The Amazon River reigns absolute as the largest river discharge into the ocean, with an annual average of ∼0.2 Sv (1Sv=106 m3s-1) of freshwater entering the northwestern Tropical Atlantic . The offshore propagation of low sea surface salinity (SSS) from the Amazon Plume into the Tropical Atlantic has been observed from drifting buoys and satellite data e.g.,. This propagation is controlled by the seasonality of the Amazon outflow as well as the North Brazil Current NBC;. Influenced by the Amazon Plume, an expanding low-SSS band spreads northwestward in winter and spring e.g.,; in summer and fall, the Amazon waters detach from the slope and loop northeastward with the NBC retroflection e.g.,. As a consequence of the spread of low salinity, a barrier layer driven by Amazon waters occupies a large part of the northwestern Tropical Atlantic e.g.,, with local and large-scale effects on climate e.g., and biogeochemical processes e.g.,.

The strongest density gradients in the ocean are found at the edge of river plumes e.g.,and references therein. From 4–20° N, the Amazon water spreads over the shelf break and intersects the warm and salty NBC, with mesoscale and submesoscale dynamics dictating near-surface motions e.g.,. The sharpest gradients at scales ranging from O (0.1–100) km are generated due to the stirring of the river plume by NBC rings e.g.,. These submesoscale fronts are important for modulating air–sea interactions e.g.,, biogeochemical processes e.g.,, and the energy cascade e.g.,. However, the challenges of observing and simulating small scales have historically hindered the understanding of such complex environments.

In the present study, we use state-of-the-art observations from the EUREC4A-OA campaign and a 1 km resolution numerical simulation to quantify surface thermohaline gradients and characterize frontal regimes in the Amazon Plume region. One objective is to provide a comprehensive picture of the surface gradients and fronts in the Amazon Plume region at different scales. Specifically, we ask: (1) How does the plume modify the scale-dependence and magnitude of density gradients? (2) What is the relative role of temperature and salinity in driving frontogenesis? (3) Do the NBC and its mesoscale dynamics contribute to the spatiotemporal distribution of these fronts? To reach our goals, we revisit in-situ observations from and to analyze density gradients at fine scales. Then, using outputs from a numerical model, we discuss the spatial distribution of fronts and spatio-temporal control mechanisms.

Saildrone USVs – Uncrewed Surface Vehicles navigating with near real-time course correction e.g., – sampled the ∼1500 km long boulevard de tourbillons region off the Amazon shelf during the EUREC4A-OA campaign see saildrones' trajectory in Fig. a. This eddy corridor connects the South and North Atlantic western boundary currents through the NBC rings, which detach from the NBC retroflection and travel toward the Caribbean Sea (Fig. a). In EUREC4A-OA, four of these saildrones were piloted in formation, approximately parallel to each other, equipped with a near-surface Seabird SBE37 Microcat CTD and a 300 kHz RD Instruments ADCP, to adaptively sample across the mesoscale eddies and submesoscale features in this region. The CTD and ADCP were mounted on the saildrones at 0.5 and 2 m below the waterline, respectively. The ADCP first bin was set at 6 m. detail the post processing of the EUREC4A-OA Saildrone data, which resulted in 1 min CTD (nominal accuracy of ±2×10-3 °C and ±3×10-3 mScm-1 for temperature and conductivity, respectively) and 5 min ADCP (±0.5 cms-1) averages. For additional details on the fleet course and in-situ sampling during the campaign, see . More on Saildrone instrumentation and observations from different campaigns are available in e.g., and .

To compare processes inside and outside the Amazon Plume, we initially derive the position of the plume, bounded by the 36 isohaline, from satellite SSS data SMAP – Soil Moisture Active Passive mission;. While other authors use a more conservative approach ; e.g.,, we choose a wider threshold as in , which comprises a larger area under the plume influence e.g.,. We then refine the limits of the plume using the high-resolution saildrone SSS measurements to separate plume and outside (Fig. b). This definition leads to a plume similar to that defined in : our plume region is virtually the same as Coadou-Chaventon's (2024) “Amazon” region, whereas the region outside roughly corresponds to the author's “Upstream” and “Downstream” areas combined. Overall, this definition suffices in isolating the core plume processes from those on the outside, both embedded in warm waters brought by the NBC (Fig. c).

The along-track measurements obtained by the saildrones provide a multi-scale overview of the surface properties, while drones sailing parallel allow the quantification of 2D gradients. We computed the mean values and gradients simultaneously over a mean trajectory between the four saildrones (see inset in Fig. a) from 17 January 2020–23 February 2020. The final resolution of the dataset over the mean trajectory is Δs=1 km (2.5× the saildrones' mean along-track resolution). With data from the four drones, we follow the method in to obtain temperature (T), salinity (S), horizontal velocities u and v, and their corresponding gradients over this trajectory. Briefly, we gather the measurements of 3–4 saildrones (depending on their mutual distances, on average 2.5±1.0 km) within a moving 8 km/120 min-radius circle around the reference trajectory points. The size of the moving circle allows us to avoid temporal variability contaminating the averaged data in each circle, obtaining 138.8±37.9 synoptic measurements for the saildrones, the synoptic scale is ≃40 km; cf. per iteration, i.e., every time the circle moves. At each iteration, we fit a set of linear functions of the form 1ϕ=ϕ0+∂xϕx-x‾+∂yϕy-y‾, which provide the mean ϕ0 values, alongside the zonal and meridional gradients ∂xϕ and ∂yϕ. We solve Eq. () for ϕ=(u,v,T,S) at every point of the mean trajectory by applying a least squares plane fit to all (x,y) observations within the moving circle cf.. These gradients are gradually smoothed by increasing Δs and the radius of the circle proportionally. Our choice is set for the smallest resolved scale under an accepted error threshold.

Saildrone instrumentation errors are documented in . The uncertainty of observed density gradients could also be affected by e.g., variations in the fleet formation, time lag between drones, and dominant winds over along-current sampling. The choice of the mean trajectory resolution (1 km) and the size of the moving circle (8 km) reduce these and other associated errors, particularly in derived quantities. Still, we estimate the errors of the coefficients in Eq. () by calculating the formal least-square uncertainties. For density gradients, we adopt a 95 % confidence interval for temperature and salinity gradients; in other words, we discard gradients smaller than twice their associated error, which removes 7 % of the 1 km-binned data.

The model is CROCO Coastal and Regional Ocean COmmunity model, based on the Regional Oceanic Modeling System;. The simulation belongs to the GIGATL family hereafter GIGATL1 over the Atlantic Ocean with a nominal horizontal resolution of 1 km and 100 terrain-following vertical levels. The simulation uses hourly CFSR Climate Forecast System Reanalysis; atmospheric forcing, a turbulence closure scheme for parameterization of vertical mixing, and SRTM30plus bathymetry . The Amazon River input is a monthly climatology from . Barotropic tidal forcing at the boundaries and tidal potential and self attraction are from TPXO7.2 and GOT99.2b. Boundary conditions are from SODA Simple Ocean Data Assimilation;. GIGATL1 is initialized in July 2007 from the 3 km simulation GIGATL3, which was in turn initialized with SODA in January 2004. GIGATL1 and GIGATL3 share the same configuration except for the horizontal resolution and initial condition. We draw snapshots and time series from 20 months of hourly surface output from the simulated years 2008 and 2009.

We first analyze a model snapshot exhibiting characteristics similar to those observed during the EUREC4A-OA campaign. The modeled plume on 25 March 2008, 6 h partially occupies the 59–47° W, 4.5–15° N region where we conduct our analyses (Fig. a). The chosen snapshot also captures the NBC retroflection next to the plume, a scenario depicted from satellite data during the campaign see inset map in Fig. a and Fig. 1 from. The choice of a single snapshot to sample the plume and outside comes from the idea of minimizing plume-related processes being captured outside and vice-versa, since we use a simple salinity criterion to separate the regions. Rather than obtaining a direct comparison with the campaign (the modeled plume lags one month behind observations; see Appendix B), our focus is to show that the model is able to represent density gradients and T–S relations similar to the observations.

As for the observations, we start by comparing 1-D density differences along multiple zonal transects within the plume and outside, taken at 0.5° longitude and latitude intervals within the 59–47° W; 4.5–14.5° N region (Fig. b). However, contrary to observations, in the model we adopt a varying salinity threshold to define the plume: the 0.25 quantile – one-fourth of the SSS probability distribution, hereafter Q25 – which separates the strongest SSS gradients of the plume from the outside (see Appendix C). This moving threshold helps to capture the plume over a longer period than in the observations, while preserving its seasonal cycle.

The model reproduces the key observational signatures: stronger and more variable density gradients within the plume than outside, salinity dominance of fronts, and net frontogenesis both within the plume and outside. The main limitation is that the contrast between both regions is less pronounced in the simulation, owing to the model's effective resolution smoothing the sharpest submesoscale gradients and blurring the plume boundary. Keeping this in mind, we benefit from the model's spatial and temporal coverage to interpret the dynamical mechanisms behind the observed frontal characteristics.

Our detailed model analyses below begins by focusing on comparisons with observations collected by the saildrones.

At fine scales, the stirring and straining of tracers by mesoscale and submesoscale motions add to the low-frequency dynamics of the large scales e.g.,. In the model, they control the spatial diversity of T–S gradients (Fig. a), maintained by the joint action of the Amazon River plume and the NBC . Their interaction gives rise to all T–S relationships, producing different types of gradients (Fig. a and b between 56–52° W; 8–10° N; see also Figs.  and ). On 25 March, a retroflecting NBC carrying warm waters rolls up a plume filament. Within the core of the impinging plume filament, the dominance of salinity gradients is striking (-π4<Tu<π4; Fig. a). East of the plume, the NBC continues to advect freshwater northward, while the swirling motion associated with NBC rings folds gradients into layered structures characterized by alternating dominance of plume salinity and NBC temperature.

Notably, the scenario shows the formation of an uncompensated front (Tu; Fig. a) at the northern edge of the Amazon plume carried offshore by the NBC. Along the retroflecting current, ΔS>0 surpassed by ΔT>0 sets up a temperature-dominated, uncompensated front edge that delimits the NBC–plume interaction region (Fig. a). Thus, fronts detaching from the near-shore region are unambiguously related to the NBC retroflection stirring the Amazon Plume. Within the core of the plume, in turn, both compensated and uncompensated fronts are now dominated by salinity. These fronts are still associated with the interaction of the NBC and the Amazon Plume, but occur mostly inside the plume (Fig. a).

The frontogenetic tendency of buoyancy (Fig. b) display distinct fronts over three domains, with particular dynamics and different geometry: i.

Shelf fronts: broken-up fronts parallel to shore occupy the core of the plume over the continental shelf. Strong τb of these fronts follow the coastal flow over the shelf and may be broken-up by processes acting on timescales shorter than the flow advection, such as interaction with normal-to-shore tidal currents e.g., and air–sea interactions e.g.,;

Although not quantified here, submesoscale fronts associated with frontogenesis at 1 km scale also play a role in cascading energy from the submeso scale to smaller scales . With a submesoscale resolving simulation, showed net frontogenesis associated with river plumes, with ageostrophic convergence near the surface dominating the dynamics of frontal sharpening. In our region, this process will complete (and likely at some point overcome) the classical frontogenesis driven by the nondivergent mesoscale stirring due to the NBC.

