[1] To simulate ozone (O 3 ) air quality in future decades over the eastern United States, a modeling system consisting of the NASA Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model, the Pennsylvania State University/National Center for Atmospheric Research mesoscale regional climate model (MM5), and the Community Multiscale Air Quality model has been applied. Estimates of future emissions of greenhouse gases and ozone precursors are based on the A2 scenario developed by the Intergovernmental Panel on Climate Change (IPCC), one of the scenarios with the highest growth of CO 2 among all IPCC scenarios. Simulation results for five summers in the 2020s, 2050s, and 2080s indicate that summertime average daily maximum 8-hour O 3 concentrations increase by 2.7, 4.2, and 5.0 ppb, respectively, as a result of regional climate change alone with respect to five summers in the 1990s. Through additional sensitivity simulations for the five summers in the 2050s the relative impact of changes in regional climate, anthropogenic emissions within the modeling domain, and changed boundary conditions approximating possible changes of global atmospheric composition was investigated. Changed boundary conditions are found to be the largest contributor to changes in predicted summertime average daily maximum 8-hour O 3 concentrations (5.0 ppb), followed by the effects of regional climate change (4.2 ppb) and the effects of increased anthropogenic emissions (1.3 ppb). However, when changes in the fourth highest summertime 8-hour O 3 concentration are considered, changes in regional climate are the most important contributor to simulated concentration changes (7.6 ppb), followed by the effect of increased anthropogenic emissions (3.9 ppb) and increased boundary conditions (2.8 ppb). Thus, while previous studies have pointed out the potentially important contribution of growing global emissions and intercontinental transport to O 3 air quality in the United States for future decades, the results presented here imply that it may be equally important to consider the effects of a changing climate when planning for the future attainment of regional-scale air quality standards such as the U.S. national ambient air quality standard that is based on the fourth highest annual daily maximum 8-hour O 3 concentration.

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.

Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O3 concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O3-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O3 in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O3-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O3 precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O3-related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change.

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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