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  • View in gallery

    Lag–lead correlation of monthly-mean Niño-3.4 SST (solid curve) and NIO SST (dashed curve) with IMR(cmap) for the period 1979–2008.

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    Composite bimonthly-mean anomalies of (a) SST (°C) and (b) rainfall (mm day−1) and 10-m winds (m s−1, vector with the scale at top) for the DJF-only cases based on IMR(cmap). (top to bottom) DJ, FM, AM, JJ, and AS. Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test. Only wind vectors that are significant at the 90% confidence level are plotted.

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    As in Fig. 2, but for the JJAS-only cases from AM to DJ.

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    As in Fig. 2, but for the DJF&JJAS cases.

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    Composite JJAS rainfall anomalies (mm day−1) for the (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases based on IMR(cmap). Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

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    As in Fig. 2, but for (a) surface (2 m) air humidity (0.1 g kg−1) and (b) 500–200-hPa thickness (10 m).

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    Composite of normalized 3-month running mean anomalies of surface (2 m) air humidity (0.1 g kg−1) over the region of 0°–10°N and 50°–80°E (solid curves) and 500–200-hPa thickness difference (10 m) between area 20° and 40°N, 50° and 100°E and area 0° and 20°N, 50° and 100°E (dashed curves) in (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases based on IMR(cmap). Marks denote that the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

  • View in gallery

    Composite of normalized 3-month running mean anomalies of net surface shortwave radiation (W m−2, solid curve), surface latent heat flux (W m−2, dashed curve), and surface wind speed (0.1 m s−1, dotted curve) over the NIO in DJF-only cases based on IMR(cmap). Marks denote that the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

  • View in gallery

    Composite JJAS mean anomalies of velocity potential (10−6 s−1) and the corresponding divergent winds (m s−1) at (a),(c) 200 and (b),(d) 850 hPa for the (a),(b) JJAS-only and (c),(d) DJF&JJAS cases based on IMR(cmap). Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test. Scales for (left) 200 and (right) 850 hPa winds.

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    Lag–lead correlation of monthly-mean Niño-3.4 SST (solid curves) and NIO SST (dashed curves) with IMR(land) for the period (a) 1979–2008 and (b) 1948–77.

  • View in gallery

    Composite of normalized 3-month running mean anomalies of Niño-3.4 SST (solid curves), NIO SST (dashed curves), and IMR(land) (dotted curves) in (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases for the period 1901–2008 based on IMR(land) and HadISST1.1. Marks denote that the composite anomalies are significant at the 95% confidence level according to a 1-sample Student’s t test.

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    As in Fig. 7, but for the period 1948–2008 based on IMR(land) and HadISST1.1. Marks denote that the composite anomalies are significant at the 95% confidence level according to a 1-sample Student’s t test.

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    Moving correlation with a 21-yr window between JJAS Niño-3.4 SST and JJAS IMR (black curves), DJF Niño-3.4 SST and JJAS IMR (red curves), MAM NIO SST and JJAS IMR (green curves), and DJF Niño-3.4 SST and MAM NIO SST (blue curves) based on (a) ERSST3 and (b) HadISST1.1.

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Different Types of ENSO Influences on the Indian Summer Monsoon Variability

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

Observational analysis reveals three types of El Niño–Southern Oscillation (ENSO) influences on the Indian summer monsoon (ISM): indirect influence of the preceding winter [December–February (DJF)] eastern equatorial Pacific (EEP) sea surface temperature (SST) anomalies (DJF-only cases), direct influence of the concurrent summer [June–September (JJAS)] EEP SST anomalies (JJAS-only cases), and coherent influence of both the preceding winter and concurrent summer EEP SST anomalies (DJF&JJAS cases). The present study distinguishes the three types of ENSO influences and investigates the processes connecting ENSO to the ISM separately.

In the DJF-only cases, the preceding winter EEP SST anomalies induce north Indian Ocean (NIO) SST anomalies through air–sea interaction processes in the tropical Indian Ocean. The SST anomalies over the western Indian Ocean alter the surface air humidity there. Both processes favor an anomalous ISM. In the JJAS-only cases, an anomalous ISM is directly induced by ENSO through large-scale circulation changes. The meridional thermal contrast may also contribute to an anomalous ISM. In the DJF&JJAS cases, the preceding winter EEP SST anomalies induce NIO SST anomalies and change the surface air humidity over the western Indian Ocean. Concurrent summer EEP SST anomalies induce large-scale vertical motion anomalies over South Asia. Together, they lead to an anomalous ISM. The anomalous meridional thermal contrast may contribute to an anomalous ISM in late summer.

Impacts of the preceding winter EEP SST anomalies in the DJF and JJAS cases may contribute to the contemporaneous correlation between ISM and EEP SST. There are more DJF&JJAS cases before than after the late 1970s. This provides an alternative interpretation for the observed weakening in the ISM–ENSO relationship around the late 1970s.

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

Abstract

Observational analysis reveals three types of El Niño–Southern Oscillation (ENSO) influences on the Indian summer monsoon (ISM): indirect influence of the preceding winter [December–February (DJF)] eastern equatorial Pacific (EEP) sea surface temperature (SST) anomalies (DJF-only cases), direct influence of the concurrent summer [June–September (JJAS)] EEP SST anomalies (JJAS-only cases), and coherent influence of both the preceding winter and concurrent summer EEP SST anomalies (DJF&JJAS cases). The present study distinguishes the three types of ENSO influences and investigates the processes connecting ENSO to the ISM separately.

In the DJF-only cases, the preceding winter EEP SST anomalies induce north Indian Ocean (NIO) SST anomalies through air–sea interaction processes in the tropical Indian Ocean. The SST anomalies over the western Indian Ocean alter the surface air humidity there. Both processes favor an anomalous ISM. In the JJAS-only cases, an anomalous ISM is directly induced by ENSO through large-scale circulation changes. The meridional thermal contrast may also contribute to an anomalous ISM. In the DJF&JJAS cases, the preceding winter EEP SST anomalies induce NIO SST anomalies and change the surface air humidity over the western Indian Ocean. Concurrent summer EEP SST anomalies induce large-scale vertical motion anomalies over South Asia. Together, they lead to an anomalous ISM. The anomalous meridional thermal contrast may contribute to an anomalous ISM in late summer.

