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- Title
Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events.
- Authors
Finkel, Justin; O'Gorman, Paul A.
- 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. Plain Language Summary: What is the strongest rainstorm that we can expect in a given thousand‐year period? To augment the available ∼100 years of historical data and to account for climate change, computer simulations are a useful, but expensive, tool to answer such questions. A model must run for many millennia to deliver an answer with statistical confidence. Rare event algorithms provide a promising alternative simulation protocol, in which an ensemble of short simulations is biased to produce more extreme events and reweighting is used to correct for the bias when calculating statistics. However, a classical rare event algorithm fails when the events of interest are short and "bursty" (like heavy rainstorms) instead of long and slow‐moving (like anomalously hot summers). We modify the rare event algorithm to make it amenable to events resembling heavy precipitation in an idealized dynamical system with chaotic traveling waves. Key Points: Rare event algorithms may help address the challenge of simulating extreme weather events and quantifying their probabilityWhen the event of interest is sudden and transient, perturbed ensembles diversify too slowly for standard rare event algorithms to workUsing the Lorenz‐96 model as a prototype for midlatitude weather, we use early perturbation and a rejection step to gain a speedup
- Subjects
RAINSTORMS; EXTREME weather; CLIMATOLOGY; ATMOSPHERIC models; HOT weather conditions; STATISTICS
- Publication
Journal of Advances in Modeling Earth Systems, 2024, Vol 16, Issue 6, p1
- ISSN
1942-2466
- Publication type
Article
- DOI
10.1029/2024MS004264