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SST variability in the eastern intertropical Indian Ocean – On the search for trigger mechanisms of IOD events
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SST variability in the eastern intertropical Indian Ocean – On the search for trigger mechanisms of IOD events J. Kämpf , A. Kavi ⁎
College of Science and Engineering, Flinders University, Adelaide, Australia
ARTICLE INFO
ABSTRACT
Keywords: Indian Ocean Dipole Climate Variability Coastal upwelling Intertropical Oceanography
This study focusses on the variability of sea surface temperature in the eastern intertropical Indian Ocean off the coasts of Sumatra and Java - a region that is central to the Indian Ocean Dipole (IOD). Using multiplatform satellite and ARGO data, here we show that the appearance of vast areas of anomalously cold surface water off the southwest Sumatran coast during positive IOD (pIOD) events is preconditioned by upwelling favourable winds along Sumatra's central west coast (1–3°S). A simple theory of coastal upwelling along coasts of finite horizontal extent, developed in this work, can explain this SST response. While this theory is consistent with the pIOD events of the years 1994, 1997 and 2006, it uncovers the existence of another strong upwelling event in the year 2011 that is missed when calculating average SST anomalies in the wider region (i.e. the Dipole Mode Index). In addition, we also derived the surface circulation establishing in the study region during pIOD events. This circulation pattern indicates that equatorward transport of colder water by a classical upwelling jet (that we call the South West Sumatra Current) reinforces negative (cold) SST anomalies off southwest Sumatra.
1. Introduction The climate of the central and northern Indian Ocean and adjacent continents is strongly modulated by monsoons. Southwesterly winds exist over the Indian Ocean during May–September, whereas northeasterly winds prevail during November–February. This seasonal wind change leads to seasonal variations of the oceanic circulation with some regions experiencing a complete reversal of the circulation, not seen in any other ocean (Schott and McCreary, 2001; Tomczak and Godfrey, 2003). The average winds over the equatorial Indian Ocean, particularly their zonal component, are weak during monsoons and, unlike in the Atlantic and Pacific, equatorial upwelling is absent (Schott and McCreary, 2001). Relatively strong westerly wind bursts, however, appear during the transition between the monsoons, first during April–May (spring) and then again during October–November (autumn). These winds drive strong eastward currents along the equator, attaining speeds exceeding 1 m/s, called Wyrtki jets (Wyrtki, 1973). Propagating eastward in the form of internal equatorial Kelvin waves, Wyrtki jets induce a transient deepening of the thermocline near the eastern boundary. In addition, these jets also induce a net eastward shift of warm water, which leads to a poleward widening of the warm water pool near the eastern boundary. Vinayachandran et al. (2009) suggest
⁎
that this deepening and widening of the warm water pool in the eastern equatorial Indian Ocean due to Wyrtki jets is fundamental in the functioning of the coupled air-sea monsoon dynamics in the region. The Indian Ocean Dipole (IOD), first described by Saji et al. (1999), is an irregular fluctuation of sea-surface temperatures between the eastern and western intertropical Indian Ocean. This feature is monitored and described by the Dipole Mode Index (DMI) as the differences of SSTs between defined areas displayed in Fig. 1a. The regions were selected based on an empirical orthogonal function (EOF) analysis, and the term “dipole” was based on the relatively strong correlation (~ 0.7) between the DMI index and the dominant mode of variability yielded from the EOF analysis; that is, the IOD is the second EOF mode of SST variability in the Indian Ocean (Saji et al., 1999). Occasionally, the eastern tropical Indian Ocean, in particular the coast ocean off southwestern Sumatra, experiences widespread cooling during late austral winter months (July–September). SST anomalies during such cooling phases are comparable to those triggered by ENSO in the eastern equatorial Pacific in terms of magnitude (> 2 °C), spatial extent (600 km by 200 km), and duration (> 3 months). Positive IOD (pIOD) events are traditionally defined by DMI values exceeding 1 °C for a continuous period exceeding 12 weeks. After this definition, only three significant cooling events can be identified in the years 1994, 1997, and 2006 (Fig. 1b). With used of a slightly “softer” DMI
Corresponding author. E-mail address: [email protected] (J. Kämpf).
https://doi.org/10.1016/j.dsr2.2018.11.010
0967-0645/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Kämpf, J., Deep-Sea Research Part II, https://doi.org/10.1016/j.dsr2.2018.11.010
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Fig. 1. a) Definition of the surface areas used to calculate the DMI. The western region is defined by (50–70°E, 10°S to 0°N) and the eastern region by (90–110°E, 10–0°S). b) Time series (1988–2017) of SST anomalies in the areas shown in a) and the corresponding Dipole Mode Index traditionally defined as DMI = SSTwest − SSTeast. The arrows highlight three pIOD events (1994, 1997 and 2006). Source: Reynolds SST data.
completeness, it should be noted that Wyrtki (1962) was the first to observe and report the existence of upwelling off Java, whereas Susanto et al. (2001) provided a first detailed analysis of coastal upwelling off Java and Sumatra, the latter playing a central role in pIOD events.
threshold, pIOD events can also be attributed to the years of 2007 and 2008 (Luo et al., 2008; Cai et al., 2009). Contrary to the meaning of the term “dipole”, it should be pointed out the observed major cooling events in the eastern DMI area were not accompanied by pronounced warming events in the western area (see Fig. 1b). Hence, the nomenclature of this climatic index may be misleading. The IOD is an important mode of coupled atmosphere-ocean variability that affects human health in surrounding countries (Saji et al., 1999; Webster et al., 1999; Huang and Kinter, 2002; Saji and Yamagata, 2003; Vinayachandran et al., 2009). pIOD events are characterized by devastating floods over East Africa but severe droughts in countries surrounding Indonesia (Hendon, 2003; Abram et al., 2003; Kripalani et al., 2010; Cai et al., 2012; Weller and Cai, 2014) While the atmospheric and oceanographic conditions characterizing IOD variability are statistically well described, the trigger mechanisms of pIOD events is not well understood. Previous modelling studies suggest that Wyrtki jets, as they approach the eastern boundary, operate to inject and subduct surface water into the pycnocline to form a distinct barrier layer characterized by a sub-surface salinity maximum (Masson et al., 2002). Sprintall and Tomczak (1992) were the first to discuss the existence of such barrier layers in the tropics. A recent analysis of ARGO float profiles suggests that such barrier layers and their variability may affect air-sea interactions during the mature phase of IOD events (September–November) (Qiu et al., 2012). Is it unclear how much (remnant) barrier layers influence the development phase of pIOD events (July–August). Apart from energetic wind changes that lead to the formation of Wyrtki jets, it needs to be pointed out that similar but weaker westerly wind bursts frequently occur during the monsoons. These wind bursts create “intraseasonal monsoon jets” (Senan et al., 2003) which are dynamically akin to Wyrtki jets. Their impact on the thermocline structure in the eastern equatorial Indian Ocean is unknown. In this paper, we exclusively focus on SST variability in the eastern intertropical Indian Ocean, captured by the eastern DMI region (see Fig. 1a), during the onset and duration of pIOD events; that is, from June to September. The objective of this work is to uncover the mechanism triggering pIOD events. Our hypothesis is that most of the SST response of the region can be explained by classical Ekman theory of wind-driven coastal upwelling. To this end, we develop a simple theory of coastal upwelling along coastlines of finite horizontal extent to verify whether it can explain the observed SST variability of the region. For
2. Methodology 2.1. Data sources This study uses multi-year satellite data of sea surface temperature (SST), 10-m wind speed components, and sea surface height anomalies in conjunction with sparse hydrographic data from ARGO floats. Both spatial and time-series analyses are considered in our work. The spatial analysis is based on Reynolds’ SST data (Reynolds et al., 2007) on a 1° grid for the period 1982–2017 (https://www.nhc.noaa. gov/sst/). We also used NCDC/NOAA daily High-Resolution SST blended with the observations from the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite, which is available at a grid resolution of 0.25°. Absolute dynamic topography is inferred from AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic) altimeter data (https://www.aviso.altimetry.fr/en/my-aviso. html). The altimeter data have a weekly temporal resolution, a spatial resolution of ~ 1/3°, and cover the period from 1993 to 2016. The time series analysis of monthly SSTs for the locations shown in Fig. 2 is based on MODIS-Aqua SST data, provided by Asia-Pacific DataResearch Centre (APDRC) (http://apdrc.soest.hawaii.edu/data/data. php) for the period 2003–2017. From the Reynolds SST data, we also calculated a time-series of region-averaged SST anomalies for the western and eastern DMI areas, and the difference thereof, which gives the DMI (shown in Fig. 1b). Monthly wind stress vectors are derived from Version 2 of the Cross-Calibrated Multi-Platform (CCMP) wind product (Atlas et al., 2011) from the APDRC for the period 1991–2011. Wind stress components are hereby calculated from the 10-m wind velocities provided using standard bulk formulae with an air density of 1.24 kg/m3 and a wind-drag coefficient of 0.0012. Variations in density stratification in terms of mixed-layer properties (density anomaly and thickness) can strongly influence the upwelling dynamics in a region (Kämpf and Chapman, 2016). To explore this possibility, we used monthly hydrographic profiles derived from ARGO float profiles for the period 2004–2017 (BOA-Argo, Li et al., 2017). Hereby it should be noted that the ARGO coverage of the study 2
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Fig. 2. Study regions and locations (represented by “station” numbers) of the data pixels used in the analysis of wind stress, SST variability, isothermal layer depth, and derived quantities. Large rectangle: eastern SOI region (see Fig. 1). Except for station #9, which is located at ~8.5°S, the numbers of all other stations correspond to their geographical latitude. The approximate station coordinates are #0 (0°S, 98°E), #1 (1°S, 100°E), #2 (2°S, 100°E), #3 (3°S, 101°E), #4 (4°S, 102°E), #5 (5°S, 103°E), #6 (6°S, 104°E), #7 (7°S, 105°E), #8 (8°S, 107.5°E), #9 (8.5°S, 110°E). Background image: Google Earth.
