@InProceedings{MindlinGoyHurTedZil:2022:SiChSe,
               author = "Mindlin, Julia and Goyal, Rishav and Hurtado, Santiago Ignacio and 
                         Tedeschi, Renata Gon{\c{c}}alves and Zilli, Marcia",
          affiliation = "{Universidad de Buenos Aires} and {University of New South Wales} 
                         and {National University of La Plata} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {University of Oxford}",
                title = "A Simple Characterization of Sea Surface Temperature Patterns that 
                         Represent the Seasonal Evolution of El Niņo Southern Oscillation 
                         Flavors",
                 year = "2022",
         organization = "AGU Fall Meeting",
            publisher = "AGU",
             abstract = "In the last decade, much of the attention on El NinoSouthern 
                         Oscillation (ENSO), especially its teleconnections, has focused on 
                         differentiating among the events' {"}flavors{"}. Despite that, the 
                         definition of each flavor, useful when classifying events and 
                         performing composite analysis, is highly variable in the 
                         literature. Furthermore, most of the literature focuses on 
                         preferential seasons when the sea surface temperature (SST) 
                         anomalies are maximized, resulting in deep convection and Rossby 
                         Wave triggering. This work uses k-means clustering algorithms to 
                         distinguish significantly different patterns for SST monthly 
                         variability considering four SST datasets with varying spatial 
                         resolution: Kaplan Extended SST v2, HadlSST, COBE-SST2, ERSSTv5. 
                         SST anomalies are clustered over the entire tropical Pacific Ocean 
                         (140E,15S to 280E,15N) rather than restricting it to arbitrary 
                         regions as in traditional ENSO indices. The analysis also treats 
                         ENSO as an evolving system by considering the entire year, 
                         classifying monthly SST anomalies into a limited number (seven) of 
                         SST patterns (clusters). We first train the clustering algorithm 
                         with satellite-era data (1979-2013), identifying seven patterns 
                         tested against white noise using a classifiability index. The 
                         number of patterns is similar across datasets, 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 transition probabilities 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 ENSO flavors in a variety of 
                         datasets, including historical and future projections of SST. It 
                         also allows a simple representation of nonlinearity between 
                         positive and negative ENSO, with a classification method valid for 
                         any month of the year.",
  conference-location = "Chicago, IL",
      conference-year = "12-16 Dec. 2022",
        urlaccessdate = "06 jun. 2024"
}