Future Climate Emulations Using Quantile Regressions on Large Ensembles

A comprehensive statistical method to compare climate projections from large ensembles that is well suited for examining changes in the tails.

The Science

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. The team presents a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations.

The Impact

The method provides insights into how simulated temperature distributions are changing within large climate model ensembles, which can then be combined with observational data products to preserve observed spatiotemporal dependence. The results highlight large differences in local extreme projections between ensembles using different versions of the same climate model, the Community Earth System Model (CESM).


The project researchers use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out one goes into the distribution tail. The researchers apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of CESM, run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter.

Principal Investigator(s)

John Weyant
Stanford University


This work was supported in part by the U.S. Department of Energy (DOE) Program on Coupled Human and Earth Systems (PCHES) under DOE Cooperative Agreement Number DE-SC0016162.


Haugen, M. A., M. L. Stein, R. L. Sriver, and E. L. Moyer. “Future climate emulations using quantile regressions on large ensembles.” Advances in Statistical Climatology, Meteorology, and Oceanography 5, 37–55 (2019). [DOI: 10.5194/ascmo-5-37-2019].