26 June 2019

Future Climate Emulations Using Quantile Regressions on Large Ensembles

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

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. We present 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.

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 (CESM).

Summary

We 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 into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (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.

Contact
John Weyant
Stanford University
Publications
Haugen, MA, ML Stein, RL Sriver, and EJ Moyer.  2019.  "Future climate emulations using quantile regressions on large ensembles."  Advances in Statistical Climatology, Meteorology and Oceanography 5(1): 37-55.  https://doi.org/10.5194/ascmo-5-37-2019.