2.2. Theoretical considerations 2.2.1. Upwelling index The classical Bakun upwelling index (Bakun, 1973) can be calculated from
UI =
/(
o
(1)
f )cos( *) 3
where |τ| is wind-stress magnitude, ρo = 1026 kg/m is a typical seawater density, f is the Coriolis parameter, and α* is the relative angle between the wind direction and the coastline orientation (Kämpf and Chapman, 2016). This index is equivalent to the offshore Ekman volume transport per unit width of the coastline. Hereby it is important to note that Ekman layers develop on time scales of the inertia period that increases with proximity to the equator. For example, the inertia period is ~ 28 days at 1°S, but it reduces to ~ 14 days at 2°S, and ~ 10 days at 3°S. Hence, with a focus on upwelling on periods of 1–2 months, the validity of (1) becomes questionable for distances < 1° from the equator. In close vicinity of the equator, upwelling rather follows directly from offshore winds, known as the lee effect (Hela, 1976; Kämpf, 2015). In the absence of density stratification, the thickness of Ekman layers increases dramatically at low latitudes. Surface and bottom layer then overlap and partially cancel out each other (Kämpf, 2015). Nevertheless, density effects in stratified waters usually eliminate this effect, such that thin Ekman layers can still exist at low latitudes (Perlin et al., 2007).
Fig. 3. Schematic of the coastal upwelling process along a coast of finite extent Ltotal in the southern hemisphere. Full upwelling occurs within a distance of L. See text for details.
2.2.2. Coastal upwelling along a coast of infinite horizontal extent Let us consider a two-layer fluid with a pycnocline located at a depth of ho in a coastal ocean that is exposed to uniform upwelling favourable wind. For a coastline of infinite horizontal extent, the resultant upwelling of the pycnocline is spatially uniform along the coast, and it is possible to estimate the time, tupwell, it takes until the pycnocline intersects the sea surface – a situation called full upwelling (Cushman-Roisin, 1994). This time scale is derived in the following. In order to conserve volume, the vertical upwelling speed, w, assumed constant over the width of the upwelling zone, r, has to balance the offshore volume transport, UI, of the surface Ekman layer. This gives:
Fig. 4. Schematic of individual and cumulative changes of the thickness of the surface layer along segments of the coastal ocean. See text for more details.
region prior to 2011 is extremely sparse. As far as we are aware, however, the ARGO database is the best information on density stratification in the study region available. Surface density anomalies were calculated from the equation of state of seawater ignoring pressure effects (Gill, 1982) using surface temperature and salinity values with reference to values found at 100-m depth. Mixed-layer depths were derived from the thickness of isothermal layers (see Li et al., 2017) which include barrier layers as sub-structures. Note that the ARGO data indicate that barrier layers are usually absent during austral winter months (see Section 3.4) and therefore irrelevant in the context of this study.
w = UI /r
*/( o c )
(2)
where UI is defined in (1), τ* is the coast-parallel wind stress, and c is the phase speed of internal gravity waves (which is the same as that of internal coastal Kelvin waves), and the width of the upwelling zone, r, 3
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Fig. 5. Spatial distributions of sea surface temperature (°C) for a) the average of three pIOD events (1994, 1997 and 2006), and b) the average of all other 26 years from the period 1988–2016. CI = 0.2 °C. Panel c) displays the differences of values shown in a) and b). Values are from NOAA-OI data for the months from July to November. The rectangle in both panels shows the eastern DMI area.
corresponds to the internal radius of deformation (Cushman-Roisin, 1994), r = c / f . The phase speed is given by:
c=
g h*
coast where the water column remains undisturbed during the process. This location can be defined as “upstream boundary”, x = 0. The equator acts as an upstream boundary for the Sumatran situation. In due course of upwelling favourable winds, the pycnocline then slopes upward away from upstream boundary and eventually intersects the sea surface (outcrops) near the coast at the location, x = L, hereafter referred to as “point of full upwelling” (Fig. 3). Again, this full upwelling occurs over the time span determined by (4). In contrast to the case with an infinite coastline, however, the pycnocline does never reach the sea surface upstream from the point of full upwelling. On the other hand, full upwelling develops downstream from this point. For x > L the upwelling front has separated from the coast with a distance that linearly increases with x. It is clear that, under the assumptions made, full upwelling only develops along a coast of finite horizontal extent if L < Ltotal. The upwelling pattern progresses along the coast as an internal Kelvin wave at the speed of internal gravity waves (Gill, 1982), which implies:
(3)
where reduced gravity is g' = Δρ/ρo g with g = 9.81 m/s2 being acceleration due to gravity and Δρ being the vertical density difference between the layers, and the height scale can be expressed as h* = ho(1 − ho/H), where H is the total water depth of the coastal ocean. The time is takes for full upwelling to develop can now be estimated from (Kämpf and Chapman, 2016):
tupwell = ho/ w = rho/ UI = cho o / *
(4)
where the overbar denotes a temporal average. Note that the time scale (4) is invariant to geographical latitude. For typical values (g' ≈ 0.03 m/s2, ho ≈ 50 m, τ* = 0.1 Pa), (4) gives a time scale of ~ 7 days, which is of the order of the timescale of mid-latitude synoptic weather events. 2.2.3. Coastal upwelling along a coast of finite horizontal extent In contrast to the above assumptions, most coastlines have a finite horizontal extent, Ltotal. This detail changes the upwelling development considerably. Now the upwelling starts to develop from one side of the
tupwell = ho/ w = L/ c
(5)
where L = c tupwell is the distance travelled by the internal Kelvin wave until the pycnocline has intersected the sea surface. When combining 4
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Fig. 6. Same as Fig. 5, but showing contours of absolute dynamic topography (ADT, m) based on daily AVISO data for the period 1992–2016. CI = 0.02 m. Panel a) shows the average distribution for three pIOD events (1994, 1997 and 2006). Panel b) shows the average from the other 22 years. Arrows illustrate directions of resultant geostrophic surface currents: SJC – South Java Current, SWSC – South West Sumatra Current, SEC – South Equatorial Current, EC – Equatorial Current, ITF – Indonesian Through Flow.
Fig. 7. Monthly time series of SST data for the years from 2002 to 2016 at different locations (see Fig. 2). The 2006 data and other unusual features are highlighted.
the above relations, we yield the final expression for calculation of the distance L over which full upwelling develops; that is,
L=
gho2 (1
h)/ cos( *) with
h = ho/ H
(iii) greater thickness of the surface mixed layer. If we assume that the bottom layer of the two-layer ocean is of infinite depth (or, equivalently, that the surface layer is much thinner than the bottom layer, called the reduced-gravity model), then we yield:
(6)
Since the Coriolis parameter does not appear in the latter equation, the distance L is invariant of geographical latitude. According to (6), the distance L increases (i.e. full upwelling is spatially delayed) with:
L=
(i) weaker wind forcing, (ii) enhanced density stratification, and
gho2/ cos( *)
(7)
which suggests a quadratic impact of ho on L. This simplification, however, can lead to an overestimation of L. 5
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Fig. 8. Monthly SST time series for 2006 shown at all study locations (see Fig. 2). The line thickness of curves increases with proximity to the equator. The ellipse indicates the period of substantial surface cooling. The arrow indicates that this cooling signal appears first in the south from where it progresses equatorward.
The wind conditions may vary significantly along the extended coastlines, such as that of Sumatra and Java. In this case, the theory presented above can be extended by splitting the coastline into segments (Fig. 4). Hereby; the contribution of each segment to the total change of ho can be calculated from:
h i = h o Li / L
(8)
Fig. 10. Monthly values of westward (offshore) wind stress, τx (Pa), near the equator for the period 1991–2011. The years of 1994, 1997, 2006 (and 2011) are highlighted.
where Li is the coastline length of the i-th segment and L, calculated from (6), is based on the coast-parallel wind stress in this segment. Since the vertical displacement of the pycnocline accumulates along the coast in the direction of Kelvin wave propagation, the cumulative mixed-layer change in segments follows from:
flux anomalies nor lateral heat flux anomalies induced by variations in oceanic currents, which can also modify SST anomalies.
i
hi =
hj j=1
3. Results and discussion
(9)
To this end, the method developed here identifies segments along a coastline favourable for the development of full upwelling; that is, Δhi > ho. Note that downwelling-favourable wind in a segment can offset the upwelling induced in “upstream” segments. For the western Sumatran coastline, we start the integration of (9) at 1°S (see Fig. 2) and not at the equator where rotational effects are absent. Note that bathymetric irregularities such as shelf-break canyons may trigger enhanced localized upwelling (see Kämpf, 2012). Such regional features are ignored in the context of this work. It should also be pointed out that the theory presented here exclusively considers regional wind effects, but neither effects due to regional air-sea heat
3.1. Spatial analysis of pIOD events SST anomalies off the coast of Sumatra are a dominant part of IOD variability (see Fig. 1b). Wyrtki (1962) suggested that, under influence of the Southeast Monsoon from June to November, the upwelling of cold water is a regular feature in coastal waters off the southern coasts of Java. This is confirmed here (Fig. 5a–b), noting that the Java upwelling appears more pronounced in pIOD years (Fig. 5b). Interestingly, our analysis identifies two years (2010 and 2016) when the Java upwelling is completely absent (see Fig. 7d). In contrast to regular upwelling off Java, the coastal waters off
Fig. 9. Monthly values of coast-parallel wind stress, τ*(Pa), at selected locations for the period 1991–2011. The years of 1994, 1997, 2006 (and 2011) are highlighted. 6
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Fig. 11. Time series of monthly values of b) mixed layer depth (MLD, m) and barrier layer thickness (BLT, m) and c) mixed-layer density anomalies relative to the density at 100 m (Δρ, kg/m3) for the period 2004–2017. The values are averaged for all data between 2°S and 5°S. Data source (BOA-Argo, Li et al., 2017). Panel a) displays the definitions of the relevant vertical scales. The depth of the surface mixed layer (Dsigma) is determined from a density criterion. Isothermal layer depth (DT02) is taken as the depth at which the temperature has decreased by 2 °C relative to surface waters. See Li et al. (2017) for more details.
