The physical setting explains why this region is so difficult to simulate. The Bay of Bengal is the largest semi-enclosed bay in the global ocean, and its northern boundary is closed, so the entire basin responds intensely to the twice-yearly reversal of the monsoon winds. From May to September, strong southwesterly winds dominate; from November to February, weaker northeasterlies take over. During the transitional months, westerly winds over the equatorial Indian Ocean generate disturbances that propagate toward the bay as equatorial and coastal Kelvin waves and westward-moving Rossby waves, reshaping the thermocline and upper-ocean stratification on seasonal to interannual timescales. On top of this remote forcing sits an extraordinary local input: the Ganges–Brahmaputra–Meghna river system discharges more than 4.4 × 10¹¹ cubic meters of freshwater each year, spreading a buoyant, sediment-laden lens across the shelf that dominates the hydrography up to roughly 21.5°N. This freshwater cap produces intense haline stratification, a barrier layer between the halocline and the isothermal layer, and, in winter, temperature inversions in which warm subsurface water becomes trapped between cooler surface and deeper layers.

Those inversions are a signature feature of wintertime hydrography in the northern bay, and the CTD profiles captured them in remarkable detail. Nine of the twelve coastal stations showed temperature inversions, and several stations, including stations 4, 5, 6, 9, and 12, displayed multiple inversion layers stacked at different depths. At stations 5 and 6, a sharp thermal inversion appeared near 15 meters in the in situ data, yet the model completely missed the feature. At stations 9 and 10, small but distinct mid-depth temperature drops were present in the observations but absent from the model output. Station 3 revealed a mixed layer only 4 to 5 meters deep, while the model showed no clear mixed-layer signature at all. The largest discrepancy occurred at station 12, where the observed mixed layer reached nearly 19 meters but the model profile ended around 11 meters. Only a handful of stations, such as stations 2 and 8, showed broadly similar profile shapes between observations and model, and even there the agreement was only approximate.

The coastal observations also revealed how shallow the winter mixed layer really is on this shelf. Mixed-layer depths from the CTD casts ranged from just 4 to 12 meters, with a mean of 5.18 meters at the northeastern stations and 10.31 meters at the northwestern stations. The authors attribute the shallower northeastern mixed layers to stronger near-surface stratification, while the relatively deeper northwestern values suggest somewhat greater vertical mixing or weaker surface stabilization near the river mouths. A time series of mixed-layer depth averaged over the northern bay showed the layer deepening slightly to a peak of about 11.4 meters around 8 February before shoaling again through late February and March. The model, by contrast, barely varied at all, holding steady between 9.8 and 9.9 meters throughout the entire three-month period and capturing none of the observed fluctuation. In station-by-station comparisons, the model generally overestimated mixed-layer depth, exceeding observations by roughly 3 to 5 meters at stations 1, 5, and 6, while underestimating at stations 11 and 12.

Offshore, the picture was somewhat better but still flawed. The team compared three Argo float profiles, located at 17.50°N 88.50°E, 18.50°N 90.50°E, and 19.50°N 92.50°E, against the model for January through March. Below about 80 to 100 meters, the model performed reasonably well, reproducing the depth of the well-developed thermocline and the general stratification, and both datasets showed a gradual deepening of the thermocline from January to March. In the upper 50 meters, however, the model diverged sharply from the floats. Argo revealed strong salinity-driven stratification and wintertime temperature inversions that the model did not fully reproduce, and collocated comparisons at 5 meters depth showed a consistent warm near-surface temperature bias alongside a tendency to underestimate salinity. Density differences followed the same pattern, with negative density biases in freshwater-dominated regions. The authors caution that because only three Argo sites were available over a short seasonal window, these bias patterns should be read as preliminary and location-specific rather than a complete regional assessment.

