Linear Inverse Modeling of Large-Scale Atmospheric Flow Using Optimal Mode Decomposition

Abstract

Abstract Linear inverse modeling or principal oscillation pattern (POP) analysis is a widely applied tool in climate science for extracting from data dominant spatial patterns together with their dynamics as approximated by a linear Markov model. The system is projected onto a principal linear subspace and the system matrix is estimated from data. The eigenmodes of the system matrix are the POPs, with the eigenvalues providing their decay time scales and oscillation frequencies. Usually, the subspace is spanned by the leading principal components (PCs) and empirical orthogonal functions (EOFs). Outside of climate science, this procedure is now more commonly referred to as dynamic mode decomposition (DMD). Here, we use optimal mode decomposition (OMD) to address the full linear inverse modeling problem of simultaneous optimization of the principal subspace and the linear operator. The method is illustrated on two pedagogical examples and then applied to a three-level quasigeostrophic atmospheric model with realistic mean state and variability. The OMD models significantly outperform the EOF/DMD models in predicting the time evolution of the large-scale flow modes. The advantage of the OMD models stems from finding more persistent modes as well as from better capturing the nonnormality of the linear operator and the associated nonmodal growth. The dynamics of the large-scale flow modes turn out to be markedly non-Markovian and the OMD modes are superior to the EOF/DMD modes also in a modeling setting with a higher-order vector autoregressive process. The OMD modes could also be used as basis functions for a nonlinear dynamical model although they are not optimized for that purpose. Potential applications of the OMD method in weather and climate science include ENSO or MJO prediction, reduced-rank data assimilation, and generation of initial perturbations for ensemble prediction.