Using a rare event sampling technique to quantify extreme El Ni\~no event statistics

TL;DR

This paper demonstrates that the TEAMS rare event sampling algorithm can efficiently generate statistically accurate extreme El Niño event data, significantly reducing computational costs compared to traditional long simulations.

Contribution

The study applies TEAMS to an ENSO model, showing it accurately estimates extreme event return times with much less computational effort than direct numerical simulations.

Findings

TEAMS reproduces DNS return time estimates accurately.

TEAMS reduces computational cost by about 80%.

Method is applicable to complex climate models.

Abstract

Extreme El Ni\~no events, such as occurred in 1997--1998, can induce severe weather on a global scale, with significant socioeconomic impacts that motivate efforts to understand them better. However, extreme El Ni\~no events are rare, and even in a very long direct numerical simulation (DNS) occur too infrequently for robust statistical characterization. This study seeks to generate extreme El Ni\~no event model data at a lower cost, while preserving statistical fidelity, using a rare event sampling technique, which preferentially devotes computational resources toward extreme events by generating a large, branched ensemble of interrelated trajectories through successive targeted perturbations. We specifically use the ``trying-early adaptive multi-level splitting'' (TEAMS) algorithm, which is well-suited for El Ni\~no's relative timescales of predictability and event duration. We apply TEAMS to the Zebiak-Cane model, an intermediate-complexity ENSO model for which it is feasible to run a long DNS (500000 years) for validation. We compare extreme El Ni\~no event return time estimates from TEAMS to those from the long DNS to assess TEAMS' accuracy and efficiency. We find that TEAMS accurately reproduces the return time estimates of the DNS at about one fifth the computational cost. Therefore, TEAMS is an efficient approach to study rare ENSO events that can be plausibly applied to full-complexity climate models.