INTRODUCTION

The dependence of industry and life with the use of oil and gas is very significant; the demand for these two products and their derivatives is extremely high (IEA, 2009; EIA, 2011; Lara, 2014; Prates et al., 2006). Large oil and gas extraction corporations are increasingly expanding into new areas, exploring and exploiting more and more natural gas and oil reserves in the deep ocean. These exploration and exploitation processes are subject to risk of accidents (O'Rourke and Connolly, 2003; Skogdalen et al., 2011), which can endanger human life and contaminate the ocean and the littoral.

The exploration and production of oil at sea is a risk-filled activity (Skogdalen and Vinnem, 2012; Small et al., 2014), as well its research (Abimbola et al., 2014; Neff et al., 1987). It requires dangerous tasks such as drilling rocks in ultra-deep regions, facing very high pressures and handling large volumes of oil and gas. This activity analyzes the large volume of information generated in the initial stages of the investigation, gathering a reasonable knowledge about the depth, thickness and behavior of existing rock layers in a sedimentary basin and the hydrodynamics of the region. Based on this knowledge, the best places to drill in the basin are chosen.

In recent decades, the environmental impacts of oil spills have occurred throughout the world and marine flora and fauna have been frequently submitted to environmental impacts caused by these events (Teal and Howarth, 1984; Ugochukwu and Ertel, 2008). Contamination of marine and coastal environments by oil has been a concern for environmentalists around the world (Mendelssohn et al., 2012). Oil residues and by-products can cause serious consequences for human life (Charrouf and Guillaume, 2008), coastal ecosystems and socioeconomic activities (de Andrade et al., 2010), and has been the subject of many debates. This type of impact causes real environmental catastrophes, with incalculable and often irreversible damage to the environment. Human activities, such as fishing and tourism are also compromised (Garza-Gil et al., 2006), causing great economic damages.

Finally, there has been a progressive decrease in the number of accidents and in the volume of oil discharged internationally (ITOPF, 2016). The reduction of accidents is associated with greater control and care in operations involving the exploration, exploitation, transport and storage of oil, which reflects an increase in the level of environmental responsibility of the society.

Environmental sensitivity to oil spills has been studied some decades ago by Gundlach and Hayes (1978). These authors constructed sensitivity maps containing three types of spatial information: the classification of the sensitivity of environments, biological resources and human resources of recreational, subsistence or commercial value. Thus, the classification of
the environment is made according to its physical characteristics, oil permanence and conditions of cleaning and removal.

The Environmental Sensitivity Charts to Oil Spills are essential tools and a primary source of information for contingency planning and for the implementation of response actions to oil pollution incidents, allowing the identification of environments with protection priority and possible areas of sacrifice, allowing the correct allocation of resources and the proper mobilization of containment and cleaning equipment (Carvalho and Gherardi, 2003; Michel et al., 1978).

The impacts of an oil spill and blowout can be minimized if locations most sensitive to contact with the oil can be protected. The previous knowledge of oil spills and explosions under the sea can help in the distribution of available resources to give a more effective response to these types of accidents. The understanding of oceanographic processes in the oil and gas blowouts is fundamental for its prevention and mitigation. In case of oil and gas blowouts, companies would have contingency planning and mitigation to reduce the impact on the environment.

The increase of the oil and gas production in marine environment verified above all in the mid-year 2000 (E&P Forum/UNEP, 1997; U.S. DOE, 1999; U.S. EIA, 1998; U.S. NOIA, 2005) brought the need of more precise models for evaluation of the transport and dispersion of these mixtures in the oceans, as well as their effects on the environment. Initially, Fannelop and Sjoen (1980) focused on the case of the evolution of a single gas plume in the ocean. These authors proposed an approach of simplified analytical modeling, with non-dimensional solutions, and applicable in cases of spills in shallow waters. In this model, the gas expansion all through the water column was estimated considering the classical theory of ideal gas. As a result, the solution for this model brings the dimensions of the cone of gas formed along the water column (starting from the ocean floor), and in consequence, the diameter of the gas plume in the surface.

Meanwhile, the Fannelop and Sjoen (1980) approach does not consider horizontal advection of gas through the currents. Some advance on this initial idea was proposed by Friedl and Fanneløp (2000), when routines were added that considered the elevation in the surface of the sea provoked by the reaching of the gas to the surface (fountain effect, Fig. 1 in Friedl and Fanneløp (2000)). In deeper waters, for instance, the gas cannot be treated as an ideal mixture (Chen and Yapa, 2001), and new interaction processes among the two means gas and liquid was observed, mainly when the local pressure is very different to the atmospheric pressure. In this new generation of mathematical models, created to represent simultaneously oil and gas plumes in marine environment, two more important physiochemical processes were considered, formation of gas hydrate (Chen and Yapa, 2001; Topham, 1984) and gas dissolution (Johansen,
2003; Zheng and Yapa, 2002) in seawater. In shallow waters, the gas dissolution is neglectable (Johansen, 2000).

However, in deep waters, the travel time for the gas through the water column is longer, also rising the dysphasic flow time. Adding to this the fact that the gas solubility increases with the environmental pressure (Johansen, 2000), it is observed frequently that under natural conditions of low temperature and high pressure. The gas tends to form hydrates, which significantly change the ascension velocity of the gaseous plume along the water column (Chen and Yapa, 2001; Johansen, 2003; Topham, 1984). The first models plume evolution of gas developed did not foresee in their equations these two mechanisms (dissolution and hydration), limiting itself to its application in deeper ocean areas (Chen and Yapa, 2001). Yapa and Zheng (1997), for instance, proposed a set of equations to predict the space-time evolution of plumes formed by spills in intermediate waters, considering only the advective transport of the gas as function of its characteristics (volumetric composition) and the environmental thermodynamic conditions (temperature fields, salinity, pressure and density). In the 90's these important mechanisms were introduced into the modeling of a plume of gas in the marine environment (Chen and Yapa, 2001; Reed et al., 1999; Yapa et al., 2001; Zheng and Yapa, 2000).

In this work will be analyzed the dilution capacity of the oil/gas effluents overflow in a blowout event as a result of the simultaneous effects of sea currents, winds and surface and subsurface thermodynamic processes. Three different locations were selected at the continental shelf of Brazil, near the Amazon River mouth and adjacent oceanic areas. This analysis will be held through mathematical modeling of hydrodynamics and gas/oil dispersion plumes in the study region.

## STUDY AREA

The Amazon Continental Shelf is a high priority area for the conservation of biodiversity (dos Santos et al., 2016; da Silva Junior and Magrini, 2014) and the Amazon River mouth represents the limit of the distribution of several sponges, lobster, stony corals and snapper and others shallow water fishes, among other groups of coastal and reef organisms (Moura et al., 2016). Our region of study is framed in 52°-40°W/4°S-10°N with 0.25° of resolution, covering an area of 9464500 km2. Fig. 1 shows the bathymetry with the positioning of points P1, P2 and P3 over the continental shelf in the northern coast of Brazil and the location of the deepwater oil exploration blocks.
<figure><img src="image_42.png" /></figure>

**Figure 1.** The bathymetry and distribution of the slope in the continental shelf off northern Brazil are represented the isobaths of 160, 3000 and 4000 m in black continuous lines. The green rectangle represents the Amazon mouth basin and the magenta one represents Pará-Maranhão-Barreirinhas basin. The oil and gas exploration blocks are represented by black polygons. The location of P1, P2 and P3 is represented by blue points above of exploration blocks where P1, P2 and P3 are placed - block FZA-M-88 (P1), block PAMA-M-337 (P2) and block BAR-M-21 (P3). The coral reef appears in red color.

The most important oceanic currents around the Amazon region are North Brazil Current (NBC), North Equatorial Countercurrent (NECC) and South Equatorial Current (SEC). According to Stramma and England (1999) the SEC is divided into four branches: the Southern South Equatorial Current (sSEC), the Central South Equatorial Current (cSEC), the northern South Equatorial Current (nSEC) and equatorial South Equatorial Current (eSEC). The eSEC and cSEC give origin to NBC; the latter is also fed by nSEC. At each season the SEC has a strong flow the
westwards (about 0.3 ms⁻¹) near the equator and weaker (0.1-0.15 ms⁻¹) in a broad band south of 10°S (Peterson and Stramma, 1991).

NBC flows from northwestward (Lentz, 1995) along the northeastern continental margin of Brazil as a coastal current developing an retroflexion in rings form (Dessier and Donguy, 1994; Fratantoni et al., 1995; Goes et al., 2005; Johns et al., 1990; da Silveira et al., 2000). This makes a turn to the east driven by the wind and feeds the NECC, the NBC reaches speeds of 0.75-1.00 ms⁻¹ (Arnault et al., 1999). During the summer, in response to a shift towards the north of the trade winds, the NECC forms eastwards, intensifying significantly during the winter (Grodsky and Carton, 2002; Richardson and Walsh, 1986). The NECC is located between 3-10°S and is considered as the northern boundary for SEC (Peterson and Stramma, 1991). The nSEC is a water source that reinforces the NECC (Stramma and England, 1999). The northern limit of NECC when present is the North Equatorial Current (NEC). The mean speed eastward of NECC is 0.42 ms⁻¹ (Fratantoni, 2001).

