1. Introduction
The Indian Ocean (IO) sea surface temperature (SST) profoundly impacts the Asian monsoon (Kulkarni et al. 2007; Schott et al. 2009; Yang et al. 2010). In recent decades, it has been reported that the tropical IO SST has experienced rapid warming, with more statistically significant and faster warming in autumn since the late 1990s (Levitus et al. 2005; Du et al. 2013). Previous studies have revealed that boreal autumn IO SST anomalies have a significant influence on the climate in East Africa (Clark et al. 2003; Behera et al. 2005), Australia, and China (Ashok et al. 2003; Qiu et al. 2015). Understanding IO SST variability is an essential task in regional climate research and climate prediction.
The Indian Ocean dipole (IOD) is an important ocean–atmosphere coupled mode in the tropical IO during boreal autumn. The opposing SST anomalies appear in the western and eastern tropical IO, accompanied by anomalous zonal winds along the equator and anomalous alongshore winds off Sumatra (Saji et al. 1999; Webster et al. 1999). During a positive IOD event, SST cooling off Sumatra suppresses local atmospheric convection and drives anomalous easterly winds (Cai et al. 2014). The anomalous wind further induces coastal upwelling and enhances surface evaporation (EVP) (Li et al. 2003). This in turn shoals the thermocline and enhances SST cooling in the southeastern IO, as in Bjerknes feedback (Liu et al. 2014; Ng et al. 2018). Recent studies have revealed that there are different types of IOD events. Du et al. (2013) suggested that the anomalous wind over the equatorial IO develops in early summer and then leads to the occurrence of unseasonable IOD events due to the wind EVP and thermocline response. Guo et al. (2015) also found that wind–EVP–SST (WES; Xie and Philander 1994) feedback plays an important role in forming the anomalous zonal SST gradient, which causes particular IOD events to appear in the second year of El Niño–Southern Oscillation (ENSO). The zonal wind anomalies converging in the central tropical IO may induce IOD Modoki events (Endo and Tozuka 2016). In particular, the development of the warm SST disturbance in the subtropical southeastern IO promotes the formation of the negative IOD (Terray et al. 2005, 2007).
There is also a dipole mode in the subtropical southern IO with SST dipole anomalies in the southwestern and northeastern parts of the southern IO, which is called the subtropical Indian Ocean dipole (SIOD; Behera and Yamagata 2001). Morioka et al. (2013) pointed out that the change in the Mascarene high may generate the SIOD by the mixed layer response and WES feedback. Recently, the IOD event in 2017 rapidly decayed from the positive to the negative phase in autumn; this decay was influenced by the SIOD (Zhang et al. 2018). The heat transport induced by the Mascarene high anomalies in the southern IO also plays an important role in modulating the IOD formation (Tozuka et al. 2007). Therefore, a combination of the IOD and SIOD should be considered when we study the impact of the IO SST in autumn. In addition, there has been a basinwide warming in the tropical IO since the 1950s (Du and Xie 2008; Yao et al. 2016).
The relationship between SST and EVP variability plays an important role in the air–sea interaction (Wu and Kirtman 2005; Wu et al. 2006). Previous studies have focused on the air–sea interaction of the SST–EVP relationship in the tropical IO. Wang et al. (2003) revealed that the EVP anomalies induced by anomalous anticyclones in the tropical southern IO have a strong influence on generating large-scale dipole SST anomaly patterns. The negative EVP–SST tendency correlations in the subtropical IO indicated the atmospheric contribution to the SST change (Wu et al. 2006). The reducing wind speed and EVP lead to an increase in SST by WES feedback, which sustains SST warming (Du et al. 2009). The reduction in EVP over the southeastern subtropical IO can cause a weakened IOD structure (Roxy et al. 2011). In addition, Yao et al. (2016) suggested that the latent heat flux from atmospheric forcing is conducive to the formation of IO basinwide warming types. Furthermore, the relationship between SST and EVP is also significantly affected by different factors (Wu et al. 2006). Wajsowicz and Schopf (2001) suggested that the significant changes in EVP from month to month are due to the differences in the relative contributions of the wind speed and the air–sea specific humidity difference to EVP. Richter and Xie (2008) pointed out that the current ocean EVP increases by 2% °C−1, which is a much lower rate than the atmospheric water vapor rate of increase of 7% °C−1. The surface relative humidity, stability, and wind speed anomalies slow the rate of increase in the EVP. Tao et al. (2015) considered that the EVP anomalies are due to anomalous air–sea temperature differences, which play a crucial role in ENSO-related tropical IO warming. Bollasina and Nigam (2009) analyzed the relationship between SST and EVP in the IO and found that the increase in wind speed over the Somali rapids enhances the upwelling of cold waters, and the SST and EVP are inversely correlated. However, although many previous studies about the air–sea interaction of dipole anomaly patterns in the tropical IO have been conducted, the interaction and physical mechanism responsible for the dipole anomaly in the tropical and subtropical IO is still unclear.
Considering the large-scale dipole anomaly patterns previously reported, what is the relationship between autumn SST and EVP over the IO in the IOD and SIOD patterns? Is the autumn SST–EVP relationship in the IO modulated by the IOD and SIOD configuration? What is the mechanism that influences the autumn SST–EVP relationship? In this study, we investigated the relationship between IO SST and EVP in autumn and examined the associated mechanisms. The rest of this paper is arranged as follows. Section 2 introduces the datasets and methods used in this study. The interdecadal shift of the relationship between SST and EVP is investigated during boreal autumn, and the main factor of the SST–EVP relationship is given in section 3. Section 4 analyzes the possible spatial pattern of the coupling modes on the SST–EVP relationship, explores the relevant mechanisms of the negative SST–EVP relationship in the IOD and SIOD patterns and further discusses possible causes of the interdecadal shift of the SST–EVP relationship. The final section provides our conclusions and a discussion of the results.
2. Datasets and methods
a. Datasets
Five reanalysis datasets are used in this study: the objectively analyzed air–sea fluxes (OAFlux) with a horizontal resolution of 1° × 1° (Yu and Weller 2007); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis with a horizontal resolution of 0.75° × 0.75° (ERA-Interim; Dee et al. 2011); the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) with a horizontal resolution of 0.625° × 0.5° (Bosilovich et al. 2017); the Japanese 55-year Reanalysis (JRA-55) with a horizontal resolution of 1.25° × 1.25° (Kobayashi et al. 2015); and the NCEP–DOE Atmospheric Model Intercomparison Project II reanalysis (NCEP-R2) with a horizontal resolution of 2.5° × 2.5° (Kanamitsu et al. 2002). The OAFlux, ERA-Interim, MERRA-2, and JRA-55 datasets are regridded onto 2.5° × 2.5° grids because the horizontal resolutions of the reanalysis datasets are not consistent. These five datasets include the variations in EVP, surface air temperature, surface specific humidity, and surface wind speed. The ensemble monthly mean of these five datasets (AVE) is employed to reduce the observation errors. We also use the monthly mean SST datasets from version 5 of the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature (ERSSTv5), which has a horizontal resolution of 2° × 2° (Huang et al. 2017). Associated variations in atmospheric circulation fields, including the sea level pressure (SLP) and wind fields, are obtained from the NCEP–NCAR reanalysis I project (NCEP-R1) with a horizontal resolution of 2.5° × 2.5° (Kalnay et al. 1996).
