A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature

• Activity Number: 500
• Type: Invited
• Date/Time: Wednesday, August 5, 2009 : 2:00 PM to 3:50 PM
• Sponsor: JASA, Applications and Case Studies
• Author(s): Ricardo T. Lemos and Bruno Sanso*+
• Companies: Universidade de Lisboa and University of California, Santa Cruz
• Keywords: Climatology ; Discrete Process Convolutions ; Parallel Processing ; Hierarchical Models

Abstract

We consider the problem of fitting a statistical model to thirty years of sea surface temperature records collected over the North Atlantic. The purpose of the model is to produce an atlas of oceanic properties, including climatological mean fields, estimates of historical trends and a spatio-temporal reconstruction of the anomalies. These products are of interest to climate change and climate variability research, numerical modeling and remote sensing analyzes. Our model improves upon the current tools used by oceanographers. It accounts for non-isotropic and non-stationary space and time dependencies, owing to its use of discrete process convolutions. Particular attention is given to the handling of massive data sets. For this we use compact support kernels that allow efficient parallelization of the MCMC. Result are compared to the World Ocean Atlas v2.