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Publication Date
1 October 2018

Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression

Seasonal changes in temperature distributions in climate model ensembles are investigated using quantile regression.
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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. We present here a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes due to anthropogenic global warming.


The work provides new insights and techniques to analyze seasonal changes in temperature distributions using quantile analysis. The method is particularly suited for analyzing extreme temperature in large climate model ensembles.


The abundance of data available in large single model ensembles allows using quantile regression to estimate high quantiles accurately within a single model structure, avoiding assumptions about the shape of the tail of the distribution that are required to apply extreme value theory. The quantile regression approach described here enables the study of seasonal transitions with a flexible framework that allows different combinations of basis functions for seasonality, long-term trends, and changes in seasonality as appropriate for different datasets. While we analyze only temperature here, our method is intended to be general enough to be applied to other climate variables such as precipitation or humidity. These detailed insights into climate variable distributions may be valuable for risk assessment studies that emphasize extreme events.

Point of Contact
John Weyant
Stanford University
Funding Program Area(s)