## THE REGIONAL OCEAN MODELING SYSTEM (ROMS)

The ROMS model was used to reproduce climatological and spatial-time variability of oceanic circulation and thermohaline variability in the study area. ROMS is an open source programming, which is effectively created by an extensive group of engineers and researchers, with more than 400000 lines of FORTRAN code, coordinating primitive conditions in a rotational arrangement of free surface, utilizing the Boussinesq approximation, the hydrostatic guess and the adjust of the vertical force (Panzer et al., 2013; Shchepetkin and McWilliams, 2005; Song and Haidvogel, 1994). This was adjusted to various geographic areas of the world where great outcomes were gotten (Haidvogel et al., 2000; Penven et al., 2000; Silva et al., 2009).

The area used for the ROMS model simulations is framed in 60.5°-14.5°W/15.5°S-24.5°N. The bathymetry grid has 183 x 159 nodes with 32 levels in the vertical, of which 20 are in the upper 500 m depth. The ETOPO2 (Smith and Sandwell, 1997) topography database was used in the vertical discretization with 2 min of horizontal resolution. The surface forcings were obtained from monthly mean climatology of Comprehensive Ocean Atmosphere Data Set (COADS05) (Da Silva et al., 1994) with 0.5° of horizontal resolution. Tides are an important process in mixing the river freshwater plumes with the open ocean and are obtained from the TPXO7 (Egbert and Erofeeva, 2002; Egbert et al., 1994), which has altimetry data from several satellites to improve the accuracy of the results obtained through the hydrodynamic model (D'Onofrio et al., 2012; Wang, 2004). All lateral boundaries are considered open. For the lateral boundary and initial conditions all variables were obtained from the monthly mean of World Ocean Atlas 2009 (WOA2009) (Antonov et al., 2010; Locarnini et al., 2010) with horizontal resolution of 1°. The monthly mean river discharge was obtained from Obidos and Tucurui gauge stations (Dai and
Trenberth, 2002), while the monthly climatology of sea surface temperature (SST) in the rivers discharge points was also obtained from WOA2009.

We have carried out a numerical experiment with ROMS model to characterize the hydrodynamic: salinity, temperature, and currents on the western tropical north Atlantic (WTNA) taking into account the discharges of the Amazon and Pará Rivers. These rivers release freshwater into the WTNA, giving the geographical configuration of Amazon River Delta. There are four inputs from the river to WTNA: Canal do Norte, Baía de Santa Rosa, Canal Perigoso and Canal do Jurupari and they were placed in four cells of the grid. Considering the width of each channel the contribution was calculated for each one with the same monthly temperature distribution for the four inputs nodes in the Amazon River Delta and different in the input node of the Pará River. We run ROMS model for 11 years but our analysis are restricted to the last 3 years of simulation.

The model validation of sea surface salinity (SSS), SST, potential salinity and temperature on the surface was implemented using the Simple Ocean Data Assimilation (SODA) version 2.2.4 (Carton et al., 2000). This dataset presents a spatial resolution of 0.5° and was used for a period of 20 years (1991 to 2010).

## THE GAS_DOCEAN MODEL

Pollutant dispersion models have been developed to the ocean, combining analytical expressions, Eulerian or Lagrangian formalism (De Dominicis et al., 2013). One of the advantages of these models is their flexibility to assimilate different types of dataset, as the case with the output data of other models, such as the ROMS model (North et al., 2011) or even using in situ current and thermohaline data (Leite et al., 2014).

In this study the evolution of the oil/gas plumes through the water column was estimated based on the theory proposed by Chen and Yapa (2004), Fannellop and Sjoen (1980), Friedl and Fanneløp (2000), Yapa et al., (2001), Zheng and Yapa (1998), Zheng and Yapa (2000) and Zheng and Yapa (2002). These studies were used for the elaboration of computational routines that represent the simultaneous transport of oil and natural gas. This model was previously used by Leite et al. (2014) in the evaluation of natural gas plumes that resulted from an eventual blowout in the oceanic floor. The new implementations to the GAS_DOCEAN code (version 3.0) allow the simulations of simultaneous evolution of the plumes of oil and natural gas. The adopted methodology follows the theoretical-experimental approach proposed by Chen and Yapa (2004).

High pressure and temperature may induce the formation of hydrates starting from lighter hydrocarbon chains (Chen and Yapa, 2004; Zheng and Yapa, 2002). With the formation of hydrates, the upwelling time for the gas along the water column will be incremented, since
their presence will induce the reduction of ascending velocity of the gas plume (Chen and Yapa, 2001; Johansen, 2003; Topham, 1984). The physical-chemical characteristics of the oil, necessary for simulations, were then calculated from each depth through parameterized equations inserted in the mathematical model, and taken from experimental curves obtained in laboratory.

In order to estimate time step (Δt), Lee and Cheung (1990) suggested Eq. 1 and was later used by Dasanayaka and Yapa (2009), Premathilake et al. (2016) and Yapa and Zheng (1997) obtaining good results

$$ \Delta t = 0.1 \frac{b_0}{|V_0|} \qquad /1/ $$

where Δt is time step in s, b₀ is ratio of initial control-volume in m and V₀ is initial blowout velocity in ms⁻¹.

## SCENARIOS FOR THE SIMULATION OF OIL AND GAS PLUMES

The points P1, P2 and P3 (Fig. 1) were selected according to the position of the oil and gas exploration blocks in the north continental shelf of Brazil and the analysis of oil sensitivity charts (SAO charts) of the areas subject to oil spill and blowouts in the region comprising the continental shelves of Amazon and Pará-Maranhão-Barreirinhas basins. P1 is located at 50°W, 5.25°N and belongs to the FZA-M-88 exploration block; P2 at 44.5°W, 0.5°N, corresponds to the PAMA-M-337 exploration block and P3 is located in BAR-M-21 exploration block at 42.75°W, 1°S (ANP, 2017). The parameters used in the simulations of oil/gas plumes are those reported by Chen and Yapa (2004) with the exception of those shown in the Tab. 1.

<table><caption>**Table 1.** Parameterization of GAS_DOCEAN.</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Ratio between the bubble core width and the buoyant jet diameter</td><td>0.8</td></tr><tr><td>Gas density</td><td>5 x 10<sup>-3</sup> kg m<sup>-3</sup></td></tr><tr><td>Initial shear entrainment coefficient</td><td>0.083</td></tr><tr><td>Gas flow</td><td>50 kg s<sup>-1</sup></td></tr><tr><td>Oil flow</td><td>100 kg s<sup>-1</sup></td></tr><tr><td>Mass transfer coefficient of dissolution</td><td>10<sup>-9</sup> m s<sup>-1</sup></td></tr></tbody></table>

## OCEAN CIRCULATION AND THERMOHALINE STRUCTURE
The main variables influencing the behavior of oil spills and blowouts are temperature, salinity and marine currents. The vertical profiles of the speed of the current are corroborated using the SODA dataset, with spatial resolution of 0.25°. Also the average values of the zonal component are compared in region Western North Equatorial Countercurrent (WNECC) framed at (50°-40°W/5°-8°N), whose average values were reported by Richardson and Reverdin (1987).

Fig. 2 depicts that the numerical mean seasonal currents are able to reproduce the advection patterns already reported by many authors (Fratantoni, 2001; Grodsky and Carton, 2002; Haidvogel et al., 2000; Malanotte-Rizzoli et al., 2000), The fundamental actor in the area ins the NBC, which forms large anticyclonic rings shed by the current of the northwestern whirl along the Norte Brazilian continental shelf.

The Amazon plume is transported along the Brazilian continental shelf by the NBC to the northwest during December-January-February (DJF) (Fig. 2), increasing its transportation in the months March-April-May (MAM) and reaching its maximum during June-July-August (JJA) (Muller-Krager et al., 1988; Salisbury et al., 2011). When NBC retroflexion occurs, it is transported eastward by the NECC during the period of September-October-November (SON) (Coles et al., 2013; Foltz et al., 2015; Moon and Song, 2014; Muller-Krager et al., 1988).

The NECC is the main current that transports the Amazon plume eastward. Varona et al. (unpublished data) compared the zonal component ($u$) differences between ROMS model results and Surface Currents from Diagnostic model (Maximenko and Hafner, 2010) in an area associated to the east of the NECC, located at 48°-41°W/2°-8°N (EPLUME). Concluding that values of $u$ indicate a gradually increasing from 0.2 $ms^{-1}$ to 0.3 $ms^{-1}$, from JJA to SON, between 4.5° and 9.5°N, finding the maximum bias (0.2 $ms^{-1}$) between 5.5° and 6.5°N in July and between 4° and 5°N in October. The mean bias is 0.1 $ms^{-1}$. These results reveal that ROMS model represents quite well the dynamics of the NECC variability. The average of the zonal component in WNECC is 0.384 $ms^{-1}$ during SON, according to reported by Richardson and Reverdin (1987), being 0.410 $ms^{-1}$.
<figure><img src="image_43.png" /><figcaption>**Figure 2.** ROMS model outputs of the seasonal marine currents during the boreal winter (DJF-December, January, February), spring (MAM-March, April, May), summer (JJA-June, July, August), and fall (SON-September, October, November). The magenta polygon represents the Amazon River plume.</figcaption></figure>

