Long-Range Atmospheric Predictability Using Space–Time Principal Components

Abstract

The long-term predictability of 70-kPa geopotential heights is examined by a series of hindcast experiments over a validation period of 40 years using empirical models. Only the North Atlantic sector is considered. Significant skill is found up to lead times of one to two months for forecasts of time averages and of weather regime occurrence frequencies. The empirical schemes produce forecasts of the conditional probability of occurrence of a predictand within its natural terciles. These probabilistic forecasts are compared for two sets of predictors. The (spatial) principal components of the Atlantic large-scale flow (S-PCs) and its space?time principal components (ST-PCs) obtained from multichannel singular spectrum analysis (MSSA). These latter predictors achieve a good compromise between explained variance and predictability. In particular, the skill of a one-step model, where predictand's conditional probabilities are obtained directly from an analog method, is compared with a two-step model, which first forecasts the ST-PCs and then specifies the predictand's conditional probabilities. The one- step model is systematically beaten by the ST-PC scheme for lead times beyond 10 days. An attempt is made to explain why ST-PCs perform better than S-PCs in the long run by applying the forecast schemes to a simple low-order chaotic dynamical system. The key factor seems to be that for a dynamical system displaying low-frequency behavior and nonlinear spells of oscillations, the MSSA expansion gathers these phenomena into a few leading ST-PCs. These ST-PCs are therefore good candidates to quantify the concept of atmospheric ?predictable? components.