Negative surface chlorophyll concentration anomalies in the southeastern Arabian Sea during 2015 and 2019 summers

doi:10.21203/rs.3.rs-2504269/v1

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Negative surface chlorophyll concentration anomalies in the southeastern Arabian Sea during 2015 and 2019 summers

https://doi.org/10.21203/rs.3.rs-2504269/v1

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Two extremely low surface chlorophyll concentration events in the southeast Arabian Sea (SEAS, 6^oN-15^oN, 72^oE-77^oE) during summers of 2015 and 2019 have been found since 1998. Although warm sea surface temperature (SST) and low nutrients are the direct cause for the anomalously low surface chlorophyll concentration, the physical processes leading to the warm SST anomalies during 2015 and 2019 summer are different. Satellite observations, model outputs and reanalysis data are used to explore the related mechanisms. In 2019, the combined effects of northward local wind anomaly due to extreme positive Indian ocean dipole (IOD) and westward-propagating downwelling Kelvin wave driven by the easterly anomaly in eastern Sri Lanka weaken the upwelling in the SEAS, leading to warm SST anomaly and suppressing the upward transport of the subsurface nutrients to the surface. A weaker positive IOD occurred in 2015, leading to stronger upwelling in the SEAS than during 2019. Yet, seawater in the SEAS experienced extreme warming (lowest SST exceeded 28.5^oC) due to the development of super El Niño in 2015. The significant seawater warming can shoal mixed layer and prevent the nutrients in the subsurface from reaching surface, which is unfavorable for the chlorophyll growth. The thermal balance analysis suggests that the extreme warming in the SEAS was mainly related to more downward shortwave radiation and zonal advection.

surface chlorophyll concentration

Indian ocean dipole

upwelling

extreme warming

The Southeastern Arabian Sea (SEAS) is adjacent to the southwest coast of India and is subject to Indian monsoon climate with significant seasonal variations. Strong upwelling occurs in the SEAS during summer (June to October), starting in March, peaking in June-August, and then weakening after September (Banse 1959; Shetye 1984; Shetye et al. 1990; Rao et. al. 2005, 2008; Gupta et al. 2016; Vinayachandran et al. 2021). Upwelling carries colder and nutrient-rich water from the subsurface upward to the surface, leading to chlorophyll bursts (Longhurst 1993; Arìstegui et al. 2009; Ravichandran et al. 2012).

Wind is the main dynamic factor that drives global upwelling system. In the SEAS, upwelling can be driven by both local and remote wind (Sharma 1978; McCreary et al. 1993; Smitha et al. 2008; Rao et al. 2009). Influenced by Peninsular India, the southwesterly deflects clockwise during summer monsoon, almost parallel to the west coast of India (Kurian and Vinayachandran 2007). The southwesterly drives offshore Ekman transport with upward replenishment of the subsurface water and the positive wind stress curl drives upward Ekman pumping, which can increase upwelling (e. g. Hareesh Kumar and Anand 2016; Vinayachandran et al. 2021). On the other hand, the ocean wave processes forced by wind in the equator and western Bay of Bengal also have a significant effect on the upwelling in the SEAS (Suresh et al., 2016, 2018; Amol, 2018, 2020; Thushara and Vinayachandran, 2020). This remote forcing can affect the variability of chlorophyll concentration in the SEAS by modulating the thermocline depth. However, the dominant role of local and remote forcing on the formation of the SEAS upwelling system in different years remains controversial (Shankar et al. 2002; Suresh et al. 2013; Hareesh Kumar and Anand 2016; Vinayachandran et al. 2021).

