Abstract

The tropical Indian Ocean dipole/zonal mode (IOD) is phase locked with the austral winter and spring seasons. This study describes three types of the IOD in terms of their peak time and duration. In particular, the authors focus on a new type that develops in May–June and matures in July–August, which is distinctively different from the canonical IOD, which may develop later and peak in September–November or persist from June to November. Such “unseasonable” IOD events are only observed since the mid-1970s, a period after which the tropical Indian Ocean has a closer relationship with the Pacific Ocean. The unseasonable IOD is an intrinsic mode of the Indian Ocean and occurs without an ensuing El Niño. A change in winds along the equator is identified as a major forcing. The wind change is in turn related to a weakening Walker circulation in the Indian Ocean sector in austral winter, which is in part forced by the rapid Indian Ocean warming. Thus, although the occurrence of the unseasonable IOD may be partially influenced by oceanic variability, the authors’ results suggest an influence from the Indian Ocean warming. This suggestion, however, awaits further investigation using fully coupled climate models.

1. Introduction

As one of the most important interannual climate modes in the Indian Ocean, the tropical Indian Ocean (TIO) dipole/zonal mode (IOD) has a strong impact on the Indian Ocean rim and beyond (Saji et al. 1999; Webster et al. 1999). A positive IOD features an anomalously strong west–east sea surface temperature (SST) gradient and easterly winds along the equatorial Indian Ocean, compared to the climatological state. Since the discovery of the IOD, there has been a vigorous debate regarding the relationship between the IOD and El Niño–Southern Oscillation (ENSO), a major mode of global climate variability on the interannual time scale (Allan et al. 2001; Yamagata et al. 2002). The consensus is that about half of the IOD events occur in conjunction with ENSO but others develop as a consequence of intrinsic variations of the TIO (e.g., Yamagata et al. 2004; Meyers et al. 2007). ENSO affects the IOD through a change in the Walker circulation. For example, an atmosphere–ocean coupled model experiment reveals that ENSO could delay the formation of the IOD until boreal summer, and IOD events independent from ENSO would form as early as the boreal spring (Behera et al. 2006). In years of co-occurrence, ENSO variability is found to affect the periodicity, strength, and formation processes of the IOD (e.g., Annamalai et al. 2005b; Behera et al. 2006), but the IOD could also influence ENSO development (Luo et al. 2008).

There has been an increase in the IOD frequency and strength in recent decades (e.g., Abram et al. 2008; Cai et al. 2009a,c; Xie et al. 2010; Zheng et al. 2010). Proxy data, such as tree-ring and coral-oxygen-isotope records, as well as ocean observations, reveal a strengthening in the relationship between the IOD and ENSO since the mid-1970s (Abram et al. 2008; Yuan and Li 2008; D’Arrigo et al. 2008).

The present study investigates the behavior of the IOD over the past six decades and finds a change in IOD characteristics after the mid-1970s. We identify a distinctive IOD type that operates only in the austral winter season and only since the mid-1970s. Terminated by processes related to the Madden–Julian oscillation (MJO; Madden and Julian 1972), such short-lived events have little linkage to ENSO, despite the strengthening ENSO–IOD relationship. We then explore the long-term changes in the Walker circulation and oceanic conditions as a probable cause for the generation of this type of IOD event.

2. Data

We use extended reconstructed SST (ERSST; Smith et al. 2008) to form indices and to examine the associated anomalies. Monthly SSTs from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) provided by the National Oceanic and Atmospheric Administration (NOAA) (Woodruff et al. 2011) and a reconstructed IOD mode index (DMI) from coral-oxygen-isotope records (Charles et al. 1997; Abram et al. 2008) are used to confirm our findings. The reconstructed index covers the period 1846–1994. The DMI is defined as the average in the difference between the western TIO (10°S–10°N, 50°–70°E) and the southeastern TIO (10°S–0°, 90°–110°E). An oceanic index for ENSO, the Niño-3.4 index, defined as the average of SST anomalies in region (5°N–5°S, 120°–170°W), is used to describe ENSO variability. Atmospheric circulation fields are derived from the National Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al. 1996) and the Special Sensor Microwave Imager (SSM/I), reconstructed sea level pressure (SLP) is from the Hadley Centre Sea Level Pressure dataset (HadSLP; Allan and Ansell 2006), rain rate is from the Global Precipitation Climatology Project (GPCP; Adler et al. 2003), and sea surface height (SSH) is from the Simple Ocean Data Assimilation (SODA), version 2.2.4 (Carton and Giese 2008), and satellite altimetry. In addition, reconstructed XBT temperatures are used to provide ocean thermocline conditions (Ishii and Kimoto 2009).

