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.