The interannual variability of the ocean and atmosphere in the tropical Indian Ocean are often influenced by large-scale climate events, including the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO) (Schott and McCreary 2001; Schott et al. 2009). Surface chlorophyll concentration anomalies in the SEAS during IOD and/or ENSO are associated with basin-wide wind changes in Indian Ocean that determine local upwelling and mixing (Murtugudde et al. 1999; Vidya and Kurian 2018; Sankar et al. 2019) or remote effects on the variations of thermocline depth (Rao and Behera 2005; Girishkumar et al. 2012; Currie et al. 2013), regulating nutrients supply to the photosynthetic zone. Recent studies have shown that surface chlorophyll concentration anomaly in the SEAS has significant negative correlation with IOD, but insignificant relation with ENSO (Currie et al. 2013; Shafeeque et al. 2021a, b). IOD events can affect the intensity of upwelling in the SEAS by altering local wind and remote wind anomalies in the tropical eastern Indian Ocean (Vallivattathiam et al. 2017; Nigam et al. 2018; Sankar et al. 2019). Local wind stress and remote effect near Sri Lanka during 2016 extreme negative IOD led to an unprecedented enhancement of surface chlorophyll concentration in the SEAS (Thushara and Vinayachandran 2020). The frequency of extreme positive IOD events may increase due to greenhouse warming in the future, leading to dramatic changes in marine ecology of Indian Ocean (Cai et al. 2014; Wang et al. 2017). IOD events showed a large diversity caused by a combination of internal processes in the Indian Ocean and remote forcing such as ENSO (Du et al. 2013; Sun et al. 2015; Jiang et al. 2022), with each showing unique pattern of wind and sea surface temperature in Indian Ocean (Endo and Tozuka 2016; Fan et al. 2017; McKenna et al. 2020). Therefore, the response of surface chlorophyll in the SEAS to different IOD events is worth to study.

Satellite observations indicate extreme negative anomalies (the two strongest events since 1998) in surface chlorophyll concentrations in the SEAS during both 2015 and 2019 summers. Both two summers experienced positive IOD, with moderate intensity in 2015 and extreme in 2019 (Du et al. 2020; Wang et al. 2020). It is noticed that the 2015 positive IOD was also accompanied by a super El Niño event in the Pacific Ocean (Santoso et al. 2017; Zhang et al. 2018). This study focuses on how surface chlorophyll in the SEAS during the summers responds to these two different positive IOD events. Data and methods are presented in Section 2. Section 3 describes the variability of chlorophyll concentrations in the SEAS during 2015 and 2019 summer monsoon. In Section 4, the physical processes controlling the abnormal chlorophyll concentrations in the SEAS during the 2015 and 2019 summer monsoon are analyzed. Summary and discussions are given in Section 5.

2.1 Data

The 1998–2020 monthly surface chlorophyll data (1/24°×1/24°) used to describe the distribution of surface chlorophyll are from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v3.1; Sathyendranath et al. 2018). High-resolution blended analysis of daily Sea surface temperature (SST, 1993–2020, 1/4°×1/4°) is obtained from National Oceanic and Atmospheric Administration (Reynolds et al. 2007). The sea surface level anomalies (SLA) are provided by Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO, Le Traon et al. 1998; Ducet et al. 2000). Wind and air-sea flux data during 1993–2019 are from the European Center for Medium-Range Weather Forecasting ERA5 (Hersbachet al. 2018). The Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) provides horizontal and vertical current, temperature, and salinity data, with a horizontal resolution of 1/4°×1/4° and 50 layers from 5 to 5900 m, and net sea surface heat flux, shortwave radiation, and mixed layer depth (Menemenlis et al. 2008). The surface current vectors from 1998 to 2020 were replaced with the Ocean Surface Current Analysis-Real Time (OSCAR) with a horizontal resolution of 1° × 1° and 5 days intervals (Bonjean and Lagerloef 2002; Johnson et al. 2007). Three-dimensional Ocean temperature and salinity data of Argo from China Argo Real-time Data Center is also used in the analysis (Li et al. 2017).

2.2 Methods

2.2.1 Climate indices

Niño 3.4 index is calculated using SST anomaly between 5°N to 5°S and 170°W to 120°W. Dipole Mode Index (DMI, Saji et al. 1999) is defined as the difference in SST anomaly during western equatorial Indian Ocean (50°E to 70°E, 10°S to 10°N) to southeastern equatorial Indian Ocean (90°E to 110°E, 10°S to 0°). The time series of the first empirical orthogonal function (EOF) mode of SST variability over the tropical Indian Ocean (40–110°E, 20°S-20°N) is defined as the Indian Ocean Basin Mode (IOBM) index (Yang et al. 2007).

2.2.2 Upwelling indices

The SST in the coastal upwelling region is colder than in the non-upwelling region due to offshore Ekman transport associated with coastal wind stress. Therefore, the upwelling strength can be expressed based on the SST or coastal wind stress intensity.

Upwelling index based on SST (UI_sst) difference between coastal region and offshore region is calculated (Nykjaer and VanCamp 1994) as follows,

$${UI}_{sst}\left(\text{R2}\text{ }\text{- R1}\right)={SST}_{offshore}-{SST}_{coast}$$

The subscript “offshore” refers to the area where water depth is greater than 200m, the subscript “coast” represents the coastal region (water depths < 200m) in Eq. (1). In this study, the coastal (R1) refers to the area shallow than 200m isobath, the offshore (R2) refers to the area along the 200m isobath at the distance of 1.5^o to 3^o to the west.

