Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times

Abstract

Climate science needs more efficient ways to study high-impact, low-probability extreme events. Ensemble boosting, a form of rare event sampling, offers a novel strategy to extract more information from those occasional simulated events, by perturbing them slightly to probe alternative scenarios immediately instead of waiting many simulation-years for the next event. But statistical accuracy and efficiency depend on the perturbation details. In particular for sudden and transient events like precipitation, performance of boosting depends sensitively on the \emph{advance split time} (AST), which must be long enough before the event to let the ensemble diversify, but not so much as to destroy the event. In pursuit of principled guidelines, we study the effect of AST for sampling tracer fluctuations in a quasigeostrophic flow, an idealized but informative model of midlatitude storm track dynamics. We formulate AST selection as an optimization problem for statistical fidelity with a ground truth. Since ground truth is not known in practice, we propose a proxy objective function of \emph{thresholded entropy}, which rewards ensembles with both a high mean and a large spread. We show that ensemble boosting, when given a well-chosen AST and equipped with methods to estimate probabilities, can accurately sample extremes at long return periods. We furthermore find evidence that thresholded entropy successfully identifies an optimal AST, which is roughly 1-3 eddy turnover timescales in the quasigeostrophic system. Moreover, this proxy captures the \emph{variation} of AST with the target location of the tracer within the flow field, suggesting generalizability to climate models. Large-scale deployment of our method will require further development in adaptive optimization strategies, but our work here is an essential first step for establishing what must be optimized.

arXiv commentPublished in Nonlinear Processes in geophysics

References in corpus (17)

• Computation of extreme heat waves in climate models using a large deviation algorithm
• Extremes and Recurrence in Dynamical Systems
• A variational approach to probing extreme events in turbulent dynamical systems
• Rare event algorithm study of extreme warm summers and heat waves over Europe
• Practical rare event sampling for extreme mesoscale weather
• A New Mathematical Framework for Atmospheric Blocking Events
• Computing return times or return periods with rare event algorithms
• Are extreme dissipation events predictable in turbulent fluid flows?
• Convergence of extreme value statistics in a two-layer quasi-geostrophic atmospheric model
• Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples
• Application of Adaptive Multilevel Splitting to High-Dimensional Dynamical Systems
• Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain
• Collapse of transitional wall turbulence captured using a rare events algorithm
• Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
• Heterogeneity of the Attractor of the Lorenz '96 Model: Lyapunov Analysis, Unstable Periodic Orbits, and Shadowing Properties
• Constructing Extreme Heatwave Storylines with Differentiable Climate Models
• Rare event sampling for moving targets: extremes of temperature and daily precipitation in a general circulation model