The broader oceanographic context of the 2020 winter helps explain the observed structures. Sea-level anomaly remained negative across the entire northern bay, ranging from about −0.32 to −0.36 meters in January and February and deepening to roughly −0.44 meters by March, with the strongest negative anomalies concentrated near the mouths of the Ganges–Brahmaputra system. Surface currents showed only weak variability, but subsurface currents told a different story: at 55 meters depth, currents shifted direction considerably and strengthened through the season, while at 21 meters they remained weak and variable. These depth-dependent current changes align closely with the thermal features seen in vertical sections, including warm subsurface layers of 25.4 to 26.6 °C trapped between cooler 23 to 24 °C waters in January and February, a cold intrusion between 90.4°E and 91.6°E that strengthened into March, and a downward penetration of warm water between 89.4°E and 90.2°E in February. Hovmöller diagrams confirmed a sudden cooling of roughly 2 to 2.5 °C around 21.3 to 21.5°N in February, followed by broad warming to about 26 °C in March as the winter monsoon weakened and wind stress declined from peaks of 0.04 to 0.054 pascals in the northeastern bay.

Why does a global model with a roughly 9-kilometer horizontal resolution and 5-meter vertical spacing near the surface struggle so much here? The authors point to a combination of factors rather than a single culprit. The model’s discrete vertical levels inherently smooth thin mixed layers, sharp thermohaline gradients, and narrow inversion layers that the CTD, sampling at roughly 0.2-meter intervals, resolves easily. Its global configuration and tripolar ORCA12 grid may not adequately represent regional subsurface processes, and the satellite datasets used to set initial conditions are often poorly suited to coastal and estuarine zones, forcing the use of pseudo-observation methods that can miss delicate features. Complex deltaic bathymetry, tide–river interactions, and the sheer magnitude of freshwater discharge add further difficulty. Importantly, the authors note that the region’s observational scarcity itself weakens the data assimilation that models depend on, although their analysis does not establish a direct causal link between sparse data and model bias. The result is a model that treats the northern shelf as an averaged, interpolated approximation of a far more intricate reality.

The observational gap itself is sobering. Despite the Argo program’s more than two million temperature–salinity profiles since 1999, fewer than 50 collocated Argo observations were reported for the extreme northern bay between 2015 and 2019, because the floats simply cannot sample the shallow Bangladesh shelf where freshwater input, sediment loading, and boundary currents are strongest. The in situ record consists almost entirely of short campaign-based surveys: a 2018 Nansen cruise with 38 CTD stations, a winter 2016 campaign with 15 casts, the 2020 dataset of 12 profiles used here, and roughly 60 nearshore stations near Cox’s Bazar collected in 2021–2022. To the authors’ knowledge, none of these CTD data are openly accessible, making their twelve profiles, to their knowledge, the only open-source coastal CTD data available for the region. Satellites offer no easy substitute, since they measure mainly the surface and often lack reliable algorithms for optically complex coastal waters, and validating satellite products requires exactly the kind of in situ data that is missing.

The stakes extend well beyond academic model evaluation. Two of the four major fishing grounds of the Bay of Bengal lie along the northeastern Bangladesh coast, suggesting a possible connection between episodic upwelling, nutrient supply, and fisheries distribution, yet the relationships among monsoon forcing, stratification, and nutrient delivery remain poorly understood precisely because strong stratification may limit vertical nutrient transfer. The bay’s freshwater-dominated upper ocean also shapes air–sea heat fluxes, cyclone response, and the monsoon system itself, so errors in simulating its mixed layer and barrier layer can propagate into regional climate predictions. The authors argue that continuous, large-scale, integrated monitoring of the northern bay is essential, not only for characterizing its subsurface structure but for building a regional model capable of accurate forecasting. Until such sustained observations exist, they conclude, the northern Bay of Bengal will remain what their title calls it: a blind spot, where even the best global models cannot see the delicate, fast-changing structures that define this extraordinary shelf sea.

Subject of Research: Winter upper-ocean hydrography and model performance in the data-scarce northern Bay of Bengal

Article Title: Inadequate in situ observations make the northern Bay of Bengal a blind spot for models

Article References: Inadequate in situ observations make the northern Bay of Bengal a blind spot for models. (n.d.). https://doi.org/10.1007/s44289-026-00148-y

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00148-y

Keywords: Bay of Bengal, oceanography, CTD profiles, Argo floats, mixed layer depth, thermal inversion, barrier layer, monsoon, ocean modeling, CMEMS reanalysis, freshwater stratification, in situ observations

A new study published in the journal Climate Dynamics by Hui Teng, Xinyu Lin, and Yun Qiu of the Third Institute of Oceanography in Xiamen, China, offers the most detailed answer yet. Drawing on more than three decades of ocean reanalysis and observational data spanning 1980 to 2015, the team shows that the interannual variability of sea surface salinity in the Bay of Bengal is tightly coupled to ENSO events, and that this coupling unfolds in two distinct, seasonally locked stages with strikingly different spatial fingerprints.

