Idealized hydrodynamical numerical model dataset with no-river runoff at the western tropical North Atlantic

Open Res Eur. 2023 Apr 28:3:67. doi: 10.12688/openreseurope.15747.1. eCollection 2023.

Abstract

The western tropical North Atlantic (WTNA) is a very complex region, with the influence of intense western boundary currents in connection with equatorial zonal currents, important atmospheric forcings (e.g Intertropical Convergence Zone), mesoscale activities (e.g NBC rings), and the world's largest river discharge as the Amazon River runoff. The volume discharge is equivalent to more than one-third of the Atlantic river freshwater input, with a plume that spreads over the region reaching the northwestward Caribbean Sea and eastward longitudes of 30°W, and influencing from physical to biological structures. Therefore, in order to enable and encourage more understanding of the region, here we present a dataset based on an idealized scenario of no river runoff of the Amazon River and Par ´a River in the WTNA. The numerical simulations were conducted with a regional oceanic modeling system (ROMS) model and three pairs of files were generated with the model outputs: (i) ROMS-files, with the parameters of the ROMS-outputs raw data in a NetCDF format and monthly and weekly frequencies; (ii) MATLAB-files, which contain oceanographic parameters also in monthly and weekly frequencies; and (iii) NetCDF-files, with oceanographic parameters again in monthly and weekly frequencies. For each file, we present the coordinates and variable names, descriptions, and correspondent units. The dataset is available in the Science Data Bank repository (doi: https://doi.org/10.57760/sciencedb.02145).

Keywords: Amazon River; Dataset; Hydro-thermodynamics; Matlab; NetCDF; No-River runoff; ROMS model; Western tropical Atlantic.

Grants and funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No [817578] (Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management [TRIATLAS]). J. Araujo and M. Araujo acknowledges the funding support of the Brazilian Research Network on Global Climate Change - Rede CLIMA (FINEP grants 01.13.0353-00)