Distributed lag models (DLM) are regression models that include multiple lagged exposure variables as covariates. They are frequently used to model the relationship between daily mortality and short-term air pollution exposures. Specifying a maximum lag number is but one of the difficulties in using a DLM for environmental epidemiology. We propose an easily extendible ensemble post-processing approach. The resultant estimates are both more parsimonious, approaching zero with increasing lag, and more efficient. The benefits are shown to be robust under various simulation scenario's and illustrated with data from the National Morbidity, Mortality and Air Pollution Study.