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Decadal climate variability in the southern Indian Ocean could be predicted up to 10 years ahead, according to a study led by Dr. MORIOKA at Application Laboratory, the Japan Agency for Marine-Earth Science and Technology (JAMSTEC: ASAHIKO Taira, President) and his colleagues. To investigate the predictability, the scientists have used sea surface temperature (SST) observation data during 1982 to 2015 to initialize a state-of-the-art coupled general circulation model, the Scale Interaction Experiment Frontier Research Center 2 for Global Change (SINTEX-F2*) with a simple SST-nudging scheme. Their decadal reforecast experiments demonstrated moderately high prediction skills of yearly mean SST in the southwest Indian Ocean at 10-year lead time.

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.

The monsoon circulation in the Northern Indian Ocean (NIO) is unique since it develops in response to the bi-annual reversing monsoonal winds, with the ocean currents mirroring this change through directionality and intensity. The interaction between the reversing currents and topographic features have implications for the development of the Island Mass Effect (IME) in the NIO. The IME in the NIO is characterized by areas of high chlorophyll concentrations identified through remote sensing to be located around the Maldives and Sri Lanka in the NIO. The IME around the Maldives was observed to reverse between the monsoons to downstream of the incoming monsoonal current whilst a recirculation feature known as the Sri Lanka Dome (SLD) developed off the east coast of Sri Lanka during the Southwest Monsoon (SWM). To understand the physical mechanisms underlying this monsoonal variability of the IME, a numerical model based on the Regional Ocean Modeling System (ROMS) was implemented and validated. The model was able to simulate the regional circulation and was used to investigate the three-dimensional structure of the IME around the Maldives and Sri Lanka in terms of its temperature and velocity. Results revealed that downwelling processes were prevalent along the Maldives for both monsoon periods but was applicable only to latitudes above 4°N since that was the extent of the monsoon current influence. For the Maldives, atolls located south of 4°N, were influenced by the equatorial currents. Around Sri Lanka, upwelling processes were responsible for the IME during the SWM but with strong downwelling during the NEM. In addition, there were also regional differences in intra-seasonal variability for these processes. Overall, the strength of the IME processes was closely tied to the monsoon current intensity and was found to reach its peak when the monsoon currents were at the maximum.

Accurately estimating the ocean's subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model's accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.

Coastal waters support a diverse range of marine life and contribute to the country's economy. Seawater quality has a significant impact on the ecological sustainability and biological productivity of coastal and marine ecosystems (DHEENAN et al., 2014; DHEENAN et al., 2016). However, population growth and industrialization in the coastal regions have steadily increased the anthropogenic pressure, resulting in seawater quality degradation along the coast. Anthropogenic activities such as land-based runoff, sewage discharge, industrial & aquaculture effluent and eutrophication in the coastal environment could impact the aquatic biota of the region. Consequently, coastal pollution has become a global issue that requires intervention through the application of monitoring programs and improvement of the seawater quality through a mitigation management system. The combined effects of salinity and temperature influence the coastal water, and nutrient content is responsible for productivity, therefore information on these parameter's distribution in different coastal ecosystems is important (SATPATHY et al., 1986). Among the numerous inorganic elements required for life support in marine coastal ecosystems, nitrogen, phosphorous, and silicates are believed to be more significant than the others because they play a vital role in phytoplankton abundance, growth, and metabolism (Barath KUMAR et al., 2018). The distribution and behavior of nutrients in the coastal environment, particularly in the nearshore environment, varies greatly depending on local variables such as anthropogenic activities, fresh water influx, tidal variation, and biological activity such as phytoplankton intake and regeneration. Although several studies on water quality have been conducted in other Indian coastal regions (RENJITH et al., 2015; JHA et al., 2015; YUVARAJ et al., 2018; SATHEESWARAN et al., 2019; RATMAN et al., 2022), there is relatively less work carried out on the seawater quality char

Ports and neighbouring cities function as connectors between land and water and have long accommodated a substantial flow of goods and services. Port cities in the Western Indian Ocean (WIO) region and the Global South (GS) are rapidly and inevitably expanding as the demand for global trade increases. However, this expansion has numerous impacts on the surrounding marine ecosystem and the socio-economic livelihoods of local communities. We propose a framework to evaluate the sustainability of port cities in the WIO region and more broadly for cities in the GS. Through an exploratory approach, a systematic literature review (SLR) was undertaken to identify existing themes on port city and marine ecosystem sustainability indicator frameworks. The results revealed a strong bias towards sustainability publications designed for port cities in Global North. The approach developed from this study focuses on the socio-economic and environmental attributes relevant to ports in the WIO region and for GS countries. This draws from the Drivers, Pressures, States, Impacts and Responses (DPSIR) framework and includes 78 indicators. The indicators are designed to identify and report on the complex land and sea interdependencies of port cities. To test the validity of these indicators their interdependencies were examined through a Causal Network (CN) structure which identified 12 priority DPSIR CN. These were also mapped to the UNSDGs enabling the wider applicability and transferability of the framework. The resulting framework enables port cities in emerging economies to establish robust sustainable reporting systems and provides a framework that offers a unique lens for evaluating interactions embedded in the land and sea continuum.

Most climate forecast agencies failed to make successful predictions of the strong 2020/2021 La Niña event before May 2020. The western equatorial Pacific warm water volume (WWV) before the 2020 spring failed to predict this La Niña event because of the near neutral state of the equatorial Pacific Ocean in the year before. A strong Indian Ocean Dipole (IOD) event took place in the fall of 2019, which is used as a precursor for the La Niña prediction in this study. We used observational data to construct the precursory relationship between negative sea level anomalies (SLA) in the southeastern tropical Indian Ocean (SETIO) in boreal fall and negative Niño 3.4 sea surface temperature anomalies index one year later. The application of the above relation to the prediction of the 2020/2021 La Niña was a great success. The dynamics behind are the Indo-Pacific "oceanic channel" connection via the Indian Ocean Kelvin wave propagation through the Indonesian seas, with the atmospheric bridge playing a secondary role. The high predictability of La Niña across the spring barrier if a positive IOD should occur in the previous year suggests that the negative SETIO SLA in fall is a much better and longer predictor for this type of La Niña prediction than the WWV. In comparison, positive SETIO SLA lead either El Niño or La Niña by one year, suggesting uncertainty of El Niño predictions.

Mobile app encourages Indian fishers to free entangled whale sharks.

The values used for natural mortality (M) are very influential in stock assessment models, affecting model outcomes and management advice. Natural mortality is one of the most difficult demographic parameters to estimate, and there is often limited information about the true levels. Here, we summarise the evidence used to estimate natural mortality at age for the four main stocks of yellowfin tuna (Indian, Western and Central Pacific, Eastern Pacific, and Atlantic Oceans), including catch curves, tagging experiments, and maximum observed age. We identify important issues for estimating M such as variation with age linked to size, maturity state or senescence, and highlight information gaps. We describe the history of natural mortality values used in stock assessments by the tuna Regional Fisheries Management Organisations responsible for managing each stock and assess the evidence supporting these values. In June 2021, an online meeting was held by the Center for the Advancement of Population Assessment Methodology (CAPAM), to provide advice and guidance on practices for modelling natural mortality in fishery assessments. Based on approaches presented and discussed at the meeting, we develop a range of yellowfin tuna natural mortality estimates for each stock. We also recommend future research to improve these estimates of natural mortality.