Fig. 3 shows the comparison of SST between the ROMS model and SODA dataset. SST presents higher temperatures than the SODA dataset in the area of the NBC retroflection, mainly during SON to DJF and in the NECC area. The cause of this large seasonal variation is probably a dynamic adjustment due to changes in the wind forcing over the tropical Atlantic and both local and remote wind stress that may play an important role in the SST variability (Bourles et al., 1999; Johns et al., 1998; Sharma et al., 2009). Tab. 2 summarizes the main results of simulated SST and SODA dataset, evidencing greater difference in DJF (1.27°C), when the plume feeds the NECC. The ROMS model is well balanced in JJA.
<figure><img src="image_44.png" /><figcaption>**Figure 3.** Mean seasonal cycle of the difference between the SST (°C) of the ROMS model and the SODA dataset During the boreal winter (DJF-December, January, February), spring (MAM-March, April, May), summer (JJA-June, July, August), and fall (SON-September, October, November).</figcaption></figure>

<table><caption>**Table 2.** Seasonal comparison of salinity and temperature at surface between the ROMS model and SODA dataset in EPLUME area. Mean value ± standard deviation.</caption><thead><tr><th rowspan="2"></th><th colspan="2">SSS</th><th colspan="2">SST</th></tr><tr><th>SODA (psu)</th><th>ROMS (psu)</th><th>SODA (°C)</th><th>ROMS (°C)</th></tr></thead><tbody><tr><th>DJF</th><td>35.97 ± 0.02</td><td>35.89 ± 0.06</td><td>27.43 ± 0.05</td><td>28.35 ± 0.15</td></tr><tr><th>MAM</th><td>35.85 ± 0.09</td><td>36.02 ± 0.03</td><td>27.41 ± 0.07</td><td>27.72 ± 0.14</td></tr><tr><th>JJA</th><td>35.32 ± 0.09</td><td>35.66 ± 0.11</td><td>27.95 ± 0.02</td><td>28.43 ± 0.09</td></tr><tr><th>SON</th><td>35.54 ± 0.06</td><td>35.41 ± 0.10</td><td>28.01 ± 0.03</td><td>28.99 ± 0.09</td></tr></tbody></table>
The difference of sea surface salinities between the ROMS model and SODA dataset is shown in Fig. 4. Mean simulated SSS presents lower salinities than SODA dataset in the area of the NBC retroreflection mainly during MAM. In the months of SON, lower values of SSS follow the NECC.

<figure><img src="image_45.png" /></figure>

**Figure 4.** Mean seasonal cycle of the difference between the SSS (psu) of the ROMS model and the SODA dataset During the boreal winter (DJF-December, January, February), spring (MAM-March, April, May), summer (JJA-June, July, August), and fall (SON-September, October, November).

Tab. 2 above also shows a summary of the mean seasonal salinity cycles for the EPLUME area. The maximum difference observed was in JJA (0.34 psu) and the minimum difference was in DJF (0.08 psu). The ROMS model overestimates the SST, with DJF period being the worst-agreement and the MAM, the best one.

Fig. 5(a) shows the vertical profiles of the temperature at point P1 of the SODA dataset and the ROMS model in the DJF and JJA periods. The vertical profiles of the temperature are
better adjusted below 300 m depth in both periods. In depths above 300 m the ROMS model overestimates the temperature and the main differences range between 0.7 to 1.7°C. In JJA period there is a maximum difference of 0.04 ms⁻¹ below 300 m and above this depth, the average difference was 0.17 ms⁻¹ with a maximum difference of 0.21 ms⁻¹ from 150 to 180 m depth (Fig. 5(b)). The profile of the current speed is well adjusted below the 300 m depth in the DJF period. The maximum difference in the DJF period was 0.18 ms⁻¹ on the surface.

<figure><img src="image_46.png" /><figcaption>**Figure 5.** (a) Temperature and (b) current speed vertical profiles in DJF (December-January-February) and JJA (June-July-August) periods for SODA and ROMS at point P1.</figcaption></figure>

In the north coast of Brazil, the continental shelf extends from 2 to 3° from the coast, followed by the continental slope with an approximate width of 1° varying the depths from 500 to 1900 m depth. The numerical simulation of oil/gas blowouts were located at 3 points of this continental slope.

Fig. 6(a) shows the mean seasonal velocity of the marine current, averaged between the surface and 100 m depth. The most intense currents form a band approximately wide and confined to the coastline, corresponds to the NBC. This current is more intense in the winter especially over the northwest part of the coast where it turns toward the east (in correspondence with the NBC retroflection), reaching speeds higher than 0.9 ms⁻¹. In the spring, we found the less intense currents of the whole year with maximum values eastward of the Amazon River mouth, reaching up to 0.7 ms⁻¹. In the summer, the currents in the NBC
intensify vary from 0.5 ms⁻¹ to speeds higher than 0.9 ms⁻¹. In the autumn, as in the winter, the turn of the current to the east appears, better defining the NBC retroflection. Higher speeds are found along the coast more to the northwest and in the NBC retroflection, oscillating the current speed from 0.5 ms⁻¹ to speeds higher than 0.9 ms⁻¹.

<figure><img src="image_47.png" /></figure>

**Figure 6.** ROMS output of mean seasonal cycle of the currents measured (a) between the surface and 100 m depth (b) between 1000 and 1200 m depth during the winter (DJF-December, January, February), spring (MAM-March, April, May), summer (JJA-June, July, August), and fall (SON-September, October, November). The centers of the red circle represent the positions of the simulation points P1, P2 and P3.

The center of the red circles represents the positions of the P1, P2 and P3 simulation points. In winter the average speed is 0.25 ms⁻¹, at P1, extremely low (0.01 ms⁻¹) at P2 and it reaches 0.15 ms⁻¹ at P3. In the spring, we find the lowest speeds of the year at P1, P2 and P3, oscillating between 0.01 and 0.25 ms⁻¹. In the summer and in the autumn at P2 and P3, current speeds reach the minimum value (0.01–0.15 ms⁻¹), increasing at P1 with respect to spring.

Fig. 6(b) shows the mean seasonal behavior of marine currents between 1000 and 1200 m depth. In general, at these depths the currents are very low, oscillating their speed between 0.01 and 0.15 ms⁻¹. These currents are slightly less intense in the spring and a little more intense in the fall, mainly above 4°N and between 47 and 50°W; and between 0.5-2°N and 42-45°W, reaching 0.15 ms⁻¹, moving this last pattern towards the southeast in the winter.
In the winter we find speeds of 0.15 ms⁻¹ at P3 and between 0.01–0.03 ms⁻¹ at P1 and P2. The least intense currents are in the spring at all 3 points (0.03 – 0.04 ms⁻¹). Similar speeds are found at P2 and P3 in the summer, increasing slightly at P1 to 0.06 ms⁻¹. In the autumn, speeds at P3 do not differ from the summer, being somewhat lower at P1 and reaching up to 0.15 ms⁻¹ at P2.

## OIL AND GAS PLUMES EVOLUTION

Fig. 7 shows the monthly evolution of oil/gas plumes at P1. During the entire evaluated period, the plumes were type 3, with exception to September, where they reached the maximum diameter below 1000 m depth. Above this level, the diameter of the plumes began to decrease gradually until reaches the surface, oscillating between 54–76.5 m. The plumes reached the largest diameter in August, November and December and the lowest diameter was observed between May–July (Tab. 3).

<table><caption>Table 3. Monthly diameter and displacement of oil/gas plumes at points P1, P2 and P3.</caption><thead><tr><th rowspan="2"></th><th colspan="3">P1</th><th colspan="3">P2</th><th colspan="3">P3</th></tr><tr><th>Mean diameter (m)</th><th>Displacement (m)</th><th>Evolution Time (hours)</th><th>Mean Diameter (m)</th><th>Displacement (m)</th><th>Evolution Time (hours)</th><th>Mean Diameter (m)</th><th>Displacement (m)</th><th>Evolution Time (hours)</th></tr></thead><tbody><tr><td>Jan</td><td>56.4</td><td>(0.71, -0.70)</td><td>8.04</td><td>75.5</td><td>(-0.30, 0.20)</td><td>7.26</td><td>58.9</td><td>(0.37, -0.32)</td><td>6.30</td></tr><tr><td>Feb</td><td>65.3</td><td>(0.50, -0.45)</td><td>7.97</td><td>60.1</td><td>(-0.35, 0.26)</td><td>7.25</td><td>58.7</td><td>(-0.13, 0.22)</td><td>6.30</td></tr><tr><td>Mar</td><td>62.1</td><td>(0.71, -0.71)</td><td>8.06</td><td>54.1</td><td>(-0.33, 0.24)</td><td>7.25</td><td>58.7</td><td>(-0.09, 0.23)</td><td>6.30</td></tr><tr><td>Apr</td><td>63.3</td><td>(0.36, -0.36)</td><td>7.96</td><td>58.4</td><td>(0.14, -0.15)</td><td>7.26</td><td>58.7</td><td>(-0.19, 0.26)</td><td>6.30</td></tr><tr><td>May</td><td>54.5</td><td>(0.69, -0.73)</td><td>8.05</td><td>60.7</td><td>(-0.10, 0.15)</td><td>7.25</td><td>58.8</td><td>(0.20, -0.18)</td><td>6.30</td></tr><tr><td>Jun</td><td>54.0</td><td>(0.30, -0.28)</td><td>7.95</td><td>55.3</td><td>(-0.52, 0.34)</td><td>7.26</td><td>58.8</td><td>(0.18, 0.24)</td><td>6.30</td></tr><tr><td>Jul.</td><td>54.3</td><td>(0.38, 0.41)</td><td>7.96</td><td>73.1</td><td>(-0.54, 0.32)</td><td>7.27</td><td>58.8</td><td>(0.10, -0.03)</td><td>6.30</td></tr><tr><td>Aug</td><td>74.1</td><td>(-0.24, 0.22)</td><td>7.96</td><td>61.0</td><td>(-0.56, 0.30)</td><td>7.26</td><td>58.8</td><td>(-0.44, 0.64)</td><td>6.33</td></tr><tr><td>Sep</td><td>60.3</td><td>(-0.02, -0.01)</td><td>7.96</td><td>56.5</td><td>(-0.47, 0.25)</td><td>7.26</td><td>58.7</td><td>(-0.28, 0.36)</td><td>6.30</td></tr><tr><td>Oct</td><td>61.4</td><td>(0.35, -0.33)</td><td>7.97</td><td>79.5</td><td>(-0.44, 0.26)</td><td>7.25</td><td>58.7</td><td>(-0.39, 0.47)</td><td>6.31</td></tr><tr><td>Nov</td><td>70.6</td><td>(0.36, -0.24)</td><td>7.97</td><td>54.5</td><td>(-0.42, 0.19)</td><td>7.25</td><td>58.7</td><td>(-0.19, 0.32)</td><td>6.30</td></tr><tr><td>Dec</td><td>76.5</td><td>(0.54, -0.69)</td><td>8.02</td><td>79.7</td><td>(-0.26, 0.16)</td><td>7.26</td><td>58.7</td><td>(-0.37, 0.55)</td><td>6.32</td></tr></tbody></table>
Varona et al., Influence Of Underwater Hydrodynamics On Oil And Gas Blowouts Off Amazon River Mouth.

