1. Introduction
The Indo-Pacific region is a major component of the global climate system, which hosts some of Earth’s most dominant sources of climate variability, El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD). The world’s warmest and most expansive pool of ocean waters straddles the Maritime Continent, regulating the global climate. Within the warm pool, vigorous atmospheric convection forms the ascending branch of the Walker circulation, fed by the easterly Pacific trade winds, which pile up water toward the Maritime Continent, thereby creating a pressure difference that drives voluminous oceanic flow from the Pacific to the Indian Ocean through the Indonesian Archipelago (Wyrtki 1987). This cross-basin oceanic flow, termed the Indonesian Throughflow (ITF), forms an integral part of the global ocean circulations, maintaining the state of Earth’s climate and its variability (e.g., Hirst and Godfrey 1993; Gordon and Fine 1996; Murtugudde et al. 1998; Vranes et al. 2002; Jochum et al. 2009; Santoso et al. 2011; Sprintall et al. 2014; Kajtar et al. 2015; Hu et al. 2015; Sprintall et al. 2020).
Changes to ITF transport influence heat and freshwater balance in the Indo-Pacific region that is important for climate across various time scales (Vranes et al. 2002; Feng et al. 2013; Ummenhofer et al. 2017; Jin and Wright 2020). The ITF is also involved in the recharge and discharge of equatorial Pacific warm water during ENSO events (e.g., McGregor et al. 2014), which can trigger IOD occurrences (e.g., Yang et al. 2015). Thus, not only do ENSO and the IOD affect the ITF (e.g., Sprintall and Revelard 2014; Hu and Sprintall 2016), but changes in the ITF may in turn impact ENSO and the IOD (Lee et al. 2002; Yuan et al. 2013; Kajtar et al. 2015). Understanding how the ITF is linked to ENSO and the IOD is therefore important for discerning the impacts of these interactions, particularly as the climate system is expected to continue to change under global warming with projected increase in the frequency of extreme ENSO and IOD events (Cai et al. 2020, 2021). While our knowledge of Indo-Pacific linkages has improved (Wang 2019; Cai et al. 2019), the extent to which ITF variability and its links to ENSO and the IOD are represented across a wide range of climate models that are used to make future projections is still not clear. Here we investigate this issue using 20 climate models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5).
As inferred from observational and model-based studies, the warm (El Niño) and cold (La Niña) phases of ENSO generally correspond with anomalously weak and strong ITF transport, respectively (Clarke and Liu 1994; Meyers 1996; England and Huang 2005; Santoso et al. 2011; Gordon et al. 1999; Sprintall and Revelard 2014; van Sebille et al. 2014; Hu and Sprintall 2016). The Walker circulation weakens during an El Niño, as the Pacific easterly trade winds relax, associated with anomalously warm central-eastern equatorial Pacific sea surface. This leads to lower-than-normal sea level in the western Pacific, consequently reducing the interbasin pressure gradient that drives the ITF. The converse occurs during a La Niña. Given the prominence of ENSO on ocean–atmospheric circulation changes in the Indo-Pacific region, the impact of the IOD can be difficult to discern. The positive phase of the IOD, marked by anomalously cold and warm sea surface in the eastern and western tropical Indian Ocean, respectively, often co-occurs with developing El Niño (e.g., Cai et al. 2011). Conversely, negative IODs tend to occur with La Niña events. There is, however, increasing evidence for a significant IOD impact on the ITF (e.g., Sprintall et al. 2009; Liu et al. 2015; Sprintall and Revelard 2014; Pujiana et al. 2019). For instance, Pujiana et al. (2019) found up to 40% reduction in ITF transport during the weak 2016 La Niña that was attributed to the concurrent negative IOD, in which downwelling Kelvin waves associated with anomalous westerly winds off Sumatra and Java led to higher-than-normal sea level at the ITF outflow region, thereby reducing the interbasin pressure gradient.
Our understanding of ITF variability is hampered by sparse observations over discontinuous periods of direct current measurement within the Indonesian seas since only the 1990s, primarily in the Makassar Strait (e.g., Gordon et al. 2008; Susanto et al. 2012)—a key ITF channel that accounts for about 80% of the total ITF transport (Gordon et al. 2010, 2019). As such, many investigations of ITF variability rely on estimates of ITF transport derived using observed temperature profiles across northwestern Australia and Java (Meyers 1996), on remotely sensed altimeter data (Sprintall and Revelard 2014), and on the use of numerical models, such as ocean reanalysis and state-of-the-art climate models (e.g., Murtugudde et al. 1998; England and Huang 2005; Potemra and Schneider 2007; van Sebille et al. 2014). The link between ITF transport and ENSO reported across these studies varies, with the strength of the correlation coefficients ranging from about 0.3 to 0.6. These differences should largely stem from the contrasting methodologies, data products, and models used, but there are physical processes modulating ENSO impacts that may be captured to varying extents across studies. Using an intermediate complexity model, Murtugudde et al. (1998) found that fixing winds over the Indian Ocean to climatology led to a dramatic increase in the ENSO–ITF correlation (from 0.31 to 0.65), illustrating that Indian Ocean processes counteract the impact of ENSO (see also Meyers 1996; Masumoto 2002). Potemra and Schneider (2007) showed using an ocean reanalysis and two coupled models that the ITF exhibits opposing transport anomalies between the upper 100 m and the deeper layer (see also Potemra et al. 2003). An enhanced upper-layer transport was found to be associated with anomalous easterly winds south of Java. A reduced transport at the subsurface below 100 m was linked to anomalous westerlies in the equatorial Pacific and anomalous easterlies in the equatorial Indian Ocean, a typical condition during an El Niño event. The latter also occurs during a positive IOD. Thus, the impact on the total depth-integrated ITF transport would not be as apparent than if the anomalous transports were of uniform polarity across depths. The partitioning of ITF variability into upper and lower layers has also been shown in observation-based studies (Molcard et al. 1996, 2001; Sprintall et al. 2009; Susanto et al. 2012; Sprintall and Revelard 2014; Gordon et al. 2019). For instance, during the weak 2006/07 El Niño, which coincided with a strong positive IOD event, an enhanced and reduced southward flow was observed above and below 100 m, respectively, in both the Makassar Strait (Susanto et al. 2012) and the ITF outflow passages (Sprintall et al. 2009).
It is not yet clear to what extent ITF variability and its link to ENSO and IOD vary across climate models that are used for future climate projections. Climate models simulate a diverse range in the representation of ENSO and IOD, exhibiting notable biases (e.g., Saji et al. 2006; Cai et al. 2011; Liu et al. 2013; Taschetto et al. 2014; Jourdain et al. 2016; McKenna et al. 2020). In particular, the overly strong IOD amplitude bias has persisted throughout generations of climate models, due to an overly active Bjerknes feedback in the southeastern tropical Indian Ocean (Cai and Cowan 2013). In this study, we investigate how ITF variability and its interplay with ENSO and IOD are represented across models and how this may be affected by model biases. Our analysis shows that ITF variability is model dependent, strongly influenced by the simulated ENSO, the IOD, and their linkage. The simulated ITF variability is shown to exhibit vertical structures which are distinctly linked to ENSO and the IOD. We demonstrate that by examining these vertical structures, the impact of model biases can be better understood, and this is also important for understanding the response of ITF to climate change.
The rest of the paper is organized as follows. Section 2 presents the implemented models and the analysis approach. The results are presented in section 3, covering ITF seasonal cycle, characteristics of the variability, relationships with ENSO and IOD, and intermodel correlations to reveal the factors that influence the simulated ITF variability. Section 4 concludes the paper with a summary and discussion on the ramification of our results on future ITF variability.
2. Data and methods
Our analysis focuses on the historical simulations of the 20 CMIP5 models listed in Fig. 1. We analyze a common 93-yr historical period (1907–99) across the models to obtain the longest span without any missing data in our model archives. As a qualitative comparison and to facilitate further understanding of the CMIP5 models result, we also examine the SODA-2.2.4 ocean reanalysis (Giese and Ray 2011) from 1970 to 2008, while keeping in mind that a reanalysis itself would differ from direct observations. SODA-2.2.4 is based on the Parallel Ocean Program (POP) ocean model assimilating various observations, with an average resolution of 0.25° × 0.4° × 40 vertical levels, and the 5-day averaged output is mapped onto monthly averaged uniform 0.5° × 0.5° × 40 level grid (Carton and Giese 2008). For ease of calculation across several different models, which have varying geographical configuration of the Maritime Continent depending on model resolutions (see Fig. S1 and Table S1 in the online supplemental material), we define the ITF as the depth-integrated transport across ocean portions along a specified transect line between Sumatra and Australia (green line in Fig. 1a) that separates the Pacific from the Indian Ocean. The bathymetry along this transect for all the models is provided in Fig. S2. The transport integrated across this transect and over the full depth (i.e., ITF total transport) is equivalent to the difference in depth-integrated mass streamfunction between the easternmost and westernmost boundary of the ITF gateway, thus strictly defining the ITF based on mass conservation of the global ocean circulation (Santoso et al. 2011). The sign convention here is positive for an enhanced ITF and negative for a weakened ITF. The CMIP5 models simulate a multimodel mean of 15.3 Sv (1 Sv ≡ 106 m3 s−1) in ITF total transport, comparable to the SODA-2.2.4 reanalysis of 16.9 Sv.
Annual cycle of depth-integrated ITF transport (ITFtotal) calculated across the transect in (a) presented as the anomaly in (b) over 1970–2008 for the SODA-2.2.4 reanalysis (thick black line) and for the historical period of 1907–99 for the CMIP5 models (ensemble mean in thick red line). Sign convention is positive for enhanced ITF transport. Colored thin curves correspond to colored model names. This color coding is used in the rest of all applicable figures. (c),(d) The vertical structure of ITF transport annual cycle per unit depth for the reanalysis and CMIP5 multimodel mean, respectively. The mean values of the ITF depth-integrated transport across the green transect line for the reanalysis and CMIP multimodel mean are indicated in (a). The transect line stretches from 0°, 103°E to 17°S, 130°E where the ITF calculation for all the models is done over ocean grid-points (see supplemental Fig. S2 for model bathymetry along this transect).
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
To further investigate the characteristics and drivers of ITF variability, an empirical orthogonal function (EOF) analysis is applied on the integrated transport within each vertical layer to extract the vertical modes of ITF variability in each model and reanalysis. To facilitate intermodel comparisons, ocean transport for all the models is first linearly interpolated onto the vertical levels of the SODA reanalysis. Given the uneven distribution of the data due to irregular vertical grid thickness, each anomaly matrix is first weighted by the thickness of the vertical grids prior to EOF computation. EOF analysis has been previously used to study observed ITF variability (Molcard et al. 1996, 2001; Susanto et al. 2012), although the period of analysis was too short to sufficiently resolve interannual variations.
To reveal the drivers of ITF variability, we perform correlation analysis between ITF transport time series with climate variables such as sea surface temperature (SST), wind stress, and upper ocean heat content (averaged temperature above 300 m) within the SODA reanalysis and each of the CMIP5 models. For ENSO and IOD variability, we utilize the Niño-3.4 (SST anomalies averaged over 5°S–5°N, 170°–120°W) and Dipole Mode Index (DMI; difference in anomalous SSTs between 10°S–10°N, 50°–70°E and 10°S–0°, 90°–110°E; Saji et al. 1999), respectively. ENSO events are defined as when the amplitude of Niño-3.4 anomalies averaged from December of ENSO development year to the subsequent February (DJF) exceeds 0.5 standard deviation. IOD events are defined as when the amplitude of DMI anomalies averaged from September to November (SON) exceeds 0.5 standard deviation. The SODA Niño-3.4 and DMI time series (detrended with seasonal cycle removed) are highly correlated to those of the NOAA Extended Reconstructed SST (ERSST) version 3b, with correlation coefficients of 0.98 and 0.85, respectively. The ENSO magnitude is comparable between the two reanalyses (Fig. S3a), but the SODA IOD amplitude is about 20% larger, although it is still at the low end of the CMIP5 models of which the multimodel mean magnitude is ∼60% larger than in ERSST (Fig. S3b). Overly large IOD amplitude is a persistent bias over generations of climate models, with many of the CMIP5 models also exhibiting overly regular ENSO and IOD variability (McKenna et al. 2020) as indicated by sharp spectral peaks (Figs. S3c,d).
Unless stated otherwise, all time series have been detrended using a second-order polynomial fit with monthly climatological means (entire record as baseline) removed. Analyses focusing on interannual variability further incorporate a removal of 11-yr running mean and an application of a Butterworth low-pass filter to remove signals with periodicities shorter than 18 months. Statistical significance for the correlation of time series is evaluated based on consideration of the effective degrees of freedom arising from autoregressive properties of a geophysical time series (Davis 1977).
3. Results
a. General characteristics of ITF variability
The CMIP5 multimodel mean and the SODA reanalysis are consistent in exhibiting an apparent annual cycle of the ITF, reaching a maximum in austral winter and a minimum in austral summer (Fig. 1b), consistent with previous studies based on models and observations (e.g., Masumoto and Yamagata 1996; Lee et al. 2010; Shinoda et al. 2012; Liu et al. 2015; Gordon et al. 2019). The CMIP5 ensemble exhibits notable intermodel spread (Fig. 1b): the peak-to-trough amplitude of seasonal cycle in the ITF total transport (Fig. 1b) ranges from 3.6 to 18 Sv, with a mean of ∼9 Sv, similar to the SODA reanalysis. This intermodel range for the 20 CMIP5 models is notably larger than the 4.9–8.7-Sv range for the 14 ocean reanalysis products analyzed by Lee et al. (2010), which is expected since CMIP models, unlike ocean reanalysis, are not constrained to assimilated observational data.
The ITF annual cycle is confined in the upper 100 m above the mean thermocline due to monsoonal forcing. Semiannual anomalies occur below, consistent with semiannual wind variations in the equatorial Indian Ocean that generate Kelvin waves toward the Maritime Continent (e.g., Sprintall et al. 2000; Iskandar et al. 2005; Lee et al. 2010; Susanto et al. 2012; Gordon et al. 2019). Upward phase propagation reveals a signature of downward penetration of Kelvin wave energy (e.g., Sprintall et al. 2009; Drushka et al. 2010). Both the reanalysis and the CMIP5 multimodel mean exhibit such vertical structures in a strikingly close correspondence to each other (Figs. 1c,d).
Beyond the seasonal cycle, the ITF displays apparent interannual variability as highlighted in Fig. 2a, which shows the ITF total transport time series in the SODA reanalysis with the seasonal cycle removed. Also visible in Fig. 2 is an increasing trend since the 1990s, linked to the strengthening of the tropical Pacific Walker circulation (e.g., Feng et al. 2011) and the associated salinity effect (Hu and Sprintall 2017). The transport range is ∼15 Sv, which is of similar order to that seen in observations at different survey sections (e.g., Liu et al. 2015; Gordon et al. 2019). With the long-term trend removed, the SODA reanalysis exhibits a standard deviation of 2.7 Sv in the ITF total transport, while the CMIP5 models range from 0.8 to 3.1 Sv with a multimodel mean of 2.2 Sv, comparable to the reanalysis. Power spectral analysis reveals that the total ITF transport variability in the SODA reanalysis exhibits peak variability with periods of 4–8 years per cycle (Fig. 2b), coinciding more with the ENSO time scale (3–5 years per cycle) than the IOD (2–4 years per cycle). This tendency is somewhat similar in the CMIP5 multimodel (Fig. 2b, thin curves). The power spectrum at each depth level for the reanalysis and CMIP5 multimodel mean (Figs. 2c,d) shows the majority of the variability being contained in the upper 100 m across a diverse range of time scales, with interannual variability becoming more dominant with depth. Calculating the power spectrum with transport variability at each depth normalized by its respective standard deviation reveals the dominant time scales (Figs. 2c,d, contours). Specifically, the interannual variability is most dominant at 100–200 m, with intraseasonal variability being prominent within the surface layer (0–50 m). The vertical structure of ITF variability is relevant for understanding the impact of ENSO and IOD as discussed next.
(a) Time series of ITFtotal with monthly climatological mean removed (black) in SODA-2.2.4. The detrended time series is shown in blue, highlighting the early-twenty-first-century enhancement of ITF transport shown in black. (b) Power spectrum of standardized ITFtotal (black), Niño-3.4 (red), and DMI (blue) time series in the SODA reanalysis (thick curves) and CMIP5 ensemble mean (light colored thin curves). (c) Power spectrum of ITF time series at each depth level in the SODA reanalysis (red shading), with the power spectrum based on standardized time series shown in purple contours. The transport time series is first detrended with monthly means removed. (d) As in (c), but for the power spectrum averaged across the 20 CMIP5 models.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
b. Link with ENSO and IOD
We first note that the year-to-year variability of the ITF total transport in the SODA reanalysis exhibits temporal behavior that is broadly consistent with observations across the IX1 transect that provide a geostrophic transport estimate based on expendable bathythermograph (XBT) temperature records (Meyers 1996; Liu et al. 2015; Feng et al. 2018). Relative to existing surveys at other choke points (Molcard et al. 1996; Sprintall et al. 2009; Gordon et al. 2019), the IX1, which stretches from Fremantle, Western Australia, to the Sunda Strait, Indonesia, would be the closest to our transect (Fig. 1a; see Fig. S4a) and has the longest record dating back to 1983. Feng et al. (2018) presented the observed time series of the IX1 geostrophic transport anomalies referenced to 700 m (their Fig. 2). Although an exact match with our time series (Fig. 2a) is not expected, there is notable agreement between the two, with transport anomalies appearing to coincide with ENSO phases [cf. our Fig. S4b with Fig. 2 of Feng et al. (2018)]: anomalously high transports occurred in 1988/89, 1995/96, 1999–2001, early 2006, and 2007/08, coincident with La Niña conditions, whereas anomalously low transports in 1986–88, 1991/92, 1997/98, and 2002–05 were concurrent with El Niño conditions.
The tendency for a weaker and stronger ITF to occur with El Niño and La Niña, respectively, is marked by a statistically significant negative correlation in Figs. 3a and 3c. The CMIP5 models overall capture the expected link between ENSO and ITF, albeit with large intermodel differences, namely a weaker ITF corresponding with an El Niño and a stronger ITF corresponding with a La Niña. The CMIP5 multimodel averaged lag correlation between the Niño-3.4 index and ITF total transport (ITFtotal; Fig. 3a) reveals a maximum correlation coefficient (r) of about −0.30, comparable to the SODA reanalysis (r = −0.38; statistically significant above the 95% confidence level), and the correlations double when considering only variability on interannual time scales (Fig. 3c). The CMIP5 intermodel spread in the correlations is notably large, with a range of about 0.4.
(a) Correlations between ITFtotal and Niño-3.4 as a function of lag time in months for the SODA reanalysis (thick black line) and CMIP5 models (thin colored curves; ensemble mean in thick red line). (b) As in (a), but for DMI. Positive (negative) time lags indicate Niño-3.4 and DMI leading (lagging) ITF time series. (c),(d) As in (a) and (b), respectively, but based on filtered time series to isolate interannual variability. Correlation coefficient cut-off values for the statistical significance at the 95% confidence level are indicated by red and black dashed horizontal lines, respectively, for the reanalysis and CMIP5 models.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
The relationship between ITFtotal with the DMI, on the other hand, is not statistically significant for either the CMIP5 multimodel mean or the reanalysis (Figs. 3b,d), even though IOD events are associated with large atmospheric and oceanic anomalies that affect sea level at the ITF outflow passages (Sprintall and Revelard 2014). The apparent lack of a link between ITFtotal transport and IOD can be expected to be a result, at least in part, of a counteracting effect of ENSO. During a positive IOD event, which peaks in austral spring, anomalous surface-layer cooling occurs off Java and Sumatra, and thus a lower sea level at the ITF outflow region, which tends to increase the throughflow (e.g., Sprintall and Revelard 2014); as such, the IOD should be positively correlated to the ITF, but it is not the case in Figs. 3b and 3d. However, a positive IOD often coincides with an El Niño, through anomalous atmospheric subsidence over the Maritime Continent as the Indo-Pacific Walker circulation weakens. This ENSO–IOD co-occurrence is reflected in a positive correlation between the Niño-3.4 and DMI of r = 0.49 using the SODA monthly data (r = 0.39 for ERSST), significant above the 99% level, when the DMI leads Niño-3.4 by 2 months (r ∼ 0.7 if correlating September–November averaged DMI and December–February averaged Niño-3.4 for both reanalyses). Such co-occurring tendency means that the positive IOD-enhanced ITF tends to be counteracted by El Niño–induced ITF weakening via an anomalously low western Pacific sea level associated with weaker Pacific trade winds; the converse applies for negative IOD and La Niña co-occurrences. The significant negative ENSO-ITFtotal correlation and the absence of a significant positive IOD–ITFtotal correlation (Fig. 3), which would otherwise indicate a dominating IOD effect, suggests the overall dominance of the ENSO forcing on the ITF total transport.
The counteracting interplay between ENSO and IOD on the ITF involves opposing transport anomalies between the surface and subsurface layers, as revealed by compositing transport anomalies according to ENSO and IOD phases (Fig. 4). For both the CMIP5 ensemble (Fig. 4a) and reanalysis (Fig. 4e), anomalously weak transport occurs in the subsurface during an El Niño, peaking at around 100–200-m depth where interannual variability dominates (Figs. 2c,d). A signature of upward propagation of anomaly is apparent, which is analogous to that occurring on subannual time scales (Figs. 1c,d), transmitting the subsurface anomaly progressively toward the surface over about 6 months (see also Sprintall et al. 2009). Upward propagation is also evident in Makassar Strait current observation from 2004 to 2017 (Gordon et al. 2019) which shows transport over 300–760 m leading that in the upper 0–300-m layer. This upward propagation feature contributes to a tendency for a longer lagged response of the ITF to ENSO toward the surface as seen in the reanalysis (supplemental Figs. S5 and S6), a response that varies across the CMIP5 models, which will be discussed in section 3c. Upward propagation has been suggested to be indicative of downward penetration of Kelvin wave energy (McCreary 1984) associated with zonal wind forcing, which has strong intraseasonal component in the equatorial Indian Ocean. However, the sequence of interannual climate forcing could also contribute as illustrated below.
Composites of interannual transport anomalies at each depth level (i.e., in units of Sv m−1) according to (a) El Niño events, (b) positive IOD events, (c) La Niña events, and (d) negative IOD events, averaged across the 20 CMIP5 models. (e)–(g) As in (a)–(d), but for the SODA reanalysis. Shading indicates composites that are significant above the 90% confidence level. The composites are shown from January of the ENSO and IOD development year (months 1–12) to December of the following year (months 13–24). ENSO and IOD respectively peak around December to February (months 12–14) and September to November (months 9–11). Value in the bottom left of each panel indicates the respective number of events, of which the proportion is overall comparable between the CMIP5 and reanalysis relative to the different record lengths (93 years for CMIP5, 39 years for reanalysis).
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
Overlying the anomalously weak subsurface transport is an anomalously enhanced surface-layer transport. A similar vertical dipole pattern is seen in the positive IOD composite (Figs. 4b,f), with the surface intensification, which is particularly prominent in the CMIP5 models, being notably more pronounced compared to the El Niño composite. These tendencies, but in the opposite sense, are overall also seen in the La Niña and negative IOD composites (Figs. 4c,d,g,h). Such structure of opposing anomalous transports between the surface and subsurface has been identified in limited observations, particularly during concurrent ENSO and IOD phases (Sprintall et al. 2009; Susanto et al. 2012; Sprintall and Revelard 2014). This has been suggested to be associated with large-scale atmospheric divergence/convergence over the Maritime Continent during ENSO–IOD concurrence (Potemra and Schneider 2007).
During a positive IOD (and a strong El Niño, which tends to induce a positive IOD condition), easterly wind anomalies prevail during boreal summer to autumn in the eastern tropical Indian Ocean, which leads to upwelling off Java and Sumatra in turn locally enhancing the easterly anomalies (e.g., Schott et al. 2009). Through the associated lowered sea level height and enhanced southward Ekman transport, an increased ITF transport occurs in the upper layer. Downwelling Kelvin waves tend to occur below, though intermittently (e.g., Horii et al. 2008), and can contribute to transport reduction at depth (e.g., Potemra and Schneider 2007). In the western Pacific, upper-ocean heat content decrease linked to El Niño–induced westerly winds in the equatorial Pacific, which peaks following El Niño maturity (e.g., McPhaden et al. 2020), leads to subsurface transport reduction. Reduced transport can be further prolonged at the surface through an increase in heat content off Sumatra and Java associated with the swing toward the opposite phase of the IOD (Feng and Meyers 2003). This, coupled with the tendency for the effect of ENSO and IOD to cancel each other out during ENSO developing phase, may explain the lagged response of the depth-integrated ITF to ENSO seen in observations as noted by Liu et al. (2015) and Feng et al. (2018). In several models (Figs. 3a,c) though, the ITFtotal instead leads Niño-3.4 by 3–6 months. This is associated with a more robust deep transport response in these models that appears to peak during ENSO developing phase (around August; Fig. 4), and a prolonged surface anomaly associated with the delayed IOD bias (discussed in section 3c). The latter would have a persistent counteracting effect on subsurface anomaly extending into ENSO decay phase.
When ENSO co-occurs with IOD events (i.e., El Niño co-occurring with positive IOD, or La Niña co-occurring with negative IOD), the transport anomaly amplitude tends to be comparable between the surface and subsurface (Fig. S5), as opposed to the stronger surface anomaly during the IOD (Fig. 4). Taken together, these results suggest that the IOD and ENSO have a stronger impact at the surface and subsurface, respectively. The weaker subsurface impact of the IOD might be attributed to the strong presence of intraseasonal Kelvin waves in the equatorial Indian Ocean (Horii et al. 2008; Iskandar et al. 2005; Drushka et al. 2010), which can lead to destructive interference between upwelling and downwelling anomalies. Support for an IOD impact on surface transport is underscored by the pervasiveness of the surface anomalies in the CMIP5 IOD composite (Figs. 4b,d) which, relative to the reanalysis (Figs. 4f,h), potentially indicates an impact of the overly strong IOD amplitude bias (see below). In addition, the CMIP5 composite for positive IOD events without El Niño, which is a rare occurrence in observations, exhibits even stronger surface transport intensification, with no appreciable anomalies at depth (figure not shown).
Some asymmetry between positive and negative climate phases is noticeable, especially in the reanalysis, although this still points to the distinct impact between ENSO and IOD discussed above. For instance, the negative surface anomalies during La Niña are not statistically significant (Fig. 4g), unlike in the El Niño case. The positive subsurface anomalies in the negative IOD composite are notably weaker than in the La Niña composite (cf. Figs. 4g,h), while in the positive IOD counterpart they are comparable to the El Niño composite (cf. Figs. 4e,f). This asymmetry partly stems from fewer co-occurrences of negative IOD events with La Niña events than co-occurrences of positive IOD events with El Niño events, over the reanalysis period used here in which all positive IOD events co-occurred with an El Niño (Fig. 4f; see also Fig. S5c). Nonlinearity in the general properties of ENSO and IOD is also expected to play a role, although this may be less apparent in climate models, which overall tend to underestimate the observed nonlinearity (e.g., McKenna et al. 2020). Investigation into such asymmetry is beyond the scope of this paper, and here we focus on the distinct impact of ENSO and IOD in general.
To further characterize the vertical structures of ITF variability and examine the associated mechanisms, we perform an empirical orthogonal function (EOF) decomposition on transport anomalies above 1200 m capturing most of the transport variability, which intensifies toward the surface (Fig. 2). The patterns of anomalous SST, zonal wind stress, and upper ocean heat content associated with these EOFs are then assessed. The analysis was applied to the CMIP5 and reanalysis data with bandpass filtering to isolate processes on interannual time scales (Fig. 5). For comparison, an analysis based on raw data with the long-term trend and seasonal cycles removed was also conducted (Fig. S7). The first (EOF1) and second (EOF2) modes together explain more than 80% of the total variability (Fig. 5a; see also Fig. S7a) and are thus the focus of our discussion, focusing on variability over interannual time scales. These two principal structures are analogous to those extracted using EOFs on 1-yr-long current observations in two key ITF outflow passages, the Timor Passage (March 1992–April 1993; Molcard et al. 1996) and Ombai Strait (December 1995–November 1996; Molcard et al. 2001).
First two leading EOF modes of the vertical profile of interannual ITF transport variability and link with ENSO and IOD. (a) Percentage of variance explained by each mode of variability. Black circles indicate those for the SODA reanalysis, and colored crosses for the CMIP5 models. The (b) first (ITFM1) and (c) second (ITFM2) modes are shown as the regression of transport per unit depth onto the corresponding principal component (PC) time series. (d)–(g) Lag correlation between the corresponding PC time series with the climate indices. Positive lags indicate climate indices leading ITFM1 and ITFM2. Thick black and thick red lines indicate quantities for the reanalysis and CMIP5 multimodel mean, respectively. Correlation coefficient cut-off values corresponding to statistical significance at the 95% confidence level are indicated by black and red dashed horizontal lines respectively for the reanalysis and CMIP5 multimodel mean.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
The CMIP5 multimodel mean and the SODA reanalysis (Figs. 5b,c; see also Figs. S7b,c) show that EOF1 exhibits surface-intensified anomalous transport (hereafter referred to as ITFM1), while EOF2 exhibits opposite anomalies between the surface and subsurface (ITFM2). Correlating the respective principal component (PC) time series against Niño-3.4 and DMI reveals that the link between ITF transport with both ENSO and IOD is captured by ITFM2. The reanalysis and CMIP5 multimodel mean are consistent in exhibiting high correlation coefficients that are significant above the 95% confidence level (Figs. 5f,g; see also Figs. S7f,g). This demonstrates that the baroclinic structure, characterized by anomalously strong surface transport accompanied by weak subsurface transport, is associated with El Niño and positive IOD events (and the converse for La Niña and negative IOD events). In the case of ITFM1 though, which appears to be equivalent barotropic in structure, there is apparent disagreement between the reanalysis and the CMIP5 models (Figs. 5d,e; see also Figs. S7d,e). In the reanalysis, ITFM1 and ENSO are linked, with El Niño leading to weaker-than-normal surface-intensified transport about 6 months later. In the CMIP5 multimodel mean, there is no such clear relationship, given the large intermodel differences. Conversely for the IOD, the CMIP5 models consistently simulate anomalously strong surface-intensified transport associated with a positive IOD (Fig. 5e), but no such relationship is found in the reanalysis. This contrast between CMIP5 and reanalysis is likely due to a combination of spurious IOD influence in CMIP5 models associated with the overly strong IOD bias, and a lack of ENSO-independent IOD events in observations. However, agreement between the CMIP5 and reanalysis is seen in the unfiltered data (Fig. S7e), thus indicating that ITFM1 is also associated with intraseasonal processes in the Indian Ocean, and that the disagreement is on interannual time scales.
