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Pattern recognition of ocean pH

Pattern recognition of ocean pH Abstract The manuscript shows how the few, scattered, latest measurements of ocean pH lacking a proper spatial and time coverage do not permit a meaningful computation of global trends, as the ocean pH is strongly variable in latitude, longitude and depth and very likely is subject to the multi-decadal oscillations that have been identified in the atmospheric and ocean systems. The proposed mathematical model is based on the assumption that the monthly averaged ocean pH may be described by the superposition of a linear trend and inter-annual, decadal and multi-decadal oscillations, with linear and sinusoidal regression coefficients requiring data that are presently unavailable. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Engineering de Gruyter

Pattern recognition of ocean pH

Nonlinear Engineering , Volume 5 (3) – Sep 1, 2016

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2192-8010
eISSN
2192-8029
DOI
10.1515/nleng-2016-0005
Publisher site
See Article on Publisher Site

Abstract

Abstract The manuscript shows how the few, scattered, latest measurements of ocean pH lacking a proper spatial and time coverage do not permit a meaningful computation of global trends, as the ocean pH is strongly variable in latitude, longitude and depth and very likely is subject to the multi-decadal oscillations that have been identified in the atmospheric and ocean systems. The proposed mathematical model is based on the assumption that the monthly averaged ocean pH may be described by the superposition of a linear trend and inter-annual, decadal and multi-decadal oscillations, with linear and sinusoidal regression coefficients requiring data that are presently unavailable.

Journal

Nonlinear Engineeringde Gruyter

Published: Sep 1, 2016

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