Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model:Impact on the Simulation of Variability

When altimetric data is assimilated,3DVAR and Ensemble Optimal Interpolation(EnOI) have different ways of projecting the surface information downward.In 3DVAR,it is achieved by minimizing a cost function relating the temperature,salinity,and sea level.In EnOI,however,the surface information is propagated to other variables via a stationary ensemble.In this study,the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model.Sea level anomalies(SLA) from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model.Results show that the standard deviation of sea level was improved by both methods,but the EnOI was more effective in the central/eastern Pacific.Meanwhile,the SLA evolution was better reproduced with EnOI than with 3DVAR.Correlations of temperature with the reanalysis data increased with EnOI by 0.1-0.2 above 200 m.In the eastern Pacific below 200 m,the correlations also increased by 0.2.However,the correlations decreased with 3DVAR in many areas.Correlations with the independent TAO profiles were also compared at two locations.While the correlations increased by up to 0.2 at some depths with EnOI,3DVAR generally reduced the correlations by 0.1-0.3.Though both methods were able to reduce the model-data difference in climatological sense,3DVAR appears to have degraded the simulated variability,especially during El Nin o-Southern Oscillation events.For salinity,similar results were found from the correlations.This tendency should be considered in future SLA assimilations,though the comparisons may vary among different model implementations.

Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model: Impact on the Simulation of Variability

When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation (EnOI) have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies (SLA) from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1--0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1--0.3. Though both methods were able to reduce the model--data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during El Nino--Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.