<figure><img src="image_48.png" /></figure>

**Figure 7.** Monthly oil/gas plume evolution at P1. The solid lines represent the west (-) and east (+) directions. The dashed lines represent the north (-) and south (+) directions. The red color is the displacement of the plumes from the bottom. The black color represents the radius to the west and north. The blue color represents the radius to the east and south.

The largest displacements of the plume from the blowout point were approximately 1 m in January and May. The mean arrival time of the oil/gas plumes to the surface was 7.99 hours and difference between the months was minimal.

Fig. 8 shows the monthly evolution of the oil/gas plumes at P2. As already verified in P1, all the plumes are of type 3. All the plumes reach their maximum diameter below 1000 m,
decreasing their diameter until they reach the surface (54.1-79.7 m). The largest diameters in the surface corresponded to the months of January, October and December. The largest displacements of P2 plume occurred from June to August and were approximately 0.64 m (Tab. 3). The average time of arrival at the surface was 7.26 hours.

<figure><img src="image_49.png" /></figure>

**Figure 8.** Monthly oil/gas plume evolution at P2. The solid lines represent the west (-) and east (+) directions. The dashed lines represent the north (-) and south (+) directions. The red color is the displacement of the plumes from the bottom. The black color represents the radius to the west and north. The blue color represents the radius to the east and south.
The pattern of oil/gas plume evolution already observed at the points P1 and P2 (type 3) was also verified at P3 (Fig. 9). The plumes at P3 reaching its largest diameter below 1000 m depth except for the months of May and July, where the diameter at the surface was almost invariant during all months (58.7–58.9 m). As shown in Tab. 3, the displacement of the plumes from its point of origin occurred in August and was 0.78 m. The plumes evolution time was practically the same, oscillating between 6.30 and 6.33 hours.

<figure><img src="image_50.png" /><figcaption>**Figure 9.** Monthly oil/gas plume evolution at P3. The solid lines represent the west (-) and east (+) directions. The dashed lines represent the north (-) and south (+) directions. The red color is the displacement of the plumes from the bottom. The black color represents the radius to the west and north. The blue color represents the radius to the east and south.</figcaption></figure>
Fig. 10(a) shows the vertical profiles of the current velocity for March at point P1 and for November at point P3. In both profiles, the plume simulations show that the maximum diameters of these were reached below 1000 m depth (Fig. 7 and Fig. 9). The bottom speed of points P1 and P3 is greater than 0.1 ms-1 and less than 0.2 ms-1, thus maintaining the entire water column up to 200 m depth. This behavior is very similar to the rest of the profiles at all points, with the exception of September, at point P1, and of May and July, at point P3.

<figure><img src="image_51.png" /><figcaption>**Figure 10.** Vertical profiles of the current speed (a) at P1 (March) and P3 (November) and (b) at P1 (September) and P3 (May and July).</figcaption></figure>

In September, at P1 (Fig. 10(b)), the current speed at the bottom was almost zero (approximately 0.02 ms-1). From the bottom, the velocity grew very slowly as the depth decreased to 1000 m (0.12 ms-1). The speed began to increase a little faster from 1000m depth to the surface, which means that the maximum diameter was reached between 900 and 1000 m depth. In May at P3 (Fig. 10(b)) the behavior of the plume in the bottom was also almost zero, but the increase in speed began near the 1500 m depth, which was much slower than in the previous case. Up to almost 200 m depth the speed did not exceed 0.2 ms-1 and, above that, where the plume reached its maximum diameter, it began to grow sharply until exceeding 0.5 ms-1 on the surface. At this same point, in July (Fig. 10(b)), the current speed was less than 0.1 ms-1 and maintained that value up to 1500 m depth. It began to decrease to approximately 850 m depth and then, it increased from above this depth, reaching its maximum diameter.
Using the coefficient 0.2 in the Eq. 1 did not get good results because the bottom current speed at the three points was very small, $|V_0| \to 0$, which this implied that $\Delta t \to \infty$. Thus it was necessary to adjust the coefficient to estimate $\Delta t$. The Eq. 2 below was then considered.

$$ \Delta t = \begin{cases} 0.025 \frac{b_0}{|V_0|} & \text{for P1 and P2} \\ 0.0375 \frac{b_0}{|V_0|} & \text{for P3} \end{cases} /2 $$

## CONCLUSION

This study characterizes the behavior of the plumes produced by the oil/gas blowouts in deep waters located at the continental slope in the northwest coast of Brazil, combining the ROMS and the GAS_DOCEAN models. The salinity and temperature of the hydrodynamic model were well adjusted to the SODA dataset, both on the surface and in the vertical profiles. All oil/gas plumes were type 3 and the oceanographic characteristics in the 3 points of the numerical simulations similar.

At P1, the mean diameter at the surface varied between 54–76.5 m and, at the bottom, the plume displacement from its origin was approximately 1 m. The average time it took to reach the surface was 7.99 hours; however, it was slower than in p2 (7.26 hours). The largest distance of displacement from P2 point of origin was 0.64 m and its average diameter on the surface ranged 54.1–79.7 m.

At P3, the displacement of the plume from its origin was 0.78 m. The mean diameter on the surface at this point varied between 58.7 and 58.9 m. The plume evolution time to the surface remained almost invariant (6.30–6.33 hours). The small plume displacement from its origin is due to the low speeds in the vertical profile.

To obtain good results in the numerical simulations of the plumes, it was necessary to adjust the coefficient for $\Delta t$ computation. The equation suggested by Lee and Cheung (1990) was modified to $\Delta t = 0.025 b_0 / |V_0|$ for P1 and P2; and $\Delta t = 0.0375 b_0 / |V_0|$ for P3.

## ACKNOWLEDGEMENTS

The first author thanks to Human Resources Program (PRH-47) of the Agência Nacional do Petróleo (ANP) for the concession of PhD scholarships. This work is also part of the Project Pólo de Interação para o Desenvolvimento de Estudos conjuntos em Oceanografia do Atlântico Tropical (PILOTE) and is a contribution of the Project Simulating the Amazon River Plume and its Impacts using Climate Models (CAPES-TAMU, Grant 99999.003440/2015-00).
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A PAPER "AMAZON RIVER PLUME INFLUENCE IN THE WESTERN TROPICAL ATLANTIC DYNAMIC VARIABILITY"

This annex presents the manuscript entitled *Amazon River plume influence in the Western Tropical Atlantic dynamic variability*, submitted July 7, 2017 in *Dynamics of Atmospheres and Oceans*. Current status: Submit Revision.

The main objective is quantify how much the Amazon River plume impacts on the dynamics of the Western Tropical North Atlantic. The role of Amazon and Pará Rivers plumes in the temperature, salinity and dynamics of the main surface currents is investigated.
# Amazon River plume influence on Western Tropical Atlantic dynamic variability

H. L. Varona<sup>a,*</sup>, D. Veleda<sup>a</sup>, M. Silva<sup>a</sup>, M. Cintra<sup>b</sup>, M. Araujo<sup>a,c</sup>

## Abstract

This study focuses on analysing the potential impact of the Amazon and Pará Rivers on the salinity, temperature and hydrodynamics of the Western Tropical North Atlantic (WTNA) region between 60.5°-24°W and 5°S-16°N. The Regional Ocean Model System (ROMS) was used to simulate ocean circulation with 0.25° horizontal resolution and 32 vertical levels. Two numerical experiments were performed considering river discharge and river input. Temperature and salinity distributions obtained numerically were compared with Simple Ocean Data Assimilation (SODA) and in situ observations from the Prediction Research Moored Array in the Tropical Atlantic (PIRATA) buoys located at 38°W8°N and 38°W12°N. Surface currents were compared with Surface Currents from Diagnostic model (SCUD). Once we verified that model results agreed with observations, scenarios with and without river discharges were compared. The difference between both simulations in the Sea Surface Temperature distribution was smaller than 2°C, whereas the Sea Surface Salinity (SSS) changed by approximately 8 psu in the plume area close to the coast from August to December and reaching SSS differences of approximately 4 psu in the region of the North Equatorial Counter Current (NECC). The surface current velocities are stronger in the experiment with river discharge, mainly in the NECC area from September to December and close to the coast in June to August. The results show that river discharges also cause a phase shift in the zonal currents, anticipating the retroflection of the North Brazil Current by two months and enhancing eastward NECC transport, which is in agreement with observations. The Mixed Layer Depth and Isothermal Layer Depth in the presence of river discharge is 20-50 m shallower over the entire extension of the Amazon plume compared with the situation without continental inflows. As a consequence, stronger Barrier Layers develop in the river plumes, reducing the Oceanic Heat Content in the WTNA.