pIOD events, cold water upwelling off Sumatra creates another upwelling jet – to be called the South East Sumatra Current (SESC) – that forms a north-westward continuation of the SJC (Fig. 6a). In interaction with the SEC, this creates a broad north-westward flow moving colder water into the DMI region. It is obvious that the SESC operates to induce advective cooling in the region, which may substantially reinforce negative SST anomalies during pIOD events. Monthly data of SST off the south coast of Java confirm that coastal upwelling is a regular feature of the region (Fig. 7d). This cooling normally commences in May/June and lasts until October/November each year. Enhanced and prolonged cooling occurred in the pIOD year of 2006 with a magnitude exceeding 4 °C. Interestingly, coastal waters off the south coast of Java did not show signs of surface cooling in the years of 2010 and 2016. 3.2. Time series analysis: SST variability In contrast to the Java region, waters off the southwest coast of Sumatra experience pronounced cooling events only rarely (Fig. 7c). In the period from 2003 to 2017, strong transient surface cooling of > 4 °C developed in July-September in the pIOD year of 2006. Similar, but slightly weaker cooling events are indicated for the years of 2007 and 2011. The SST development in 2007 is not unexpected, given that this year classifies as a weaker pIOD event (Luo et al., 2008). But the observed cooling in 2011 is rather surprising, given that the DMI does not highlight this particular year (see Fig. 1b). Note that, as for the Java region, the years of 2010 and 2016 mark unusually “warm phases” for the waters off the southwest coast of Sumatra. The central shelf of Sumatra (~ 3°S) experiences normally relatively little seasonal SST variations except in pIOD years when substantial cooling of > 5 °C occurs in the region (Fig. 7b). The eastern equatorial Indian Ocean shows a relatively high intra-annual SST variability with variations of ± 1° (Fig. 7a). This feature is not surprising, given that the equator is a waveguide for internal Kelvin waves that tend to dissipate farther away from the equator. Despite these relatively strong SST fluctuations, the cooling event associated with the pIOD event in 2006 can also be identified near the equator. However, with a closer look at the SST development, we find that the peak of the cooling phase in 2006 occurred first off southwest Sumatra before progressively moving towards the equator where it arrived around half a month later (Fig. 8).
Fig. 12. Inter-annual variations of a) isothermal layer thickness, ho, and b) density anomalies (surface values minus value at 100 m depth) for the period 2004–2017. The values are averaged for all values between 2°S and 5°S. Only values for July, August and September are shown. Data source (BOA-Argo, Li et al., 2017).
southwestern Sumatra show normally little or no sign of full upwelling (Fig. 5b), as previously concluded by Saji and Yamagata (2003). Note that, normally, an extensive warm pool of SSTs > 29 °C exists around the equator off northwest Sumatra. This normal setting changes significantly in pIOD years (1994, 1997 and 2006) when an extensive area of anomalously cold water appears off the southwestern Sumatran coast and the equatorial warm pool weakens considerably (Fig. 5a). The anomaly distribution (Fig. 5c) highlights that pIOD events are characterized by major SST changes centred at ~ 5°S; that is, directed off the southwest coast of Sumatra. Note that unlike pIOD events, negative IOD events are structural similar to the average distributions and therefore not explicitly discussed here. Normally, the surface circulation (derived from absolute dynamic topography) in the study region consists of the upwelling-enforced South Java Current (SJC), described by Sprintall et al. (1999), that feeds into the South Equatorial Current (SEC) south of the latitude of ~ 10°S (Fig. 6b), which is consistent with historical in-situ data (Quadfasel et al., 1996). In addition, an equatorial current (EC) moves warmer equatorial surface water both eastward and southward into the DMI region, where it recirculates and joins the SEC. In contrast, during
3.3. Time series analysis: upwelling-favourable wind stress The time series of monthly wind stresses parallel to the coast shows that upwelling favourable winds exists along the southern Java coast 7
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Table 1 Values of Δhi (m) according to (9) for each segment i (which is characterized by its geographical latitude) along the coastlines of Sumatra and Java using August wind data for the years 1991–2011 and ho = 50 m and Δρ ≈ 2.75 g/m3. The width of coastal segments is ΔLi ≈ 150 km, except for ΔL8 ≈ 300 km. Segments of full upwelling (i.e., Δhi > 50 m) are highlighted in red. Yellow cells indicate situations in which the pycnocline come close (< 10 m) to the sea surface. Blue cells indicate the years in which more than half of the upwelling is provided as preconditioning north of 3°S.
from April to November each year (Fig. 9d). Overall, the region experiences little interannual variability in the wind conditions, which implies that the warming phases seen in Fig. 7d cannot be explained by changing wind conditions. While seasonably upwelling-favourable winds exist along the entire Sumatran coastline south of the equator (Fig. 9a–c), overall the onset of such winds generally develops later than along the coast of Java. Moreover, there are significant interannual variations of the magnitude of the upwelling-favourable winds. The pIOD years of 1994, 1997 and 2006 all experienced the overall highest upwelling-favourable wind stresses of ~ 0.1 Pa between 1°S and 3°S, which is more the double the normal situation. Interestingly, the year 2011 also belongs to this category of unusually high upwellingfavourable wind stresses. Note that despite the regular existence of upwelling-favourable winds along most of Sumatra's coast, full coastal upwelling does rarely develop here (see Fig. 7). Time series of monthly zonal wind stresses near the equator reveal the unusual development of westward zonal wind components in the pIOD years of 1994, 1997 and 2006 and in 2011, not seen in other years (Fig. 10). In these particular years, an unusual westward wind component starts develop in August to peak around September/October. Overall these zonal wind components, which have the potential to induce upwelling near the equator, seem to follow rather than trail surface cooling events (see Fig. 7a). It should be pointed out that, again, the year 2011 appears with coastal wind characteristics similar to those existing during pIOD events.
average mixed-layer properties in coastal waters along Sumatra's west coast. The vertical density stratification in the eastern intertropical Indian Ocean varies considerably over the months of a year. This includes variations of both depth and density of the surface mixed layer and the formation of barrier layers (Fig. 11). Wyrtki jets play a key role in this variability (e.g. Vinayachandran et al., 2009). Nevertheless, pIOD events tend to develop during the relatively stable (southeast) monsoon period and not during the times of rapid monsoon changes. From the sparse ARGO data available it appears that both thickness of the isothermal layer, ho, and the density anomaly of this layer (with respect to the seawater density at 100-m depth) display only little interannual variations in these months of interest (Fig. 12). For instance, ho varies by ± 10 m from an average of ~ 50 m, while the average density anomaly of Δρ ≈ 2.75 kg/m3 varies by ± 0.75 kg/m3. Given these relatively small variations, it is reasonable to use average values of both parameters in the theory presented in Section 2.2.3. When using all available data, our theory (9) reveals pronounced interannual variations of the point of full upwelling along the Sumatran coast (Table 1). In some years, such as in 2010, upwelling remains suppressed along the entire Sumatran coast, a feature which can be linked to the existence of downwelling-favourable winds from the equator to a geographical latitude of 3°S (Table 2). A similar wind pattern is seen in 1998. On the other hand, our theory suggests the point of full upwelling progresses equatorward to a geographical latitude of 5°S in the years of 1994, 1997, 2006, 2007, and 2011. While four of these five instances are pIOD years, our theory suggests that the winds in 2011 should also create full upwelling off southwest Sumatra, which will be further explored in the next section. The individual upwelling contributions in each segment of the coast (Table 2) confirms that it is the northern segments of the Sumatran
3.4. Why does the Sumatran upwelling only develop infrequently? This section applies the theory on coastal upwelling along coasts of finite extents (see Section 2.2.3) which requires information about 8
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Table 2 Same as Table 1 but showing values of δhi (m) according to (8). Highlighted are the years in which upwelling-favourable coastal winds are unusually strong in the near-equatorial region. Positive (negative) values indicate upwelling (downwelling).
Fig. 14. Same as Fig. 13, but for September 2011. Data source: NCDC/NOAA.
Fig. 13. Distribution of sea surface temperature for September 2006. Data source: NCDC/NOAA.
Despite the associated uncertainties, however, it should be pointed out that the model is also supported by the observed equatorward progression of SST anomalies in a pIOD year (see Fig. 8). During the development towards the steady-state shape of the pycnocline (see Fig. 3), full upwelling will appear first in the south before progressing northward to its steady-state location of the point of full upwelling. Equatorward of this point, however, the thermocline is located closer to the sea surface due to the partial-upwelling process, such that wind stirring is expected to also trigger lower SSTs in those regions.
coast between the equator and 3oS that induce substantial partial upwelling of > 25 m in the pIOD years of 1994, 1997 and 2006, and in the year of 2011. This suggests that it is this only this preconditioning that enables the development of full upwelling off southwest Sumatra. Note that variations of ho and Δρ (within the indicated ranges) do not significantly change these results. Indeed, our conceptual model is highly simplified as it exclusively considers regional wind conditions as the driver of SST anomalies. 9
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Given that there are strong intraseasonal variability of currents including the Indonesian Through Flow (Masumoto et al., 2005) and mesoscale eddy activities (Feng and Wijffels, 2002; Yu and Potemra, 2006) in the tropical Indian Ocean, we cannot totally rule out the possibility that other effects not captured in this analysis may influence the development of pIOD events. Future studies should focus on the influence that intraseasonal monsoon jets have on the density stratification in the region. While some recent modelling studies indicate that the frequency of pIOD events has increased under the influence of global warming (e.g. Cai et al., 2009), it should highlighted that our study finds that coastal upwelling was fully suppressed in the entire region including Java coastal waters in the years of 2010 and 2016. This feature, which could be linked to global warming, deserves further study. Acknowledgements
Fig. 15. Same as Fig. 13, but for September 2016. Data source: NCDC/NOAA.
