Nitracentric/Hydrographic Classification and Prediction of Nitrate Profiles for Oceanographic Stations Under the Influence of Mesoscale Eddies in the Gulf of Mexico - Velásquez-Aristizábal, Jorge A.; Camacho-Ibar, Víctor F.; Durazo, Reginaldo; Valencia-Gasti, José A.; Lee-Sánchez, Erika; Trasviña-Castro, Armando (Frontiers in Marine Science, 2022)
In the ocean, nitrogen availability controls primary production and, by extension, energy fluxes through food webs. Mesoscale eddies are dominant modulators of the spatial distributions of physical and biogeochemical properties in the Gulf of Mexico (GM), including the availability of nitrate + nitrite (NN). In this study, we explore a station classification based on the integrated concentration of NN, which we call the “nitracentric classification,” and a classification based on hydrographic variables to identify stations under the influence of mesoscale eddies. We analyzed profiles of hydrographic data obtained with CTD casts and the NN concentrations obtained from discrete samples collected during the oceanographic campaign XIXIMI–5, which was conducted in June 2016 in the deep–water region of the GM. The best station separation was produced when the NN concentration was integrated between the surface and 200 m depth, which was supported by the station classification based on the hydrographic variables that we call the Best Fit Variables (BFVs), such as the depth of the 20 °C isotherm and the depth of the 26 kg m–3 isopycnal. With our classification, a better separation between station groups was obtained when compared to those that rely on the use of altimetric variables and hydrographic criteria that have been previously employed to study biogeochemical and physical processes in the GM. We obtained parameterizations that allow for the nitrate stock to be predicted and nitrate profile between 100–500 m to be reproduced at any given station based on a single value of a BFV obtained from a CTD cast. Our results allow for nitrate stocks and profiles to be calculated under different scenarios from hydrographic databases that are already available for the GM and for these to be predicted from hydrographic cast data at the time of sampling.