Plastic debris is the most common and exponentially increasing human pollutant in the world's ocean. The distribution and impact of plastic in the Pacific and Atlantic oceans have been the subject of many publications but not so the Indian Ocean (IO). Some of the IO rim countries have the highest population densities globally and mismanagement of plastic waste is of concern in many of these rim states. Some of the most plastic-polluted rivers empty into the IO, with all this suggesting that the IO receives a tremendous amount of plastic debris each year. However, the concentration, distribution, and impacts of plastics in the IO are poorly understood as the region is under-sampled compared to other oceans. In this review, we discuss sources and sinks, which are specific to the IO. We also discuss unique atmospheric, oceanographic, and topographic features of the IO that control plastic distribution, such as reversing wind directions due to the monsoon, fronts, and upwelling regions. We identify hotspots of possible plastic accumulation in the IO, which differ between the two hemispheres. In the southern IO, plastics accumulate in a garbage patch in the subtropical gyre. However, this garbage patch is not well defined, and plastics may leak into the southern Atlantic or the Pacific Ocean. There is no subtropical gyre and associated garbage in the northern IO due to the presence of landmasses. Instead, the majority of buoyant plastics most likely end up on coastlines. Finally, we identify the vast knowledge gaps concerning plastics in the IO and point to the most pressing topics for future investigation.

Yellowfin tuna (Thunnus albacares, Bonnaterre, 1788; YFT) face extensive exploitation pressure worldwide owing to the attractive economics of these fisheries. A better understanding of how oceanography influences the distribution of YFT (or highly migratory species more generally) will improve the ability for fisheries management policies to achieve conservation (e.g., maintain a stocks reproductive capacity), economic (e.g., maximize gross domestic product), and social (e.g., optimize successful fishing opportunities) objectives. Such ecosystem-based linkages, when combined with real-time remote sensing data, provide the predictive framework for enhancing favourable fishing opportunities and limiting excessive harvest. We examined factors influencing YFT distribution and behaviour using pop-up satellite archival tags and remotely sensed oceanographic data from two regions in the northern Indian Ocean. Tagged individuals did not exhibit significant deep diving or diurnal behaviour, and preferred ambient temperatures of 26–30°C and 25–29°C in the Arabian Sea (AS) and the Bay of Bengal (BoB), respectively. In general, tagged YFT were found to be in waters with sea surface temperatures of 26–29°C, 60% of the time and within a sea surface height-anomaly of ±6 cm 70% of the time. YFT avoided moving below the relatively shallow oxycline depth, which is indicative of the stratified waters of the AS and the BoB. Low dissolved oxygen levels are likely a limiting factor for tuna movement given their high oxygen demand. The northern Indian Ocean provides an opportunity to study climate impacts on fish distribution and movement, and our findings provide a basis for understanding how habitat and migratory patterns may be altered under climate change.

Commercial landings of sardines are known to show strong year‐to‐year fluctuations. A key driver is thought to be environmental variability, to which small forage fish are especially sensitive. We examined the utility of including environmental covariates in forecasts for landings of the Indian oil sardine using a long‐term time series of quarterly catches. Potentially influential variables examined included precipitation, upwelling intensity, sea surface temperature (SST), and chlorophyll‐a concentration. All of these have been shown to be important for oil sardine growth and survival, spawning and/or movement into the nearshore fishing regions. However, improving out‐of‐sample landings forecasts using environmental covariates has often proven elusive. We tested the inclusion of environmental covariates in forecast models using generalized additive models, which allow for non‐linear responses, and dynamic linear models, which allow for time‐varying responses. Only two environmental covariates improved out‐of‐sample prediction: the 2.5‐year average regional SST and precipitation over land during June–July. The most significant improvement was with the SST covariate and post‐monsoon landings with a 19%–22% reduction in mean‐squared prediction error. Models with the second best covariate, monsoon precipitation over land, provided a 4%–8% reduction in prediction error. We also tested large‐scale ocean climate teleconnection indices. One, an index of the Atlantic Multidecadal Oscillation, also improved out‐of‐sample predictions similarly to the multiyear average regional SST. The earth's changing climate is associated with both rapid warming in the Western Indian Ocean and changes to monsoon rainfall patterns. Our work highlights these as key variables that can improve forecasting of oil sardine landings.

The resolution of the model emerges to be an important factor in simulating the real oceanic features. In this paper, the performance of two coupled bio-physical models, having spatial resolutions 1/12° (∼9 km) and 1/4° (∼25 km) configured using Regional Ocean Modeling System (ROMS), have been evaluated in simulating upper ocean dynamics of the Arabian Sea. A comparison of the model simulated physical and biogeochemical fields with the observations from remote sensing, in-situ ship-borne, and Biogeochemical-Argo (BGC-Argo) floats showed that the high-resolution model reproduced the ocean physical and biogeochemical dynamics, and their seasonality more efficiently. The upper ocean dynamics associated with the variability of mixed layer depth, persistent occurrence of deep chlorophyll maxima, and seasonal phytoplankton blooms. as well as deep ocean characteristics of oxygen minimum zone were much effectively captured by the high-resolution model than by its counterpart. Similarly, the former model performed very well in reproducing the upwelling dynamics over the eastern continental shelf indicating that the open ocean-coastal coupling has been better established. Our analysis indicates that the realistic representation of the eddy fields by the high-resolution model leads to the better representation of the ocean fields in comparison to the coarse-resolution model.