Lag correlations between PC time series and grid point zonal winds and upper-ocean heat content on interannual time scales (Fig. 6) reveal consistency between the reanalysis and CMIP5 multimodel mean for the forcing of ITFM2, which is underpinned by large-scale anomalous divergence/convergence over the Indo-Pacific warm pool (Figs. 6b,d). The association implies that interannual transport variability marked by anomalously strong surface transport and anomalously weak subsurface transport, as captured by ITFM2, is linked to easterly wind anomalies across the equatorial Indian Ocean and westerly anomalies across the equatorial Pacific, consistent with the results of Potemra and Schneider (2007) based on a reanalysis and two models. The atmospheric divergence reduces upper-ocean heat content, and thus a lowered sea level and shoaled thermocline, across the western Pacific to eastern Indian Ocean, and increases heat content in the eastern Pacific and western Indian Ocean. Such conditions are characteristic of a co-occurrence between El Niño and a positive IOD (the converse for La Niña and negative IOD), as indicated in the correlation pattern for SSTs (Figs. S8b,d).
Correlation patterns of gridpoint zonal wind stress (τx; contours) and upper-ocean heat content (HC; color shading) against (a),(c) ITFM1 and (b),(d) ITFM2 in (a),(b) the CMIP5 multimodel mean and (c),(d) reanalysis at different lag times. Black and purple contours mark positive and negative correlations between τx and ITF anomalies, respectively. Positive lags indicate ITFM1 and ITFM2 lagging τx and heat content anomalies. Only correlation coefficients that are statistically significant at the 95% confidence level are shown. Analysis is based on interannual time series.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
For the ITFM1 interannual forcing, the reanalysis and CMIP5 multimodel mean are not consistent with each other (Figs. 6a,c). In the reanalysis, stronger and weaker surface intensified transports are respectively associated with anomalous easterly and westerly winds in the equatorial Pacific that occur several months prior. This suggests that the reanalysis ITFM1 is a lagged response to ENSO, as indicated by a La Niña–like pattern at 6-month lead time in Fig. S8c. In contrast, the CMIP5 ITFM1 is associated with anomalous winds and upper-ocean heat content changes within the tropical Indian Ocean, thus suggesting that ITFM1 (i.e., surface-intensified transport variability) in CMIP5 models tends to be associated with processes internal to the Indian Ocean, evidently the IOD (Fig. 5e; see also Fig. S8a).
Thus, the difference between the CMIP5 models and the SODA reanalysis is largely manifested in ITFM1 transport variability and the associated forcing. This disagreement applies exclusively for interannual time scale processes, as the unfiltered data show a closer agreement between the CMIP5 models and reanalysis, in particular between ITFM1 and IOD (Fig. S7e). In the unfiltered data, in association with ITFM1, both CMIP5 and reanalysis indicate localized zonal wind anomalies just south of the Maritime Continent between Java and northern Australia (Figs. S9a,c), with weak anomalous cooling in the eastern equatorial Indian Ocean signifying a positive IOD (Fig. S10). Associated with anomalous easterlies in this region, upper-ocean heat content is reduced south of Java, contributing to an increased ITF. The local wind is typically rich in high-frequency variability. Indeed, unlike ITFM2, which is dominated by interannual variability, ITFM1 contains significant intraseasonal variability (Fig. S11), and so does the DMI (Fig. 2b). Thus, the most apparent discrepancy between the reanalysis and CMIP5 is attributed to the interannual variability of surface-intensified transport as depicted by ITFM1.
c. Intermodel relationships
In this section, we investigate the CMIP5 intermodel differences by examining intermodel correlations across various variables. First, there is a strong link between the magnitude of interannual variability in the ITF total transport (ITFtotal) across the models with the simulated ENSO amplitudes: models simulating stronger ENSO producing stronger interannual variability in ITFtotal (r = 0.70; Fig. 7a). The intermodel correlation is not statistically significant however when using the unfiltered data (Table 1), underscoring the role of higher-frequency variability. In both unfiltered and filtered (i.e., interannual) cases, the intermodel correlation is significant for the DMI amplitude (Table 1), given substantial intraseasonal component in the DMI (Fig. 2b). The ITFtotal variability is also found to be positively correlated to the magnitude of zonal wind variability south of the Maritime Continent (
Intermodel relationships between ENSO and IOD amplitude and ITF variability: (a),(b) total transport, (c),(d) transport between surface to 100 m (ITF0–100), and (e),(f) transport between 100 and 300 m (ITF100–300). ENSO amplitude is measured as standard deviation of Niño-3.4 index. IOD amplitude measured as standard deviation of the DMI. Analysis is based on data detrended with seasonal mean removed and filtered to focus on interannual variability. Multimodel mean is indicated with a red square and reanalysis with a black square. Correlation coefficients significant above the 95% confidence level are indicated in boldface; p values are shown in parentheses.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
Intermodel correlation of variability amplitude between pairs of indices that represent ENSO (Niño-3.4), IOD (DMI), zonal wind stress south of the Maritime Continent (
As suggested by the analysis in section 3b, ENSO exerts a stronger imprint on ITF transport variability in the subsurface than in the surface layer, which in contrast is more strongly linked to the IOD. This is reflected in the intermodel relationships: stronger ENSO and IOD amplitudes across models tend to correspond with stronger interannual variability in the subsurface (100–300 m; ITF100–300) and surface layer (0–100 m; ITF0–100), respectively (Figs. 7c–f). The correlations are statistically significant above the 95% confidence level. Without EXM, the strengths of the linkage decrease, but the relative importance between the ENSO and IOD influence on surface and subsurface transports remains robust. Note that in the unfiltered data the relationship with IOD amplitude is stronger for ITF100–300 than ITF0–100 (Table 1). This however stems from the tendency for models that simulate stronger ENSO amplitude to also produce stronger IOD variability (Table 1). The association is stronger in the unfiltered data, indicating that such tendency may not necessarily be solely due to the coupling between ENSO and IOD but also to other factors such as the level of stochasticity in the models that can influence the variability of both ENSO and the IOD.
