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Water mass distributions in the Southern Ocean derived from a parametric analysis of mixing water masses
de Brauwere, A.; Jacquet, S.H.M.; De Ridder, F.; Dehairs, F.; Pintelon, R.; Schoukens, J.; Baeyens, W. (2007). Water mass distributions in the Southern Ocean derived from a parametric analysis of mixing water masses. J. Geophys. Res. 112(C02021): 16 pp.
In: Journal of Geophysical Research. American Geophysical Union: Richmond. ISSN 0148-0227, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

    Mengprocessen; Multivariate analyse; Watermassa's; PS, Zuidelijke Oceaan [Marine Regions]; Marien

Auteurs  Top 
  • de Brauwere, A., meer
  • Jacquet, S.H.M., meer
  • De Ridder, F., meer
  • Dehairs, F., meer

    An empirical method to reconstruct distributions of mixing water masses is developed and applied to two Southern Ocean (Australian sector) data sets. The method is a parametric extension of the well-known Optimum Multiparameter (OMP) analysis, therefore called POMP (parametric OMP). The main development consists of parameterizing the mixing fractions as a function of position (e.g. latitude and depth) and estimating the parameters of the resulting function, instead of estimating the mixing fractions for every sampling point individually. Because this enables to reduce the total number of unknowns to be estimated, the proposed adjustments result in more robust estimates of the mixing fractions, as illustrated on the Southern Ocean CLIVAR-SR3 data set (early spring 2001). An additional consequence of the new working scheme is that the mixing distributions are smoothed. It is emphasized that the degree of smoothing should be chosen with care in order not to neglect any significant features. Statistical rules to do this are applied. Application of the POMP method to the second Southern Ocean data set (SAZ'98, summer 1998) revealed the importance of a careful selection of the mixing source water types. This study was limited to the subantarctic region and upper 1000 m, where variations in water mass characteristics and distributions are most important. Indeed, it seems necessary to redefine the source water characteristics for each data set. The direction of these changes suggests that interannual variability or long term changes of water masses overwhelm the seasonal variability.

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