The seasonal and interannual variations of the barrier layer
thickness (BLT) in the tropical Indian Ocean (TIO) is investigated in this
study using the Simple Ocean Data Assimilation version 3 (SODA v3) ocean
reanalysis dataset. Analysis of this study suggests energetic but divergent
seasonal variabilities of BLT in the western TIO (5∘ N–12∘ S, 55–75∘ E) and the eastern TIO
(5∘ N–12∘ S, 85–100∘ E). For
instance, the thicker barrier layer (BL) is observed in the western TIO
during boreal winter as a result of decreasing sea surface salinity (SSS)
and deeper thermocline, which are associated with the intrusion of
freshwater flux and the weakened upwelling, respectively. On the contrary,
the variation of BLT in the eastern TIO mainly corresponds to the variation
in thermocline depth in all seasons. The interannual variability of BLT with
the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO) is
explored. During the mature phase of positive IOD events, a thinner BL in the
eastern TIO is attributed to the shallower thermocline, while a thicker BL
appears in the western TIO due to deeper thermocline and fresher surface
water. During negative IOD events, the thicker BL only occurs in the eastern
TIO, corresponding to the deeper thermocline. During ENSO events, prominent
BLT patterns are observed in the western TIO corresponding to two different
physical processes during the developing and decaying phase of El Niño
events. During the developing phase of El Niño events, the thicker BL in the
western TIO is associated with deepening thermocline induced by the westward
Rossby wave. During the decaying phase of El Niño events, the
thermocline is weakly deepening, while the BLT reaches its maxima induced by
the decreasing SSS.
Introduction
The upper ocean traditionally included only the mixed layer and the
thermocline. The terminology “barrier layer” (BL) was recently introduced as
the mixed layer depth (MLD) was redefined from using the temperature (de
Boyer Montégut et al., 2004) to using the oceanic density (Kara et al.,
2000; Mignot et al., 2007). The barrier layer thickness (BLT) is simply the
depth between the bottom of the mixed layer defined by density and the top
of the thermocline (Lukas and Lindstrom, 1991; Masson et al., 2002;
Sprintall and Tomczak, 1992). Although the BL is much thinner than the other
two layers, it plays a key role in oceanic dynamics and air–sea interaction.
For example, the BL helps to sustain the heat in the mixed layer by
isolating the temperature in the upper ocean from the cooling entrainment.
Accordingly, BL is crucial in the formation of the El Niño–Southern
Oscillation (ENSO) and contributes to the formation of the different ENSO
types (conventional ENSO and ENSO Modoki) (Singh et al., 2011; Maes, 2002;
Maes et al., 2005, 2006). Also, the spatial structure of BLT
driven by special variation in Ekman drift is crucial for the formation of
monsoon cyclones in the pre-monsoon season (Thadathil et al., 2007;
Vinayachandran et al., 2002; Masson et al., 2005; Neetu et al., 2012).
The variability of BLT is mainly affected by the change of MLD and
thermocline due to various mechanisms, such as heavy precipitation, oceanic
currents, wind stress, and oceanic waves (Bosc et al., 2009; Mignot et al.,
2007; Masson et al., 2002; Qu and Meyers, 2005). For instance, a thicker BL
mainly presents in the areas beneath the Intertropical Convergence Zone
(ITCZ) with decreasing sea surface salinity (SSS) due to abundant rainfall (Vialard and Delecluse,
1998) or large river discharge (Pailler et al., 1999). The strong wind
stress anomalies could also contribute to thickening the BL via deepening the
thermocline (Seo et al., 2009).
Compared to the tropical Pacific and the Atlantic Ocean, the tropical Indian
Ocean (TIO) is characterized by a shallower thermocline in the west (Yokoi
et al., 2012, 2008; Yu et al., 2005) and stronger interannual variation of
upper-ocean temperature in the east (Li et al., 2003; Saji et al., 1999),
which provides a unique region to evaluate the seasonal and interannual
variabilities of BLT.
