Abstract
The Indian Ocean Dipole (IOD) is a prominent mode of climate variability
in the tropical Indian Ocean (IO). It exerts a significant influence on
biological activities in this region. To elucidate the biological
response to the IOD, previous research has introduced the Biological
Dipole Mode Index (BDMI). However, the delineation of the region by the
BDMI has limitations in capturing IOD-induced chlorophyll variations in
the IO. By analyzing observational data and historical simulations from
a Coupled Model Intercomparison Project (CMIP) model, this study shows
that chlorophyll levels in the IO exhibit a dipole pattern in response
to IOD. During the developing and mature phases of the IOD, we observe a
substantial decrease in chlorophyll in the south-southwest of India,
contrasting with a pronounced increase in the southeast of the IO. This
response is attributed to anomalous southeasterly winds induced by IOD,
which enhance nutrient upwelling in the southeastern IO and suppress it
in the south-southwest of India, resulting in corresponding changes in
surface chlorophyll blooms. Based on this finding, we propose a new
Biological Dipole Index that more robustly explains the surface
chlorophyll response to IOD in the tropical IO. This study highlights
the profound influence of IOD on oceanic chlorophyll and underscores the
importance of a more comprehensive understanding of the associated
biophysical interactions.
Introduction
Marine phytoplankton play a key role in global biogeochemical cycles
(Field et al 1998), contributing to the marine food web
(Richardson and Schoeman 2004) and regulating the global climate and
carbon cycle (Murtugudde et al 2002, Gregg et al 2003).
Previous studies have reported that surface chlorophyll-a (hereafter
chlorophyll) concentrations are an important indicator of phytoplankton
productivity (Field et al 1998). The western Indian Ocean (IO)
has been found to be one of the most productive regions in the tropical
oceans (Lee et al 2005), and therefore many studies have
conducted to understand the associated dynamics of biophysical
interactions (Goes et al 2005, Wiggert et al 2005). For
example, Roxy et al (2016) investigated the changes in
chlorophyll concentration in the western IO and reported an alarming
decline in phytoplankton during summer. Further they showed that the
enhanced ocean stratification driven by the rapid warming in the IO is a
key factor for the declining chlorophyll in the IO. In addition, the
biological productivity exhibits a pronounced seasonal cycle in the IO
as a result of monsoon variability, which modulates upwelling,
entrainment and convective mixing in the upper ocean (McCreary et
al 1996, Kone et al 2009, Vijith et al 2016). On the
other hand, the seasonal evolution of surface chlorophyll in the IO has
been found to be significantly modulated by tropical climate
variability, such as El Nino-Southern Oscillation (ENSO, Behrenfeldet al 2006, Wiggert et al 2009, Currie et al 2013,
Racault et al 2017) and Indian Ocean Dipole (IOD, Brewin et
al 2012, Park and Kug 2014). Thus, the chlorophyll in the IO is
robustly influenced by the local and remote forcings.
The IOD is a prominent and dynamic climate phenomenon that exerts a
profound influence on weather and climate patterns throughout the IO
region (Saji et al 1999). This interannual air-sea coupled
climate mode is characterized by a dipole pattern in sea surface
temperature (SST) anomalies between the western and eastern equatorial
IO (Saji et al 1999). IOD typically manifests in two phases: the
positive phase is characterized by warmer SST anomalies in the western
IO (10° S-10° N, 50° E-70° E) and cooler SST anomalies in the eastern IO
(10° S-0°, 90° E-110° E), and vice versa for the negative phase. These
SST anomalies trigger variations in the atmospheric circulation, leading
to shifts in the position of the Walker circulation, changes in monsoon
patterns and altered precipitation regimes in the affected regions (Sajiet al 1999, Webster et al 1999). Thus, the IOD is a
crucial component of the Earth’s climate system (Saji and Yamagata
2003), affecting not only regional climate variability, but also having
far-reaching effects on weather systems (Chan et al 2008),
agriculture (Ashok and Saji 2007), ecosystems (Marchant et al2006, Park and Kug 2014) and socio-economic conditions (Feng et
al 2022) in countries bordering the IO.
In addition to the effects on the physical structure of the IO, there
are biological consequences of IOD with potential importance for the
marine ecosystem, fishery resources and carbon sequestration (Currieet al 2013). The IOD-induced changes in the surface circulation
can lead to upwelling/downwelling and mixing and regulate the supply of
nutrients to the surface causing robust changes in surface chlorophyll
in the IO (Sarma 2006, Chen et al 2013). However, the
relationship between IOD and ocean biogeochemistry is complex and
depends on the specific phase of IOD (positive or negative) and their
regional effects (Sari et al 2020). Therefore, the biological
response to IOD has been widely studied (Vinayachandran et al2002, Chen et al 2013, Currie et al 2013, Sankar et
al 2019, Thushara and Vinayachandran 2020, Shi and Wang 2021). For
example, Murtugudde et al (1999) reported a strong phytoplankton
bloom in the eastern equatorial IO, an area characterized by low
climatological productivity, due to the intense positive IOD of 1997.