Upwelling index based on Ekman offshore transport over the SEAS (UI_ek, Bakun 1973), is calculated as follows,

$${UI}_{ek}\left(along coast\right)=\frac{{\tau }_{coast}}{{\rho }_{0}f}$$

where ${ \rho }_{0}=1024kg·{m}^{-3}$ is the seawater density, ${\tau }_{coast}$ is the sum of the projection of the meridional and zonal wind stress in the direction of the coast, f is the Coriolis parameter.

2.2.3 Ekman vertical velocity

Wind-driven Ekman vertical velocity (${W}_{ek}$) including Ekman pumping and Ekman transport (Thushara and Vinayachandran 2020), which can be written,

$${W}_{ek}=\frac{\nabla \times \overrightarrow{\tau }}{{\rho }_{0}f}+\frac{\beta {\tau }_{coast}}{{\rho }_{0}{f}^{2}}$$

where $\nabla \times \overrightarrow{\tau }$ is the wind stress curl and $\beta =\frac{\partial f\left(y\right)}{\partial y}$ .

2.2.4 Mixed layer heat budget

Based on previous studies (Sayantani et al. 2016; Nigam et al. 2018; Jyothi et al. 2022), the mixed layer heat budget can be calculated as follow,

$$\frac{\partial T}{\partial t}=\frac{({Q}_{net}-{Q}_{pen})}{\rho {C}_{p}h}-\left(u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}\right)-\frac{\varDelta T}{h}W++residual$$

$${Q}_{pen}=0.47\times {Q}_{shortwave}\times (V1\times {e}^{-h/\xi 1}+\left(1-V1\right)\times {e}^{-h/\xi 2})$$

$$W={w}_{h}+\frac{\partial h}{\partial t}, \varDelta T=(T-{T}_{-h})$$

T is the average temperature in mixed layer. The h is mixed layer depth. The $\varDelta T=T-{T}_{-h}$ represents the difference between the average temperature of the mixing layer and the temperature in bottom of mixed layer. ${w}_{h}$ is the velocity at the bottom of the mixed layer. ${Q}_{net}$ is the net sea surface heat flux, including long-wave radiation, short-wave radiation, latent heat flux and sensible heat flux. The ${Q}_{pen}$ is component of solar shortwave radiation below the mixed layer depth (Sweeney et al. 2005), $\text{w}\text{h}\text{e}\text{r}\text{e} V1=0.58$, $\xi 1=0.35$, $\xi 2=23$ (Nigam et al. 2018); $\rho$ is seawater density; ${C}_{p}$ is seawater specific heat capacity, ${C}_{p}=4096 J·{(kg·℃)}^{-1}$. The u and v are horizontal velocity, $w$ is vertical velocity.

To diagnose thermal balance anomalies, each variable is divided into climatological average and interannual variability (e.g., ${T}_{mld}=\stackrel{-}{{T}_{mld}}+{{T}_{mld}}^{{\prime }}$). The mixed layer heat budget Eq. (5) can be rewritten as,

$\frac{\partial {{T}_{mld}}^{{\prime }}}{\partial t}=\frac{{({Q}_{net}-{Q}_{pen})}^{{\prime }}}{\rho {C}_{p}h}-({u}^{{\prime }}\frac{\partial \stackrel{-}{{T}_{mld}}}{\partial x}+{v}^{{\prime }}\frac{\partial \stackrel{-}{{T}_{mld}}}{\partial y}+\stackrel{-}{u}\frac{\partial {T}_{mld}^{{\prime }}}{\partial x}$

$+\stackrel{-}{v}\frac{\partial {T}_{mld}^{{\prime }}}{\partial y}+{u}^{{\prime }}\frac{\partial {T}_{mld}^{{\prime }}}{\partial x}+{v}^{{\prime }}\frac{\partial {T}_{mld}^{{\prime }}}{\partial y})-\frac{1}{h}({W}^{{\prime }}\varDelta \stackrel{-}{T}+\stackrel{-}{W}\varDelta {T}^{{\prime }}$

$+{W}^{{\prime }}\varDelta {T}^{{\prime }})+residual$

The term on the left side of Eq. (7) is the mixed layer temperature trend (TT). The first term on the right of the Eq. (7) is the heat flux (HF) term. The second term is the horizontal advection (ADV) of mixed layer temperature. The third term is vertical entrainment flux (VEF). The last item is a residual.

