4D-Var data assimilation using satellite sea surface temperature to improve the tidally-driven interior ocean dynamics in the Indo-Australian Basin

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Abstract

Estimating ocean processes in regions with sparse observations, and where dynamics are dominated by strong barotropic and baroclinic tides, poses a significant modelling challenge. In this study, a Four-Dimensional Variational (4D-Var) data assimilation method, incorporating remotely sensed sea surface temperature (SST) data observations only, was used to improve estimates of the internal tide dynamics in the ocean between Australia and Indonesia. In this region, local internal tide generation, propagation and interference of the internal tides is highly variable and dependent on the 3D density field and mesoscale dynamics. The results of this study show that assimilation of remotely sensed SST data not only reduced the surface temperature root mean squared error (RMSE) from 0.8 °C to 0.3 °C, and removed a 0.4 °C cold bias (compared to a non-assimilative model), but also improved estimates of through-water-column variables – with the latter evaluated against ARGO profiling floats and two independent mooring sites. Overall, the depth-averaged temperature RMSE was reduced by as much as 38%, and ocean currents by ∼20% at the two mooring sites. The improvements were most significant in the top 100 m, where the internal tide signature was significantly corrected despite no depth-varying variables being assimilated. The study also showed that assimilating 2 km resolution SST observations resulted in more realistic SST wavenumber power spectral density estimates than those without assimilating data; in particular, the scheme reduced the energy at mesoscale length scales and improved the spectral slope towards the smaller submesoscale dynamics. It is concluded that 4D-Var assimilation using only SST improves estimates of upper 100 m ocean dynamics in tropical regions where there is sensitivity to the air-sea fluxes.
Original languageEnglish
Article number101969
Number of pages17
JournalOcean Modelling
Volume171
DOIs
Publication statusPublished - Mar 2022

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