To investigate SST variability in the IO, we also adopted the Indian Ocean basin mode (IOBM) index, which is defined as the SST anomalies averaged over the tropical IO (20°S–20°N, 40°–110°E) (Xie et al. 2009). The IOD index is defined as the SST anomaly differences between the western (10°S–10°N, 50°–70°E) and eastern (10°S–0°, 90°–110°E) IO (Saji et al. 1999), and the SIOD index is similarly defined as the SST anomaly differences between the western (45°–30°S, 45°–75°E) and eastern (25°–15°S, 80°–100°E) parts of the southern IO (Yan et al. 2009). In this study, we focus on the relationship between SST and EVP in the IO (30°–120°E, 45°S–30°N) during boreal autumn.
b. EVP decomposition method
We used a diagnostic partitioning of the bulk aerodynamic formulation for surface EVP following Richter and Xie (2008):
where CE is the exchange coefficient and ρa, U, and RH are the surface air density, surface wind speed, and surface relative humidity, respectively. Also, S = Tair − SST represents the stability of the near-surface air, where Tair is the surface air temperature. The term qs is the Clausius–Clapeyron relation for the saturation specific humidity of water as a function of temperature:
where q0 is a constant,
and a, b, and c are constants (Emanuel 1994). We can then express the EVP anomalies EVP′ through a first-order Taylor series expansion (Bosilovich et al. 2017):
Here, the partial derivatives are calculated from monthly climatology, and the prime denotes a departure from monthly climatology, which, in our case, is based on the period 1980–2016. The right side of the equation represents the Newtonian cooling term (SST), stability term S, relative humidity term (RH), wind speed term U, transfer coefficient term CE, which is calculated in some datasets by dividing the EVP by ρaU[qs(SST) − RH × qs(SST + S)] in Eq. (1), and the residual term (RES). The Newtonian cooling term represents the oceanic response. The stability term, relative humidity term, and wind speed term could be regarded as atmospheric forcing (Du et al. 2009; Yao et al. 2016). The results of this paper are evaluated from ERSSTv5 and AVE without special instructions. The EVP decomposition method used in this study can quantitatively separate the effects of different factors on EVP and identify factors that determine EVP from the perspective of sea–air interaction.
Moreover, the monthly mean SST tendency is calculated using the SST in the next month minus the SST in the previous month (Wang et al. 2003; Wu et al. 2006). Singular value decomposition (SVD; Iwasaka and Wallace 1995) and composite analysis are applied to extract the coupling modes of SST and EVP in the IO.
3. Air–sea interaction in the reversed SST–EVP relationship between 1980–90 and 2005–15
a. Interdecadal change in the autumn SST–EVP relationship over the IO
Figure 1a depicts the time evolution of the area-averaged autumn SST and EVP anomalies over the IO during 1980–2016. Both exhibit consistent negative anomalies before the late 1990s. After that, the SST and EVP anomalies show opposite signs. Different EVP datasets exhibit a similar spread in EVP anomalies. These results indicate that the relationship between IO SST and EVP may change around the late 1990s. To further demonstrate the change in the SST–EVP relationship, the 11-yr sliding correlation coefficients of the area-averaged autumn SST and EVP are shown in Fig. 1b. A clear interdecadal shift in the SST–EVP relationship occurred in approximately 1999, displaying a change from a positive to a negative correlation. Similarly, previous studies pointed out an interdecadal change in the late 1990s (Feng et al. 2018; Qiao et al. 2017; Zou et al. 2018). SST anomalies are positively correlated with EVP anomalies over the IO for the period 1980–90, with a positive correlation coefficient of 0.55, exceeding the 90% confidence level. The significant negative SST–EVP correlation coefficient over the IO reaches −0.56 during 2005–15. Therefore, the periods 1980–90 and 2005–15 are selected for a comparative analysis of the interdecadal change in the autumn SST–EVP relationship. In addition, the SST–EVP correlations for these two periods are consistent with those obtained from the EVP and other SST datasets (Table S1 in the online supplemental material), suggesting a robust finding.
(a) Area-averaged time series of autumn SST (red line; °C) and EVP (mm day−1; the blue line is AVE EVP, the dashed lines are five reanalysis datasets, and the horizontal solid line indicates the zero-value line). (b) The 11-yr sliding correlation coefficient between area-averaged autumn SST and EVP over the IO (30°–120°E, 45°S–30°N) during 1980–2016. The horizontal black lines indicate the zero-value line (solid) and the significant correlations exceeding 90% confidence level by the two-tailed t test (dashed).
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
The spatial distributions of the pointwise SST–EVP correlations and SST tendency–EVP correlations during 1980–90 and 2005–15 are presented in Fig. 2. For 1980–90, significant positive SST–EVP correlations are observed in the equatorial IO and the west of Australia, which contribute to the strong positive SST–EVP relationship. Consistent with previous studies (Wu et al. 2006), large negative SST tendency–EVP correlations are found in the northern and subtropical southern IO, exceeding the 95% confidence level. We calculate the lead–lag correlations of the SST and EVP in the west of Australia (Fig. S1 in the online supplemental material). Strongly positive SST–EVP correlations are observed when the SST leads the EVP. Thus, the positive SST–EVP correlations in the equatorial IO and the west of Australia imply that the SST impacts the EVP, mainly leading to a positive SST–EVP relationship over the IO during 1980–90. The negative SST tendency–EVP correlations indicate atmospheric forcing of SST in the northern IO and subtropical southern IO during 1980–90 (Wu and Kirtman 2005). For 2005–15, positive SST–EVP correlations are observed in some small areas near the equatorial IO. The negative SST–EVP correlations dominate the Arabian Sea and the subtropical southern IO except for only a small portion at 30°S latitude. Corresponding to the negative SST–EVP correlations, significant negative SST tendency–EVP correlations appear in the northern and subtropical southern IO. These results indicate that the atmospheric forcing of SST mainly contributes to the negative SST–EVP relationship over the IO during 2005–15. The mechanism of the contribution of the EVP to SST anomalies will be discussed in the next section.
