  Climate change is a massive multidimensional shift. Temperature shifts, in
particular, have important implications for urbanization, agriculture, health,
productivity, and poverty, among other things. While much research has
documented rising mean temperature \emph{levels}, we also examine range-based
measures of daily temperature \emph{volatility}. Specifically, using data for
select U.S. cities over the past half-century, we compare the evolving time
series dynamics of the average temperature level, AVG, and the diurnal
temperature range, DTR (the difference between the daily maximum and minimum
temperatures). We characterize trend and seasonality in these two series using
linear models with time-varying coefficients. These straightforward yet
flexible approximations provide evidence of evolving DTR seasonality and stable
AVG seasonality.
