Many, but not all, observational epidemiological studies of ozone (O(3)) air pollution have yielded significant associations between variations in daily ambient concentrations of this pollutant and a wide range of adverse health outcomes. We evaluate some past epidemiological studies that have assessed the short-term association of O(3) with mortality, and investigate one possible reason for variations in their O(3) effect estimate, i.e., differences in their approaches to the modeling of weather influences on mortality. For all of the total mortality-air pollution time-series studies considered, the combined analysis yielded a relative risk, RR=1.036 per 100-ppb increase in daily 1-h maximum O(3) (95% CI: 1.023-1.050). However, the subset of studies that specified the nonlinear nature of the temperature-mortality association yielded a combined estimate of RR=1.056 per 100 ppb (95% CI: 1.032-1.081). This indicates that past time-series studies using linear temperature-mortality specifications have underpredicted the premature mortality effects of O(3) air pollution. For Detroit, MI, an illustrative analysis of daily total mortality during 1985-1990 also indicated that the model weather specification choice can influence the O(3) health effects estimate. Results were intercompared for alternative weather specifications. Nonlinear specifications of temperature and relative humidity (RH) yielded lower intercorrelations with the O(3) coefficient, and larger O(3) RR estimates, than a base model employing a simple linear spline of hot and cold temperature. We conclude that, unlike for particulate matter (PM) mass, the mortality effect estimates derived by time-series analyses for O(3) can be sensitive to the way that weather is addressed in the model. The same may well also be true for other pollutants with largely temperature-dependent formation mechanisms, such as secondary aerosols. Generally, we find that the O(3)-mortality effect estimate increases in size and statistical significance when the nonlinearity and the humidity interaction of the temperature-health effect association are incorporated into the model weather specification. We recommend that a minimization of the intercorrelations of model coefficients be considered (along with other critical factors such as goodness of fit, autocorrelation, and overdispersion) when specifying such a model, especially when individual coefficients are to be interpreted for risk estimation.