Spatial distributions of pointwise correlation coefficients (a),(b) between the autumn SST and EVP anomalies and (c),(d) between the autumn EVP and SST tendency during (left) 1980–90 and (right) 2005–15. The stippling indicates significance at the 95% confidence level.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
Previous studies pointed out that the most pronounced SST signals are the IOBM and IOD in the tropical IO, which are strongly modulated by ENSO (Zheng et al. 2011; Roxy et al. 2011). We calculate the sliding SST–EVP correlation by linear regression concerning the IOBM index, aiming to remove the IOBM impacts (Fig. 3). The significant positive SST–EVP correlations over the IO weaken during 1980–90. Thus, the positive SST–EVP relationship is largely attributed to the IOBM. The negative SST–EVP correlations show no significant change during 2005–15. This implies that there are different driving factors between the positive and negative SST–EVP relationships. In addition, after removing ENSO and IOD variability, similar features are observed during 1980–90. This suggests that ENSO and the IOD play a small role in the positive SST–EVP relationship (Fig. S2 in the online supplemental material). Although we have focused our attention on the SST forcing–induced positive SST–EVP correlation, the impact of atmospheric forcing on the northern and subtropical southern IO is also important (Fig. 2c). Therefore, it can be concluded that the IOBM plays an important role in the positive SST–EVP relationship during 1980–90, especially in the equatorial IO and the west of Australia. The atmospheric forcing of SST exerts an important impact on the negative SST–EVP relationship during 2005–15, especially in the northern and subtropical southern IO.
The 11-yr sliding correlation coefficients between the area-averaged autumn EVP and SST anomalies after removing the IOBM during 1980–2016. The horizontal black lines indicate the zero-value line (solid) and the significant correlations exceeding 90% confidence level by the two-tailed t test (dashed).
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
b. Factors affecting the negative SST–EVP relationship over the IO
The above results concluded that atmospheric forcing of SST has a significant impact on the negative SST–EVP relationship. To quantify the contribution of different factors to the negative SST–EVP relationship, we analyze the role of different factors affecting the SST–EVP relationship using the EVP decomposition method [Eq. (2)].
Figure 4 shows the correlation between the EVP from the atmospheric forcing part and the SST during 1980–90 and 2005–15. For 1980–90, significant negative SST–RH correlations are observed in the northern IO, and negative SST–U correlations are observed in the northern and subtropical southern IO, which contribute to the weak negative SST–EVP correlations in the Arabian Sea and the subtropical southern IO (Fig. 2a). This indicates that atmospheric forcing of SST plays an important role in the SST–EVP relationship in addition to the IOBM impact. For 2005–15, compared to the SST–EVP correlations, the SST–U correlations exhibit similar features, implying that the wind speed is the main factor affecting the negative correlations. The enhanced negative SST–S and SST–RH correlations contribute to the negative SST–EVP relationship in the southeastern IO. In addition, the significant positive SST–S correlations dominate north of 10°S during the two periods. This may be related to SST warming, which leads to a decrease in the air–sea temperature difference and thus an increase in the EVP in the tropical IO (Zheng et al. 2011). Therefore, the wind speed is the major cause of the negative SST–EVP relationship after the late 1990s. A covariance analysis method, as applied by Yu (2011), is utilized to estimate the contribution of the wind speed. Wind speed explains a substantial proportion of the overall EVP variance, approximately 71.5%, consistent with the finding of Yu (2007).
Spatial distributions of correlation coefficients between the EVP from the atmospheric forcing part and SST anomalies: (a),(b) the stability term–SST correlation; (c),(d) the relative humidity term–SST correlation; and (e),(f) the wind speed term–SST correlation during (left) 1980–90 and (right) 2005–15. The stippling indicates significance at the 95% confidence level.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
The above results indicate that the EVP caused by wind speed has an important impact on the SST, which leads to the negative SST–EVP relationship. In particular, the area-averaged wind speed–induced EVP anomalies over the IO show that the weakening wind speed reduces the EVP and thus induces SST warming after the late 1990s (Fig. 1a), which resembles WES feedback. It is noted that WES feedback plays a key role in the negative SST–EVP relationship over the IO. Recent studies also suggested that the IO wind speed has significantly weakened in the past decade (Padmakumari et al. 2013; Du et al. 2013).
4. The inverse phases of the IOD and SIOD cause the negative SST–EVP correlation over the IO
The positive SST–EVP relationship is closely related to the IOBM during 1980–90, and the negative SST–EVP relationship is mainly related to the change in wind speed by WES feedback during 2005–15. We assume that the coupling mode of SST and EVP changed after the late 1990s. Thus, we focus on the contrast between 1980–90 and 2005–15 in the following SVD analyses to understand how the coupling modes related to the SST–EVP relationship have changed.
a. The link between the IOD and SIOD configuration and the SST–EVP relationship
To determine the relationship between SST and EVP, we use the SVD method to identify the distribution of the coupled modes of SST and EVP in the IO during 1980–90 and 2005–15. Figures 5a and 5b show the heterogeneous correlation maps for the first SVD mode (SVD1) between SST and EVP in the IO for 1980–90, which accounts for 56.4% of the total squared covariance. The anomalous SST pattern is marked by basinwide warming in the IO and is similar to the IOBM pattern (Fig. 5a). The correlation coefficients between the corresponding temporal coefficients in SVD1 and the autumn IOBM index are shown in Table 1. The correlation coefficient between the time coefficient of the SST in SVD1 and the IOBM is 0.96, exceeding the 99% confidence level. Consistent with previous results, the IOBM plays a key role in the SST–EVP relationship during 1980–90. Positive EVP anomalies are observed over the IO except over the Arabian Sea and a small portion of the subtropical southern IO (Fig. 5b). The time coefficients for SVD1 of the SST and EVP shown in Fig. 5c are highly correlated (the correlation coefficient is 0.89), showing significant negative anomalies. This implies that negative IOBM-related SST anomalies induce negative EVP anomalies and lead to a positive SST–EVP correlation.
Heterogeneous correlation coefficients for the first SVD mode associated with autumn (a) SST and (b) EVP pattern in the IO during 1980–90. (c) The corresponding temporal coefficients (the blue line represents SST, and the red line represents EVP); their correlation coefficient is written in the upper-left corner. The percentage in (a) indicates the explained variance. In (a) and (b), the stippling indicates significance at the 95% confidence level. The coefficients are normalized by their standard deviation.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
Correlation coefficients: the temporal coefficients of the SST in SVD1 and IOBM during 1980–90 and the temporal coefficients of the EVP and IOD/SIOD during 2005–15. A single asterisk indicates significance at the 95% confidence level, and a double asterisk indicates significance at the 99% confidence level.