Impacts of the preceding winter EEP SST anomalies in the DJF and JJAS cases may contribute to the contemporaneous correlation between ISM and EEP SST. There are more DJF&JJAS cases before than after the late 1970s. This provides an alternative interpretation for the observed weakening in the ISM–ENSO relationship around the late 1970s.

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

1. Introduction

The El Niño–Southern Oscillation (ENSO) and the Indian summer monsoon (ISM) are two interacting components in the climate system (Webster and Yang 1992). On one hand, the ISM variability is largely affected by ENSO (Webster et al. 1998). On the other hand, the ISM can feed back onto ENSO through modulating western equatorial Pacific winds (Meehl 1997; Chung and Nigam 1999; Kirtman and Shukla 2000; Wu and Kirtman 2003). The present study focuses on the influence of ENSO on ISM.

ENSO can influence ISM directly via large-scale circulation changes over the Indo-western Pacific that suppress or enhance ISM (Webster and Yang 1992; Chen and Yen 1994; Yang and Lau 1998; Lau and Nath 2000; Wang et al. 2001). ENSO can also indirectly lead to an anomalous ISM by modulating the north Indian Ocean (NIO) sea surface temperature (SST) (Kawamura et al. 2001; Wu and Kirtman 2004), the meridional thermal contrast over South Asia (Yang and Lau 1998; Kawamura 1998), the atmospheric moisture content over the tropical Indian Ocean (Ashok et al. 2004; Wu and Kirtman 2004), and the latitudinal position of the intertropical convergence zone (ITCZ) over Indonesia in the spring (Ju and Slingo 1995). Park et al. (2010) suggested that stronger low-level moisture transport and reduced moist stability associated with a warmer NIO following the 1982/83 and 1997/98 strong El Niño events increase the Indian monsoon rainfall despite a weakened monsoon circulation, thus providing a delayed effect of El Niño on the Indian monsoon rainfall (Shukla 1995; Webster et al. 1998). The relationship between the Pacific SST and ISM is different between the two types of influences. The overall correlation results from a combined effect of various processes. As such, correlation or composite analyses without distinguishing different types of ENSO influences may not be able to reveal a clear signal. Can a clear ENSO signal be identified by separating the different types of ISM–ENSO connections? One purpose of the present study is to distinguish the different types of connections between ENSO and ISM and examine their relative contribution to the overall ISM–ENSO correlation.

The NIO SST anomalies can be induced by ENSO through an atmospheric bridge (Klein et al. 1999; Alexander et al. 2002) and regional atmosphere–ocean interaction processes in the tropical Indian Ocean (Kawamura et al. 2001; Wu and Kirtman 2004; Wu et al. 2008; Wu 2009; Wu and Yeh 2010). The ENSO-induced NIO SST anomalies in turn can contribute to the ISM variability. Thus, another purpose of this study is to examine the role of NIO SST anomalies in different types of ISM–ENSO connections.

The ISM–ENSO relationship has experienced an obvious weakening in the late 1970s (e.g., Kripalani and Kulkarni 1999; Krishna Kumar et al. 1999; Krishnamurthy and Goswami 2000). Previous studies have attributed this weakening to mean state change (Krishna Kumar et al. 1999; Chang et al. 2001). Krishna Kumar et al. (1999) attributed it to the global-warming-induced shift in anomalous Walker circulation and an increase in surface temperature over Eurasia. Chang et al. (2001) attributed it to the strengthening and poleward shift of the jet stream over the North Atlantic. Ashok et al. (2001, 2004) indicated the impact of the Indian Ocean dipole (IOD) on the ISM–ENSO relationship. Some studies suggested that the change in the SST anomaly pattern in the equatorial Pacific may affect the ISM–ENSO relationship (Krishna Kumar et al. 2006; Ashok et al. 2007). Stochastic processes may also contribute to the interdecadal change in the ISM–ENSO relationship (Gershunov et al. 2001; Wu and Kirtman 2003). The third purpose of this study is to examine whether the weakening of the ISM–ENSO relationship after the late 1970s can be linked to changes in the relative frequency of different types of ISM–ENSO connections.

The organization of the text is as follows. Section 2 describes the datasets used in the present study. In section 3, we reveal different types of ENSO influences on ISM through an examination of the correspondence of the ISM index with eastern equatorial Pacific (EEP) SST anomalies in the preceding winter and concurrent summer. In section 4, we separate the different types of ISM–ENSO connections and examine the temporal and spatial evolution of the composite anomalies for each type. The processes for connecting ENSO to ISM are then investigated in section 5. In section 6, we perform an analysis using long-record data for validation. The interdecadal change in the number of different types of ISM–ENSO connections is then examined in section 7. Section 8 presents the summary and discussions.

2. Datasets

The Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) (Xie and Arkin 1997) is used as a proxy for rainfall observations. The CMAP rainfall analysis is on a 2.5° × 2.5° grid and is available starting from January 1979. The observed all-Indian station rainfall from June through September (JJAS) (Parthasarathy et al. 1993) for the period 1871–2008 is used in the present study. This rainfall is obtained through the Web page of the Indian Institute of Tropical Meteorology (www.tropmet.res.in).

The present study uses SST from the National Oceanic and Atmospheric Administration (NOAA)’s extended reconstructed SST, version 3 (ERSST3) (Smith et al. 2008), which is provided by NOAA’s Office of Oceanic and Atmospheric Research (OAR) Earth System Research Laboratory (ESRL) Physical Science Division (PSD), Boulder, Colorado, from its Web site (www.cdc.noaa.gov/). This SST dataset has a resolution of 2.0° × 2.0° and is available from 1854 to the present. This study also uses SST from the Met Office Hadley Centre Sea Ice and Sea Surface Temperature version 1.1 (HadISST1.1) (Rayner et al. 2003), available online (www.metoffice.gov.uk/hadobs/hadisst). The HadISST dataset has a resolution of 1.0° × 1.0° and is available from 1870 to the present.

