## 1 Introduction  
Sea surface salinity (SSS) influences ocean-atmosphere interactions which control weather and climate patterns. Understanding ocean-atmosphere interactions in the western tropical Atlantic is essential to the development of new parameterizations schemes for numerical weather prediction (NWP) and climate models [1] that support forecasting over this region. The need to improve weather and climate prediction models over this region is driven by the fact that many Caribbean Small Island Developing States (SIDS) located in the region heavily utilize NWPs to support severe weather forecasting as part of their multi-hazard early warning system and climate models to drive their climate change adaptation policies and programmes. Studies have shown that salinity stratification as a result of riverine outflows from large South American river systems, in particular the Amazon River system, that make their way into the Northern Atlantic Ocean, reduce upper ocean cooling during hurricane passage supporting hurricane intensification [2, 3]. SSS modulates both vertical mixing and Sea Surface Temperature (SST) [1]. The Barrier Layer (BL) produced as a result of freshwater influxes may produce biases in SST by controlling vertical mixing and entrainment of cooler water into the Ocean Mixed Layer (OML) [4]. The freshwater fluxes described also carrynutrients and organic matter which can produce ecological challenges that impact fisheries, marine biodiversity and tourism [5]. Observations of ocean salinity and density provide information on ocean dynamics especially in areas with complex ocean processes such as the North Brazil Current (NBC) region [6; 7; 8]. Salinity variations provide insight into ocean circulation and air sea fluxes which in turn impact the atmospheric boundary layer, resulting in changes to the Earth's climate [9]. Salinity is also used for monitoring the movements of water masses along with vertical exchange of water between surface and subsurface layers [10]. Historically, in situ measurements of salinity and temperature were taken from ships using bucket and thermosalinograph (TSG) measurements and were sparse on a global scale [11]. This improved over time with the implementation of the Argo Float programme, which included in situ observations of salinity [12]. This global network, consisting of autonomous Argo Profiling floats produces 3\({}^{\circ}\) spatial resolution salinity data at 10-day intervals. Although useful for many applications, these spatial and temporal resolutions are too coarse to provide useful salinity observations at the mesoscale level [13]. In 2010, the first global satellite ocean salinity measurements became available from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, which retrieved salinity at a spatial resolution of 40 km and a 23-day repeat cycle. In 2011, National Aeronautics and Space Administration's (NASA) Aquarius mission began providing salinity at a spatial resolution of 100 km and a 7-day repeat cycle [14]. This was followed by NASA's Soil Moisture Active Passive (SMAP) mission in 2015, which has a resolution of 40 km and a repeat cycle of 8 days [15]. Although space-based platforms improved the acquisition of salinity data, there are still inaccuracies when making measurements near to the coast and at high latitudes, due to limitations of the L-band microwave radiometers used to measure salinity [16]. Ocean modelling provides another approach for estimating global ocean salinity. Ocean modelling commenced in the 1960s with the development of simple ocean circulation models [17]. These older models were incapable of simulating ocean processes at spatial resolutions finer than 100 km and many typically had temporal resolutions of approximately three months. However, improvements over the years have led to the development of models capable of simulating submesoscale ocean behavior.