Salinity Assimilation Using S(T): Covariance Relationships

Abstract

Assimilation of salinity into ocean and climate general circulation models is a very important problem. Argo data now provide far more salinity observations than ever before. In addition, a good analysis of salinity over time in ocean reanalyses can give important results for understanding climate change. Here it is shown from the historical ocean database that over large regions of the globe (mainly midlatitudes and lower latitudes) variance of salinity on an isotherm S(T) is often less than variance measured at a particular depth S(z). It is also shown that the dominant temporal variations in S(T) occur more slowly than variations in S(z), based on power spectra from the Bermuda time series. From ocean models it is shown that the horizontal spatial covariance of S(T) often has larger scales than S(z). These observations suggest an assimilation method based on analyzing S(T). An algorithm for assimilating salinity data on isotherms is then presented, and it is shown how this algorithm produces orthogonal salinity increments to those produced during the assimilation of temperature profiles. It is argued that the larger space and time scales can be used for the S(T) assimilation, leading to better use of scarce salinity observations. Results of applying the salinity assimilation algorithm to a single analysis time within the ECMWF seasonal forecasting ocean model are also shown. The separate salinity increments coming from temperature and salinity data are identified, and the independence of these increments is demonstrated. Results of an ocean reanalysis with this method will appear in a future paper.

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

“…Some studies (e.g., Carton et al, 2000;Huang et al, 2008;Yan et al, 2004) uses climatological T-S relations typically calculated from the World Ocean Atlas (e.g., Locarnini et al, 2010). Vertical shifts of water masses in background (model) T-S profiles are applied in Haines et al (2006); Ricci et al (2005). Coupled T-S Empirical Orthogonal Function (EOF) modal decomposition is also employed to reconstruct vertical temperature and salinity profiles in Dobricic et al (2005); Fujii & Kamachi (2003); Maes et al (2000).…”

The link between the Barents Sea and ENSO events simulated by NEMO model

“…The data assimilation method of (Haines et al, 2006); Smith and Haines, 2009), previously implemented within NEMO, was used for the assimilation. Referred to as the S(T ) scheme this is a two-step sequential scheme for hydrographic data based on optimal interpolation.…”

“…In the second step salinity profiles (S) are assimilated along isotherms (i.e. S(T ), see Haines et al, 2006). With the S(T ) increments being spread along isotherms this means that corrections to a particular water mass do not influence adjacent water masses, (Haines et al, 2012) This table gives a definition of all the experiments used in this paper.…”

Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System

“…There are many areas where the effect of temperature on S300 is contrary to the dire ct effect of using salinity [85]. S3 uses the algorithm from [86] to correct salinity along isotherms to maintain important water mass properties, a technique that [87] show s improves upon conventional depth-level assimilation.…”