El Niño and La Niña During 1916-1920 and 1996-2000 in a Large Ensemble

Abstract:

A 56-member ensemble of ocean reanalyses is used to explore strong El Niño and La Niña events in two 5-year periods, 1916 to 1920 and 1996 to 2000, that have markedly different quantities of observations. To generate the 56 forcing fields, we produced a 56 member atmospheric reanalysis (20CR system) prescribing as boundary conditions 8 different sea surface temperature (SST) estimates from an ocean reanalysis system, the SODA with sparse input (SODA.si1), resulting in 8 sets of 7 ensemble members each. These reanalyses are part of an overall effort to run ocean and atmosphere reanalyses iteratively in an effort to improve historical reanalyses in periods of sparse observations.

The reanalyses are used to explore ENSO diversity in the tropical Pacific Ocean and its global effects. Early results from the two periods show that the representation of the same ENSO event varies widely in strength, duration, and location among the 56 ensemble members indicating that the atmospheric forcing makes a significant difference in how SODAsi resolves the event. For example, the warming during the 1918/1919 event in some members is far in the eastern equatorial Pacific Ocean while in other members the major warming is in the western and central Pacific. During the better-observed 1990s, the ensemble variance is smaller, but members still show substantial differences in the magnitude of events. Additionally, similarities among the results of each atmospheric reanalysis set generated with the same SODAsi.1 SST suggest that the state estimates are strongly dependent upon the SST boundary condition. These reanalyses are used to understand the effects of both high and low frequency atmospheric dynamics on ENSO strength and position, as well as explore the state of the ocean leading up to the strongest La Nina event on record (1916-1917). The results add to what is previously known about ENSO in order to improve ENSO predictability, as well as highlight the importance of data assimilation.