2. Key Laboratory of Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;
3. Chengdu University of Information Technology, Chengdu 610225
There is a significantly strong and narrow westerly circling the subtropical Northern Hemisphere (NH) in the upper troposphere in summer, which plays an important role in maintaining the transformations and balances of the global atmospheric angular momentum and energy (Endlich and McLean, 1957; Cressman, 1981). In particular, this strong and narrow subtropical westerly in the upper troposphere is usually associated with frontal systems, in which disturbances and storms can easily develop and result in severe weather (Uccellini and Johnson, 1979; Gao et al., 1992; Uccellini, 1998). It is found that the strongest zonal wind in the NH subtropical westerly is typically located at 200 hPa, with three steady centers of strong wind speeds situated in East Asia, North America, and Middle East, respectively (Berggren et al., 1958; Kuang and Zhang, 2005; Jin, 2012). The subtropical westerly has its strongest centers appearing over East Asia, where it is called the East Asian subtropical jet (EASJ) when its central wind speed exceeds the threshold of 30 m s–1 (Ding et al., 1988). In this paper, we abbreviate the subtropical westerly over East Asia as SWEA for simplicity. Being noted that the EASJ is embedded in the SWEA and can be regarded as a kind of strongest wind perturbation in the SWEA. Considerable existing studies have shown that the changes of the location and intensity of EASJ or SWEA in summer have an important influence on the seasonal transition of atmospheric circulation in East Asia, drought and flood in rainy season in eastern China, and summer monsoon activity in East Asia (Ye et al., 1958; Li et al., 2004; Lu, 2004; Tao and Wei, 2006; Jin et al., 2012; Xiao and Zhang, 2012; Lu et al., 2013). However, these studies that focus on the more general wind perturbation (WP) in the SWEA and its association with weather and climate of East Asia have been relatively limited.
Kuang and Zhang (2005) found that on annual scale, the variation of the EASJ is mainly influenced by seasonal variation of solar radiation, the special land–sea distribution in East Asia, and the large-scale topography of the Tibetan Plateau. The EASJ activity is also affected by quasi-steady teleconnection wave trains along the global westerly belt (Hoskins and Ambrizzi, 1993; Ambrizzi et al., 1995). It has been demonstrated that in the subtropical tropopause, which is an important region for Rossby wave propagation, the EASJ acts as a wave guide (Hoskins and Karoly, 1981; Terao, 1999a, b), along which synoptic-scale wave packets propagate from west to east, leading to the so called “downstream dispersion effect” that controls the weather and climate in the midlatitude areas of the NH (Hoskins and Ambrizzi, 1993; Ambrizzi et al., 1995).
In Asia, teleconnection wave trains along the westerly belt are significant in summer (Lu et al., 2002). These teleconnection wave trains, which are also termed as the “Silk Road pattern” (Enomoto et al., 2003), are a result of quasi-stationary Rossby wave trains propagating along the SWEA in the upper troposphere (Chen and Huang, 2012). The “Silk Road pattern” influences meridional displacement of the EASJ (Lu et al., 2002; Ding and Wang, 2005; Lin, 2010), which not only results in the advancement or delay of the seasonal cycle of atmospheric circulation during July–August (Liao and Tao, 2004), but also has significant impacts on the northward and southward movements of the northwestern Pacific subtropical high (NPSH) and the rain belt in East Asia (Tao and Wei, 2006).
In addition to the impact of quasi-stationary planetary-scale wave trains on the meridional displacement of the EASJ as mentioned above, previous studies have shown that baroclinic synoptic-scale waves also exert influences on the development and maintenance of the SWEA disturbance. For instance, Mei and Guan (2009) demonstrated that baroclinic waves in the upper troposphere could organize into Rossby wave packets during the Meiyu period of 1998 and propagated to their downstream area. The energy dispersion associated with the baroclinic wave packets leads to the accumulation of energy for rainstorms in the Yangtze River valley. The quasi-stationary wave train (7 days < period < 30 days) along the SWEA provides a favorable background for the development of high-frequency baroclinic waves (period ≤ 7 days). By analyzing the large-scale characteristics and transient disturbance activities in the upper troposphere, Ren et al. (2010) proposed that a stronger wintertime EASJ is often accompanied with weaker synoptic-scale transient disturbance.
The studies mentioned above, which were mainly based on the wave–flow interaction and Rossby wave energy dispersion theory, reveal the interdependence among the SWEA, EASJ, quasi-stationary planetary-scale wave trains, and high-frequency baroclinic synoptic-scale waves. Nonetheless, regarding the WP in the SWEA, the following basic questions remain unclear from both objective and quantitative analyses: (1) Is the WP in the SWEA significantly characterized by planetary- and synoptic-scale waves in June–July–August (JJA)? (2) What is the relationship between the SWEA perturbation on different scales and the rain belt in summer over East China? The purpose of the present investigation is to provide objective and quantitative analyses of the spatiotemporal features of the 200-hPa WP in the SWEA on different scales. We also try to find the difference in the SWEA perturbation between the Meiyu season in the Yangtze–Huaihe region and the rainy period in North China.
This paper is organized as follows. The data and methods are described in Section 2. Based on the spectral resolution, spatial distributions of the 200-hPa WP in the SWEA on different scales during the summertime are analyzed in Section 3. The daily evolution features of the 200-hPa WP in the SWEA and its relation to the typical summer rainy season in eastern China are presented in Section 4. Section 5 contains a summary of the main results and some concluding comments.2 Data and methods 2.1 Data
The data used in this study are the gridded NCEP/NCAR reanalysis data (Kalnay et al., 1996). Following Kuang et al. (2014) and Lin and Lu (2009), we chose to analyze 200-hPa zonal wind (U-wind). Daily data from 56 summers (summer is defined as the 92-day period starting from 1 June) during 1960–2015, available on 2.5° × 2.5° (longitude × latitude) grids, have been analyzed.
The 24-h cumulative precipitation data are derived from the observations collected at 2426 national meteorological stations of China. The observations have been interpolated to 1° × 1° grids by using the Cressmen method.2.2 Methods
Based on Fourier theory (Tsay and Kao, 1978), the spectral decomposition is used for spatial spectral decomposition of the NH 200-hPa U-wind field. In this study, the 200-hPa U-wind field is separated into the following five WP fields on different spectral scales: the total WP (i.e., the difference between the 56-yr climatological mean original U-wind field and the zonal average wind, containing waves on all scales), the composite WP (wavenumbers 1–12), the planetary-scale WP (wavenumbers 1–6; also called the quasi-stationary wave), the synoptic-scale WP (wavenumbers 7–12; also called the high-frequency baroclinic wave), and the small-scale WP (wavenumbers greater than 12).
The SWEA position index and intensity index as defined by Jin et al. (2012) are computed. The SWEA position index is defined as the latitude averaged from those latitudes that have the largest U-wind speed along each individual longitude at 200 hPa over a key region of 20°–55°N, 110°–130°E. Meanwhile, the SWEA intensity index is defined as the U-wind averaged from the largest U-wind speed value along each individual longitude over the key region mentioned above.
The Morlet wavelet method (Farge, 1992) is used to analyze the periodic oscillation of the SWEA position. The two-tailed t-test is used to determine whether the result is statistically significant.3 The 200-hPa WP in the SWEA 3.1 Spatial distribution
The climatological mean U-wind original field and its corresponding perturbation field at 200 hPa in JJA during 1960–2015 are shown in Figs. 1a, b, respectively. A strong U-wind belt at 200 hPa over the NH is observed in Fig. 1a. Three local wind speed maxima within the westerly belt are located in East Asia, North America, and Middle East, respectively. The wind speeds in the centers in East Asia and North America gradually decrease to the west, breaking in the east coasts of Atlantic and Pacific, respectively, and thus form two independent jet stream belts—one is from North America to the east coast of Atlantic, and the other is from North Africa to the east coast of Pacific. Belonging to the latter, The EASJ locates over East Asia–West Pacific. Figure 1a displays that the axis of the EASJ is located near 40°N with the center maximum wind speed greater than 30 m s–1. It is also noted that the EASJ is the strongest westerly system in the whole NH, and a similar result was also found in some previous studies (Cressman, 1981; Ding et al., 1988). As far as the spatial distribution of the SWEA is concerned, it is shown that the westerly wind belt has a large north–south span from north of 35°N to the polar zone (Fig. 1a). However, the WP in the SWEA concentrates along the EASJ, and is also zonally unevenly distributed with its two centers overlapped coincidentally with those of EASJ (Fig. 1b), indicating that the WP in the SWEA can reflect disturbance of the westerly wind belt more significantly than the pure EASJ. Concerning the intensity, the ratio of the maximum WP (e.g., 12 m s–1 in Fig. 1b) to the maximum EASJ or SWEA intensity (e.g., 33 m s–1 in Fig. 1a) is 35%. A similar analysis is also performed on the spatial distribution of the SWEA in wintertime (figure omitted). It is found that the WP in the SWEA in winter is also zonal and concentrated around the EASJ, and the maximum WP intensity is obviously stronger, with the center of 33 m s–1 accounting for about 45% of the maximum EASJ or SWEA intensity (73 m s–1). This further demonstrates the necessity and rationality of investigating WP in the SWEA in addition to the EASJ.3.2 Multi-scale characteristics
The atmospheric perturbation usually consists of multi-waves on different scales, which determine the fluctuation pattern of atmospheric circulation and thus affect the global distribution of weather and climate. Scale analysis of the key systems of atmospheric circulation is one of the important practices in meteorological research and application. Using the Fourier spectral decomposition, the original 200-hPa U-wind field is separated into the total WP field, composite WP field, planetary-scale WP field, synoptic-scale WP field, and small-scale WP field in JJA during 1960–2015. Figures 1b–f illustrate the spatial distributions of these perturbations versus the climatological mean fields during the same time as in Fig. 1a, respectively.
The position, intensity, and wavelike shape of the composite WP field (Fig. 1c) are similar to those of the total WP (Fig. 1b), indicating that the composite WP can approximately represent the total WP. Figure 1d reveals that the position and wavelike shape of the planetary-scale WP field are also similar to those of the total WP (Fig. 1b), except that the planetary-scale WP has a little weaker intensity than the total WP. Moreover, the zonal distribution of wavenumber 4 is the dominant wave pattern in the planetary-scale WP. Figure 1e demonstrates that the synoptic-scale WP field can be represented by the longitudinal distribution of wavenumber 7, and the wavelike shape and position of the synoptic-scale WP field are very different from those of the total WP (Fig. 1b). However, the positive intensity centers of the synoptic-scale WP are located around 40°N, where it coincides with the axis of the EASJ or the SWEA. More importantly, the top three strongest centers in the synoptic-scale WP field located around 50°, 90°, and 150°E are coincident with those of the three maximum speed centers within the EASJ belt as shown in Fig. 1a. Figure 1f demonstrates that the small-scale WP field can be represented by the longitudinal distribution of the dominant wavenumber 14, and the smaller-scale WP with the maximum perturbation over 1 m s–1 mainly occurs within the upper-level easterly jet over the tropical area around 10°–20°N. However, the magnitude of the small-scale WP around 40°N where the EASJ maintains is below 0.4 m s–1, which is one or two orders of magnitude less than the total WP. This result suggests that the westerly WP in the SWEA is dominated by planetary- and synoptic-scale waves. The small-scale perturbation is active mainly within the upper-level easterly jet over the tropical area.
Figure 2 shows the statistical correlation coefficients between plenary-scale WP field, synoptic-scale WP field, and the total WP field for the 200-hPa U-wind in JJA during 1960–2015, respectively. The correlation coefficients above 0.027 are significant at the 0.05 level by the two tailed t-test. It is shown that correlation between the total WP and the planetary-scale WP obviously differs from the correlation between the total WP and the synoptic-scale WP. Specifically, the correlation coefficients between the total WP and the planetary-scale WP are all above 0.87 and decrease gradually from the polar region to low latitudes (Fig. 2a). In particular, the correlation coefficients within the EASJ belt are all above 0.9 and distributed uniformly. In contrast, the correlation coefficients between the total WP and the synoptic-scale WP are all below 0.57 and appear more scattered in the eastern NH (Fig. 2b). Three areas of large correlation coefficients over the EASJ belt are located around 50°, 90°, and 150°E, which are consistent with the positive centers of the synoptic-scale WP and the total WP as shown in Figs. 1b, e.
In summary, the planetary-scale WP with the dominant wavenumber 4 is highly correlated with the total WP, indicating that the planetary-scale WP is the key factor determining the wavelike shape and location of the EASJ or the SWEA WP. The three highest correlation centers between the synoptic-scale WP and the total WP are overlapped with the three positive centers of the synoptic-scale WP, meaning that the synoptic-scale WP is closely related to the intensity fluctuation of the SWEA WP (although the planetary-scale WP dominates the shape and location of the SWEA WP). Statistically significant relationships of the spatial patterns shown in this study indicate the important influence of planetary- and synoptic-scale waves on the position and intensity of the EASJ or the SWEA WP. Similar results can also be found in other studies. For instance, Yang and Zhang (2007) pointed out that the perturbation kinetic energy of Rossby waves had significant influences on the position of the EASJ, that is, anomalously strong (weak) perturbation kinetic energy of Rossby waves along the EASJ could lead to southward (northward) than normal position and stronger (weaker) than normal intensity of the EASJ. Xiang (2011) indicated that climatologically, transient eddy (synoptic-scale) perturbations are helpful for the maintenance of the EASJ, and there is a direct dynamical relation between transient eddy perturbations and the EASJ variation.4 Variations of the WP in the SWEA on different scales
Previous studies have shown that the variation of SWEA is characterized by seasonal northward (southward) migration and weakened (strengthened) intensity from winter to summer (summer to winter), influencing the position and intensity of the rain belt in eastern China (Lin and Lu, 2005). In this section, we try to discuss the variation of position, intensity, and period of the WP on different scales within the SWEA and their association with the summer rainfall in eastern China.4.1 Position variation
Based on the SWEA position index definition given in Section 2.2, the 56-yr mean daily SWEA position indexes for the WP on different scales are calculated from 1 June to 31 August, to analyze the daily evolution of the SWEA position index and the meridional displacement of the SWEA in association with the typical rainy season in eastern China. The solid blue and red lines in Fig. 3a illustrate daily evolution of the SWEA position index for the total WP and the planetary-scale WP, respectively. It is clear that the SWEA for the total WP and the planetary-scale WP synchronously and steadily moves from south to north and reaches the northernmost point of 43°N by the end of July, and then slowly retreats to the south again. Similar to the results in Lin and Lu (2009) about the relationship between the SWEA position and the rain belt in boreal summer in eastern China, the daily evolution of the SWEA position corresponds to seasonal variation of the rain belt, which also steadily moves from south in early June to north in late July and then slowly retreats to the south (Fig. 3c). Specifically, the positions of the SWEA for the total WP and the planetary-scale WP swing between 35° and 36°N during 1–16 June, corresponding to the pre-summer rainy season in South China when the WP in the SWEA is located to the north of the main rain belt by about 12 latitudes. The position of the SWEA for the total and planetary-scale WPs slowly shifts northward from 37° to 39°N during 16 June–16 July, when Meiyu rainfall occurs in the Yangtze–Huaihe region. During the Meiyu period, the position of the WPs in the SWEA is located to the north of the main rain belt by about eight latitudes. The position of the SWEA for the total and planetary-scale WPs further moves northward again from 40°N to the northernmost position around 43°N during 17–29 July and then gradually moves southward before early August, which coincides with the rainy period over North China. During this period, the position of the WPs in the SWEA is located to the north of the main rain belt by about five latitudes. Hence, along with the meridional shift of the SWEA and rain belt in eastern China, the latitude difference between the two gradually reduces during JJA. Furthermore, the influence mechanism of the SWEA on the three typical rainy seasons over eastern China is worth a further investigation in the future.
The SWEA position index for the planetary-scale WP (solid red line in Fig. 3a) displays a variation pattern similar to that of the position index for the total WP. However, the position index for the synoptic-scale WP (solid green line in Fig. 3a) oscillates within 38°–40°N, and does not show significant meridional displacement in JJA. Therefore, it is deduced that the meridional movement of summer rain belt in eastern China is mainly dominated by the SWEA position of the planetary-scale WP. In addition, it is worth noting that the position indexes for the total, planetary-scale, and synoptic-scale WPs coincide during 29 June–16 July. This period corresponds to the Meiyu period in the Yangtze–Huaihe region, which seems to imply that the combined effect of the planetary- and synoptic-scale waves along the SWEA is more significant in the Meiyu period over the Yangtze–Huaihe region. It may also possibly explain why the intensity of the SWEA during the Meiyu period in the Yangtze–Huaihe region is stronger than the summer-mean SWEA intensity (Jin et al., 2012).
Annual correlation coefficients of the SWEA position index for the total WP with those for the planetary- and synoptic-scale WPs at 200 hPa in JJA during 1960–2015 are shown in Fig. 3b. The coefficients greater than 0.205 are statistically significant at the 0.05 level by the two tailed t-test. The correlation coefficient of the SWEA position index between the total and planetary-scale WPs is positive with large values. Overall, there exists a significant interannual variation in the correlation coefficient between the total and planetary-scale WPs. The largest correlation coefficient of 0.97 appears in 1967, while the smallest correlation coefficient of 0.73 appears in 2001. Moreover, the correlation coefficient is above 0.8 in 49 out of the total 56 years. In contrast, the correlation of the SWEA position index between the total and synoptic-scale WPs is comparably weak as the correlation coefficient varies between –0.2 and 0.4 and does not pass the significance test in 30 out of the total 56 years. However, it can still be concluded that the contribution of synoptic-scale to total WPs of the SWEA is in general positive because the negative correlation coefficients are generally small and do not pass the significance test. Similar conclusion is also reported in other studies. For example, Xiang and Yang (2012) verified the positive feedback of transient vorticity forcing on the position variation of the EASJ, i.e., transient perturbations always make the EASJ located more northward or southward. This result is now confirmed by the positive feedback of the synoptic-scale WP to the SWEA position obtained in this study.
In summary, considering both the climatological mean and the daily evolution, the SWEA position is dominated by quasi-stationary wave associated with the planetary-scale WP, which also influences the position of the summer rain belt in eastern China. The contribution of the synoptic-scale WP to the position of the SWEA is comparably small. Meridional displacement of the SWEA on different scales vibrates within the latitude range of meridional displacement of westerly activity, presenting different amplitudes and corresponding oscillation periods. Based on this conclusion, Section 4.3 will further discuss the period characteristics of the SWEA perturbation on different scales.4.2 Intensity variation
Based on the SWEA intensity index definition, the 56-yr mean daily SWEA intensity indexes for different scale perturbations are calculated from 1 June to 31 August, to analyze the daily evolution and the intensity change of the SWEA in association with the typical rainy season in eastern China. The daily evolution of the SWEA intensity index for the total WP (blue solid line in Fig. 4a) shows that the SWEA intensity for the total WP steadily weakens and reaches the weakest by the end of July, and then slowly intensifies again. The SWEA intensity index for the planetary-scale WP (red solid line in Fig. 4a) changes simultaneously with that for the total WP. The planetary-scale WP is positively correlated with the total WP (Fig. 4b). The correlation coefficients are greater than 0.8 in 55 out of the total 56 years. The smallest correlation coefficient of 0.78 appears in 2002, while the largest correlation coefficient of 0.97 appears in 2015. The SWEA intensity index for the synoptic-scale WP oscillates with small amplitude within the range of 4–6 m s–1, and is weakly and negatively correlated with that for the total WP. There are only 7 out of the total 56 years when the correlation coefficients pass the significance test. The biggest negative correlation coefficient of –0.42 appears in 1981 (Fig. 4b).
As shown in Figs. 3a, 4a, following the northward (southward) movement of SWEA for the total and planetary-scale WPs, their perturbation intensities weaken (enhance) correspondingly, whereas the intensity of the synoptic-scale WP enhances (weakens) and the intensity difference between the total and planetary-scale WPs increases (decreases). In addition, the configurations of the position and intensity of the planetary- and synoptic-scale WPs are different during different rainy periods in eastern China. Specifically, during the rainy period (late July to early August) over North China when the SWEA position is in the northernmost point (above 40°N), the intensity of the planetary-scale WP in the SWEA is weak within 10–12 m s–1, while the intensity of the synoptic-scale WP in the SWEA is the strongest with values greater than 6 m s–1. This indicates that the synoptic-scale wave exerts more important influence during this rainy period than that during other rainy periods. During the Meiyu period (16 June–16 July) over the Yangtze–Huaihe region, the situation is reversed. As the SWEA slowly moves to around 37°–39°N, the intensity of the planetary-scale WP in the SWEA varies within the range of 15–20 m s–1, while the intensity of the synoptic-scale WP is in its weak phase with values of around 5 m s–1. Apparently, the planetary-scale wave is dominant during this rainy period. Similar results are also found in the study of Yang and Zhang (2007) from the perspective of energy disturbance. They demonstrated that the EASJ is southward (northward) of the mean and stronger (weaker) than usual when the perturbation kinetic energy enhances (weakens).