Section 2.1 lists the sources of data used in this work. This project did not receive external funding. The authors declare no conflict of interest. This work makes use of Argo data (http://doi.org/10.17882/42182).
3.5. What happened in the years of 2011 and 2016?
References Abram, N.J., Gagan, M.K., McCulloch, M.T., Chappell, J., Hantoro, W.S., 2003. Coral reef death during the 1997 Indian Ocean Dipole linked to Indonesian wildfires. Science 301, 952–955. Atlas, R., Hoffman, R.N., Ardizzone, J., Leidner, S.M., Jusem, J.C., Smith, D.K., Gombos, D.A., 2011. Cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteorol. Soc. 92, 157–174. https://doi.org/10.1175/2010BAMS2946.1. Bakun, A., 1973. Coastal Upwelling Indices, West Coast of North America, 1946–71 (NOM Tech. Rep. NMFS SSRF-671). pp. 103. Cai, W., Sullivan, A., Cowan, T., 2009. Climate change contributes to more frequent consecutive positive Indian Ocean Dipole events. Geophys. Res. Lett. 36, L23704. https://doi.org/10.1029/2009GL040163. Cai, W., van Rensch, P., Cowan, T., Hendon, H.H., 2012. An asymmetry in the IOD and ENSO teleconnection pathway and its impact on Australian climate. J. Clim. 25, 6318–6329. Cushman-Roisin, B., 1994. Introduction to Geophysical Fluid Dynamics. Prentice Hall, New Jersey. Feng, M., Wijffels, S., 2002. Intraseasonal variability in the South Equatorial Current of the east Indian Ocean. J. Phys. Oceanogr. 32, 265–277. https://doi.org/10.1175/ 1520-0485(2002)032<0265:IVITSE>2.0.CO;2. Gill, A.E., 1982. Atmosphere-Ocean Dynamics. Academic Press, San Diego, CA. Hela, I., 1976. Vertical velocity of the upwelling in the sea. Soc. Sci. Fenn. Comm. Phys.Math. 46, 9–24. Hendon, H.H., 2003. Indonesian rainfall variability: impacts of ENSO and local air-sea interaction. J. Clim. 16, 1775–1790. Huang, B., Kinter, J.L., 2002. Interannual variability in the tropical Indian ocean. J. Geophys. Res. 107. https://doi.org/10.1029/2001JC001278. Kämpf, J., 2012. Lee effects of localized upwelling in a shelf-break canyon. Cont. Shelf Res. 42, 78–88. https://doi.org/10.1016/j.csr.2012.05.005. Kämpf, J., 2015. Interference of wind-driven and pressure gradient-driven flows in shallow homogeneous water bodies. Ocean Dyn. 65 (11), 1399–1410. https://doi. org/10.1007/s10236-015-0882-2. Kämpf, J., Chapman, P., 2016. Upwelling Systems of the World. Springer, Cham, Switzerland. Kripalani, R.H., Oh, J.H., Chaudhari, H.S., 2010. Delayed influence of the Indian Ocean Dipole mode on the East Asia–West Pacific monsoon: possible mechanism. Int. J. Climatol. 30, 197–209. Li, H., Xu, F., Zhou, W., Wang, D., Wright, J.S., Liu, Z., Lin, Y., 2017. Development of a global gridded Argo data set with Barnes successive corrections. J. Geophys. Res. Oceans 122. https://doi.org/10.1002/2016JC012285.6. Luo, J.-J., Behera, S., Masumoto, Y., Sakuma, H., Yamagata, T., 2008. Successful prediction of the consecutive IOD in 2006 and 2007. Geophys. Res. Lett. 35, L14S02. https://doi.org/10.1029/2007GL032793. Masumoto, Y., Hase, H., Kuroda, Y., Matsuura, H., Takeuchi, K., 2005. Intraseasonal variability in the upper layer currents observed in the eastern equatorial Indian Ocean. Geophys. Res. Lett. 32, L02607. https://doi.org/10.1029/2004GL021896. Masson, S., Delecluse, P., Boulanger, J.-P., Menkes, C., 2002. A model study of the seasonal variability and formation mechanisms of the barrier layer in the eastern equatorial Indian Ocean. J. Geophys. Res. 107 (C12), 8017. https://doi.org/10.1029/ 2001JC000832. Perlin, A., Moum, J.N., Klymak, J.M., Levine, M.D., Boyd, T., Kosro, P.M., 2007. Organization of stratification, turbulence, and veering in bottom Ekman layers. J. Geophys. Res. 112, C05S90. https://doi.org/10.1029/2004JC002641. Qiu, Y., Cai, W., Li, L., Guo, X., 2012. Argo profiles variability of barrier layer in the tropical Indian Ocean and its relationship with the Indian Ocean Dipole. Geophys. Res. Lett. 39, L08605. https://doi.org/10.1029/2012GL051441.
A few years shows unusual and unexpected SST variability. The year 2011, for instance, indicates the existence of strong upwelling during August/September off southwest Sumatra and similar wind conditions all along the Sumatran coast. Why does this year not appear marked as pIOD event in the DMI? On the other hand, there were two years, 2010 and 2016, where surface cooling failed to develop despite the existence of robust upwelling-favourable winds that were very similar to the normal situation. What caused this feature? The SST distribution in September 2006 at the peak of the pIOD event in September (Fig. 13) serves as a means of comparison to be able to answer these questions. The surface SST distribution in September 2006 demonstrates the full extent of a pIOD event (Fig. 13). Extensive coastal upwelling with SSTs < 24°C has developed along the southern coast of Java and this upwelling extends equatorward along the southwest Sumatran coastline. The SST distribution for September 2011, on the other hand, had a pattern very similar to that of September 2006 (Fig. 14), which is consistent with the analysis presented in Tables 1, 2. Hence, the only reason why the DMI methodology has not picked up this year as an unusual year (see Fig. 1a) is that a slight relative warming in other regions has overshadowed the cooling signature induced by coastal upwelling. In how much the overall atmospheric circulation pattern in 2011 was similar to that of pIOD events remains for future investigations. On the other hand, significant and widespread warming of surface waters occurred in September 2016 (Fig. 15) and, similarly, in September 2010 (data not shown). It is reasonable to relate the appearance of such extreme heat waves in the region to the absence of upwelling off the southern coastline of Java. Future studies should investigate whether similar heat waves have existed prior to 2003. 4. Summary and conclusions In this study, the authors present a simple theory of coastal upwelling along coasts of finite horizontal extent. This theory can explain the SST variability observed in coastal waters off the southwestern coast of Sumatra – a dominant driver of the Indian Ocean Dipole. Findings suggest that partial upwelling north of 3°S is a key preconditioning process for the development of full upwelling along the southwestern coast of Sumatra. The authors believe that this identification of the likely trigger mechanism of pIOD events provides the scientific community with a starting point for investigations of the impact of climate change on the monsoon dynamics and variability in the Indian Ocean. 10
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J. Kämpf, A. Kavi Quadfasel, D., Frische, A., Cresswell, G., 1996. The circulation in the source area of the South Equatorial Current in the eastern Indian Ocean. J. Geophys. Res. 101 (C5), 12483–12488. Reynolds, R.W., Smith, T.M., Liu, C., Chelton, D.B., Casey, K.S., Schlax, M.G., 2007. Daily high-resolution blended analyses for sea surface temperature. J. Clim. 20, 5473–5496. Saji, N.H., Goswami, B.N., Vinayachandran, P.N., Yamagata, T., 1999. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363. Saji, N.H., Yamagata, T., 2003. Structure of SST and surface wind variability during Indian Ocean Dipole Mode years: COADS observations. J. Clim. 16, 2735–2751. Schott, F.A., McCreary, J.P., 2001. The monsoon circulation of the Indian Ocean. Prog. Oceanogr. 51, 1–123. Senan, R., Sengupta, D., Goswami, B.N., 2003. Intraseasonal “monsoon jets” in the equatorial Indian Ocean. Geophys. Res. Lett. 30, 1750. https://doi.org/10.1029/ 2003GL017583. Sprintall, J., Tomczak, M., 1992. Evidence of the barrier layer in the surface layer of the tropics. J. Geophys. Res. 97, 7,305–7,316. Sprintall, J., Chong, J.C., Syamsudin, F., Morawitz, W., Hautala, S., Bray, N., Wijffels, S., 1999. Dynamics of the South Java Current in the Indo-Australian Basin. Geophys.
Res. Lett. 26, 2493–2496. Susanto, R.D., Gordon, A.L., Zheng, Q., 2001. Upwelling along the coasts of Java and Sumatra and its relation to ENSO. Geophys. Res. Lett. 28, 1599–1602. Tomczak, M., Godfrey, J.S., 2003. Regional Oceanography: An Introduction, second ed. Daya Publishing House, New Delhi. Vinayachandran, P.N., Francis, P.A., Rao, S.A., 2009. Indian Ocean Dipole: processes and impacts. In: Mukunda, N. (Ed.), Current Trends in Science (Platinum Jubilee Special Publication). Indian Academy of Sciences, Sadashivanagar, Bangalore, pp. 569–589. Yu, Z., Potemra, J., 2006. Generation mechanism for the intraseasonal variability in the Indo-Australian basin. J. Geophys. Res. 111, C01013. https://doi.org/10.1029/ 2005JC003023. Webster, P.J., Moore, A.M., Loschnigg, J.P., Leben, R.R., 1999. Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401, 356–359. Weller, E., Cai, W., 2014. Meridional variability of atmospheric convection associated with the Indian Ocean Dipole Mode. Sci. Rep. 4, 3590. https://doi.org/10.1038/ srep03590. Wyrtki, K., 1962. The upwelling in the region between Java and Australia during the southeast-monsoon. Aust. J. Mar. Freshw. Res. 13, 217–225. Wyrtki, K., 1973. An equatorial jet in the Indian Ocean. Science 181, 262–264.