Surface heat fluxes used in the present study are from the National Oceanography Centre Southampton (NOCS) surface flux dataset version 2.0 (Berry and Kent 2009), which is a monthly-mean gridded dataset of marine surface measurements and derived fluxes constructed using optimal interpolation. The NOCS surface fluxes are available on a 1° × 1° grid from 1973 to the present. The data are obtained from the Research Data Archive (RDA), which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). The bias of input variables, the bias adjustment, and the uncertainty of the flux output are discussed in detail in Berry and Kent (2009).

The present study uses monthly-mean atmospheric variables from the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis version 2 (Kanamitsu et al. 2002) for the period 1979–2008 and from NCEP–NCAR (Kalnay et al. 1996) for the period 1948–2008. The analysis includes surface (10 m) winds and surface (2 m) air humidity on a T62 Gauss grid (about 1.9° × 1.9°) and winds at 850 and 200 hPa on a regular 2.5° × 2.5° grid. The reanalysis product is provided by the NOAA–Cooperative Institute for Research in Environmental Sciences (CIRES) Climate Diagnostics Center, Boulder, Colorado, and is obtained online (ftp://ftp.cdc.noaa.gov/).

3. Types of ISM–ENSO associations

The Indian monsoon rainfall (IMR) is represented using JJAS rainfall averaged over the region of 5°–25°N, 60°–100°E based on CMAP, which, for brevity, is denoted as IMR(cmap). The criterion for an anomalous ISM is that the magnitude of IMR anomalies exceeds the 0.7 standard deviation. While the choice of this criterion is subjective, we note that an analysis using the 0.5 standard deviation as the criterion has obtained similar results. The Niño-3.4 (5°S–5°N, 170°–120°W) SST is used as an index for ENSO. The criterion for ENSO follows the CPC monitoring, that is, the magnitude of 3-month running mean Niño-3.4 SST anomalies with respect to the climatology for the period 1971–2000 exceeding 0.5°C for a minimum of 5 consecutive 3-month running means. The NIO SST is represented using the area mean over the region of 5°–25°N, 60°–100°E. For convenience, we denote the season in the same year as the ISM using a suffix “(0).” For example, DJF(0) denotes the preceding December–February (DJF) and JJAS(0) the concurrent summer.

Figure 1 shows the lag correlation of monthly-mean Niño-3.4 and NIO SST with respect to IMR(cmap). A negative correlation is seen for Niño-3.4 SST from the simultaneous summer to the following winter, indicating a weak (strong) ISM in El Niño (La Niña) developing years. The correlation is weaker than previous studies based on a longer record, including the period before 1970s (Yasunari 1990; Kirtman and Shukla 2000), which is because of the weakening of the ISM–ENSO relationship around the late 1970s (e.g., Krishna Kumar et al. 1999; Krishnamurthy and Goswami 2000). A positive correlation is seen for the Niño-3.4 SST in the preceding winter. NIO SST shows a positive correlation during late spring and summer. The correlation changes to negative in fall, suggesting a negative feedback of ISM on the NIO SST (Rao and Goswami 1988; Meehl 1997; Lau and Nath 2000; Krishnamurthy and Kirtman 2003; Wang et al. 2003; Wu and Kirtman 2004, 2007).

Fig. 1.
Fig. 1.

Lag–lead correlation of monthly-mean Niño-3.4 SST (solid curve) and NIO SST (dashed curve) with IMR(cmap) for the period 1979–2008.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

To further understand the influence of ENSO on ISM, we have compared the year-to-year variations of IMR(cmap) and Niño-3.4 SST in DJF(0) and JJAS(0). Detailed inspection of these variations reveals three types of associations between Niño-3.4 SST and IMR. In the first type, a positive (negative) DJF(0) Niño-3.4 SST anomaly is followed by a positive (negative) IMR anomaly with the requirement that the JJAS(0) Niño-3.4 SST anomaly is not opposite of the IMR anomaly to distinguish from the third type of relationship mentioned below. For simplicity, this type of association is denoted as DJF-only cases. In the second type, a positive (negative) JJAS(0) Niño-3.4 SST anomaly is accompanied by a negative (positive) IMR anomaly with the condition that the DJF(0) Niño-3.4 SST anomaly is not of the same sign as the IMR anomaly. This type of association is denoted as JJAS-only cases. In the third type, the Niño-3.4 SST anomaly switches from positive (negative) in DJF(0) to negative (positive) in JJAS(0) and the IMR anomaly is positive (negative). This type of association is denoted as DJF&JJAS cases. In the DJF-only cases, Niño-3.4 SST anomalies decrease or maintain from DJF(0) to JJAS(0), indicating that ENSO either decays or persists. In the JJAS-only cases, Niño-3.4 SST anomalies increase or maintain from DJF(0) to JJAS(0), suggesting that ENSO either develops or persists. In the DJF&JJAS cases, ENSO switches its phase during the half-year period. The number of cases for the above-mentioned 3 types of years is listed in Table 1. There are 6 DJF-only cases, 4 JJAS-only cases, and 3 DJF&JJAS cases for the period 1979–2008.

Table 1.

Cases for different types of ISM–ENSO relationships. The first, second, and third rows are for the cases based on IMR(cmap) for the period 1979–2008, IMR(land) for the period 1979–2008, and IMR(land) for the period 1948–77, respectively.

Table 1.

Note that there are other anomalous ISM years that have occurred in the absence of ENSO. For these cases, other factors, such as internal atmospheric dynamics (Goswami 1998), may play important roles. There are also ENSO years that are not accompanied by an anomalous ISM. In the present study, we focus on the cases when ENSO is accompanied by an ISM for understanding the processes for the ENSO influence. We will not discuss why in some cases ENSO does not lead to an anomalous ISM or why an anomalous ISM occurs without ENSO.