The first stage arrives with the northeast monsoon, the dry winter season, and peaks between December of the developing El Niño year and the following February. During this window, the researchers found that sea surface salinity drops significantly across the southern Bay of Bengal, the Andaman Sea, and the eastern equatorial Indian Ocean, while at the same time salinity rises markedly in the northern bay. It is a seesaw pattern: as one end of the basin freshens, the other grows saltier, and the two changes are physically connected rather than coincidental.

The mechanism behind the southern freshening, according to the study, lies in the winds. El Niño’s influence on the tropical atmosphere generates an anomalous anticyclonic circulation over the region, a giant swirl of air that spins the surface ocean along with it. This circulation drives a southward advection of low-salinity water, pushing the bay’s famously fresh surface waters down toward the southern bay, the Andaman Sea, and the equatorial Indian Ocean. In effect, El Niño spreads the freshwater signature of the northern bay across a much wider swath of the tropical Indian Ocean than it would normally occupy.

The northern bay, meanwhile, moves in the opposite direction for a different reason. The western boundary current of the bay, the current that flows along India’s eastern coast, intensifies during this stage of an El Niño event. That intensification enhances the northward transport of salty water drawn from the south, delivering a saltier payload to the northern reaches of the basin. The result is a simultaneous freshening in the south and salinification in the north, a dipole of sorts that had not previously been resolved with this level of seasonal precision.

Then, as the seasons turn, the pattern itself turns. During the southwest monsoon stage of El Niño events, particularly in August and October of the year following the event’s onset, the salinity map flips into a new configuration. Salinity decreases sharply in the central and southern bay and in the eastern equatorial Indian Ocean, but now it increases in both the northern bay and the Andaman Sea. The two stages are not mirror images of each other; they are governed by different combinations of forces.

In the summer stage, the study identifies two processes working together to freshen the southern bay. El Niño enhances precipitation over the region, dumping additional freshwater directly onto the sea surface, while an anomalous cyclonic circulation, opposite in sense to the winter anticyclone, advects even more low-salinity water southward. The salinity increase in the northern bay during this stage arises from a strengthened northward advection of high-salinity water combined with a reduction in freshwater input, as the weakened summer monsoon delivers less river runoff and less direct rainfall to the basin’s head.

Crucially, the researchers found that La Niña events, the cool phase of the oscillation, run the same machinery in reverse. Correlation and regression analyses applied to the 36-year record show that the cold phase produces salinity anomalies of opposite sign through the same set of oceanic and atmospheric pathways. This symmetry is a hallmark of a linear response, suggesting that the bay’s salinity system can be modeled and predicted with relatively straightforward tools once the ENSO state is known, at least to first order.

The technical foundation of the work is a careful synthesis of independent datasets. The team used version 3.3.1 of the Simple Ocean Data Assimilation reanalysis, the World Ocean Atlas 2018 climatology, satellite-derived sea surface salinity from the Soil Moisture and Ocean Salinity mission, precipitation from the Global Precipitation Climatology Project, evaporation and heat fluxes from the Objectively Analyzed air–sea Fluxes product, and atmospheric fields from the ERA5 reanalysis. ENSO state was tracked with the standard Niño-3.4 index, and the Indian Ocean Dipole, the basin’s other dominant climate mode, was monitored with the Dipole Mode Index to disentangle the two influences.

Why does this matter beyond the tidy world of salinity budgets? The Bay of Bengal’s freshwater stratification is a linchpin of the regional climate system. A barrier layer of low-salinity water sitting atop denser, saltier water prevents wind-driven mixing from bringing cool water to the surface, allowing sea surface temperatures to stay high and sustaining atmospheric convection that feeds the monsoon. By showing that ENSO systematically redistributes the bay’s salinity in two predictable seasonal stages, the study provides a physical basis for anticipating how the barrier layer, and by extension monsoon convection, may respond when an El Niño or La Niña is brewing in the Pacific.

