Locally Stationary Interpolation of Argo Float Data for Improved Estimates of Ocean Climate

• Activity Number: 90 - Invited EPoster Session
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 8:30 PM to 10:30 PM
• Sponsor: ASA
• Author(s): Mikael Kuusela*
• Companies: Carnegie Mellon University
• Keywords: local kriging; nonstationarity; non-Gaussianity; ocean heat content; oceanography; climate science

Abstract

Argo floats measure seawater temperature and salinity in the upper 2000 meters of the global ocean. Statistical analysis of the resulting spatio-temporal dataset is challenging due to its nonstationary structure and large size. We propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure. We also investigate Student-t distributed fine-scale variation as a means to account for non-Gaussian heavy tails in ocean temperature data. Cross-validation studies comparing the proposed approach with the existing state-of-the-art demonstrate clear improvements in point predictions and show that accounting for the nonstationarity and non-Gaussianity is crucial for obtaining well-calibrated uncertainties. These techniques can be used to obtain improved estimates of ocean climate and dynamics. As an example, we present an application in estimating the heat content of the global ocean, a quantity that is of central importance for understanding changes in the Earth's climate system.