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Sea Surface Salinity Variability from a Simplified Mixed Layer Model of the Global Ocean : Volume 4, Issue 1 (15/01/2007)

By Michel, S.

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Book Id: WPLBN0004020999
Format Type: PDF Article :
File Size: Pages 66
Reproduction Date: 2015

Title: Sea Surface Salinity Variability from a Simplified Mixed Layer Model of the Global Ocean : Volume 4, Issue 1 (15/01/2007)  
Author: Michel, S.
Volume: Vol. 4, Issue 1
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2007
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Chapron, B., Reul, N., Tournadre, J., & Michel, S. (2007). Sea Surface Salinity Variability from a Simplified Mixed Layer Model of the Global Ocean : Volume 4, Issue 1 (15/01/2007). Retrieved from http://worldlibrary.net/


Description
Description: Laboratoire d'Océanographie Spatiale, IFREMER, Brest, France. A bi-dimensional mixed layer model (MLM) of the global ocean is used to investigate the sea surface salinity (SSS) balance and variability at daily to seasonal scales. Thus a simulation over an average year is performed with daily climatological forcing fields. The forcing dataset combines air-sea fluxes from a meteorological model, geostrophic currents from satellite altimeters and in situ data for river run-offs, deep temperature and salinity. The model is based on the slab mixed layer formulation, which allows many simplifications in the vertical mixing representation, but requires an accurate estimate for the Mixed Layer Depth. Therefore, the model MLD is obtained from an original inversion technique, by adjusting the simulated temperature to input sea surface temperature (SST) data. The geographical distribution and seasonal variability of this effective MLD is validated against an in situ thermocline depth. This comparison proves the model results are consistent with observations, except at high latitudes and in some parts of the equatorial band. The salinity balance can then be analysed in all the remaining areas. The annual tendency and amplitude of each of the six processes included in the model are described, whilst providing some physical explanations. A map of the dominant process shows that freshwater flux controls SSS in most tropical areas, Ekman transport in Trades regions, geostrophic advection in equatorial jets, western boundary currents and the major part of subtropical gyres, while diapycnal mixing leads over the remaining subtropical areas and at higher latitudes. At a global scale, SSS variations are primarily caused by horizontal advection (46%), then vertical entrainment (24%), freshwater flux (22%) and lateral diffusion (8%). Finally, the simulated SSS variability is compared to an in situ climatology, in terms of distribution and seasonal variability. The overall agreement is satisfying, which confirms that the salinity balance is reliable. The simulation exhibits stronger gradients and higher variability, due to its fine resolution and high frequency forcing. Moreover, the SSS variability at daily scale can be investigated from the model, revealing patterns considerably different from the seasonal cycle. Within the perspective of the future satellite missions dedicated to SSS retrieval (SMOS and Aquarius/SAC-D), the MLM could be useful for determining calibration areas, as well as providing a first-guess estimate to inversion algorithms.

Summary
Sea surface salinity variability from a simplified mixed layer model of the global ocean

Excerpt
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