An Efficient Description of the Dynamics of Barotropic Flow

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Abstract
A potentially efficient description of the atmospheric circulation is investigated in the context of a barotropic spectral model, truncated to T21. The model circulation evolves around a realistic winter climate and has a reasonable low-frequency variability. This study is motivated by the observation that the atmosphere continuously generates coherent structures, which perhaps are better represented by empirical orthogonal functions (EOFs) than by spherical harmonics. Therefore, the proposition is made to project the dynamical equations onto the dominant EOFS. Ambiguities in the formulation of an EOF model are clarified. Careful attention is paid to the integral constraints of an EOF model. As a reference for the performance of the EOF models, a T20 version of the T21 model is used. The T21 model has 231 variables; the T20 version 210. Deterministic predictions of the flow of the T21 model by the EOF model truncated to only 20 EOFs turn out to be substantially better than the predictions by the ... Abstract A potentially efficient description of the atmospheric circulation is investigated in the context of a barotropic spectral model, truncated to T21. The model circulation evolves around a realistic winter climate and has a reasonable low-frequency variability. This study is motivated by the observation that the atmosphere continuously generates coherent structures, which perhaps are better represented by empirical orthogonal functions (EOFs) than by spherical harmonics. Therefore, the proposition is made to project the dynamical equations onto the dominant EOFS. Ambiguities in the formulation of an EOF model are clarified. Careful attention is paid to the integral constraints of an EOF model. As a reference for the performance of the EOF models, a T20 version of the T21 model is used. The T21 model has 231 variables; the T20 version 210. Deterministic predictions of the flow of the T21 model by the EOF model truncated to only 20 EOFs turn out to be substantially better than the predictions by the ...

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