The spatial distribution of Tu shows large areas dominated by salinity and not restricted to the plume. Temperature-dominated gradients also appear, mostly associated with remarkable flow structures driven by the NBC. Over these structures, a succession of compensated and reinforced density gradients appear to be related to the advection and stirring of freshwater filaments by the NBC Fig. ; see also, e.g.,. In the northern part of the domain, surface gradients can be dominated by salinity even 1200 km away from the continental shelf. This relatively low salinity may be the result of older strong discharges still present in the region while slowly mixing with local waters e.g.,. But how does low SSS reach as far as 20° N? Which processes are involved in the spatio-temporal distribution of gradients and how do they relate to density fronts? A Hovmöller diagram in Fig. a illustrates the spatio-temporal evolution (propagation) of surface salinity versus temperature dominance over density gradients. Moreover, a timeseries of pdfs (Fig. b) shows the temporal evolution of compensation and reinforcement on density by T–S gradients within the plume.

To construct Fig. , we use Tu to quantify both the T–S relations and their effects on density gradients. First, we calculate the percentage of salinity dominance in surface gradients (i.e., where |Tu|<π/4), averaged longitudinally between 59–47° W. From 6–18° N, we binned |Tu| in 0.05° intervals over 20 months of hourly-averaged snapshots, at 24 h frequency (Fig. a). Then, since Tu varies linearly, from the interpretation of Tu, it is possible to define the % of compensation or reinforcement in R by interpolating Tu in each quadrant on a 0 %–100 % scale. For every snapshot, we select points where S<Q25 and repeat the computation to obtain daily pdfs.

Between 6–9° N, the prominent seasonality of salinity and temperature dominance reveals a competition between the NBC and the Amazon Plume. In Fig. a, % Sal. is highly correlated with the Amazon Plume forcing r2=0.93; cf.not shown and strongest from April to June. The weak discharge in autumn opens room for the NBC at low latitudes (Fig. a). When the NBC is strongest e.g.,, the region between 6–9° N shows the highest % Temp., up to 70 %. The NBC influence rapidly fades offshore, and a north–south, quasi-stationary (∼80%)% Sal. band appears from April on. Driven by a growing Amazon Plume, a secondary branch of % Sal. begins to expand offshore in late March , at roughly 11–4 kmd-1 (Fig. a), consistent with previous drifter observations trapped within NBC rings e.g.,.

This propagating branch characterizes a “transition zone” (9–15° N) influenced by the advection of salinity gradients by the NBC and its associated mesoscale processes and mean surface currents that drive the plume toward the north e.g.,. Strong fronts can be formed by the stirring of salinity gradients by the NBC retroflection and rings. Mesoscale stirring gives rise to gradients that can quickly evolve to submesoscale fronts by an exponential sharpening of those gradients . Stirring and trapping of the plume by the NBC mesoscale activity could also explain the bifurcation of the % Sal. signal into a direct northward branch and a second branch that breaks off and slowly moves poleward after a virtually stationary period between 9 and 12° N, probably entrained within mesoscale structures Fig. a; e.g.,. Indeed, low SSS anomalies (which drive density gradients in the area) were observed north of the NBC retroflection between June and November e.g.,. Low SSS anomalies in this region may be damped by evaporation e.g.,, although precipitation does not appear to reduce SSS within the plume .

The northernmost part of the domain (15–18° N) shows the most persistent dominance of salinity on density gradients throughout the year. Away from the NBC and its associated (sub)mesoscale features, low SSS from the quasi-stationary branch is slowly mixed locally. A few months later, the secondary branch of the plume arrives. This high salinity dominance remains in the region until the onset of the “main branch” of the following year, and the number of fronts increases substantially in the transition zone (Fig. b). Even with salinity dominance at the surface, the isotropic areas with anisotropic fronts show characteristics of a “submesoscale soup” e.g.,, where fronts are linked to mixed-layer instabilities and the dynamics is fundamentally controlled by the mixed-layer cycle e.g.,.

