The Indian Ocean Dipole

The Indian Ocean Dipole Saji N. Hameed

Summary Discovered at the very end of the 20th century, the Indian Ocean Dipole (IOD) is a mode of natural climate variability that arises out of coupled ocean-atmosphere interaction in the Indian Ocean. It is associated with some of the largest changes of ocean-atmosphere state over the equatorial Indian Ocean on inter-annual time scales. IOD variability is prominent during the boreal summer and fall seasons, with its maximum intensity developing at the end of the boreal-fall season. Between the peaks of its negative and positive phases, IOD manifests a markedly zonal seesaw in anomalous sea surface temperature (SST) and rainfall - leading, in its positive phase, to a pronounced cooling of the eastern, and a moderate warming of the western and central, equatorial Indian Ocean; this is accompanied by deficit rainfall over the eastern, and surplus rainfall over the western, Indian Ocean. Changes in mid-tropospheric heating, accompanying the rainfall anomalies, drive wind anomalies that anomalously lift the thermocline in the equatorial eastern Indian Ocean and anomalously deepen them in the central Indian Ocean; the thermocline anomalies further modulate coastal and open ocean upwelling, thereby influencing biological productivity and fish catches across the Indian Ocean. The hydro-meteorological anomalies that accompany IOD exacerbate forest fires in Indonesia and Australia and brings floods and infectious diseases to equatorial East Africa. The coupled ocean-atmosphere instability that is responsible for generating and sustaining IOD develops on a mean state that is strongly modulated by the seasonal cycle of the Austral-Asian monsoon; this setting gives the IOD its unique character and dynamics, including a strong phase-lock to the seasonal cycle. While IOD operates independently of El Nino Southern Oscillation (ENSO), the proximity between the Indian and Pacific Oceans, and the existence of oceanic and atmospheric pathways, facilitate mutual interactions between these tropical climate modes.

Introduction The study of the ocean and the atmosphere as mutually interacting systems has provided deep insights into the workings of the climate system(Bjerknes, 1969; Gill, 1982; McCreary and Anderson, 1985; Yamagata, 1985). Some of the spectacular fluctuations of climate state from one year to the other owe their origin to instabilities of the coupled ocean-atmosphere system; these instabilities are understood to be a fundamental driver, among other things, of the occasional failures of the Indian monsoon, extensive droughts in Indonesia and Australia, and floods over East Africa and Peru. It is the tropics, in particular, which provide a source region for the most significant climate perturbations generated by ocean-atmosphere interactions: here, conditions favour the maintenance of coupled instabilities over several seasons, leading to a distinct modulation of the seasonal cycle. In the tropics, these coupled instabilities manifest as distinct, recurrent patterns of sea surface temperature (SST), wind, and rainfall anomalies; further, they influence a significant portion of the globe, through planetary waves excited by their anomalous tropical heating patterns, raising their importance even more. The first of the tropical coupled phenomena to be studied, the El Nino-Southern Oscillation (ENSO), arises out of ocean-atmosphere interactions in the tropical Pacific. The tropical Atlantic also exhibits an ENSO-like mode, sometimes referred to as the Atlantic Nino. Towards the end of the 20th century, a new mode of coupled ocean-atmosphere interaction was discovered over the tropical Indian Ocean; known as the Indian Ocean Dipole Mode (IOD), this important phenomenon will be discussed in this article. IOD’s discovery was an important milestone in the study of ocean-atmosphere interactions. Prior to the discovery of IOD, climate scientists thought that the tropical Indian Ocean did not generate an inherent mode of coupled variability; in fact, the conventional view maintained that the tropical Indian Ocean passively responded to, and amplified, ENSO associated variations(Wallace et al., 1998). The concept of IOD arising as an inherent climate mode of the Indian Ocean took a while to get established as various debates vigorously examined aspects of IOD(Saji and Yamagata, 2003b; Yamagata et al., 2004), including its validity and independence from ENSO. Some of these important issues and their resolution will be considered, among other things, in this review. However, before we do this, we will set the stage by reviewing the developments that led to the discovery of IOD and its exposition as an inherent 2/46

coupled mode of the tropical Indian Ocean. We then discuss how many unique aspects of IOD are due to the peculiar mean state upon which the coupled instability driving IOD develops. Finally, some of the important impacts associated with the phenomenon will be reviewed.

The discovery of IOD Inherent modes of climate variability, resulting from coupled ocean-atmosphere interaction, were discovered, in the 1960’s, in the tropical Pacific Ocean(Bjerknes, 1969) and, in the 1980’s, in the Atlantic Ocean(Hisard, 1980; Zebiak, 1993). In contrast, till nearly the end of the 20th century, it was widely believed that such a mode did not exist in the tropical Indian Ocean(Wallace et al., 1998); discussions regarding year-to-year variations of SST in the Indian Ocean focused on a basin-wide anomaly pattern that was remotely driven by ENSO(Lau and Nath, 1996; Weare, 1979). Further, a strong relation between Indian summer monsoon rainfall and ENSO was recognized for a long time(Goswami et al., 1999; Pant and Parthasarathy, 1981). Thus, in the beginning of the year 1999, the prevailing view on Indian Ocean climate variations was that the Indian Ocean was incapable of generating its own coupled mode, and that the year-to-year variations in SST and rainfall over the Indian sector were mostly forced by ENSO(Wallace et al., 1998). However, by the middle of that year, this paradigm was strongly challenged by discoveries that revealed new, unexpected facets of climate variations within the Indian Ocean region. In early 1999, researchers at the newly established Frontier Research Center for Global Change (FRCGC) in Japan documented an interesting pattern of climate variability that occurred during 1994 in the tropical Indian Ocean(Behera et al., 1999; Vinayachandran et al., 1999). This singular event, which took the climate community by surprise, was characterized by strong westward wind anomalies over the central equatorial Indian Ocean that persisted for a long time from April to October; accompanying this was an unusual pattern of SST variation characterized by cool SST anomalies in the eastern tropical Indian Ocean(Vinayachandran et al., 1999), and warm SST anomalies in the central and western Indian Ocean. Further, newly available sea surface height (SSH) measurements, from satellite remote sensing, showed that these were accompanied by abnormally low SSH at the eastern Indian Ocean(Behera et al., 1999; Meyers, 1996; Potemra and Lukas, 1999), while hydrographic measurements revealed that the normally 3/46