4. Temporal–spatial evolution of composite anomalies

To understand the processes by which ENSO influences the ISM in different types of years, we examine the temporal evolution of composite anomalies of SST, rainfall, and surface winds. In constructing the composite, we have reversed the anomalies in the years when the IMR anomaly is negative and group these together with the years when the IMR anomaly is positive. The statistical significance of composite anomalies is determined based on a 1-sample Student’s t test. The obtained composites are shown in Fig. 2 for the DJF-only cases, Fig. 3 for the JJAS-only cases, and Fig. 4 for the DJF&JJAS cases. The following description refers to the cases when the IMR anomaly is positive, but it is applicable to the cases when the IMR anomaly is negative except for a reversal in the sign of the anomalies.

Fig. 2.
Fig. 2.

Composite bimonthly-mean anomalies of (a) SST (°C) and (b) rainfall (mm day−1) and 10-m winds (m s−1, vector with the scale at top) for the DJF-only cases based on IMR(cmap). (top to bottom) DJ, FM, AM, JJ, and AS. Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test. Only wind vectors that are significant at the 90% confidence level are plotted.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for the JJAS-only cases from AM to DJ.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

Fig. 4.
Fig. 4.

As in Fig. 2, but for the DJF&JJAS cases.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

In the DJF-only cases, positive SST anomalies in the EEP weaken in December–January (DJ) and in August–September (AS) (Fig. 2a). In the NIO, positive SST anomalies are seen in summer. In February–March (FM) and April–May (AM), tropical Indian Ocean SST anomalies are positive south of the equator and weak north of the equator, forming a cross-equatorial SST gradient. After summer, SST anomalies decrease in the NIO. Above-normal rainfall and anomalous westerlies over the equatorial central Pacific weaken from winter to summer (Fig. 2b), which is consistent with the changes in the tropical Pacific SST anomalies. In the tropical Indian Ocean, rainfall and wind anomalies display obvious changes during the period. In DJ, an east–west contrast of rainfall anomalies is accompanied by anomalous easterly winds near the equator. A north–south asymmetric rainfall and wind anomalies develop in FM and mature in AM. In June–July (JJ), above-normal rainfall is seen over the Arabian Sea, which is accompanied by anomalous easterly winds. In AS, above-normal rainfall is observed over the Indian subcontinent and the Bay of Bengal, accompanied by an anomalous cyclone over the Arabian Sea. Note that the northeast Indian rainfall anomalies in summer tend to be different from other Indian regions.

In the JJAS-only cases, negative SST anomalies increase in the EEP (Fig. 3a). NIO SST anomalies are weak in spring and negative SST anomalies develop in AS. There are negative rainfall and easterly anomalies over the equatorial Pacific (Fig. 3b), typical of those in La Niña years. These negative rainfall anomalies move eastward from JJ to DJ. Positive rainfall anomalies increase over the Maritime Continent from JJ to October–November (ON). In JJ, positive rainfall anomalies dominate the Arabian Sea, the Indian subcontinent, and the Bay of Bengal. In AS, positive rainfall anomalies are mainly in the Bay of Bengal and central India and negative rainfall anomalies appear to extend from the head of the Bay of Bengal to northern India. Negative rainfall anomalies develop over the tropical Indian Ocean in ON.

In the DJF&JJAS cases, SST anomalies in the EEP switch from positive in winter to negative in summer (Fig. 4a) by definition. Positive SST anomalies are maintained in the tropical Indian Ocean during the period. Rainfall and wind anomalies over the tropical Pacific (Fig. 4b) display notable changes, following the switch of EEP SST anomalies. Positive rainfall anomalies are seen over the eastern Arabian Sea and western India in JJ and extend to cover most of India in AS.

The rainfall anomalies in the ISM region display noticeable differences between early summer (JJ) and late summer (AS). For example, in the JJAS-only cases and in the mean monsoon trough region extending from the head of the Bay of Bengal to northwest India, the rainfall is below normal in AS but above normal is JJ (Fig. 3b). To understand which one (JJ or AS) is dominant for the whole summer (JJAS), we show composite rainfall anomalies for JJAS (Fig. 5). A comparison with Figs. 2b, 3b, and 4b indicates that the late summer rainfall anomalies have a relatively larger contribution to the whole summer rainfall anomalies in the DJF-only cases and DJF&JJAS cases, whereas the early summer rainfall anomalies have a relatively larger contribution in the JJAS-only cases.

Fig. 5.
Fig. 5.

Composite JJAS rainfall anomalies (mm day−1) for the (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases based on IMR(cmap). Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

Previous studies indicated the role of surface moisture over the tropical Indian Ocean (Wu and Kirtman 2004) and the meridional thermal contrast over South Asia (Kawamura 1998; Kawamura et al. 2001) in the ISM variability. Here, we examine composite anomalies of surface (2 m) air humidity and upper-tropospheric (500–200 hPa) thickness for the different types of years. Figure 6 shows composite anomalies for the DJF-only cases. There are positive surface air humidity anomalies over the western Indian Ocean in AM and JJ (Fig. 6a). The thickness anomalies are positive in the tropical Indian Ocean and negative in subtropical Asia during DJ–JJ (Fig. 6b). As such, the land–sea thermal contrast anomaly is negative over South Asia. One feature to note is that the negative thickness anomalies move westward over the subtropics. This feature has been pointed out in previous studies (Liu and Yanai 2001; Miyakoda et al. 2003).

Fig. 6.
Fig. 6.

As in Fig. 2, but for (a) surface (2 m) air humidity (0.1 g kg−1) and (b) 500–200-hPa thickness (10 m).

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

The surface moisture and thermal contrast is further compared in Fig. 7. The surface air humidity shown in these figures is the area-mean value over the western Indian Ocean (WIO; 0°–10°N, 50°–80°E), following Wu and Kirtman (2004). This region is chosen because the increased moisture in this region may be transported to the ISM domain by mean southwesterly winds and thus may contribute to a stronger ISM. The thermal contrast is represented using a 500–200-hPa thickness difference between the ranges 20°–40°N, 50°–100°E and 0°–20°N, 50°–100°E, following Kawamura (1998), which is denoted as IMI(thick).

Fig. 7.
Fig. 7.

