Climate change is a  multidimensional shift.  While much research has documented rising mean temperature levels , we also examine range-based measures of daily temperature 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 at a given location ). 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 , stable AVG seasonality , and conditionally Gaussian but heteroskedastic innovations for both DTR and AVG .
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 levels , we also examine range-based measures of daily temperature 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 .
A
massive
A
Temperature shifts, in particular, have important implications for urbanization, agriculture, health, productivity, and poverty, among other things.
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levels
levels
R
volatility
volatility
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at a given location
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,
and
D
, and conditionally Gaussian but heteroskedastic innovations for both DTR and AVG