For 2005–15, nearly 60% of the total squared covariance is explained by SVD1. The anomalous SST pattern is a zonal dipole over the tropical IO and northeast–southwest direction anomalies in the subtropical southern IO, which bears a close resemblance to the mixture of the positive phase of IOD and negative phase of SIOD (Fig. 6a). Negative EVP anomalies are observed in the Arabian Sea and northeast of the subtropical southern IO. Positive EVP anomalies are seen in a small portion of the equatorial IO and southwest of the subtropical southern IO (Fig. 6b). The correlation coefficients between the time coefficients of the EVP in SVD1 related to the IOD and SIOD are 0.71 and −0.65 (statistically significant at the 95% confidence level), respectively. The time coefficients for SVD1 of the SST and EVP are highly correlated (the correlation coefficient is 0.84) and exhibit positive phases. These results imply that the autumn negative SST–EVP relationship is significantly linked to the IOD and SIOD configuration. We suppose the importance of the IOD and SIOD configuration in causing a negative SST–EVP relationship. Thus, different main factors lead to the interdecadal change in the SST–EVP relationship.
As in Fig. 5, but during 2005–15.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
b. The inverse phases of the IOD and SIOD lead to the negative SST–EVP relationship
The autumn IOD and SIOD index results are given in Fig. 7. The IOD index is dominated by the negative phase (13 of 19) before 1999, whereas the SIOD index is a positive phase (11 of 19). More than half of the positive IOD events (9 of 17) and negative SIOD events (10 of 17) occurred after 1999, which is generally opposite to that before 1999 (Table S3 in the online supplemental material). Consequently, the typical IOD and SIOD events are selected using the criterion that values of the normalized time series should exceed 0.3 standard deviations (Table 2). Notably, the conclusions presented in this study are only weakly affected if we adopt other thresholds (such as 0.5 standard deviations; Table S2 in the online supplemental material). We find that positive IOD and negative SIOD (pIOD–nSIOD) events are seen during 2005–15 and that negative IOD and positive SIOD (nIOD–pSIOD) events appear during 1980–90. We exclude the other three configurations from our analysis, which correspond to only two years or less (Table 2). The selected IOD and SIOD events are similar to previous studies (Behera et al. 2008; Hoell et al. 2017). A composite analysis is conducted to reveal the differences between the pIOD–nSIOD and nIOD–pSIOD events. Figure 8 displays the composite anomalies for SST, surface wind, SST tendency and EVP in autumn during the pIOD–nSIOD and nIOD–pSIOD events.
Autumn (a) IOD index and (b) SIOD index during 1980–2016.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
Typical years for the autumn IOD and SIOD configurations during 1980–90 and 2005–15.
Composite maps of (a),(b) anomalous SST (°C; color shading) and surface wind (m s−1; vectors) and (c),(d) SST tendency (°C month−1; color shading) and EVP (mm day−1; contours, with contour interval of 0.1 mm day−1) anomalies for the (left) pIOD–nSIOD and (right) nIOD–pSIOD events. The stippling indicates significance of the EVP values at the 95% confidence level. The vector winds that are shown are above the 95% confidence level.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
For the pIOD–nSIOD events, the cooling is strongest in the south of the equator trapped to the west coast of Sumatra. Elongated warm SST anomalies stretch in a southeasterly direction toward Australia from the western tropical IO. Meanwhile, anomalous easterlies appear in the central-eastern equatorial IO, and anomalous westerlies appear in the southwestern IO (Fig. 8a). Positive EVP anomalies are observed in the central equatorial IO and the southwestern IO. Negative EVP anomalies are observed in the northern IO and the west of Australia. The composite results resemble those in SVD analysis, showing the negative SST–EVP relationship, similar to the tripolar structure reported by Ummenhofer et al. (2009). These results indicate that the SST–EVP relationship is negative in the northern and subtropical southern IO. Moreover, the anomalous SST tendency pattern has some similarities with the EVP pattern in the northern and subtropical southern IO (Fig. 8c). These results indicate that the importance of EVP in generating large-scale SST anomalies.
In comparison, for the nIOD–pSIOD events, significant negative SST anomalies are observed in the western equatorial IO to the west of Australia, accompanied by westerly wind anomalies in the eastern equatorial IO and southeasterly wind anomalies in the southwestern tropical IO (Fig. 8b). In particular, the wind anomalies in the southwestern IO are more insignificant than those for the pIOD–nSIOD events, indicating that the wind speed in the nIOD–pSIOD events contribute only slightly to the SST anomalies over the subtropical southern IO. The anomalous EVP is mainly located in the tropical IO, similar to the IOD pattern. This suggests that the EVP anomalies generally have phases opposite to those of the SST anomalies. The overall patterns of the SST tendency and EVP are similar (Fig. 8d), indicating the contribution of anomalous EVP to the SST anomalies. Considering the previous results, the impact of the IOBM and the nIOD–pSIOD patterns during 1980–90 result in a positive SST–EVP relationship in the equatorial IO and a weak negative SST–EVP relationship in the Arabian Sea and the subtropical southern IO (Fig. 2a). Thus, the positive SST–EVP relationship during 1980–98 is mainly attributed to the IOBM and influenced by the atmospheric forcing of SST in nIOD–pSIOD events. In contrast, the pIOD–nSIOD pattern is prominent after 1999 (Table 2). The pIOD–nSIOD events during 2005–15 lead to a negative SST–EVP relationship. In general, the autumn SST variations in the inverse phases of the IOD and SIOD are primarily driven by the EVP from atmospheric forcing part. The interdecadal change in the SST–EVP relationship is mainly due to the impact of the IOBM and the inverse phase of the IOD and SIOD. The negative SST–EVP relationship is mainly related to the inverse phases of the IOD and SIOD.
c. Different mechanisms driving the inverse phases of the IOD and SIOD
How the negative SST–EVP relationship caused by the EVP from atmospheric forcing part is connected to the inverse phases of the IOD and SIOD is studied next. We further decompose the EVP under the inverse phases of the IOD and SIOD by the EVP decomposition method [Eq. (2)]. Figure 9 shows the EVP decomposition terms for the pIOD–nSIOD and nIOD–pSIOD events. Figure 10 separately shows atmospheric circulation conditions for the pIOD–nSIOD and nIOD–pSIOD events.
(a),(b) The stability term (mm day−1); (c),(d) the relative humidity term (mm day−1); and (e),(f) the wind speed term (mm day−1) for the (left) pIOD–nSIOD and(right) nIOD–pSIOD events. The stippling indicates significance at the 95% confidence level.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
As in Fig. 9, but for the (a),(b) SLP (hPa) and (c),(d) 850-hPa wind fields (m s−1; blue wind vectors represent values at the 95% confidence level) and divergence field (10−6 s−1; shaded).