**Keywords:** ROMS, Western Tropical North Atlantic, Amazon River discharge, Ocean circulation, Mixed Layer, Barrier Layer, Oceanic Heat Content.

<footnote><sup>a</sup>Department of Oceanography, DOCEAN, Federal University of Pernambuco, Recife-PE, Brazil.</footnote>

<footnote><sup>b</sup>Federal University of Rio Grande do Norte, Natal-RN, Brazil.</footnote>

<footnote><sup>c</sup>Brazilian Research Network on Global Climate Change, Rede CLIMA, S. José dos Campos-SP, Brazil.</footnote>

<footnote>*Corresponding author: humberto.varona@yadex.com</footnote>
# 1 Introduction

The circulation in the Western Tropical North Atlantic Ocean (WTNA) performs an important role in the interhemispheric transport of mass, heat, and salt and in the thermohaline overturning cell (Schmitz Jr. and McCartney, 1993; Bourlès et al., 1999a; Silva et al., 2009a; Veleda et al., 2012). Furthermore, a strong western boundary current, the North Brazil Current (NBC) is the main conduit for cross-equatorial transport of South Atlantic upper-ocean water as part of the Atlantic meridional overturning cell (Johns et al., 1998). This current flows north westward, intercepting the Amazon and Pará River freshwater discharges along the Brazilian north coast.

The Western Tropical North Atlantic Ocean is also a region with a complex system of zonal currents and counter-currents forced by subtropical gyres and the action of the trade winds in both hemispheres (Stramma et al., 2005). The NBC is considered a low latitude strong western-boundary current (Garzoli et al., 2004; Fratantoni and Richardson, 2006; Akuetevi and Wirth, 2015), and it is periodically retroflected near 6-8°N and separated away from the boundary, turning anti-cyclonically for more than 90°, and forming anticyclonic eddies exceeding 450 km in overall diameter (Richardson et al., 1994; Garzoli et al., 2004; Fratantoni and Richardson, 2006). The NBC retroflection feeds the North Equatorial Counter Current (NECC), an eastward zonal current that contributes to the formation of the anticyclonic current rings (Castelão and Johns, 2011). The NBC rings are a significant contributor to transporting water across current gyres and between hemispheres in the tropical Atlantic (Bourlès et al., 1999a; Johns et al., 1998; Schott et al., 2003; Stramma et al., 2005).

The Amazon River plume flows into the WTNA near the equator and is carried north westward along the Brazilian shelf by the NBC (Muller-Krager et al., 1988; Salisbury et al., 2011). This is the main source of freshwater in the world, with an average discharge of approximately 222,800 m³s⁻¹, it deposits almost 20% of the global river discharge onto the equatorial Atlantic Ocean continental shelf (Goulding et al., 2003; Barthem et al., 2004). The Amazon River plume extends thousands of kilometres over the North Atlantic Ocean arriving at the Caribbean Sea (Müller-Karger et al., 1989; Johns et al., 1990). Strong seasonal variations in this current system occur in response to trade wind variability and seasonal migration of the atmospheric Intertropical Convergence Zone (ITCZ) between its southern position in boreal winter, and its northern position in boreal summer (Xie and Carton, 2004). This leads to northward transport of Amazon waters in boreal winter, and eastward transport of Amazon water in the NECC in boreal spring
through autumn (Muller-Krager et al., 1988; Lentz, 1995; Fratantoni and Glickson, 2002; Coles et al., 2013; Foltz et al., 2015). Thus, the influence of Amazon water is felt far from the river mouth through enhancement of surface stratification leading to the formation of barrier layers (Silva et al., 2005; Ffield, 2007; Coles et al., 2013; Grodsky et al., 2014). Thus, the Amazon River plume is thought to influence the surface ocean heat balance and its interaction with the atmosphere in the WTNA.

The Western Tropical North Atlantic ocean is a region with intense land-ocean interaction, characterised by complex material transport, mixed layer depth changes (Grodsky et al., 2012; Coles et al., 2013) and high biogeochemical activity (Lefèvre et al., 2010; Ibánhez et al., 2017; Araujo et al., 2014, 2017), giving rise to alterations in local and remote oceanic processes. For example, river discharge use to be a small component of the open ocean salinity balance, but the magnitude of the Amazon freshwater source is so important that the discharged volume reaches two-fold the net evaporation minus precipitation budget over the north western tropical Atlantic (Ferry and Reverdin, 2004). Thus, in addition to the physical and weather/climate impacts, the Amazon and Pará Rivers also inject terrestrially derived sediments, nutrients, and coloured as well as transparent dissolved organic matter that can also be traced thousands of kilometres from the river mouth (Hu et al., 2004). The Biological community structure is strongly influenced by these terrigenous inputs of dissolved organic matter and nutrients, as well as by the induced changes in the stratification in the upper ocean (Stukel et al., 2014), leading to a globally significant uptake of atmospheric carbon dioxide in the river plume area (Cooley et al., 2007; Subramaniam et al., 2008; Yeung et al., 2012).

Several recent studies have used in situ observation data, satellite products, general circulation models (OGCMs) and regional models to explain the spatial and temporal variability of the Amazonian plume and its interaction with the NBC rings (Fratantoni and Glickson, 2002; Ffield, 2007; Korosov et al., 2015). Schmidt et al. (2011), for example, implemented an operational forecasting system using a high-resolution model to resolve the migration rate of the NBC rings on a short time scale. Other recent work links the intensification of hurricanes to the spreading of Amazonian freshwater discharges due to the impact of haline stratification on reduction of the vertical heat flux (Balaguru et al., 2012; Grodsky et al., 2012; Newinger and Toumi, 2015) and the periodic movement of the NBC rings (Ffield, 2007).
In this work, we used a climatological modelling approach to investigate the role of the Amazon and Pará River plumes on the circulation and thermohaline variability in the Amazon River-Ocean continuum (Araujo et al., 2017). Two numerical experiments were conducted, contrasting the scenarios with and without the contribution of continental freshwater to the tropical Atlantic. We focused here on the impact of river inflows on the circulation (NBC-NECC system) and temperature/salinity distributeons, which induce important changes in isothermal/mixed/barrier layer formation and oceanic heat content in the Western Tropical North Atlantic Ocean. The model configuration, simulation scenarios, observational dataset and analysis procedures are described in Section 2. In addition to simulation considering the discharges of the Amazon and Pará Rivers in the presence of islands (Scenario RRF), an idealised configuration without continental contributions was also simulated (Scenario NRF). The results from scenario RRF are compared with the available observational datasets in order to validate the simulation. Then, differences between two scenarios are examined. Simulation results, including model validation, are presented and discussed in Section 3. The last section provides the conclusions and outcome perspectives of this work.

# 2 Data and methodology

## 2.1 Numerical modelling experiments

In this work, we use the Regional Ocean Modeling System (ROMS), an open source software, which has been continuously developed by a large community of scientists, with more than 400,000 lines of FORTRAN code. ROMS integrates the primitive Reynolds equations in a rotational free surface system of free surface, using the Boussinesq approximation, the hydrostatic approximation and the balance of vertical momentum (Shchepetkin and McWilliams, 2005; Song and Haidvogel, 1994; Panzer et al., 2013). This was adapted to different geographic regions of the world where good results were obtained (Haidvogel et al., 2000; Penven et al., 2000; Silva et al., 2009a; Tchamabi et al., 2017).

The region of study is framed in 60.5°-24°W and 5°S-16°N (Fig. 1) with 0.25° of resolution (approximately 27.8 km), covering an area of 4916·10³ km² corresponding to a 183×159 node grid with 32 levels in the vertical, 12 of which are in the upper 100 m and 20 in the 500 m. The ETOPO2 (Smith and Sandwell, 1997) topography database was used in the vertical discretisation
with 2min of resolution. The red rectangles in Fig. 1 indicate the regions used to evaluate model performance where strong seasonal changes in ocean circulation and thermohaline structure are observed; REG1 corresponds to the NBC retroflection region, and REG2 represents the NECC area. The locations of PIRATA buoys are also plotted in the figure.

<figure><img src="image_52.png" /></figure>

Figure 1: The model domain framed in 60.5°-24°W/5°S-16°N. The colour bar represents ocean bathymetry and the red rectangles correspond to two regions used for model validation: REG1 (48-45°W/4-12°N) and REG2 (40-28°W/6-10°N). PIRATA buoys at 38°W8°N and 38°W12°N are also indicated in the figure.