Consistently, similar responses were also observed during the 2019
positive IOD event (Shi and Wang 2021). Although similar blooms were
observed during the 2006 event, Wiggert et al (2009) showed that
the biological responses to the 1997 event were more intense, suggesting
that IOD intensity is an important factor for the intensity of
chlorophyll. Furthermore, Wiggert et al (2009) showed that the
1997 and 2006 events caused a decrease in surface chlorophyll in the
Arabian Sea (AS), an area characterized by high climatological
productivity. On the other hand, several studies have reported an
increase in surface chlorophyll in the southeastern AS during negative
IOD events due to enhanced Ekman suction and coastal upwelling, as well
as a shoaling thermocline in the region (e.g., Thushara and
Vinayachandran 2020). Thus, in addition to the differences in
chlorophyll intensity, the biological responses to IOD also show a
diverse spatial structure.
Given that IOD influences the chlorophyll variability of the tropical
IO, Shi and Wang (2021) proposed a biological IOD, and later the authors
introduced the biological dipole mode index (BDMI, Shi and Wang 2022) to
characterize and quantify IOD-induced changes in surface chlorophyll in
the IO. The BDMI is defined as the difference between the average
chlorophyll anomaly in the western IO (10° S-10° N, 50° E-70° E) and the
eastern IO (10° S-0°, 90° E-110° E), where the chosen region is the same
as that used to define the dipole mode index (DMI). However, there are
some limitations in the index region that prevent an accurate
representation of the chlorophyll variability in response to IOD in the
IO. As shown in the composite analysis of positive and negative IODs
(Fig. S1), IOD-induced changes in chlorophyll remain weak in the western
equatorial IO. This raises questions about the accuracy of using
chlorophyll in the western equatorial IO to define the BDMI in previous
studies, and therefore possible clarifications are sought to understand
the IOD-induced chlorophyll changes (e.g., intensity and spatial
pattern) in the tropical IO. Furthermore, understanding these
interactions is crucial for understanding the broader ecological and
environmental consequences of IOD events over the Indian Ocean.
Therefore, in the present study we investigate the biological
consequences of IOD.
Data and methods
To investigate the effect of IOD on chlorophyll, we used monthly
chlorophyll from the European Space Agency Ocean Color Climate Change
Initiative (ESA-OC-CCI v4, Sathyendranath et al 2018). In
addition, we used monthly SST data from extended reconstructed SST
(ERSSTv5, Boyin et al 2017), 10 m zonal and meridional winds from
National Centers for Environmental Prediction (NCEP2), subsurface
temperature from NCEP Global Ocean Data Assimilation System (GODAS,
Kanamitsu et al 2002), and surface wind stress from European
Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5).
Along with observations, we used historical simulations from 13 CMIP6
coupled general circulation models, including chlorophyll, SST,
low-level (850 hPa) winds, subsurface temperature, and nutrients
(nitrate and phosphate, Table S1), to provide supporting evidence for
the observed relationships. It should be mentioned that these 13 models
(Table S1) among CMIP6 models are selected because they only provide
full 3-D datasets of ocean biogeochemistry. However, many of the CMIP
models fail to reproduce the spatial pattern of climatology in
chlorophyll concentrations and interannual variability in the IO (Roxyet al 2016). Therefore, following Roxy et al (2016), we
selected a single model to investigate the biophysical response to IOD
based on the pattern correlation coefficient (PCC > 0.5) of
mean chlorophyll and the interannual variability of chlorophyll
(standard deviation) (Fig. S2). The model chosen is the Geophysical
Fluid Dynamics Laboratory Earth System Model (GFDL-ESM4, Dunne et
al 2020). GFDL-ESM4 simulates the mean climatology of chlorophyll
concentrations and the interannual variability of chlorophyll with a
relatively small bias compared to the other 12 CMIP6 models (Fig. S2).
In addition, the dipole mode index (DMI) is calculated as the difference
between the averaged SST anomaly in the western Indian Ocean (10° S-10°
N, 50° E-70° E) and the eastern Indian Ocean (10° S-0°, 90° E-110° E,
Saji et al 1999). We also examined the biological dipole mode
index (BDMI) introduced by Shi and Wang (2022). The BDMI is defined as
the difference of average chlorophyll anomaly in the western Indian
Ocean (10° S-10° N, 50° E-70° E) and the eastern Indian Ocean (10° S-0°,
90° E-110° E). Here, all anomalies are calculated by removing the
seasonal cycle and the long-term linear trend. The datasets have been
analyzed for the period 1997-2020 for the observations and for the
period 1850-2014 for the CMIP6 simulations.
Results