Supporting Information for "Regional extreme precipitation events: robust inference from credibly simulated GCM variables"
General Circulation Models (GCMs) have been demonstrated to produce estimates of precipitation, including the frequency of extreme precipitation, with substantial bias and uncertainty relative to their representation of other fields. Thus, while theory predicts changes in the hydrologic cycle under anthropogenic warming, there is generally low confidence in future projections of extreme precipitation frequency for specific river basins. In this paper, we explore whether a GCM simulates large-scale atmospheric circulation indices that are associated with regional extreme precipitation (REP) days more accurately than it simulates REP days themselves, and thus whether conditional simulation of the precipitation events based on the circulation indices may improve the simulation of REP events. We show that a coupled Geophysical Fluid Dynamics Laboratory GCM simulates too many springtime REP days in the Ohio River Basin in historical (1950-2005) simulations. The GCM, however, does credibly simulate the distributional and persistence properties of several indices (which represent the large-scale atmospheric pressure gradient, local atmospheric moisture content, and local vertical velocity) that are shown to modulate the likelihood of REP occurrence in the reanalysis/observational record. We show that simulation of REP events based on the GCM-based atmospheric indices greatly reduces the bias of GCM REP frequency relative to the observed record. The simulation is conducted via a Bayesian regression model by imposing the empirical relationship between observed REP occurrence and the reanalysis-based atmospheric indices. Application of this model to future (2006-2100) representative concentration pathway 8.5 scenario suggests an increasing trend in springtime REP incidence in the study region. The proposed approach of simulating precipitation events of interest, particularly those poorly represented in GCMs, with a statistical model based on climate indices that are reasonably simulated by GCMs could be applied to subseasonal to seasonal forecasts as well as future projections.
Regional-scale extreme rainfall and flooding are temporally and spatially associated with the occurrence of tropical moisture exports (TMEs) in the Ohio River Basin (ORB). TMEs are related to but not synonymous with atmospheric rivers, which refer to specific filiamentary organizational processes. TMEs to the ORB may be driven by strong, persistent ridging over the Eastern United States and troughing over the Central United States, creating favorable conditions for southerly flow and moisture transport from the Gulf of Mexico and Caribbean Sea. However, the strong inter-annual variation in TME activity over the ORB suggests dependence on global-scale features of the atmospheric circulation. We suggest that this synoptic dipole pattern may be viewed as the passage of one or more high-wavenumber, transient Rossby waves. We build a multi-level hierarchical Bayesian model in which the probability distribution of TME entering the ORB is a function of the phase and amplitude of the traveling waves. In turn, the joint distribution of the phase and amplitude of this wave is modulated by hemispheric-scale features of the atmospheric and oceanic circulation, and the amplitude and synchronization of quasi-stationary Rossby waves with wavenumber 1-4. Our approach bridges information about different features of the atmospheric circulation which inform the predictability of TME at multiple time scales and develops existing understanding of the atmospheric drivers of TMEs beyond existing composite and EOF studies.