Four lateral boundaries are considered open in simulations. In the lateral boundary and initial conditions all variables were constrained by the monthly mean of the 2009 World Ocean Atlas, WOA2009 (Locarnini et al., 2010; Antonov et al., 2010) with a resolution of 1°. The surface forcings were obtained from the monthly mean climatology of the Comprehensive Ocean-Atmosphere Data Set (COADS05) (Da Silva et al., 1994) with 0.5° of resolution. Tides are an important process in mixing the river freshwater plumes with the open ocean and are obtained
from the TPXO7 (Egbert et al., 1994; Egbert and Erofeeva, 2002), which has altimetry data from
several satellites to improve the accuracy of the results obtained through the hydrodynamic model
(Wang, 2004; D'Onofrio et al., 2012). The monthly mean river discharge was obtained from the
Obidos and Tucurui gauge stations (Dai and Trenberth, 2002), and the monthly climatology of
Sea Surface Temperature (SST) in the river discharge points was also obtained from WOA2009.

We performed two numerical experiments to estimate the potential impact of the Amazon and Pará Rivers on the salinity, temperature and surface currents of the WTNA. In the first experiment, hereafter referred to as River Runoff (RRF), the Amazon and Pará Rivers release freshwater into the WTNA. Given the geographical configuration of the Amazon River Delta, there are four inputs from the river to the WTNA (Fig. 2(a)): Canal do Norte, Baia de Santa Rosa, Canal Perigoso and Canal do Jurupari. The inputs are placed in four cells of the grid. Considering the channel width, the contribution was calculated for each one, distributed as 14.47%, 37.27%, 29.13% and 19.13%, respectively (Fig. 2(b)), with the same monthly temperature distribution for the four input nodes in the Amazon River Delta and different in the input node of the Pará River (Fig. 2(c)). In the second experiment, the No-River Runoff (NRF), the Amazon and Pará Rivers do not release freshwater into the WTNA, keeping the same model parametrisation and initial and boundary conditions. We ran each experiment for 11 years (spin-up), but our analyses are focused on the last simulation year.

<figure><img src="image_53.png" /><figcaption>Figure 2: (a) Location map of the Amazon River delta and its four inputs (North channel, Santa Rosa Bay, Dangerous channel and Jurupari channel) from the river to WTNA. (b) Monthly distribution of discharge (m³s⁻¹) of the Amazon River (blue lines) and Pará River (black line), from the Obidos and Tucuruí gauge stations. (c) Monthly distribution of temperature (°C) of the Amazon River (blue line) and Pará River (black line).</figcaption></figure>
## 2.2 Mixed layer depth (MLD), isothermal layer depth (ILD), barrier layer thickness (BLT) and oceanic heat content (OHC) criteria

The MLD was defined as the depth where the density increases from the surface value due to a prescribed temperature decrease of 0.2°C ($\Delta T = -0.2^{\circ}C$) while maintaining constant surface salinity. The MLD was mathematically defined by Sprintall and Tomczak (1992) and de Boyer Montégut et al. (2007) as

$$ \Delta\sigma = \sigma(T + \Delta T, S, P_0) - \sigma(T, S, P_0) $$

where $\Delta\sigma$ is the density difference for the same change in temperature $\Delta T$ at constant salinity, $T$ and $S$ are the values of temperature and salinity at the reference depth ($Z_{REF}$) and $P_0$ is the pressure at the ocean surface. The ILD is the depth at which the temperature is equal to $T + \Delta T$. In this study, we consider $Z_{REF}=0$, corresponding to the SST obtained in simulations.

The Barrier Layer (BL) may prevent heat exchange between the oceanic mixed layer (MLD) and deeper water, influencing the SST and ensuring greater isolation along the Mixed Layer Depth (MLD). The thicker the BL is, the less heat exchange exists between deep cold water and the oceanic mixed layer. The BLT is calculated as

$$ BLT=ILD-MLD $$

The transfer of mass, momentum and energy through the mixing layer is an important feature influencing oceanic circulation and changes with the atmosphere. The ILD determines the heat content and the mechanical inertia of the layer that interacts directly with the atmosphere (de Boyer Montégut et al., 2004). Changes in oceanic heat content play an important role in sea level rise due to thermal expansion. The quantity of energy stored per unit area in the ocean (OHC) between levels $Z_{REF}$ and $h$ is defined according to Jayne et al. (2003) as

$$ OHC = \rho_0 C_p \int_{Z_{REF}}^{h} T(z) dz $$
where OHC is Oceanic Heat Content in $Jm^{-2}$, $\rho_0 = 1025$ kg m$^{-3}$ is the representative density of seawater at the sea surface, $C_p = 4 \cdot 10^3 J(kg^{-1}°C)^{-1}$ is the specific heat of seawater at constant pressure at the sea surface (Levitus et al., 2005), $T(Z)$ is a vertical temperature (°C), Z is Depth (m), $Z_{REF}=0$ and h is the maximum depth to calculate the OHC.

As the depth increases, the temperature oscillation decreases and below the active ocean layer, there are practically no annual variations in temperature. The OHC is more related to the thickness of the isothermal layer than to the temperature directly; thus, we plan to study only the quantity of heat in the active layer of the ocean, numerically integrating the temperature in each vertical profile in the grids of the RRF and NRF experiments, from surface to $h = ILD$.

## 2.3 Model validation: Comparison with observations

For model validation, we compare numerical Sea Surface Temperature and Sea Surface Salinity (SSS) variability with the Simple Ocean Data Assimilation (SODA) version 2.2.4 (Carton et al., 2000a, 2000b; Carton and Giese, 2008), which has a spatial resolution of 0.5° and integration period of 20 years (1991-2010).

Surface currents are compared with the Surface Current form Diagnostic model (SCUD) (Maximenko and Hafner, 2010), with spatial resolution of 0.5° and period of 2000-2008. To compare numerical results to previous findings reported by Richardson and Reverdin (1987), the average values of the zonal component of surface velocity were also calculated for two different regions of the NECC: the Western NECC (WNECC - 50-40°W/5-8°N), and the Eastern NECC (ENECC - 30-25°W/5-8°N).

Other than considering the overall differences between the RRF and NRF scenarios at the whole integration domain, the river induced circulation and thermohaline changes were examined at two highly dynamic areas of the WTNA (red rectangles in Fig. 1): (i) the REG1 region (48-45°N/4-12°N) corresponding to an NBC retroflection area; and (ii) the REG2 region (40-28°W/6-10°N), representing the zonal band NECC pathway.

Subsurface temperature and salinity distributions and variability obtained from the RRF scenario were compared to the observed climatology of the PIRATA buoys located at 38°W/8°N and 38°W/12°N (Servain et al., 1998; Bourlès et al., 2008), constructed during 2000-2015. The
PIRATA mooring design allows high frequency measurements of ocean temperature at 11 levels (i.e., 1, 20, 40, 60, 80, 100, 120, 140, 180, 300, 500 m), salinity at 4 levels (1, 20, 40, 120 m), and meteorological variables at the sea surface, that are transmitted and kept immediately available on the Web after their validation (https://www.pmel.noaa.gov/gtmba/).

Except the previously mentioned comparison of the zonal component of current velocity, the results presented and discussed in the next section focus on the differences between the RRF and NRF experiments during: boreal winter (DJF: December, January, February); boreal spring (MAM: March, April, May), boreal summer (JJA: June, July, August) and boreal autumn (SON: September, October, November).

# 3 Results and discussion

## 3.1 Model evaluation: Validation of the RRF experiment

The ROMS monthly climatology output is compared with the SODA dataset to evaluate model results and validate the RRF experiment. The differences in Sea Surface Salinity between the model simulation and SODA climatology (RRF-SODA) are shown in Fig. 3. The results indicate that lower SSS values were obtained from ROMS simulations than from SODA in the area of the NBC retroflection (during April to June - boreal spring) and in some meanders of the NECC zonal corridor (during October to December - boreal autumn).
<figure><img src="image_54.png" /><figcaption>Figure 3: Seasonal variability of the differences in SSS distributions (psu) between the RRF experiment and SODA climatology in the WTNA.</figcaption></figure>

The differences between the SST climatology obtained from simulation and from SODA are shown in Fig. 4. The modelled mean SSTs present in general higher monthly averaged temperatures than SODA in the area of the NBC retroflection and the NECC, mainly during boreal autumn and early/mid-winter.
<figure><img src="image_55.png" /><figcaption>Figure 4: Seasonal variability of the differences in SST distributions (°C) between the RRF experiment and SODA climatology in the WTNA.</figcaption></figure>

A comparison between seasonal averaged SSS (columns 1 and 2) and SST (columns 3 and 4) values obtained from simulations (RRF) and from SODA in REG2 area (NECC region, Fig. 1) is shown in Tab. 1. The mean value±standard deviation is indicated in the first set of rows, and the range (minimum–maximum) in the second set for each season. The results indicate overall good agreement between SST and SSS generated by the RRF scenario and SODA. The maximum SSS difference in area REG2 (0.27 *psu*) is verified during boreal summer (June to August) and a minimum SSS difference (0.06 *psu*) during boreal spring (March to May). For SST, columns 3 and 4 in Tab. 1 show greater differences between RRF simulation and SODA in the REG2 area during boreal winter (December to February) (1.27°C), when we still have the Amazon river
plume feeding the NECC. The RRF simulation is better adjusted during boreal summer (June to August).