3.1 Anomaly pattern over the tropical Indian Ocean

IOD events are generally accompanied by the opposite patterns of sea surface temperature anomalies (SSTA) in the eastern and western basins of equatorial Indian Ocean (Saji et al. 1999). Both 2015 and 2019 are positive IOD events (Table 1), with negative (positive) SSTA in the tropical southeastern (western) Indian Ocean (Fig. 1). Surface chlorophyll concentrations are generally negatively correlated with the SSTA, therefore, negative (positive) anomalies in surface chlorophyll concentrations occur in the western (southeastern). Based on the patterns of surface chlorophyll concentration anomalies across the Indian Ocean during 2015 and 2019 summer, large-scale air-sea interaction related to positive IOD events might be the main factor (Schott et al. 2009; Shafeeque et al. 2021a). The tropical southeastern Indian Ocean is the east pole of IOD events and usually considered as the dominant pole, with thermocline shoaling and strong upwelling occurring during positive IOD events (Saji et al. 1999). As the result, cold SSTA and chlorophyll bursts can be seen in this area during positive IOD event. Stronger surface chlorophyll anomaly in the tropical southeastern Indian Ocean during 2019 summer corresponds well with the strong intensity of positive IOD (Fig. 1b). Although the anomaly patterns of surface chlorophyll concentration in tropical southeastern Indian Ocean show large differences during 2015 and 2019 summer monsoon, similarly strong negative anomalies are observed in the SEAS (Fig. 1).

Table 1

DMI (^oC) and Niño 3.4 index (^oC) for 2015 and 2019, and the correlation coefficients between surface chlorophyll concentration in the SEAS during 1998–2020 summers and DMI and Niño 3.4 index. The (*) indicate the indices greater than 1.5 standard deviations. The (**) indicates the correlation above the 99% significance level.
	DMI	Nino 3.4
2015	0.30*	1.60*
2019	0.75*	0.60
Correlation coefficients	-0.65**	-0.18

3.2 Extreme negative anomalies in the Southeast Arabian Sea

The horizontal distribution of surface chlorophyll concentrations showed large negative anomalies (< -0.3 mg·m^− 3) over the SEAS in 2015 and 2019 summer (Fig. 1). Further analysis reveals that surface chlorophyll concentrations in the SEAS during 1998 to 2020 exhibit strong seasonal variations, with higher values in the boreal summer than winter (Fig. 2a). There are also significant interannual variations of surface chlorophyll concentrations in the SEAS during summers, with standard deviation exceeding 1mg·m^− 3 (Fig. 2c). Extremely positive anomaly of surface chlorophyll concentrations in 2016 can be easily seen in Fig. 1a (green line), which is consistent with Thushara and Vinayachandran (2020). It is also noticed that there are significant negative anomalies of surface chlorophyll in the SEAS during both 2015 and 2019 summers, with the maximum anomaly magnitude exceeding − 0.5 mg·m^− 3 in September (Fig. 1c) and being the two weakest years in past 20 years.

The surface chlorophyll concentrations in SEAS have significant negative correlation with the DMI index (Correlation coefficient = -0.65, Table. 1). However, surface chlorophyll concentration anomalies in the SEAS show similar intensity under different IOD events, which indicates that other effects may contribute to the negative surface chlorophyll concentration anomalies in 2015.

Upwelling (downwelling) in the SEAS can cause increases (decreases) in surface chlorophyll concentrations (Shankar et al. 2019). The mean (6^oN-15^oN) vertical current shows that the weak downwelling anomaly (less than 0.15m·day^− 1) occurs over the area of 72^oE-74^oE during 2015 summer, with intensity weaker in the upper layer (Fig. 3a). No downwelling anomalies can be found in coastal region (74^oE-76^oE). Moreover, the thermocline depth (i.e., 23°C isotherm line depth, D23) in the SEAS had weak subsidence (less than 10 m) in 2015 summer, which gradually deepens from west to east (Fig. 3a). However, a strong downwelling anomaly (reach 0.3m·day^− 1) is observed in the SEAS during 2019 summer (Fig. 3b). The regions of strong downwelling anomaly can be found in upper 100m layer along 73^oE-76^oE and lower layer (50m-150m) over 76^oE-77^oE. The thermocline depth sinks significantly (20m-30m) in the SEAS during 2019 summer. It is clear that the upwelling in 2019 summer is much weaker than in 2015, especially in the upper 50m.

