Deep ocean dynamics and thermal/salinity structures cannot be observed by satellite remote sensors directly, but can be estimated with the help of models using satellite data. Which processes and structures can be estimated?
Data assimilation is a natural approach for merging observations (in-situ and satellite) and model data to obtain estimates of either observed or unobserved variables. For instance, we are interested in obtaining estimates of the North Atlantic meridional overturning circulation (NAMOC) by applying that methodology. Preliminary results suggest that this could be a successful endeavor.
Assimialtion of SLA into Indian ocean circulation model gave improvements in temperature profile below 200m and upto 500m using EnOI method. May be the assimilation of SST and SSS is needed for mixed layer processess.
Historical temperature and salinity profiles provide a good answer. A covariance relation between T(z) and S(z) can be constructed given the observations. These can also be extended to include the cross-covariance with geopotential. The result is that sea surface temperature (SST) and sea surface salinity (SSS) observations provide information within the mixed layer since properties are well mixed throughout. However the SST and SSS provide little information below the mixed layer. The sea surface height anomaly (SSHA) reflects T and S variations throughout the water column. Because the ocean acts mainly as a first mode baroclinic system (two layers with warm water above the thermocline and cold water below), a large fraction of T and S variability throughout the water column is related to the SSHA and can be estimated through historical data relations.
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