<table><caption>Table 1: Comparison between seasonal SSS (columns 1 and 2) and SST (columns 3 and 4) values obtained from simulations (RRF scenario) and from SODA in the REG2 area (NECC region, Fig. 1). The mean value±standard deviation is indicated in the first rows, and value ranges (minimum–maximum) in the second rows.</caption><thead><tr><th rowspan="2">Period</th><th colspan="2">SSS</th><th colspan="2">SST</th></tr><tr><th>SODA<br>(psu)</th><th>RRF<br>(psu)</th><th>SODA<br>(°C)</th><th>RRF<br>(°C)</th></tr></thead><tbody><tr><td>DJF</td><td>35.86±0.02<br>(35.56–36.10)</td><td>35.63±0.07<br>(34.94–36.19)</td><td>26.90±0.04<br>(26.02–27.48)</td><td>28.17±0.14<br>(26.47–29.12)</td></tr><tr><td>MAM</td><td>36.09±0.02<br>(35.87–36.32)</td><td>36.15±0.01<br>(35.81–36.47)</td><td>26.74±0.10<br>(25.60–27.61)</td><td>27.05±0.27<br>(25.73–28.31)</td></tr><tr><td>JJA</td><td>35.82±0.03<br>(35.43–36.15)</td><td>36.09±0.05<br>(35.46–36.51)</td><td>27.77±0.07<br>(27.15–28.30)</td><td>27.77±0.23<br>(26.27–28.84)</td></tr><tr><td>SON</td><td>35.41±0.07<br>(34.89–35.87)</td><td>35.23±0.13<br>(34.19–36.26)</td><td>28.26±0.04<br>(27.84–28.74)</td><td>28.63±0.13<br>(27.63–29.69)</td></tr><tr><td>Annual</td><td>35.79±0.03<br>(35.44–36.11)</td><td>35.78±0.08<br>(35.10–36.36)</td><td>27.42±0.07<br>(26.65–28.03)</td><td>27.91±0.19<br>(26.53–28.99)</td></tr></tbody></table>

Fig. 5 shows the comparison between near surface (0-120 m depth for salinity and 0-500 m depth for temperature) seasonal variation of the vertical distributions of temperature and salinity obtained from the RFF scenario and from PIRATA observations at 38°W8°N and 38°W12°N. This figure indicates generally good agreement between model results and in situ measurements, but in order to have a statistical confirmation of model capacity to reproduce observations, we performed two-sample t-test. Prior to performing t-test, in situ and numerical profiles were normalised since the initial dataset showed non-normal distributions as verified by the one-sample Kolmogorov-Smirnov test.
The simulated salinity at the position 38°W8°N (Fig. 5(a)) shows a seasonal evolution of vertical structure similar to that observed from PIRATA data, in particular during boreal spring (August to December), when lower salinity Amazonian waters are transported eastward by the NECC. The main differences are verified from June to October, in the first 40 m, just after the beginning of NBC retroflection, when the freshwater river plume starts to feed the NECC (Coles et al., 2013; Grodsky et al., 2014). T-test indicated no statistically significant differences between vertical distributions of salinity issued from the RRF experiment and PIRATA data (p=0.9966, α = 0.05). The temperature evolution obtained numerically is also well adjusted to measurements (Fig. 5(b)), with no significant differences between the potential model and PIRATA data (p=0.8287, α = 0.05).

<figure><img src="image_56.png" /></figure>
Figure 5: Hovmöller diagrams of temperature and salinity obtained from model simulation (red dashed lines) and PIRATA observations (blue solid lines) at: 38°W8°N (a) salinity (psu) and (b) temperature (°C); and 38°W12°N (c) salinity (psu) and (d) temperature (°C).

The vertical profiles of modelled salinity are in general agreement with in situ measurements at the position of the PIRATA buoy 38°W12°N (Fig. 5(c)). A mean difference of 0.4 psu is seen and no significant differences were found between model outputs and observations (p=0.9857, α = 0.05). The evolution of near surface vertical temperature structure is also similar to the measurement along the year (Fig. 5(d)), with some discrepancies between 100–120 m depth, just below the thermocline depth. Again, t-test indicated no significant differences between the model and measurements (p=0.8137) for a significance level of 0.05.

The NECC is the main current transporting the Amazon River plume eastward (Grodsky et al., 2014). We now compare the seasonal evolution of the zonal components of surface velocity longitudinally averaged in the REG2 limits (40-28°W) obtained numerically (RRF scenario) (Fig. 6(a)) to that issued from the SCUD dataset (Fig. 6(b)). Both cases show a gradual increase of the zonal component from July to October, between 4.0 and 10°N, and decreasing during boreal autumn (September to November). The maximum difference between the model and SCUD was found between 5.5 and 6.5°N in July and between 4 and 5°N in October, with an averaged difference of 0.1 ms⁻¹. These results reveal that the model simulation represents the dynamics of the NECC variability in the WTNA quite well.
<figure><img src="image_57.png" /><figcaption>Figure 6: Hovmöller diagrams of the zonal component of surface velocity longitudinally averaged in the REG2 limits (40-28°W): (a) Model (RRF) simulation; (b) SCUD dataset.</figcaption></figure>

Finally, to compare model results to previous in situ observations, we calculated the averaged zonal component of surface velocity obtained numerically during boreal autumn at the WNECC and ENECC regions as 0.384 ms⁻¹ and 0.226 ms⁻¹, respectively. These values are very close to those reported by Richardson and Reverdin (1987) for the same period and regions (0.410 ms⁻¹ and 0.215 ms⁻¹, respectively).

## 3.2 Impact of river plumes: Comparison between the RRF and NRF experiments

In this section, we compare the seasonal evolution of the differences between the RRF and NRF scenarios (RRF-NRF). It aims to investigate the influence of river discharges on the surface ocean circulation and thermohaline structure in the WTNA.

### 3.2.1 Impacts of thermohaline structure and changes on ILD, MLD, BLT and OHC

Fig. 7(a) shows the seasonal variation of the difference of SSS distributions in the WTNA obtained from model results with (RRF) and without (NRF) the Amazon and Pará River discharges. The RRF experiment shows lower salinity confined to the coast from December to February. From March to May, the RRF scenario shows evidence of lower salinity at the NBC retroflection area. From June to August, the plume spreads northward, and from September to
November, the plume is transported eastward by the NECC. The river scenario shows 10–12 psu lower salinity values along the coast than does the NRF case, which represents the seasonal cycle of the Amazon plume well. In the NECC area, the continental inflows generate lower SSS values of 4 psu. These results are in agreement with previous observations and modelling efforts (Silva et al., 2009b, 2010; Coles et al., 2013; Korosov et al., 2015; Newinger and Toumi, 2015; Araujo et al., 2017).

<figure><img src="image_58.png" /><figcaption>Figure 7: Seasonal evolution of the difference of: (a) SSS (psu); and (b) SST (°C) between the RRF (River discharges) and NRF (No-river discharges) simulations during boreal winter (DJF:</figcaption></figure>
December, January, February), boreal spring (MAM: March, April, May), boreal summer (JJA: June, July, August), and boreal autumn (SON: September, October, November).

Fig. 7(b) shows the differences in seasonal SST distributions for both situations (RRF-NRF). Although not significant, the results indicate that river discharges induce a warming near the Amazon River mouth, in particular during boreal spring/summer and at the NBC retroflection area in boreal autumn. A small cooling is also verified very close to the coastline at the left side of the river mouth. In the open ocean SST changes are not very sensitive to the river inputs. These results are in agreement with previous observations (Silva et al., 2010; Newinger and Toumi, 2015; Araujo et al., 2017).

Changes in temperature and salinity distributions due to the inflow of Amazon and Pará River freshwater modify the thickness and evolution of the Isothermal (ILD), Mixed (MLD) and Barrier layers (BLT) in the WTNA. Fig. 8(a) indicates that oceanic mixed layers are shallower in the RRF experiment than in the situation simulated with no-river input. These changes are located in the regions (and periods) where (when) river plumes spread into the WTNA. Indeed, the low-density layer formed by the freshwater river discharge induces MLD 20 m to 50 m shallower over the entire extension of the plume. The MLD minimum for the RRF experiment is 6 m throughout the year and the maximum fluctuates between 88 m and 100 m, being deeper in the SON period. In contrast, whereas minimum MLD values are approximately 6 m, the maximum mixed layer depth oscillates between 100 m and 120 m in the absence of river plumes (NRF experiment).

Fig. 8(b) shows the spatial distribution of the differences in the seasonally averaged distributions of ILD between the RRF and NRF experiments. Although less intense than MLD, we also find shallower ILDs in the presence of river discharges than in the simulations without continental contribution (NRF), in particular during boreal summer (JJA) and autumn (SON). As expected, there is a clear influence of SST changes over the ILD, being up to 1°C higher in the plume area for the RRF scenario than for the No-rivers simulation (Fig. 7(b)).
<figure><img src="image_59.png" /><figcaption>Figure 8: Seasonal evolution of the difference of: (a) MLD (m), (b) ILD (m), and (c) BLT (m) between the RRF (River discharges) and NRF (No-river discharges) simulations during boreal</figcaption></figure>
winter (DJF: December, January, February), boreal spring (MAM: March, April, May), boreal summer (JJA: June, July, August), and boreal autumn (SON: September, October, November).

