Detecting Climate Teleconnections With Granger Causality

Autor(es)

Silva, Filipi N. ^[1] ; Vega-Oliveros, Didier A. ^{[2, 3]} ; Yan, Xiaoran ^[4] ; Flammini, Alessandro ^[3] ; Menczer, Filippo ^{[1, 3]} ; Radicchi, Filippo ^[3] ; Kravitz, Ben ^{[5, 6]} ; Fortunato, Santo ^{[1, 3]}

Afiliação do(s) autor(es)

^[1] Indiana Univ, Network Sci Inst IUNI, Bloomington, IN 47401 - USA

^[2] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil

^[3] Indiana Univ, Luddy Sch Informat Comp & Engn, Ctr Complex Networks & Syst Res, Bloomington, IN 47401 - USA

^[4] Zhejiang Lab, Artificial Intelligence Res Inst, Hangzhou, Zhejiang - Peoples R China

^[5] Pacific Northwest Natl Lab, Atmospher Sci & Global Change Div, Richland, WA 99352 - USA

^[6] Indiana Univ, Dept Earth & Atmospher Sci, Bloomington, IN 47401 - USA

• Tipo de documento: Artigo Científico
• Fonte: Geophysical Research Letters; v. 48, n. 18 SEP 28 2021.
• Citações Web of Science: 0

Resumo

Climate system teleconnections are crucial for improving climate predictability, but difficult to quantify. Standard approaches to identify teleconnections are often based on correlations between time series. Here we present a novel method leveraging Granger causality, which can infer/detect relationships between any two fields. We compare teleconnections identified by correlation and Granger causality at different timescales. We find that both Granger causality and correlation consistently recover known seasonal precipitation responses to the sea surface temperature pattern associated with the El Nino Southern Oscillation. Such findings are robust across multiple time resolutions. In addition, we identify candidates for unexplored teleconnection responses. (AU)

• Processo FAPESP: 18/24260-5 - Análise de dados espaço-temporais baseado em redes complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 19/26283-5 - Aprendendo pistas visuais da passagem do tempo
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 16/23698-1 - Processos Dinâmicos em Aprendizado de Máquina baseados em Redes Complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado