Linkage between Interannual Variation of the East Asian Intraseasonal Oscillation and Mei-Yu Onset

Yonghong Yao School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China

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Hai Lin Recherche en Prévision Numérique Atmosphérique, Environment Canada, Dorval, Quebec, Canada

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Qigang Wu Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai, China

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Abstract

The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Yonghong Yao, yyh@nju.edu.cn

Abstract

The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Yonghong Yao, yyh@nju.edu.cn

1. Introduction

The persistent mei-yu front that initiates the summer rainy season over eastern China is associated with the advance of the East Asian summer monsoon (EASM; Tao and Chen 1987; Lau et al. 1988; Ding 1992; Murakami and Matsumoto 1994; Webster et al. 1998). The mei-yu rainband moves into the Yangtze and Huai River valleys (in China), southern Japan, and the Korean peninsula after the onset of the South China Sea (SCS) summer monsoon (Ding and Chan 2005). On average, the mei-yu starts near mid-June and ends around early July in the middle and lower Yangtze River Valley (MLYRV). The date of the mei-yu onset in the MLYRV is characterized by a remarkable interannual variability, with over a 1-month difference between the earliest and latest mei-yu onset. The onset date corresponds well to the total precipitation amount over the MLYRV during the mei-yu season. Early mei-yu onset years tend to have a longer rainy season with more rainfall, whereas late mei-yu onset years tend to have a short rainy season and less rainfall (Xu et al. 1999; Wei and Zhang 2004; Huang et al. 2012). As extreme flood events occur frequently over the MLYRV during the mei-yu season (Zhu et al. 2003; Gao et al. 2011), it is important to predict the mei-yu onset.

Previous studies show that the northward-propagating intraseasonal oscillation (ISO) plays a vital role in determining the timing of active and break cycles of the EASM system, such as the northward movement of the monsoon trough (Lau and Chan 1986) and the northward migration of the monsoonal front (Chen and Chen 1995; Tsou et al. 2005). The associated migration of the rainband is a geographically unique control of the timing of the mei-yu in China (also called baiu in Japan and changma on the Korean peninsula) (Wang and LinHo 2002; Chen et al. 2004). Previous studies, such as that of Kajikawa and Yasunari (2005), have identified distinct ISO at 10–20- and 30–60-day time scales over the tropical Indian Ocean, the SCS, and the tropical western Pacific. Lau et al. (2012) suggested that both the 10–20-day oscillation and the 30–60-day oscillation coexist in the Asian summer monsoon.

Intraseasonal oscillations at both the 10–20- and the 30–60-day time scales have been found to be associated with the interannual variation of EASM precipitation (Yang et al. 2010). The 10–20-day ISO generally propagates westward whereas the 30–60-day ISO affects the EASM through its northward propagation (Chen and Chen 1993, 1995). Previous studies have shown that the 10–20-day oscillation is a dominant mode of intraseasonal variability of precipitation in summer (Lu and Ding 1996; Fujinami and Yasunari 2009; Mao and Wu 2006; Yang et al. 2010). There are also studies that have examined the effect of the 30–60-day oscillation on summer precipitation anomalies in some individual years (Zhu et al. 2003; Yin et al. 2011). However, most previous studies focused on influences of both the 10–20- and the 30–60-day oscillations on the precipitation amount during the mei-yu season, with little attention on the mei-yu onset. The influences of the ISO on the mei-yu onset have not been documented as comprehensively as those on the total mei-yu precipitation. The main purpose of this study is to investigate the impact of ISO intensity on the mei-yu onset, as well as its contribution to prediction of the mei-yu onset.

The timing of the mei-yu onset has been found to be influenced by El Niño–Southern Oscillation (ENSO) events (Wang and Qian 2005; Gao et al. 2006; Wang et al. 2008; Zhu et al. 2008). The “traditional” El Niño is characterized by a warming (or a cooling in the case of La Niña) in the eastern equatorial Pacific that propagates toward the date line (e.g., Trenberth 1997), and this phenomenon is referred to as the eastern Pacific (EP) ENSO, including EP El Niño and EP La Niña (Kao and Yu 2009). However, in some years, substantial warming (or cooling) develops in the central Pacific instead of in the eastern Pacific, and such an event is referred to as the central Pacific (CP) ENSO, including CP El Niño and CP La Niña (Yu and Kao 2007; Kao and Yu 2009). Wang et al. (2008) found that SST anomalies in the Niño-4 region (eastern Pacific) exert more significant impacts on the mei-yu onset than those in the Niño-3 region (central Pacific), and that warm EP El Niño (cold EP La Niña) SST anomalies in the Niño-4 region in the previous winter and spring would favor an early (late) mei-yu onset. On the other hand, Wang et al. (2008) showed that an early (late) mei-yu onset is also associated with warm CP El Niño (cold CP La Niña) in the early spring season. The northward propagation of the 30–60-day ISO has also been found to be closely related to the quasi-biennial ENSO and an abrupt ENSO transition (Yun et al. 2009). This indicates that the EP ENSO or CP ENSO condition in winter and spring has significant impacts on both the ISO variability and the mei-yu onset, and the ISO–mei-yu onset relationship may be due to ENSO. Since the two types of El Niño events have different impacts on the seasonal mean climate, the Asian summer monsoon could behave differently during the entire life cycle of CP ENSO and EP ENSO. The second purpose of this study is to compare the impacts of these two types of ENSO on ISO and the mei-yu onset, and to assess their roles in the ISO–mei-yu relationship. In this study, we investigate different impacts of these two types of ENSO on ISO intensity and the mei-yu onset, with the objective of improving the prediction of the mei-yu onset.

The relationship between EASM and ENSO changed in the late 1970s (e.g., Wang 2002; Gao et al. 2006). As discussed in Gao et al. (2006), before the late 1970s (using 1951–74 China station precipitation data), an El Niño winter is often followed by a summer with a late mei-yu onset, dry conditions along the Huai River, and heavy rainfall in northern and southern China (with an opposite situation after a La Niña winter), but after the late 1970s (based on 1980–2003) the relationships were neither similar nor clearly opposite. Ye and Lu (2011) also indicated that the relationship between ENSO and the precipitation in eastern China is weaker after 1979. The ENSO index has exhibited an interdecadal change with more La Niña events before approximately 1980 and more El Niño events since then. In addition, Zhu et al. (2013), based on 1980–2000 mei-yu seasons, found a decadal seesaw-like change in mei-yu precipitation, wet in the northern and dry in the southern half of the MLYRV from 1980 to 1991 (related to CP La Niña), and reversing to dry in the northern and wet in the southern half from 1992 to 2000 (related to EP El Niño). It is unclear whether such an interdecadal change of ENSO modulates the interannual association between ENSO and the date of the mei-yu onset, so we also address whether the interannual relationship between ENSO and the mei-yu onset is stable.

The paper is organized as follows. Section 2 introduces the dataset and methodology of this study. Section 3 documents the relationship between the 30–60-day oscillation and the mei-yu onset. The relationship between spring intraseasonal oscillation intensity, SSTs, and the mei-yu onset is discussed in section 4. Section 5 develops an empirical model to predict the mei-yu onset based on the intraseasonal oscillation intensity and ENSO. Conclusions are given in section 6.

2. Data and methodology

The datasets analyzed in this study include the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Global Reanalysis (NCEP-1) (Kalnay et al. 1996) and the NOAA Optimum Interpolation (OI) SST analysis (Reynolds et al. 2002). The data of the mei-yu onset dates over the MLYRV and daily observed precipitation at 756 Chinese stations, both covering 1979–2013, are provided by the China Meteorological Administration (CMA).

The mei-yu onset date is determined based on the daily precipitation of five gauge stations over the MLYRV, namely Shanghai, Nanjing, Wuhan, Jiujiang, and Hankou. A single rainy day is first identified when the total amount of daily precipitation of the above five stations exceeds 10 mm and at least two stations report >0.1 mm. If the number of rainy days is greater than 5 in a successive 10-day period, the first single rainy day is then defined as the mei-yu onset date. More details can be found in Chen and Zhao (2000).

Several indices describe summer intraseasonal variations that affect East Asia, including the Real-Time Multivariate Madden–Julian oscillation (MJO) index (RMM; Wheeler and Hendon 2004), the boreal summer intraseasonal oscillation (BSISO) index (Kikuchi et al. 2012; Lee et al. 2013), and the East Asian–western North Pacific (EAWNP) ISO index (Lin 2013). The MJO is an eastward-propagating tropical disturbance that alternately favors or suppresses convection with a period of roughly 30–60 days (Madden and Julian 1994). However, according to Kikuchi et al. (2012), the MJO mode dominates from December to April, but from June to October the MJO is weak and the BSISO, a northward-propagating system of enhanced or suppressed EASM rainfall along the extratropical monsoon front, dominates. For real-time applications, the EAWNP ISO (similar to BSISO) is defined to focus on the ISO behavior in the EAWNP region (Lin 2013), which covers 10°S–40°N, 90°–150°E, and the index input data (specifically OLR and 850-hPa zonal wind without temporal filtering) are averaged longitudinally retaining the latitudinal variation.

The indices described above are based on combined empirical orthogonal function (EOF) or extended EOF (EEOF) analysis of the variables representing the intraseasonal oscillation. In each of these cases, the normalized principal component (PC) values of the two leading EOFs characterize the ISO, and the ISO index at each time is a (PC1, PC2) pair. If a point representing the pair is plotted on a 2D phase space, the distance from the origin is the amplitude (intensity) of the ISO, and the trajectory of consecutive points in the phase space depicts the propagation of the ISO as well as intensity changes (generally amplitude with absolute value <1 is considered weak). Normally eight phases are defined dividing the phase space, which correspond to different locations of the ISO convective activity.

Composites of the observed OLR and 850-hPa wind anomalies for the eight phases of the RMM index show an eastward propagation of the MJO around the equator (e.g., Wheeler and Hendon 2004), and those of the BSISO or EAWNP indices demonstrate a northward propagation of the Asian summer monsoon ISO (e.g., Lee et al. 2013; Lin 2013). However, since the EAWNP index was designed specifically for the EAWNP region, it explains more ISO variance in eastern China than the BSISO index (Lin 2013; Gao et al. 2016).

3. The relationship between the 30–60-day oscillation and mei-yu onset

The intraseasonal oscillation is associated with significant precipitation variability during summer over the MLYRV (Yang et al. 2010). Figure 1 compares the composites of the 30–60-day-bandpass-filtered standardized precipitation anomaly averaged over the MLYRV (29°–35°N, 115°–120°E) between nine early and seven late mei-yu onset years from 1979–2013 (late and early onset years are shown later in Fig. 3a). The earliest onset occurred on 2 June, whereas the latest onset happened on 9 July. The peak precipitation anomaly appears around mid-June during an early mei-yu onset year, but occurs in early July during a late mei-yu onset year. Such consistent anomaly features are not present in the time series of the 10–20-day-bandpass-filtered precipitation in MLYRV (not shown). The association of the 30–60-day MLYRV precipitation anomaly and the mei-yu onset as shown in Fig. 1 indicates that the 30–60-day oscillation likely determines the mei-yu onset date rather than the 10–20-day oscillation. This is consistent with previous studies that indicate that the northward movement of the 30–60-day oscillation explains most summer rainfall variability in the MLYRV (e.g., Mao et al. 2010).

Fig. 1.
Fig. 1.

The normalized daily 30–60-day-filtered precipitation anomalies from May to August averaged over the MLYRV (29°–35°N, 115°–120°E) in nine early mei-yu onset years (red line) and seven late mei-yu onset years (blue line). The specific early and late onset years are plotted on Fig. 3a.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

To further understand the correlation between the 30–60-day oscillation and mei-yu onset, Fig. 2 illustrates composites of the 30–60-day-filtered precipitation and 850-hPa wind anomalies in China for different phases of ISO according to the Lin (2013) EAWNP ISO index. During phase 1, decreased precipitation occurs in coastal southern China and a small region in northeastern China while increased precipitation occurs over the MLYRV in the area of 850-hPa southwesterly wind anomalies. This pattern is similar to the peak period of a mei-yu rainband, indicating that phase 1 corresponds well to the mei-yu mode (Lau et al. 1988). During phase 2, the increased precipitation shifts northward with the maximum positive value to the north of the Yangtze River, corresponding to the peak of the rainy season in the Huanghuai (Yellow River) Valley in east-central China. Negative precipitation anomalies persist in the coastal area of southern China. The maximum positive precipitation anomaly continues to move farther north toward northeastern China, with anomalous southwesterly wind penetrating to northeastern China in phase 3. Negative precipitation anomalies cover the area south of the Yangtze River, with anomalous northeasterly flow throughout southern China in phases 4 and 5. The negative precipitation anomaly keeps moving northward during phase 6, and is centered over the MLYRV, while positive precipitation anomalies appear in coastal southern China. In phases 7 and 8, the positive precipitation anomaly moves gradually from southern China into the MLYRV. Based on the above eight phases of the EAWNP ISO index, Gao et al. (2016) concluded that the EAWNP ISO very well describes the northward propagation of the 20–70-day intraseasonal variability in the EAWNP. In spring, when the rainbelt related to the EASM is mainly in southern China, the northernmost location of the precipitation band does not reach northern China in the Gao et al. (2016) study. However, the northward propagation of the rainbelt during boreal summer in China is less clear in terms of the eight phases of the Wheeler and Hendon (2004) RMM index, which is not surprising as the RMM index was designed to represent the eastward propagation rather than the northward movement of the ISO. This is why we use the EAWNP ISO index to investigate the relationship between the 30–60-day oscillation and mei-yu onset.

Fig. 2.
Fig. 2.

Phase-related composited normalized precipitation anomalies (shaded) and wind anomalies at 850 hPa (vectors; arrow above color bar indicates 1 m s−1) based on the 1979–2013 EAWNP ISO index during the boreal summer. Precipitation is computed from the 756 Chinese stations with daily data.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

Time series of the EAWNP ISO amplitude and the mei-yu onset date are shown in Fig. 3. On the interannual time scale (Fig. 3a), a significant negative correlation of −0.46 is obtained. On the decadal time scale from 1979 to 2013 (Fig. 3b), however, a positive correlation of 0.57 is found between the EAWNP ISO intensity and the mei-yu onset date, which is statistically significant at the 95% level according to a Student’s t test. This indicates that the EAWNP ISO intensity and mei-yu onset date are coupled differently on decadal and interannual time scales. Decadal coupling deserves future research using a longer data period than available for this study. The relationship between the ISO and mei-yu onset on the interannual time scale is more robust than that of the total variability. As we are mainly interested in the interannual variation of the mei-yu onset and its relationship with the 30–60-day oscillation, the decadal change is omitted in the following discussion. To see whether the EAWNP ISO intensity has a persistent linkage with the mei-yu onset and to explore the potential predictability with a lead–lag relationship, association between the preceding spring EAWNP ISO intensity and the mei-yu onset is further analyzed (Figs. 3c,d). The correlation of spring EAWNP ISO intensity and the mei-yu onset date is −0.44, exceeding the 95% confidence level, suggesting that the intensity of the EAWNP ISO has a strong and persistent connection with the mei-yu onset, which makes it of seasonal prediction value. Therefore, the intensity of the EAWNP ISO in April and May is a useful precursor for predicting the mei-yu onset date.

Fig. 3.
Fig. 3.

Time series of normalized EAWNP ISO intensity (red line) and the normalized date of the mei-yu onset in MLYRV (blue line) averaged over (a),(b) summer (June–August) and (c),(d) spring (April–May). (a),(c) Annual values with the decadal change removed (mei-yu onset dates are the same in both panels and range from 2 June to 9 July, but dates do not exactly correspond to the normalized values because the decadal component is removed). (b),(d) Annual values on the decadal change time scale. In (b) and (d), the red line is the ISO intensity using the right scale, and the blue line is the decadal scale average mei-yu onset date using the left scale. The correlation coefficient r between the two annual time series is shown on each panel. The circles and the solid dots in (a) identify the seven late and nine early mei-yu onset years respectively (these years are used for Fig. 1).

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

4. Impact of SST anomalies on the mei-yu onset

a. SST patterns related to the mei-yu onset

Results in section 3 indicate that there exists a statistically significant connection between the spring (AM) EAWNP ISO intensity and the mei-yu onset date. Evidence of the influence of ENSO on the mei-yu onset has been provided in previous studies (e.g., Xu et al. 2002; Zhu et al. 2008). It is of great interest to understand the roles of CP ENSO and EP ENSO in the ISO–mei-yu onset relationship. A composite analysis with respect to the intensity of the EAWNP ISO in spring is performed here to investigate the evolution of SST. Strong ISO years are those with a spring EAWNP ISO index ≥ 0.8 (9 out of 35years between 1979 and 2013), while weak ISO years are those with an EAWNP ISO index ≤−0.8 (11 years between 1979 and 2013).

The composite SST anomalies (SSTA) from January to July during years with strong spring EAWNP ISO are presented in Fig. 4 (see Fig. 6 for weak spring EAWNP ISO). In strong EAWNP ISO years (Fig. 4), the most prominent feature is a persistent EP El Niño–like SSTA in the tropical eastern Pacific, with a maximum positive SSTA amplitude in the equatorial eastern Pacific from January to July. The persistent CP La Niña–like SSTA and the SSTAs (discussed below) in the western North Pacific as well as in the Atlantic Ocean have been suggested to be closely related to an early mei-yu onset in MLYRV (Xu et al. 2002; Wang and Qian 2005; Zhu et al. 2008). It is worth noting that in Fig. 4 (strong spring EAWNP ISO years), SSTA patterns in the tropical central and western Pacific vary from winter to summer, but the warm SSTA in the tropical eastern Pacific is sustained from winter to summer, although it is weaker in March. This suggests that the EP El Niño–like SSTA may provide a favorable environment for a strong EAWNP ISO to develop in spring and to influence the mei-yu onset. In January (Fig. 4a), a uniform warming is also found in the tropical Indian Ocean, and a less significant SSTA tripole is present in the North Atlantic (with positive SSTA in the subtropical region and the area close to Greenland, and negative SSTA along the eastern coast of North America). The tripole pattern of SSTA during the winter in the North Atlantic was discussed by Wu et al. (2009) and was found to contribute to an early mei-yu onset (Xu et al. 2002). From February to March (Figs. 4b,c), the warming in the Indian Ocean weakens, while a positive SSTA appears off the eastern coast of North America (Fig. 4d). The warm SSTA in the tropical eastern Pacific strengthens again in April. In May, cold SSTAs appear in the tropical eastern Indian Ocean, the SCS (5°S–10°N, 90°–120°E) and the adjacent tropical Pacific. Cold SSTAs over the eastern Indian Ocean and SCS region in the preceding winter and spring can lead to an early SCS summer monsoon onset (Zhao and Wu 2003). The statistically significant relationship between the onset dates of the SCS summer monsoon and mei-yu was found in Zhu et al. (2016), and they suggest that the northward propagation of the subtropical western North Pacific high (WNPH) probably plays a key role on the onset of both the SCS summer monsoon and mei-yu. When the WNPH extends northward earlier than usual, southwesterly convergence to the north of the WNPH causes an early onset of the SCS summer monsoon. After the SCS summer monsoon begins, the WNPH continues propagating northward and the southwesterly convergence similarly causes an early mei-yu onset. Associated with a strong spring EAWNP ISO, the cold SSTA in the eastern Indian Ocean and SCS region can thus lead to an early mei-yu onset. In June (Fig. 4f) and July (Fig. 4g), the EP El Niño–like SSTA is sustained and becomes dominant.

Fig. 4.
Fig. 4.

Monthly composited sea surface temperature anomalies (°C) from winter to summer in years with a strong EAWNP ISO index during the preceding spring (EAWNP ISO intensity > 0.8 standard deviations; 9 out of 35 years from 1979 to 2013, as identified in Fig. 3c). Shading indicates areas with SST anomalies above the 95% confidence level.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

To better understand the relationship between EP ENSO, strong spring EAWNP ISO, and the mei-yu onset, Fig. 5 shows monthly regression maps of SSTA against the normalized EP ENSO index (https://www.ess.uci.edu/~yu/2OSC/) based on the time period from 1979 to 2013. Tropical eastern Pacific warm SSTAs (an EP El Niño pattern) are seen in all months, but are weakest in March and dominant starting May, with all eastern Pacific SSTA patterns similar to the corresponding months in Fig. 4. In addition, the CP La Niña–like SSTA in the tropical central Pacific and SSTAs in the western North Pacific in winter and spring, the North Atlantic Ocean in spring, and in the SCS in May in Fig. 5 are similar to the corresponding SSTA features shown in Fig. 4, which have been suggested to be related to early mei-yu onset in previous studies (Xu et al. 2002; Zhu et al. 2008; Zhu et al. 2016). This further indicates that a strong spring EAWNP ISO is usually associated with the preceding winter EP El Niño SSTA pattern, which is persistent from the preceding winter through summer, and leads to SST cooling over the SCS in May, favoring an early mei-yu onset.

Fig. 5.
Fig. 5.

The simultaneous regression of the sea surface temperature anomaly (°C) from winter to summer against the EP ENSO index for the same months from 1979 to 2013. Shading above the 95% confidence level has the same meaning as in Fig. 4.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

In Fig. 6, the SST anomaly in weak spring EAWNP ISO years also shows distinct features. A pronounced feature of SSTA evolution in the tropical Pacific is the transition from CP to EP El Niño in April. The January–March CP El Niño pattern reflects a warm SSTA center in the central Pacific with little warming along the equatorial South America coast. As the equatorial South America coastal SSTA warms in April and clearly exceeds the central Pacific SSTA in May and June, the situation transitions to EP El Niño. Accompanied with the change of ENSO pattern, the SSTA in both the Indian Ocean and the SCS areas changes to increasingly widespread positive values from January to May, and then persists to June. The anomalous warm SST in both the Indian Ocean and SCS favors a southwestward shift of the northwestern Pacific subtropical high, leading to a late onset of the SCS summer monsoon (Zhao and Wu 2003), and thus a late mei-yu onset (Zhu et al. 2016).

Fig. 6.
Fig. 6.

As in Fig. 4, but composite SST anomalies (°C) for years with preceding spring weak EAWNP ISO (EAWNP ISO intensity < −0.8 standard deviation; 11 out of 35 years from 1979 to 2013).

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

Figure 7 shows the monthly regression maps of SSTA with respect to the CP ENSO index (https://www.ess.uci.edu/~yu/2OSC/) from winter to the following summer based on the same years as in Fig. 5. From January to March, the positive SSTA centered in the tropical central Pacific matches the CP El Niño pattern. In April, a negative SSTA (rather than a positive SSTA as in Fig. 6d) appears in the tropical eastern Pacific (EP La Niña), while the maximum positive SSTA in the tropical central Pacific (CP El Niño) persists but is weakened. The EP La Niña–like signal is pronounced in May and still has moderate intensity in July. This suggests that the SSTA pattern during the early summer is an EP La Niña pattern. The EP La Niña pattern starts to appear from April. The transition period of CP El Niño to EP La Niña in Fig. 7d is the same month as in the weak EAWNP ISO years (Fig. 6d). The difference between Figs. 6d and 7d is that Fig. 6d shows a transition from CP El Niño to EP El Niño, while Fig. 7d shows a shift from CP El Niño to EP La Niña. Such a difference suggests that both the CP El Niño and EP El Niño contribute to the SSTA distribution during weak EAWNP ISO years (Fig. 6). The CP El Niño is prominent before April. The EP El Niño then dominates and offsets the cooling of EP La Niña due to CP ENSO, which causes the maximum warming in the eastern tropical Pacific in the following summer season.

Fig. 7.
Fig. 7.

As in Fig. 5, but showing simultaneous regression of the SST anomaly (°C) against the CP ENSO index for the same month from 1979 to 2013.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

In addition, the SST anomalies in the Indian Ocean, the western North Pacific, and the North Atlantic Ocean related to CP La Niña and EP El Niño in Figs. 5 and 7 are similar to those documented in previous studies (Zhu et al. 2008), which were considered to be related to the mei-yu onset date. This further indicates that a strong (weak) spring EAWNP ISO is usually associated with an EP El Niño (CP El Niño) SSTA pattern in the preceding winter. The SSTA pattern persists into summer, and leads to below-normal (above normal) SST over the SCS, favoring an early (late) mei-yu onset. Therefore, the preceding winter ENSO state is a significant predictor for the mei-yu onset date during both strong and weak EAWNP ISO years.

The difference in geopotential height at 500 hPa (Z500) based on strong minus weak composite EAWNP ISO years varies from winter to summer (Fig. 8) and does not show persistent patterns. The northward propagation of the northwestern Pacific subtropical high is considered to be an important factor leading to the mei-yu onset in June (Huang et al. 2012). The Z500 difference pattern in June (Fig. 8f) is weak and shows little statistical significance, indicating that the difference of Z500 between strong and weak EAWNP ISO years is not significant. This suggests that the contribution of hemispheric atmospheric circulation may be small in the relationship between the EAWNP ISO intensity and the mei-yu onset date.

Fig. 8.
Fig. 8.

The difference of Z500 (gpm) between the strong and weak spring intensity of EAWNP ISO from winter to summer [based on composites using the same years as in Fig. 4 (strong ISO) and Fig. 6 (weak ISO)]. Contour interval is 5 gpm with the zero contour omitted and negative contours dashed. Shading indicates grid points with Z500 differences above the 95% significance level.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

b. Possible impact of EP/CP ENSO on spring EAWNP ISO intensity

Previous studies suggested that an easterly vertical wind shear favors the northward propagation of MJO (Jiang et al. 2004; Yang et al. 2008; Deng et al. 2016; Wu and Song 2018) and that a rich moisture supply contributes to the growth of MJO (Deng et al. 2016; Wu and Song 2018). To show the possible influences of EP ENSO and CP ENSO on EAWNP ISO intensity during the spring, regression maps for May (1979–2013) of easterly wind shear (u velocity at 200 hPa minus 850 hPa; m s−1) and specific humidity (at 850 hPa; g kg−1) with respect to the preceding winter (January–February) EP ENSO and CP ENSO indices are shown in Fig. 9. As mentioned in the previous section, anomalous cooling (warming) of SST over the eastern Indian Ocean and SCS region in May can lead to an early (late) SCS summer monsoon onset and thus an early (late) mei-yu onset. In Fig. 9, nearly opposite May regression patterns indicate that easterly wind shear and specific humidity responses are approximately opposite following an EP versus a CP El Niño winter. Associated with the EP ENSO index (left panels), fairly large mostly insignificant negative vertical wind shear anomalies occur in a band near 30°N, and positive insignificant specific humidity anomalies are found over the EAWNP region. Therefore, after an EP El Niño winter, the background dynamics (vertical wind shear) favor the northward movement of EAWNP ISO and the thermodynamics (moisture) enhance the EAWNP ISO intensity in May. The enhanced EAWNP ISO propagates northward to cause an early onset of the SCS monsoon related to EP El Niño and an early mei-yu onset subsequently. On the other hand, after a CP El Niño winter (right panels), significant positive vertical wind shear anomalies and less significant negative specific humidity anomalies over the EAWNP region are associated with CP El Niño. This indicates that both the background dynamics and the thermodynamics are unfavorable for the EAWNP ISO northward propagation, which may lead to a less active EAWNP ISO and a delayed onset of the SCS monsoon and the mei-yu.

Fig. 9.
Fig. 9.

The regression of (a),(c) the vertical shear of zonal winds (m s−1) and (b),(d) the specific humidity (g kg−1) against (a),(b) the preceding winter (JF) EP ENSO and (c),(d) CP ENSO indices in May, based on all years 1979–2013. Anomalies above the 95% confidence level are shaded.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

5. Prediction of the mei-yu onset

Previous studies suggested that ENSO is an important predictor for the mei-yu onset (Gao et al. 2006; Zhu et al. 2008; Wang et al. 2009). The results in section 4 suggest that the intensity of the spring EAWNP ISO provides an additional physically based predictor for the mei-yu onset. An empirical prediction model of the MLYRV mei-yu onset date is developed using a linear regression method based on spring (average of daily April and May values) EAWNP ISO intensity and ENSO indices, as illustrated in Eq. (1):
e1
where y represents the date of the mei-yu onset, x1 denotes the spring EAWNP ISO intensity, and x2 and x3 refer to the spring EP ENSO and CP ENSO indices respectively. All variables including the onset date are normalized by their respective standard deviations. To assess the relative importance of the three predictors to the mei-yu onset, prediction experiments are performed with one or two individual predictors in the model. It turns out that on the interannual time scale, the spring EAWNP ISO intensity is a better predictor than either CP ENSO or EP ENSO. The correlation skill of the hindcast with respect to the observed mei-yu onset date reaches 0.56 for the three-predictor model (Fig. 9), 0.45 for the model with only the spring EAWNP ISO intensity, and 0.46 for the model with both ENSO indices. Therefore, the forecast skill of the empirical model is largely determined by the spring EAWNP intensity.

To test the predictive capability of the above empirical model with the spring EAWNP ISO intensity and both ENSO indices as predictors, a cross-validation method with a leave-eight-out strategy is adopted to predict the mei-yu onset date for the period from 1979 to 2013 (Wu et al. 2009; Lin and Wu 2011). The cross-validated estimates of the mei-yu onset date are shown in Fig. 10, with the correlation coefficient between the observations and the hindcast reaching 0.48, exceeding the 95% confidence level. Therefore, the above empirical model has a statistically significant prediction skill for the mei-yu onset date.

Fig. 10.
Fig. 10.

Comparison between the normalized observed date of the mei-yu onset from 1979 to 2013 (red line) and corresponding hindcast normalized mei-yu onset dates using regression models described in the text (green, black, and blue lines). The correlation coefficients are shown in parentheses.

Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-17-0873.1

The regression model is also applied to predict the mei-yu onset date for 2005–13 (black line in Fig. 10) under a simulated real-time environment where the regression coefficients are obtained using the data before 2005. The result shows a good agreement between the forecast and the observation, with the correlation coefficient between the observations and the forecast reaching 0.6. Because all regression predictors are available in the preceding spring, this regression model can potentially be used for operational prediction of the mei-yu onset date very early in each year.

6. Conclusions

The prediction of the mei-yu onset date has been a focal issue in dealing with the interannual variability of the EASM. While previous studies have demonstrated the important roles of CP ENSO and EP ENSO in the mei-yu onset, the usefulness of ENSO as a predictor of the mei-yu onset has decreased after the 1980s (Gao et al. 2006), as the relationship between ENSO and the mei-yu has weakened in the past two decades. We find a significant negative (positive) correlation between the EAWNP ISO amplitude in spring (April–May) and the mei-yu onset at the interannual (decadal) time scale. A strong (weak) EAWNP ISO in spring tends to be associated with an early (late) onset of the mei-yu over the MLYRV. To our knowledge, this is the first study that presents significant evidence that the spring EAWNP ISO intensity can influence the mei-yu onset, and where the ENSO effect has also been detected through the ISO–mei-yu onset relationship. A strong spring EAWNP ISO is related to an EP El Niño pattern in the tropical eastern Pacific that persists from the preceding winter through the current summer, and a weak spring EAWNP ISO is related to a CP El Niño before April and EP El Niño from May through summer. When above-normal SST over the eastern Pacific persists from the preceding winter through summer, below-normal SST occurs over the tropical Indian Ocean and SCS in May, leading to an early SCS summer monsoon onset, and the mei-yu begins earlier than normal. When the maximum above-normal SST occurs in the equatorial central Pacific from the preceding winter through April, and then shifts to the eastern Pacific and persists through June and July, above normal SST is observed over the tropical Indian Ocean and SCS in May, leading to a later-than-normal mei-yu onset. In addition, the above-normal (below normal) SST over SCS weakens (strengthens) the thermal land–sea contrast in East Asia, likely resulting in a weak (strong) summer monsoon and a possible late (early) SCS summer monsoon onset (Zhao and Wu 2003). Therefore, the significant correlation between spring EAWNP ISO intensity and the mei-yu onset indicates that both the EAWNP ISO intensity and the mei-yu onset are likely influenced by some common forcing, such as SSTA patterns in the tropical Pacific, the Indian Ocean, the SCS, and the North Atlantic Ocean.

Our results suggest that the different types of ENSO play important roles in the linkage between spring EAWNP ISO intensity and the mei-yu onset since the SST anomaly patterns over the Indian Ocean, the SCS, and the North Atlantic Ocean are also associated with the evolution of the ENSO pattern from the preceding winter to summer. An easterly (westerly) vertical wind shear and positive (negative) specific humidity anomalies over the EAWNP region in May are associated with the EP (CP) El Niño, which is favorable (unfavorable) for the northward propagation of the EAWNP ISO and the enhancement of the EAWNP ISO intensity. However, the proposed mechanism needs to be further investigated by numerical model experiments in the future.

The intimate linkage between spring EAWNP ISO intensity and the mei-yu onset presented in this study provides another important source of predictability for real-time seasonal predictions of the mei-yu onset date. A correlation coefficient around 0.45 is produced by a linear regression model using one predictor (spring EAWNP ISO intensity) or two predictors (preceding spring CP ENSO and EP ENSO indices). However, the EAWNP ISO index is significantly correlated with both ENSO indices, which influence the EAWNP ISO in their own way in different seasons, and a high prediction correlation of 0.56 can be achieved with all three predictors. This empirical model can be applied in real-time forecasts of the mei-yu onset.

Acknowledgments

This work is funded by the National Natural Science Foundation of China (Grant 41375076) and the National Key Scientific Research Plan of China (Grant 2012CB956002), and is also supported by the Jiangsu Collaborative Innovation Center for Climate Change and CMA-NJU Joint Laboratory for Climate Prediction Studies.

REFERENCES

  • Chen, T., and J. Chen, 1993: The 10–20-day mode of the 1979 Indian monsoon: Its relation with the time variation of monsoon rainfall. Mon. Wea. Rev., 121, 24652482, https://doi.org/10.1175/1520-0493(1993)121<2465:TDMOTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T., and J. Chen, 1995: An observational study of the South China Sea monsoon during the 1979 summer: Onset and life cycle. Mon. Wea. Rev., 123, 22952318, https://doi.org/10.1175/1520-0493(1995)123<2295:AOSOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T., S. Wang, W. Huang, and M. Yen, 2004: Variation of the East Asian summer monsoon rainfall. J. Climate, 17, 744762, https://doi.org/10.1175/1520-0442(2004)017<0744:VOTEAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., and Z. Zhao, 2000: Precipitation in Rainy Season in China: Prediction and Applications (in Chinese). China Meteorological Press, 419 pp.

  • Deng, L., T. Li, J. Liu, and M. Peng, 2016: Factors controlling the interannual variations of MJO intensity. J Meteor. Res., 30, 328340, https://doi.org/10.1007/s13351-016-5113-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Y., 1992: Summer monsoon rainfalls in China. J. Meteor. Soc. Japan, 70, 373396, https://doi.org/10.2151/jmsj1965.70.1B_373.

  • Ding, Y., and J. C.-L. Chan, 2005: The East Asian summer monsoon: An overview. Meteor. Atmos. Phys., 89, 117142, https://doi.org/10.1007/s00703-005-0125-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujinami, H., and T. Yasunari, 2009: The effects of midlatitude waves over and around the Tibetan Plateau on submonthly variability of the East Asian summer monsoon. Mon. Wea. Rev., 137, 22862304, https://doi.org/10.1175/2009MWR2826.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., Y. Wang, and J. He, 2006: Weakening significance of ENSO as a predictor of summer precipitation in China. Geophys. Res. Lett., 33, L09807, https://doi.org/10.1029/2005GL025511.

    • Search Google Scholar
    • Export Citation
  • Gao, H., S. Yang, A. Kumar, Z. Z. Hu, B. H. Huang, Y. Q. Li, and B. Jha, 2011: Variations of the East Asian mei-yu and simulation and prediction by the NCEP Climate Forecast System. J. Climate, 24, 94108, https://doi.org/10.1175/2010JCLI3540.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, J., H. Lin, L. You, and S. Chen, 2016: Monitoring early-flood season intraseasonal oscillations and persistent heavy rainfall in South China. Climate Dyn., 47, 38453861, https://doi.org/10.1007/s00382-016-3045-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, Q., L. Wang, Y. Li, and J. He, 2012: Determination of the onset and ending of regional Meiyu over Yangtze-Huaihe River Valley and its characteristics (in Chinese). J. Trop. Meteor., 28, 749756.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 10221039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kajikawa, Y., and T. Yasunari, 2005: Interannual variability of the 10–25- and 30–60-day variation over the South China Sea during boreal summer. Geophys. Res. Lett., 32, L04710, https://doi.org/10.1029/2004GL021836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615632, https://doi.org/10.1175/2008JCLI2309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal representation of the tropical intraseasonal oscillation. Climate Dyn., 38, 19892000, https://doi.org/10.1007/s00382-011-1159-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., G. J. Yang, and S. H. Shen, 1988: Seasonal and intraseasonal climatology of summer monsoon rainfall over East Asia. Mon. Wea. Rev., 116, 1837, https://doi.org/10.1175/1520-0493(1988)116<0018:SAICOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., S. Yang, and D. E. Waliser, 2012: Intraseasonal Variability in the Atmosphere–Ocean Climate System. 2nd ed. Springer, 613 pp.

    • Crossref
    • Export Citation
  • Lee, J., B. Wang, M. Wheeler, X. Fu, D. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493509, https://doi.org/10.1007/s00382-012-1544-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H., 2013: Monitoring and predicting the intraseasonal variability of the East Asian–western North Pacific summer monsoon. Mon. Wea. Rev., 141, 11241138, https://doi.org/10.1175/MWR-D-12-00087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H., and Z. Wu, 2011: Contribution of the autumn Tibetan Plateau snow cover to seasonal prediction of North American winter temperature. J. Climate, 24, 28012813, https://doi.org/10.1175/2010JCLI3889.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, E., and Y. Ding, 1996: Low frequency oscillation in East Asia during the 1991 excessively heavy rain over Changjiang–Huaihe River Basin. Acta Meteor. Sin., 19, 200208.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., and G. Wu, 2006: Intraseasonal variations of the Yangtze rainfall and its related atmospheric circulation features during the 1991 summer. Climate Dyn., 27, 815830, https://doi.org/10.1007/s00382-006-0164-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., Z. Sun, and G. Wu, 2010: 20–50-day oscillation of summer Yangtze rainfall in response to intraseasonal variations in the subtropical high over the western North Pacific and South China Sea. Climate Dyn., 34, 747761, https://doi.org/10.1007/s00382-009-0628-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, M., and J. Matsumoto, 1994: Summer monsoon over the Asian continent and western North Pacific. J. Meteor. Soc. Japan, 72, 719745, https://doi.org/10.2151/jmsj1965.72.5_719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, S. Y., and L. X. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsou, C.-H., P.-C. Hsu, W.-S. Kau, and H.-H. Hsu, 2005: Northward and northwestward propagation of 30-60 day oscillation in the tropical and extratropical western North Pacific. J. Meteor. Soc. Japan, 83, 711726, https://doi.org/10.2151/jmsj.83.711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and LinHo, 2002: Rainy season of the Asian-Pacific summer monsoon. J. Climate, 15, 386398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., 2002: Instability of the East Asian summer monsoon–ENSO relations. Adv. Atmos. Sci., 19, 111, https://doi.org/10.1007/s00376-002-0029-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., J. He, X. Liu, J. Lu, and B. Wu, 2008: Previous stronger signal of anomalous Meiyu onset over Yangtze–Huaihe River Valley and its analysis. Meteor. Mon., 34, 3540.

    • Search Google Scholar
    • Export Citation
  • Wang, J., J. He, X. Liu, and B. Wu, 2009: Interannual variability of the Meiyu onset over Yangtze–Huaihe River Valley and analysis of its previous strong influence signal. Chin. Sci. Bull., 54, 687695, https://doi.org/10.1007/s11434-008-0534-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., and Y. Qian, 2005: The affection of sea-surface temperature anomaly to Meiyu onset date of Yangtze-Huaihe River valley (in Chinese). Quart J. Appl. Meteor., 16, 193204.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., V. O. Magaña, T. N. Palmer, J. Shukla, R. A. Tomas, M. Yanai, and T. Yasunari, 1998: Processes, predictability, and the prospects for prediction. J. Geophys. Res., 103, 1445114510, https://doi.org/10.1029/97JC02719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., and J. J. Zhang, 2004: Climatic variation of Meiyu in the middle-lower reaches of Changjiang River during 1885–2000 (in Chinese). Quart. J. Appl. Meteor., 15, 313321.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., and H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, R., and L. Song, 2018: Spatiotemporal change of intraseasonal oscillation intensity over the tropical Indo-Pacific Ocean associated with El Niño and La Niña events. Climate Dyn., 50, 12211242, https://doi.org/10.1007/s00382-017-3675-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Z., B. Wang, J. Li, and F. Jin, 2009: An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, https://doi.org/10.1029/2009JD011733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, H., J. He, and Y. Yao, 1999: Interannual variability of the Meiyu onset and its association with the atmospheric circulation in the previous winter and possible causes (in Chinese). J. Nanjing Inst. Meteor., 22, 246252.

    • Search Google Scholar
    • Export Citation
  • Xu, H., J. He, and M. Dong, 2002: Interannual variability of the Meiyu onset and its association with North Atlantic oscillation and SSTA over North Atlantic (in Chinese). Acta Meteor. Sin., 59, 694705.

    • Search Google Scholar
    • Export Citation
  • Yang, J., B. Wang, and B. Wang, 2008: Anticorrelated intensity change of the quasi-biweekly and 30–50-day oscillations over the South China Sea. Geophys. Res. Lett., 35, L16702, https://doi.org/10.1029/2008GL034449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, J., B. Wang, and Q. Bao, 2010: Biweekly and 21–30 day variabilities of the subtropical East Asian monsoon over the lower reach of Yangtze River Basin. J. Climate, 23, 11461159, https://doi.org/10.1175/2009JCLI3005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, H., and R. Lu, 2011: Subseasonal variation in ENSO-related East Asian rainfall anomalies during summer and its role in weakening the relationship between the ENSO and summer rainfall in eastern China since the late 1970s. J. Climate, 24, 22712284, https://doi.org/10.1175/2010JCLI3747.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, Z., Y. Wang, and D. Yuan, 2011: Analysis of the interannual variability of the Meiyu quasi-biweekly oscillation and its previous strong influence signal (in Chinese). Trans. Atmos. Sci., 34, 297304.

    • Search Google Scholar
    • Export Citation
  • Yu, J., and H. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, https://doi.org/10.1029/2006JD007654.

    • Search Google Scholar
    • Export Citation
  • Yun, K., B. Ren, K. Ha, J. C.-L. Chan, and J. Jhun, 2009: The 30–60 day oscillation in the East Asian summer monsoon and its time dependent association with the ENSO. Tellus, 61A, 565578, https://doi.org/10.1111/j.1600-0870.2009.00410.x.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., and A. Wu, 2003: Numerical experiments for the influences of SST anomalies over the South China Sea–eastern tropical Indian Ocean on the South China Sea monsoon. J. Trop. Meteor., 19, 2735.

    • Search Google Scholar
    • Export Citation
  • Zhu, C., T. Nakazawa, J. Li, and L. Chen, 2003: The 30–60 day intraseasonal oscillation over the western North Pacific Ocean and its impacts on summer flooding in China during 1998. Geophys. Res. Lett., 30, 1952, https://doi.org/10.1029/2003GL017817.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., D. Huang, Y. Zhang, X. Kuang, and Y. Huang, 2013: Decadal changes of Meiyu rainfall around 1991 and its relationship with two types of ENSO. J. Geophys. Res., 118, 97669777, https://doi.org/10.1002/jgrd.50779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, W., J. Pan, B. Zhou, and Y. Wang, 2016: Onset process of South China Sea summer monsoon in 2011 and its relationship with Meiyu over the middle and lower reaches of the Yangtze River (in Chinese). J. Nanjing Inst. Meteor., 39, 3745.

    • Search Google Scholar
    • Export Citation
  • Zhu, X., Z. Wu, and J. He, 2008: Anomalous Meiyu onset averaged over the Yangtze River valley. Theor. Appl. Climatol., 94, 8195, https://doi.org/10.1007/s00704-007-0347-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
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  • Chen, T., and J. Chen, 1993: The 10–20-day mode of the 1979 Indian monsoon: Its relation with the time variation of monsoon rainfall. Mon. Wea. Rev., 121, 24652482, https://doi.org/10.1175/1520-0493(1993)121<2465:TDMOTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T., and J. Chen, 1995: An observational study of the South China Sea monsoon during the 1979 summer: Onset and life cycle. Mon. Wea. Rev., 123, 22952318, https://doi.org/10.1175/1520-0493(1995)123<2295:AOSOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T., S. Wang, W. Huang, and M. Yen, 2004: Variation of the East Asian summer monsoon rainfall. J. Climate, 17, 744762, https://doi.org/10.1175/1520-0442(2004)017<0744:VOTEAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., and Z. Zhao, 2000: Precipitation in Rainy Season in China: Prediction and Applications (in Chinese). China Meteorological Press, 419 pp.

  • Deng, L., T. Li, J. Liu, and M. Peng, 2016: Factors controlling the interannual variations of MJO intensity. J Meteor. Res., 30, 328340, https://doi.org/10.1007/s13351-016-5113-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Y., 1992: Summer monsoon rainfalls in China. J. Meteor. Soc. Japan, 70, 373396, https://doi.org/10.2151/jmsj1965.70.1B_373.

  • Ding, Y., and J. C.-L. Chan, 2005: The East Asian summer monsoon: An overview. Meteor. Atmos. Phys., 89, 117142, https://doi.org/10.1007/s00703-005-0125-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujinami, H., and T. Yasunari, 2009: The effects of midlatitude waves over and around the Tibetan Plateau on submonthly variability of the East Asian summer monsoon. Mon. Wea. Rev., 137, 22862304, https://doi.org/10.1175/2009MWR2826.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., Y. Wang, and J. He, 2006: Weakening significance of ENSO as a predictor of summer precipitation in China. Geophys. Res. Lett., 33, L09807, https://doi.org/10.1029/2005GL025511.

    • Search Google Scholar
    • Export Citation
  • Gao, H., S. Yang, A. Kumar, Z. Z. Hu, B. H. Huang, Y. Q. Li, and B. Jha, 2011: Variations of the East Asian mei-yu and simulation and prediction by the NCEP Climate Forecast System. J. Climate, 24, 94108, https://doi.org/10.1175/2010JCLI3540.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, J., H. Lin, L. You, and S. Chen, 2016: Monitoring early-flood season intraseasonal oscillations and persistent heavy rainfall in South China. Climate Dyn., 47, 38453861, https://doi.org/10.1007/s00382-016-3045-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, Q., L. Wang, Y. Li, and J. He, 2012: Determination of the onset and ending of regional Meiyu over Yangtze-Huaihe River Valley and its characteristics (in Chinese). J. Trop. Meteor., 28, 749756.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 10221039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kajikawa, Y., and T. Yasunari, 2005: Interannual variability of the 10–25- and 30–60-day variation over the South China Sea during boreal summer. Geophys. Res. Lett., 32, L04710, https://doi.org/10.1029/2004GL021836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615632, https://doi.org/10.1175/2008JCLI2309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal representation of the tropical intraseasonal oscillation. Climate Dyn., 38, 19892000, https://doi.org/10.1007/s00382-011-1159-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., G. J. Yang, and S. H. Shen, 1988: Seasonal and intraseasonal climatology of summer monsoon rainfall over East Asia. Mon. Wea. Rev., 116, 1837, https://doi.org/10.1175/1520-0493(1988)116<0018:SAICOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., S. Yang, and D. E. Waliser, 2012: Intraseasonal Variability in the Atmosphere–Ocean Climate System. 2nd ed. Springer, 613 pp.

    • Crossref
    • Export Citation
  • Lee, J., B. Wang, M. Wheeler, X. Fu, D. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493509, https://doi.org/10.1007/s00382-012-1544-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H., 2013: Monitoring and predicting the intraseasonal variability of the East Asian–western North Pacific summer monsoon. Mon. Wea. Rev., 141, 11241138, https://doi.org/10.1175/MWR-D-12-00087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H., and Z. Wu, 2011: Contribution of the autumn Tibetan Plateau snow cover to seasonal prediction of North American winter temperature. J. Climate, 24, 28012813, https://doi.org/10.1175/2010JCLI3889.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, E., and Y. Ding, 1996: Low frequency oscillation in East Asia during the 1991 excessively heavy rain over Changjiang–Huaihe River Basin. Acta Meteor. Sin., 19, 200208.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., and G. Wu, 2006: Intraseasonal variations of the Yangtze rainfall and its related atmospheric circulation features during the 1991 summer. Climate Dyn., 27, 815830, https://doi.org/10.1007/s00382-006-0164-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., Z. Sun, and G. Wu, 2010: 20–50-day oscillation of summer Yangtze rainfall in response to intraseasonal variations in the subtropical high over the western North Pacific and South China Sea. Climate Dyn., 34, 747761, https://doi.org/10.1007/s00382-009-0628-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, M., and J. Matsumoto, 1994: Summer monsoon over the Asian continent and western North Pacific. J. Meteor. Soc. Japan, 72, 719745, https://doi.org/10.2151/jmsj1965.72.5_719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, S. Y., and L. X. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsou, C.-H., P.-C. Hsu, W.-S. Kau, and H.-H. Hsu, 2005: Northward and northwestward propagation of 30-60 day oscillation in the tropical and extratropical western North Pacific. J. Meteor. Soc. Japan, 83, 711726, https://doi.org/10.2151/jmsj.83.711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and LinHo, 2002: Rainy season of the Asian-Pacific summer monsoon. J. Climate, 15, 386398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., 2002: Instability of the East Asian summer monsoon–ENSO relations. Adv. Atmos. Sci., 19, 111, https://doi.org/10.1007/s00376-002-0029-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., J. He, X. Liu, J. Lu, and B. Wu, 2008: Previous stronger signal of anomalous Meiyu onset over Yangtze–Huaihe River Valley and its analysis. Meteor. Mon., 34, 3540.

    • Search Google Scholar
    • Export Citation
  • Wang, J., J. He, X. Liu, and B. Wu, 2009: Interannual variability of the Meiyu onset over Yangtze–Huaihe River Valley and analysis of its previous strong influence signal. Chin. Sci. Bull., 54, 687695, https://doi.org/10.1007/s11434-008-0534-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., and Y. Qian, 2005: The affection of sea-surface temperature anomaly to Meiyu onset date of Yangtze-Huaihe River valley (in Chinese). Quart J. Appl. Meteor., 16, 193204.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., V. O. Magaña, T. N. Palmer, J. Shukla, R. A. Tomas, M. Yanai, and T. Yasunari, 1998: Processes, predictability, and the prospects for prediction. J. Geophys. Res., 103, 1445114510, https://doi.org/10.1029/97JC02719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., and J. J. Zhang, 2004: Climatic variation of Meiyu in the middle-lower reaches of Changjiang River during 1885–2000 (in Chinese). Quart. J. Appl. Meteor., 15, 313321.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., and H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, R., and L. Song, 2018: Spatiotemporal change of intraseasonal oscillation intensity over the tropical Indo-Pacific Ocean associated with El Niño and La Niña events. Climate Dyn., 50, 12211242, https://doi.org/10.1007/s00382-017-3675-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Z., B. Wang, J. Li, and F. Jin, 2009: An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, https://doi.org/10.1029/2009JD011733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, H., J. He, and Y. Yao, 1999: Interannual variability of the Meiyu onset and its association with the atmospheric circulation in the previous winter and possible causes (in Chinese). J. Nanjing Inst. Meteor., 22, 246252.

    • Search Google Scholar
    • Export Citation
  • Xu, H., J. He, and M. Dong, 2002: Interannual variability of the Meiyu onset and its association with North Atlantic oscillation and SSTA over North Atlantic (in Chinese). Acta Meteor. Sin., 59, 694705.

    • Search Google Scholar
    • Export Citation
  • Yang, J., B. Wang, and B. Wang, 2008: Anticorrelated intensity change of the quasi-biweekly and 30–50-day oscillations over the South China Sea. Geophys. Res. Lett., 35, L16702, https://doi.org/10.1029/2008GL034449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, J., B. Wang, and Q. Bao, 2010: Biweekly and 21–30 day variabilities of the subtropical East Asian monsoon over the lower reach of Yangtze River Basin. J. Climate, 23, 11461159, https://doi.org/10.1175/2009JCLI3005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, H., and R. Lu, 2011: Subseasonal variation in ENSO-related East Asian rainfall anomalies during summer and its role in weakening the relationship between the ENSO and summer rainfall in eastern China since the late 1970s. J. Climate, 24, 22712284, https://doi.org/10.1175/2010JCLI3747.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, Z., Y. Wang, and D. Yuan, 2011: Analysis of the interannual variability of the Meiyu quasi-biweekly oscillation and its previous strong influence signal (in Chinese). Trans. Atmos. Sci., 34, 297304.

    • Search Google Scholar
    • Export Citation
  • Yu, J., and H. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, https://doi.org/10.1029/2006JD007654.

    • Search Google Scholar
    • Export Citation
  • Yun, K., B. Ren, K. Ha, J. C.-L. Chan, and J. Jhun, 2009: The 30–60 day oscillation in the East Asian summer monsoon and its time dependent association with the ENSO. Tellus, 61A, 565578, https://doi.org/10.1111/j.1600-0870.2009.00410.x.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., and A. Wu, 2003: Numerical experiments for the influences of SST anomalies over the South China Sea–eastern tropical Indian Ocean on the South China Sea monsoon. J. Trop. Meteor., 19, 2735.

    • Search Google Scholar
    • Export Citation
  • Zhu, C., T. Nakazawa, J. Li, and L. Chen, 2003: The 30–60 day intraseasonal oscillation over the western North Pacific Ocean and its impacts on summer flooding in China during 1998. Geophys. Res. Lett., 30, 1952, https://doi.org/10.1029/2003GL017817.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., D. Huang, Y. Zhang, X. Kuang, and Y. Huang, 2013: Decadal changes of Meiyu rainfall around 1991 and its relationship with two types of ENSO. J. Geophys. Res., 118, 97669777, https://doi.org/10.1002/jgrd.50779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, W., J. Pan, B. Zhou, and Y. Wang, 2016: Onset process of South China Sea summer monsoon in 2011 and its relationship with Meiyu over the middle and lower reaches of the Yangtze River (in Chinese). J. Nanjing Inst. Meteor., 39, 3745.

    • Search Google Scholar
    • Export Citation
  • Zhu, X., Z. Wu, and J. He, 2008: Anomalous Meiyu onset averaged over the Yangtze River valley. Theor. Appl. Climatol., 94, 8195, https://doi.org/10.1007/s00704-007-0347-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    The normalized daily 30–60-day-filtered precipitation anomalies from May to August averaged over the MLYRV (29°–35°N, 115°–120°E) in nine early mei-yu onset years (red line) and seven late mei-yu onset years (blue line). The specific early and late onset years are plotted on Fig. 3a.

  • Fig. 2.

    Phase-related composited normalized precipitation anomalies (shaded) and wind anomalies at 850 hPa (vectors; arrow above color bar indicates 1 m s−1) based on the 1979–2013 EAWNP ISO index during the boreal summer. Precipitation is computed from the 756 Chinese stations with daily data.

  • Fig. 3.

    Time series of normalized EAWNP ISO intensity (red line) and the normalized date of the mei-yu onset in MLYRV (blue line) averaged over (a),(b) summer (June–August) and (c),(d) spring (April–May). (a),(c) Annual values with the decadal change removed (mei-yu onset dates are the same in both panels and range from 2 June to 9 July, but dates do not exactly correspond to the normalized values because the decadal component is removed). (b),(d) Annual values on the decadal change time scale. In (b) and (d), the red line is the ISO intensity using the right scale, and the blue line is the decadal scale average mei-yu onset date using the left scale. The correlation coefficient r between the two annual time series is shown on each panel. The circles and the solid dots in (a) identify the seven late and nine early mei-yu onset years respectively (these years are used for Fig. 1).

  • Fig. 4.

    Monthly composited sea surface temperature anomalies (°C) from winter to summer in years with a strong EAWNP ISO index during the preceding spring (EAWNP ISO intensity > 0.8 standard deviations; 9 out of 35 years from 1979 to 2013, as identified in Fig. 3c). Shading indicates areas with SST anomalies above the 95% confidence level.

  • Fig. 5.

    The simultaneous regression of the sea surface temperature anomaly (°C) from winter to summer against the EP ENSO index for the same months from 1979 to 2013. Shading above the 95% confidence level has the same meaning as in Fig. 4.

  • Fig. 6.

    As in Fig. 4, but composite SST anomalies (°C) for years with preceding spring weak EAWNP ISO (EAWNP ISO intensity < −0.8 standard deviation; 11 out of 35 years from 1979 to 2013).

  • Fig. 7.

    As in Fig. 5, but showing simultaneous regression of the SST anomaly (°C) against the CP ENSO index for the same month from 1979 to 2013.

  • Fig. 8.

    The difference of Z500 (gpm) between the strong and weak spring intensity of EAWNP ISO from winter to summer [based on composites using the same years as in Fig. 4 (strong ISO) and Fig. 6 (weak ISO)]. Contour interval is 5 gpm with the zero contour omitted and negative contours dashed. Shading indicates grid points with Z500 differences above the 95% significance level.

  • Fig. 9.

    The regression of (a),(c) the vertical shear of zonal winds (m s−1) and (b),(d) the specific humidity (g kg−1) against (a),(b) the preceding winter (JF) EP ENSO and (c),(d) CP ENSO indices in May, based on all years 1979–2013. Anomalies above the 95% confidence level are shaded.

  • Fig. 10.

    Comparison between the normalized observed date of the mei-yu onset from 1979 to 2013 (red line) and corresponding hindcast normalized mei-yu onset dates using regression models described in the text (green, black, and blue lines). The correlation coefficients are shown in parentheses.

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