Auto-updating plot of data from Climate Prediction Center.

The term "El Niño" (or similarly "ENSO") describes the episodic warming and cooling of the eastern tropical Pacific on a timescale of several years. The figure above displays several decades of average eastern tropical Pacific sea surface temperatures. A tendency to vary irregularly on a timescale of roughly 3-7 years (i.e., interannually) is evident.

A motivation to study the dynamics of El Niño is the possibility of improvement in prediction ability (the major events in 1982 and 1998 each had roughly 2,000 deaths attributed to them). Furthermore, in light of increased concern about climatic response to human-induced global change, improved understanding of El Niño dynamics may allow us to better assess potential feedbacks between tropical Pacific variability (which has global impacts) and global warming.

Rapidly varying weather, and especially westerly wind bursts (tropical Pacific weather events with a timescale of weeks), have been frequently suggested to drive ENSO. We are investigating the possibility that westerly wind bursts are in fact modulated by ENSO itself. In our first study of this hypothesized two-way feedback, we combined analysis of satellite scatterometer data with experiments carried out using an ENSO computer forecast model of intermediate complexity, and we found that the inclusion of observationally-based westerly wind burst modulation by ENSO has a huge effect on simulated interannual variability. We extended this work using a more sophisticated ENSO model which combines an ocean general circulation model with a statistical atmospheric model (i.e., a hybrid coupled model). We added an explicit westerly wind burst component to the model atmosphere with guidance from a twenty-three year observational record, and we parameterized westerly wind burst occurrence such that the likelihood of an event depends on the western Pacific warm pool extent. The modulation of westerly wind bursts strongly affected simulated ENSO characteristics in this model.