eastward flowing Wyrtki jet had considerably weakened during the boreal spring of 1994(Reppin et al., 1999). This singular Indian Ocean event, in 1994, set the stage for the discovery of IOD at FRCGC; there, further research revealed that such patterns were not unique for 1994, but also occurred in other years(Saji et al., 1999). Among these events, the 1961 event is of particular importance - bearing remarkable similarity to 1994, this year was also characterized by positive SST anomalies in the west and central Indian Ocean and negative anomalies further east(Flohn, 1987; Reverdin et al., 1986); further, the SST anomalies cooccurred with large anomalies of cloud cover and surface winds over the tropical Indian Ocean. This event had caught the attention of the famous German climatologist Hermann Flohn(Craig, 2005) whose research eventually demonstrated that atmospheric circulation anomalies accompanying this unusual Indian Ocean climate signal was behind the catastrophic East African rains of 1961(Flohn, 1987; Grove, 1996; Kite, 1981; Lamb, 1966; Odingo, 1962). The excessive rainy season of 1961/62 over East Africa, which peaked in November 1961, was a remarkable event that caused worldwide attention because of its impact on the White Nile. The excessive rains of 1961 had caused a discontinuous rise of Lake Victoria; the subsequent discharge of the White Nile continued for at least twenty years after the event; further, there were large scale floods all over equatorial East Africa, resulting in the loss of homes and lives and damage to crops, while emergency food had to be flown into marooned villages(Conway, 2002). The event caused huge economic damages: an estimate, by Odingo, (1962) suggests that the total flood damage costs, at the time for Kenya, to have been around five million British pounds. This mega event, over East Africa, was accompanied by severe climate anomalies over other countries surrounding the Indian Ocean: India experienced the highest monsoon rainfall ever since records were set up(Grove, 1996); at the same time, large parts of the Indonesian archipelago suffered a devastating drought(Flohn, 1987). Most interestingly, there was no El Nino in the Pacific. Noting this remarkable event and its occurrence during a non-ENSO year, Reverdin et al., (1986) suggested that the 1961 event must have involved coupled ocean-atmosphere interactions within the Indian Ocean. However, during this time, neither the general characteristics of IOD events, nor the nature of ocean-atmosphere interaction leading to IOD events had been established. While the 1994 event provided the first evidence of the role of the subsurface ocean in IOD events, it 4/46

was not widely noted, except by a few researchers. In contrast, the IOD event that occurred three years later received worldwide attention. This was, perhaps, because the 1997 IOD event occurred (Birkett et al., 1999; Murtugudde et al., 2000; Webster et al., 1999) during the strongest El Nino ever observed in the 20th century. In this year, too, East Africa experienced widespread heavy rainfall, and Indonesia a devastating drought; however, unlike during the 1994 and 1961 IOD events, Indian rainfall was close to normal(Conway, 2002). Although it occurred during a strong El Nino event, the 1997 IOD event exhibited strong co-evolution of oceanic and atmospheric variables over the Indian Ocean; this led some researchers to argue that the event arose out of ocean-atmosphere interactions within the Indian Ocean(Murtugudde et al., 2000; Webster et al., 1999); others, though they examined the same data, argued that Indian Ocean variability was driven by the El Nino(Chambers et al., 1999; Yu and Rienecker, 1999). While the above studies all focused on individual events, Saji et al., (1999) took a different approach. They sought to understand the common features associated with these unusual events in order to explore their underlying dynamics, and to understand their relation to ENSO. To this end, they devised a simple method to identify similar events from observations. This method involved the construction of an SST based index, the so-called Dipole Mode Index or DMI. The DMI, defined as the difference in SST anomaly between the western (50 - 70 E, 10 S - 10 N) and the south eastern (90 - 110 E, 10 S - Equator) equatorial Indian Ocean, was designed with two considerations in mind - the first exploited the fact that SST anomaly exhibited a dipole-like structure when these events were at their peak strength. Secondly, it provided a simple filter to remove the ENSO induced basin-wide SST anomaly pattern. Note that although SST anomaly in the eastern tropical Indian Ocean is a defining aspect of IOD events, the presence of the ENSO induced basin-wide SST anomaly renders it impossible to separate these two SST modes only using SST from the eastern tropical Indian Ocean. Using the DMI, Saji et al., (1999) identified six extreme IOD events, viz. that of 1961,1967,1972,1982,1994 and 1997, during the 1958 to 1998 period. Of these, the 1972, 1982 and 1997 coincided with strong El Nino events. However, there were IOD events that occurred independent of which phase ENSO was in at the time of IOD’s development: thus, the events of 1961, 1967 and 1994 coincided with no ENSO, a La Nina and a weak El Nino respectively. This led Saji et al., (1999) to conclude that IOD events were 5/46

independent of ENSO; further, they also cited the weak simultaneous correlation between the time series of DMI and an ENSO index as another evidence in support of their conclusion. To account for the existence of IOD events, they invoked the existence of an inherent mode of climate variability over the tropical Indian Ocean that was generated and maintained by ocean-atmosphere interactions. The original analysis of Saji et al., (1999) used a composite IOD event to argue for the existence of an inherent coupled mode in the tropical Indian Ocean. In the following section, we will recreate their arguments for coupled air-sea interactions during IOD. However, instead of using composite analysis, here we will examine the strong IOD event of 2006 - an event that occurred some years after Saji et al., (1999) published their results. Anatomy of an IOD event

(a) SST

(b) SSH

(c) OLR

Figure 1. Spatial structure of anomalies for the 2006 IOD event during its development (top), mature (middle), and dissipating (bottom) phases. The shaded contours in (a), (b), and (c) are for SST, SSH, and OLR anomalies respectively. Surface wind anomalies are overlaid as vectors. Data sources: SST TMI(Wentz et al., 2000), SSH - AVISO(Le Traon et al., 1998), OLR - NOAA(Liebmann and Smith, 1996), and Surface winds - NCEP Reanalysis 1(Kalnay et al., 1996). Fig. 1a displays the evolution of SST and surface wind anomalies during the 2006 IOD event. Note that the evolution of the 2006 event is remarkably similar to the composite IOD event(Saji et al., 1999). 6/46

Figure 2. The co-evolution of the time series of DMI (dark grey bars) and equatorial (80-110E, 5S-5N) zonal wind anomalies(light grey bars) during the 2006 IOD event. Note the clear lag in the establishment of SST anomalies over the western (60-80E, 10S - 10N, red line) relative to that over the eastern (90-110E, 10S-Equator, blue line) equatorial Indian Ocean. The time series shown here are normalized by their respective standard deviations. The developing stage (top panel of Fig. 1a) is characterized by cool SST anomalies that appear south of Sumatra and Java; these are accompanied by low SSH anomalies over the same region and strengthened southeasterly trades at the southeastern Indian Ocean; at the same time, the normally eastward directed surface winds along the equator have considerable weakened. Further, at this stage, warm SST anomalies are only weakly developed over the central tropical Indian Ocean; in the ensuing months, cool SST anomalies intensify, while migrating towards the Equator along the Indonesian coastline. Thus, there is a clear lag in the development of warm SST anomalies in the central and west Indian Ocean, with a SST dipole structure clearly established only during the boreal fall (Fig. 2). At this time, the dipole structure is also very clear in SSH and Outgoing Longwave Radiation (OLR) anomalies; the latter field is a proxy for rainfall. The co-evolution of the zonal SST gradient with the equatorial zonal wind anomalies, clearly depicted in Fig. 2, is a characteristic feature of IOD events; further, it provides strong empirical evidence for ocean-atmospheric interaction as the inherent mechanism driving IOD. IOD’s temporal evolution is characterized by an initiation phase in late spring to early summer; there is a rapid development of coupled variability during summer and a peak in fall; the dissipating phase, occurring in early winter, is characterized by the abrupt disappearance of cool SST anomalies in the eastern Indian Ocean. It can be shown that the SST, SSH and OLR fields displayed in Fig. 1 are coupled to each other. The 7/46

low SSH anomaly, in the eastern half, is a dynamic response to the equatorial easterly wind anomaly and is shaped by equatorial and coastal Kelvin waves(Feng et al., 2001; McCreary, 1976; S. A. Rao and Behera, 2005; S. A. Rao et al., 2002). Low SSH anomalies, in turn, lead to cool SST anomalies through oceanic processes, such as entrainment, that couple the subsurface and surface ocean temperatures(Behera et al., 1999; Cai et al., 2013; Murtugudde et al., 2000; Vinayachandran et al., 2007, 1999).

