Quantile-based Bias Correction and Uncertainty Quantification of Extreme Event Attribution Statements
Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. Climate models may have biases in their representation of extreme events.
There are two important results from this work. The first result is that we present a methodology tailored to bias correct the extreme events simulated by climate models. This is necessary to make better use of the CMIP5 database of climate models. The second, and more significant result, concerns a finding about the uncertainty in attributing the human influence on individual climate and weather extreme events. We present a method to estimate the lower bound of the change in risk of events when the upper bound includes infinity. We find that this lower bound, using this one-sided estimate, is insensitive to the magnitude of the extreme event. This important finding means that observational uncertainty is not critical to determining whether humans have had a significant influence on an actual extreme event. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model.
Citation: Soyoung Jeon, Christopher J. Paciorek, Michael F. Wehner (2016) Quantile-based Bias Correction and Uncertainty Quantification of Extreme Event Attribution Statements. Weather and Climate Extremes. Early online release. DOI:10.1016/j.wace.2016.02.001