The study of Lu et al. (2013) indicated that the upper-level jet stream in summer over East Asia tends to move southward with a tendency of increased intensity in the recent 50 years, but the intensity of the westerly has shown a weakening trend since the middle 1990s. As shown in Figs. 4a, b, the correlation of the intensity of the planetary- and synoptic-scale WPs with the intensity of the total WP in the SWEA also reveals different characteristics before and after the 1990s with a decreased correlation. Further investigation should be conducted to study whether the interdecadal variation of the SWEA position and intensity is related to the wave activities caused by the planetary- and synoptic-scale WPs in the SWEA.
Perturbations of the subtropical westerly influencing Meiyu in the Yangtze–Huaihe region are associated with both the planetary- and synoptic-scale WPs in the SWEA. Existing studies have shown that perturbations on different scales exert different impacts on Meiyu. Yang and Zhang (2007) claimed that during the year of strong disturbance of Rossby waves along the SWEA, the upper, middle, and lower tropospheric circulations and vertical velocity of the whole layer are all favorable for the strengthening of tropical monsoon circulation in East Asia and reinforcement of the Meiyu front as well as the southward displacement of the northwestern Pacific subtropical high (NPSH). In their case study of the anomalous Meiyu season of 1998, Mei and Guan (2009) found that there existed quasi-stationary wave trains (planetary-scale perturbations) that served as the background field for high-frequency waves (synoptic-scale perturbations) during the Meiyu period. Originating from the regions to the east of the Caspian Sea, the baroclinic waves in the upper troposphere were organized into the Rossby wave packets during the 1998 Meiyu period and then propagated downstream along the SWEA. The energy dispersion associated with the baroclinic wave packets led to the accumulation of energy for rainstorms in the Yangtze River valley.4.3 Periodic analysis
In this section, we try to reveal the periodic oscillation characteristics of the SWEA perturbations on different scales, and answer the following questions: What are the exact oscillation periods for the positions of different scale WPs in the SWEA? What is the configuration of these periods for the positions of different scale WPs in the SWEA during the typical rainy season over eastern China?
By means of wavelet transform, the temporal variation of daily SWEA position index for 56-yr mean 200-hPa U-wind field (original field), total WP, planetary-scale WP, and synoptic-scale WP can be filtered on the time frequency domain from 1 June to 31 August. As shown in Fig. 5, three wavelet variance peaks are detected for the above four position indexes on the frequency domain during the 46 days. Wavelet variance curves of the original field, the total WP, and the planetary-scale WP agree well with the first two peaks occurring on 38 and 21 days, respectively. The third peak of the wavelet variance for the mean field occurs on 10 days, while the third peak for the total WP and the planetary-scale WP occurs on 13 days. The synoptic-scale WP presents a unique characteristic of the oscillation frequency, with the main period peaking on 16 days and the other two periods on 8 and 28 days, respectively. Actually, the periods of 16 and 28 days do not have any physical significance for the synoptic-scale WP of the SWEA, and the periods of 21 and 38 days make no sense for other perturbations as well.
Table 1 shows the first three main periods of annual position index for the different scale WPs and their corresponding statistical frequencies during 1960–2015. The first three main periods correspond to the three most frequently occurred cycles during the 56 years. It is clear that the first three periods of the original field are 8, 15, and 13 days, respectively. The first three periods of the total WP and the planetary-scale WP are 12, 13, and 14 days, respectively. The first three periods of the synoptic-scale WP are 5, 6, and 7 days, respectively. This reveals that the periods of the original field are actually the comprehensive results of the synoptic-scale WP superimposed on the planetary-scale WP. The temporal variation of the total WP exhibits quasi-biweekly (13 days) oscillation, consistent with that of position of the planetary-scale WP. Meanwhile, weekly (6 days) oscillation is the main period of the synoptic-scale WP. It can be probably inferred that quasi-biweekly (13 days) oscillation is significant in the original field due to the impact of the planetary-scale WP during its strongest stage, whereas weekly (6 days) oscillation becomes a bit more important when the synoptic-scale WP enhances from late July to early August.
|Original field||Total WP||Planetary-scale WP||Synoptic-scale WP|
|Top three periods (day)||8/15/13||14/13/12||12/13/14||6/5/7|
Previous studies revealed that the typical rainy season in boreal summer in China also evolves with significant periodic variation characteristics. For instance, quasi-biweekly oscillation is significant during the Meiyu (e.g., Lau et al., 1988; Chen et al., 2000). The high-frequency precipitation episodes are characterized with obvious 3–8-day oscillation, which continues from the very beginning to the end of the rainy season in North China (Liu, 2009). Such obvious periodic characteristics of precipitation in the typical rainy season are apparently related to impacts of the SWEA perturbations, especially the WPs on planetary and synoptic scales.
