A space-time model for joint modeling of ocean temperature and salinity levels as measured by Argo floats

The world's climate is to a large extent driven by the transport of heat and fresh water in the oceans. Regular monitoring, studying, understanding and forecasting of temperature and salinity at different depths of the oceans are a great scientific challenge. Temperature at the ocean surface can be measured from space. However salinity cannot yet be measured by satellites, and space-based measurements can only ever give us values at the surface. Until recently temperature and salinity measurements within the oceans have had to come from expensive research ships. The Argo float program has been funded by various nations to collect actual measurements and rectify this problem. A Bayesian hierarchical model is proposed in this paper describing the spatio-temporal behavior of the joint distribution of temperature and salinity levels. The model is obtained as a kernel-convolution effect of a single latent spatio-temporal process. Additional terms in the mean describe non-stationarity arising in time and space. Predictive Bayesian model selection criteria have been used to validate the models using data for the year 2003. Illustrative annual prediction maps along with their uncertainty maps are also obtained. The Markov chain Monte Carlo methods are used throughout in the implementation.