As presented for MLD and ILD, Fig. 8(c) shows the spatial distribution of the seasonally averaged differences of BLT distributions between the RRF and NRF experiments. The discharge of freshwater from the rivers plays a fundamental role in the formation of barrier layers along the extensions of the river plume spreading into the WTNA. The maximum difference in BLT is verified at the Amazon mouth extending north westward when the river plume is transported towards the Caribbean Sea. During boreal autumn (SON) the differences in BLT extend to the east following plume transport by the NECC. In the absence of rivers, the BL is almost non-existent in the plume area, and we find 100% of the BLT in the range of 1–35 m. In the RRF experiment the BLT reaches 82–94 m in the DFJ, MAM and JJA periods and up to 110 m in boreal autumn (SON). Similar magnitudes and BLT distributions were reported by the in situ observations of Pailler et al. (1999) and Silva et al. (2005, 2009b and 2010).

It is expected that space and time modifications to temperature distribution due to river inflows should also induce changes in oceanic heat content (OHC) in the WTNA. Simulation results indicate that higher accumulations of heat in the ILD without rivers varied from 1.1 to 1.3·10¹⁰ Jm⁻², whereas the maximum values of OHC in the RRF experiment ranged from 1.0 to 1.2·10¹⁰ Jm⁻². Fig. 9 presents the seasonally averaged differences in OHC between the simulations with and without rivers (RRF-NRF) indicating that rivers reduce oceanic heat storage, in particular along the plume spreading areas. The largest differences ranged between -0.7 and -0.3·10¹⁰ Jm⁻², during the periods of MAM, JJA and SON, confirming that variation in ILD is the main factor influencing the OHC difference between the RRF and NRF experiments.
<figure><img src="image_60.png" /><figcaption>Figure 9: Mean seasonal cycle of difference in OHC (J m⁻²) integrated from Z REF to ILD. Boreal winter (DJF: December, January, February), boreal spring (MAM: March, April, May), boreal summer (JJA: June, July, August), and boreal autumn (SON: September, October, November).</figcaption></figure>

### 3.2.2 Impact on ocean circulation and changes to NBC and NECC

Fig. 10 shows the differences in ocean surface circulation between numerical results obtained from the RRF and NRF scenarios. The seasonal cycle provides evidence of the rivers' impact on the WTNA regions under the influence of freshwater plumes (NBC retroflection and NECC areas). Stronger surface velocities are present from June to November and a well-defined meandering/ring structure is highlighted from September to November. These differences (reaching 1 ms⁻¹) emphasize the role of the Amazon river plume in the dynamics of the NBC-NECC system, including NBC retroflection, formation of NBC rings and eastward NECC meandering/transport.
<figure><img src="image_61.png" /><figcaption>Figure 10: Seasonal evolution of the difference in surface currents (ms⁻¹) between the RRF (River discharges) and NRF (No-river discharges) simulations during boreal winter (DJF: December, January, February), boreal spring (MAM: March, April, May), boreal summer (JJA: June, July, August), and boreal autumn (SON: September, October, November).</figcaption></figure>

To evaluate the influence of Amazon inflow on the dynamics of the NBC retroflection we compare the evolution of the longitudinally averaged (REG1 limits 48-45°W) zonal component of the surface currents obtained from the RRF and NRF experiments (Fig. 11(a) RRF and (b) NRF). The results show overall similar patterns in both simulations with the zonal current intensity reaching a maximum of 0.5 ms⁻¹. In both cases, stronger zonal velocity appears in January-February (between 7° and 8°N), with a core of less intense but still significant eastward transport centred at 9°N in March-April. However, during the second half of the year, when river discharges are stronger (Goulding et al., 2003; Barthem et al., 2004; Araujo et al., 2014), there is evidence for differences in the space and time evolution of zonal currents between the RRF and NRF scenarios. Although eastward currents start to occur in the middle of August in both scenarios, stronger and broader eastward transport is verified when the Amazon and Pará Rivers are considered. The period of maximum zonal current values is also different for both situations, whereas the cores of high eastward velocity are verified since the beginning of August in the RRF
simulation; those maximum values are shifted to mid-September in the NRF case. The differences in intensity and space-time evolution of the zonal surface currents in the second part of the year reveals how river plumes change ocean circulation in the WTNA, in particular over the NBC retroflection area and the NECC pathway (see also Fig. 10).

<figure><img src="image_62.png" /><figcaption>Figure 11: Hovmöller diagrams of the zonal component of surface velocity longitudinally averaged in the REG1 limits (48-45°W): (a) RRF scenario (River discharges); (b) NRF scenario (No-river discharges).</figcaption></figure>

## 4 Conclusions

This study uses observations and numerical modelling to investigate the influence of the Amazon and Pará Rivers' discharge on the surface ocean circulation and thermohaline variability in the Western Tropical North Atlantic Ocean (WTNA). Regional climatological modelling is used to conduct two numerical experiments with (RRF) and without (NRF) continental freshwater inflow. Our analyses focused on the impact of rivers on the circulation (NBC-NECC system) and temperature/salinity distribution that induce changes in the isothermal/mixed/barrier layer formation and oceanic heat content.

The model results considering real situations (with rivers) are in good agreement with previous observations and reanalysis efforts, showing similar patterns of thermohaline distribution and ocean surface circulation in the WTNA. The rivers' impact on sea surface salinity variability is evident. The space and time variability of SSS in the area of the NBC retroflection and NECC is
clearly identified, showing a confinement of the lower salinity waters near the coast from
December to February and spreading eastward along the NECC pathway from September to
November. The SST fields change near the Amazon River mouths, reducing the salinity by
approximately 8 psu.

A warm core of SST is concentrated at the left side of the Amazon mouth from September to
November, following the NBC retroflection area, due to the influence of warmer river
temperature. However, in the open ocean, SST changes are not significantly sensitive to river
discharges.

The computed differences between the RRF (Rivers discharges) and NRF (No-rivers discharges) scenarios indicate a strong impact of river plumes on ocean circulation in the WTNA. Compared to the NRF experiment, the RRF experiment increases the surface current up to 1 ms⁻¹. Stronger velocity occurs from June to November and well-defined meandering/ring generation structures are highlighted from September to November. The results also emphasize the role of the Amazon plume in the dynamics of the NBC retroflection, as well as in the eastward NECC transport. The comparisons of longitudinally averaged zonal components of surface currents in the NBC retroflection area for the RRF and NRF experiments show overall similar patterns during the first half of the year, when river discharges are lower. However, during the second semester, the beginning of maximum eastward transport (zonal currents reaching 0.5 ms⁻¹) is two months delayed in the absence of continental inflows, and is also weaker and limited to a narrower latitude band compared to the observed RRF scenario.

The Amazon and Pará Rivers also impact the isothermal/mixed/barrier layer dynamics in the WTNA. Induced MLD and ILD are 20–50 m shallower over the entire extension of the river plumes when river discharges are considered, resulting in the maximum BLT at the river mouths, extending north westward as the plume is transported towards the Caribbean during boreal spring and summer. Higher values of BLT are observed along the zonal NECC pathway during boreal autumn, after eastward NBC retroflection.

Changes in near surface thermohaline structure drive oceanic heat content. The results indicate that modifications in isothermal/mixed/barrier layers due to river input result in less oceanic heat storage in the WTNA. Differences up to 0.1·10¹⁰ Jm⁻² are verified in the large portion of the Western Tropical North Atlantic occupied by less salty plume waters.
In summary, the results indicate the Amazon and Pará Rivers' discharges impact the thermohaline structure in the WTNA as follows: (i) modifying seasonal salt distribution variability in the river plume area, (ii) inducing shallower Mixed and Isothermal layers as a response to salinity changes, (iii) enhancing Barrier Layer formation and increasing its depth, and (iv) reducing Ocean Heat storage capacity. Furthermore, ocean surface circulation is modified by the following factors: (v) anticipating the NBC retroflection (by about two months), and (vi) enhancing the eastward NECC transport/spreading of low salinity water into the central tropical Atlantic.

Considering all the evidence for the influence of the Amazon and Pará Rivers on the dynamics of the WTNA Ocean, it seems interesting as a next step to investigate the effects of these physical alterations and of the riverine nutrient and organic material contributions on the biogeochemical cycles of the Western Tropical North Atlantic region. In this case, in addition to the analyses of observations and in situ measurements, the use of coupled physical-biogeochemical modelling appears to be an exciting research method to be explored.

## Acknowledgements

The first author thanks to Human Resources Program (PRH-47) of the Agência Nacional do Petróleo (ANP) for the concession of PhD scholarships. This paper is a contribution of the Brazilian Research Network on Global Climate Change, FINEP/ Rede CLIMA Grant Number 01.13.0353-00. The authors thank to the Brazilian National Institute of Science and Technology for Tropical Marine Environments" INCT AmbTropic (CNPq/FABESB grants 565054/2010-4 and 8936/2011), the Project Simulating the Amazon River Plume and its Impact using Climate Models, Grant CAPES-TAMU 003440/2015-00, the Project Modeling the light field in the waters of Equatorial Atlantic under the effects of the Amazon River discharge: implication to biogeochemical processes and primary production, MEC/MCTI/CAPES/CNPq/FAPs Grant: 401326/2012-8; D. V. thanks to Project ProdPluma - Modelo Regional de Produtividade Primária da Pluma do Amazonas (CNPq grant 460687/2014-0).
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