Hence, these results can provide references for medium-range forecasting of large-scale precipitation in boreal summer in East Asia. For example, during the Meiyu season in the Yangtze–Huaihe region when the SWEA moves northward to the geographic location around 37°–39°N, quasi-biweekly (13 days) oscillation is dominant because the planetary-scale WP contributes to the major variation of the SWEA. During the rainy period of North China when the SWEA moves to 40°N, besides the quasi-biweekly oscillation, weekly (6 days) oscillation should also be considered because the synoptic-scale WP intensifies during this period.5 Conclusions and discussion
By using daily NCEP/NCAR reanalysis data and daily precipitation observations in China from 1 June to 31 August 1960–2015, this study analyzes in detail the temporal and spatial characteristics of the WP in the SWEA on different scales in association with the summer rainy seasons in eastern China. The results are as follows.
(1) In the climatological mean fields, the 200-hPa WP in the SWEA is collocated with the EASJ. The distribution of the 200-hPa WP in the SWEA is quasi-zonal, with its centers overlapped with those of the EASJ. The WP in the SWEA mainly comprises planetary- and synoptic-scale waves. The planetary-scale WP is the key factor that determines the shape, intensity, and location of the EASJ or SWEA perturbations. For the synoptic-scale WP, its three positive centers located along 40°N coincide with the intensity centers of the WP in the EASJ or SWEA, which can be regarded as the intensity fluctuation superimposed on the planetary-scale WP.
(2) In both the climatological mean and the daily WP fields, the meridional displacement and intensity varia-tion of the SWEA WP have a close relation to those of the quasi-stationary planetary-scale wave, but are only weakly correlated with the synoptic-scale wave. Addition-ally, following the northward (southward) movement of the SWEA, the intensities of the total and planetary-scale WPs weaken (enhance) while the intensity of the synoptic-scale WP enhances (weakens).
(3) The position and intensity variations of the WP in the SWEA on different scales and oscillation periods show different modes during the two typical rainy seasons in eastern China. For example, during the Meiyu season (16 June–16 July) in the Yangtze–Huaihe region when the SWEA moves northward slowly to the region around 37°–39°N, low-frequency variation of quasi-biweekly (13 days) oscillation in its geographic location becomes significant, which is largely attributed to the strong (weak) planetary-scale (synoptic-scale) component. In the rainy season over North China (from late July to early August), the WP in the SWEA strives northward beyond 40°N, showing both quasi-biweekly (13 days) and weekly (6 days) oscillations in its position. Meanwhile, the planetary-scale WP in the SWEA becomes weakened whereas the synoptic-scale WP intensifies.
After filtering out the small-scale perturbations and removing the zonal-mean component from the original wind field, the perturbations in the remained field can better represent the movement of the SWEA or EASJ and show a clearer relationship between the SWEA and the summer rain belt in eastern China. The above results on the relationships between the WPs in the SWEA on different scales, their corresponding oscillation periods, and summer rainfall in eastern China would provide useful information for analyzing the variation of summer rain belt in eastern China. These results are also valuable references for medium-range weather forecasting.
This paper reveals the characteristics of spatial distribution, temporal variation, and oscillation characteristic of the WP in the SWEA on different scales, and investigates the relationship between the SWEA perturbations and the summer typical rainy seasons in eastern China. More efforts should be dedicated to investigating the in-depth influence mechanism of different scale SWEA perturbations on summer rainfall in China. For instance, it is worth analyzing the distribution of wave packets and energy dispersion for different scale SWEA perturbations and their impacts on summer rainfall in China. Furthermore, many previous studies assumed that extreme weather and climate events in the midlatitudes are caused by enhanced waviness of the upper-level jet stream (e.g., Francis and Vavrus, 2012, 2015). For example, Lu and Fu (2010) demonstrated that the upper-level jet stream in summer would enhance and move southward with increased annual variation rate in the future and the relationship between the jet stream and summer precipitation would also become closer. This highlights the importance of further analysis on the relationship between the EASJ together with SWEA and the summer rainfall in China from the perspective of the wave and flow interaction.
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