The weakening upwelling during the 2015 and 2019 summers are accompanied by the increasing SST in the SEAS (Fig. 4a). Surface chlorophyll concentrations in the SEAS during summer showed a significant negative correlation with SSTA (-0.64). It is noteworthy that sea temperature in upper 30m is warmer in 2015 is stronger than in 2019, even though the upwelling intensity is stronger in 2015 (Fig. 4). In other words, the intensity of upwelling in the SEAS during 2015 summer does not match the upper ocean temperature and surface chlorophyll concentrations (Figs. 2, 3 and 4). To disentangle the differences in the physical processes that led to the reduction of chlorophyll concentration in 2015 and 2019 summer, it is necessary to analyze the causes of the upwelling intensity changes and to examine the physical processes affecting seawater temperature variability.

Wind is the main contributor to drive the upwelling in the SEAS during summer, including both local and remote forcing. The wind anomaly patterns in Arabian Sea show significant differences between in 2015 and 2019 summers, while the wind anomaly patterns in other parts of Indian Ocean do not differ much between both years (Fig. 5). In 2015, the Arabian Sea is predominated by a northeasterly anomaly, with the positive (negative) wind stress curl anomaly in the south (north). In contrast to 2015 summer, the wind stress curl anomaly is positive (negative) at the north (south) side of the Arabian Sea in 2019 summer. Meanwhile, wind anomaly in the Arabian Sea is weak in 2019 summer, but there is significant poleward anomaly in the SEAS. The inconsistent wind pattern in 2015 and 2019 summers will contribute differently to the upwelling intensity in SEAS.

Upwelling indexes over the SEAS during 2018–2020 are shown in Fig. 6. Both the SST (UI_sst) and Ekman pumping (UI_ek) induced upwelling peaks in summer and have strong interannual variability, with the weakest in 2019 summer (Figs. 6a and 6b). UI_sst is calculated based on the SST differences between the coastal and offshore regions, which is a direct reflection of the upwelling intensity. UI_ek is an upwelling index calculated from the local wind stress, which is an important factor for upwelling intensity changes. The two indexes exhibit large differences comparing to the climatology (average over the period of 1998–2020). UI_ek in 2015 exceeds the climatology monsoon season, but UI_sst is below the average; UI_ek in 2019 reaches the lowest during summer for 1998–2020, but UI_sst does not reach the lowest. The poleward coastal wind and negative wind curl anomaly in the SEAS during 2019 summer inhibits the offshore Ekman transport and pumping, weakening the upwelling (Figs. 5 and 6) and deepens the thermocline depth (Fig. 3b). Compared to that during 2019 summer, the UI_sst and UI_ek in 2015 are stronger and closer to the climatology, showing stronger upwelling strength. Despite the occurrence of positive IOD events in both 2015 and 2019, and poleward coastal wind anomalies were observed in SEAS, the wind stress rotational anomalies are opposite (Fig. 5). Correspondingly, the wind-driven vertical velocity anomaly (W_ek) in the SEAS in 2015 (2019) is stronger (weaker) than the 23-year (1998–2020) average (Fig. 6c). The UI_sst and UI_ek for 2015 and 2019 do not correspond well, suggesting that local wind field anomalies are not the only factor controlling upwelling strength.

In addition to the local wind forcing, non-local wave processes derived by remote winds can also affect the strength of coastal upwelling. These wave processes affect upwelling by modulating sea surface height and thermocline depth, with positive (negative) sea surface height anomalies (SLA) representing enhanced (weakened) upwelling. Easterly anomalies in equatorial eastern Indian Ocean induced by positive IOD events (Saji et al. 1999; Fig. 5) can force the equatorial upwelling Kelvin waves and propagate into the SEAS to affect SLA. The upwelling Kelvin waves can enhance the upwelling, so the equatorial upwelling Kelvin waves driven by equatorial eastern Indian Ocean do not contribute to weaken the upwelling in SEAS during the summer monsoon. Recent studies have shown that the IOD events induce wind anomalies in eastern Sri Lanka, driving westward coastal Kelvin waves that can quickly affect the SEAS (Suresh et al. 2016, 2018). The easterly and negative wind stress curl anomalies occurred in eastern Sri Lanka (5°N) starts in May, driving the downwelling Kelvin waves (positive SLA) and propagating westward into the SEAS, which can suppress upwelling and deepen the thermocline depth (Figs. 3, 5 and 7). No significant westward propagation process can be seen in 2015 summer (Fig. 7a), which may be due to the local wind field favoring upwelling and suppressing positive SLA in SEAS. As shown in Figure. 7b, during the 2019 summer, the SLA positive anomaly extend from the southern Bay of Bengal (especially around Sri Lanka) to the SEAS and seems to have a westward propagation process. The strong easterly winds and negative wind stress curl anomalies from eastern Sri Lanka during 2019 summer (Fig. 5b) drive downwelling Kelvin waves to propagate westward into the SEAS, suppressing upwelling and deepening thermocline (Figs. 3b).