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
For the pIOD–nSIOD events, the stability term anomalies are positive on the east coast of South Africa to Somalia and the Arabian Sea. The wind speed term shows the meridional structure with negative anomalies on either side of the equatorial IO and positive anomalies on the equatorial IO and the southwestern IO. Combined with surface wind field anomalies (Fig. 8a), the easterly (southeasterly) anomalies over the north (south) of the eastern equatorial IO lead to a decrease (increase) in the wind speed and thus negative (positive) wind speed term anomalies on the north (south) of the equatorial IO. The wind anomalies strengthen (weaken) the prevailing wind and thus the positive (negative) wind speed term anomalies on the southwestern (northeastern) subtropical southern IO. The spatial distribution of the wind speed term is similar to that of the EVP anomalies (Fig. 9e). It is clear that wind speed plays a major role in the formation of EVP changes, which is in agreement with the previous conclusions. Furthermore, the IO is overlaid by the negative SLP anomalies in Fig. 10a. The anomalous cyclone and anticyclone are found in the center of the subtropical southern IO and Australia, respectively (Fig. 10c). On the north branch of the anomalous cyclone, westerly wind anomalies strengthen the prevailing winds, leading to increased EVP in the southwestern IO, which also corresponds to the wind speed term, consistent with the results of previous studies (Kripalani et al. 2010; Yamagami and Tozuka 2015). The changes in EVP will affect SST by WES feedback (Fig. 8a), which is consistent with the findings of Wu and Kirtman (2007). Thus, the negative SST–EVP relationship is largely attributed to the influence of the wind speed on the EVP. These results indicate that WES feedback exists in the pIOD–nSIOD events. In addition, the relative humidity term exhibits significant positive anomalies in the central equatorial IO, corresponding to a reduced relative humidity induced by low-level moisture divergence (Fig. 10c). This could mean that, in addition to the wind speed, other factors may have an important role in inducing EVP anomalies in the equatorial IO.
In contrast, for the nIOD–pSIOD events, the relative humidity term and wind speed term have phases that are opposite to those for pIOD–nSIOD events and generally weaker anomalies. The distributions of the relative humidity term and EVP anomalies are similar. This implies that low-level moisture plays a key role in EVP change. Furthermore, a significant SLP gradient is observed in the IO (Fig. 10b), which corresponds to the SST anomalies. An anomalous anticyclone and an anomalous cyclone are observed in the center of the subtropical southern IO and the eastern equatorial IO (Fig. 10d). Compared to pIOD–nSIOD events, in the nIOD–pSIOD events, the center position of the Mascarene high moves northward, which may cause insignificant zonal wind anomalies and a significant wind divergence effect. Thus, the anomalous cyclone (anticyclone) induced by low-level convergence (divergence) leads to the suppression (promotion) of EVP by increasing low-level moisture in the IO (Fig. 10d), which is consistent with the relative humidity term anomalies (Fig. 9d). This indicates that an anomalous cyclone (anticyclone) leads to low-level convergence (divergence), thus suppressing (enhancing) EVP via low-level moisture–EVP feedback; this finding is consistent with those of previous studies (Jones and Weare 1996; Marshall et al. 2008). The similar spatial structure of the anomalous anticyclones and cyclones in the IO induces a similar structure in the wind speed term between the pIOD–nSIOD and nIOD–pSIOD events. In addition, the stability term shows significant negative anomalies in the western-central northern IO. The stability term may play a larger role in the northern IO (Fig. 9b).
The above analyses demonstrate that anomalous low-level cyclones and anticyclones are responsible for the EVP pattern and SST dipole pattern in the tropical and subtropical IO (Wang et al. 2003). These results indicate the contribution of WES feedback to the pIOD–nSIOD pattern and the contribution of low-level moisture–EVP–SST feedback to the nIOD–pSIOD pattern. These inverse phases of the IOD and SIOD are related to the negative SST–EVP relationship but have different mechanisms. After the late 1990s, the pIOD–nSIOD pattern dominates the IO, thus leading to the importance of WES feedback. The mechanisms responsible for the pIOD–nSIOD and nIOD–pSIOD patterns are summarized in a schematic diagram (Fig. 11). It is shown that the negative (positive) EVP in the west of Australia (southwestern IO and eastern equatorial IO) leads to positive (negative) SST, which is due to the decrease (increase) in surface wind speed corresponding to the cyclone in the southwestern IO and anticyclone in the northeastern part of the southern IO in the pIOD–nSIOD events. Compared to pIOD–nSIOD events, the positive (negative) EVP in the center of southern IO (eastern equatorial IO) leads to a negative (positive) SST, which is due to the decrease (increase) in surface relative humidity corresponding to anticyclone divergence in the southern IO and cyclone convergence in the eastern equatorial IO during the nIOD–pSIOD events.
Schematic diagram of the mechanisms responsible for the negative SST–EVP correlations in (a) pIOD–nSIOD and (b) nIOD–pSIOD events in autumn. The red or blue curved arrows represent the wind speed–induced EVP positive or negative anomalies, respectively, in (a) and the relative humidity–induced EVP positive or negative anomalies, respectively, in (b). The anomalous anticyclone and cyclone over the IO are denoted by a black ellipse with arrows in (a). The brown dashed vectors in (a) represent the anomalous wind. The open arrows in (b) represent the low-level convergence and divergence.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
Why did the nIOD–pSIOD pattern almost disappear after 1999? Figure 12 displays the difference in the autumn SLP between 1980–90 and 2005–15. The significant negative SLP difference can be observed in almost the whole IO, similar to the results shown in Fig. 9a. The temporal evolution of the SLP anomalies (figure not shown) shows a maximum downward trend, reaching −0.15 hPa per decade. This indicates that the weakened Mascarene high is conducive to the pIOD–nSIOD pattern, leading to the contribution of WES feedback to SST anomalies after 1999.
Differences in the autumn SLP between 1980–90 and 2005–15 (2005–15 minus 1980–90; hPa); the stippling indicates significance at the 95% confidence level.
Citation: Journal of Climate 33, 23; 10.1175/JCLI-D-20-0009.1
Notably, the anomalous EVP plays an important role in affecting large-scale SST anomalies. The WES feedback is considered to be the response of atmospheric forcing, and several studies have pointed to the importance of atmospheric regulation (Li et al. 2011; Yao et al. 2016). This analysis reveals that internal air–sea interaction within the IO is key to producing the inverse phases of the IOD and SIOD.
