Abstract
Five years of Topex/Poseidon (T/P) and ERS sea level anomaly (SLA) data (1994–1998) are assimilated every 10 days into a primitive equation model of the tropical Pacific ocean. The data assimilation technique used here is a reduced-order Kalman filter derived from the Singular Evolutive Extended Kalman (SEEK) filter [J. Mar. Syst. 16(3–4) (1998) 323] with an error covariance matrix parameterised by a subset of multivariate 3D global empirical orthogonal functions (EOFs). The assimilation run is compared to the free run and to independent data from the TAO network. The impact of sea-surface height (SSH) assimilation on surface and subsurface temperature and currents is estimated in the equatorial band. In a second stage, temperature data from the TAO array are assimilated in the same conditions as in the first stage. The comparison between the results of the two assimilation experiments is made mainly with a view to gaining insights into the mean sea-surface height (MSSH) for the assimilation of altimeter data, and more generally, into the question of biases. XBT observations and TAO array data are then used to build a physically more consistent mean sea-surface height for assimilation of SLA data. Results from the assimilation of altimeter data referenced to this new MSSH show significant improvements.
Highlights
The El Nino – Southern oscillation (ENSO) phenomenon is the most significant process of climate variability at the interannual time scale in the tropical Pacific ocean, and is recognized as having an impact on the whole planet
One basic objective of this paper will be to demonstrate that a statistical assimilation scheme is able to improve the tropical Pacific ocean state simulated by a primitive equation ocean model for the years 1994– 1998, a period which encompasses the most important El Nino phenomenon of the past century
This paper describes the assimilation of altimeter (T/P and ERS satellite) and temperature (TAO array) data into a primitive equation model of the tropical
Summary
The El Nino – Southern oscillation (ENSO) phenomenon is the most significant process of climate. It is important to obtain an accurate estimate of the ocean initial conditions before forecasting climate variability using coupled models, and data assimilation is a way to do this (e.g., Canizares et al, 2001). Two methods are generally used in data assimilation, the statistical approach of the Kalman filter (e.g., Fukumori, 1995; Gourdeau et al, 2000; hereafter G00) or the variational approach (e.g., Greiner and Arnault, 2000; Bonekamp et al, 2001; Vossepoel and Behringer, 2000) Both the Kalman filter and the variational approach derive from the same estimation problem, their algorithmic implementation in complex ocean models may require different simplifications or approximations, leading to different solutions. One basic objective of this paper will be to demonstrate that a statistical assimilation scheme is able to improve the tropical Pacific ocean state simulated by a primitive equation ocean model for the years 1994– 1998, a period which encompasses the most important El Nino phenomenon of the past century.
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