A Reanalysis of the Extended Multivariate ENSO Index (MEI.ext) and Comparison of the 1877-78 and 2015-16 El Nino Events

The El Nino Southern Oscillation (ENSO) is the most robust, coupled ocean-atmospheric component of intraseasonal-interannual variability on the globe. The Extended Multivariate ENSO Index (MEI.ext) is originally defined by Wolter and Timlin (2011) as the combined, bi-monthly, leading principle component analysis (PCA) of Sea Level Pressure (SLP) and Sea Surface Temperature (SST) over the Tropical Pacific domain in the HADSST2 and HADSLP2 datasets. Although useful for broad interpretations of long-term ENSO variability, the original Extended MEI and the Principal Components that comprise it are based upon relatively older and defunct data, fail to account for the significant climatic and observational mean state changes in the past century and a half, uncertainties in the SLP and SST fields, and is not available in near real-time. Hence, this paper is intended to present a major improvement upon the original Extended Multivariate ENSO Index (MEI.ext) to increase the quality and reliability of the data, as well as depict the astounding similarities in evolution and amplitude between the 1877-78 and 2015-16 “Super” El Ninos, which are among the strongest ENSO events observed within the past few centuries. The reanalysis of the Extended MEI confirms previous findings that ENSO variability was much larger near the turn of the 20th and 21st centuries, with a relative lull in the mid 20th century, and exhibits similar timing, duration, and amplitude to available indices with most ENSO events. The reanalyzed Extended MEI attempts to maximize both longevity & data assimilation over the entire 165-year instrumental record, as well as be available for real-time scrutiny. Its creation was largely motivated by the unusually strong & prolonged 2014-16 “Super” El Nino, initialization for global climate models, climate risk assessment, as well as assessing the elusive role greenhouse gases potentially play in ENSO variability.