Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression
Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. In this paper, we present a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method’s utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles.