variability is related to tropical Pacific and Indian Ocean SST anomalies on both interannual and interdecadal time scales and the mid- to high-latitude EAWM variability is associated with tropical Indian Ocean SST anomalies only on the interdecadal time scale.

The above studies demonstrated the necessity of separating the northern (midlatitude) and southern (low latitude) modes of the EAWM variability. This points to a new direction of understanding the EAWM variability and its factors. The present study distinguishes from the above studies in the following two aspects. First, these previous studies are mostly based on correlation and regression analysis with respect to one specific index although the analysis has been done for the northern and southern components separately. Because of the fact that the two components are not independent, the obtained features and factors do not indicate what happens when only one component is abnormal while the other is normal. In this study, we perform a composite analysis considering various combinations of the two components. By this approach, we can identify features and factors when both components are abnormal and when only one component is abnormal. As such, we will be able to identify individual and coherent features and factors related to the two modes of the EAWM variability. This will lead to a better understanding of what type of climate anomalies may likely occur. Second, some previous studies do not separate the interannual and interdecadal components of the EAWM variability. As indicated by previous studies, the factors for interannual and interdecadal variability of the EAWM may be different (Jhun and Lee 2004; Wang et al. 2010; Wang and Chen 2010; He and Wang 2013). Thus, it is necessary to separate the interannual and interdecadal time scales for a better understanding of the factors for the variability of two EAWM components. In this study, we will focus on the interannual variability of the EAWM. The interdecadal variability of the EAWM will be addressed in future studies.

Wang et al. (2010) extracted two dominant modes of the EAWM variability based on surface air temperature anomalies. Liu et al. (2012) derived the two modes of the EAWM based on surface wind anomalies. Since winds are of more dynamical meaning and the most fundamental feature of the winter monsoon is the wind distribution, in this study we define the two modes using surface meridional winds, following Liu et al. (2012). We will compare the wind modes with the temperature modes.

The organization of the text is as follows. Section 2 introduces the datasets and methods used in the present study. Section 3 discusses the definition of the two indices for the northern and southern modes of the EAWM, respectively. The features associated with the northern and southern modes of the EAWM are described in section 4. The climate anomalies associated with the two modes are presented in section 5. Section 6 investigates the factors for the variability of the two EAWM modes. Section 7 provides a summary and discussion.

2. Data and methods

The present study used monthly mean sea level pressure (SLP), geopotential height, wind, and surface temperature fields from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996). The resolution of the NCEP–NCAR reanalysis data is 2.5° × 2.5° except for surface variables, which have a resolution of T62. The reanalysis is available after 1948. The sea surface temperature (SST) data used in the present study were taken from the National Oceanic and Atmospheric Administration (NOAA) extended reconstructed SST (ERSST version 3b) and the horizontal resolution is 2° in latitude and longitude (Smith et al. 2008). The SST data start from 1854.

The monthly mean surface air temperature and monthly total surface precipitation used in this study were obtained from the University of Delaware data with land-only coverage and a resolution of 0.5° × 0.5° (Matsuura and Willmott 2009). This land surface dataset is available from 1901 to 2010. We also used monthly temperature and precipitation data of China with both 160 and 730 stations provided by the China Meteorological Administration.

The sea ice concentration data gridded at 1° × 1° resolution since 1870 were obtained from the Met Office Hadley Center (Rayner et al. 2003). The snow cover data (version 4) were derived from the National Snow and Ice Data Center (NSIDC) (Brodzik and Armstrong 2013). The original snow cover in the Northern Hemisphere is at weekly intervals for the period 3 October 1966–31 December 2010. We have converted the raw snow cover data to a regular 1° × 1° grid for our analyses.

The following indices were also used in this study: the Arctic Oscillation index, the North Atlantic Oscillation (NAO) index, and the oceanic Niño index (ONI; 3-month running mean of SST anomalies in the Niño-3.4 region) produced by the Climate Prediction Center (CPC; http://www.cpc.ncep.noaa.gov/). The ONI was used as an ENSO index in the present study. The Siberian high (SH) index is defined as the regionally averaged winter SLP anomaly over the region 40°–65°N, 80°–120°E.

The primary data used in this study cover the period from December 1962 to February 2012 and the 1962 winter represents the three months from December 1962 to February 1963. Since we focus on the interannual time scale, all the variables have been subjected to a bandpass time filtering with a period of 2–9 years to exclude the interdecadal variations. This reduces the interference of the interdecadal component that may include uncertainty (Wu et al. 2005) with the results on interannual time scales.

The present study employs the EOF and rotated EOF (REOF) to extract the dominant spatial patterns of winter anomalies. Correlation and regression analysis is applied to derive the relationship between two indices or the spatial pattern related to a specific index. Composite analysis is performed to obtain distribution of anomalies for different types of years. The Student's t test is used to examine the significance of the correlation coefficient and the composite anomalies.

3. Definition of the two modes of the EAWM variability

To distinguish the EAWM variability at different latitudinal domains, we need to define the northern and southern modes of the EAWM variability. Our approach follows that of Liu et al. (2012), but with further analysis of validation. Liu et al. (2012) defined the low-latitude and mid- to high-latitude modes based on the distribution of the standard deviation and an EOF analysis of the 1000-hPa meridional winds during winter over East Asia. The two modes are represented by 1000-hPa meridional wind anomalies averaged over the region of 10°–25°N, 105°–135°E (the lower-latitude mode, EAWM-L) and 30°–50°N, 110°–125°E (the mid- to high-latitude mode, EAWM-M), respectively.

Figure 1 displays the winter mean 1000-hPa meridional winds and the corresponding standard deviation. Relatively large mean winds are observed over oceanic regions along the coast (Fig. 1a). This is expected since the friction is smaller over the water than over the land. There are two regions with relatively large standard deviation (Fig. 1b). One is the South China Sea and the Philippine Sea between 10° and 25°N. The other is located in the mid- to high-latitude land region, extending northeastward from northern China. The distribution of the standard deviation agrees with that obtained by Liu et al. (2012).

Fig. 1.
Fig. 1.

(a) Climatological mean and (b) standard deviation of December–February (DJF) mean meridional winds at 1000-hPa based on the period 1962–2011. The contour interval is 2 m s−1 in (a) and 0.2 m s−1 in (b). Shading is applied for highlighting purpose. The two boxes denote the regions that are used to define the N-index and the S-index.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

As Liu et al. (2012), we performed an EOF analysis of the 1000-hPa meridional wind anomalies in winter. The derived spatial patterns of the leading EOF modes are similar to those obtained by Liu et al. (2012). In the leading EOF mode, which explains about 24.5% of the total variance, two large loading regions are seen over the South China Sea/Philippine Sea and the region from North China to east of the Lake of Bengal (Fig. 2a). In comparison, the loading in the southern region is about 2 times of that in the northern region. This differs from Liu et al. (2012), who obtained somewhat larger loading in the northern region than in the southern region. This difference is likely due to the difference in the analysis period. Liu et al. (2012)'s EOF analysis covers a period back to 1951, whereas the present EOF analysis starts from 1962. The NCEP–NCAR reanalysis lower-level winds may overestimate interdecadal variability in mid- to high latitudes of East Asia (Wu et al. 2005), in particular in the early years. Inclusion of the data in the 1950s may increase the loading in the northern region. The second EOF mode, which accounts for about 13.8% of the total variance, displays opposite loading between the northern South China Sea/northern Philippine Sea and the midlatitude East Asia (Fig. 2b). The pattern is similar to that obtained by Liu et al. (2012).

Fig. 2.
Fig. 2.

The (a) first and (b) second EOF mode, and the (c) first and (d) second rotated EOF mode, of DJF mean meridional wind anomalies at 1000 hPa for the period 1962–2011. The shading is applied for highlighting purpose. The two boxes denote the regions used to define the N-index and the S-index.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

According to the distribution of standard deviation and the loading of the first two EOF modes, we may use area-mean 1000-hPa meridional wind anomalies over the South China Sea and midlatitude East Asia to define a southern mode and a northern mode, as was done by Liu et al. (2012). From the spatial patterns of the first two modes, we expect that the meridional wind variations over the South China Sea and midlatitude East Asia have a coherent part as well as an opposite part. Based on the definition of the northern mode and the southern mode using the meridional winds over the northern region and the southern region, we should expect to see both coherent and independent variations of the two modes.

To confirm the above definition, we perform a rotated EOF analysis. An advantage of the REOF analysis is that it can capture regional features. Interestingly, the first two REOF modes show large loading in the South China Sea/Philippine Sea and midlatitude East Asia, respectively (Figs. 2c,d). The percent variances explained by the two modes are close to each other (12.8% and 11.6%). This confirms that the meridional wind variations in the above two regions have specific features of nearly equal importance. The correlation coefficient of the first two modes is 0.31 (calculated using the two principal component time series), which confirms that the two modes have both coherent and independent variations.

Comparing the above figures (Figs. 1 and 2), the maximum loading region in the mid- to high-latitude East Asia for the first two EOF modes (Figs. 2a,b), the largest loading region in the second REOF mode (Fig. 2d), and the area with large standard deviation over East Asian land (Fig. 1b) generally coincide with each other. Similarly, the maximum loading region in low-latitude East Asia for the first two EOF modes (Figs. 2a,b), the large loading region in the first REOF mode (Fig. 2c), and the area with large standard deviation in the South China Sea/Philippine Sea (Fig. 1b) are in good agreement with each other. Accordingly, we define area-mean 1000-hPa meridional wind anomalies over the regions of 10°–25°N, 105°–135°E and 35°–55°N, 110°–125°E as indices, denoted the S-index and N-index, respectively, to represent the southern and northern modes of the EAWM variability. The southern region we use is the same as in Liu et al. (2012), whereas the northern region differs slightly (about 5° north) from Liu et al. (2012). This 5° shift is based on the distribution of the loading in the second REOF mode. We multiplied the original indices by −1 so that a high index corresponds to a strong EAWM (large northerly winds).

In the following analysis, we focus on the interannual time scale. The N-index and S-index have been subjected to time filtering to exclude variations with periods longer than 9 years (Fig. 3). As shown in the figure, the two indices exhibit pronounced interannual variations, and they are of the same sign in some years but not in other years. During the period of 1962–2011, there were 20 years in which the two indices had opposite signs, accounting for 40% of the total years. The correlation coefficient between the N-index and the S-index is 0.50, exceeding the 95% confidence level. This indicates that the interannual variations of the N-index and S-index are not independent and the EAWM variability may display coherent features in some years.

Fig. 3.
Fig. 3.

The normalized interannual time series of the N-index (solid curve) and the S-index (dashed curve) for the period 1962–2011. The two horizontal lines represent the values of ±0.5.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

Based on the ±0.5 standard deviations of the two indices, there are 13 strong northern EAWM years, 18 weak northern EAWM years, 17 strong southern EAWM years, and 15 weak southern EAWM years during 1962–2011. Based on the combination of the two indices, the abnormal years can be classified into eight categories (Table 1). Different categories feature various characteristics of an anomalous EAWM. For example, when both the N-index and S-index are larger than +0.5, an anomalous EAWM is featured, extending from the midlatitude East Asia to the South China Sea; whereas when the N-index is larger than +0.5 and the S-index is normal, an anomalous EAWM occurs only in the midlatitude East Asia. These different cases indicate whether the northern and southern modes have coherent anomalies or only one mode is abnormal with the other mode in a normal state. Thus, these different cases feature climate anomalies with different spatial coverage associated with the EAWM variability. It is important to understand under what conditions these situations occur respectively and what are the impacts of various factors from the perspective of monitoring and predicting the climate anomalies associated with the EAWM variability.

Table 1.

Number of years and cases for different combinations of northern and southern EAWM indices.

Table 1.

In Table 1, there are 9 years with the two indices both positive and 10 years with the two indices both negative. There are 3 (6) years when the N-index is positive (negative) and the S-index is normal. There are 6 (4) years when the S-index is positive (negative) and the N-index is normal. Only in 1 (2) year(s) do the N-index and the S-index display opposite anomalies. From the above, there are more chances when the northern and southern modes are in phase than when they are opposite. Because of the small number of opposite anomaly cases, we do not consider this type of case further. The number of cases when only one index is positive or negative is relatively small. In the following composite analysis, we combine the positive and negative index years with the anomalies in the negative index years reversed. As such, the obtained composites only represent features common to both positive and negative index years, but do not show features unique to either positive or negative index years. We note that the climate anomalies in the in-phase northern and southern mode years are likely widespread as both modes are abnormal. In contrast, in years when only one mode is abnormal, the climate anomalies are likely less so. As these two types of situations have different impacts, it is important to distinguish them for purposes of mitigating the damages that may be induced by anomalous EAWM. Note that there are more negative N-index cases than positive N-index cases when the S-index is normal. This asymmetric feature is probably due to the asymmetric influence of anomalous lower boundary conditions.

Liu et al. (2013) did a similar classification of the years based on the two indices. However, because they did not separate the interannual and interdecadal components, they obtained a classification of years that differs largely from ours. For example, their EAWM-M index is biased to positive value before the mid-1960s and to negative value during late 1960s and 1970s. They also obtain more cases (8) when the two indices have opposite anomalies. As such, the composite anomalies obtained by Liu et al. (2013) may include a large interdecadal contribution. As pointed out by Wu et al. (2005), the NCEP–NCAR reanalysis may overestimate the interdecadal change over East Asia in the SLP and lower-level meridional winds. Thus, the composite anomalies obtained by Liu et al. (2012, 2013) may include some uncertainty.

4. Coherent and independent variations of the northern and southern modes

In this section, we perform regression and composite analyses to understand the features associated with the northern and southern modes of the EAWM variability. The main purpose of this section is to unravel features common as well as unique to the two modes. The traditional regression analysis may not be able to capture the features unique to one of the modes since the two indices are not independent. On the other hand, the composite analysis has the advantage of revealing features unique to a specific type of the EAWM variability determined based on the different combinations of the two modes according to Table 1. We start with a regression analysis and then perform the composite analysis. Note that all the analyses below are based on the interannual components.

a. Regression analysis

Figure 4 shows the SLP, 850-hPa wind, and SST anomalies in winter obtained by regression against the N-index and S-index, respectively. Common to a positive N-index and S-index, positive and negative SLP anomalies are observed over the Eurasian continent and the North Pacific, respectively (Figs. 4a,b). SLP anomalies are negative over the tropical Indian Ocean and western Pacific and positive over the tropical eastern Pacific. Negative and positive SLP anomalies dominate North America and the North Atlantic Ocean, respectively. In comparison, positive SLP anomalies over the Eurasian continent appear at lower latitudes for a positive S-index than for the N-index. The signals in the SLP field are larger over the tropics and North America through the North Atlantic for the southern mode than for the northern mode.

Fig. 4.
Fig. 4.

Anomalies of DJF (a),(b) SLP, (c),(d) 850-hPa wind, and (e),(f) SST obtained by regression on the (left) N-index and (right) S-index, based on the period 1962–2011. The contour interval is 0.5 hPa in (a) and (b) and 0.2°C in (e) and (f). The wind scale is shown on the top-right corner in (c) and (d). The shaded areas denote that the SLP [in (a) and (b)], either u-wind or υ-wind [in (c) and (d)], and SST [in (e) and (f)] anomalies are significant at the 95% confidence level.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

The obtained 850-hPa wind anomalies show large anomalous northerlies along the coast of East Asia extending to the equatorial South China Sea (Figs. 4c,d). Associated are an anomalous cyclone over the midlatitude North Pacific and an anomalous anticyclone over mid- and high-latitude Eurasia. Over the tropics, anomalous easterlies and westerlies are observed over the Pacific and Indian Oceans, respectively, leading to pronounced convergence over the Maritime Continent. An anomalous anticyclone is present over the midlatitude North Atlantic. In comparison, the wind anomalies over the Eurasian continent appear more significant for the N-index, whereas those over the tropics are more significant for the S-index.

The obtained SLP and lower-level wind anomalies corresponding to the northern and southern modes are similar to the temperature southern mode of Wang et al. (2010). The similarity is higher at mid- and high latitudes for the northern mode and at mid- and low latitudes for the southern mode. The northern temperature mode derived by Wang et al. (2010) has no corresponding feature compared to the modes in the present analysis. The traditional EAWM features large meridional winds along the East Asia coast, which is associated with the pressure contrast between the Eurasian land and the North Pacific. The northern temperature mode corresponds to large SLP anomalies over the high-latitude land, but very weak anomalies over the North Pacific (see Fig. 6b of Wang et al. 2010). As such, it appears that this mode is not related to the traditional EAWM variability. The distribution of surface temperature anomalies obtained by regression on the N-index and the S-index (not shown) is similar to that corresponding to the southern temperature mode of Wang et al. (2010), but not to the northern temperature mode.

The 850-hPa wind anomalies corresponding to the southern mode are similar to those of the EAWM-L obtained by Liu et al. (2012, 2013) (see Fig. 4a of Liu et al. 2012). The 850-hPa wind anomalies corresponding to the northern mode are similar to those of the EAWM-M obtained by Liu et al. (2012, 2013) at mid- and high latitudes, but shows a large difference at low-latitudes (see Fig. 4b of Liu et al. 2012). This discrepancy is attributed to the large interdecadal variability involved in the EAWM-M of Liu et al. (2012), in particular in the early years. Again, this points to the necessity of separating the interannual and interdecadal components of the EAWM variability.

The distribution of the SST anomalies corresponding to the N-index and the S-index displays a similar pattern in the tropical Indian and Pacific Oceans (Figs. 4e,f). Negative SST anomalies are seen in the equatorial central and eastern Pacific, the tropical Indian Ocean, the South China Sea, and the subtropical western North Pacific and positive SST anomalies are present in the tropical western North and South Pacific. The distribution features a La Niña state. In comparison, the magnitude of the SST anomalies appears smaller for the N-index than for the S-index. From the above SST anomaly distribution, it may be inferred that both northern and southern modes are associated with tropical Indo-Pacific SST anomalies. However, it is uncertain whether the two modes are both physically linked to the SST anomalies or only one mode (e.g., the southern mode) is linked to the SST anomalies and the other mode (e.g., the northern mode) shows significant correlation with the SST anomalies because of its correlation with the first mode. We will use composite analysis to address this issue later.

While the SST anomaly pattern for the S-index is similar to that corresponding to the EAWM-L obtained by Liu et al. (2013) (see their Fig. 7a), the SST anomaly pattern for the N-index differs largely from that corresponding to the EAWM-M obtained by Liu et al. (2013, see their Fig. 7b) in the tropical Indian and Pacific Oceans. This difference is because of the contribution of the interdecadal component in EAWM-M. As pointed out by Liu et al. (2013), after removing the interdecadal component of the SST variations, the EAWM-M has a weak correlation with the tropical Indian Ocean SST anomalies.

b. Composite analysis

Figure 5 shows the composite maps of the SLP anomalies for different combinations of the N-index and S-index listed in Table 1. When the two modes are both strong (N-index > +0.5 and S-index > +0.5), negative SLP anomalies cover the midlatitude North Pacific, tropical western North Pacific, and tropical Indian Ocean and positive SLP anomalies cover most of the Eurasian continent and tropical eastern Pacific (Fig. 5a). Positive SLP anomalies are also seen in the midlatitude North Atlantic. When the two modes are both weak (N-index < −0.5 and S-index < −0.5), opposite SLP anomalies are observed (Fig. 5b). One difference is that the SLP anomalies over the tropical Indian Ocean is less significant when the two modes are weak than strong.

Fig. 5.
Fig. 5.

Composite maps of DJF SLP anomalies (hPa) for different cases listed in Table 1. The dashed contours denote that the anomalies are significant at the 95% confidence level according to the Student's t test.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

When only the northern mode is abnormal (N-index > +0.5 or N-index < −0.5 and S-index ~ 0), significant SLP anomalies are limited to the mid- and high latitudes and the SLP anomalies in the tropics are weak (Fig. 5c). In contrast, when only the southern mode is abnormal (S-index > +0.5 or S-index < −0.5 and N-index ~ 0), significant SLP anomalies are observed in the tropics and the anomalies in mid- and high-latitude East Asia are weak (Fig. 5d). The above contrast, on the one hand, demonstrates that our composite analysis can reveal distinct features in SLP associated with the northern and southern modes. This is an advantage over the traditional regression analysis. On the other hand, it clearly demonstrates that the two modes are affected by different pressure systems. The northern mode is mainly affected by the mid- to high-latitude pressure systems with a contrast of the SLP changes between the North Pacific and the mid- and high-latitude Eurasian continent. The southern mode is mainly related to changes in the tropics with a contrast of the SLP anomalies between the eastern Pacific and western Pacific/Indian Ocean/Atlantic Ocean. An increase in SLP is observed in the midlatitude North Atlantic when one mode is abnormal and the other mode is normal with a larger amplitude for the southern mode than for the northern mode (Figs. 5c,d).

Figure 6 shows composite maps of 850-hPa winds for different types of years. When the northern and southern modes are strong simultaneously, anomalous northerlies extend from the midlatitude East Asia all the way to the equatorial South China Sea (Fig. 6a). To the east is a large anomalous cyclone dominating the midlatitude North Pacific. To the northwest is an anomalous anticyclone covering mid- and high-latitude Eurasia. There is an anomalous anticyclone over midlatitude North Atlantic. In the tropics, anomalous westerlies from the Indian Ocean and anomalous easterlies from the Pacific Ocean converge over the Maritime Continent and an anomalous cyclone is present around the Philippines. There is an anomalous confluence south of the Philippines. When the two modes are weak simultaneously, nearly opposite anomalous wind fields are obtained (Fig. 6b).

Fig. 6.
Fig. 6.

Composite maps of DJF 850-hPa wind anomalies (m s−1) for different cases listed in Table 1. The wind scale is shown on the top-right corner. The shaded areas denote that either u-wind or υ-wind anomalies are significant at the 95% confidence level according to the Student's t test.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

Liu et al. (2013) present composite 850-hPa wind anomalies when the EAWM-L and EAWM-M are both strong or weak (see their Figs. 3a,b). The spatial pattern of the wind anomalies obtained in the present study is similar to that obtained by Liu et al. (2013) over East Asia and part of the North Pacific. However, the magnitude of wind anomalies along the East Asian coast appears larger in Liu et al. (2013), likely because of the inclusion of the interdecadal component in Liu et al. (2013)'s composite composed of cases different from the present study.

When the northern mode is strong and the southern mode is normal, anomalous winds are large over the mid- and high latitudes, including an anomalous cyclone over the midlatitude North Pacific, an anomalous anticyclone over mid- and high-latitude Eurasia, and an anomalous anticyclone over midlatitude North Atlantic (Fig. 6c). However, anomalous northerlies along the East Asian coast are confined to the midlatitudes. The results demonstrate that the northern mode has a closer relation to the midlatitude circulation systems than the tropical systems. When the southern mode is strong and the northern mode is normal, the wind anomalies over the tropical Indian and Pacific Oceans are large and significant, and so are the anticyclonic wind anomalies over midlatitude North Atlantic (Fig. 6d). The wind anomalies over the Eurasian landmass are weak and those over the North Pacific display a southwest–northeast contrasting pattern. Large anomalous northerlies along the East Asian coast are confined to south of 30°N. The anomalous cyclone over the Philippine Sea becomes stronger. The results further indicate that the northern mode is mainly affected by the mid- to high-latitude circulation systems and the southern mode has a larger impact by the tropical and the North Atlantic circulations.

We have also compared composite 500-hPa geopotential height and 200-hPa wind anomalies among the different category of years (not shown). When only the northern mode is abnormal, the 500-hPa geopotential anomalies are confined to the Eurasian continent and the North Pacific, indicating a large change in the East Asian trough intensity, but no obvious anomalies over the tropics. The 200-hPa winds feature significant anomalies over Eurasia, the North Pacific, and the tropical Indo-western Pacific region, which signifies important changes in the upper-level midlatitude westerly jets over East Asia. When only the southern mode is abnormal, large 500-hPa geopotential anomalies span the global tropics as well as the North Pacific through North America and the North Atlantic Ocean. Significant 200-hPa wind anomalies are observed over the global tropics and the North American/North Atlantic region. Combined with the 850-hPa wind anomalies, this indicates that the southern mode is closely associated with an anomalous Walker circulation. Indeed, composite zonal vertical circulation along the equator shows an intensification of upward motion over the Maritime Continent and downward motion over equatorial central and eastern Pacific and equatorial western Indian Ocean when the southern mode is strong (not shown).

5. Climate anomalies associated with the northern and southern modes

Figure 7 shows composite land surface air temperature anomalies in different types of years. When the northern and southern modes are strong simultaneously, a significant cooling region extends from 50°N to the tropics and warming occurs over the northern Eurasian continent (Fig. 7a). When the two modes are both weak, the temperature anomalies display a similar spatial pattern but with opposite signs (Fig. 7b). In comparison, the anomalies over the high latitudes appear more significant in the weak EAWM composite and those over the midlatitudes and tropics are more significant in the strong EAWM composite. When only the northern mode is abnormal, the temperature anomaly distribution displays a triple pattern over Asia, with positive anomalies over Southeast Asia to southwestern China, negative anomalies over midlatitude Asia and eastern China, and positive anomalies over high-latitude Asia (Fig. 7c). When only the southern mode is abnormal, significant negative positive anomalies are confined to south of 20°N and significant positive anomalies are seen over northeastern Asia (Fig. 7d). The anomalies over eastern China are small.

Fig. 7.
Fig. 7.

As in Fig. 5, but for land surface temperature anomalies (°C).

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

The temperature anomalies have a good correspondence with the wind anomalies. Along the East Asian coast, lower temperature corresponds to anomalous northerly winds (Fig. 7c versus Fig. 6c). Over the high-latitude land, higher temperature is associated with anomalous anticyclone and anomalous southerly winds (Fig. 7c versus Fig. 6c). The higher temperature over the coast of northeast Asia is related to anomalous easterly winds from the North Pacific that bring warmer air from the ocean (Fig. 7d versus Fig. 6d).

From the difference in the distribution of temperature anomalies among different composites, the temperature anomalies are more widespread and have a much larger meridional extension when both the northern and southern modes are abnormal. In contrast, when only one of the modes is abnormal, the temperature anomalies induced by the EAWM variability are limited to specific regions. Thus, it is important to know whether the northern and southern modes experience in-phase anomalies or only one of the modes is likely abnormal from the perspective of prediction of the winter climate anomalies. This also points to the necessity of understanding the respective factors for the northern mode and the southern mode for the purpose of predicting the status of the two modes in specific years.

Compared to the temperature anomalies, the precipitation anomalies are less organized (Fig. 8). When the two modes are both strong, below-normal precipitation is seen over midlatitude Asia and eastern China (Fig. 8a), which occurs likely because of anomalous northerly winds (Fig. 6a) that bring cold and dry air from higher latitudes. Weak positive anomalies are present over high-latitude Asia in association with anomalous southerly winds. When the two modes are both weak, an opposite distribution tends to appear (Fig. 8b), which is consistent with the reversal of wind anomalies. When only the northern mode is strong, the distribution of precipitation anomalies displays more small-scale features, with alternatively positive, negative, positive, and negative signs from southern China, north and northeastern China, the west coast of the Okhotsk Sea, and far northeast Asia (Fig. 8c). When only the southern mode is strong, the precipitation anomaly distribution displays a contrast between high-latitude and midlatitude Asia (Fig. 8d). When both modes are strong or weak, the precipitation anomalies tend to be of the same sign as the temperature anomalies in the mid- and high latitudes. This in-phase relationship has been noted in previous studies (Trenberth and Shea 2005; Wu et al. 2013), occuring because cold air in winter cannot hold moisture and thus there is a less chance of precipitation.

Fig. 8.
Fig. 8.