Figure 3. Numerical simulation of surface winds with a linearized primitive equation atmospheric model forced by a patch of diabatic heating anomalies (shaded contour) over the eastern equatorial Indian Ocean. Thick contours represent diabatic heating anomalies of -0.8 and -1.0 K/day; a rainfall rate of 10mm/day corresponds to a diabatic heating rate of about 2.5 K/day. Over the eastern Indian Ocean, the standard deviation of rainfall during the early summer is about 4mm/day

The feedback loop, described above, discusses how surface winds affect SST through its impact on ocean dynamics. To close this loop, we need to consider how SST anomalies feedback to the surface wind anomalies. The immediate impact of cool SST anomalies is to reduce reduce rainfall in the vicinity of the eastern equatorial Indian Ocean; this introduces diabatic heating anomalies in the middle troposphere that can force additional surface wind anomalies(Gill, 1980). We now show that the nature of surface wind anomalies introduced by the SST anomaly is such that it can further enhance the original SST perturbation and thus lead to the establishment of a positive feedback. For this purpose, we introduce the results of a numerical simulation of surface winds in Fig. 3 to a diabatic heating (shaded contour) representative of IOD events (Hameed et al., 2017). Interested readers are referred to Watanabe and Kimoto, (2000) for the details of the atmospheric model used in this simulation. Here, we only briefly describe the model and the experiment before discussing the 8/46

results further. The atmospheric model (T42 resolution, 20 vertical sigmal levels) uses the so-called primitive equations to model the dynamics of atmospheric flows. The model equations are linearized about a specified background state. For the experiment, we used the model linearized about the boreal summer climatological atmospheric state. Momentum damping in the model is through a linear drag that mimics Rayleigh friction. The model has no physics to generate diabatic heating, so we have prescribed an idealized heating that approximates the horizontal structure OLR anomaly shown in the top panel of Fig. 1a. In the vertical we prescribed the diabatic heating to have a single maximum at the midtroposphere. The surface wind anomalies simulated by the numerical model in response to negative diabatic heating anomalies at the eastern equatorial Indian Ocean have a structure very similar to that shown in Fig. 1. This implies that the response to the SST anomaly acts in such a way as to enhance the SST anomaly further. In other words, a positive feedback loop between surface winds and SST involving thermocline and mid-troposheric thermal perturbations as intermediary processes exists, which can explain the genesis and maintenance of IOD variability. The presence of such a feedback has also been demonstrated in multiple coupled model experiments conducted by various groups all over the world(Behera et al., 2006; Crétat et al., 2016; Gualdi et al., 2003; H. Wang et al., 2016; Yang et al., 2015) The reader may notice that, although the preceding discussion provides an overall description of the coupled ocean-atmospheric processes leading to IOD variability, it fails to explain certain important features that stand out from Figs. 1 and 2. For example, it is not clear why IOD is so strongly phaselocked to the annual cycle, and why it dissipates rather quickly after the peak. Another interesting feature concerns the relation between the SSH and SST anomaly. One can notice that, although the SSH anomaly is very symmetric about the equator at the eastern Indian Ocean, the SST anomaly is manifested only south of the equator. The answer to these unique features of IOD variability can be sought in the mean thermal structure of the Indian Ocean, which is strongly affected by the monsoonal circulation and its strong annual cycle.

Understanding the unique features of IOD variability The IOD phenomenon is generated over a unique atmospheric ocean state over the equatorial Indian Ocean. Understanding this unique state is important for understanding the properties of IOD, including 9/46

its spatial structure and the mechanisms that maintain and dissipate it; further, such an understanding will also help advance projections of future IOD behavior.

Figure 4. Longitude-depth section of ocean temperatures averaged along the equator (5S to 5N). Data are monthly mean climatological values averaged between June and November. Data source: CARS2009(Ridgway et al., 2002)

Fig. 4 depicts the depth-longitude section of ocean temperatures(Ridgway et al., 2002) averaged over 5S to 5N. Along the equator, there is a pronounced zonal tilt of the thermocline in all the three oceans; however, the direction of the zonal thermocline tilt over the equatorial Indian Ocean is unique: the thermocline is deepest at its eastern end - in sharp contrast to the other oceans, where it is shallowest at the eastern end. Over the Pacific and Atlantic oceans, easterly trades drive equatorial upwelling, leading to a shallow thermocline at the east. In addition, the westward wind stress on the equatorial ocean, acting in the presence of the western boundary, makes the thermocline deeper at the west. On the other hand, surface wind stress over the equatorial Indian Ocean is towards the east throughout the year; this creates a deep thermocline in the equatorial eastern Indian Ocean. 10/46

A shallow thermocline is conducive for ocean-atmosphere interaction on the interannual timescales, as the study of the ENSO phenomenon has amply demonstrated(McCreary, 1976; McCreary and Anderson, 1985; Philander, 1990). The shallower the thermocline, the more effectively is the sea surface temperature influenced by temperature perturbations in the thermocline(S. Xie et al., 2002). By effectively coupling the thermocline to the surface ocean, a shallow thermocline allows for feedback from the subsurface ocean to affect the tropical atmosphere, which is sensitive to sea surface temperature perturbations.

(a) Temperature (50m depth)

(b) Chlorophyll

Figure 5. Zones of near-equatorial upwelling are depicted with maps of ocean temperature at 50m (left) and satellite observed surface chlorophyll concentration (right). Surface winds are plotted as vectors on the left panel. Data are monthly mean climatological values averaged between June and November. Data Sources: ocean temperature - CARS 2009(Ridgway et al., 2002), surface wind - NCEP reanalysis(Kalnay et al., 1996), and chlorophyll -SeaWifs(Hooker et al., 1992).

On the other hand, the temperature perturbations in the thermocline are a result of oceanic processes directly related to wind stress perturbations at the ocean-atmosphere interface; one such process occurs when surface winds force internal waves at the thermocline(McCreary, 1976). The subsequent propagation of internal waves, as westward propagating Rossby waves and eastward propagating Kelvin waves, also allows wind perturbations to affect thermoclines located far from the forcing region. The surface wind perturbation can also locally affect subsurface temperatures by changing the rate of oceanic upwelling. Where the thermocline is deep, any subsurface perturbation, in response to wind forcing, has weaker feedback to SST and thereby to winds. Therefore, a deep thermocline is not conducive for oceanatmosphere interaction to develop. In the presence of sharp horizontal gradients, SST can also be effectively modulated by wind-forced ocean currents through advective processes. The eastern edge of the warm pool is an area where such 11/46