Because observations with reasonable samplings are only available in the past six decades (e.g., ICOADS and HadSLP), we focus on the period of 1948–2010, although the GPCP rain rate is only available after 1979; merged satellite SSH are from 1993 to 2010; SSM/I 10-m wind is from 1987 to 2010; reconstructed XBT temperatures are up to 2008; and coral δ18O in the Seychelles and west of Sumatra are up to 1994 and 1997, respectively. In Figs. 15, a linear trend and the mean seasonal climatology are removed. Anomalies of the above variables are referenced to each period.

Fig. 1.

Evolution of the three types of the IOD and their composite: DMI (°C) for (a) the unseasonable IOD, (b) the normal IOD, (c) the prolonged IOD, and (d) a comparison of the composites. Events in the composite are listed in each panel.

Fig. 1.

Evolution of the three types of the IOD and their composite: DMI (°C) for (a) the unseasonable IOD, (b) the normal IOD, (c) the prolonged IOD, and (d) a comparison of the composites. Events in the composite are listed in each panel.

Fig. 2.

Bimonthly [May–June (MJ), July–August (JA), September–October (SO), and November–December (ND)] average composite anomalies, over six unseasonable IOD events, of (a)–(d) SST (°C; shading) and wind vectors over the 95% confidence level according to a t test, (e)–(h) SLP (hPa; shading) and GPCP rain rate (mm month−1; contour), and (i)–(l) latent heat flux (W m−2; shading, where a negative value indicates ocean losing heat) and solar radiation (W m−2; contour).

Fig. 2.

Bimonthly [May–June (MJ), July–August (JA), September–October (SO), and November–December (ND)] average composite anomalies, over six unseasonable IOD events, of (a)–(d) SST (°C; shading) and wind vectors over the 95% confidence level according to a t test, (e)–(h) SLP (hPa; shading) and GPCP rain rate (mm month−1; contour), and (i)–(l) latent heat flux (W m−2; shading, where a negative value indicates ocean losing heat) and solar radiation (W m−2; contour).

Fig. 3.

As in Fig. 2, but for the composite of five prolonged IOD events. Note that the scale is changed.

Fig. 3.

As in Fig. 2, but for the composite of five prolonged IOD events. Note that the scale is changed.

Fig. 4.

As in Fig. 2, but for the composite of five normal IOD events.

Fig. 4.

As in Fig. 2, but for the composite of five normal IOD events.

Fig. 5.

Madden–Julian oscillation and oceanic Kelvin wave: NCEP zonal winds (m s−1) in (a) the east (5°S–5°N, 80°–100°E) and (b) the west (5°S–5°N, 50°–70°E) equatorial Indian Ocean for six unseasonable IOD events, where the anomalies (shading) relative to seasonal variations (smoothed curve) are highlighted, and (c) weekly satellite altimetry SSH anomalies (cm; shading) along the equator in 2003, 2007, and 2008, superimposed with weekly SSM/I wind anomalies [contour interval of 2 m s−1]. The red line in (c) indicates a Kelvin wave terminating the SST cooling in the east. The data span from 1993 to 2010. Long-term linear trends are removed. All anomalies refer to their seasonal-mean climatology.

Fig. 5.

