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J. Mar. Sci. Eng. 2017, 5(4), 57;

Regime Changes in Global Sea Surface Salinity Trend

Institut de Ciències del Mar - CSIC, Barcelona 08003, Spain
Integrated Statistics, Woods Hole 02543, MA, USA
Independent Research, West Tisbury 02575, MA, USA
Author to whom correspondence should be addressed.
Received: 17 October 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 27 November 2017
(This article belongs to the Section Physical Oceanography)
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Recent studies have shown significant sea surface salinity (SSS) changes at scales ranging from regional to global. In this study, we estimate global salinity means and trends using historical (1950–2014) SSS data from the UK Met Office Hadley Centre objectively analyzed monthly fields and recent data from the SMOS satellite (2010–2014). We separate the different components (regimes) of the global surface salinity by fitting a Gaussian Mixture Model to the data and using expectation–maximization to distinguish the means and trends of the data. The procedure uses a non-subjective method (Bayesian information criterion) to extract the optimal number of means and trends. The results show the presence of three separate regimes: Regime A (1950–1990) is characterized by small trend magnitudes; Regime B (1990–2009) exhibited enhanced trends; and Regime C (2009–2014) with significantly larger trend magnitudes. The salinity differences between regime means were around 0.01. The trend acceleration could be related to an enhanced global hydrological cycle or to a change in the sampling methodology. Understanding past SSS changes can provide insight into future climate evolution by complementing the knowledge acquired in recent decades from long-term temperature record analyses. View Full-Text
Keywords: sea surface salinity; regime change; global trend; SMOS; expectation–maximization sea surface salinity; regime change; global trend; SMOS; expectation–maximization

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Aretxabaleta, A.L.; Smith, K.W.; Kalra, T.S. Regime Changes in Global Sea Surface Salinity Trend. J. Mar. Sci. Eng. 2017, 5, 57.

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