Composite of normalized 3-month running mean anomalies of surface (2 m) air humidity (0.1 g kg−1) over the region of 0°–10°N and 50°–80°E (solid curves) and 500–200-hPa thickness difference (10 m) between area 20° and 40°N, 50° and 100°E and area 0° and 20°N, 50° and 100°E (dashed curves) in (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases based on IMR(cmap). Marks denote that the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

In the DJF-only cases (Fig. 7a), negative thickness difference anomalies persist from winter to summer. There are positive anomalies of surface air humidity in the WIO, indicating an increase in the moisture availability upstream of the ISM region. In the JJAS-only cases (Fig. 7b), the thickness difference anomalies are positive in summer. The largest thickness difference anomalies lag the rainfall anomalies, indicating that the thermal contrast anomaly may be a result of anomalous heating associated with the enhanced monsoon rainfall (Liu and Yanai 2001). Surface air humidity anomalies are small. In the DJF&JJAS cases (Fig. 7c), the thickness difference anomalies are negative in winter and spring and positive after July. As in the JJAS-only cases, the thermal contrast anomaly itself may be induced by anomalous monsoon heating (Liu and Yanai 2001). Surface air humidity anomalies in the WIO are large and positive from winter to early summer.

5. Processes for the influence of ENSO on the ISM

As reviewed in the introduction, ENSO can influence the ISM via atmospheric circulation change or by modulating the NIO SST, the moisture content over the tropical Indian Ocean, and the land–sea thermal contrast over South Asia. This section discusses the processes of the ENSO influences corresponding to the different types of relationships separately.

a. DJF-only cases

In the DJF-only cases, the EEP SST anomalies in summer are still positive (Fig. 2a) and thus they are not favorable to the above-normal ISM. The NIO SST anomalies are positive in JJ and AS (Fig. 2a) and thus may contribute to above-normal ISM (Fig. 2b). According to Lindzen and Nigam (1987), the positive SST anomalies and associated gradients can reduce the surface pressure and induce lower-level convergence over the NIO. Indeed, lower-level anomalous convergence dominates over the Arabian Sea and the Indian subcontinent, as indicated by the decrease of easterlies from the western Bay of Bengal to the Arabian Sea in JJ and the convergence of westerlies and easterlies over the eastern Arabian Sea and western India in AS (Fig. 2b). This suggests that the influence of ENSO on ISM is likely through NIO SST and that concurrent EEP SST anomalies have an opposing effect.

The anomalies in the tropical Indian Ocean in the DJF-only cases are similar to those of the asymmetric mode (Wu et al. 2008). As discussed in detail in Wu et al. (2008) and Wu (2009), anomalous northwesterly winds over the southwestern tropical Indian Ocean (DJ and FM) (Fig. 2b) oppose the mean winds, leading to a reduction in surface wind speed and latent heat flux. This contributes to the SST warming in the southwestern tropical Indian Ocean and the formation of a cross-equatorial SST gradient (FM and AM) (Fig. 2a). The SST gradient then induces asymmetric rainfall and wind anomalies in AM (Fig. 2b). Warm SST anomalies develop in the NIO in JJ (Fig. 2a) due to enhanced incoming shortwave radiation associated with suppressed cloudiness and suppressed latent heat flux associated with reduced surface wind speed (AM). In turn, the warm SST anomalies lead to lower-level convergence and above-normal rainfall in JJ and AS.

To understand how NIO SST anomalies are induced, we show area-mean surface heat flux anomalies averaged over the NIO for the DJF-only cases (Fig. 8). There is more incoming shortwave radiation in relation to suppressed precipitation during winter through early summer. The suppression of precipitation is linked to the remote forcing from the Pacific Ocean and the asymmetric mode (Fig. 2b). The upward surface latent heat is reduced because of the weakened surface winds in spring and early summer, as anomalous easterlies oppose mean westerly winds. These surface heat flux anomalies contribute to the NIO warming in spring (Fig. 2a). The incoming shortwave radiation decreases in association with above-normal rainfall and upward surface latent heat flux increase in late summer and fall, likely because of both enhanced surface winds and warmer SST. These changes contribute to the decrease of the SST anomalies in the NIO after summer.

Fig. 8.
Fig. 8.

Composite of normalized 3-month running mean anomalies of net surface shortwave radiation (W m−2, solid curve), surface latent heat flux (W m−2, dashed curve), and surface wind speed (0.1 m s−1, dotted curve) over the NIO in DJF-only cases based on IMR(cmap). Marks denote that the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

It is known that surface heat fluxes include large uncertainty. To estimate the impact of this uncertainty, we have performed a composite analysis using the total bias estimation provided by the NOCS flux dataset. The obtained shortwave radiation anomaly bias is about −0.5 to −1.5 W m−2, and the latent heat flux anomaly bias fluctuates between +1.5 and −1.0 W m−2 in the NIO region. These values are much smaller than peak shortwave radiation and latent heat flux composite anomalies seen in Fig. 8.

There are positive surface air humidity anomalies over the WIO in AM and JJ (Figs. 6a and 7a). The increase in surface air humidity may be induced by positive SST anomalies during late spring and early summer (Fig. 2a). Indeed, the temporal evolutions of surface air humidity and SST anomalies in the WIO are quite consistent (Figs. 6a and 2a). The larger amount of air humidity increases the moisture transport to India by southwesterly winds in summer and thus favors a stronger ISM.

The thickness anomalies are positive in the tropics and negative in the subtropics (Fig. 6b), leading to a negative thermal contrast anomaly. The positive thickness anomalies in the tropical Indian Ocean are related to the Pacific Ocean warming that induces tropics-wide tropospheric warming through thermodynamic adjustment (e.g., Chiang and Sobel 2002). The negative thickness anomalies over the subtropics appears as a Rossby wave–type response to suppressed heating over the western North Pacific–Bay of Bengal, induced by warm EEP SST anomalies (Kawamura 1998; Kawamura et al. 2003). Thus, the thermal contrast does not contribute to the above-normal ISM.

b. JJAS-only cases

In the JJAS-only cases, both EEP and NIO SST anomalies are negative in summer (Fig. 3a). Thus, the above-normal ISM cannot be explained by local negative SST anomalies. Instead, they are likely induced by a direct SST forcing in the Pacific Ocean through large-scale circulation changes. The influence of ENSO on the ISM through the planetary-scale circulation change has been known since the days of Walker (1924). Many previous studies have applied this to explain the ENSO–ISM connection (e.g., Webster and Yang 1992; Ju and Slingo 1995; Lau and Nath 2000). Indeed, the composite velocity potential and divergent wind anomalies show upper-level convergence and lower-level divergence over the equatorial central Pacific, and upper-level divergence and lower-level convergence over the South Asia–tropical Indian Ocean–Maritime Continent–Australia region (Figs. 9a and 9b).