ENSO and the IOD are linked through the atmospheric Walker circulation, and this interaction in turn affects atmospheric circulation particularly in the vicinity of the Maritime Continent over which anomalous atmospheric convergence and divergence due to ENSO and IOD occur. As such, there is a significant intermodel correlation between the magnitude of local wind forcing (
(a) Lag correlation between ENSO and IOD. Positive lag indicates ENSO leading IOD, and vice versa for negative lag. Red curve indicates CMIP5 multimodel mean, solid black for SODA, and dashed black for ERSST. (b) Composite of DMI according to El Niño and La Niña phases indicated in red and blue, respectively. (c) Composite of DMI according to positive IOD and negative IOD phases indicated in red and blue, respectively. In (b) and (c), solid (dashed) curves indicate reanalysis (CMIP5 multimodel mean) with filled circle, empty circle, and triangle markers indicating statistical significance above the 90% confidence level for SODA, ERSST, and CMIP5 multimodel mean, respectively. (d)–(f) Intermodel relationship between ENSO–IOD time lag and (d) IOD persistence, (e) correlation coefficients of DJF average Niño-3.4 vs SON average DMI, and (f) correlation coefficients of DJF average Niño-3.4 vs following year SON average DMI. ENSO-IOD time lag is defined as the time (in months) at which the correlation between Niño-3.4 and DMI [in (a)] reaches a positive maximum. IOD persistence is defined as the time at which the autocorrelation of the DMI crosses zero. Red square indicates CMIP5 multimodel mean, black square for SODA, and empty circle for ERSST. All analyses are based on interannual time series.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
In the real system, the IOD tends to lead ENSO by about a season (Fig. 8a), reflecting the peak of IOD in boreal autumn preceding ENSO maturity in winter. However, in 15 out of the 20 CMIP5 models, it tends to be the converse, with El Niño and La Niña respectively leading positive and negative IOD by an average of 3 months (Fig. 8a). This bias seems to be related to the simulated ENSO teleconnection, rather than simply the representation of the IOD itself, as the delayed bias is much more apparent in the multimodel composite of monthly DMI according to El Niño and La Niña phases (Fig. 8b) than the composite based on IOD events (Fig. 8c). Previous studies have pointed out that positive IOD events in models can occur following El Niño events or persist longer than observed (Cai et al. 2005; Saji et al. 2006; Cai et al. 2011; Jourdain et al. 2016). In the extreme case, Cai et al. (2005) found in their model that positive IOD events often occur three seasons following El Niño events. They suggested that this was a consequence of crudely representing Java–Timor topography as a single zonal landmass that would lead to spurious intrusion of El Niño–induced upwelling Rossby waves from the Pacific into the southern coast of Java. In the real ocean, these waves propagate off the coast of northwestern Australia. There is an indication that model resolution indeed plays a role: models with the coarsest horizontal resolution (CanESM2, the three IPSL models, MIROC-ESM-CHEM, and MPI-ESM-LR; see Table S1) exhibit strong delayed IOD bias (3–6 months). The longer persistence of the simulated IOD can also be attributed to persistent El Niño (e.g., Jourdain et al. 2016). We find that the delayed IOD occurrence relative to ENSO (i.e., a positive ENSO–IOD time lag) contributes to the overall longer persistence of the simulated IOD than the reanalysis (measured as the time when the DMI autocorrelation crosses zero). The intermodel correlation (r = 0.66; r = 0.71 without EXM) is significant above the 99% confidence level (Fig. 8d). This positive ENSO–IOD time lag also explains why the ENSO–IOD synchronous correlation across the models appears to be weaker overall compared to reanalysis (Fig. 8e), as underscored by a high intermodel correlation of r = −0.74 (r = −0.61 without EXM) significant above 99% confidence level. On the other hand, it also reflects a stronger tendency for positive IOD events to occur in the year following El Niño events in the models (Fig. 8f).
The prolonged/delayed IOD in the models is expected to affect how ENSO influences the ITF, especially in the surface layer where the impact of IOD is most prominent. Because El Niño and La Niña correspondingly lead to a weaker and stronger surface transport in the following year (Fig. 4), these anomalies would tend to be counteracted by the prolonged/delayed effect of positive and negative IODs, respectively (see section 3b). Indeed, the longer the IOD lags ENSO, the weaker the ENSO influence is on the ensuing ITF0–100 (Fig. 9a), underscored by an intermodel correlation of 0.61 (r = 0.76 without EXM) significant above the 99% confidence level. Specifically, this means that the longer a positive IOD lags an El Niño, the weaker the influence of El Niño is on the ensuing reduction in upper-layer ITF transport. As described in section 3b, anomalous surface-intensified ITF transport represents the leading mode of ITF variability (ITFM1; Figs. 5a,b). In the reanalysis, a reduced surface transport associated with ITFM1 is more of a lagged response to an El Niño with less apparent link to the IOD (Fig. 5d). In the CMIP5 models, on the other hand, ITFM1 is instead prominently linked to the IOD (Fig. 5e) with positive and negative IOD respectively corresponding to enhancement and reduction in ITFM1-associated transport. Thus, the models’ tendency for a delayed positive IOD counteracts the upper-layer ITF reduction that typically follows an El Niño. The prevalence of the IOD in driving ITFM1 in the CMIP5 models is linked to the magnitude of
Intermodel relationships of (a) ENSO–IOD time lag (see Fig. 8) and maximum negative correlation between Niño-3.4 and ITF0–100 over which Niño-3.4 leads ITF0–100 (supplemental Fig. S6c), (b) standard deviation of zonal wind variability and maximum correlation between DMI and ITFM1 (refer to Fig. 5e), and (c) the ratio of variance explained by ITFM1 and ITFM2 (Fig. 5a) and maximum negative correlation between Niño-3.4 and ITFtotal (Fig. 3c). Red square indicates CMIP5 multimodel mean; black square indicates SODA.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
4. Summary and discussions
Up until now, there has been no systematic multimodel study on ITF variability linked to Indo-Pacific climate variability; namely ENSO and the IOD. This is examined here using 20 CMIP5 models and the SODA-2.2.4 reanalysis. We found that overall the CMIP5 models capture several properties of ITF transport that are qualitatively consistent with the SODA reanalysis, although the CMIP5 intermodel differences are substantial. For instance, the CMIP5 models simulate an ITF total transport (ITFtotal) that reaches a maximum in austral winter and a minimum in austral summer, with a peak-to-trough amplitude of ∼9 Sv in the multimodel mean seasonal cycle, consistent with the reanalysis, but with a large intermodel range of ∼4–18 Sv. In terms of variability of the ITF total transport, with long-term trends removed, the standard deviation of the CMIP5 models ranges from 0.8 to 3.1 Sv, with a multimodel mean of 2.2 Sv that is slightly weaker than that of the reanalysis at 2.7 Sv. The discrepancy is larger when focusing on variability at interannual time scales where the CMIP5 multimodel mean is ∼1 Sv and the reanalysis 1.5 Sv. This indicates the different roles of the simulated ENSO and IOD in the CMIP5 models on the ITF variability.