processes have been demonstrated to affect air-sea interaction during ENSO events (Clarke, 2008; Gill, 1983; Picaut et al., 1997). However, over the eastern equatorial Indian Ocean, the horizontal temperature gradient is not large enough for such processes to facilitate ocean-atmosphere interaction. Thus, on the face of this unique situation, it may be thought that ocean-atmosphere interactions of the kind present during ENSO cannot develop over the equatorial Indian Ocean. How then does the oceanatmosphere interaction needed to maintain IOD develop in the unique setting of the equatorial Indian Ocean? The answer to this mystery lies in the existence of a unique shallow thermocline region that is maintained by coastal upwelling south of the equator, off Java in the eastern equatorial Indian Ocean(Wyrtki, 1962). Fig. 5a depicts ocean temperatures at 50m depth in the near equatorial Indian Ocean. It is seen that subsurface temperatures at this depth increases dramatically to the east, with the west coast of Sumatra experiencing temperatures in excess of 29◦ C. The climatological surface wind field overlaid on top of the temperature contours reveals that this is closely related to the easterly wind stress experienced by the equatorial Indian Ocean. On the other hand, subsurface temperatures cooler than 25◦ C occur in three zones, two of them located south of the equator. The upwelling associated with all of these zones is evidenced by their high chlorophyll concentrations (Fig. 5b). Here, we focus on the coastal upwelling zone along the Java coast situated around 8◦ S, as it is considered to be the most significant factor in the initiation and maintenance of IOD events (Delman et al., 2016). Here, upwelling is a seasonal phenomenon(Qu et al., 2005; Varela et al., 2016; Wyrtki, 1962), and is caused by along-shore trade winds, which are most effective in producing coastal upwelling between June and November; i.e during the boreal summer monsoon season(Varela et al., 2016). It is to be noted, however, that while the seasonal cycle of these trade winds are important in modulating upwelling on the annual timescale, their perturbation on interannual timescales is not significant enough to produce interannual changes in coastal upwelling intensity. For these, the most important factor is the variability of surface winds over the equatorial Indian Ocean(Chen et al., 2016; Delman et al., 2016), which generate oceanic equatorial Kelvin waves that propagate along the equator and subsequently along the Sumatra-Java coasts as coastally trapped Kelvin waves. 12/46

(a) Depth-Latitude cross section (Aug)

(b) Time-Latitude cross section

(c) Depth-Latitude cross section (Mar)

Figure 6. Upwelling off Java is most clearly developed during the southeast summer monsoon season. Climatological Ocean temperature section at 110 E during August (left) and March (right); note that temperatures between 23 and 24◦ C is shaded white. The annual cycle of mean ocean temperature, averaged from the surface to 50m, is plotted along 110E (middle). Source: CARS 2009(Ridgway et al., 2002)

Fig. 6a depicts a vertical cross-section of ocean temperatures along 110◦ E, from 15◦ S to 8◦ S. The climatological mean temperatures during August are shown in the figure. We can see a sharp rise of isotherms from about 15◦ S towards the Java coast. For example, the 25◦ C isotherm rises from 100m depth around 15◦ S to about 20m depth at the coast of Java. Interestingly, the warmest ocean temperatures in this cross-section do not occur at the surface, as one might expect, but at a depth of 60m around 14◦ S. This core of warm ocean temperatures reflect the transport of relatively warm and fresh water from the Pacific Ocean to the Indian Ocean through the Indonesian seas; the so-called Indonesia Through Flow (ITF) (Sprintall et al., 2009; Wijffels and Meyers, 2004). The coastal upwelling along Java has a strong seasonal cycle. To depict this, we show the depth averaged ocean temperatures from the surface to 50m, as a function of the seasonal cycle in Fig. 6a; off Java, SST cooler than 26◦ C is found between June and November, with the strongest cooling occurring in August; during this period, the cooling also has its maximum meridional extent, reaching as far south as 11◦ S. It is clear that conditions favourable for upwelling, and by implication ocean-atmosphere interaction, exist only during the southeast monsoon. After November, ocean temperatures warm up 13/46

considerably, reaching 27.5◦ C in March at the Java coast; further, the signal of upwelling disappears as the thermocline becomes flat from 15◦ S to the coast of Java, while migrating down to a depth of about 60m; this is associated with the reversal of monsoon winds along the coast of Java during the northwest monsoon, which favours downwelling(Varela et al., 2016), rendering conditions unfavourable for feedback from the thermocline to the sea surface. This annual cycle of upwelling is very important in understanding the strong phase-locking of IOD to the annual cycle. We note that a second zone of oceanic upwelling exists in the central to west Indian Ocean, just to the south of the equator(S. Xie et al., 2002). Here, upwelling is produced by an entirely different property of the surface wind: it is the negative wind curl, between the southeasterly trades and equatorial westerlies, that is responsible for the unique open ocean upwelling zone. Fig. 7 shows the correlation between ocean temperature anomalies at 100m depth with that at the surface, for the four seasons. The data for this calculation is from the SODA2.4 reanalysis(Carton and Giese, 2008), and covers the period 1958 to 2010. In the south equatorial Indian ocean, strong correlation between the subsurface and surface temperatures are noted in the vicinity of the Java upwelling zone; to its west, positive correlations occur in the open ocean upwelling zone in the west and central Indian Ocean. Further, there is a strong seasonality in the correlation, with maximum positive correlations observed during boreal summer and fall. The positive correlation between SST and subsurface temperatures in the eastern Indian Ocean reflect the seasonal cycle of upwelling in this region, with the positive relation disappearing once the northwest monsoon sets in, and the mean thermocline deepens off Java. This effectively cuts off the coupling between the subsurface and the surface, and abruptly terminates the positive feedback loop between the surface wind and SST anomalies. At the same time, the reduced cloudiness associated with IOD (Fig. 1c) implies that the ocean surface receives abnormally high solar insolation, which tends to increase the SST anomaly. This is reflected in the negative correlation between SST and subsurface temperatures seen in Fig. 7 during the boreal winter, with positive SST anomaly developing at the end of the IOD cycle, while subsurface temperatures are still cooler than normal. Thus, it is the unique seasonal setting of the equatorial subsurface ocean, driven by the strong annual cycle of the Asian monsoon, that controls the spatio-temporal features of the IOD event - in particular, its strong phase-locking to the seasonal cycle and its abrupt dissipation in early boreal winter(Saji et al., 1999). 14/46

Figure 7. Correlation coefficients of ocean temperature at 100 m depth with that at the surface over the tropical Indian Ocean during boreal (a) spring, (b) summer, (c) fall and (d) winter. Thick line contours denote absolute correlations of 0.4 and 0.6. Data Source: SODA reanalysis v2.4(Carton and Giese, 2008)

Positive and negative IOD events So far, we introduced IOD as a powerful modulator of Indian Ocean climate that renders the normally warm and wet climate of the eastern Indian Ocean cooler and dryer than normal. This is however only the positive phase of IOD; in its negative phase, IOD drives Indian Ocean climate in the opposite direction, rendering wetter than normal conditions at the equatorial eastern Indian Ocean. To depict this remarkable see-saw in ocean-atmosphere state during the positive and negative phases of IOD, Fig. 8 shows the state of SST, rainfall, surface winds, and sea level during two strong IOD events. In this figure, the data are not plotted as departures from normal conditions, but the actual state of the variable itself is plotted. In each sub-figure, the top panel depicts conditions during the strong negative IOD of 2005; the bottom panel is for the 2006 (positive) IOD. What is remarkable about the figures is the striking east-west migration of climate state between the phases of IOD. In the negative phase, extremely warm SST covers the eastern Indian Ocean, nearly cutting off the seasonal upwelling off Java (Fig. 8a,8c); there is a pronounced zonal SST gradient, with temperatures clearly increasing from west to east along the equator. As a result of the warm eastern 15/46