The strong seasonality of BLT has been observed in some subregions of the
TIO, such as the southeastern Arabian Sea, the Bay of Bengal, and the
southeastern TIO (Schott et al., 2009). These regions are also characterized
by the strong seasonality of SSS due to different hydrological processes
(Rao, 2003; Subrahmanyam et al., 2011; Zhang et al., 2016; Zhang and Du,
2012). Overall, the seasonality of BLT in the TIO is partly consistent with
the change of SSS due to the impact of freshwater (Masson et al., 2002; Qu
and Meyers, 2005).
The interannual variability of BLT in the southeastern TIO can be partly
explained by Indian Ocean Dipole (IOD) events (Qiu et al., 2012). During positive IOD year
(e.g., 2006), thinner BL in the southeastern TIO is mainly led by the shallower
thermocline induced by the upwelling Kelvin wave in the presence of weakly
shoaling MLD. In negative IOD year (e.g., 2010), a thicker BL is expected due
to the extending of the thermocline. Furthermore, at the subseasonal scale,
the zonal SSS gradient driven by the freshwater advection results in a
thicker BL to sustain a fresh and stable MLD (Drushka et al., 2014).
Existing studies on the interannual variability of BLT were mainly focused
on specific years and lacked long-term evaluation. More importantly, the
interannual variability of both thermocline and SSS are supposed to be
associated with ENSO events (Grunseich et al., 2011; Rao and Sivakumar,
2003; Subrahmanyam et al., 2011; Zhang et al., 2013), but relationships
between BLT and ENSO are scarcely reported in the TIO. Also, the relative
impact of SSS and thermocline depth on the variability of BLT is still
unclear and is not systematically investigated in the TIO. Thus, the
evolution of the seasonal and interannual variabilities of BLT and its
relationship with SSS and thermocline anomalies are still highly desired.
The Simple Ocean Data Assimilation (SODA) version 3 ocean reanalysis dataset
covers time series data from 1980 to 2015, which may be adequate for such
purpose.
The remainder of this paper is arranged as follows. In Sect. 2, we briefly
describe the datasets and methods. Comparisons of the BLT variability
interpreted from both observed and reanalysis datasets in the TIO are
presented in Sect. 3. Section 4 presents the seasonal variability of the
BLT in the TIO, while its interannual variability is shown in Sect. 5.
Finally, a summary and discussions are given in Sect. 6.
Data and methods
Two datasets are used in this study to investigate the variability of BLT in
the TIO. The first one is the monthly global gridded observation and
reanalysis products with 1∘ horizontal resolution from
2005 to 2015, which is compiled from Argo profiles products provided by the
French Research Institute for Exploration of the Sea (Ifremer: http://www.ifremer.fr/cerweb/deboyer/mld/Subsurface_Barrier_Layer_Thickness.php, last access: June 2020). BLT is
calculated as the difference between TTDDTm02 and MLD:
BLT=TTDDTm02-MLD,
where TTDDTm02 is the depth of the top of thermocline, which is
defined as the depth at which the surface temperature is 0.2∘
cooler than the sea surface temperature and is hereafter referred to as the
isothermal layer depth (ILD). MLD is the mixed layer depth defined by
oceanic density at which depth the density is 0.03 kg m-3 larger than
that of the surface (de Boyer Montégut et al., 2007; Mignot et al.,
2007).
Another dataset is the latest released SODA version 3 reanalysis data
(1980–2015) with a horizontal resolution of 0.5∘ which is
hereafter denoted as SODA v3 data and can be accessed from the Asia-Pacific
Data-Research Center (APDRC: http://apdrc.soest.hawaii.edu/datadoc/soda_3.3.1.php, last access: July 2020). SODA
v3 has reduced systematic errors in the upper ocean and has improved the
accuracy of the poleward variability in the tropic (Carton et al., 2018). It
has 26 vertical levels with a 15 m resolution near the sea surface. We
adopted the same Ifremer equation to calculate SODA BLT as the difference
between density and temperature-defined MLD.