Fig. 9.
Fig. 9.

Composite JJAS mean anomalies of velocity potential (10−6 s−1) and the corresponding divergent winds (m s−1) at (a),(c) 200 and (b),(d) 850 hPa for the (a),(b) JJAS-only and (c),(d) DJF&JJAS cases based on IMR(cmap). Thick contours denote regions where the composite anomalies are significant at the 90% confidence level according to a 1-sample Student’s t test. Scales for (left) 200 and (right) 850 hPa winds.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

Surface air humidity anomalies over the WIO are small (Fig. 7b), which appears to be related to local negative SST anomalies (Fig. 3a). As such, the moisture change is not a factor for the ISM. Positive thickness difference anomalies are seen in summer and fall (Fig. 7b). Thus, the thermal contrast anomaly, after being induced by anomalous monsoonal heating, may, in turn, contribute to an anomalous ISM in late summer.

c. DJF&JJAS cases

In the DJF&JJAS cases, EEP SST anomalies switch from positive in winter to negative in summer (Fig. 4a). NIO SST anomalies are positive from winter to summer. Thus, both the preceding and concurrent EEP SST anomalies may contribute to the strong ISM. Preceding positive EEP SST anomalies induce positive NIO SST anomalies through atmospheric circulation change (Klein et al. 1999). Positive SST anomalies in the NIO reduce surface pressure (Lindzen and Nigam 1987). Indeed, negative sea level pressure anomalies develop in AM over the WIO and extend to India and the Bay of Bengal in JJ and AS (figure not shown). However, lower-level convergence displays large spatial variations (not shown), which is probably related to the weakness of the SST gradient (Fig. 4a) (Lindzen and Nigam 1987; Soman and Slingo 1997). Negative SST anomalies in the EEP force lower-level divergence and upper-level convergence over the tropical Pacific, which leads to upper-level divergence over the South Asia–tropical Indian Ocean–Australia region (Figs. 9c and 9d). This signifies the remote forcing of EEP SST on the ISM.

Large and positive surface air humidity anomalies are seen in the WIO (Fig. 7c), which is consistent with positive SST anomalies (Fig. 4a). The large increase in moisture availability contributes importantly to an anomalous ISM. Positive thermal contrast anomalies are seen after July (Fig. 7c). Thus, the contribution of the thermal contrast to an anomalous ISM may be limited to the late summer.

6. Analysis of long-record data

One weakness of the analyses in the previous sections is that the number of cases for each type of relationship is limited during the analysis period. To validate these results, we perform an analysis with available long-record Indian land rainfall and reconstruction SST. Before the analysis using long-record data, we perform a comparative analysis using the land rainfall for the same period as CMAP rainfall to examine whether the results based on CMAP rainfall can also be obtained based on land-only rainfall. We use JJAS all-Indian rainfall based on land-only stations as an alternative for the IMR, which, for brevity, is denoted as IMR(land). The correlation coefficient between IMR(cmap) and IMR(land) is 0.61 for the period 1979–2008, which is significant at the 99% confidence level.

Figure 10a shows the lag correlation of monthly-mean Niño-3.4 and NIO SST with respect to IMR(land). Similar to IMR(cmap), a negative correlation is seen for Niño-3.4 SST from summer to winter. A positive correlation is seen for the Niño-3.4 SST in the preceding winter, but it is weaker compared to IMR(cmap). Thus, the ocean rainfall has a better positive correlation with the preceding winter Niño-3.4 SST compared to the land rainfall. The correlation of NIO SST with IMR(land) is weak during spring and summer and changes to negative in fall. The difference of correlation between IMR(cmap) and IMR(land) in late spring and summer suggests that NIO SST influences the ocean rainfall, but its influence on the land rainfall is relatively weak, consistent with Meehl and Arblaster (2002).

Fig. 10.
Fig. 10.

Lag–lead correlation of monthly-mean Niño-3.4 SST (solid curves) and NIO SST (dashed curves) with IMR(land) for the period (a) 1979–2008 and (b) 1948–77.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

The number of cases for the 3 types of years based on IMR(land) are listed in the second row of Table 1. There are 3 DJF-only cases, 4 JJAS-only cases, and 2 DJF&JJAS cases. In comparison, there are more DJF-only cases for IMR(cmap) than for IMR(land) (6 vs 3), which is consistent with the difference seen in the correlation between DJF(0) Niño-3.4 SST and JJAS(0) IMR. We have performed the composite analysis based on the cases for IMR(land). The results are very similar to those based on IMR(cmap) though the number of cases are different. Briefly, in the DJF-only cases, positive NIO SST anomalies and positive WIO humidity anomalies are seen in late spring–summer, negative thermal contrast anomalies are seen during the preceding winter–early summer; in the JJAS-only cases, negative NIO SST anomalies are seen in summer, humidity anomalies in the WIO are small, and positive thermal contrast anomalies are observed in summer; in the DJF&JJAS cases, positive NIO SST anomalies and positive WIO humidity anomalies maintain from the preceding spring to summer, and positive thermal contrast anomalies are present in late summer.

For the analysis using long-record data for the period 1901–2008, we have applied the harmonic analysis to remove the variations with periods longer than 8 yr. This is necessary since the Indian Ocean SST shows a long-term warming trend. From inspection of year-to-year variations of the Niño-3.4 SST and IMR(land), we identify different types of years as in the recent period. The cases for the 3 types of relationships are listed in Table 2 for both ERSST3 and HadISST1.1. Figure 11 shows the temporal evolution of composite IMR(land), Niño-3.4 SST, and NIO SST anomalies based on HadSST1.1. The results based on ERSST3 are very similar though there are differences in the cases (Table 2).

