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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/4878NQ5
Repositorysid.inpe.br/mtc-m21d/2022/12.13.19.35
Metadata Repositorysid.inpe.br/mtc-m21d/2022/12.13.19.35.48
Metadata Last Update2023:01.03.16.46.27 (UTC) administrator
Secondary KeyINPE--PRE/
Citation KeyMindlinGoyHurTedZil:2022:SiChSe
TitleA Simple Characterization of Sea Surface Temperature Patterns that Represent the Seasonal Evolution of El Niño Southern Oscillation Flavors
Year2022
Access Date2024, June 02
Secondary TypePRE CI
2. Context
Author1 Mindlin, Julia
2 Goyal, Rishav
3 Hurtado, Santiago Ignacio
4 Tedeschi, Renata Gonçalves
5 Zilli, Marcia
Group1
2
3
4 DIPTC-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Universidad de Buenos Aires
2 University of New South Wales
3 National University of La Plata
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 University of Oxford
Conference NameAGU Fall Meeting
Conference LocationChicago, IL
Date12-16 Dec. 2022
PublisherAGU
History (UTC)2022-12-13 19:35:48 :: simone -> administrator ::
2023-01-03 16:46:27 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
AbstractIn 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.
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