temperatures (Fig. 8b), heavy rainfall occurs over Indonesia (Fig. 8c), while conditions are markedly dry in the western Indian Ocean. These are accompanied by very strong westerlies over the central equatorial Indian ocean, leading to a pronounced east-west slope in the sea-level(Fig. 8d). These conditions nearly reverse during a strong positive IOD event, when the warmest temperatures around the equator are not at the eastern end, but over the central Indian Ocean. SST over the eastern Indian Ocean drops markedly below 27◦ C, leading to a nearly complete disappearance of rainfall (D’Arrigo and Wilson, 2008; Saji and Yamagata, 2003a) over the southeastern Indian Ocean; instead, the rainband migrates to the warmer oceans to the north and west, leading to unusually persistent and abundant rainfall over Sri Lanka(Zubair et al., 2003) and Equatorial East Africa(Behera et al., 2005; Birkett et al., 1999; Conway, 2002; Flohn, 1987; Saji and Yamagata, 2003a; Ummenhofer et al., 2009b). There is a complete disappearance or reversal of the westerly surface wind jet over the equatorial central Indian Ocean, which nearly flattens or reverses the east-west slope of sea-level. So far, we have looked at the properties of IOD through the two events of 2005 and 2006. It is of interest to examine other IOD events that are recorded in the observational record, and to see how their features compare with that during 2005 and 2006. In order to do this, we have identified positive and negative IOD events for the period 1958-2015, based on the methodology of Saji and Yamagata, (2003b). This methodology not only uses the DMI to detect IOD events, but also checks if SST anomalies over the eastern Indian Ocean and surface wind anomalies over the equatorial Indian Ocean are in the right phase. Specifically, SST over the eastern Indian Ocean is required to be cooler than normal for the event to be classified as a positive IOD, and warmer than normal for it to be a negative IOD. Strong events are IOD’s that are required to have DMI and other criteria above 1 standard deviation for more than 3 months; however, moderate events need only hold these criteria above the 0.5 standard deviation threshold. Further, the basin wide anomaly associated with ENSO evolution was filtered out of the SST anomalies, as discussed in section 6. Using this methodology, six strong positive IOD events (1961, 1963,1967,1994,1997 and 2006) and six strong negative IOD events (1960, 1975,1992,1996,1998 and 2005) are seen to occur between 1958 and 2015; Fig. 9 depicts all the IOD events detected during this period. To a first order, negative IOD events are the mirror image of positive IOD events; however, there are 16/46

some important differences(Cai et al., 2012; Ng and Cai, 2016; Ummenhofer et al., 2009b). Figure. 11 illustrates this using SSH and zonal wind anomalies during positive (a,c) and negative (b,d) IOD events. A prominent difference is in the amplitude - positive events are stronger than negative events. This can be understood as a consequence of the deep thermocline in the eastern Indian Ocean: a shallowing thermocline is more effective in generating a surface cooling than a deepening one is in generating surface warming(Cai et al., 2013). The Fourier spectra of the DMI time series, shown in Fig. 10a, brings out three distinct peaks at 2, 3 and 4.5 years. Interestingly, this spectra is unique in that there is little indication of an increase of power with period. The clear biennial signal reflects the tendency for IOD events of opposite phase to follow each other(Feng and Meyers, 2003; Saji et al., 1999); the time scale is determined by the width of the Indian Ocean, and the time taken for equatorial waves to travel to the western boundary from the eastern Indian Ocean, and back to the eastern boundary after reflection(Feng and Meyers, 2003). The 3 and 4.5 year timescales appear to be return periods associated with IOD, with the periodicity shifting over time (Fig. 10b, Ummenhofer et al., 2017). For example the strong 3 year peak is associated with a prominent 3-year return period of IOD during the 1990’s. Also notice the longer return periods observed before and after the 1990’s. To examine the common structure of IOD events, we resort to a correlation analysis using the DMI time series. In Figs.12 and 13, the covariation of September to November (SON) averaged data of surface winds, SST, land rainfall and marine cloudiness anomalies with DMI is depicted. All the data are correlated with SON values of DMI, the season when IOD attains its peak. Besides this, the June to August (JJA) averaged values of rainfall and marine cloudiness is also correlated with SON DMI. The correlation of DMI with surface wind and SSH anomalies for the boreal fall season is shown in Fig. 12, while that with marine cloudiness and land rainfall is shown in Fig. 13. The remarkable structural similarity between these maps and the case study shown in Fig. 1 is very striking. In addition to this, the correlations maps also help gauge the contribution of IOD to regional variability. As expected (Fig. 2), there is a strong correlation between the SST variability associated with IOD and equatorial zonal winds, with the correlation coefficient exceeding 0.8 over the central equatorial Indian Ocean. Similarly, IOD can be seen to contribute to a significant part of the sea-level variability 17/46

over the tropical Indian Ocean. The prominent dipole structure of SSH anomalies and SST anomalies associated with IOD during the peak phase is also reflected on rainfall anomalies (Fig. 13). Over the ocean, there are no sustained direct observations of rainfall. The only dataset that can be considered a proxy for rainfall variations prior to the satellite era is marine cloudiness; these are essentially observations of cloud cover from a ship deck. Correlating cloudiness anomalies with the DMI provides a qualitative look at the structure of rainfall patterns associated with IOD. There is a striking similarity with the pattern of rainfall anomalies observed during 2006. Further, the correlation map nicely align with that obtained from correlation analysis between DMI and land rainfall, completing the picture of IOD’s signature on regional rainfall.

IOD-ENSO Interaction The mechanisms that generate and sustain IOD lie entirely within the equatorial Indian Ocean, just as ENSO’s genesis mechanisms lie entirely within the Pacific basin. However, the physical proximity between the Indian and Pacific basins and the existence of oceanic(Wijffels and Meyers, 2004) and atmospheric pathways (Barnett, 1984; Hameed et al., 2017) between them raise questions on the possibility and nature of interactions between the two phenomenon - a question first raised by McCreary and Anderson, (1985). The problem of IOD-ENSO interaction is rendered interesting due to the significant correlation that exists between the time series of IOD and ENSO during the boreal fall season: SON values of DMI and Nino3 are correlated at 0.55. This correlation has been interpreted in multiple ways over the years (for a review, see Saji and Yamagata, (2003b) and Yamagata et al., (2004)). Co-occurences of El Nino with positive IOD, and La Nina with negative IOD, accounts partly (Fig. 9) for the large correlation. However, external factors such as the global warming trend, decadal variations, and the ENSO-induced basin-wide SST anomaly introduce spurious zonal SST gradient in Indian Ocean SST(Saji and Yamagata, 2003b), which also partly contributes to DMI’s high correlation with Nino3 during boreal fall. Before we can interpret the correlation between DMI and Nino3 indices in terms of physical processes, it is necessary to remove spurious zonal gradients introduced by extraneous factors. As mentioned in section 3, the need to construct DMI as a zonal difference of SST anomaly was to filter out ENSO18/46

