Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events

Abstract

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and “ensemble boosting,” which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz-96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models.