The findings also sharpen the picture of the tropical Indian Ocean as an active participant in global climate variability rather than a passive responder. Prior work had established links between the Indian Ocean Dipole and salinity anomalies in the equatorial Indian Ocean, and earlier modeling studies had identified processes controlling bay salinity variability in general. What the new analysis adds is a coherent, seasonally resolved framework that ties the bay’s salinity seesaw directly to the evolution of ENSO events, complete with the specific current systems and wind-driven circulations responsible at each stage.

For forecasters and climate scientists, the practical implications are considerable. Because the winter-stage salinity dipole emerges during the peak of an El Niño event, and the summer-stage pattern follows with a predictable lag, sea surface salinity in the bay could serve as an observable fingerprint of ENSO’s downstream reach, one that satellites now monitor routinely. As climate change continues to alter both the strength of ENSO events and the freshwater delivery from Himalayan-fed rivers, understanding the baseline mechanics of this coupling becomes essential for projecting the future of the South Asian monsoon, one of the most consequential climate systems on the planet.

Subject of Research: Interannual variability of sea surface salinity in the Bay of Bengal and its relationship with ENSO

Article Title: Interannual variabilities of sea surface salinity in the Bay of Bengal and its relationship with ENSO

Article References: Teng, H., Lin, X., & Qiu, Y. (2026). Interannual variabilities of sea surface salinity in the Bay of Bengal and its relationship with ENSO. Climate Dynamics, 64(10), Article 425. https://doi.org/10.1007/s00382-026-08383-x

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08383-x

Keywords: Bay of Bengal, sea surface salinity, ENSO, El Niño, La Niña, Indian Ocean, monsoon, ocean circulation, Climate Dynamics, barrier layer, Indian Ocean Dipole, climate variability

The study, published in the journal Discover Oceans, was led by C. M. Jimna Janardhanan of Cochin University of Science and Technology together with P. Anand and R. P. Raju of India’s Naval Physical and Oceanographic Laboratory, along with colleagues from the National Institute of Oceanography and Kerala University of Fisheries and Ocean Studies. Using two decades of temperature and salinity measurements spanning 2000 to 2020, drawn from the World Ocean Database 2018 and the international Argo float program, the team mapped where inversions form, how strong they become, and what they do to the sound speed structure of the upper ocean.

The North Indian Ocean is uniquely suited to producing these inversions. It is bounded to the west by the Arabian Sea and to the east by the Bay of Bengal, and its upper layers are transformed each year by the monsoon cycle. During the southwest monsoon from June to September, and again in the early post-monsoon period, enormous volumes of freshwater pour into the basin from monsoonal rainfall and from great river systems, chief among them the Ganges–Brahmaputra and the Irrawaddy. This freshwater dilutes the surface, creating intense salinity stratification: light, fresh water floats atop dense, salty water and resists mixing.

That stratification has a remarkable consequence. It isolates the shallow surface mixed layer from the deeper thermocline, forming what oceanographers call a barrier layer. When winter cooling strips heat from the surface, the isolated surface water cools faster than the water trapped just beneath it, and a warm layer becomes sandwiched between cool surface water and the cold thermocline below. This is the surface layer temperature inversion. The research team found the most dramatic examples in the northern Bay of Bengal, where the temperature difference across the inversion layer reaches about 5 degrees Celsius, and in the South Eastern Arabian Sea, where it approaches 3 degrees. Such strong inversions, the authors note, are unique to the North Indian Ocean; comparable shallow inversions observed in the Pacific, such as in the Oyashio–Kuroshio frontal zone, do not reach these gradients.

The seasonal evolution follows a clear rhythm. In October, localized inversions first appear in the Krishna–Godavari basin of the western Bay of Bengal, with temperature differences up to 2 degrees Celsius and inversion layer thicknesses of 40 to 50 meters. By November, inversions spread to the northeastern bay, hugging the coast. December brings organized, basin-scale inversions to both the northern Bay of Bengal and the South Eastern Arabian Sea, with temperature differences of roughly 5 and 3 degrees respectively and layer thicknesses of 20 to 80 meters. January sees inversions across nearly the entire bay, often farther offshore, before the pattern retreats and weakens through February and March.