induced basin-wide SST anomaly Because of this differencing procedure, any zonally inhomogeneous extraneous signal will project on the DMI; neither the global warming signal(Alory et al., 2007), nor the decadal signal(Han et al., 2014) in Indian Ocean SST is zonally homogeneous; this has been shown to bias the correlation between IOD and ENSO(Meyers et al., 2007; Saji and Yamagata, 2003b). The magnitude of the spurious DMI introduced by ENSO-induced basin-wide SST anomaly is particularly large(Saji and Yamagata, 2003b). The basin-wide SST anomaly is a consequence of equatorial atmospheric adjustment to ENSO’s mid-tropospheric diabatic heating perturbations(Yulaeva and Wallace, 1994): atmospheric Kelvin waves that emanate from the diabatic heating anomalies spread the ENSO signal over the equatorial belt; the ENSO signal is non-uniformly expressed on Indian Ocean SST anomalies, with the signal established much later over the eastern (Meyers et al., 2007; Saji and Yamagata, 2003b) than over the western and central tropical Indian Ocean (eastern Indian SST anomaly maximally correlates with Nino3 at six-months lag, while western Indian SST anomaly lags it by only 3 months). The definition of DMI assumes that there is no zonal homogeneity in the ENSO footprint, but as discussed this is not correct; hence, a spurious zonal SST gradient not associated with IOD is introduced. The spurious effect may be reduced by eliminating the estimated basin-wide anomaly: one way is using lagged regression with ENSO (Saji and Yamagata, 2003b); another way is to use lagged EOF analysis (Meyers et al., 2007). Removing the spurious DMI values resulted in the correlation dropping to 0.43 from the original value of 0.55 ( a 39% drop in the variance explained relative to the original). If the correlation, reported in the preceding paragraph, is interpreted in terms of the rate of cooccurrence between the two modes, a correlation of 0.43 implies that about 19% of IOD and ENSO events co-occur. In Fig. 9, we have also denoted the co-occurrence of ENSO events during moderate and strong IOD events. ENSO is considered to have co-occurred if the Nino3 index exceeded a normalized amplitude of 0.5σ for moderate IOD events and 1σ for strong IOD events, when averaged over the period of IOD development (June to November). From this, it can be seen that the positive IOD events of 1963, 1972, 1976, 1982 and 1997 co-occured with El Nino events, while the negative IOD events of 1964, 1971, 1981, 1996 and 2013 co-occurred with La Nina events. However, this implies a higher co-occurrence rate of about 30%. Thus, the interpretation of the correlation representing co-occurrence is perhaps not appropriate. 19/46

Then, why is the ENSO-IOD correlation substantially weaker in the light of a 30% rate of cooccurrence? A clear explanation emerges from the consideration of the high scatter between the intensity of IOD and ENSO events (Fig. 14). Strong IOD events occur during strong (1963,1997) and weak (1994,2006) El Nino as well as La Nina (1961,1967) events. Notice also the lack of IOD events during the strong El Nino of 2015 and the strong La Nina of 2007. From Fig. 14, one can notice that there is a propensity for La Nina and negative IOD to often happen together. This is also seen in the correlations, when upon removing negative IOD events from the calculation, the correlation becomes insignificant (0.11). The regression line (red line) between Nino3 and DMI is also shown in Fig. 14. One can see that it is not a very good fit for most of the data points and is strongly affected by outliers. In fact, upon removing the five extreme years of 1996,2010 (negative IOD with La Nina) and 1972,1982 and 1997 (positive IOD with El Nino) the correlation drops to 0.28. One way to account for the consistent, but non-linear relation between IOD and ENSO, is that ENSO can on occasion trigger IOD (Annamalai et al., 2003; Feng and Meyers, 2003), with the strength of IOD controlled by the character of coupled-instability and external noise (such as those associated with intra-seasonal oscillations) within the tropical Indian Ocean basin, while on other occasions IOD may be triggered from within the Indian Ocean. For this argument to be valid, ENSO dynamics should be capable of introducing significant anomalies over the eastern Indian Ocean, which is the key region for the genesis of IOD events. Fig. 15 shows the correlation of Nino3 index with SST and its regression with surface winds over the period 1958 to 2015. Due to the correlation with IOD, such an analysis would also contain the impacts of IOD as well. To deduce the impact of ENSO alone there are various possible ways. For example, Saji and Yamagata, (2003a) uses partial correlation analysis. Here, as an alternative method, we removed co-occurring events from the analysis: specifically, we removed the 1972,1982,1998,1997 and 2010 events before performing the correlation/regression analysis. In early summer, ENSO is associated with weak westerly wind anomalies over the equatorial Indian Ocean (Fig. 15, also see Fig. 14 of Saji and Yamagata, (2003b)), that act to decrease upwelling over Java. However despite this, one may notice weak cool SST anomalies between Java and Australia. Wajsowicz and E. K. Schneider, (2001) have shown that reduction of the Indonesian Throughflow can result in cool SST anomalies between Java and Australia. During El Nino, the Indonesian Throughflow is reduced due to the lowering of sea-level over 20/46

the western Pacific. Therefore despite the sign of ENSO associated winds being unfavourable, cool SST can be introduced over the eastern Indian Ocean. However the core of the Indonesian throughflow is clearly separated from the Java upwelling zone (Fig. 6a) and the SST anomaly thus introduced is quite weak. Therefore it is questionable whether ocean-atmosphere teleconnections associated with ENSO are strong enough to trigger an IOD event early on its development phase. The signal does get stronger in the fall season, as easterly wind anomalies associated with ENSO get stronger (Saji and Yamagata, 2003a,b). However by this time, it may be too late to initiate IOD development. On the other hand, when an ENSO event co-occurs with an IOD, these mechanisms may strengthen the cooling over the eastern Indian Ocean and allow the IOD event to persist longer. However, since correlation does not provide any information on the direction of causality, it is perfectly logical to consider the other alternative, viz., that it is IOD that affects ENSO. Hameed et al., (2017) provides an analysis that demonstrates that IOD events can have a strong impact on ENSO evolution. They argue from observational analysis and modeling experiments that the 1994 and 2006 El Nino like events were a result of IOD forcing. Further, they demonstrate that super El Ninos - the likes of 1972, 1982, and 1997 events that are characterized by strong SST variability in the far-eastern Pacific - are a result of the interaction between IOD and ENSO dynamics.

Regional Impacts The most striking feature of IOD is the marked dipole structure, signifying an opposing tendency of oceanatmosphere perturbations oriented in a zonal direction across the Indian Ocean. This marked east-west migration of ocean-atmosphere state associated with IOD drives significant changes in the the oceanic and atmospheric environment with severe implications for regional societies, economies and ecosystems. We will review some of these as described in the literature. For simplicity, we will interpret these impacts with reference to the positive phase of IOD. In terms of ocean circulation, one of the most significant features of the Indian Ocean is the Indonesian throughflow (ITF). Being the only major low-latitude connection in the global oceans, the Indonesian seas permit the transfer of Pacific waters into the Indian Ocean. The importance of the ITF for the global thermohaline circulation(Hirst and Godfrey, 1994; Schiller et al., 1998) and global atmospheric 21/46

