A Low-Order Data-Driven Model of ENSO Diversity

El Niño and La Niña are dominant patterns of climate variability that can have wide-reaching impacts on weather and ecosystems worldwide. Scientists often use mathematical models to study these events, including Linear Inverse Models (LIMs), which analyze past data to make predictions. However, standard LIMs struggle to capture certain asymmetric features of El Niño and La Niña events, like their uneven strength and their spatial footprint. For instance, intense El Niños tend to develop quickly and decay rapidly, while La Niñas often linger longer but are not as extreme. In this study, we introduce a modified model, the Non-Gaussian LIM (NG-LIM), which better represents these asymmetries between El Niño and La Niña. This modified model generates a broader range of synthetic events, providing a valuable tool for understanding these climate patterns.