What makes this study distinctive is its classification scheme. Rather than simply cataloging inversions, the researchers grouped ten representative stations across the basin into six inversion types according to the dominant physical mechanisms driving them. Four processes can create an inversion: net heat loss from the sea surface, low-salinity river water influx, advection of cold low-salinity water over warmer salty water, and penetrative solar radiation that deposits heat below the surface. In the northern and eastern Arabian Sea, surface heat loss alone does the work. In the northeastern Arabian Sea, heat loss combines with runoff from the Indus River. In the South Eastern Arabian Sea, inversions form when the East India Coastal Current carries cold, fresh water from the Bay of Bengal over the warm, saline local waters, aided by solar radiation. Along India’s east coast, heat loss, river runoff, and advection all conspire. In the head of the Bay of Bengal, the immense freshwater discharge of the Ganges–Brahmaputra and Irrawaddy systems dominates, producing an intensely stratified surface layer shallower than 10 meters.

The acoustic payoff comes from how these structures reshape the sound speed profile. Because sound speed in seawater increases with temperature, pressure, and salinity, a warm inversion layer can deepen the sonic layer depth, the depth to which sound speed increases near the surface and within which acoustic energy becomes trapped in a surface duct. Using the UNESCO equation of state for sound speed and a ray-based propagation model called cTraceo at a frequency of 3000 hertz, typical of anti-submarine warfare sonars, the team simulated transmission loss with and without inversions at each station, placing the sound source both inside the inversion layer at 10 meters depth and below it, over a sand-silt-clay seabed.

The results split cleanly along mechanistic lines. Where river water influx is not the dominant cause, inversions deepen the sonic layer and extend its reach. At stations representing inversion types I, III, IV, and VI, transmission loss remained below 80 decibels uniformly from the surface down to 50 meters out to ranges of 20 kilometers or more during inversion conditions, whereas in non-inversion conditions the same threshold was breached in patches beyond just 5 kilometers. In other words, the inversion turns the upper ocean into an efficient acoustic waveguide. Where freshwater dominates, however, the opposite occurs: intense haline stratification prevents the sonic layer from deepening, it shoals, and sound propagates less effectively. At the northeastern Arabian Sea station, the region with transmission loss below 60 decibels shrank from 10 kilometers in non-inversion conditions to 5 kilometers during inversion; at the freshwater-dominated Bay of Bengal stations, the well-illuminated depth shoaled from 60 meters to about 30 meters. When the acoustic source sits below the inversion layer, none of these changes matter much, because the duct lies out of reach.

The researchers also quantified the duct’s behavior through its cut-off frequency and limiting ray angle, parameters that determine which sound frequencies get trapped and which ray paths escape. Notably, the cut-off frequency decreased during inversions at stations near India’s east coast and Sri Lanka, where advection and heat loss dominate, broadening the range of frequencies that can duct. Because diffraction leakage from a 50-meter surface duct is negligible at 3000 hertz, the ray model’s conclusions are robust at operational sonar frequencies, though the authors caution that at lower frequencies, below the cut-off, ducting fails entirely and inversion-driven variability becomes irrelevant.

The implications reach beyond naval acoustics. Any system that relies on predictable underwater sound, from tsunami early warning networks to acoustic tomography of ocean heat content, must contend with the seasonal emergence and decay of these warm layers across the North Indian Ocean. The authors emphasize that their mechanism assignments rest on climatological data and previous process studies, and that profile-specific attribution would require concurrent observations and numerical simulations. Their proposed next step is an integrated ocean-acoustic modeling framework capable of capturing how inversions steer sound in three dimensions. For now, the message is clear: every winter, a hidden thermal architecture spreads across the northern Indian Ocean, quietly redrawing the map of where sound can and cannot travel, and anyone listening beneath the waves must read that map to be heard.

Subject of Research: Surface layer temperature inversions in the North Indian Ocean and their effects on underwater acoustic propagation

Article Title: Distribution of surface layer temperature inversion in the North Indian Ocean and associated acoustic propagation characteristics

Article References: Jimna Janardhanan, C. M., Anand, P., Raju, R. P., Sabu, A. K., Krishnan, A. R. A., Sajeev, R., & Thadathil, P. (2026). Distribution of surface layer temperature inversion in the North Indian Ocean and associated acoustic propagation characteristics. Discover Oceans, 3(1), Article 50. https://doi.org/10.1007/s44289-026-00163-z

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00163-z

Keywords: surface layer temperature inversion, North Indian Ocean, Bay of Bengal, Arabian Sea, underwater acoustics, sonic layer depth, surface duct, barrier layer, sonar propagation, monsoon freshwater, sound speed profile, ocean stratification