The ensemble of T–S gradients within the plume exhibits a seasonal cycle in the distribution of compensated and reinforced fronts (Fig. b). An expanding plume in spring is still mostly contained northwest along the coast and creates a SSS gradient from the shelf to the open ocean. Assuming salinity βΔS largely dominates over αΔT (Fig. a), Tu is virtually zero and, in most density gradients, compensation or reinforcement can decrease or intensify them by only about 20 % (although Fig. b does not distinguish salinity or temperature dominance), the rest being essentially driven by salinity Fig. b – months June–August; see also.

In the first half of the year, warmer SST near the shelf presents a negative temperature gradient towards offshore e.g.,, which may reinforce density fronts associated with low SSS from the plume (Fig. b – months February–May). From June to August, the transport by the NBC and its rings increases and pushes the plume to its maximum extent. In late fall and winter (October–December), a shift in the distribution towards compensation indicates the evolution of gradients within the plume. This is the period when compensation mechanisms through air–sea interaction will be most effective within the plume, for differential surface cooling will act due to a mixed-layer depth-dependent heat loss e.g.,. Conversely, a positive heat flux in spring and summer would reinforce the salinity-dominated density gradients to the point where the unbalanced heat uptake leads to dominant temperature gradients on density. However, one must bear in mind that compensation and reinforcing are scale-dependent, so assumptions such as net seasonal heat fluxes involve processes other than mixed layer instabilities and air–sea heat fluxes acting on the mixed-layer depth, SSS, and SST .

In the Mississippi River plume, showed an overall increase in the tails of Tu pdfs during winter. Moreover, the authors show with a Lagrangian framework that, even if an initial state presents a distribution of Tu centered at zero, a preference for compensated gradients arises over time. This shift occurs gradually during autumn, with a clear predominance of compensated gradients at the beginning of wintertime. We recall that our analysis considers only density gradients with SSS lower than Q25, which can introduce a bias toward reinforced gradients: the mixing of “the old plume” usually creates higher SSS, and evolving fronts in this region can be removed from the analysis.

While air–sea interactions are known to induce compensation to the strongest T–S gradients at small scales in the Amazon Plume , facing the gradients galore of the northwestern Tropical Atlantic brings a broader perspective related to the region's peculiar dynamics. Next, we suggest how these dynamics set up some T–S relations observed in the region.

In the previous sections, we showed regions with fronts related to the interaction between the Amazon Plume and the NBC. They are numerous near shore and broken up possibly due to further interactions with high-frequency processes. Offshore, elongated fronts drawn from this near-shore pool align with flow structures. They often come as freshwater filaments advected by the NBC that pierce through a higher salinity background (Fig. a) and roll up into a warm-core NBC ring Fig. b; e.g.,.

From observations, we suggested that the interaction between the Amazon Plume and the NBC could induce a variety of Tu. Indeed, the resulting front sketched in Fig. c shows compensation and reinforcement on opposite sides of the front. This asymmetry arises because the low-salinity filament is embedded in a warmer NBC temperature background. On the warm (inner) side of the filament, the temperature gradient opposes the salinity gradient, producing compensation. On the cooler (outer) side, temperature increases as salinity decreases toward the filament core, so they reinforce each other on the density gradient. The key driver is therefore the sign change in the salinity gradient across the filament, while the temperature gradient remains roughly uniform.

The blowout of T–S gradients in Fig. d and e selects areas where |βΔS|≥0.01 m-1. It further illustrates how salinity and temperature combine to sustain compensation and reinforcement at both sides of the arched filament (Fig. f). Crossing the main filament, temperature gradients do not display a clear change in sign due to somewhat uniform west–east gradients. This pattern was briefly reported by in a cold filament in the Bay of Bengal and appears repeatedly during the formation of NBC rings on our simulation.

