Science Source
Quantile-based bias correction and uncertainty quantification of extreme event attribution statements
- States that extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events
- States that attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals
- States that many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence; however, such climate models may have biases in their representation of extreme events
- Accounts for discrepancies in the probabilities of extreme events between observational datasets and model datasets
- Demonstrates an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio
- This methodology accounts for various components of uncertainty in estimation of the risk ratio
- Presents an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity
- Demonstrates the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model, finding that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event