Salinity and temperature in the first level (5 m) are adopted as the SODA SSS
and sea surface temperature (SST), respectively. The thermocline depth is
defined as the depth of the 20∘ isotherms.
Monthly SST between 1980 and 2015 on a grid of 1∘×1∘ is
acquired from Hadley Center Global Sea Ice and Sea Surface Temperature
(HadISST: https://climatedataguide.ucar.edu/climate-data/sst-data-hadisst-v11, last access: July 2020) to
calculate the Nino3.4 index. The Nino3.4 index is the average SST anomaly in
the area of 5∘ N–5∘ S, 170–120∘ W. Monthly precipitation data are obtained from CMAP (Climate Prediction Center (CPC):
http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=13195, last access: August 2020). Monthly zonal wind stress is obtained from SODA.
Seasonal distributions of the BLT climatology obtained from Argo
(a–d) and SODA (e–h) from 2005 to 2015 in the Indian Ocean. Units: m. The
thicker green line is the zero BLT line from Argo and the dashed blue lines
represent the areas of the western TIO (5∘ N–12∘ S, 55–75∘ E)
and the eastern TIO (5∘ N–12∘ S, 85–100∘ E), respectively. The two
thin black lines represent the latitudes of 12∘ S and 5∘ N, respectively.
The significance of simultaneous and lead–lag correlations is evaluated in
this study with a Student's t test. In all the datasets, we removed the
annual cycle of each parameter before the interannual correlation analysis.
Composite analysis is also employed to evaluate the interannual variability
of BLT using a Monte Carlo significance test. The Monte Carlo process is
that the IOD/El Niño/La Niña years are randomly shuffled (10 000 times)
for each month, and a mean Student's t test is used to calculate the t
statistic for the selected areas. The mean of the t statistic generated by
the random simulations exceeding that of the actual t value is determined and
assessed at the 5 % significance level. The positive and negative IOD
years are provided by the Bureau of Meteorology (http://www.bom.gov.au/climate/iod/, last access: July 2020), and the El Niño and La Niña years
are obtained from the Golden Gate Weather Services (https://ggweather.com/enso/oni.htm, last access: August 2020). Monthly mean values are averaged over
3 sequential months for different seasons, e.g., December–January–February
(DJF) for boreal winter, March–April–May (MAM) for boreal spring,
June–July–August (JJA) for boreal summer, and September–October–November
(SON) for boreal autumn. All the area-averaged parameters shown in this
study are weighted by the cosine of the latitude.
Seasonal cycle of the region-averaged BLT for SODA and Argo: (a)
the western TIO (12∘ S–5∘ N, 55–75∘ E) and (b)
the eastern TIO (12∘ S–5∘ N, 85–100∘ E).
BLT in the Indian Ocean
BLT in the TIO calculated from SODA v3 is first validated against Argo float
observations from 2005 to 2015. As shown in Fig. 1, the seasonal BLT
climatology obtained from SODA v3 is biased thinner in the Bay of Bengal in
all seasons compared to that derived from Argo. This thinner BL in SODA v3
is probably because it lacks the runoff data from the Bay of Bengal as input
in its reanalysis (Carton et al., 2018; Carton and Giese, 2008).
Additionally, SODA BLT fails to capture the BLT feature on the west coast of
Africa and the northwestern Arabian Sea (see the white areas right of the green
line), where no BLT is expected due to the salinity inversion. However, for
the area of interest in the TIO (5∘ N–12∘ S, 55–100∘ E), the BLT in SODA v3 shows a coherent
spatial pattern with the Argo BLT, where BL is, in general, thicker in the
east and thinner in the west. The seasonal evolution of BLT in the east
obtained from SODA is consistent with that from Argo, shown as a decreasing
trend from boreal winter to spring and an increasing trend from boreal
summer to autumn.