Extreme low surface chlorophyll in 2019 can be well demonstrated by weak upwelling (strong downwelling), which is related by both local wind and remote wave response. However, the strong upwelling in 2015 due to local wind cannot be offset by the weak downwelling induced by remote wave response (Fig. 7a), therefore the upwelling by the combined local and remote wind forcing also cannot explain the extreme low surface chlorophyll concentration in 2015 summer.

Upper ocean temperature changes have an important effect on chlorophyll concentration. Upper ocean warming can inhibit vertical mixing by increasing the water stratification, thereby reducing the nutrients in subsurface waters upward transport to surface (Behrenfeld et al. 2006; Boyce et al. 2010; Doney et al. 2012; Behrenfeld 2016; Roxy et al. 2016; Sankar et al. 2019). Significant negative correlation (approximately − 0.64) can be found between SST anomalies and surface chlorophyll concentration anomalies (Fig. 4a). SST in the SEAS during 2015 summer reaches extreme value (minimum SST > 28.5^oC) in the last 23 years (Fig. 8a). At the same time, mixed layer temperature warming in the SEAS is observed (Fig. 8b).

5.1 Mixed layer heat budget in 2015

The interannual variations in SST over tropical Indian Ocean are often associated with ENSO changes (Xie et al. 2016; Cai et al. 2019; Wang et al. 2019). The El Niño event can affect the Indian Ocean through an atmospheric bridge mechanism (e.g., Klein et al. 1999; Alexander et al. 2002; Lau and Nath 2003; Zhang et al. 2015). To understand the relative contributions of atmospheric and oceanic processes to control the mixed layer temperature, a mixed layer heat budget Eq. (7) was used to analyze the mixed layer warming in the SEAS during 2015 summer (Fig. 9).

The HF term in the Eq. (7) is mainly controlled by the net sea surface heat flux, including long-wave radiation (LWR), short-wave radiation (SWR), latent heat flux (LHF) and sensible heat flux (SHF). From March to September of 2015, the mixed layer temperature trend (TT) in the Eq. (7) is positive and temperature over the SEAS is abnormally high (black line in Fig. 9a). The results show that the HF has an important influence on the mixed layer temperature variation in 2015 (Fig. 9a), with shortwave radiation being the main factor (Fig. 9b). Although the short-wave radiation from February to May in 2015 is negative (< -5W·m^− 2), the latent heat and sensible heat are positive (> 5W·m^− 2), so the contribution of HF to warm mixed layer temperature is positive; however, the short-wave radiation is positive (reaching 20W·m^− 2) from June to September and has a strong heating effect in mixed layer warming. In addition to atmospheric heating, the horizontal advection (ADV term) in mixed layer contributes significantly to the mixed layer temperature warming during 2015 summer. Among the horizontal convection, ${-u}^{{\prime }}\frac{\partial \stackrel{-}{{T}_{mld}}}{\partial x}$, $-\stackrel{-}{v}\frac{\partial {T}_{mld}^{{\prime }}}{\partial y}$ and ${-u}^{{\prime }}\frac{\partial {T}_{mld}^{{\prime }}}{\partial x}$ contribute positively to the warming in 2015 summer, while ${-v}^{{\prime }}\frac{\partial \stackrel{-}{{T}_{mld}}}{\partial y}$, $-\stackrel{-}{u}\frac{\partial {T}_{mld}^{{\prime }}}{\partial x}$ and $-{v}^{{\prime }}\frac{\partial {T}_{mld}^{{\prime }}}{\partial y}$ contribute negatively. ${-u}^{{\prime }}\frac{\partial \stackrel{-}{{T}_{mld}}}{\partial x}$ and ${-u}^{{\prime }}\frac{\partial {T}_{mld}^{{\prime }}}{\partial x}$ reach maximum values during summer, indicating that zonal advection anomalies have a significant effect on mixed layer temperature warming. The vertical process (VEF term) has a weak negative contribution to the mixed layer temperature increase during 2015 summer.

