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Ocean primary production is the basis of the marine food web, sustaining life in the ocean via photosynthesis, and removing carbon dioxide from the atmosphere. Recently, a small but significant decrease in global marine primary production has been reported based on ocean color data, which was mostly ascribed to decreases in primary production in the northern Indian Ocean, particularly in the Bay of Bengal. Available reports on primary production from the Bay of Bengal (BoB) are limited, and due to their spatial and temporal variability difficult to interpret. Primary production in the BoB has historically been described to be driven by diatom and chlorophyte clades, while only more recent datasets also show an abundance of smaller cyanobacterial primary producers visually difficult to detect. The different character of the available datasets, i.e., direct counts, metagenomic and biogeochemical data, and satellite-based ocean color observations, make it difficult to derive a consistent pattern. However, making use of the most highly resolved dataset based on satellite imaging, a shift in community composition of primary producers is visible in the BoB over the last 2 decades. This shift is driven by a decrease in chlorophyte abundance and a coinciding increase in cyanobacterial abundance, despite stable concentrations of total chlorophyll. A similar but somewhat weaker trend is visible in the Arabian Sea, where satellite imaging points towards decreasing abundances of chlorophytes in the north and increasing abundances of cyanobacteria in the eastern parts. Statistical analysis indicated a correlation of this community change in the BoB to decreasing nitrate concentrations, which may provide an explanation for both the decrease in eukaryotic nitrate-dependent primary producers and the increase in small unicellular cyanobacteria related to Prochlorococcus, which have a comparably higher affinity to nitrate. Changes in community composition of primary producers and an

COrona VIrus Disease (COVID) 2019 pandemic forced most countries to go into complete lockdown and India went on complete lockdown from 24th March 2020 to 8th June 2020. To understand the possible implications of lockdown, we analyze the long-term distribution of Net Primary Productivity (NPP) in the North Indian Ocean (NIO) and the factors that influence NPP directly and indirectly, for the period 2003-2019 and 2020 separately. There exists a seasonal cycle in the relationship between Aerosol Optical Depth (AOD), Chlorophyll-a (Chl-a) and NPP in agreement with the seasonal transport of aerosols and dust into these oceanic regions. In Arabian Sea (AS), the highest Chl-a (0.58 mg/m3), NPP (696.57 mg/C/m2/day) and AOD (0.39) are observed in June, July, August, and September (JJAS). Similarly, maximum Chl-a (0.48 mg/m3) and NPP (486.39 mg/C/m2/day) are found in JJAS and AOD (0.27) in March, April, and May (MAM) in Bay of Bengal. The interannual variability of Chl-a and NPP with wind speed and Sea Surface Temperature (SST) is also examined, where the former has a positive and the latter has a negative feedback to NPP. The interannual variability of NPP reveals a decreasing trend in NPP, which is interlinked with the increasing trend in SST and AOD. The analysis of wind, SST, Chl-a, and AOD for the pre-lockdown, lockdown, and post lockdown periods of 2020 is employed to understand the impact of COVID-19 lockdown on NPP. The assessment shows the reduction in AOD, decreased wind speeds, increased SST and reduced NPP during the lockdown period as compared to the pre-lockdown, post-lockdown and climatology. This analysis is expected to help to understand the impact of aerosols on the ocean biogeochemistry, nutrient cycles in the ocean biogeochemical models, and to study the effects of climate change on ocean ecosystems.

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.

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.

Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling - regarded as the largest in the WIO and the fifth most important upwelling system globally - takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

The Seychelles-Chagos Thermocline Ridge (SCTR, 5°S-10°S, 50°E-80°E) is a unique open-ocean upwelling region in the southwestern Indian Ocean. Due to the negative wind stress curl between the equatorial westerlies and southeasterly trade winds, SCTR is known as a strong upwelling region with high biological productivity, providing a primary fishing zone for the surrounding countries. Given its importance in shaping the variability of the Indian Ocean climate by understanding the sea-air interaction and its dynamics, the simulation of SCTR is evaluated using outputs from the Coupled Model Intercomparison Project Phase Sixth (CMIP6). Compared to observations, 23 out of 27 CMIP6 models tend to simulate considerably deeper SCTR thermocline depth (defined as the 20°C isotherm depth (D20))- a common bias in climate models. The deep bias is related to the easterly wind bias in the equatorial to southern Indian Ocean, which is prominent in boreal summer and fall. This easterly wind bias produces a weak annual mean Ekman pumping, especially in the boreal fall. Throughout the year, the observed Ekman pumping is positive and is driven by two components: the curl term, is associated with the wind stress curl, leads to upwelling during boreal summer to fall; the beta term, is linked to planetary beta and zonal wind stress, contributes to downwelling during boreal spring to fall. However, the easterly wind bias in the CMIP6 increases both the positive curl and negative beta terms. The beta term bias offsets the curl term bias and reduces the upwelling velocity. Furthermore, the easterly wind bias is likely caused by the reduced east-west sea surface temperature (SST) difference associated with a pronounced warm bias in the western equatorial Indian Ocean, accompanied by the east-west mean sea level pressure gradient over the Indian Ocean. Furthermore, this study finds local wind-induced Ekman pumping to be a more dominant factor in thermocline depth bias than Rossby waves,