High resolution 3-D temperature and salinity fields derived from in situ and satellite observations

This paper describes an observation-based approach that efficiently combines the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 yr) are merged with the lower accuracy but high-resolution synthetic data derived from satellite altimeter and sea surface temperature observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations, and salinity fields from altimeter observations, through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolutionary nature of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method, and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50% of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30% of the signal can be reconstructed from altimeter observations, making the in situ observing system essential for salinity estimates. The in situ observations (step 2 of the method) further reduce the differences between the gridded products and the observations by up to 20% for the temperature field in the mixed layer, and the main contribution is for salinity and the near surface layer with an improvement up to 30%. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to describe qualitatively the temperature variability patterns for the period from 1993 to 2009

A global comparison of Argo and satellite altimetry observations

Differences, similarities and complementarities between Sea Level Anomalies (SLA) deduced from altimeter measurements and dynamic height anomalies (DHA) calculated from Argo in situ temperature (<i>T</i>) and salinity (<i>S</i>) profiles are globally analyzed. SLA and DHA agree remarkably well and, compared to previous studies, Argo dataset allows an improvement in the coherence between SLA and DHA. Indeed, Argo data provides a much better spatial coverage of all oceans and particularly the Southern Ocean, the use of an Argo mean dynamic height, the use of measured salinity profiles (versus climatological salinity), and the use of a deeper reference level (1000 m versus 700 m). The large influence of Argo salinity observations on the consistency between altimetry and hydrographic observations is particularly demonstrated with an improvement of 35% (relative to the SLA minus DHA signal) by using measured salinity profiles instead of climatological data. The availability of observations along the Argo float trajectories also provides a means to describe the sea level variability of the global ocean both for the low frequency and the mesoscale part of the circulation. Results indicate that sea level variability is dominated by baroclinic signal at seasonal to inter-annual periods for all latitudes. In the tropics, sea level variability is baroclinic for meso-scale to interannual periods and at high latitudes, sea level variability is barotropic with also deep baroclinic signals (i.e. influence of deep temperature and salinity signals) for intra seasonal and mesoscale periods. These results emphasize the need to separate the different time and space scales in order to improve the merging of the two data sets. The qualitative study of seasonal to interannual SLA minus DHA signals finally reveals signals related to deep ocean circulation variations and basin-scale barotropic signals. Future work is, however, needed to understand the observed differences and relate them to different forcing mechanisms