5.2 Mechanisms of mixed layer warming in 2015 summer

The mixed layer heat budget in the SEAS for 2015 summer may be related to the development of El Niño. Previous results show that the 2015 El Niño began to develop in early summer, and its intensity is the strongest in the past 23 years (Levine and McPhaden 2016; Chen et al. 2017). The IOB indexes are used to reflect SST anomalies in the Indian Ocean basin. IOD, El Niño and IOB begin to develop in late spring and early summer of 2015, then peak in September, November, and the following spring, respectively (Fig. 10). Chowdary and Gnanseelan (2007) showed that IOB is more intense when IOD and El Niño events occur simultaneously, with net seas surface heat fluxes (mainly latent heat flux and shortwave radiation) playing a dominant role. Our results show that shortwave radiation in 2015 summer played an important role in the SEAS warming (Fig. 9c). The IOB generally lags El Niño by several months (Du et al. 2009; Xie et al. 2016; Santoso et al. 2017), but the warming in 2015 occurred almost simultaneously with El Niño (Fig. 10). Recent studies have shown that west-type positive IODs (i.e., strong positive SST anomalies in the western Indian Ocean) are stimulated by El Niño that develop in late spring and early summer (Fan et al. 2017; Jiang et al. 2022). Anticyclonic anomalies and negative wind stress curl anomalies were found in the northern Arabian Sea and South China Sea-Bay of Bengal in 2015 (Fig. 5). Similar to the results of Fan et al. (2017) and Jiang et al. (2022), the anticyclonic wind field anomalies in the northern Arabian Sea weakened the summer monsoon and wind-evaporation-SST (WES) feedback (Xie and Philander 1994), resulting in the tropical western Indian Ocean warming, followed by an enhanced east-west temperature (east-cooling-west-warming) gradient in the tropical Indian Ocean and IOD started to develop. The anticyclonic anomaly and weaker monsoon lead to reduced rainfall in the SEAS, enhancing the solar shortwave radiation fluxes and weakened longwave, sensible, and latent heat fluxes (Figs. 10c, d, e, and f). The anticyclonic circulation nomaly over the South China Sea-Bay of Bengal enhances the easterly over the eastern tropical Indian Ocean and further strengthen the east-west SST gradient (i.e., positive IOD enhancement). The reason why the super El Nino event did not generate strong positive IOD in 2015 is due to the influence of the deeper thermocline in the eastern tropical Indian Ocean in the last decade (Zhang et al. 2018). Even though the early development of 2015 super El Niño does not force a strong positive IOD, it still heats the SEAS, causing extreme warming during 2015 summer (Fig. 8).

The El Niño induced ocean warming in the SEAS appears to be more pronounced in the mixed layer, while wind field changes have a greater effect on the depth of the thermocline (Fan et al. 2017; Shankar et al. 2019). Seawater temperature above 30 m (below 30 m) in SEAS in 2015 (2019) summer is warmer (Figs. 3 and 4b). This suggests that the mechanism for the extreme negative surface chlorophyll anomaly in SEAS in 2015 summer may not be the same as in 2019. The thermocline depth and upwelling intensity in the SEAS during 2015 summer do not reach extreme anomalies (Figs. 3a and 6), and the contribution of mixed layer warming to extreme negative surface chlorophyll concentration anomaly needs to be considered. The mixed layer warming results in weaker mixing, making it difficult for nutrients to reach the surface layer and inhibiting the growth of chlorophyll concentration.

Upwelling in the SEAS during the summer, bringing cold water and nutrients from the subsurface upward to the surface, makes this region a highly productive basin. Satellite observations over the past 20 years indicate that, the sea surface chlorophyll concentrations in SEAS in 2015 and 2019 summers showed extremely low levels. Weaker upwelling and warmer mixed layer temperature in the SEAS are the two main factors contributing to the negative anomalies in surface chlorophyll concentration. Surface chlorophyll concentrations in the SEAS are negatively correlated with IOD (Shafeeque et al. 2021b), with positive IOD events occurring in both 2015 and 2019 (Table. 1). However, the 2019 IOD intensity is almost twice as intense as in 2015, and the surface chlorophyll concentrations in SEAS are all extreme negative anomalies.