5. Conclusions and discussion
This study investigated the boreal autumn SST and EVP relationship over the IO during 1980–2016. The significant positive correlation of autumn SST and EVP over the IO during 1980–90 changes to a negative correlation during 2005–15. The pointwise SST–EVP correlations show that the positive SST–EVP correlations in the equatorial IO and the west of Australia contribute to the positive SST–EVP relationship during 1980–90, and the negative SST–EVP correlations in the northern and subtropical southern IO contribute to the negative SST–EVP relationship during 2005–15. The lead–lag SST–EVP correlations indicate the importance of SST forcing in the west of Australia during 1980–90. The pointwise SST tendency–EVP correlations show the importance of SST forcing in the equatorial IO during 1980–90 and the contribution of anomalous EVP from atmospheric forcing part to SST anomalies in the northern and subtropical southern IO during 1980–90 and 2005–15. For 1980–90, the positive SST–EVP relationship was mainly contributed by the impact of the IOBM on EVP in the equatorial IO and the west of Australia. The EVP anomalies influence the SST anomalies by atmospheric forcing of SST in the northern and subtropical southern IO that is mainly related to WES feedback and contribute to the negative SST–EVP relationship during 2005–15. The wind speed is a key factor leading to the negative SST–EVP correlations during 2005–15 by the EVP decomposition method.
Moreover, the coupled modes of autumn SST and EVP in the IO reflect the negative SST–EVP relationship, which is related to the inverse phases of the IOD and SIOD. For the pIOD–nSIOD events, the positive (negative) EVP anomalies corresponds to the negative (positive) SST anomalies in the southwestern IO (the northern IO and the west of Australia), which has a similar structure to SST tendency. The nIOD–pSIOD events have consistent features with opposite signs. The results indicate that the autumn SST variations in the inverse phases of the IOD and SIOD are primarily driven by the EVP from atmospheric forcing part. The inverse phases of the IOD and SIOD are key to the negative SST–EVP relationship. The pIOD–nSIOD events that dominate during 2005–15 lead to the negative SST–EVP relationship. The interdecadal change in the SST–EVP relationship is mainly due to the impact of the IOBM and the inverse phases of the IOD and SIOD.
There are different mechanisms driving the inverse phases of the IOD and SIOD. The nIOD–pSIOD events correspond to an anticyclone in the center of the subtropical southern IO and a cyclone in the eastern equatorial IO. The low-level convergence (divergence) leads to the suppression (promotion) of EVP via low-level moisture–EVP feedback in the IO. The relative humidity is the key factor of the EVP anomalies. The pIOD–nSIOD events correspond to a cyclone in the center of the subtropical southern IO and an anticyclone in Australia. The wind anomalies strengthen (weaken) EVP anomalies and thus the negative (positive) SST anomalies to the south (north) of the equatorial IO and in the southwestern (northeastern) subtropical IO. WES feedback plays a key role in pIOD–nSIOD events. Thus, WES feedback plays an important role in SST anomalies after 1999. Furthermore, the SLP change between 1980–90 and 2005–15 is related to favorable conditions in pIOD–nSIOD events. Our study highlights the importance of internal air–sea interactions on large-scale SST anomalies over the IO.
Although our results emphasize that WES feedback has profound impacts on the negative SST–EVP relationship during 2005–15, ocean processes (e.g., wind-induced oceanic mixing and Indonesian throughflow) are important air–sea coupling processes that could also impact SST anomalies. The reduced SLP is conducive to low-level convergence, which may have further suppressed the EVP after the late 1990s. This low-level moisture–EVP feedback is similar to that in the nIOD–pSIOD pattern. The SST warming also contributes to low-level convergence. Walsh et al. (1996) proposed that the decreased SLP over the central Arctic is associated with cyclonic wind forcing. This implies that a reduced wind speed promotes wind convergence in the IO. Therefore, WES feedback is probably not the only feedback that affects the negative SST–EVP relationship during 2005–15.
Several studies have shown that the formation of the IOD coincides with the development of El Niño in the Pacific. In addition, the local correlation does not consider the ENSO impacts. To examine whether ENSO has an impact on the local correlation, the local correlation is recalculated by linear regression with the Niño-3.4 index to remove the impacts of ENSO. The result implies that the impact of ENSO on the boreal autumn IO SST–EVP relationship is weak. Meanwhile, ENSO has little impact on the inverse phases of the IOD and SIOD. The existence of the IOD and SIOD in the absence of El Niño, such as in 2012, is reflected in the pIOD–nSIOD events. Although ENSO has little effect on the interdecadal change in the SST–EVP relationship, we find significant positive SST–EVP correlations in the tropical IO during El Niño events, which may lead to an insignificant SST–EVP correlation during 1990–2005. In this study, we only stress the importance of EVP in the IOD and SIOD configuration. However, the difference between the wind speed anomalies and divergence induced by the cyclone and the interaction between the IOD and SIOD are not yet clear. In summary, the interaction between SST and EVP and its effect on the ocean should be paid more attention as a feedback of the external forcing of the ocean on the atmosphere.
Acknowledgments
We very much appreciate the comments by the three anonymous reviewers and the editor. These comments led to a significant improvement in the paper. The authors declare no conflict of interest with this publication. This work was supported by the National Natural Science Foundation of China through Grant 41530531, the National Key Research and Development Program of China through Grant 2017YFC1502303, the National Natural Science Foundation of China through Grants 41875096, 41705053, and 41875097, the Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China, and the “High-level Talent Support Program” funding of Yangzhou University.
Data availability statement
The OAFlux dataset was provided by the Woods Hole Oceanographic Institution (http://oaflux.whoi.edu). The ERA-Interim dataset was obtained from the ECMWF (https://apps.ecmwf.int/datasets). The MERRA2 dataset was obtained from the NASA Earth Science Data (https://earthdata.nasa.gov). The JRA-55 dataset was provided by the JMA Data Distribution System (https://jra.kishou.go.jp). The NCEP reanalysis and ERSSTv5 were obtained from the NOAA/ESRL Physical Sciences Division (https://www.esrl.noaa.gov/psd). The IOBM, IOD and SIOD indices were provided by the National Climate Center (NCC) of China (https://cmdp.ncc-cma.net/cn/prediction.htm).
REFERENCES
Ashok, K., Z. Guan, and T. Yamagata, 2003: Influence of the Indian Ocean dipole on the Australian winter rainfall. Geophys. Res. Lett., 30, 1821, https://doi.org/10.1029/2003GL017926.
Behera, S. K., and T. Yamagata, 2001: Subtropical SST dipole events in the southern Indian Ocean. Geophys. Res. Lett., 28, 327–330, https://doi.org/10.1029/2000GL011451.
Behera, S. K., J.-J. Luo, S. Masson, P. Delecluse, S. Gualdi, A. Navarra, and T. Yamagata, 2005: Paramount impact of the Indian Ocean dipole on the East African short rains: A CGCM study. J. Climate, 18, 4514–4530, https://doi.org/10.1175/JCLI3541.1.