As in Fig. 5, but for land precipitation anomalies (mm month−1).

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

The distribution of temperature and precipitation anomalies in the composite when both modes are abnormal resembles that corresponding to the temperature southern mode obtained by Wang et al. (2010) (see their Figs. 6c, 7c, 10a, and 10b). This temperature mode is associated with anomalous northerlies extending from the midlatitude East Asia to the South China Sea (see Figs. 6c and 10a of Wang et al. 2010). This feature is also seen in our lower-level wind composite (Figs. 6a,b). This comparison suggests that the temperature southern mode is related to both northern and southern modes of the meridional winds. The temperature northern mode in Wang et al. (2010) corresponds to temperature anomalies of the same sign north of 30°N (see Figs. 6b and 9a of Wang et al. 2010), which is not seen in any of our composites. This indicates that the temperature northern mode has no direct correspondence with the leading wind modes.

To focus on climate anomalies in China, we perform a composite analysis of precipitation and temperature anomalies based on station observations (not shown). The strong northern mode is related to cooling in northwest, north, and northeast China and the strong southern mode is accompanied by warming in northeast China. When both modes are strong, the cooling is more widespread, covering most of China except for far northeast China where there is warming. Opposite anomalies tend to occur when both modes are weak. The strong northern mode features below-normal precipitation in north and northeast China. The strong southern mode is accompanied by below-normal precipitation in southeastern and north China. When both modes are strong (weak), below-normal (above-normal) precipitation covers most of eastern China. The obtained results are similar to those based on the University of Delaware surface temperature and precipitation data.

6. Factors for the variability of the northern and southern modes

Previous investigations have indicated the impacts of various factors on the EAWM variability. These include tropical Pacific SST (e.g., Chen et al. 2000; Wang et al. 2000), the AO (e.g., Wu and Huang 1999; Gong et al. 2001), and the snow cover over the Eurasian continent (e.g., Jhun and Lee 2004; Wang et al. 2010). In this section, we analyze the relationship of the northern and southern modes with these factors separately to understand their respective influences on the two modes. Again, we focus on the interannual variations of the EAWM. As noted in the regression analysis, both the northern and southern modes are related to SST anomalies in the tropical Pacific, but because of the coherent variations inherent to the two modes, it is unknown whether the tropical Pacific SST anomalies exert direct influences on both the northern and southern modes.

Figure 9 shows composite SST anomalies in different types of years. When both modes are strong, negative SST anomalies are observed in the equatorial central and eastern Pacific, the tropical Indian Ocean, the South China Sea, and the subtropical western North Pacific, whereas positive SST anomalies are present in the tropical western North Pacific (Fig. 9a). Opposite SST anomalies are seen when both modes are weak (Fig. 9b). When only the northern mode is abnormal, SST anomalies in the equatorial regions are weak (Fig. 9c). When only the southern mode is abnormal, significant SST anomalies appear in the tropics, with negative anomalies in the equatorial central and eastern Pacific, tropical Indian Ocean, and subtropical western North Pacific, and positive SST anomalies in the tropical western North Pacific (Fig. 9d). The difference of composite SST anomalies between the cases when only the northern mode is abnormal and the cases when both modes are abnormal indicates that tropical Indian and Pacific SST anomalies have little direct impact on the northern mode, but have large impacts on the southern mode. This suggests that the apparent SST anomalies in tropical Indo-Pacific Ocean seen in the regression with respect to the northern mode result from the coherent variations between the northern and southern modes.

Fig. 9.
Fig. 9.

As in Fig. 5, but for SST anomalies (°C).

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

The North Atlantic Ocean displays significant SST anomalies when both modes are weak (Fig. 9b), but not when both modes are strong (Fig. 9a). These SST anomalies may form in response to atmospheric changes. When both modes are weak, anomalous westerlies over the subtropics (Fig. 6b) reduce surface wind speed and surface latent heat flux, contributing to SST warming. The anomalous cyclone over the midlatitudes induces upwelling, leading to SST cooling. After they are formed, these SST anomalies may in turn affect the EAWM variability through atmospheric teleconnection (e.g., Watanabe 2004). When the southern mode is abnormal, opposite winds anomalies (Fig. 6d) tend to induce positive SST anomalies in the midlatitudes (Fig. 9d).

Negative SST anomalies are observed in the region around Japan when both modes are strong (Fig. 9a) and either mode is strong (Figs. 9c,d). When both modes are weak, opposite SST anomalies appear in the above region (Fig. 9b). These SST anomalies may be explained by anomalous winds. Anomalous northerly winds are observed in the above region when both modes are strong (Fig. 6a) and either mode is strong (Figs. 6c,d). These anomalous winds may bring more air from the land where the air temperature is relatively low. This may lead to an increase in the sea–air temperature and humidity difference, enhancing the upward turbulent surface heat fluxes and thus leading to SST cooling (Wu and Kinter 2010). In contrast, anomalous winds are southerly when both modes are weak (Fig. 6b). The same effect with opposite changes leads to SST warming. Opposite SST anomalies are seen in the midlatitude central North Pacific in Fig. 9b versus Figs. 9a, 9c, and 9d. This appears to be consistent with the opposite wind anomalies over the midlatitude North Pacific (Fig. 6).

The processes for the tropical Pacific SST influence on the southern mode have been discussed in previous studies (Wang et al. 2000; Wu and Wang 2000). The anomalous cooling in equatorial central Pacific associated with lower SST there induces anomalous upper-level divergence and ascent over the Philippine Sea, favoring the development of an anomalous lower-level cyclone (Wang et al. 2000). The accompanying in situ SST warming also favors the formation of an anomalous lower-level cyclone over the western North Pacific through modulating the lower-level convergence and atmospheric stability (Wu and Wang 2000). The anomalous cyclone enhances the northerly winds to its northwest flank (Zhang et al. 1996; Wang et al. 2000; Wu et al. 2003), leading to a stronger southern mode.