This analysis reveals that the physical processes controlling the two extreme low chlorophyll concentration events in 2015 and 2019 summers are different (Fig. 11). The low chlorophyll concentration appears to be associated with extreme positive IOD event for 2019 summer. The anomalous poleward local wind and negative wind curl anomalies inhibit upwelling in the SEAS during 2019 summer. At the same time, the easterly anomalies in eastern Sri Lanka drive the westward propagation of downwelling Kelvin wave, suppressing the development of upwelling and deepening the thermocline depth in the SEAS. The local and remote wind anomalies in 2019 summer result in deeper thermocline and weaker upwelling in the SEAS, making it difficult for nutrients to reach the surface layer and inhibiting the growth of surface chlorophyll concentrations. The processes of positive IOD in 2019 and negative IOD in 2016 (Thushara and Vinayachandran 2020) on surface chlorophyll in the SEAS are similar, opposite wind anomaly patterns triggered opposite strong surface chlorophyll anomalies. Positive IOD event also occurs in 2015, but with weaker easterly anomaly in the eastern Sri Lanka and positive wind stress curl anomaly in the SEAS, resulting in stronger upwelling intensity in 2015 than in 2019. On the other hand, we found the SEAS experienced extreme warming in 2015, with the lowest SST over the SEAS exceeding 28.5^oC. Since the SSTA and upwelling intensity in 2015 and 2019 summers do not match, we further analyze the causes of seawater warming by using heat budget. The results show that the extreme warming in 2015 summer is mainly related to the development of super El Niño. The early development of 2015 El Niño weakening the southwest monsoon and warming the western Indian Ocean by more solar radiation and stronger zonal advection in mixed layer.

The diversity of IOD events makes the response of chlorophyll concentration in the SEAS to different IODs more complex. This study focuses on the main reasons for the decrease in chlorophyll concentration in the SEAS under the influence of two IOD events with different intensities. The response processes of chlorophyll concentration in the SEAS differ due to the different locations of the wind and SST pattern of the IOD events. Although there is no significant correlation between ENSO events and surface chlorophyll concentrations in the SEAS (Table. 1), the development of 2015 super extreme El Niño may play an important role inhibiting surface chlorophyll concentrations in the SEAS by inducing surface warming over the area. Chlorophyll concentrations in the ocean have important implications for marine fisheries and material cycling. With global warming, the frequency and intensity of more diverse IOD events are increasing, which will inevitably lead to dramatic changes in surface chlorophyll concentrations and require further study.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (92158204), GuangDong Basic and Applied Basic Research Foundation (2022A1515010945), the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0306), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20060500). The numerical calculation is supported by the high-performance computing division.

Funding

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HM, HK, YL and DX. The first draft of the manuscript was written by HM and all authors commented on previous versions of the manuscript. All authors read, edit and approved the final manuscript.

Data Availability

The OCCCI data were downloaded from http://www.esa-oceancolour-cci.org/. The ECMWF ERA5 data were obtained at https://cds.climate.copernicus.eu/. The AVISO Sea level data were downloaded from http://marine.copernicus.eu/. The OSCAR surface current products were downloaded at ftp://podaacftp.jpl.nasa.gov/allData/oscar/preview/L4/oscar_third_deg. We obtained the SST data from https://www.psl.noaa.gov/data/gridded/index.html. The ECCO2 data were downloaded from http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=4837. The ARGO data were obtained at http://www.argo.org.cn.

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Negative surface chlorophyll concentration anomalies in the southeastern Arabian Sea during 2015 and 2019 summers

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Abstract

Figures

1 Introduction

2 Data And Methods

2.1 Data

2.2 Methods

2.2.1 Climate indices

2.2.2 Upwelling indices

2.2.3 Ekman vertical velocity

2.2.4 Mixed layer heat budget

3 Surface Chlorophyll Concentration Anomalies During 2015 And 2019 Summer Monsoons

3.1 Anomaly pattern over the tropical Indian Ocean

3.2 Extreme negative anomalies in the Southeast Arabian Sea

4 Physical Mechanisms For The Extreme Negative Surface Chlorophyll Concentrations In Seas

5 Extreme Warming In Mixed Layer Temperature During 2015 Summer

5.1 Mixed layer heat budget in 2015

5.2 Mechanisms of mixed layer warming in 2015 summer

6 Conclusions And Discussions

Declarations

Acknowledgements

References

Additional Declarations

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