11
Contents lists available at ScienceDirect
Deep-Sea Research Part II journal homepage: www.elsevier.com/locate/dsr2
SST variability in the eastern intertropical Indian Ocean – On the search for trigger mechanisms of IOD events J. Kämpf , A. Kavi ⁎
College of Science and Engineering, Flinders University, Adelaide, Australia
ARTICLE INFO
ABSTRACT
Keywords: Indian Ocean Dipole Climate Variability Coastal upwelling Intertropical Oceanography
This study focusses on the variability of sea surface temperature in the eastern intertropical Indian Ocean off the coasts of Sumatra and Java - a region that is central to the Indian Ocean Dipole (IOD). Using multiplatform satellite and ARGO data, here we show that the appearance of vast areas of anomalously cold surface water off the southwest Sumatran coast during positive IOD (pIOD) events is preconditioned by upwelling favourable winds along Sumatra's central west coast (1–3°S). A simple theory of coastal upwelling along coasts of finite horizontal extent, developed in this work, can explain this SST response. While this theory is consistent with the pIOD events of the years 1994, 1997 and 2006, it uncovers the existence of another strong upwelling event in the year 2011 that is missed when calculating average SST anomalies in the wider region (i.e. the Dipole Mode Index). In addition, we also derived the surface circulation establishing in the study region during pIOD events. This circulation pattern indicates that equatorward transport of colder water by a classical upwelling jet (that we call the South West Sumatra Current) reinforces negative (cold) SST anomalies off southwest Sumatra.
1. Introduction The climate of the central and northern Indian Ocean and adjacent continents is strongly modulated by monsoons. Southwesterly winds exist over the Indian Ocean during May–September, whereas northeasterly winds prevail during November–February. This seasonal wind change leads to seasonal variations of the oceanic circulation with some regions experiencing a complete reversal of the circulation, not seen in any other ocean (Schott and McCreary, 2001; Tomczak and Godfrey, 2003). The average winds over the equatorial Indian Ocean, particularly their zonal component, are weak during monsoons and, unlike in the Atlantic and Pacific, equatorial upwelling is absent (Schott and McCreary, 2001). Relatively strong westerly wind bursts, however, appear during the transition between the monsoons, first during April–May (spring) and then again during October–November (autumn). These winds drive strong eastward currents along the equator, attaining speeds exceeding 1 m/s, called Wyrtki jets (Wyrtki, 1973). Propagating eastward in the form of internal equatorial Kelvin waves, Wyrtki jets induce a transient deepening of the thermocline near the eastern boundary. In addition, these jets also induce a net eastward shift of warm water, which leads to a poleward widening of the warm water pool near the eastern boundary. Vinayachandran et al. (2009) suggest
⁎
that this deepening and widening of the warm water pool in the eastern equatorial Indian Ocean due to Wyrtki jets is fundamental in the functioning of the coupled air-sea monsoon dynamics in the region. The Indian Ocean Dipole (IOD), first described by Saji et al. (1999), is an irregular fluctuation of sea-surface temperatures between the eastern and western intertropical Indian Ocean. This feature is monitored and described by the Dipole Mode Index (DMI) as the differences of SSTs between defined areas displayed in Fig. 1a. The regions were selected based on an empirical orthogonal function (EOF) analysis, and the term “dipole” was based on the relatively strong correlation (~ 0.7) between the DMI index and the dominant mode of variability yielded from the EOF analysis; that is, the IOD is the second EOF mode of SST variability in the Indian Ocean (Saji et al., 1999). Occasionally, the eastern tropical Indian Ocean, in particular the coast ocean off southwestern Sumatra, experiences widespread cooling during late austral winter months (July–September). SST anomalies during such cooling phases are comparable to those triggered by ENSO in the eastern equatorial Pacific in terms of magnitude (> 2 °C), spatial extent (600 km by 200 km), and duration (> 3 months). Positive IOD (pIOD) events are traditionally defined by DMI values exceeding 1 °C for a continuous period exceeding 12 weeks. After this definition, only three significant cooling events can be identified in the years 1994, 1997, and 2006 (Fig. 1b). With used of a slightly “softer” DMI
Corresponding author. E-mail address: [email protected] (J. Kämpf).
https://doi.org/10.1016/j.dsr2.2018.11.010
0967-0645/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Kämpf, J., Deep-Sea Research Part II, https://doi.org/10.1016/j.dsr2.2018.11.010
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Fig. 1. a) Definition of the surface areas used to calculate the DMI. The western region is defined by (50–70°E, 10°S to 0°N) and the eastern region by (90–110°E, 10–0°S). b) Time series (1988–2017) of SST anomalies in the areas shown in a) and the corresponding Dipole Mode Index traditionally defined as DMI = SSTwest − SSTeast. The arrows highlight three pIOD events (1994, 1997 and 2006). Source: Reynolds SST data.
completeness, it should be noted that Wyrtki (1962) was the first to observe and report the existence of upwelling off Java, whereas Susanto et al. (2001) provided a first detailed analysis of coastal upwelling off Java and Sumatra, the latter playing a central role in pIOD events.
threshold, pIOD events can also be attributed to the years of 2007 and 2008 (Luo et al., 2008; Cai et al., 2009). Contrary to the meaning of the term “dipole”, it should be pointed out the observed major cooling events in the eastern DMI area were not accompanied by pronounced warming events in the western area (see Fig. 1b). Hence, the nomenclature of this climatic index may be misleading. The IOD is an important mode of coupled atmosphere-ocean variability that affects human health in surrounding countries (Saji et al., 1999; Webster et al., 1999; Huang and Kinter, 2002; Saji and Yamagata, 2003; Vinayachandran et al., 2009). pIOD events are characterized by devastating floods over East Africa but severe droughts in countries surrounding Indonesia (Hendon, 2003; Abram et al., 2003; Kripalani et al., 2010; Cai et al., 2012; Weller and Cai, 2014) While the atmospheric and oceanographic conditions characterizing IOD variability are statistically well described, the trigger mechanisms of pIOD events is not well understood. Previous modelling studies suggest that Wyrtki jets, as they approach the eastern boundary, operate to inject and subduct surface water into the pycnocline to form a distinct barrier layer characterized by a sub-surface salinity maximum (Masson et al., 2002). Sprintall and Tomczak (1992) were the first to discuss the existence of such barrier layers in the tropics. A recent analysis of ARGO float profiles suggests that such barrier layers and their variability may affect air-sea interactions during the mature phase of IOD events (September–November) (Qiu et al., 2012). Is it unclear how much (remnant) barrier layers influence the development phase of pIOD events (July–August). Apart from energetic wind changes that lead to the formation of Wyrtki jets, it needs to be pointed out that similar but weaker westerly wind bursts frequently occur during the monsoons. These wind bursts create “intraseasonal monsoon jets” (Senan et al., 2003) which are dynamically akin to Wyrtki jets. Their impact on the thermocline structure in the eastern equatorial Indian Ocean is unknown. In this paper, we exclusively focus on SST variability in the eastern intertropical Indian Ocean, captured by the eastern DMI region (see Fig. 1a), during the onset and duration of pIOD events; that is, from June to September. The objective of this work is to uncover the mechanism triggering pIOD events. Our hypothesis is that most of the SST response of the region can be explained by classical Ekman theory of wind-driven coastal upwelling. To this end, we develop a simple theory of coastal upwelling along coastlines of finite horizontal extent to verify whether it can explain the observed SST variability of the region. For
2. Methodology 2.1. Data sources This study uses multi-year satellite data of sea surface temperature (SST), 10-m wind speed components, and sea surface height anomalies in conjunction with sparse hydrographic data from ARGO floats. Both spatial and time-series analyses are considered in our work. The spatial analysis is based on Reynolds’ SST data (Reynolds et al., 2007) on a 1° grid for the period 1982–2017 (https://www.nhc.noaa. gov/sst/). We also used NCDC/NOAA daily High-Resolution SST blended with the observations from the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite, which is available at a grid resolution of 0.25°. Absolute dynamic topography is inferred from AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic) altimeter data (https://www.aviso.altimetry.fr/en/my-aviso. html). The altimeter data have a weekly temporal resolution, a spatial resolution of ~ 1/3°, and cover the period from 1993 to 2016. The time series analysis of monthly SSTs for the locations shown in Fig. 2 is based on MODIS-Aqua SST data, provided by Asia-Pacific DataResearch Centre (APDRC) (http://apdrc.soest.hawaii.edu/data/data. php) for the period 2003–2017. From the Reynolds SST data, we also calculated a time-series of region-averaged SST anomalies for the western and eastern DMI areas, and the difference thereof, which gives the DMI (shown in Fig. 1b). Monthly wind stress vectors are derived from Version 2 of the Cross-Calibrated Multi-Platform (CCMP) wind product (Atlas et al., 2011) from the APDRC for the period 1991–2011. Wind stress components are hereby calculated from the 10-m wind velocities provided using standard bulk formulae with an air density of 1.24 kg/m3 and a wind-drag coefficient of 0.0012. Variations in density stratification in terms of mixed-layer properties (density anomaly and thickness) can strongly influence the upwelling dynamics in a region (Kämpf and Chapman, 2016). To explore this possibility, we used monthly hydrographic profiles derived from ARGO float profiles for the period 2004–2017 (BOA-Argo, Li et al., 2017). Hereby it should be noted that the ARGO coverage of the study 2
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Fig. 2. Study regions and locations (represented by “station” numbers) of the data pixels used in the analysis of wind stress, SST variability, isothermal layer depth, and derived quantities. Large rectangle: eastern SOI region (see Fig. 1). Except for station #9, which is located at ~8.5°S, the numbers of all other stations correspond to their geographical latitude. The approximate station coordinates are #0 (0°S, 98°E), #1 (1°S, 100°E), #2 (2°S, 100°E), #3 (3°S, 101°E), #4 (4°S, 102°E), #5 (5°S, 103°E), #6 (6°S, 104°E), #7 (7°S, 105°E), #8 (8°S, 107.5°E), #9 (8.5°S, 110°E). Background image: Google Earth.
