ENSO Remote Forcing

Abstract

Author(s): Kug, Jong‐Seong; Vialard, Jerome; Ham, Yoo‐Geun; Yu, Jin‐Yi; Lengaigne, Matthieu | Editor(s): McPhaden, Michael J; Santoso, Agus; Cai, Wenju | Abstract: The El Nino‐Southern Oscillation (ENSO) is a coupled ocean‐atmosphere phenomenon of variability that is a leading source of seasonal climate prediction skill across the globe. The first ENSO prediction was made in the mid‐1970s, but it was another 10–15 years before operational centers, using simple, coupled climate models, began to make routine ENSO predictions. These early forecast models were succeeded in the 1990s by more sophisticated dynamical and statistical models, which created the basis for real‐time seasonal outlooks over the globe. These models, and more recent multimodel ensembles, also inform our understanding and estimates of the predictability and prediction skill of ENSO, which varies seasonally and from decade to decade. ENSO predictability largely stems from slowly evolving oceanic conditions, with short‐term atmospheric fluctuations often limiting predictability on seasonal timescales. Despite improved models and better initializations, prediction skill remains low for forecasts passing through the boreal spring, the so‐called spring prediction barrier. Furthermore, prediction skill and predictability have varied significantly over the past couple decades. Higher skill and predictability are evident during periods of larger amplitude ENSO events (e.g., Eastern Pacific El Nino), whereas lower skill/predictability is associated with lower amplitude events (e.g., Central Pacific El Nino). These natural variations in our ability to predict ENSO, together with challenges during 2014–2016, motivate the search for understanding of how anthropogenic warming will influence seasonal ENSO prediction.