circulation(N. Schneider, 1998) are well recorded in the literature. On the seasonal timescales, the strong annual cycle of the monsoons have a strong impact on the ITF transport(Masumoto and Yamagata, 1996), with the strongest transports during the southeast monsoon, when coastal upwelling is well developed and sea level shallows at the Java coast(Wyrtki, 1987). The strong wind perturbations at the equator during IOD events (Fig. 1, 8c) can therefore substantially modulate the ITF(Meyers, 1996; Sprintall et al., 2009) through the wind’s impact on sealevel at the eastern Indian Ocean (Fig. 8d). During a positive IOD event, the sealevel becomes abnormally low over the eastern Indian Ocean through the impact of equatorial winds. This leads to an increase of the pressure head between the Pacific and Indian Oceans(Masumoto and Yamagata, 1996; Meyers, 1996; Wijffels and Meyers, 2004; Wyrtki, 1987) and can increase ITF transport during positive IOD years. However, some of the positive IOD events have in the past occurred at the same time as an El Nino event. El Nino events are associated with reduced sea level in the western Pacific. This can lead to a decrease of the pressure head between the oceans and lead to a reduced ITF transport. Only in recent years is detailed observations of the ITF beginning to be available. These observations are beginning to provide insights into the control of ITF during IOD and ENSO events (Hu and Sprintall, 2016; Liu et al., 2015; Meyers, 1996; Sprintall and Révelard, 2014; Wijffels and Meyers, 2004). Of particular interest are the observations described by (Sprintall et al., 2009) of the opposing anomalies of ITF transport during the negative and positive IOD events of 2005 and 2006. Staying with the ocean, another important implication of the anomalies of sealevel and upwelling intensity during IOD is on Indian Ocean’s biological productivity. The seasonal upwelling off Java controls biological productivity in that region (Fig. 5b); thus, the strong SSH variability during IOD events have large implications for fisheries, both coastal and offshore (Amri, 2012; Lumban-Gaol et al., 2015). It is during the upwelling season that catches of pelagic species such as sardine(Ghofar, 2005) and tuna (Lumban-Gaol et al., 2015) are the highest. Further, offshore transport of the upwelled water and nutrients, by submesoscale filaments and eddies, inject plankton-rich waters into key fish spawning areas(Matsuura et al., 1997), which lie south of Java. However, fish catch is not only related to fish abundance, but it is also constrained by practical matters such as the depth to which fishing lines are cast. An interesting situation is seen with respect to the Bigeye tuna(Thunnus obesus), which are abundant in the eastern Indian Ocean; these prefer an ambient temperature of 10-15◦ C, which is normally found at 22/46

depths of 150m to 400m(Hanamoto, 1987; Lumban-Gaol et al., 2015). However, the tuna longline used in conventional fishing vessels is set to reach water depths of only 100m to 280m. While biological productivity off Java is controlled by IOD, the thermocline depth of the waters where Bigeye tuna is abundant is also strongly controlled by the ITF transport. Although during positive IOD years, the thermocline can migrate upwards leading to a shallowing of the Bigeye tuna fishing layer by at least 50m(Lumban-Gaol et al., 2015), the increased ITF transport can counter this. When a positive IOD co-occurs with El Nino enhanced biological productivity can lead to fish abundance; at the same time, the shallower thermocline enables the tuna longline to penetrate deep into the fishing layer, thereby increasing the fish catch. These observations appear to be consistent with the substantial increases of Bigeye tuna catch during the cooccuring IOD/El Nino of 1997(Amri, 2012; Lumban-Gaol et al., 2015; Syamsuddin et al., 2013), and with the observation that the catch was not substantially high during the 1994 IOD event. In contrast, at the western Indian Ocean, primary productivity is negatively correlated with IOD(Lan et al., 2013): Lan et al., (2013) find that during positive IOD events, catch distributions of tuna were restricted to the northern and western margins of the Indian Ocean; further, they find that during negative IOD events tuna catches expands into the central regions of the western Indian Ocean; these observations are consistent with the sea-level distribution shown in Fig. 8d associated with positive and negative IOD events. On the other hand, the zonal variation of IOD’s precipitation across the Indian Ocean wrecks havoc on countries situated both on the eastern and western coasts of the Indian Ocean. During positive IOD events, Indonesia suffers from severe droughts (Saji and Yamagata, 2003a); these, persisting over two seasons (Fig. 13), often trigger forest fires with severe implications for air quality and health across the whole of Southeast Asia (Kunii et al., 2002). The forest fires of Indonesia are unique in that the continuous burning of peat, for a season or more(Page et al., 2002), produces high aerosol concentrations, leading to significant reduction of visibility(Y. Wang et al., 2004); the seed for these fires are anthropogenic activities (related to forest degradation and clearance activities), whose impact is magnified by the droughts(Wooster et al., 2012). Since the 1980’s, the rapid and dramatic change of land-use and population density over Indonesia has further exacerbated this problem(Field et al., 2009). Indonesian drought and forest fires are traditionally ascribed to El Nino, but, after the discovery of IOD, it is becoming clear that the droughts over parts of Indonesia are more strongly controlled by IOD(Saji and Yamagata, 2003a); 23/46

this explains the large droughts during IOD years of 1961, 1963, 1991, 1994 and 2006(Field et al., 2009), which occurred when conditions were near normal in the Pacific. Although IOD brings in higher than normal rainfall over Equatorial East Africa(Behera et al., 2005; Saji and Yamagata, 2003a), this impact is usually more disastrous than beneficial. IOD’s impact on rainfall is felt strongly during the so-called short rainfall season of October-November-December; the rainfall anomalies are strongest between 10◦ S and 10◦ N, and extend from the Indian Ocean coast till about 25◦ E (see Fig. 13 and Fig. 16, also (Conway et al., 2005)). In strong IOD years, massive hydrological anomalies occur: over the 1961-1964 period, which experienced two strong IOD events, the cumulative river flow anomaly for the White Nile upstream of the Sudd, the Blue Nile, Atbara, Congo, Tana, and Zambezi rivers was 1428 km3 , totalling the annual flow of the Congo(Conway et al., 2005) - the second largest river in the world, by discharge; after the 1997 IOD event, levels of Lake Victoria, Lake Tanganyika, and Lake Malawi rose by ca. 1.7m, 2.1m and 1.8m respectively (Birkett et al., 1999). The widespread flooding and inundation of low lying areas, during positive IOD years, also incur major socioeconomic impacts across East Africa, due to damage to life and infrastructure(Conway, 2002; Odingo, 1962), massive displacement of people from flooded areas(Conway et al., 2005), and infectious diseases(Hashizume et al., 2012; Munyua et al., 2010; Nguku et al., 2010). A particularly severe health hazard is the Rift Valley Fever (RVF), a mosquito-borne viral disease primarily affecting domesticated animals, that also causes mild to life-threatening disease conditions in humans(Baba et al., 2016). In 1997, RVF was widespread in the Horn of Africa, involved 5 countries, and caused a loss of ca. 100,000 domestic animals and ca. 90,000 human infections(Woods et al., 2002). The economic impact was further amplified due to the bans on livestock export from the region(P. D. Little et al., 2001). In 2006, RVF returned to East Africa, causing 75,000 human infections and 350 deaths, while spreading to larger areas than in 1997. The wide-ranging impacts of the disease on the livestock and other sectors induced an economic loss of over Ksh 2.1billion (US$ 32 million) on the Kenyan economy alone(Rich and Wanyoike, 2010), and caused overall economic losses in East Africa exceeding $60 million(P. Little, 2009). Note that rainfall anomalies over equatorial East Africa are often incorrectly ascribed to ENSO(Saji and Yamagata, 2003b). The reason why ENSO appears to be associated with rainfall anomalies over Equatorial East Africa is due to the co-occurrence of IOD and ENSO events. To illustrate this, and to 24/46

