Linearization of an Oceanic General Circulation Model for Data Assimilation and Climate Studies

Abstract

A recipe for the linearization and state reduction of a general circulation model (GCM) is evaluated in a North Pacific test basin. The underlying assumption is that modern GCMs are, or will become, sufficiently accurate so that large-scale differences with the real ocean are small and have linear physics. Model Green?s functions are used to construct a reduced-order linear model that compares favorably with the large-scale response of the GCM away from the western boundary. In a numerical example, the linear model is applied to the estimation of the large-scale internal structure of a simulated ocean using pseudotomographic and altimetric measurements. The sensitivity of the solution to a priori statistical assumptions is analyzed. Several algorithmic improvements are explored to render the estimation procedure more efficient, more accurate, and easier to implement than in previous studies.