Behera, S. K., J.-J. Luo, and T. Yamagata, 2008: Unusual IOD event of 2007. Geophys. Res. Lett., 35, L14S11, https://doi.org/10.1029/2008GL034122.
Bollasina, M., and S. Nigam, 2009: Indian Ocean SST, evaporation, and precipitation during the South Asian summer monsoon in IPCC-AR4 coupled simulations. Climate Dyn., 33, 1017, https://doi.org/10.1007/s00382-008-0477-4.
Bosilovich, M. G., F. R. Robertson, L. Takacs, A. Molod, and D. Mocko, 2017: Atmospheric water balance and variability in the MERRA-2 reanalysis. J. Climate, 30, 1177–1196, https://doi.org/10.1175/JCLI-D-16-0338.1.
Cai, W., A. Santoso, G. Wang, E. Weller, L. Wu, K. Ashok, Y. Masumoto, and T. Yamagata, 2014: Increased frequency of extreme Indian Ocean dipole events due to greenhouse warming. Nature, 510, 254–258, https://doi.org/10.1038/nature13327.
Clark, C. O., P. J. Webster, and J. E. Cole, 2003: Interdecadal variability of the relationship between the Indian Ocean zonal mode and East African coastal rainfall anomalies. J. Climate, 16, 548–554, https://doi.org/10.1175/1520-0442(2003)016%3c0548:IVOTRB%3e2.0.CO;2.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Du, Y., and S.-P. Xie, 2008: Role of atmospheric adjustments in the tropical Indian Ocean warming during the 20th century in climate models. Geophys. Res. Lett., 35, L08712, https://doi.org/10.1029/2008GL033631.
Du, Y., S.-P. Xie, G. Huang, and K. Hu, 2009: Role of air–sea interaction in the long persistence of El Niño–induced north Indian Ocean warming. J. Climate, 22, 2023–2038, https://doi.org/10.1175/2008JCLI2590.1.
Du, Y., W. Cai, and Y. Wu, 2013: A New type of the Indian Ocean dipole since the mid-1970s. J. Climate, 26, 959–972, https://doi.org/10.1175/JCLI-D-12-00047.1.
Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.
Endo, S., and T. Tozuka, 2016: Two flavors of the Indian Ocean dipole. Climate Dyn., 46, 3371–3385, https://doi.org/10.1007/s00382-015-2773-0.
Feng, G., M. Zou, S. Qiao, R. Zhi, and Z. Gong, 2018: The changing relationship between the December North Atlantic Oscillation and the following February East Asian trough before and after the late 1980s. Climate Dyn., 51, 4229–4242, https://doi.org/10.1007/s00382-018-4165-8.
Guo, F., Q. Liu, S. Sun, and J. Yang, 2015: Three types of Indian Ocean dipoles. J. Climate, 28, 3073–3092, https://doi.org/10.1175/JCLI-D-14-00507.1.
Hoell, A., C. Funk, J. Zinke, and L. Harrison, 2017: Modulation of the Southern Africa precipitation response to the El Niño Southern Oscillation by the subtropical Indian Ocean dipole. Climate Dyn., 48, 2529–2540, https://doi.org/10.1007/s00382-016-3220-6.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Iwasaka, N., and J. M. Wallace, 1995: Large scale air sea interaction in the Northern Hemisphere from a view point of variations of surface heat flux by SVD analysis. J. Meteor. Soc. Japan, 73, 781–794, https://doi.org/10.2151/jmsj1965.73.4_781.
Jones, C., and B. C. Weare, 1996: The role of low-level moisture convergence and ocean latent heat fluxes in the Madden and Julian oscillation: An observational analysis using ISCCP data and ECMWF analyses. J. Climate, 9, 3086–3104, https://doi.org/10.1175/1520-0442(1996)009<3086:TROLLM>2.0.CO;2.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631.
Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001.
Kripalani, R. H., J. H. Oh, and H. S. Chaudhari, 2010: Delayed influence of the Indian Ocean dipole mode on the East Asia–west Pacific monsoon: Possible mechanism. Int. J. Climatol., 30, 197–209, https://doi.org/10.1002/joc.1890.
Kulkarni, A., S. S. Sabade, and R. H. Kripalani, 2007: Association between extreme monsoons and the dipole mode over the Indian subcontinent. Meteor. Atmos. Phys., 95, 255–268, https://doi.org/10.1007/s00703-006-0204-9.
Levitus, S., J. Antonov, and T. Boyer, 2005: Warming of the world ocean, 1955–2003. Geophys. Res. Lett., 32, L02604, https://doi.org/10.1029/2004GL021592.
Li, G., B. Ren, J. Zheng, and C. Yang, 2011: Trend singular value decomposition analysis and its application to the global ocean surface latent heat flux and SST anomalies. J. Climate, 24, 2931–2948, https://doi.org/10.1175/2010JCLI3743.1.
Li, T., B. Wang, C.-P. Chang, and Y. Zhang, 2003: A theory for the Indian Ocean dipole–zonal mode. J. Atmos. Sci., 60, 2119–2135, https://doi.org/10.1175/1520-0469(2003)060<2119:ATFTIO>2.0.CO;2.
Liu, L., S.-P. Xie, X.-T. Zheng, T. Li, Y. Du, G. Huang, and W.-D. Yu, 2014: Indian Ocean variability in the CMIP5 multi-model ensemble: The zonal dipole mode. Climate Dyn., 43, 1715–1730, https://doi.org/10.1007/s00382-013-2000-9.
Marshall, A. G., O. Alves, and H. H. Hendon, 2008: An enhanced moisture convergence–evaporation feedback mechanism for MJO air–sea interaction. J. Atmos. Sci., 65, 970–986, https://doi.org/10.1175/2007JAS2313.1.
Morioka, Y., T. Tozuka, and T. Yamagata, 2013: How is the Indian Ocean subtropical dipole excited? Climate Dyn., 41, 1955–1968, https://doi.org/10.1007/s00382-012-1584-9.
Ng, B., W. Cai, T. Cowan, and D. Bi, 2018: Influence of internal climate variability on Indian Ocean dipole properties. Sci. Rep., 8, 13500, https://doi.org/10.1038/s41598-018-31842-3.
Padmakumari, B., A. K. Jaswal, and B. N. Goswami, 2013: Decrease in evaporation over the Indian monsoon region: Implication on regional hydrological cycle. Climatic Change, 121, 787–799, https://doi.org/10.1007/s10584-013-0957-3.
