When modeling environmental time series, proper accounting of periodic phenomena is often critical. Seasonal, diurnal effects and their interactions are the most common, but other periodicities may also be present, such as those related to characteristics of the Earth’s orbital cycles. These periodic phenomena may affect not just the mean of the distribution but also the variability and shape. Climate change may affect seasonal and diurnal cycles, further complicating their modeling and estimation. This talk will explore these issues through some examples, including daily surface temperatures and coastal water levels. Block bootstrapping will be the main tool for producing defensible uncertainty estimates without having to model complex temporal and, when modeling multiple locations simultaneously, spatio-temporal dependencies.