Madden–Julian oscillation and oceanic Kelvin wave: NCEP zonal winds (m s−1) in (a) the east (5°S–5°N, 80°–100°E) and (b) the west (5°S–5°N, 50°–70°E) equatorial Indian Ocean for six unseasonable IOD events, where the anomalies (shading) relative to seasonal variations (smoothed curve) are highlighted, and (c) weekly satellite altimetry SSH anomalies (cm; shading) along the equator in 2003, 2007, and 2008, superimposed with weekly SSM/I wind anomalies [contour interval of 2 m s−1]. The red line in (c) indicates a Kelvin wave terminating the SST cooling in the east. The data span from 1993 to 2010. Long-term linear trends are removed. All anomalies refer to their seasonal-mean climatology.

3. Classification of IOD events

Seasonal-mean June–August (JJA) and September–November (SON) SST, as well as monthly-mean indices, are constructed for the DMI and the IOD eastern pole. The eastern pole indices are used to focus on the initial phase of IOD development. When the amplitude of a possible event reaches a one standard deviation value of its respective index, it is referred to as an IOD event. A 4–84-month bandpass filter is applied to remove the intraseasonal and long-term variations. Three types of IOD events are possible: events that develop and mature mostly within JJA, events that develop and mature mostly within SON, and events that develop in JJA and mature in SON. These are referred to as unseasonable IOD, normal IOD, and prolonged IOD, respectively (Table 1). The latter two types are termed the canonical IOD. The unseasonable IOD has received little attention in the literature and has only been noticed recently as aborted IOD events in the development of canonical episodes (Rao and Yamagata 2004; Rao et al. 2009). Over the period considered, there are six unseasonable IOD, five normal IOD, and five prolonged IOD events (Fig. 1), although the 1991 IOD may be seen as an unseasonable episode followed by a normal event.

Table 1.

Classification of the three types of IOD. Years in boldface denote a significant event (amplitude over 1.5 standard deviations).

Classification of the three types of IOD. Years in boldface denote a significant event (amplitude over 1.5 standard deviations).
Classification of the three types of IOD. Years in boldface denote a significant event (amplitude over 1.5 standard deviations).

An examination reveals that negative IOD events are generally weak (figure not shown). Previous studies suggest that a weak negative IOD is related to a decadal variation of the IOD, with strong and frequent occurrences of positive IOD events associated with El Niño events since the 1960s (e.g., Kripalani and Kumar 2004; Tozuka et al. 2007; Ihara et al. 2008). During our period of data coverage, there are three unseasonable negative IOD events (1973, 2001, and 2009), compared with six positive events. The analysis in the following section also suggests that a change in the atmospheric circulation favors a positive IOD-like response in the Indian Ocean. As such, we consider only the positive events.

In terms of the amplitude of the three types, the unseasonable IOD is about two-thirds of the canonical IOD types. In terms of temporal evolution, although some unseasonable IOD events may persist into September, they tend to mature in July. In contrast, the normal IOD events tend to form in July, peak in October, and end by early December. The prolonged IOD events evolve gradually, although they may commence their development earlier than the unseasonable IOD; they usually reach their peak in September, and their demise in December comes abruptly. Thus, the evolution of the prolonged IOD is more asymmetric, with a slow development in contrast to a rapid demise.

4. Circulation features associated with unseasonable IOD events

Focusing on the unseasonable IOD, Fig. 2 shows the associated bimonthly composite anomalies. In May–June, water off Sumatra–Java cools while the rest of the TIO warms. Easterly wind anomalies are generated in the eastern equatorial TIO and southeasterlies off Sumatra–Java, whereas the southeastern TIO experiences an anticyclonic wind circulation anomaly (Fig. 2a). Correspondingly, latent heat flux loss increases off Sumatra–Java, contributing to the regional cooling (Fig. 2i). In July–August, SST anomalies reach their peak, with maximum warming on both sides of the equator (Fig. 2b). The convergence of easterly and westerly anomalies indicates anomalous convection that develops at the central equatorial region, consistent with an east–west low–high rainfall structure during May–August (Figs. 2e,f). Rainfall over land shows a weaker anomaly around the TIO rim than over the ocean, with increased rainfall in central Africa and the Indian Peninsula and decreased rainfall in the northwest corner of Australia (Fig. 2e,f). Increased latent heat loss continues to favor a cooling, while an increase in solar radiation damps the SST cooling off Sumatra–Java (Fig. 2b,j). In September–October, increased solar radiation, as well as strengthened westerly wind anomalies, terminates the anomalous cooling in the east (Figs. 2c,k). Thereafter, no IOD signature is observed in the ocean or in the atmosphere (Figs. 2d,h,l).

