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A Maldives Beach Awash in Bioluminescent Phytoplankton Looks Like an Ocean of Stars.

Chlorophyll-a can be used as a proxy for phytoplankton and thus is an essential water quality parameter. The presence of phytoplankton in the ocean causes selective absorption of light by chlorophyll-a pigment resulting in change of the ocean color that can be identified by ocean color remote sensing. The accuracy of chlorophyll-a concentration (Chl-a) estimated from remote sensing sensors depends on the bio-optical algorithm used for the retrieval in specific regional waters. In this work, it is attempted to estimate Chl-a from two currently active satellite sensors with relatively good spatial resolutions considering ocean applications. Suitability of two standard bio-optical Ocean Color (OC) Chlorophyll algorithms, OC-2 (2-band) and OC-3 (3-band) in estimating Chl-a for turbid waters of the northern coastal Bay of Bengal is assessed. Validation with in-situ data showed that OC-2 algorithm gives an estimate of Chl-a with a better correlation of 0.795 and least bias of 0.35 mg/m3. Further, inter-comparison of Chl-a retrieved from the two sensors, Landsat-8 OLI and Sentinel-2 MSI was also carried out. The variability of Chl-a during winter, pre-monsoon, and post-monsoon seasons over the study region were inter-compared. It is observed that during pre-monsoon and post-monsoon seasons, Chl-a from MSI is over estimated compared to OLI. This work is a preliminary step toward estimation of Chl-a in the coastal oceans utilizing available better spatially resolved sensors.

Shifts in phytoplankton phenology were observed in the Strait of Malacca (SM) and Sunda Shelf (SS), which were speculated to be potentially related to global warming and climate anomaly events. Such interactions between phytoplankton structure and physico-chemical factors were less known in narrow straits. Therefore, the spatial distribution pattern and diversity of surface phytoplankton assemblage, local hydrology, and nutrient regimes were investigated over the SM and SS (South China Sea, SCS) during 2017 and 2018 pre-monsoon season (spring). Diatoms, dinoflagellates, and cyanobacteria were representatives of microphytoplankton in the survey area. Total phytoplankton abundance peaked near Singapore Strait (SGS) and diminished toward SS. From the lower ratio of diatoms to dinoflagellates (<3) in SS, we deduced lower carbon pump efficiency here. In agreement with the modeled results proposed previously, cold conditions (negative Indian Ocean Dipole, IOD) were more suitable for high diatom (especially centric forms) abundance, while warm scenarios (positive IOD/El Niño period as in 2017) seemed to favor dinoflagellates and/or cyanobacteria. Specifically, diatom proportion increased by 30% and dinoflagellate, cyanobacteria reduced by 71%, 75% in response to shifts of climate anomaly from 2017 cruise to 2018 cruise. This study between field microalgae and physical and chemical conditions would be helpful to launch large-scale climate model, biogeochemistry, and carbon cycling in future research.

Coastal waters are dynamic because of anthropogenic activities that contribute nutrients and contaminants. These changes have the potential to alter patterns of primary production and thus pelagic food webs. Here, we investigated the spatial variation of the phytoplankton community and its response to changing environmental variables at 84 stations along the five coastal districts of Tamil Nadu (TN). During the present study, 85 phytoplankton species were recorded, such as diatoms (64), dinoflagellates (18), silicoflagellates (1), and Cyanophyceae (2). The maximum phytoplankton abundance was recorded on the Thanjavur coast and gradually decreased towards the south coast of Tamil Nadu. Among the phytoplankton community, 50% was dominated by pennate diatoms, attributed to higher NO3− concentrations in the coastal waters due to agricultural discharge. Cluster analysis revealed that Ramanathapuram and Tirunelveli formed a closed cluster, whereas Thanjavur and Pudukottai formed a separate closed cluster associated with higher nutrient and metal concentrations, highlighting the difference in physicochemical parameters between the northern and southern districts of the TN coast. Relatively high nutrient concentrations in the coastal waters of northern districts are of greater concern, which could impact the coastal ecosystem. Coastal eutrophication is becoming a widespread phenomenon, causing disruption in the food chain and ecosystem balances and hence requiring regular monitoring and management.

Tropical regions experience a diverse range of dense clouds, posing challenges for the daily reconstruction of chlorophyll-a concentration data. This underscores the pressing need for a practical method to reconstruct daily-scale chlorophyll-a concentrations in such regions. While traditional data reconstruction methods focus on single variables and rely on specific factors to infer missing data at specific locations, these single-variable methods may falter when applied to tropical oceans due to the scarcity of available data. Fortunately, all oceanographic variables undergo similar atmospheric and marine dynamic processes, creating internal relationships between them. This allows for the reconstruction of missing data through correlations between variables. Thus, this study introduces a multivariate reconstruction approach using the extended data interpolating empirical orthogonal function (ExDINEOF) method to reconstruct missing daily-scale chlorophyll-a concentration data. The ExDINEOF method considers the simultaneous relationships among multiple variables for data reconstruction in tropical oceans. To verify the method's robustness, missing data were reconstructed during the formation and passage of severe tropical cyclone Hudhud through the Bay of Bengal. The results demonstrate that ExDINEOF outperforms traditional data reconstruction methods, exhibiting favorable spatial distribution and enhanced accuracy within the dynamic tropical marine environment. Furthermore, an assessment of marine physical environmental factors associated with chlorophyll-a concentration data provides additional evidence for the ExDINEOF method's accuracy. Notably, the ExDINEOF method offers comprehensive spatial distribution aligned with underlying physical mechanisms governing phytoplankton distribution patterns, detailed phytoplankton growth, bloom, extinction variations in time series, satisfactory accuracy, and comprehensive local-level details.

The oxygen minimum zone has a significant effect on primary production, marine biodiversity, food web structure, and marine biogeochemical cycle. The Arabian Sea oxygen minimum zone (ASOMZ) is one of the largest and most extreme oxygen minimum zones in the world, with a positional decoupling from the region of phytoplankton blooms. The core of the ASOMZ is located to the east of the high primary production region in the western Arabian Sea. In this study, a coupled physical-biogeochemical numerical model was used to quantify the impact of ocean circulation and settling of particulate organic matters (POMs) on the decoupling of the ASOMZ. Model results demonstrate that the increased (decreased) dissolved oxygen replenishment in the western (central) Arabian Sea is responsible for decoupling. The oxygen-rich intermediate water (200-1,000 m) from the southern Arabian Sea enters the Arabian Sea along the west coast and hardly reaches the central Arabian Sea, resulting in a significant oxygen replenishment in the western Arabian Sea high-productivity region (Gulf of Aden) but only a minor contribution in the central Arabian Sea. Besides that, the POMs that are remineralized to consume central Arabian Sea dissolved oxygen comprises not only local productivity in winter bloom but also the transport from the western Arabian Sea high-productivity region (Oman coast) in summer bloom. More dissolved oxygen replenishment in the western Arabian Sea, and higher dissolved oxygen consumption and fewer dissolved oxygen replenishment in the central Arabian Sea could contribute to the decoupling of the ASOMZ and phytoplankton productive zone.