Two subregions are highlighted to evaluate the seasonal and interannual
variabilities of SODA BLT, namely western TIO (5∘ N–12∘ S,
55–75∘ E) and eastern TIO (5∘ N–12∘ S, 85–100∘ E). Since these two
subregions not only represent the zonal difference of the BLT in the TIO
but also include the well-known areas of the Seychelles–Chagos Thermocline
Ridge (SCTR; 60–75∘ E, 12–5∘ S) and the eastern IOD area
(IODE; 10∘ S–Equator, 90–100∘ E) (Manola et al., 2015; Yokoi et al., 2012, 2008). As
shown in Fig. 2, region-averaged BLT obtained from SODA v3 in the western
TIO is greater than that of Argo, especially during boreal summer and
autumn. In the eastern TIO, SODA v3 BLT is quite comparable with that of
Argo, except for slight discrepancies in June and July. The trend of BLT
seasonality obtained from SODA v3 and Argo is, however, overall consistent,
suggesting the robustness of using SODA v3 data in interpreting the BLT
variabilities in the TIO.
Due to the insufficient temperature–salinity observations, we only compare
the interannual variability of the SODA v3 BLT with Argo between 2005 and
2015. As shown in Fig. 3, the interannual variability of BLT from SODA v3
and Argo is very consistent in both the western and eastern TIO. The
correlation coefficients between SODA v3 and Argo for the western and
eastern TIO are 0.75 and 0.90, respectively. Results in Fig. 3 confirm
that SODA v3 is adequate to evaluate the long-term seasonal and interannual
variabilities of the BLT in the TIO.
Interannual time series of the region-averaged BLT for SODA and
Argo: (a) the western TIO (12∘ S–5∘ N, 55–75∘ E)
and (b) the eastern TIO (12∘ S–5∘ N, 85–100∘ E).
The seasonal and interannual variations of MLD and ILD averaged over the
western and eastern TIO are also presented in Fig. 4 to investigate the
dominant drivers for the BLT variability. Overall, the seasonal
variabilities of MLD and ILD present a consistent annual cycle in both
subregions. The seasonality of BLT, however, exerts discrepancies between
these two regions (Fig. 4a and b). Specifically, a semi-annual cycle of
BLT is observed in the western TIO, compared to an annual cycle of BLT
observed in the eastern TIO. The interannual variabilities of BLT are also
different in the western and eastern TIO (Fig. 4c and d). In the western
TIO, the interannual variability of BLT is more related to the ILD
variation. For example, the years with thicker BL in the western TIO are
associated with deeper ILD, such as 1982, 1983, 1991, and 1996. On the
contrary, in the eastern TIO, the relative impact of MLD and ILD on the
interannual variability of BLT cannot be discriminated. For instance, deeper
BLT occurs in 1981, 1985, and 1996, corresponding to relatively shallower
MLD, while the other years of deeper BLT, such as 1994, 1999, and 2001, are
associated with deeper ILD. Additionally, the interannual correlation
coefficients between BLT and MLD are -0.07 and -0.25 for the western and
eastern TIO, respectively, and the correlations coefficients between BLT and
ILD are 0.47 and 0.38 in those two subregions. The low correlation
coefficients suggest that neither MLD nor ILD can fully explain the BLT
variabilities in the TIO. Therefore, the difference of BLT variabilities in
the western and eastern TIO needs to be further explained. In the subsequent
analysis, the mixed layer variables, including SST and SSS, and thermocline
depth are selected to explain the BLT variabilities in the TIO.
The seasonal and interannual variations of BLT, MLD, and ILD: (a, c)
the western TIO (12∘ S–5∘ N, 55–75∘ E) and (b, d)
the eastern TIO (12∘ S–5∘ N, 85–100∘ E).