Throughout this process, the equatorial Pacific shows virtually no coherent SST anomalies (Figs. 2a–d). No proceeding El Niño forms, suggesting that this type of event is independent from ENSO. Indeed, the 2007 and 2008 events were each followed by a La Niña event (Cai et al. 2009b). In comparison, Figs. 3 and 4 depict the evolution of the normal and prolonged events. The difference lies in an SLP anomaly over the Maritime Continent and the eastern tropical Pacific, which is particularly conspicuous after September (Figs. 3, 4). As an important part of the Southern Oscillation, there is an SLP anomaly over the Maritime Continent and the eastern tropical Pacific in the September–October and November–December months in the canonical IOD (Figs. 3g,h, 4g,h), whereas such a positive SLP anomaly is absent in the unseasonable events (Figs. 2g,h). For the canonical IOD events, with the El Niño sustaining an anticyclonic atmospheric circulation over the southeastern Indian Ocean (Yu and Rienecker 1999), easterly wind anomalies off Sumatra and Java develop and persist into November–December (Figs. 3c,d, 4c,d). In comparison, the unseasonable IOD is weak, short lived, and confined to the Indian Ocean (Figs. 2c,d). Note that the SST anomalies over the tropical Pacific coherent with the prolonged IOD are not statistically significant at the 95% confidence level according to a t test (Figs. 3c,d), but those coherent with the normal IODs are (Figs. 4c,d). Three out of the five IOD (of both IOD types) events occur in conjunction with an El Niño (Table 1). Out of the three concurrences, one significant El Niño (marked in bold font in Table 1) occurs with a prolonged IOD and two significant events occur with a normal IOD (Table 1). The difference in the coherent significant events probably accounts for the difference in the significance of the associated SST anomalies (Figs. 3c,d, 4c,d).

The generation of unseasonable IOD in the absence of an El Niño is due to IOD-favorable wind conditions, as observed during events independent from ENSO (Annamalai et al. 2003; Drbohlav et al. 2007; Roxy et al. 2011). In the southeastern TIO, the climatological winds are southeasterly/easterly from April to October, particularly off Sumatra–Java (Hellerman and Rosenstein 1983). During May–August, as long as southeasterlies/easterlies prevail in the southeastern/eastern TIO (Fig. 2a,b), the positive feedback through wind evaporation (Figs. 2i,j), coastal upwelling, and thermocline shoaling induces an SST cooling off Sumatra–Java and contributes to the establishment of a zonal temperature gradient that defines an IOD (Figs. 2a,b). A question arises as to why an unseasonable IOD decays before September (Figs. 1a, 2c), in spite of mean seasonal southeasterly winds that are present along the Sumatra–Java coast (Fig. 2b).

A thermocline response is important for the evolution of unseasonable IOD events. During July–August, zonal wind anomalies along the equator are of opposite sign to those off Sumatra–Java (Fig. 2b), which prohibit a full thermocline response that accompanies the development of other types of the IOD, creating a condition for an unseasonable IOD to decay rapidly. The wind anomalies are sometimes related to strong convection-enhancing MJO activities over the TIO as suggested by Rao and Yamagata (2004) and Rao et al. (2009). Figure 3 of the present study illustrates the associated processes of MJO. West of Sumatra, easterly anomalies dominate in May or June and grow in strength in July and August (Fig. 5a). In contrast, over the western equatorial Indian Ocean, westerly wind anomalies are observed in mid-May 1991, August 2003, July 2007, and July 2008. In general, these westerly wind anomalies in the western equatorial Indian Ocean excite warm Kelvin waves and terminate the upwelling in the eastern equatorial Indian Ocean in the following month (Fig. 5c). However, significant differences exist among events. For example, the IOD termination in 2007 and 2008 is due to an incoming downwelling Kelvin wave from the west, triggered by strong MJO activities in July (Figs. 5b,c). The eastward propagation of the MJO also helps terminate the cooling in the east. In 2003, the wind in the eastern TIO reverses to westerlies in early August (Figs. 5a,c), which lead to a fast demise of cold SST anomalies (Rao et al. 2009).