2.2. Theoretical considerations 2.2.1. Upwelling index The classical Bakun upwelling index (Bakun, 1973) can be calculated from
UI =
/(
o
(1)
f )cos( *) 3
where |τ| is wind-stress magnitude, ρo = 1026 kg/m is a typical seawater density, f is the Coriolis parameter, and α* is the relative angle between the wind direction and the coastline orientation (Kämpf and Chapman, 2016). This index is equivalent to the offshore Ekman volume transport per unit width of the coastline. Hereby it is important to note that Ekman layers develop on time scales of the inertia period that increases with proximity to the equator. For example, the inertia period is ~ 28 days at 1°S, but it reduces to ~ 14 days at 2°S, and ~ 10 days at 3°S. Hence, with a focus on upwelling on periods of 1–2 months, the validity of (1) becomes questionable for distances < 1° from the equator. In close vicinity of the equator, upwelling rather follows directly from offshore winds, known as the lee effect (Hela, 1976; Kämpf, 2015). In the absence of density stratification, the thickness of Ekman layers increases dramatically at low latitudes. Surface and bottom layer then overlap and partially cancel out each other (Kämpf, 2015). Nevertheless, density effects in stratified waters usually eliminate this effect, such that thin Ekman layers can still exist at low latitudes (Perlin et al., 2007).
Fig. 3. Schematic of the coastal upwelling process along a coast of finite extent Ltotal in the southern hemisphere. Full upwelling occurs within a distance of L. See text for details.
2.2.2. Coastal upwelling along a coast of infinite horizontal extent Let us consider a two-layer fluid with a pycnocline located at a depth of ho in a coastal ocean that is exposed to uniform upwelling favourable wind. For a coastline of infinite horizontal extent, the resultant upwelling of the pycnocline is spatially uniform along the coast, and it is possible to estimate the time, tupwell, it takes until the pycnocline intersects the sea surface – a situation called full upwelling (Cushman-Roisin, 1994). This time scale is derived in the following. In order to conserve volume, the vertical upwelling speed, w, assumed constant over the width of the upwelling zone, r, has to balance the offshore volume transport, UI, of the surface Ekman layer. This gives:
Fig. 4. Schematic of individual and cumulative changes of the thickness of the surface layer along segments of the coastal ocean. See text for more details.
region prior to 2011 is extremely sparse. As far as we are aware, however, the ARGO database is the best information on density stratification in the study region available. Surface density anomalies were calculated from the equation of state of seawater ignoring pressure effects (Gill, 1982) using surface temperature and salinity values with reference to values found at 100-m depth. Mixed-layer depths were derived from the thickness of isothermal layers (see Li et al., 2017) which include barrier layers as sub-structures. Note that the ARGO data indicate that barrier layers are usually absent during austral winter months (see Section 3.4) and therefore irrelevant in the context of this study.
w = UI /r
*/( o c )
(2)
where UI is defined in (1), τ* is the coast-parallel wind stress, and c is the phase speed of internal gravity waves (which is the same as that of internal coastal Kelvin waves), and the width of the upwelling zone, r, 3
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Fig. 5. Spatial distributions of sea surface temperature (°C) for a) the average of three pIOD events (1994, 1997 and 2006), and b) the average of all other 26 years from the period 1988–2016. CI = 0.2 °C. Panel c) displays the differences of values shown in a) and b). Values are from NOAA-OI data for the months from July to November. The rectangle in both panels shows the eastern DMI area.
corresponds to the internal radius of deformation (Cushman-Roisin, 1994), r = c / f . The phase speed is given by:
c=
g h*
coast where the water column remains undisturbed during the process. This location can be defined as “upstream boundary”, x = 0. The equator acts as an upstream boundary for the Sumatran situation. In due course of upwelling favourable winds, the pycnocline then slopes upward away from upstream boundary and eventually intersects the sea surface (outcrops) near the coast at the location, x = L, hereafter referred to as “point of full upwelling” (Fig. 3). Again, this full upwelling occurs over the time span determined by (4). In contrast to the case with an infinite coastline, however, the pycnocline does never reach the sea surface upstream from the point of full upwelling. On the other hand, full upwelling develops downstream from this point. For x > L the upwelling front has separated from the coast with a distance that linearly increases with x. It is clear that, under the assumptions made, full upwelling only develops along a coast of finite horizontal extent if L < Ltotal. The upwelling pattern progresses along the coast as an internal Kelvin wave at the speed of internal gravity waves (Gill, 1982), which implies:
(3)
where reduced gravity is g' = Δρ/ρo g with g = 9.81 m/s2 being acceleration due to gravity and Δρ being the vertical density difference between the layers, and the height scale can be expressed as h* = ho(1 − ho/H), where H is the total water depth of the coastal ocean. The time is takes for full upwelling to develop can now be estimated from (Kämpf and Chapman, 2016):
tupwell = ho/ w = rho/ UI = cho o / *
(4)
where the overbar denotes a temporal average. Note that the time scale (4) is invariant to geographical latitude. For typical values (g' ≈ 0.03 m/s2, ho ≈ 50 m, τ* = 0.1 Pa), (4) gives a time scale of ~ 7 days, which is of the order of the timescale of mid-latitude synoptic weather events. 2.2.3. Coastal upwelling along a coast of finite horizontal extent In contrast to the above assumptions, most coastlines have a finite horizontal extent, Ltotal. This detail changes the upwelling development considerably. Now the upwelling starts to develop from one side of the
tupwell = ho/ w = L/ c
(5)
where L = c tupwell is the distance travelled by the internal Kelvin wave until the pycnocline has intersected the sea surface. When combining 4
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Fig. 6. Same as Fig. 5, but showing contours of absolute dynamic topography (ADT, m) based on daily AVISO data for the period 1992–2016. CI = 0.02 m. Panel a) shows the average distribution for three pIOD events (1994, 1997 and 2006). Panel b) shows the average from the other 22 years. Arrows illustrate directions of resultant geostrophic surface currents: SJC – South Java Current, SWSC – South West Sumatra Current, SEC – South Equatorial Current, EC – Equatorial Current, ITF – Indonesian Through Flow.
Fig. 7. Monthly time series of SST data for the years from 2002 to 2016 at different locations (see Fig. 2). The 2006 data and other unusual features are highlighted.
the above relations, we yield the final expression for calculation of the distance L over which full upwelling develops; that is,
L=
gho2 (1
h)/ cos( *) with
h = ho/ H
(iii) greater thickness of the surface mixed layer. If we assume that the bottom layer of the two-layer ocean is of infinite depth (or, equivalently, that the surface layer is much thinner than the bottom layer, called the reduced-gravity model), then we yield:
(6)
Since the Coriolis parameter does not appear in the latter equation, the distance L is invariant of geographical latitude. According to (6), the distance L increases (i.e. full upwelling is spatially delayed) with:
L=
(i) weaker wind forcing, (ii) enhanced density stratification, and
gho2/ cos( *)
(7)
which suggests a quadratic impact of ho on L. This simplification, however, can lead to an overestimation of L. 5
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Fig. 8. Monthly SST time series for 2006 shown at all study locations (see Fig. 2). The line thickness of curves increases with proximity to the equator. The ellipse indicates the period of substantial surface cooling. The arrow indicates that this cooling signal appears first in the south from where it progresses equatorward.
The wind conditions may vary significantly along the extended coastlines, such as that of Sumatra and Java. In this case, the theory presented above can be extended by splitting the coastline into segments (Fig. 4). Hereby; the contribution of each segment to the total change of ho can be calculated from:
h i = h o Li / L
(8)
Fig. 10. Monthly values of westward (offshore) wind stress, τx (Pa), near the equator for the period 1991–2011. The years of 1994, 1997, 2006 (and 2011) are highlighted.
where Li is the coastline length of the i-th segment and L, calculated from (6), is based on the coast-parallel wind stress in this segment. Since the vertical displacement of the pycnocline accumulates along the coast in the direction of Kelvin wave propagation, the cumulative mixed-layer change in segments follows from:
flux anomalies nor lateral heat flux anomalies induced by variations in oceanic currents, which can also modify SST anomalies.
