A Simple Nonlinear and End-Member-Free Approach for Obtaining Ocean Remineralization Patterns

Open Access

Abstract

13 pages, 7 figures, 4 tables, supplementary material http://journals.ametsoc.org/doi/suppl/10.1175/JTECH-D-17-0090.1 ; The variability of a biogeochemical property in the ocean is the outcome of both nonconservative (such as respiration and photosynthesis) and conservative (mixing of water masses with distinct concentrations at origin) processes. One method to separate both contributions is based on a multiple regression of the biogeochemical property in terms of temperature θ and salinity S as conservative proxies of water masses. This regression delivers the variability related to the conservative fraction and hence allows for identifying the residual as the biogeochemical anomaly. Here, the standard multiple linear regression (MLR) method, which assumes that water masses mix locally and linearly, is compared with a nonlinear polynomial regression (PR) over the entire (θ, S) space. The PR method has two important advantages over MLR: allows for simultaneous nonlinear mixing of all water masses and does not require knowing the end-member water types. Both approaches are applied to data along 7.5°N in the equatorial Atlantic Ocean, and the biogeochemical anomalies are calculated for humic-like fluorescent dissolved organic matter, apparent oxygen utilization, and nitrate—all of them related through in situ remineralization processes. The goodness of both approaches is assessed by analyzing the linear dependence and the coefficient of correlation between the anomalies. The results show that the PR method can be applied over the entire water column and yet retains the local variability associated with nonconservative processes. The potential of the PR approach is also illustrated by calculating the oxygen–nitrate stoichiometric ratio for the entire 7.5°N transatlantic section ; This research was supported by the Spanish government through projects MOC2 (CTM2008-06438-CO2) and VA-DE-RETRO (CTM2014-56987-P). Patricia De La Fuente would like to thank the Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (AGAUR) for its financial support through an FI-AGAUR fellowship ; Peer Reviewed