5. Decadal variations of unseasonable IOD events

The unseasonable IOD events are dissimilar to a canonical IOD described by early studies (e.g., Saji et al. 1999) in at least two respects: no development of a proceeding El Niño and a conspicuous absence in the early epoch of 1948–75. In comparison, half of the canonical IOD events precede an El Niño, although these IOD events could develop as an independent ocean–atmosphere coupled phenomenon. These two features are tantalizing and paradoxical since the IOD–ENSO correlation actually increases after the mid-1970s (Fig. 6) (e.g., Yuan and Li 2008). To understand the associated dynamics, calculation of variance of the DMI using a 21-yr running window for each calendar month is carried out to examine variations on decadal time scales (Fig. 7a; the result is recorded in the middle year of the window). Significant decadal changes emerge with two peaks centering on July and October after the mid-1970s. A similar calculation is conducted for the monthly IOD eastern pole (Fig. 7b). A coral DMI, as an independent index, reconstructed from δ18O in the Seychelles and west of Sumatra (Charles et al. 1997; Abram et al. 2008), up to the years 1994 and 1997, respectively, further confirms the decadal change in the mid-1970s, though there are differences due to sampling location and depth (Figs. 7a,b).

Fig. 6.

Running (21 yr; centered at the middle year of the window) correlation of the DMI in JJA (red) and SON (blue) with the Niño-3.4 NDJ SST index. The dashed line denotes statistical significance at the 95% confidence level according to a t test.

Fig. 6.

Running (21 yr; centered at the middle year of the window) correlation of the DMI in JJA (red) and SON (blue) with the Niño-3.4 NDJ SST index. The dashed line denotes statistical significance at the 95% confidence level according to a t test.

Fig. 7.

Root-mean-square variance of (a) the DMI derived from ERSST (shading) and coral δ18O anomaly (coral DMI normalized relative to 1960–90; contour) in the Seychelles ~(4.6°S, 55.8°E) and the Mentawai Islands off Sumatra ~(0°, 98°E) at 6–7-m water depth (Charles et al. 1997; Abram et al. 2008) (data are only available up to 1994 and 1997, respectively), (b) SST (shading) in the IOD eastern pole (10°S–0°, 90°–110°E) and coral δ18O anomaly (‰; contour) in the Mentawai Islands, (c) SSH (cm; shading) and upper thermocline depth (m; contour) averaged over the IOD eastern pole, and (d) surface zonal wind (m s−1) along the equator (5°S–5°N, 70°–90°E), using a 21-yr running window, centered at the middle year, for each month. Month 1 corresponds to January and so on.

Fig. 7.

Root-mean-square variance of (a) the DMI derived from ERSST (shading) and coral δ18O anomaly (coral DMI normalized relative to 1960–90; contour) in the Seychelles ~(4.6°S, 55.8°E) and the Mentawai Islands off Sumatra ~(0°, 98°E) at 6–7-m water depth (Charles et al. 1997; Abram et al. 2008) (data are only available up to 1994 and 1997, respectively), (b) SST (shading) in the IOD eastern pole (10°S–0°, 90°–110°E) and coral δ18O anomaly (‰; contour) in the Mentawai Islands, (c) SSH (cm; shading) and upper thermocline depth (m; contour) averaged over the IOD eastern pole, and (d) surface zonal wind (m s−1) along the equator (5°S–5°N, 70°–90°E), using a 21-yr running window, centered at the middle year, for each month. Month 1 corresponds to January and so on.

A comparison of Figs. 7a,b shows that the IOD eastern pole accounts for most of the variations in the DMI, consistent with the finding of previous studies regarding the importance of the IOD eastern pole (e.g., Meyers et al. 2007). The maximum SST variance in March (Fig. 7b) is forced by ENSO, which reflects the uniform warming in the TIO before the warming decays in the subsequent months (Du et al. 2009). A correlation of the DMI in JJA with Niño-3.4 in the following November, December, or January using a 21-yr running window shows no statistically significant correlations after the mid-1970s (Fig. 6), confirming the absence of an associated (following) El Niño event.