Seasonal variation
It is well known that the area with the thickest BL in the TIO corresponds
to the freshest surface water, while the areas of the thinnest BL
corresponds to the saltiest surface water (Agarwal et al., 2012; Felton et
al., 2014; Han and McCreary, 2001; Vinayachandran and Nanjundiah, 2009). The
spatial features of BLT and SSS in different seasons are presented in Fig. 5,
where the seasonality of SSS and BLT does not co-vary, especially near
the Equator. For example, surface saltier water in the western TIO elongates
eastward during boreal winter and spring and retreats during boreal summer
and autumn, while BLT does not vary accordingly. In the eastern TIO, BLT
presents a more prominent seasonality than that of SSS, with a maximum in
boreal autumn.
The seasonal distributions of SSS (unit: psu; a–d) and BLT (unit:
m; e–h) in the Indian Ocean from 1980 to 2015. The two dashed black lines
represent the latitudes of 12∘ S and 5∘ N, respectively.
Figure 6 shows the in-phase correlations of SST and SSS anomalies with BLT
anomalies. Here, the SSS, SST, and BLT anomalies have been averaged as
functions of longitude vs. time in the western and eastern TIO,
respectively. The seasonal BLT–SST relationship in the western TIO is not
robust as only a few areas exceed the 95 % significance level (see Fig. 6a).
A short-term (less than 2 months) negative correlation between BLT
and SST anomalies can be observed in the eastern TIO during boreal winter.
This negative BLT–SST correlation also exists when the HadISST data are
employed (figure not shown). Compared with the seasonal BLT–SST
relationship, the seasonal BLT–SSS relationship is more prominent in the
TIO, especially in the western TIO (Fig. 6b). This negative correlation
between BLT and SSS starts from January and extends to June.
Simultaneous correlations along the area of
12∘ S–5∘ N for (a) SST and (b) SSS anomalies with respect
to BLT anomalies. Shaded areas exceed the 95 % significance level, while
the shaded red and blue areas represent the areas with the positive and
negative correlation coefficients, respectively.
Lead–lag crossing correlations of BLT with SSS (a–d) and
thermocline (e–h) anomalies for January (JAN), April (APR), July (JUL), and
October (OCT) along the area of 12∘ S–5∘ N from 1980 to
2015. Shaded areas exceed the 95 % significance level. Positive lag means
SSS (thermocline) leads BLT, and vice versa. Blue (red) shaded areas
represent the negative (positive) correlation. The thick dashed black line
represents the in-phase correlation.
To further understand the seasonal relationship of BLT with SSS and
thermocline, we adopt the lead–lag crossing correlation analysis for BLT
anomalies with respect to SSS and thermocline depth anomalies in January
(JAN), April (APR), July (JUL), and October (OCT). The significant area of
the lead–lag negative correlation between SSS and BLT is mainly located in the
western TIO (Fig. 7a–d), which is consistent with that of their in-phase
correlation (Fig. 6b). During boreal winter, spring, and autumn, the
variation of SSS can affect BLT variability in the next 2 months (Fig. 7a
and d). For example, fresher (saltier) water in October in the western
TIO can lead to thicker (thinner) BL in November and December. The positive
correlation between BLT and the thermocline depth is very prominent in the
western TIO, particularly in January. The variation of the thermocline in
January has an impact on BLT variations up to the next 4 months (Fig. 7e).
During boreal autumn, a strong BLT–thermocline correlation mainly
occurs in the eastern TIO. The variation of the thermocline in October could
have an impact on BLT variations in the 3 successive months (Fig. 7h).
Seasonal variation in the western TIO (12∘ S–5∘ N,
55–75∘ E) (a, c) and the eastern TIO
(12∘ S–5∘ N, 85–100∘ E) (b, d).
The top figures show the depth–time plots of the upper-ocean salinity
(shaded), the thermocline depth (green line), isothermal layer (dashed black
line), and mixed layer (blue line). The bottom figures show the freshwater
flux (P-E) and zonal component of the wind stress (U_WS)
anomalies in the corresponding areas.