Qiao, S., P. Hu, T. Feng, J. Cheng, Z. Han, Z. Gong, R. Zhi, and G. Feng, 2017: Enhancement of the relationship between the winter Arctic Oscillation and the following summer circulation anomalies over central East Asia since the early 1990s. Climate Dyn., 50, 3485–3503, https://doi.org/10.1007/s00382-017-3818-3.
Qiu, Y., W. Cai, X. Guo, and B. Ng, 2015: The asymmetric influence of the positive and negative IOD events on China’s rainfall. Sci. Rep., 4, 4943, https://doi.org/10.1038/srep04943.
Richter, I., and S.-P. Xie, 2008: Muted precipitation increase in global warming simulations: A surface evaporation perspective. J. Geophys. Res., 113, D24118, https://doi.org/10.1029/2008JD010561.
Roxy, M., S. Gualdi, H.-K. L. Drbohlav, and A. Navarra, 2011: Seasonality in the relationship between El Niño and Indian Ocean dipole. Climate Dyn., 37, 221–236, https://doi.org/10.1007/s00382-010-0876-1.
Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363, https://doi.org/10.1038/43854.
Schott, F. A., S.-P. Xie, and J. P. McCreary, 2009: Indian Ocean circulation and climate variability. Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245.
Tao, W., G. Huang, K. Hu, X. Qu, G. Wen, and H. Gong, 2015: Interdecadal modulation of ENSO teleconnections to the Indian Ocean basin mode and their relationship under global warming in CMIP5 models. Int. J. Climatol., 35, 391–407, https://doi.org/10.1002/joc.3987.
Terray, P., S. Dominiak, and P. Delecluse, 2005: Role of the southern Indian Ocean in the transitions of the monsoon-ENSO system during recent decades. Climate Dyn., 24, 169–195, https://doi.org/10.1007/s00382-004-0480-3.
Terray, P., F. Chauvin, and H. Douville, 2007: Impact of southeast Indian Ocean sea surface temperature anomalies on monsoon-ENSO-dipole variability in a coupled ocean–atmosphere model. Climate Dyn., 28, 553–580, https://doi.org/10.1007/s00382-006-0192-y.
Tozuka, T., J.-J. Luo, S. Masson, and T. Yamagata, 2007: Decadal modulations of the Indian Ocean dipole in the SINTEX-F1 coupled GCM. J. Climate, 20, 2881–2894, https://doi.org/10.1175/JCLI4168.1.
Ummenhofer, C. C., A. Sen Gupta, A. S. Taschetto, and M. H. England, 2009: Modulation of Australian precipitation by meridional gradients in east Indian Ocean sea surface temperature. J. Climate, 22, 5597–5610, https://doi.org/10.1175/2009JCLI3021.1.
Wajsowicz, R. C., and P. S. Schopf, 2001: Oceanic influences on the seasonal cycle in evaporation over the Indian Ocean. J. Climate, 14, 1199–1226, https://doi.org/10.1175/1520-0442(2001)014<1199:OIOTSC>2.0.CO;2.
Walsh, J. E., W. L. Chapman, and T. L. Shy, 1996: Recent decrease of sea level pressure in the central Arctic. J. Climate, 9, 480–486, https://doi.org/10.1175/1520-0442(1996)009<0480:RDOSLP>2.0.CO;2.
Wang, B., R. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian-Australian monsoon variation. J. Climate, 16, 1195–1211, https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2.
Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401, 356–360, https://doi.org/10.1038/43848.
Wu, R., and B. P. Kirtman, 2005: Roles of Indian and Pacific Ocean air–sea coupling in tropical atmospheric variability. Climate Dyn., 25, 155–170, https://doi.org/10.1007/s00382-005-0003-x.
Wu, R., and B. P. Kirtman, 2007: Regimes of seasonal air–sea interaction and implications for performance of forced simulations. Climate Dyn., 29, 393–410, https://doi.org/10.1007/s00382-007-0246-9.
Wu, R., B. P. Kirtman, and K. Pegion, 2006: Local air–sea relationship in observations and model simulations. J. Climate, 19, 4914–4932, https://doi.org/10.1175/JCLI3904.1.
Xie, S.-P., and S. G. H. Philander, 1994: A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus, 46A, 340–350, https://doi.org/10.3402/tellusa.v46i4.15484.
Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730–747, https://doi.org/10.1175/2008JCLI2544.1.
Yamagami, Y., and T. Tozuka, 2015: Interdecadal changes of the Indian Ocean subtropical dipole mode. Climate. Dyn., 44, 3057–3066, https://doi.org/10.1007/s00382-014-2202-9.
Yan, H.-M., C.-Y. Li, and W. Zhou, 2009: Influence of subtropical dipole pattern in southern Indian Ocean on ENSO events (in Chinese). Chin. J. Geophys., 52, 2436–2449.
Yang, J., Q. Liu, and Z. Liu, 2010: Linking observations of the Asian monsoon to the Indian Ocean SST: Possible roles of Indian Ocean basin mode and dipole mode. J. Climate, 23, 5889–5902, https://doi.org/10.1175/2010JCLI2962.1.
Yao, S.-L., G. Huang, R.-G. Wu, X. Qu, and D. Chen, 2016: Inhomogeneous warming of the tropical Indian Ocean in the CMIP5 model simulations during 1900–2005 and associated mechanisms. Climate Dyn., 46, 619–636, https://doi.org/10.1007/s00382-015-2602-5.
Yu, L., 2007: Global variations in oceanic evaporation (1958–2005): The role of the changing wind speed. J. Climate, 20, 5376–5390, https://doi.org/10.1175/2007JCLI1714.1.
Yu, L., 2011: A global relationship between the ocean water cycle and near-surface salinity. J. Geophys. Res., 116, C10025, https://doi.org/10.1029/2010JC006937.
Yu, L., and R. A. Weller, 2007: Objectively analyzed air–sea heat fluxes for the global ice-free oceans (1981–2005). Bull. Amer. Meteor. Soc., 88, 527–540, https://doi.org/10.1175/BAMS-88-4-527.
Zhang, L., Y. Du, and W. Cai, 2018: A spurious positive Indian Ocean dipole in 2017. Sci. Bull., 63, 1170–1172, https://doi.org/10.1016/j.scib.2018.08.001.
Zheng, X. T., S. P. Xie, and Q. Liu, 2011: Response of the Indian Ocean basin mode and its capacitor effect to global warming. J. Climate, 24, 6146–6164, https://doi.org/10.1175/2011JCLI4169.1.
Zou, M., S. Qiao, T. Feng, Y. Wu, and G. Feng, 2018: The inter-decadal change in anomalous summertime water vapour transport modes over the tropical Indian Ocean–western Pacific in the mid-1980s. Int. J. Climatol., 38, 2672–2685, https://doi.org/10.1002/joc.5452.