A Framework for Detection and Attribution of Regional Precipitation Change: Application to the United States Historical Record
Rainfall is a critical component of the water cycle, and any deviations from “normal” rainfall can have disastrous consequences for managing water resources. Therefore, it is highly important to understand what causes prolonged periods of heavy rain and drought. Unfortunately, determining cause and effect for changes in rainfall is generally quite difficult when considering regions such as the United States. Here, we develop an approach for identifying changes to rainfall that determines cause and effect in a new way. We primarily examine changes caused by human activity such as air pollution from coal-burning power plants and carbon dioxide emissions.
Unlike existing approaches, our method allows us to discover changes in average and unusual rainfall events for specific locations and specific time points. We can be sure that the approach is well-founded since it is based on a systematic assessment of artificial data sets that have known properties. In this work, we focus on changes in rainfall over the United States, but the methodology could easily be extended to look at rainfall in other parts of the globe.
Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales, the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. We use the output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. The hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, but we note that the conclusions here will likely fail to hold in other geographic regions and under future warming.