This paper describes the characteristic space and time scales in time series of ambient ozone data. The authors discuss the need and a methodology for cleanly separating the various scales of motion embedded in ozone time series data, namely, short-term (weather related) variations, seasonal (solar induced) variations, and long-term (climate-policy related) trends, in order to provide a better understanding of the underlying physical processes that affect ambient ozone levels. Spatial and temporal information in ozone time series data, obscure prior to separation, is clearly displayed by simple laws afterward. In addition, process changes due to policy or climate changes may be very small and invisible unless they are separated from weather and seasonality. Successful analysis of the ozone problem, therefore, requires a careful separation of seasonal and synoptic components.The authors show that baseline ozone retains global information on the scale of more than 2 months in time and about 300 km in space. The short-term ozone component, attributable to short-term weather and precursor emission fluctuations, is highly correlated in space, retaining 50% of the short-term information at distances ranging from 350 to 400 km; in time, short-term ozone resembles a Markov process with 1-day lag correlations ranging from 0.2 to 0.5. The correlation structure of short-term ozone permits highly accurate predictions of ozone concentrations up to distances of about 600 km from a given monitor. These results clearly demonstrate that ozone is a regional-scale problem.

The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov-Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.

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To study the underlying forcing mechanisms that distinguish the days with high ozone concentrations from average or nonepisodic days, the observed and model-predicted ozone time series are spectrally decomposed into different temporal components; the modeled values are based on the results of a three-month simulation with the Urban Airshed Model-Variable Grid Version photochemical modeling system. The ozone power spectrum is represented as the sum of four temporal components, ranging from the intraday timescale to the multiweek timescale. The results reveal that only those components that contain fluctuations with periods equal to or greater than one day carry the information that distinguishes ozone episode days from nonepisodic days. Which of the longer-term fluctuations is dominant in a particular episode varies from episode to episode. However, the magnitude of the intraday fluctuations is nearly invariant in time. The promulgation of the 8-h standard for ozone further emphasizes the importance of longerterm fluctuations embedded in ozone time series data. Furthermore, the results indicate that the regional photochemical modeling system is able to capture these features. This paper also examines the effect of simulation length on the predicted ozone reductions stemming from emission reductions. The results demonstrate that for regulatory purposes, model simulations need to cover longer time periods than just the duration of a single ozone episode; this is necessary not only to perform a meaningful model performance evaluation, but also to quantify the variability in the efficacy of an emission control strategy.

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