A simple nonlinear and endmember-free approach for obtaining ocean remineralization patterns

A simple nonlinear and endmember-free approach for obtaining ocean remineralization patterns AbstractThe variability of a biogeochemical property in the ocean is the outcome of both non-conservative (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 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 non-linear polynomial regression (PR) over the entire (θ, S) space. The PR method has two important advantages over MLR: allows simultaneous non-linear mixing of all water masses and does not require knowing the endmember 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. We assess the goodness of both approaches by analyzing the linear dependence and 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 non-conservative 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

A simple nonlinear and endmember-free approach for obtaining ocean remineralization patterns

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0090.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThe variability of a biogeochemical property in the ocean is the outcome of both non-conservative (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 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 non-linear polynomial regression (PR) over the entire (θ, S) space. The PR method has two important advantages over MLR: allows simultaneous non-linear mixing of all water masses and does not require knowing the endmember 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. We assess the goodness of both approaches by analyzing the linear dependence and 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 non-conservative 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.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Oct 5, 2017

References

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