The features of the unseasonable IOD described above indicate that this new type is an independent entity rather than a prelude to the canonical IOD. The evolution of the unseasonable IOD indicates a coupling, a Bjerknes-like positive feedback, between the ocean and the atmosphere (Bjerknes 1969). The initial change in the equatorial easterlies strengthens in response to a stronger zonal SST gradient in the TIO, leading to a shoaling thermocline, strengthened upwelling, and a further SST decrease in the IOD eastern pole (Fig. 2).

Variance of SSH and the depth of 24°C temperature in the IOD eastern pole illustrates a significant signal in the JJA season (Fig. 7c), which is not observed prior to the mid-1970s. Those changes have a good correspondence in the equatorial zonal winds (Fig. 7d). This equatorial zonal wind did display a weak signal around the 1960s and the early 1970s (Fig. 7c), with consistently weak variance in the thermocline (Fig. 5c) that is not statistically significant. The pre-1970 signature may be an indication of the prolonged IOD events (1961, 1967, and 1972) (Table 1), which commence their development in April–May months.

An examination shows that the monsoon experiences no corresponding change (Fig. 8). Variance of a monsoon index, defined by Webster and Yang (1992), shows a minimum in the JJA season and bears no significant decadal changes (Fig. 8a). Correlation of the DMI with the monsoon index does not pass the 95% significance level in the JJA season (Fig. 8b). An increasing inverse correlation in SON after the mid-1980s reflects a closer normal IOD–Indian monsoon relationship (e.g., Ashok et al. 2001). These results rule out an involvement of the monsoon in the evolution of the unseasonable IOD. What then causes the enhanced ocean and atmosphere coupling after the mid-1970s?

Fig. 8.

(a) Root-mean-square variance of the Webster and Yang monsoon index (contour interval of 1 m s−1) and (b) running (21 yr) correlation, centered at the middle year of the window, of the DMI with the Webster and Yang monsoon index in JJA (red) and SON (blue). The dashed line denotes statistical significance at the 95% confidence level according to a t test.

Fig. 8.

(a) Root-mean-square variance of the Webster and Yang monsoon index (contour interval of 1 m s−1) and (b) running (21 yr) correlation, centered at the middle year of the window, of the DMI with the Webster and Yang monsoon index in JJA (red) and SON (blue). The dashed line denotes statistical significance at the 95% confidence level according to a t test.

6. Mechanism and discussion

The easterly anomalies off Sumatra are closely related to atmospheric circulation along the equator, which is referred to as the Walker circulation. As the Walker circulation in the Pacific weakens in a global warming scenario (Vecchi et al. 2006; Deser et al. 2010), is there a corresponding weakening in the Indian Ocean sector, as recently postulated by Tokinaga et al. (2012)?

The TIO, as a whole, displays a fast warming rate after the mid-1970s (Alory et al. 2007; Du and Xie 2008), but the rate is neither uniform across the basin (Cai et al. 2007) nor the season. A robust TIO warming is seen in the 1960s, almost two decades earlier than the Pacific (Fig. 9a; Deser et al. 2010). In particular, the west TIO warms at a faster rate than the east (Cai et al. 2009a), which is expected to cause a response in the atmospheric circulation. In addition, a weak seasonal dependence is seen with a more robust and faster warming in the months of June–November (Fig. 9b). In the meantime, the SLP difference between the west and the east (west minus east) shows a well-defined decrease in May–October after the late-1970s (Fig. 9c), which is opposite to the SLP difference of the mean state, implying a weakening Walker circulation (Tokinaga et al. 2012), consistent with the finding for the Pacific using the Southern Oscillation index (SOI) (e.g., Power and Smith 2007).

Fig. 9.

