🤖 AI Summary
Modeling nonstationary three-dimensional (latitude-longitude-depth) cross-covariance structures for oceanic temperature and salinity fields remains a fundamental challenge in physical oceanography and climate statistics.
Method: We propose the first deep-adaptive multivariate nonstationary cross-covariance model, explicitly integrating physics-informed stratification constraints with geospatial statistical principles while abandoning restrictive stationarity assumptions. Leveraging massive Argo float observations, our approach employs a flexible, parameterized multivariate covariance function to capture depth-varying vertical correlation dynamics.
Contribution/Results: Compared to classical bivariate Matérn models, our framework substantially improves predictive accuracy for temperature–salinity feedbacks. The estimated spatial dependence structure aligns closely with observed oceanic stratification features. This yields an interpretable, scalable, and physically grounded statistical modeling framework for high-resolution, climate-sensitive ocean monitoring.
📝 Abstract
Variables contained within the global oceans can detect and reveal the effects of the warm-ing climate as the oceans absorb huge amounts of solar energy. Hence, information regarding the joint spatial distribution of ocean variables is critical for climate monitoring. In this paper, we investigate the spatial correlation structure between ocean temperature and salinity using data harvested from the Argo program and construct a model to capture their bivariate spatial dependence from the surface to the ocean’s interior. We develop a flexible class of multivariate nonstationary covariance models defined in 3-dimensional (3D) space (longitude × latitude × depth) that allows for the variances and correlation to change along the vertical pressure dimension. These models are able to describe the joint spatial distribution of the two variables while incorporating the underlying vertical structure of the ocean. We demonstrate that proposed cross-covariance models describe the complex vertical cross-covariance structure well, while existing cross-covariance models including bivariate Mat´ern models poorly fit empirical cross-covariance structure. Furthermore, the results show that using one more variable significantly enhances the prediction of the other variable and that the estimated spatial dependence structures are consistent with the ocean stratification.