As mentioned in the introduction, previous investigators have studied the relationships of the EAWM with AO, NAO, ENSO, and the SH indices. Here, we analyze the relationship of these factors with the northern and southern modes of EAWM. Since there is a close relationship between the N-index and the S-index (0.5), we use partial correlation to exclude the possible interference between the two indices. We note that the correlation is calculated only for the interannual components. The results are provided in Table 2.

Table 2.

Correlation and partial correlation coefficients of the various indices with the N-index and S-index. Boldface font denotes that the correlation coefficient reaches the 95% confidence level. SH, AO, NAO, and ENSO denote the Siberian high, Arctic Oscillation, North Atlantic Oscillation, and El Niño–Southern Oscillation, respectively.

Table 2.

From Table 2, neither the N-index nor the S-index shows significant relationships with either the AO or the NAO. The N-index has a high positive correlation with the SH index and a negative correlation with ENSO, but only the partial correlation coefficient between the SH and the N-index is still significant when the effect of the S-index is excluded. The correlation coefficient and the partial correlation coefficient between the S-index and the ENSO index are both high. The AO and NAO have a weak correlation with the S-index. These results indicate that the interannual variability of the northern mode may be associated with the Siberian high and the southern mode is more closely related to ENSO. The AO and NAO have no significant impact on the EAWM variability on the interannual time scale. This is different from the interdecadal time scale at which the mid- to high-latitude EAWM variability is closely related to the AO (He and Wang 2013).

Another factor for the EAWM variability is snow (Jhun and Lee 2004; Wang et al. 2010). Here, we perform a partial correlation analysis to investigate the snow cover anomalies related to the northern and southern modes. Figure 10 shows the partial correlation of snow cover with respect to the N-index and S-index, respectively. It is obvious that the snow cover anomalies display a very different distribution between abnormal northern and southern mode years. Corresponding to a positive N-index, above-normal snow cover is seen over west Russia and most of China and below-normal snow cover appears over East Asia north of 50°N (Fig. 10a). Corresponding to a positive S-index, below-normal snow cover controls the large region from west Russia to eastern China and above-normal snow cover is observed over northeast China and Europe (Fig. 10b). However, significant correlation tends to be of limited coverage with a much smaller scale compared to temperature anomalies.

Fig. 10.
Fig. 10.

Partial correlations between the snow cover frequency and the (a) N-index and (b) S-index based on the period 1967–2009.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

The relationship of snow cover variations to the wind and temperature appears not simple. In strong northern mode years, above-normal snow cover over most of China may be linked to the northerly winds (Figs. 6c and 10a) that bring colder air from higher latitudes, which is consistent with lower surface temperature (Fig. 7c). Over East Asia north of 50°N, however, the snow cover is reduced (Fig. 10a), probably because the temperature is not very low (Fig. 7c) and the liquid precipitation (Fig. 8c) consumes the moisture transported from the ocean (Fig. 6c). In strong southern mode years, broadly, less snow cover over most of midlatitude Asia (Fig. 10d) is consistent with warmer air temperature (Fig. 7d). From the correspondence, snow cover anomalies appear to be responses to circulation and temperature changes. It is not obvious how the snow cover anomalies affect the EAWM variability. Further investigation is necessary to address this issue.

The relationship between the EAWM variability and sea ice has been investigated in previous studies (Wu et al. 1999; Honda et al. 2009; Wu et al. 2011; Inoue et al. 2012; Li and Wang 2013). Studies indicated that light sea ice in the Barents Sea and the Kara Sea may contribute to the enhancement of the Siberian high (Honda et al. 2009; Inoue et al. 2012). Wu et al. (2011) showed that the intensity of the winter Siberian high is negatively correlated with the Arctic sea ice concentration from the previous autumn to winter seasons in the eastern Arctic Ocean and Siberian marginal seas. Li and Wang (2013) pointed out that heavy Bering Sea ice cover corresponded to weaker EAWM circulations and light Bering Sea ice cover corresponded to stronger EAWM circulations.

Figure 11 shows the partial correlation of the sea ice concentration with the N-index and the S-index. The N-index displays a positive correlation with sea ice in the Bering Sea and a negative correlation with sea ice in the Okhotsk Sea (Fig. 11a). This pattern is similar to that obtained by Li and Wang (2013). Negative correlation is also observed in the Barents Sea and the Kara Sea. Since a positive N-index corresponds to an enhanced Siberian high, our result is consistent with Honda et al. (2009), Wu et al. (2011), and Inoue et al. (2012). The S-index shows a large negative correlation in the Russian and Alaska side of the Arctic Ocean, and a positive correlation in the Barents Sea (Fig. 11b). Comparing the correlation distribution, the northern and southern modes are correlated with different sea ice anomaly pattern in the Arctic region. This points to the necessity of distinguishing the two modes of the EAWM variability to understand the impacts of sea ice anomalies. Compared to the surface air temperature and SST distribution, more sea ice tends to correspond to low temperature and less sea ice to high temperature. As such, the sea ice and surface temperature variations are consistent. This consistency suggests that the sea ice change may follow the surface temperature change, but may also affect the surface temperature through modifying the surface albedo. This interactive nature of the relationship makes it difficult to separate out the sea ice effect on the circulation based on the observations. Numerical experiments, as used by previous studies (e.g., Honda et al. 2009; Wu et al. 2011), would be necessary to help clarify the different effects of the sea ice anomalies in different regions on the northern mode and the southern mode and the associated physical processes.