Long-term changes and trends: (a) SST change (°C) along the equator averaged over (5°S–5°N) relative to the 1890–1910 mean, (b) TIO SST (20°S–20°N, 40°–100°E) change (°C), (c) trends of differences in SLP (hPa) between a west (5°S–5°N, 40°–80°E) and an east (5°S–5°N, 80°–120°E) box (west minus east), (d) trends of surface zonal wind (m s−1) along the equator (5°S–5°N, 70°–90°E), and (e) trends of SSH (cm) averaged in the IOD eastern pole. Changes in (b) are referenced to the 10-yr mean of 1948–57, and a 9-yr running average is applied. Trends in (c)–(e) are calculated using a 21-yr running window for each month, centered at the middle year of the window.

Fig. 9.

Long-term changes and trends: (a) SST change (°C) along the equator averaged over (5°S–5°N) relative to the 1890–1910 mean, (b) TIO SST (20°S–20°N, 40°–100°E) change (°C), (c) trends of differences in SLP (hPa) between a west (5°S–5°N, 40°–80°E) and an east (5°S–5°N, 80°–120°E) box (west minus east), (d) trends of surface zonal wind (m s−1) along the equator (5°S–5°N, 70°–90°E), and (e) trends of SSH (cm) averaged in the IOD eastern pole. Changes in (b) are referenced to the 10-yr mean of 1948–57, and a 9-yr running average is applied. Trends in (c)–(e) are calculated using a 21-yr running window for each month, centered at the middle year of the window.

We suggest that the emergence of the unseasonable IOD is at least in part a consequence of the weakening Walker circulation induced by the TIO warming. Under the impact of such a warming pattern, the Indian Ocean is more susceptible to development of easterly wind anomalies not only in the austral spring but also in the austral winter season (Fig. 9d). It is worth noting that there is a strong seasonality in the weakening Walker circulation changes (Fig. 9c). Based on in situ observations, particularly the SLP, recent studies found that the March–September season contributes most strongly to the weakening Walker circulation in the twentieth century, but there is no evidence for a weakening in the November–January (NDJ) season (Nicholls 2008; Karnauskas et al. 2009; Compo et al. 2011).

In fact, Fig. 9c demonstrates an increase of the SLP difference (west minus east) during November–March since the late 1980s, corresponding to a strengthening Walker circulation. This result echoes the findings of the strengthening Walker circulation in recent decades diagnosed from satellite water vapor transport by Sohn and Park (2010). A faster-increasing sea level height in the western Pacific from satellite altimetry and tide gauge records also supports the strengthening of the atmospheric circulation over the tropical Pacific since the early 1990s (Merrifield 2011). Thus, although long-term changes in the water cycle favor a weakening Walker circulation in the mean state (Held and Soden 2006), short-term changes can go in either direction. However, the associated mechanism has yet to be explored. Nevertheless, we emphasize that there is a strong seasonality of the atmospheric circulation change, as indicated in Figs. 9c,d, showing that the Walker circulation in the Indian Ocean does weaken in the May–October seasons.

In the scenarios described above, the independence of the unseasonable IOD from ENSO does not contradict the intensifying ENSO–IOD correlation after the late 1970s. Although no El Niño developed following an unseasonable IOD, it does not mean a weakening relationship between the IOD and ENSO. On the contrary, more positive IODs occur concurrently with an El Niño, but these concurrent IOD events are of the canonical IOD type. During 1980–2008, when there was an accompanying El Niño in the Pacific, the Indian Ocean developed either a prolonged IOD or a normal IOD (see Table 1). However, for an unseasonable IOD event, there was not a coherent favorable condition in the Pacific. Instead, the Bjerknes feedback intrinsic to the Indian Ocean was solely responsible for the development of such an event. As prolonged IOD or normal IOD events are often reinforced by an El Niño event, they are stronger and/or able to persist for longer than the unseasonable IOD (Fig. 1d).

The unseasonable IOD is closely related to the upwelling off Sumatra–Java; the impact of which depends on the thermocline condition in the southeastern TIO, where a shoaling thermocline trend is observed (Fig. 9e), consistent with the independent result of Tokinaga et al. (2012). However, our result differs from a recent study using model simulation and short-term satellite observations by Han et al. (2010), who find little trend in the eastern TIO. This contrasting result may be partly due to a short length of in situ observations; we note the trend of sea level height in limited tide gauge around the island of Java support a shoaling thermocline in the last 30 years if the effect of ice melting in sea level rise is removed (figure not shown).

