Optimal Initial Conditions and Stochastic Forcing for Central and East Pacific ENSO Events

Abstract:

Research on the structure and evolution of individual El Niño / Southern Oscillation (ENSO) events has identified two categories of ENSO event characteristics that can be defined by maximum equatorial SST anomalies centered in the Central Pacific (around the dateline to 150°W; CP events) or in the Eastern Pacific (west of about 150°W; EP events). The distinction between these two events is not just academic: both types of event evolve differently, implying different predictability; the events tend to have different maximum amplitude; and the global teleconnection differs between each type of event. The identification of initial conditions that tend to grow into each type of event will lead to better predictability and better dynamical understanding of individual ENSO event evolution.

In this study, we use a linear inverse model framework to (i) calculate optimal initial conditions that lead to CP or EP ENSO events, (ii) identify patterns of stochastic forcing that are responsible for exciting each type of event, and (iii) investigate the relative roles of stochastic forcing and internal dynamics in generating long term variations in CP and EP ENSO event statistics. We target our analysis toward CP or EP ENSO events by constructing a CP or EP norm under which optimal initial conditions are calculated. Results highlight a fundamentally different role for the Pacific Meridional Mode in the initial condition and subsequent evolution of CP vs. EP ENSO events. Analysis of stochastic forcing shows that CP ENSO events evolve via the Seasonal Footprinting Mechanism, in which mid-latitude atmospheric variability associated with the atmospheric North Pacific Oscillation lead to ENSO events through the Pacific Meridional Mode. Finally, variations in ENSO event statistics are investigated using a linear inverse model derived from a long simulation of the National Center for Atmospheric Research Community Earth System Model. Variations in stochastic forcing vs. dynamics of the system will be discussed.