Learning ENSO Dynamics From Data

What are the most important local and remote feedbacks on ENSO, and how can they be best determined from observational data to inform model development and reduce model biases? Using atmospheric and oceanic reanalysis data for the 1979-2018 period, we have developed a 15-variable coupled Linear Inverse Model (LIM) of the tropical Indo-Pacific climate system to specifically address these questions. Our approach yields, for the first time from data, a clear picture of the competition between the destabilization of ENSO by coupled air-sea (Bjerknes) and subsurface oceanic feedbacks and its stabilization by surface shortwave flux feedbacks. The analysis also suggests that misrepresentation of the shortwave feedbacks is likely causing almost all current climate models to extend the warming of the equatorial Pacific during El Nino too far west into the western Pacific, compromising their seasonal and longer-term climate predictions around the globe through spuriously generated planetary Rossby waves. We also present evidence that misrepresentation of the shortwave feedbacks over the maritime continent is a primary contributor to the mean easterly wind and cold tongue biases in the equatorial Pacific in climate models.