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Effects of Sea Level Data Assimilation by Ensemble Optimal Interpolation and 3D Variational Data Assimilation on the Simulation of Variability in a Tropical Pacific Model

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  • 1 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

Sea level anomalies (SLA) from the Ocean Topography Experiment (TOPEX)/Poseidon are assimilated with three-dimensional variational data assimilation (3DVAR) and ensemble optimal interpolation (EnOI) for the period of 1997–2001. When sea level data are assimilated, one major concern is how to project the surface information downward. In 3DVAR, downward projection is usually achieved by minimizing a cost function that computes the relations among temperature, salinity, and sea level. In EnOI, the surface information is propagated to other variables through a stationary ensemble. Their effects on the simulated variability are evaluated in a tropical Pacific Ocean model. When compared with different datasets, it is found that effects of 3DVAR and EnOI are different in several aspects. For sea level, the standard deviation is improved by both methods, but EnOI is more effective in the central/eastern Pacific. The SLA evolution is better reproduced with EnOI than with 3DVAR. For temperature, the model–reanalysis correlations are increased by 0.1–0.2 in the top 200 m with both methods, but EnOI is more effective, especially along the thermocline depth. When compared with the Tropical Atmosphere–Ocean array (TAO) profiles, evolution of the temperature reveals that 3DVAR tends to cause more errors during ENSO events. The correlations with TAO profile are increased by 0.1–0.3 with EnOI and are generally decreased by 0.1–0.3 with 3DVAR. For salinity, both methods have weak impact on the model–reanalysis correlations above the thermocline. Relative to 3DVAR, EnOI can increase the correlation by 0.2 below the thermocline. When compared with the TAO profiles, the differences are reduced to some extent with both methods, but 3DVAR is very negative on the simulated variability.

Corresponding author address: Weiwei Fu, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: weiweifu@mail.iap.ac.cn

Abstract

Sea level anomalies (SLA) from the Ocean Topography Experiment (TOPEX)/Poseidon are assimilated with three-dimensional variational data assimilation (3DVAR) and ensemble optimal interpolation (EnOI) for the period of 1997–2001. When sea level data are assimilated, one major concern is how to project the surface information downward. In 3DVAR, downward projection is usually achieved by minimizing a cost function that computes the relations among temperature, salinity, and sea level. In EnOI, the surface information is propagated to other variables through a stationary ensemble. Their effects on the simulated variability are evaluated in a tropical Pacific Ocean model. When compared with different datasets, it is found that effects of 3DVAR and EnOI are different in several aspects. For sea level, the standard deviation is improved by both methods, but EnOI is more effective in the central/eastern Pacific. The SLA evolution is better reproduced with EnOI than with 3DVAR. For temperature, the model–reanalysis correlations are increased by 0.1–0.2 in the top 200 m with both methods, but EnOI is more effective, especially along the thermocline depth. When compared with the Tropical Atmosphere–Ocean array (TAO) profiles, evolution of the temperature reveals that 3DVAR tends to cause more errors during ENSO events. The correlations with TAO profile are increased by 0.1–0.3 with EnOI and are generally decreased by 0.1–0.3 with 3DVAR. For salinity, both methods have weak impact on the model–reanalysis correlations above the thermocline. Relative to 3DVAR, EnOI can increase the correlation by 0.2 below the thermocline. When compared with the TAO profiles, the differences are reduced to some extent with both methods, but 3DVAR is very negative on the simulated variability.

Corresponding author address: Weiwei Fu, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: weiweifu@mail.iap.ac.cn
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