The Tropical Atlantic Ocean (TAO) is characterized by having variability of meteorological and oceanographic processes, over space and time. Oscillations in the fields of temperature, salinity, density and winds are examples of variations relevant to the studies of physical oceanography, since this science is behind the study of spatial and temporal changes in ocean properties. Due to this, the TAO becomes a region of interest for study, as its dynamics involves interseasonal, interannual, interdecadal and daily changes. Such physical changes occur at different types of scales present in the TAO system, playing an important role in the circulation of properties such as mass, heat and salt between the hemispheres. Attributes such as Chlorophyll-a and Sea Surface Temperature (SST) leave marks on the ocean surface, signals that can be analyzed remotely, making it possible to verify their spatio-temporal variability. Knowing this, the objective of this work is to verify how the patterns of Chlorophyll-a and SST vary over time and space as a function of local dynamics and the coherence of these variations. For this, remote sensing data were collected, referring to the study area, covering an extensive time scale for analysis. The data obtained refer to SeaWifs and MODIS-Aqua sensors (Level-3), with a spatial resolution of 9 km, in addition to ocean models (Level-4). After that, these elements were cataloged and renamed to enable their treatment. The analysis process was performed using the python programming language. The data underwent statistical treatment (mean and standard deviation), generating climatological maps to define the subdomains for analysis. The subdomains selected, from the standard deviation climatological maps, were: mouth of the Amazon River, mouth of the Congo River and Coastal Resurgence, on the coast of Africa. After delimiting the subdomains, monthly climatological averages and standard deviations were calculated. Then, the subdomains were analyzed using Empirical Orthogonal Functions (EOF). The analysis of the two main modes of explained variance for SST and Chlorophyll-a at the mouth of the Amazon and Congo Rivers show us a seasonal variation in the propagation of the plumes, defined by the off-shore dynamics and the thermal equator. The two main modes of explained variance for coastal upwelling on the coast of Africa indicate a correlation between the seasonal processes of strengthening and weakening of the regional wind regime, which condition the occurrence and strength of coastal upwelling. In view of this, it is concluded that the patterns of variation of the Chlorophyll-a and SST properties are conditioned to the seasonal variations of meso and macroscale in the TAO, the main modes of variance of the EOFs reinforce the coherence of the main variations within the system. Finally, further, more robust studies are recommended, integrating other types of analysis and data integration such as river discharge, rainfall indices, analysis of current and wind fields and comparison with data collected in situ to aid in interpretation.
Keywords: chlorophyll-a, SST, Tropical Atlantic Ocean, EOF, remote sensing.
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