Table 2.

Cases for different types of ISM–ENSO relationship for the period 1901–2008 based on filtered data. The first and second rows are for the cases based on SST from ERSST3 and HadISST1.1, respectively.

Table 2.
Fig. 11.
Fig. 11.

Composite of normalized 3-month running mean anomalies of Niño-3.4 SST (solid curves), NIO SST (dashed curves), and IMR(land) (dotted curves) in (a) DJF-only cases, (b) JJAS-only cases, and (c) DJF&JJAS cases for the period 1901–2008 based on IMR(land) and HadISST1.1. Marks denote that the composite anomalies are significant at the 95% confidence level according to a 1-sample Student’s t test.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

In the DJF-only cases (Fig. 11a), positive Niño-3.4 SST anomalies in the preceding winter are followed by positive NIO SST anomalies during spring through early summer. This indicates that the preceding EEP SST anomalies may affect the ISM through NIO SST changes. In the JJAS-only cases (Fig. 11b), the NIO SST anomalies are negative in summer and thus are not favorable for the ISM. In these cases, negative EEP SST anomalies may directly induce an anomalous ISM via atmospheric circulation changes. In the DJF&JJAS cases (Fig. 11c), positive NIO SST anomalies are seen during winter and spring. This suggests the contribution of the preceding winter positive EEP SST anomalies to an anomalous ISM through the NIO SST change. The concurrent negative EEP SST anomalies may contribute to an anomalous ISM via direct atmospheric circulation change. Thus, both preceding and concurrent EEP SST anomalies appear to contribute to an anomalous ISM.

A corresponding temporal evolution of composite thermal contrast and surface humidity based on the filtered data for the period 1948–2008 is shown in Fig. 12. In the DJF-only cases (Fig. 12a), positive specific humidity anomalies are seen during the preceding winter through early summer, which is a favorable condition for a stronger ISM. The thermal contrast anomalies are negative in spring and early summer and positive but weak in late summer, and thus the contribution of thermal contrast appears small. In the JJAS-only cases (Fig. 12b), positive thermal contrast anomalies are seen in summer, indicating the contribution of thermal contrast to the ISM. Surface humidity anomalies are small before and during summer. In the DJF&JJAS cases (Fig. 12c), surface humidity anomalies are positive during winter through early summer, suggesting an increase of the moisture availability. Positive thermal contrast anomalies are seen in and after August, and thus the contribution of thermal contrast to an anomalous ISM is only seen in late summer.

Fig. 12.
Fig. 12.

As in Fig. 7, but for the period 1948–2008 based on IMR(land) and HadISST1.1. Marks denote that the composite anomalies are significant at the 95% confidence level according to a 1-sample Student’s t test.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

Note that there are notable differences of thermal contrast anomaly between Figs. 7 and 12. In the DJF only cases, the negative thermal contrast anomaly from the preceding winter to early summer in Fig. 12 is not as significant as in Fig. 7. In the JJAS-only cases and the DJF&JJAS cases, the positive thermal contrast anomaly appears earlier in Fig. 12 compared to Fig. 7. There are two factors that may have contributed to the above-mentioned discrepancies. One is the difference of the index for IMR: IMR(cmap) in Fig. 7 and IMR(land) in Fig. 12. The other is the difference of the period for the analysis: 1979–2008 in Fig. 7 and 1948–2008 in Fig. 12. To understand which of the two factors contributes more to the above-mentioned discrepancies, we have made composites similar to Fig. 7 but based on the IMR(land) for 1949–77 and 1979–2008, respectively (figures not shown).

A comparison shows that a positive thermal contrast anomaly in JJAS-only and DJF&JJAS cases during 1979–2008 appears one month earlier in the composite based on IMR(land) compared to the composite based on IMR(cmap). This suggests that a larger impact of a thermal contrast on land IMR than ocean IMR, consistent with Meehl and Arblaster (2002). The evolution of composite thermal contrast anomalies based on IMR(land) shows a large difference between 1948–77 and 1979–2008. During 1948–77, in the DJF-only cases, a positive thermal contrast anomaly appears in July–September and a negative thermal contrast anomaly is not seen during the preceding winter and spring. In the JJAS-only cases, a large positive thermal contrast anomaly starts in April. In the DJF&JJAS cases, a positive thermal contrast anomaly starts in May. Thus, it appears that the discrepancies between Figs. 7 and 12 have a larger contribution from the interdecadal change.

The above comparison indicates that the role of the thermal contrast in the ISM variability is larger in 1948–77 than in 1979–2008. Indeed, the correlation coefficient between IMR(land) and IMI(thick) is 0.79 for the period 1948–77, which is much larger than the correlation for the period 1979–2008 (0.33). This result is consistent with Miyakoda et al. (2003).

7. Interdecadal change in the frequency of different types of ENSO influences

In this section, we revisit the interdecadal change in the ISM–ENSO relationship around the late 1970s. We address this issue by examining the change in the relative frequency of different types of ISM–ENSO associations and comparing the relative roles of the NIO SST and the thermal contrast before and after the late 1970s.

Figure 10b shows the lag–lead correlation of monthly-mean Niño-3.4 SST and NIO SST with respect to IMR(land) for the period 1948–77. The Niño-3.4 SST in summer and the following winter displays a stronger negative correlation compared to the recent period, consistent with previous studies (Krishna Kumar et al. 1999; Krishnamurthy and Goswami 2000; Miyakoda et al. 2003). The correlation in the preceding winter is slightly lower than that in the recent period.

We have examined the year-to-year variations of IMR(land) and DJF(0) and JJAS(0) Niño-3.4 SST anomalies for the period 1948–77. The statistics of their relationship is summarized in the third row of Table 1. During the 30-yr period, there are 3 DJF-only cases, 4 JJAS-only, and 6 DJF&JJAS cases. There is a prominent change in the number of DJF&JJAS cases: 6 cases in 1948–77 and 2 cases in 1979–2008, whereas the number of JJAS-only cases are the same in the two periods. Because in the DJF&JJAS cases DJF(0) and JJAS(0) Niño-3.4 SST anomalies work coherently in influencing ISM, ENSO is more likely to overcome the internal atmospheric dynamics to induce an anomalous ISM. Thus, the apparent weakening in the relationship between an ISM and concurrent EEP SST anomalies may be related to the decrease in the number of these cases. In turn, this is related to the change in ENSO characteristics. In the former period, ENSO is more biennial and there are more cases in which ENSO switches its phase from winter to summer, whereas, in the latter period, the ENSO period is longer and there are fewer cases in which ENSO switches its phase from winter to summer (An and Wang 2000). Thus, an alternative explanation for the weakening of the ISM–ENSO relationship in the late 1970s is the change in ENSO characteristics.