If one considers submesoscale fresh filaments developing at the edge of the plume, when considering all T–S gradients this pattern of both compensation and reinforcement shall repeat over a large portion of the domain, yielding the zero-centered distributions of Tu due to the interaction with the plume and the NBC and/or local dynamics offshore. With the region being mainly frontogenetic, the set up of different fronts by the NBC–Amazon Plume interaction (e.g., Figs.  and ) could trigger distinct dynamical responses: while uncompensated fronts are unstable and act on the direct energy cascade and mixing e.g.,, the more stable, (partially) compensated fronts can be strained for longer periods, shaping horizontal transport and maintaining their associated vertical fluxes e.g.,. We call for future work to statistically address these mechanisms and their effects on the overall energy budgets of compensated and reinforced fronts.

An expanding Amazon River plume occupied the northwestern Tropical Atlantic during the EUREC4A-OA campaign as seen in the SSS maps,. As expected for the transition period from low to high discharge e.g.,, the plume was relatively well developed, and saildrones sampled both the region outside and the interior of the plume. We observed the sharp increase in the magnitude of density gradients inside the plume, which translates into strong buoyancy gradients at O (1–10) km. Unsurprisingly, T–S relations show salinity-dominated density fronts within the plume. Perhaps more interestingly, these fronts display nearly equal contributions of compensation and reinforcement by temperature gradients. Moreover, net frontogenesis is observed both outside and within the plume.

Using a 1 km resolution model, we explored how both the Tu and τb observed during EUREC4A-OA are spatially arranged when the Amazon Plume and the NBC interact. The modeled region shows greater variability and extreme fronts in the plume, although the separation between plume and outside is less distinct in the simulation. Three different frontal regimes occur in the domain, one within the plume over the continental shelf, a second associated with stirring of the plume by the NBC, and a third offshore, typical of mixed-layer controlled submesoscale activity. Qualitatively detailing the interaction between the Amazon Plume and the NBC, we show that the mesoscale stirring of the plume by the NBC produces a patchy distribution of Tu, particularly during the formation of NBC rings, where freshwater filaments are rolled up into a NBC temperature background, generating compensated density fronts in the inner portion of the filament and reinforced fronts on the outer rim.

In the northwestern Tropical Atlantic, salinity gradients representative of sharp density gradients dominate the region up to 9° N driven by the seasonality of the Amazon runoff. When the river discharge is weakest and the NBC charges into the region, temperature gradients dominate. Stirring and advection of the plume by the NBC shape the fronts in the transition region from 9–15° N. North of 15° N, the distribution of surface gradients is dominated by salinity all year long, even though in this region their dynamics are most likely to follow the temperature-driven seasonality of submesoscale turbulence e.g.,. The distribution of frontogenetic tendency suggests the northwestern Tropical Atlantic as a region of predominant frontogenesis, with both compensated and uncompensated density fronts mostly related to mesoscale low-salinity filaments stirred from the Amazon Plume by the NBC and submesoscale structures developing at the plume edge, with a clear seasonal cycle of compensation and reinforcement linked to the plume size and the NBC strength.

The frontal seascape described here, which is salinity-dominated, seasonally modulated, with coexisting compensated and reinforced fronts, has direct implications beyond physical oceanography. Compensated fronts, being more stable, sustain strong horizontal tracer gradients for longer periods, potentially influencing nutrient supply, phytoplankton patchiness e.g.,, and ecosystem connectivity e.g.,. Reinforced fronts, being unstable, drive energetic downwelling that may export surface-enriched Amazon water below the pycnocline on short timescales e.g.,. Distinguishing these two populations of fronts may therefore be essential for understanding the plume's role in regional carbon and nitrogen budgets.

As numerical models continue to improve, they rely on data acquisition at high resolution to tackle current discrepancies and parameterizations for dynamics at small scales. The unprecedented datasets from campaigns such as EUREC4A-OA and other uncrewed vehicles in the global ocean meet them in a review by, together with state-of-the-art numerical modeling provide statistical confidence in studying derived quantities at O(1km). Our current effort is to explore the dynamics depicted from EUREC4A-OA small-scale observations to help improve the fine-scale ocean modeling of the Amazon Plume outflowing into the Atlantic. Such datasets need to be continuously exploited alongside new observational and modeling efforts to improve our assessment of the Amazon Plume region.