We also examined the corresponding atmospheric forcing in the western and
eastern TIO. Figure 8 shows the seasonal evolution of the upper-ocean
salinity, MLD, ILD, the thermocline depth, the freshwater flux
(precipitation minus evaporation, P-E), and the zonal component of the wind
stress. In the western TIO, freshening of the upper-ocean water from October
to April is observed due to freshwater flux, which in turn, thickens the BL,
consistent with the analysis in Fig. 6b. In the meantime, a negative wind
stress curl mainly dominated by the zonal wind stress leads to a weakening
Ekman pumping in the western TIO. This weakened Ekman pumping inhibits the
upwelling from December to April, resulting in the thicker thermocline depth
(green line), which in turn, also makes the BL thicker. The driving factors of
the BLT seasonality in the eastern TIO are more complex than those in the
western TIO. Firstly, the seasonal evolution of SSS has a semi-annual
feature, while the freshwater flux does not. This can be explained by the
Indonesian Throughflow, which brings freshwater from the Pacific Ocean into
the eastern TIO (Shinoda et al., 2012). Secondly, the thermocline presents
the opposite seasonal cycle compared with that in the western TIO. However,
the zonal wind stress displays a similar seasonal variation in both the
western and eastern TIO. Last but not least, the salinity in the deeper
ocean varies similarly to the thermocline depth in the eastern TIO, which is
not observed in the western TIO. Thus, the freshwater flux and the
wind-driven upwelling cannot fully explain the BLT seasonality in the
eastern TIO. Felton et al. (2014) have suggested that the seasonal BLT
variation in the eastern TIO may be related to the sea level and ILD
oscillation.
The compositing seasonal variations of SSS (a, b; unit: psu), BLT
(c, d; unit: m), and the thermocline depth (e, f; unit: m) in IOD events
during the period of 1980–2015 averaged by the areas of the eastern TIO
(EIO; 12∘ S–5∘ N, 85–100∘ E) and the
western TIO (WIO; 12∘ S–5∘ N, 55–75∘ E),
separately. The blue line represents the composite in positive IOD events and the
red one represents that in negative IOD events; the green shaded area
represents the 95 % Monte Carlo significance level.
Same as Fig. 9 but the composite in the El Niño and La Niña
years.
Interannual variation
The IOD, as it modifies the zonal SST gradients along the equatorial TIO, is
a crucial climate mode on the interannual scale (Schott et al., 2009). IOD
events mostly develop and mature within boreal autumn and decay in boreal
winter (Saji et al., 1999). The IOD corresponds well with local
precipitation and wind change and has an impact on the SSS (Saji and
Yamagata, 2003). The intensity of IOD can be defined by the Dipole Mode
Index (DMI), which is the difference between SST anomalies in the region of
10∘ S–10∘ N, 50–70∘ E, and
10∘ S–Equator, 90–110∘ E (Saji et al.,
1999). Based on the DMI, we composited the monthly SSS, BLT, and the
thermocline depth anomalies for positive IOD (pIOD) and negative IOD (nIOD)
events, respectively. The corresponding years are listed in Table 1. Figure 9
presents the composited seasonal variations for our current dataset during
the period of 1980–2015. The Monte Carlo procedure has been used to evaluate
the significance of the composite variations (green shaded areas). If the
value of the variables exceeds the green shaded areas, it is assessed to be
significant at the 95 % significance level. In the eastern TIO (Fig. 9a,
c, and e), there are no prominent patterns of SSS during negative
(positive) IOD events. This is because the eastward (westward) saltier
(fresher) water advection can compensate for the reduced (increased)
precipitation due to the presence of the strong Wyrtki jet (Thompson et al.,
2006). In contrast, the thermocline and BLT display a prominent seasonal
phase-locking feature in the eastern TIO. Specifically, during the mature
and decaying phases of the positive IOD events, shallower thermocline depth
due to strong upwelling leads to thinner BL. This thinner BL provides
favorable circumstances for the cold water intrusion into the ocean surface,
which contributes to the intensification of positive IOD events (Deshpande
et al., 2014). During the mature phase of negative IOD events, a deeper
thermocline along with a thicker BL could be observed in the eastern TIO due
to the strong downwelling. In the western TIO (Fig. 9b, d, and f), the
thicker BL prominently occurs only during the mature phase of positive IOD
events that are associated with deeper thermocline and fresher surface
water. The deeper thermocline is due to wind-induced downwelling and the
fresher surface water is attributed to the westward freshwater advection and
more precipitation induced by positive IOD events in the western TIO.
