Advertisement

Climate Dynamics

, Volume 17, Issue 9, pp 687–700 | Cite as

A global ocean temperature and altimeter data assimilation system for studies of climate variability

  • S. Masina
  • N. Pinardi
  • A. Navarra

Abstract

 An ocean data assimilation (ODA) system which can assimilate both temperature and altimeter observations has been applied to the global ocean and tested between January 1993–October 1996. A statistical method has been used to convert sea surface height (SSH) anomalies observations from TOPEX/POSEIDON into synthetic temperature profiles. The innovative aspect of this method is the introduction of time dependency in the correlations used to transform the altimeter observations into temperature corrections. The assimilation system is based on a univariate variational optimal interpolation scheme applied to assimilate both in situ and synthetic temperature profiles. In addition, a longer global analysis for the upper-ocean temperature starting from January 1979 and ending November 1997, has been produced to examine the skill of sea temperature assimilation with a rather simple and practical method. The temperature analysis shows encouraging improvement over a corresponding ocean simulation when compared to independent (not assimilated) temperature data both at seasonal and interannual time scales. However, the univariate data assimilation of hydrographic data does not result in an improvement of the velocity field. In fact the assimilation of sparse in situ data can introduce unrealistic spatial variability in the temperature field which affects the velocity field in a negative way. This deficiency is partially overcome when we also assimilate altimeter observations since the coverage is complete and uniform for this data. In particular, our study shows that temperature corrections due to the altimeter signal have a positive impact on the current system in the tropical Pacific.

Keywords

Assimilation Global Ocean Assimilation System Temperature Correction Altimeter Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • S. Masina
    • 1
  • N. Pinardi
    • 2
  • A. Navarra
    • 1
  1. 1.Istituto Nazionale di Geofisica, Rome, Italy E-mail: s.masina@isao.bo.cnr.itIT
  2. 2.Corso di Laurea in Scienze Ambientali, University of Bologna, Ravenna, ItalyIT

Personalised recommendations