demonstrate the importance of IOD for East African short rains, we present four situations in Fig. 16. The top and bottom panels on the left depict composite rainfall anomalies over East Africa during positive IOD and El Nino years respectively; on the plots shown on the right, we however removed co-occurring years before the composite was calculated, in order to reveal the true impacts of IOD and ENSO. It is clear that ENSO does not strongly affect East African rainfall variations; this conclusion is in agreement with many modeling studies(Behera et al., 2005; Ummenhofer et al., 2009b). Regional impacts of IOD are also moderately felt over Australia(Ashok et al., 2003; Saji and Yamagata, 2003a), where the spatial signature of IOD varies a bit with the season(Fig. 13), and is mixed with that due to ENSO (Meyers et al., 2007; Risbey et al., 2009). Risbey et al., (2009) found that years with co-occurring negative IOD and La Nina increased rainfall over southeastern Australia, while the opposite occurred during years with an El Nino and positive IOD event. Ummenhofer et al., (2009a) suggested that IOD more than ENSO was the key driver of major droughts in the region of southeastern Australia; they demonstrate that the IOD impact explained nearly all of Australia’s iconic droughts of the twentieth century. Cai et al., (2009b) suggested that the relative preponderance of positive IOD events since the 1950s potentially accounted for a large proportion of the long-term decline in austral autumn southeastern Australian rainfall; Cai et al., (2009a) showed that out of the 21 significant bushfire seasons since 1950, 11 were preceded by a positive IOD event. Yuan and Yamagata, (2015) have found that wheat yield is reduced (increased) by 28.4%(12.8%) during positive(negative) IOD events. Surprisingly, the IOD influence over India as estimated by statistical methods appear to be weak (Saji et al., 1999). However, this is likely a consequence of IOD-ENSO interaction(Ashok et al., 2004, 2001) : when a positive IOD co-occurrs with an El Nino, the IOD induced enhancement of monsoon rainfall is countered by the suppression of the same rainfall by El Nino’s atmospheric teleconnections; they further show that during decades of weak ENSO variability, IOD is strongly correlated with Indian summer monsoon rainfall.

Future Directions As the article demonstrates, we have a fair understanding of the essential nature of the IOD phenomenon, its structure, and genesis mechanisms. There is also a great deal of progress made in exploring and understanding the impacts of IOD. There are a number of areas where further research can yield a richer and 25/46

deeper understanding of this important phenomenon and its impacts. Firstly the atmospheric and oceanic dynamics controlling IOD variability needs to be systematically explored at a basic level, especially with simplified models. Most of the understanding of IOD’s structure and mechanisms are arrived at through analysis of data obtained from observations or reanalysis products and from complex numerical simulations. However these need to be connected to the laws of fluid motion and the structure of IOD explained on the basis of these laws, so that a rich theory of IOD variability can be built upon the framework of geophysical fluid dynamics. Although this can to some extent be done by adapting the rich framework of ENSO theory, IOD variability has many unique characteristics that set it apart from ENSO. A large part of this is due to the strong annual cycle of the background state upon which IOD develops, which in turn is controlled by the monsoonal annual cycle. There are several specific areas where a dedicated study of IOD dynamics can enrich the theory of coupled ocean-atmosphere interaction. The interaction of planetary and Kelvin waves with a time varying mean state can not only deepen the understanding of the temporal aspects of IOD variability, but can be a key in understanding the seasonal structure of teleconnections excited by IOD. In the latter regard, this will also help further the understanding of the interaction between IOD and ENSO.

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(a) SST

(b) Rain

(c) Surface wind

(d) SSH

Figure 8. Absolute fields of (a) SST, (b) rainfall, (c) surface winds, and (d) SSH during opposite phases of IOD. The plots on the top row of (a), (b), (c), and (d) represent absolute fields during the mature phase of the 2005 negative IOD; bottom rows of (a), (b), (c), and (d) are similar, but for the 2006 positive IOD. Data sources are the same as in Fig. 1, except for rainfall which is from CMAP(P. Xie and Arkin, 1997).

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Figure 9. Time series of standardized DMI, averaged from June to November, from 1958 to 2015; orange(blue) bars indicate strong positive(negative) IODs; light green(turquoise) bars are moderate positive(negative) IODs. If, for a strong IOD, June to November averaged Nino3 exceeded 1σ , that El Nino/La Nina is marked by a filled circle. If, for a moderate IOD, June to November averaged Nino3 exceeded 0.5σ , that El Nino/La Nina is marked by an open circle. Data Source: COADS(Woodruff et al., 2011) from 1958 to 1981 and OISST(Reynolds et al., 2002) thereafter.

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(a) Power spectrum of DMI

(b) Wavelet spectrum of DMI

Figure 10. Spectral analysis of standardized DMI values averaged from June to November. The fourier spectrum shown in (a) has three distinct peaks around 2, 3 and 4.5 years. Peaks significant at the 95% significance level against the background spectra (red line), is shaded. The wavelet spectra shown in (b) suggests that the multiple peaks are associated with shifts of IOD periodicity over time.

Figure 11. Composite anomalies of SSH (left) and zonal surface wind (right) during positive (a,c) and negative (b,d) IOD events. Data sources: SSH - AVISO(Le Traon et al., 1998) and Surface winds NCEP reanalysis 1(Kalnay et al., 1996). 42/46

(a) zonal wind

(b) SSH

Figure 12. Correlation of (a) zonal winds and (b) with the DMI index for the boreal fall season. Data sources: SSH - SODA(Carton and Giese, 2008) is for the period 1958 to 2010. Surface winds - NCEP reanalysis 1(Kalnay et al., 1996) is for the period 1958 to 2015.

(a) JJA

(b) SON

Figure 13. Correlation of SON DMI with land rainfall and marine cloudiness during (a) JJA and (b) SON. Marine cloudiness and land rainfall were separately correlated with DMI and the correlation maps were then merged together for this plot. Data sources: Cloudiness - COADS(Woodruff et al., 2011). Land rainfall - GPCC(Ziese et al., 2011). Both data are for the period 1958 to 2014.

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Figure 14. A scatter plot with Nino3 on the x-axis and DMI on the y-axis. Both indices were averaged for the September to November period and normalized by their respective standard deviations. Years of occurrence are shown for events exceeding 1σ . IOD events occurring during El Nino are colored red, and that during La Nina are blue. Data Source: COADS(Woodruff et al., 2011) from 1958 to 1981 and OISST(Reynolds et al., 2002) thereafter.

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(a) JJA

(b) SON

Figure 15. Correlation of SON Nino3 index with Indian Ocean sea surface temperature anaomalies during (a) JJA and (b) SON. Also shown as vectors are the regression of Nino3 index with surface winds. To reduce the impact of co-occurring IOD -ENSO events on this analysis, the five extreme years of 1998, 2010 (negative IOD with La Nina) and 1972, 1982 and 1997 (positive IOD with El Nino) were removed before the correlation and regression analysis was carried out.

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(a) All events

(b) Non-cooccurring events

Figure 16. Composite rainfall anomalies of October-November-December averaged rainfall over Equatorial Africa are shown for (top) positive IOD events and (b) El Nino events over the period 1958 to 2014. In Fig. 16a all events are considered. However in Fig. 16b, co-occurring events of 1963,1972,1976,1982 and 1997 are removed from both the top and bottom plots. The IOD years considered in Fig. 16a are those during 1961, 1963, 1967, 1972, 1976, 1982, 1983, 1991, 1994, 1997, 2006, 2008, 2011 and 2012. The El Nino years considered are those during 1963, 1965, 1972, 1976, 1982, 1986, 1987, 1997, 2002, 2004, 2009 and 2014.

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