The ITF total transport is found to weaken during El Niño and strengthen during La Niña marked by CMIP5 multimodel mean in maximum correlation between the Niño-3.4 index and ITFtotal of ∼−0.3, statistically significant above 95% confidence level, with a large intermodel range of ∼0.4. The relationship with the IOD is in contrast not statistically significant. Considering that ENSO and IOD are the dominant drivers of Indo-Pacific climate variability, these relationships appear disproportionately weak. This seemingly weak relationship is due to the vertical structure of ITF variability in which surface and subsurface transports tend to exhibit opposing anomalies, a feature that was previously revealed by Potemra and Schneider (2007) based on two climate models and a reanalysis, and identified in studies based on limited observations (e.g., Sprintall et al. 2009; Susanto et al. 2012; Sprintall and Revelard 2014).
Here we further show that ITF variability can be decomposed into two primary vertical structures, with one exhibiting a surface intensified anomaly (ITFM1), and the other exhibiting anomalous opposing flows between the surface and subsurface in the upper 300 m (ITFM2). Separating the variability into these two structures reveals a discrepancy between the CMIP5 multimodel ensemble and the reanalysis. While the CMIP5 models and reanalysis are consistent in terms of ITFM2, which is shown to be a response to both ENSO and IOD, there is a strong disagreement associated with ITFM1. In the reanalysis, ITFM1 is a lagged response to ENSO, such that an El Niño leads to a surface-intensified reduction in transport about 6 months later. On the other hand, ITFM1 in the CMIP5 models is linked to the IOD, in which a positive IOD corresponds to a surface-intensified transport enhancement. The prevalence of an IOD impact on surface transport in the CMIP5 models is related to the overly strong IOD amplitude, which impacts surface transport anomalies through local wind variability. However, despite the strong IOD amplitude, the CMIP5 ITF variability tends to be weaker than that in the reanalysis. This damped ITF variability is partly attributed to the propensity for the CMIP5 models to simulate a delayed or prolonged IOD in response to ENSO, of which the effects tend to counteract each other. As noted in section 3c, the delayed IOD bias could be related to model resolution, with models with the coarsest horizontal resolution exhibiting the strongest bias.
Our results underscore that the ITF exhibits vertical structure that responds differently to ENSO and the IOD, and thus diagnosing the variations of the ITF under different climate states requires a consideration of processes in the different layers. To highlight this point, we present some results on ITF changes in response to greenhouse forcing, which clearly exhibit distinct responses in the surface and subsurface layer transports. We utilize the CMIP5 simulations under representative concentration pathways (RCP) 4.5 scenario (Taylor et al. 2012), comparing the future (2006–98) and historical (1907–99) periods. First, in terms of mean state there is a lack of a robust change in the surface layer transport (Fig. 10a), consistent with the fact that the local wind (
(a) Differences between future (2006–98; RCP4.5) and historical (1907–99) periods in ITF transport at depth levels. Multimodel difference is denoted by thick dashed line; thick red line indicates statistically significant difference at 95% significance level (evaluated using bootstrap mean test with 1000 draws). (b)–(d) Intermodel relationships between the future change in ENSO and IOD amplitude and that of ITF variability in the surface (ITF0–100) and subsurface (ITF100–300) layer.
Citation: Journal of Climate 35, 10; 10.1175/JCLI-D-21-0485.1
Previous studies have shown that there tends to be no intermodel consensus in the projected change in ENSO and IOD amplitude as measured by the conventional metrics in Niño-3.4 and DMI (e.g., Cai et al. 2020, 2021). The strong link between the projected change in ITF variability at the subsurface to the amplitude change in ENSO, as well as the IOD, implies that reducing projection uncertainty in ENSO and the IOD would also narrow the projected change in ITF variability. Further studies are warranted to further understand future changes in ITF variability, particularly given the projections of increased occurrences in extreme ENSO and IOD events in models that are able to simulate such events (Cai et al. 2020, 2021). This has implications for understanding changes to the heat and freshwater budgets over the Indo-Pacific region, which can in turn influence mean state climate and climate variability.
Acknowledgments.
The authors thank three anonymous reviewers who provided constructive feedback that led to significant improvements to the manuscript. This research is supported by the Australian Research Council (ARC). AS, WC, and MHE are supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), a joint research center between QNLM and CSIRO, and by the Australian Government’s National Environmental Science Program (NESP). JBK is supported by the ARC’s Centre of Excellence for Climate Extremes (CE170100023).
Data availability statement.
The CMIP5 model data can be downloaded from https://pcmdi.llnl.gov/mips/cmip5/data-portal.html. The SODA reanalysis version 2.2.4 data can be accessed from http://apdrc.soest.hawaii.edu/datadoc/soda_2.2.4.php. The NOAA ERSST are available from https://psl.noaa.gov/data/gridded/data.noaa.ersst.v3.html.
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