The Rise and Fall of ENSO in a Warming World: Insights from a Lag-Linear Model

TL;DR

This paper develops a lag-linear model to predict ENSO variability changes under global warming, revealing a transient increase followed by a decrease driven by ocean stratification and circulation changes, with implications for climate prediction.

Contribution

It introduces a simple yet effective lag-linear model that predicts ENSO variability evolution from global mean SST and its history, capturing 90% of simulated changes.

Findings

ENSO variability initially rises then falls under warming.

The model predicts ENSO changes using only global mean SST and history.

Faster emissions lead to stronger peak ENSO variability.

Abstract

The El Ni\~no-Southern Oscillation (ENSO) is a fluctuation in sea surface temperature (SST) and pressure across the equatorial Pacific Ocean with a period of 2-7 years. As the largest mode of interannual variability on Earth, ENSO shapes global weather and climate patterns ranging from monsoons in southern Asia to hurricanes in the Atlantic and droughts in South America. Predicting and understanding ENSO's response to greenhouse warming is essential for mitigating the impacts of climate change, yet model ensemble projections are prohibitively expensive to generate across emission scenarios and remain incompletely understood. Here, we use a hierarchy of models to show that ENSO strength undergoes a transient rise followed by a long-term fall under greenhouse warming. An East Pacific energy budget reveals that the initial increase in ENSO variability is due to enhanced upper-ocean stratification, while its subsequent decrease arises from a slowing Walker circulation and stronger surface flux damping. Building on these mechanisms, we derive a linear model which predicts the evolution of ENSO variability from only East Pacific temperature and stratification. We further show that subsurface warming, and therefore stratification, is connected to surface warming with a lag, enabling us to create a lag-linear model that explains $\sim$90\% of simulated changes in ENSO variability from only global mean SST and its history. This efficient predictor can forecast ENSO strength over time in any warming scenario, and reveals that faster emissions lead to stronger peak ENSO variability even with identical total emissions.