Rare event sampling for moving targets: extremes of temperature and daily precipitation in a general circulation model

TL;DR

This paper extends rare event sampling algorithms to efficiently estimate extreme weather event probabilities in an idealized climate model, significantly reducing computational costs.

Contribution

It introduces TEAMS, an improved rare event algorithm that handles transient midlatitude extremes, enabling faster risk assessment in complex climate models.

Findings

Achieved 5-10 times faster estimation of long return periods for temperature and precipitation extremes.

Extended the TEAMS algorithm to handle transient, fast-changing events in climate models.

Demonstrated promising results for accelerated climate risk assessment.

Abstract

Extreme weather events epitomize high cost: to society through their physical impacts, and to computer servers that simulate them to assess risk and advance physical understanding. It costs hundreds of simulation years to sample a few once-per-century events with straightforward model integration, but that cost can be much reduced with \emph{rare event sampling}, which nudges ensembles of simulations to convert moderate events to severe ones, e.g., by steering a cyclone directly through a region of interest. With proper statistical accounting, rare event algorithms can provide quantitative climate risk assessment at reduced cost. But this can only work if ensemble members diverge fast enough. Sudden, transient events characteristic of Earth's midlatitude storm track regions, such as heavy precipitation and heat extremes, pose a particular challenge because they come and go faster than an ensemble can explore the possibilities. Here we extend standard rare event algorithms to handle this challenging case in an idealized atmospheric general circulation model, achieving $\sim5-10$ times sped-up estimation of long return periods for extremes of surface temperature and daily precipitation (e.g., a return period of 150 years from 20 years of simulation). The algorithm, called TEAMS (``trying-early adaptive multilevel splitting''), was developed previously with a toy chaotic system, and relies on a key parameter -- the advance split time -- which may be estimated based on simple diagnostics of ensemble dispersion rates. The results are promising for accelerated risk assessment across a wide range of physical hazards using more realistic and complex models with acute computational constraints.