Understanding ENSO Dynamics Through a Gross Moist Stability Framework in Climate Model and Reanalysis Data

Abstract

El Niño events are major expressions of the El Niño Southern Oscillation (ENSO), with significant impacts on precipitation and temperature patterns both over the Pacific and globally. Traditional frameworks for understanding ENSO dynamics, such as sea surface temperature anomalies (SSTAs), fail to fully capture the mechanisms of El Niño onset, progression, and termination. Building on the moist static energy (MSE) framework developed by Neelin and Held (1987), this paper applies a gross moist instability (GMI) lens to reanalysis and climate model data to better understand the spatial and temporal evolution of El Niño. Neelin and Held’s precipitation approximation is a function of net vertical energy flux, gross moist stability, and vertical specific humidity gradient. This approximation highlights the importance of gross moist stability in capturing convective processes that SSTAs alone cannot resolve. In comparing model and reanalysis data, we find that the spatial patterns of GMI anomaly and precipitation anomaly are consistent in the reanalysis data, but not the model data. The precipitation anomaly for the model data matches the observed behaviour, but GMI anomaly does not. We use Neelin and Held’s approximation for precipitation which includes the variable GMI to assess what might be causing this discrepancy. We find that the precipitation approximation is a good predictor of the spatial behaviour of actual precipitation in the model and reanalysis data and is particularly strong over the Niño 4 and equatorial region. While the spatial pattern is strong, the predicted precipitation consistently underestimates the magnitude of precipitation. We attribute the underestimation to the approximation not including horizontal exports of moisture which likely contribute significantly toward this error. By decomposing the precipitation approximation, we find that the main driver of predicted precipitation is energy convergence. Gross moist stability plays a less significant role and specific humidity plays the smallest role in driving precipitation. This suggests that while there might be some compensation between variables in the precipitation approximation, the model precipitation follows the observed pattern because energy convergence, and not GMI, is the main driver. We then compare the spatial correlation coefficients between variables of the flux adjusted climate model and the reanalysis data. We find that the years with elevated SSTs during El Niño events (late Y0 and early Y+1) have slightly higher correlation than the year following the event (Y+2) and significantly higher correlation than the year preceding the event (Y-1). Moreover, the correlation is stronger for SSTs and precipitation—both emergent properties of ENSO—than energy convergence and instability—underlying processes that contribute to ENSO behaviour. These results indicate that climate models are tuned to the years with peak El Niño behaviour and likely ignore important ENSO indicators in the years surrounding El Niño events. The results also indicate that the models are tuned to the emergent properties of El Niño events rather than the underlying dynamics causing the events. Our results suggest that better representation of atmospheric convection and energetics in all years around El Niño events, but particularly the year preceding the event, could strengthen the predictive capability of climate models.