In addition, a remote influence from the Pacific may be conveyed through the Indonesian Seas. The Indonesian Throughflow (ITF) can convey changes in the Pacific through the waveguide across the Indonesian Seas (Annamalai et al. 2005a; Cai et al. 2005). The shoaling in the Pacific thermocline may be in part generated by the weakening Pacific Walker circulation (Vecchi et al. 2006). However, observed SSH shows a fast regional rise in the western tropical Pacific since 1993 (Merrifield 2011), which indicates a deepening rather than shoaling of the thermocline in the southeastern Indian Ocean. Figure 1 of Merrifield (2011) indicates that the trend of SSH off Sumatra–Java is rather small and, if anything, negative, detaching from the change in the north and west off western Australia, where the trend synchronizes with the sea level rise in the western tropical Pacific. It suggests that, in this period, the impact from the Pacific does not change the shoaling thermocline trend in the eastern equatorial Indian Ocean.

When the thermocline off Sumatra–Java shoals, indicative of conditions favorable to upwelling and cooling, more and/or stronger IOD events develop. Consistent with the long-term trend in the west–east SLP difference, the thermocline trend indicates a seasonal dependence, with significant shoaling in the austral winter and spring season since the mid-1970s. Because of the short period of observations, it is difficult to determine the extent to which it is due to the remote influence from the Pacific or is due to the local weakening Walker circulation. This question needs to be further investigated with products from the upcoming phase 5 of the Coupled Model Intercomparison Project (CMIP5).

Thus, in an environment of a global warming–induced weakening Walker circulation in the Indian Ocean, particularly in the May–October seasons, the conditions are ripe for more IOD events, so that, in addition to canonical events, a new type (i.e., the unseasonable IOD) emerges.

7. Summary

Using the peak season and duration as criteria, IOD events are classified into three types. In addition to the normal IOD and prolonged IOD, we have identified a new type of event, which we refer to as the unseasonable IOD. Although the unseasonable IOD has a similar spatial pattern and influence to the canonical IOD, substantial differences exist: this type of event is relatively weak and short lived, and it only exists after the mid-1970s, a period when the TIO and the Pacific have a closer relationship. Paradoxically, the unseasonable IOD is not coherent with El Niño and has no ensuing El Niño. Usually, easterly wind anomalies occur during the austral winter and spring seasons, which strengthens the intrinsic oscillation in the Indian Ocean. With a co-occurrence of El Niño, a developing IOD is strengthened and prolonged. Without such an El Niño event or the associated reinforcing air–sea interaction from the tropical Pacific, an IOD can develop but tends to do so in austral winter and to terminate within the season. The mechanism for termination of the new type of event is related to the MJO by oceanic Kelvin waves from the western and central equatorial TIO and/or a wind reversal in the eastern TIO. There is evidence that the weakening Walker circulation contributes to the occurrence of such unseasonable IOD events, but this needs further investigation. If confirmed, this implies more IOD activities and hence more unseasonable IOD events in a warming climate.

Acknowledgments

We thank S.-P. Xie of IPRC/UH, T. Cowan and Ben Ng of MAR/CSIRO for useful discussions, and K. Liu and L. Zhang of LTO/SCSIO for help with satellite data processing. The SSM/I wind data were obtained from the Physical Oceanography Distributed Active Archive Center (http://podaac.jpl.nasa.gov). The NCEP reanalysis was provided by the NOAA/CIRES Climate Diagnostics Center (http://www.cdc.noaa.gov). The satellite merged SSH data were obtained from APDRC in IPRC–SOEST, University of Hawaii (http://apdrc.soest.hawaii.edu). Mentawai coral δ18O data and the DMI reconstruction are provided by N. J. Abram through the NOAA/NCDC Paleoclimatology Program (Data Contribution Series 2009-137). This work is supported by MoST (2010CB950302 and 2012CB955603), CAS (XDA05090404), and NSFC (41176024) and by the Australian Climate Change Science Program and the Southeast Australia Climate Initiative.

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