i
hi =
hj j=1
3. Results and discussion
(9)
To this end, the method developed here identifies segments along a coastline favourable for the development of full upwelling; that is, Δhi > ho. Note that downwelling-favourable wind in a segment can offset the upwelling induced in “upstream” segments. For the western Sumatran coastline, we start the integration of (9) at 1°S (see Fig. 2) and not at the equator where rotational effects are absent. Note that bathymetric irregularities such as shelf-break canyons may trigger enhanced localized upwelling (see Kämpf, 2012). Such regional features are ignored in the context of this work. It should also be pointed out that the theory presented here exclusively considers regional wind effects, but neither effects due to regional air-sea heat
3.1. Spatial analysis of pIOD events SST anomalies off the coast of Sumatra are a dominant part of IOD variability (see Fig. 1b). Wyrtki (1962) suggested that, under influence of the Southeast Monsoon from June to November, the upwelling of cold water is a regular feature in coastal waters off the southern coasts of Java. This is confirmed here (Fig. 5a–b), noting that the Java upwelling appears more pronounced in pIOD years (Fig. 5b). Interestingly, our analysis identifies two years (2010 and 2016) when the Java upwelling is completely absent (see Fig. 7d). In contrast to regular upwelling off Java, the coastal waters off
Fig. 9. Monthly values of coast-parallel wind stress, τ*(Pa), at selected locations for the period 1991–2011. The years of 1994, 1997, 2006 (and 2011) are highlighted. 6
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Fig. 11. Time series of monthly values of b) mixed layer depth (MLD, m) and barrier layer thickness (BLT, m) and c) mixed-layer density anomalies relative to the density at 100 m (Δρ, kg/m3) for the period 2004–2017. The values are averaged for all data between 2°S and 5°S. Data source (BOA-Argo, Li et al., 2017). Panel a) displays the definitions of the relevant vertical scales. The depth of the surface mixed layer (Dsigma) is determined from a density criterion. Isothermal layer depth (DT02) is taken as the depth at which the temperature has decreased by 2 °C relative to surface waters. See Li et al. (2017) for more details.
pIOD events, cold water upwelling off Sumatra creates another upwelling jet – to be called the South East Sumatra Current (SESC) – that forms a north-westward continuation of the SJC (Fig. 6a). In interaction with the SEC, this creates a broad north-westward flow moving colder water into the DMI region. It is obvious that the SESC operates to induce advective cooling in the region, which may substantially reinforce negative SST anomalies during pIOD events. Monthly data of SST off the south coast of Java confirm that coastal upwelling is a regular feature of the region (Fig. 7d). This cooling normally commences in May/June and lasts until October/November each year. Enhanced and prolonged cooling occurred in the pIOD year of 2006 with a magnitude exceeding 4 °C. Interestingly, coastal waters off the south coast of Java did not show signs of surface cooling in the years of 2010 and 2016. 3.2. Time series analysis: SST variability In contrast to the Java region, waters off the southwest coast of Sumatra experience pronounced cooling events only rarely (Fig. 7c). In the period from 2003 to 2017, strong transient surface cooling of > 4 °C developed in July-September in the pIOD year of 2006. Similar, but slightly weaker cooling events are indicated for the years of 2007 and 2011. The SST development in 2007 is not unexpected, given that this year classifies as a weaker pIOD event (Luo et al., 2008). But the observed cooling in 2011 is rather surprising, given that the DMI does not highlight this particular year (see Fig. 1b). Note that, as for the Java region, the years of 2010 and 2016 mark unusually “warm phases” for the waters off the southwest coast of Sumatra. The central shelf of Sumatra (~ 3°S) experiences normally relatively little seasonal SST variations except in pIOD years when substantial cooling of > 5 °C occurs in the region (Fig. 7b). The eastern equatorial Indian Ocean shows a relatively high intra-annual SST variability with variations of ± 1° (Fig. 7a). This feature is not surprising, given that the equator is a waveguide for internal Kelvin waves that tend to dissipate farther away from the equator. Despite these relatively strong SST fluctuations, the cooling event associated with the pIOD event in 2006 can also be identified near the equator. However, with a closer look at the SST development, we find that the peak of the cooling phase in 2006 occurred first off southwest Sumatra before progressively moving towards the equator where it arrived around half a month later (Fig. 8).
Fig. 12. Inter-annual variations of a) isothermal layer thickness, ho, and b) density anomalies (surface values minus value at 100 m depth) for the period 2004–2017. The values are averaged for all values between 2°S and 5°S. Only values for July, August and September are shown. Data source (BOA-Argo, Li et al., 2017).
southwestern Sumatra show normally little or no sign of full upwelling (Fig. 5b), as previously concluded by Saji and Yamagata (2003). Note that, normally, an extensive warm pool of SSTs > 29 °C exists around the equator off northwest Sumatra. This normal setting changes significantly in pIOD years (1994, 1997 and 2006) when an extensive area of anomalously cold water appears off the southwestern Sumatran coast and the equatorial warm pool weakens considerably (Fig. 5a). The anomaly distribution (Fig. 5c) highlights that pIOD events are characterized by major SST changes centred at ~ 5°S; that is, directed off the southwest coast of Sumatra. Note that unlike pIOD events, negative IOD events are structural similar to the average distributions and therefore not explicitly discussed here. Normally, the surface circulation (derived from absolute dynamic topography) in the study region consists of the upwelling-enforced South Java Current (SJC), described by Sprintall et al. (1999), that feeds into the South Equatorial Current (SEC) south of the latitude of ~ 10°S (Fig. 6b), which is consistent with historical in-situ data (Quadfasel et al., 1996). In addition, an equatorial current (EC) moves warmer equatorial surface water both eastward and southward into the DMI region, where it recirculates and joins the SEC. In contrast, during
3.3. Time series analysis: upwelling-favourable wind stress The time series of monthly wind stresses parallel to the coast shows that upwelling favourable winds exists along the southern Java coast 7
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Table 1 Values of Δhi (m) according to (9) for each segment i (which is characterized by its geographical latitude) along the coastlines of Sumatra and Java using August wind data for the years 1991–2011 and ho = 50 m and Δρ ≈ 2.75 g/m3. The width of coastal segments is ΔLi ≈ 150 km, except for ΔL8 ≈ 300 km. Segments of full upwelling (i.e., Δhi > 50 m) are highlighted in red. Yellow cells indicate situations in which the pycnocline come close (< 10 m) to the sea surface. Blue cells indicate the years in which more than half of the upwelling is provided as preconditioning north of 3°S.
from April to November each year (Fig. 9d). Overall, the region experiences little interannual variability in the wind conditions, which implies that the warming phases seen in Fig. 7d cannot be explained by changing wind conditions. While seasonably upwelling-favourable winds exist along the entire Sumatran coastline south of the equator (Fig. 9a–c), overall the onset of such winds generally develops later than along the coast of Java. Moreover, there are significant interannual variations of the magnitude of the upwelling-favourable winds. The pIOD years of 1994, 1997 and 2006 all experienced the overall highest upwelling-favourable wind stresses of ~ 0.1 Pa between 1°S and 3°S, which is more the double the normal situation. Interestingly, the year 2011 also belongs to this category of unusually high upwellingfavourable wind stresses. Note that despite the regular existence of upwelling-favourable winds along most of Sumatra's coast, full coastal upwelling does rarely develop here (see Fig. 7). Time series of monthly zonal wind stresses near the equator reveal the unusual development of westward zonal wind components in the pIOD years of 1994, 1997 and 2006 and in 2011, not seen in other years (Fig. 10). In these particular years, an unusual westward wind component starts develop in August to peak around September/October. Overall these zonal wind components, which have the potential to induce upwelling near the equator, seem to follow rather than trail surface cooling events (see Fig. 7a). It should be pointed out that, again, the year 2011 appears with coastal wind characteristics similar to those existing during pIOD events.
average mixed-layer properties in coastal waters along Sumatra's west coast. The vertical density stratification in the eastern intertropical Indian Ocean varies considerably over the months of a year. This includes variations of both depth and density of the surface mixed layer and the formation of barrier layers (Fig. 11). Wyrtki jets play a key role in this variability (e.g. Vinayachandran et al., 2009). Nevertheless, pIOD events tend to develop during the relatively stable (southeast) monsoon period and not during the times of rapid monsoon changes. From the sparse ARGO data available it appears that both thickness of the isothermal layer, ho, and the density anomaly of this layer (with respect to the seawater density at 100-m depth) display only little interannual variations in these months of interest (Fig. 12). For instance, ho varies by ± 10 m from an average of ~ 50 m, while the average density anomaly of Δρ ≈ 2.75 kg/m3 varies by ± 0.75 kg/m3. Given these relatively small variations, it is reasonable to use average values of both parameters in the theory presented in Section 2.2.3. When using all available data, our theory (9) reveals pronounced interannual variations of the point of full upwelling along the Sumatran coast (Table 1). In some years, such as in 2010, upwelling remains suppressed along the entire Sumatran coast, a feature which can be linked to the existence of downwelling-favourable winds from the equator to a geographical latitude of 3°S (Table 2). A similar wind pattern is seen in 1998. On the other hand, our theory suggests the point of full upwelling progresses equatorward to a geographical latitude of 5°S in the years of 1994, 1997, 2006, 2007, and 2011. While four of these five instances are pIOD years, our theory suggests that the winds in 2011 should also create full upwelling off southwest Sumatra, which will be further explored in the next section. The individual upwelling contributions in each segment of the coast (Table 2) confirms that it is the northern segments of the Sumatran
3.4. Why does the Sumatran upwelling only develop infrequently? This section applies the theory on coastal upwelling along coasts of finite extents (see Section 2.2.3) which requires information about 8
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Table 2 Same as Table 1 but showing values of δhi (m) according to (8). Highlighted are the years in which upwelling-favourable coastal winds are unusually strong in the near-equatorial region. Positive (negative) values indicate upwelling (downwelling).
Fig. 14. Same as Fig. 13, but for September 2011. Data source: NCDC/NOAA.
Fig. 13. Distribution of sea surface temperature for September 2006. Data source: NCDC/NOAA.
