Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereby helping to untangle complex processes. Mode-to-mode comparison of observed and modeled data provides a mechanism for model evaluation. The decomposition methods included empirical orthogonal functions (EOF), empirical mode decomposition (EMD), and wavelet filters (WF). EOF, a linear method designed for stationary time series, is principal component analysis (PCA) applied to time-lagged copies of a given time series. EMD is a relatively new nonlinear method that operates locally in time and is suitable for nonstationary and nonlinear processes; it is not, in theory, bandwidth limited, and the number of modes is automatically determined. Wavelet filters are linear and band-width guided with the number of modes set by the analyst. The purpose of this paper is to compare the performance of decomposition techniques in characterizing time scales in meteorological and air quality variables. Aiding this comparison is an analysis of simulated time series that have features in common with observations.