A Simple Characterization of Sea Surface Temperature Patterns that Represent the Seasonal Evolution of El Niño Southern Oscillation Flavors

J MINDLIN; R GOYAL; HURTADO, SANTIAGO I.; RG TEDESCHI; M ZILLI

Chicago

Congreso; AGU Fall Meeting 2022; 2022

American Geophysical Union

In the last decade, much of the attention on El Nino–Southern Oscillation (ENSO), especially itsteleconnections, has focused on differentiating among the events' "flavors". Despite that, thedefinition of each flavor, useful when classifying events and performing composite analysis, ishighly variable in the literature. Furthermore, most of the literature focuses on preferentialseasons when the sea surface temperature (SST) anomalies are maximized, resulting in deepconvection and Rossby Wave triggering. This work uses k-means clustering algorithms todistinguish significantly different patterns for SST monthly variability considering four SSTdatasets with varying spatial resolution: Kaplan Extended SST v2, HadlSST, COBE-SST2,ERSSTv5. SST anomalies are clustered over the entire tropical Pacific Ocean (140E,15S to280E,15N) rather than restricting it to arbitrary regions as in traditional ENSO indices. Theanalysis also treats ENSO as an evolving system by considering the entire year, classifyingmonthly SST anomalies into a limited number (seven) of SST patterns (clusters). We first trainthe clustering algorithm with satellite-era data (1979-2013), identifying seven patterns testedagainst white noise using a classifiability index. The number of patterns is similar acrossdatasets, attesting to the robustness of the identified patterns. After that, we classify the dataset(1900-2020) based on these SST patterns and investigate the seasonal evolution of transitionprobabilities of the SST patterns, providing a picture of the seasonal evolution of the flavors.Given the robustness and simplicity of the method, it is easily applicable to classify ENSOflavors in a variety of datasets, including historical and future projections of SST. It also allows asimple representation of nonlinearity between positive and negative ENSO, with a classificationmethod valid for any month of the year.