TOWARD UNDERSTANDING PREDICTABILITY OF CLIMATE: A LINEAR STOCHASTIC MODELING APPROACH A Dissertation by

Toward Understanding Predictability of Climate: A Linear Stochastic Modeling Approach. (August 2003) Faming Wang, B.S., Shandong University; M.S., First Institute of Oceanography Chair of Advisory Committee: Dr. Ping Chang This dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation. Subsequently, predictabilities of two specific physical systems are investigated within such a framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection. Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However,