List of positive IOD events and negative IOD events in our study period.
Lagged correlations of (a) BLT, (b) the thermocline depth, (c)
SSS, (d) precipitation, and (e) the zonal wind stress anomalies averaged in
12∘ S–5∘ N, with the Nino3.4 index as a function of
longitude and calendar month (shaded areas exceed the 95 % significance level;
positive lagging correlations are shaded in red and negative ones are in
blue; the thick dashed black line represents the start of the decaying phase
of El Niño).
In previous studies, a significant seasonal phase-locking impact of ENSO on
the TIO has been addressed (Schott et al., 2009; Zhang and Yang, 2007). This
seasonal phase-locking impact mainly exists during the developing phase of
ENSO (boreal autumn), the mature phase of ENSO (boreal winter), and the
decaying phase of ENSO (boreal spring) in different areas of the TIO. We
composited our variables based on ENSO events from Table 2. Figure 10
presents the composited results of the seasonal variation of BLT, SSS, and
thermocline. The thinner BL is mainly associated with shallower thermocline
during the developing and mature phases of El Niño (Fig. 10c and e),
which can be explained by the anomalous easterlies along the Equator
invoked by the adjusted Walker circulation (Alexander et al., 2002; Kug and
Kang, 2006). In the western TIO, thicker BL presents two peaks during the
developing and decaying phase of El Niño events (Fig. 10d). The first
peak of thicker BLT corresponds to a peak of deepening thermocline depth due
to the westward downwelling Rossby wave and the anomalous wind stress
induced by El Niño (Kug and Kang, 2006; Xie et al., 2002). The second
peak of thicker BL is more significant, which connects to the peak of the
deepening thermocline and decreasing SSS (Fig. 10b and f).
List of El Niño events and La Niña events in our study period.
El Niño years1982198719911997La Niña years19881998199920072010
Time series of BLT, SSS, and thermocline anomalies averaged over
the western TIO (12∘ S–5∘ N, 55–75∘ E) during
boreal winter (a) and spring (b) from 1980 to
2015. Red, green, and blue lines represent BLT, SSS, and the thermocline
depth, respectively.
The pattern of BLT in the western TIO during El Niño events is the most
prominent, and its two peaks can be explained by two physical mechanisms.
Thus, we calculate the lead–lag correlations between the BLT, thermocline, and
SSS anomalies and the Nino3.4 index from 1980 to 2015 to investigate the
BLT–El Niño relationship. The correlation coefficients between the
thermocline depth anomalies and the Nino3.4 index reach significant values
during the mature period of El Niño (Fig. 11b). Also, the correlation
between thermocline and Nino3.4 index shows a time delay that is longitude
dependent, which is consistent with the result of Xie et al. (2002). This
deeper thermocline due to the westward downwelling Rossby wave induced by El
Niño affects the corresponding BL. As shown in Fig. 11a, there is a
remarkably positive correlation between BLT and the Nino3.4 index 1 month
apart. Then, the correlation between the thermocline depth anomalies and El
Niño becomes weaker during the decaying period of El Niño (Fig. 11b).
However, there is an enlarged area of correlation between BLT and
ENSO. This enlarged pattern accompanies with the appearance of a negative
correlation between SSS and ENSO (Fig. 11c). The negative SSS anomalies
due to precipitation induced by El Niño via the adjusting Walker
circulation and the westward Rossby wave in the western TIO thicken the BLT
anomalies (Fig. 11d and e).