A somewhat similar argument was proposed by Kawamura et al. (2003). They inferred that an indirect influence of ENSO on an ISM during the decay phase of ENSO is significantly different from that during the growth phase. As such, changes in the ENSO cycle may affect the ISM–ENSO relationship. They indicated that the indirect (equatorially asymmetric) influence on an ISM during the decay phase of ENSO is mainly in the early stage (June–July) of the monsoon season and that it cannot strongly regulate JJAS total rainfall amount over India, whereas the equatorially symmetric influence at the growth phase of ENSO is strong. Because of the change in the ENSO cycle, an ISM after the late 1970s tends to experience less equatorially symmetric but more equatorially asymmetric impacts of ENSO, which would lead to a weakening in the ISM–ENSO connection.

To further demonstrate the change in the ISM–ENSO relationship, we show in Fig. 13 the sliding correlation of DJF(0) and JJAS(0) Niño-3.4 SST and March–May(0) [MAM(0)] NIO SST with IMR(land) with a 21-yr window. The correlation has been calculated based on SST from both ERSST3 and HadISST1.1. The analysis for ERSST3 SST is only extended to 1901 because the interannual variation of ERSST3 SST appears small in the nineteenth century. Clearly the weakening of the correlation between JJAS(0) Niño-3.4 SST and IMR(land) in the 1980s corresponds to the weakening of the correlation between DJF(0) Niño-3.4 SST and IMR(land). The correlation between MAM(0) NIO SST and IMR(land) also experienced a weakening at nearly the same time based on ERSST3 (Fig. 13a). The correlation between DJF(0) Niño-3.4 SST and MAM(0) NIO SST experienced a weakening during the 1970s through the mid-1980s and recovered in the late 1980s.

Fig. 13.
Fig. 13.

Moving correlation with a 21-yr window between JJAS Niño-3.4 SST and JJAS IMR (black curves), DJF Niño-3.4 SST and JJAS IMR (red curves), MAM NIO SST and JJAS IMR (green curves), and DJF Niño-3.4 SST and MAM NIO SST (blue curves) based on (a) ERSST3 and (b) HadISST1.1.

Citation: Journal of Climate 25, 3; 10.1175/JCLI-D-11-00039.1

8. Summary and discussions

The present study has identified three different types of ENSO influences on the ISM based on the relationship between Niño-3.4 SST anomalies in DJF(0) and JJAS(0) and IMR anomalies in JJAS(0), and it has analyzed the corresponding processes connecting ENSO to ISM separately. In the first type (the DJF-only cases), the preceding EEP SST anomalies induce SST anomalies in the NIO through wind–evaporation and cloud–radiation changes. NIO SST anomalies, in turn, lead to anomalous ISM. In the second type (the JJAS-only cases), concurrent EEP SST anomalies directly induce an anomalous ISM through large-scale circulation changes. In the third type (the DJF&JJAS cases), ENSO switches its phase from winter to summer and both the preceding and concurrent EEP SST anomalies contribute to an anomalous ISM through ENSO-induced NIO SST anomalies and contemporary large-scale circulation changes. It is found that the thermal contrast does not play a role in an ISM in the DJF-only cases, but it is complementary in the JJAS-only and DJF&JJAS cases.

Both the JJAS-only and DJF&JJAS cases contribute to the correlation between an ISM and concurrent EEP SST anomalies. Thus, an increase in the number of either the JJAS-only cases or the DJF&JJAS cases or both can lead to a change in the ISM–ENSO relationship. The number of DJF&JJAS cases is very different before the late 1970s and after the late 1970s. Before the late 1970s, there are more DJF&JJAS cases in which the preceding and concurrent EEP SST anomalies work together to lead to an anomalous ISM. This feature is related to the short periodicity of ENSO. After the late 1970s, there are fewer DJF&JJAS cases. This is consistent with the longer period and delayed decay of ENSO after the late 1970s than before. This contrast suggests that the change in the number of DJF&JJAS cases may contribute to the apparent decrease in the correlation between ISM and summer EEP SST anomalies in the late 1970s. Thus, an alternative explanation for the interdecadal weakening in the ISM–ENSO relationship in the late 1970s is the change in ENSO characteristics.

The number of cases for each type of relationship is quite limited during the analysis period, and thus the robustness of the results obtained in the present study needs to be examined with a longer period of reliable data. Nevertheless, the results of this study reveal the necessity of separating different types of ENSO influences on ISM and analyzing the corresponding processes separately, which would help to understand the ISM–ENSO relationship and its long-term change.

The present analysis can be applied to model simulations to understand the model’s performance in simulating the ISM–ENSO relationship. Some models fail to capture the observed ISM–ENSO relationship. One suggestion would be to separate the different types of anomalous ISM years based on the relationship with the preceding and concurrent EEP SST anomalies. A comparison of the statistics, the processes connecting ENSO to ISM, and the roles of different factors with observations could help to understand why the models fail in the ISM–ENSO relationship. This could also help for identifying the deficiencies of the models.

Acknowledgments

The comments of three reviewers have led to a significant improvement of this paper. This research was initiated when RW was at COLA. RW acknowledges support from the NSF (Grant ATM-0830068), NOAA (Grants NA09OAR4310058 and NA09OAR4310186), and NASA (Grants NNX09AN50G). RW is supported by a Direct Grant of the Chinese University of Hong Kong (2021090). JC acknowledges the support of the National Basic Research Program of China Grant 2009CB421405 and the National Natural Science Foundation of China Grants 40975046 and 40810059005.

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