Fig. 11.
Fig. 11.

Partial correlations between the sea ice concentration and the (a) N-index and (b) S-index based on the period 1962–2011.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-13-00314.1

7. Summary and discussion

Given the large meridional coverage of the EAWM, the EAWM variability may differ between the low latitudes and the mid- to high latitudes, and so may the factors for the variability of low-latitude and mid- to high-latitude components of the EAWM. The present study distinguish the two components of the EAWM variability with the purpose of identifying individual and coherent features associated with the northern and southern modes and the respective factors for the variability of the two components. The northern and southern modes are determined by performing an EOF and REOF analysis of the 1000-hPa winter meridional wind anomalies over East Asia and the western North Pacific. Two indices have been defined to measure the intensity of two modes of EAWM, denoted the N-index and the S-index. While the two indices are similar to those by Liu et al. (2012), the present study focuses on the interannual variability of the EAWM.

Composite analysis clearly demonstrates features unique to each of the two EAWM modes. The northern mode is associated with changes in the mid- to high-latitude circulation systems, such as the Siberian high, the Aleutian low, the East Asian trough, and the East Asian westerly jet stream, whereas there are no obvious signals over the tropics. In contrast, the southern mode is closely related to circulation changes over the global tropics, the North Atlantic, and North America, whereas the circulation anomalies over the Eurasian landmass are weak. When the southern mode is strong, the Walker circulation along the equator is intensified and the convection over the Maritime Continent becomes stronger. When the two modes show in-phase changes, both the tropical and mid- to high-latitude circulation systems are involved in the EAWM variability.

The surface temperature and precipitation anomalies associated with the northern and southern modes display different characteristics. The positive N-index years feature higher surface temperature over the Indochina Peninsula, southwest China, and northeast Russia, and lower surface temperature over midlatitude Asia. The positive S-index years are accompanied by higher surface temperature over northeast Asia and lower surface temperature over the southern Indochina Peninsula and Indonesia. The N-index displays a contrast of rainfall anomalies between south and north of the Yangtze River. The positive S-index features below-normal precipitation over southeastern and northern China. When the N-index and the S-index are in phase, the surface temperature and precipitation anomalies are more widespread with lower (higher) surface temperature over most of China and less (more) precipitation over most of eastern China in strong (weak) EAWM years.

Different from Liu et al. (2013), the present study analyzed not only the coherent variation cases of the northern and southern modes, but also the independent cases. Thus, the present composite analysis clearly distinguishes the factors for the variability of the two EAWM components. This overcomes the ambiguity of the traditional correlation and regression analysis. The tropical Indo-Pacific SST anomalies affect the EAWM variability through its modulation on the southern component, but have no direct influence on the northern mode. There is no clear signal in the SST field for the northern mode. The results indicate that there is some predictability of climate anomalies associated with the southern mode from the SST anomalies, but the predictability of climate anomalies associated with the northern mode is likely low.

He and Wang (2013) obtained a close relationship between mid- to high-latitude EAWM and the AO. On the interannual time scale, the NAO and AO do not have an obvious influence on two modes, but the relationships between the Siberian high and the northern mode are significant. The snow cover anomaly pattern associated with the two modes is very different and appears to result from the circulation and temperature changes. It is unclear how anomalous snow cover may affect the variability of the two modes. Distinct sea ice concentration distributions are identified corresponding to the two modes, consistent with the temperature distribution. The results suggest the influence of circulation on sea ice. It remains to be investigated how the sea ice anomalies can feed back on the variability of the two modes.

The distributions of circulation, surface temperature, and precipitation anomalies obtained by regression on the two modes or by composite when the both modes are in phase resemble those corresponding to the southern temperature mode of Wang et al. (2010). This indicates that the southern temperature mode appears to be a combination of the northern and southern wind modes. The northern temperature mode of Wang et al. (2010) appears to relate to circulation changes at higher latitudes, but has no correspondence with the two wind modes.

While the definition of the two modes in the present study is similar to that of Liu et al. (2012), we focus on the interannual variations of the two modes, which is different from Liu et al. (2012, 2013). In particular, we remove the impacts of uncertainty of the interdecadal variations in the NCEP–NCAR reanalysis. Based on the composite analysis, we are able to identify the circulation features, the climate anomalies, and the possible factors corresponding to either the case when an individual mode is abnormal or the case when both modes are abnormal. This has important implications for application in the monitoring and predicting the EAWM-related climate anomalies. For example, if only tropical Indo-Pacific SST anomalies are large, we may expect that the EAWM is abnormal mainly in the low latitudes and the climate anomalies corresponding to the southern mode are likely to occur. If the factors for both the southern and northern modes are large, we may expect that the EAWM and its associated climate anomalies have a large coverage.

The presented composite maps combine positive and negative index years to reveal features robust to both strong and weak EAWM years. We have also examined composites separately for positive or negative index years (not shown). The results show that the SLP and 850-hPa wind anomalies tend to display similar features over the middle and high latitudes in positive and negative N-index years. However, the anomalies are more significant over the tropics in positive N-index years than in negative N-index years. The SST anomalies show clear signals in both tropics and midlatitudes in positive N-index years, whereas they are weak in negative N-index years. Comparing the positive and negative S-index years, SLP, 850-hPa wind, and SST anomalies are similar in the tropics, but they show differences in the middle and high latitudes. These differences between positive and negative index cases may indicate asymmetric influences of anomalous lower boundary forcing, which is beyond the scope of the present study.

Acknowledgments

The study is supported by National Natural Science Foundations of China Grants 41275081 and 41228006 and National Basic Research Program of China Grant 2014CB953902. RW acknowledges the support of a Hong Kong Research Grants Council grant (CUHK403612).

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