Abstract
Sea-surface temperature (SST) and sea-surface height (SSH) data are assimilated with a reduced-order sequential filter every 10 days over four years (1993-1996) in a 1/3-degree resolution South Atlantic primitive equation model. At every assimilation step, the misfit between observations and their model counterparts is evaluated and projected onto the dominant 10 multivariate, full-depth local modes of variability diagnosed from a free model run (without assimilation). Satellite observations are used to perform a sequential update of all model variables according to their natural correlations with the surface variables. In this paper, we perform a validation of both the mean state and the eddy flow. Over the four years of assimilation, the forecast-data RMS misfit is decreased (i) by 55% on SSH; (ii) by 40% on SST; (iii) by about 20% on temperature in the upper 700 m with regard to independent, synoptic XBT data; (iv) as deep as 3500 m on the time-averaged temperature distribution compared to a recent climatology. These statistical estimates of the assimilation performance were complemented by a more original oceanographic investigation that revealed a significant improvement in the mean circulation (in particular in the Confluence region and in the deep ocean), the position of the main fronts, the averaged level and basin-scale distribution of SSH variance, the detailed evolution of individual mesoscale structures, the vertical distribution of the eddy kinetic energy, and the salinity field. These results would further be improved by the assimilation of in-situ data below sharp thermoclines that tend to decorrelate subsurface dynamics from the observed surface.
Highlights
Satellite measurements of sea-surface temperature (SST) and sea-level anomalies (SLA)provide oceanographers with a real-time monitoring of the surface state of the world ocean
The present validation study has shown that the sequential assimilation of Sea-surface temperature (SST) and sea-surface height (SSH) data into an eddy-permitting model of the South Atlantic Ocean over four years improves the realism of the model solution, on assimilated surface variables and at depth
Available in-situ observations are signi cantly sparser in the South Atlantic than in the northern basin, so in addition to the more usual statistical evaluation of the assimilation performance we referred to different types of independent data (XBTs, temperature/salinity climatology, direct transport estimates, current meter measurements) to validate the model solution from different points of view
Summary
Provide oceanographers with a real-time monitoring of the surface state of the world ocean. They highlight the need for a better knowledge of other crucial surface variables (salinity, absolute velocities) and of subsurface processes. Our goal is to produce a consistent picture of the mean state and variability in the South Atlantic Ocean through the assimilation into a numerical model of sea-surface height (SSH) and SST data based on observations. The South Atlantic Ocean is connected to the North Atlantic, the Paci c, the Indian and the Antarctic oceans These sub-basins mostly interact in localized and eddy-active regions of the South Atlantic. The assimilation method should be able to control the highly nonlinear eddy eld and to extrapolate surface data toward the subsurface and nonobserved variables in accordance with the local dynamics and strati cation
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