To further verify the impact of IOD and ENSO events on the interannual
variation of BLT, the time series of BLT, SSS, and thermocline anomalies
averaged over the western TIO during boreal winter and spring from 1980 to
2015 are shown in Fig. 12. During boreal winter (Fig. 12a), thicker BL
and deeper thermocline could be found in 1983, 1992, and 1998, corresponding to
the mature phase of El Niño events (Table 2). During boreal spring
(Fig. 12b), thicker BL and deeper thermocline could also be observed in
the decaying phase of El Niño events, accompanied with fresher surface
water. On the other hand, the effect of IOD on the interannual variability
of BLT could also be observed in specific years, such as 1983, 1998, and
2006 (Table 1).
Summary
The seasonal and interannual variabilities of BLT in the TIO are
investigated by using the SODA v3 ocean reanalysis dataset. SODA v3
reasonably well reproduces the observed mean and variabilities of BLT in the
TIO when compared to Argo.
The dominant contributors to the BLT seasonality are different in the
western and eastern TIO. BLT in the eastern TIO is positively correlated with
the thermocline depth during boreal autumn, winter, and spring, and the
positive impact can last for the next 3–4 months. On the other
hand, BLT in the western TIO is negatively correlated with SSS during boreal
winter, spring, and autumn. The change of SSS can further control BLT
variation in up to 2 subsequent months. Additionally, the change of BLT in
the western TIO during boreal winter can also be affected by the variation
of the thermocline depth. For instance, during boreal winter, fresher
surface water and shallower thermocline depth result in thicker BL in the
western TIO; these result from freshwater flux and strong wind convergence
induced by both the winter monsoon wind and the westerlies (Yokoi et al.,
2012).
The interannual variability of BLT exerts a seasonal phase-locking pattern
during the IOD and ENSO years. In the eastern TIO, the thicker BL is led by the
deeper thermocline due to wind-induced downwelling during the mature phase
of negative IOD events. In contrast, the thinner BL is dominated by the
shallower thermocline due to wind-induced upwelling during the developing
and mature phases of positive IOD events. In the western TIO, the thicker BL
is only observed during the mature and decaying phases of positive IOD
events, along with deeper thermocline and fresher surface water.
The prominent patterns of BLT in the western TIO can only be detected during
El Niño events. According to the theory of Xie et al. (2002), warmer
water developing in the eastern tropical Pacific Ocean (El Niño)
results in anomalous easterlies and the generation of the downwelling
Rossby wave along the equatorial TIO. Thereby, the thermocline depth is
deepened in the western TIO, resulting in the thicker BL. This thickening BL
hampers the upwelling process and helps to sustain warmer SST. During the
decaying phase of El Niño events, there is an anomalous ascending branch
of the adjusted Walker circulation in the western TIO. As a result, SSS in
the western TIO decreases due to abundant precipitation. Consequently,
fresher surface water contributes to thickening the BL, which in turn
sustains the warmer SST in the western TIO.
Code availability
The code is available from the authors upon request (NCL
and MATLAB).
Data availability
The gridded ocean parameter datasets are available at the
Asian-Pacific Data-Research Center
(http://apdrc.soest.hawaii.edu/data/data.php, APDRC, 2020) and National Oceanic and
Atmospheric Administration (https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html, National Oceanic and Atmospheric Administration Physical Sciences Laboratory, 2020).
Author contributions
XuY designed the study, carried out the analysis
presented, and drafted the manuscript. ZS supervised the project, providing
edits to the manuscript. XiY helped to edit the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank A. J. George Nurser and one anonymous reviewer for their
constructive comments to improve the manuscript. The use of the following
datasets is gratefully acknowledged: the gridded ocean parameter datasets
are available at the Asian-Pacific Data-Research Center (http://apdrc.soest.hawaii.edu/data/data.php, last access: August 2020) and National Oceanic and
Atmospheric Administration
(https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html, last access: August 2020).
Financial support
The article processing charges for this open-access
publication were covered by WRS department in faculty of Geo-Information
Science and Earth Observation (ITC) in University of Twente.
Review statement
This paper was edited by A. J. George Nurser and reviewed by one anonymous referee.
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