Despite the associated uncertainties, however, it should be pointed out that the model is also supported by the observed equatorward progression of SST anomalies in a pIOD year (see Fig. 8). During the development towards the steady-state shape of the pycnocline (see Fig. 3), full upwelling will appear first in the south before progressing northward to its steady-state location of the point of full upwelling. Equatorward of this point, however, the thermocline is located closer to the sea surface due to the partial-upwelling process, such that wind stirring is expected to also trigger lower SSTs in those regions.
coast between the equator and 3oS that induce substantial partial upwelling of > 25 m in the pIOD years of 1994, 1997 and 2006, and in the year of 2011. This suggests that it is this only this preconditioning that enables the development of full upwelling off southwest Sumatra. Note that variations of ho and Δρ (within the indicated ranges) do not significantly change these results. Indeed, our conceptual model is highly simplified as it exclusively considers regional wind conditions as the driver of SST anomalies. 9
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Given that there are strong intraseasonal variability of currents including the Indonesian Through Flow (Masumoto et al., 2005) and mesoscale eddy activities (Feng and Wijffels, 2002; Yu and Potemra, 2006) in the tropical Indian Ocean, we cannot totally rule out the possibility that other effects not captured in this analysis may influence the development of pIOD events. Future studies should focus on the influence that intraseasonal monsoon jets have on the density stratification in the region. While some recent modelling studies indicate that the frequency of pIOD events has increased under the influence of global warming (e.g. Cai et al., 2009), it should highlighted that our study finds that coastal upwelling was fully suppressed in the entire region including Java coastal waters in the years of 2010 and 2016. This feature, which could be linked to global warming, deserves further study. Acknowledgements
Fig. 15. Same as Fig. 13, but for September 2016. Data source: NCDC/NOAA.
Section 2.1 lists the sources of data used in this work. This project did not receive external funding. The authors declare no conflict of interest. This work makes use of Argo data (http://doi.org/10.17882/42182).
3.5. What happened in the years of 2011 and 2016?
References Abram, N.J., Gagan, M.K., McCulloch, M.T., Chappell, J., Hantoro, W.S., 2003. Coral reef death during the 1997 Indian Ocean Dipole linked to Indonesian wildfires. Science 301, 952–955. Atlas, R., Hoffman, R.N., Ardizzone, J., Leidner, S.M., Jusem, J.C., Smith, D.K., Gombos, D.A., 2011. Cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Am. Meteorol. Soc. 92, 157–174. https://doi.org/10.1175/2010BAMS2946.1. Bakun, A., 1973. Coastal Upwelling Indices, West Coast of North America, 1946–71 (NOM Tech. Rep. NMFS SSRF-671). pp. 103. Cai, W., Sullivan, A., Cowan, T., 2009. Climate change contributes to more frequent consecutive positive Indian Ocean Dipole events. Geophys. Res. Lett. 36, L23704. https://doi.org/10.1029/2009GL040163. Cai, W., van Rensch, P., Cowan, T., Hendon, H.H., 2012. An asymmetry in the IOD and ENSO teleconnection pathway and its impact on Australian climate. J. Clim. 25, 6318–6329. Cushman-Roisin, B., 1994. Introduction to Geophysical Fluid Dynamics. Prentice Hall, New Jersey. Feng, M., Wijffels, S., 2002. Intraseasonal variability in the South Equatorial Current of the east Indian Ocean. J. Phys. Oceanogr. 32, 265–277. https://doi.org/10.1175/ 1520-0485(2002)032<0265:IVITSE>2.0.CO;2. Gill, A.E., 1982. Atmosphere-Ocean Dynamics. Academic Press, San Diego, CA. Hela, I., 1976. Vertical velocity of the upwelling in the sea. Soc. Sci. Fenn. Comm. Phys.Math. 46, 9–24. Hendon, H.H., 2003. Indonesian rainfall variability: impacts of ENSO and local air-sea interaction. J. Clim. 16, 1775–1790. Huang, B., Kinter, J.L., 2002. Interannual variability in the tropical Indian ocean. J. Geophys. Res. 107. https://doi.org/10.1029/2001JC001278. Kämpf, J., 2012. Lee effects of localized upwelling in a shelf-break canyon. Cont. Shelf Res. 42, 78–88. https://doi.org/10.1016/j.csr.2012.05.005. Kämpf, J., 2015. Interference of wind-driven and pressure gradient-driven flows in shallow homogeneous water bodies. Ocean Dyn. 65 (11), 1399–1410. https://doi. org/10.1007/s10236-015-0882-2. Kämpf, J., Chapman, P., 2016. Upwelling Systems of the World. Springer, Cham, Switzerland. Kripalani, R.H., Oh, J.H., Chaudhari, H.S., 2010. Delayed influence of the Indian Ocean Dipole mode on the East Asia–West Pacific monsoon: possible mechanism. Int. J. Climatol. 30, 197–209. Li, H., Xu, F., Zhou, W., Wang, D., Wright, J.S., Liu, Z., Lin, Y., 2017. Development of a global gridded Argo data set with Barnes successive corrections. J. Geophys. Res. Oceans 122. https://doi.org/10.1002/2016JC012285.6. Luo, J.-J., Behera, S., Masumoto, Y., Sakuma, H., Yamagata, T., 2008. Successful prediction of the consecutive IOD in 2006 and 2007. Geophys. Res. Lett. 35, L14S02. https://doi.org/10.1029/2007GL032793. Masumoto, Y., Hase, H., Kuroda, Y., Matsuura, H., Takeuchi, K., 2005. Intraseasonal variability in the upper layer currents observed in the eastern equatorial Indian Ocean. Geophys. Res. Lett. 32, L02607. https://doi.org/10.1029/2004GL021896. Masson, S., Delecluse, P., Boulanger, J.-P., Menkes, C., 2002. A model study of the seasonal variability and formation mechanisms of the barrier layer in the eastern equatorial Indian Ocean. J. Geophys. Res. 107 (C12), 8017. https://doi.org/10.1029/ 2001JC000832. Perlin, A., Moum, J.N., Klymak, J.M., Levine, M.D., Boyd, T., Kosro, P.M., 2007. Organization of stratification, turbulence, and veering in bottom Ekman layers. J. Geophys. Res. 112, C05S90. https://doi.org/10.1029/2004JC002641. Qiu, Y., Cai, W., Li, L., Guo, X., 2012. Argo profiles variability of barrier layer in the tropical Indian Ocean and its relationship with the Indian Ocean Dipole. Geophys. Res. Lett. 39, L08605. https://doi.org/10.1029/2012GL051441.
A few years shows unusual and unexpected SST variability. The year 2011, for instance, indicates the existence of strong upwelling during August/September off southwest Sumatra and similar wind conditions all along the Sumatran coast. Why does this year not appear marked as pIOD event in the DMI? On the other hand, there were two years, 2010 and 2016, where surface cooling failed to develop despite the existence of robust upwelling-favourable winds that were very similar to the normal situation. What caused this feature? The SST distribution in September 2006 at the peak of the pIOD event in September (Fig. 13) serves as a means of comparison to be able to answer these questions. The surface SST distribution in September 2006 demonstrates the full extent of a pIOD event (Fig. 13). Extensive coastal upwelling with SSTs < 24°C has developed along the southern coast of Java and this upwelling extends equatorward along the southwest Sumatran coastline. The SST distribution for September 2011, on the other hand, had a pattern very similar to that of September 2006 (Fig. 14), which is consistent with the analysis presented in Tables 1, 2. Hence, the only reason why the DMI methodology has not picked up this year as an unusual year (see Fig. 1a) is that a slight relative warming in other regions has overshadowed the cooling signature induced by coastal upwelling. In how much the overall atmospheric circulation pattern in 2011 was similar to that of pIOD events remains for future investigations. On the other hand, significant and widespread warming of surface waters occurred in September 2016 (Fig. 15) and, similarly, in September 2010 (data not shown). It is reasonable to relate the appearance of such extreme heat waves in the region to the absence of upwelling off the southern coastline of Java. Future studies should investigate whether similar heat waves have existed prior to 2003. 4. Summary and conclusions In this study, the authors present a simple theory of coastal upwelling along coasts of finite horizontal extent. This theory can explain the SST variability observed in coastal waters off the southwestern coast of Sumatra – a dominant driver of the Indian Ocean Dipole. Findings suggest that partial upwelling north of 3°S is a key preconditioning process for the development of full upwelling along the southwestern coast of Sumatra. The authors believe that this identification of the likely trigger mechanism of pIOD events provides the scientific community with a starting point for investigations of the impact of climate change on the monsoon dynamics and variability in the Indian Ocean. 10
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Res. Lett. 26, 2493–2496. Susanto, R.D., Gordon, A.L., Zheng, Q., 2001. Upwelling along the coasts of Java and Sumatra and its relation to ENSO. Geophys. Res. Lett. 28, 1599–1602. Tomczak, M., Godfrey, J.S., 2003. Regional Oceanography: An Introduction, second ed. Daya Publishing House, New Delhi. Vinayachandran, P.N., Francis, P.A., Rao, S.A., 2009. Indian Ocean Dipole: processes and impacts. In: Mukunda, N. (Ed.), Current Trends in Science (Platinum Jubilee Special Publication). Indian Academy of Sciences, Sadashivanagar, Bangalore, pp. 569–589. Yu, Z., Potemra, J., 2006. Generation mechanism for the intraseasonal variability in the Indo-Australian basin. J. Geophys. Res. 111, C01013. https://doi.org/10.1029/ 2005JC003023. Webster, P.J., Moore, A.M., Loschnigg, J.P., Leben, R.R., 1999. Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401, 356–359. Weller, E., Cai, W., 2014. Meridional variability of atmospheric convection associated with the Indian Ocean Dipole Mode. Sci. Rep. 4, 3590. https://doi.org/10.1038/ srep03590. Wyrtki, K., 1962. The upwelling in the region between Java and Australia during the southeast-monsoon. Aust. J. Mar. Freshw. Res. 13, 217–225. Wyrtki, K., 1973. An equatorial jet in the Indian Ocean. Science 181, 262–264.
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