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\begin{document}

\title{Linear and nonlinear causality in monthly atmospheric and ocean time
series}



\author[1]{Maria Gabriela Louzada Malfatti}%
\author[2]{Lucas Massaroppe}%
\author[2]{Pedro Leite da Silva Dias}%
\affil[1]{Institute of Energy and Environment}%
\affil[2]{Institute of Astronomy, Geophysics and Atmospheric Sciences}%


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  \date{May 15, 2024}


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\begin{abstract}
In meteorology, identification of teleconnections between climatic
phenomena plays an important role in the validation of atmospheric
models which are used for weather and climate prediction, as well as the
development of future climate scenarios. To evaluate the connectivity
between climatic phenomena, correlation analysis is often used, but this
type of analysis may lead to oversimplified relationships, which does
not imply causality between different scales of time. In this work,
Partial Directed Coherence (PDC) and kernel nonlinear Partial Directed
Coherence (knPDC) were used to infer the influence between atmospheric
compartments (atmosphere and ocean), allowing the detection of linear
and nonlinear connections, respectively, between variables
representative of important climatic variability modes. Teleconnections
patterns were divided into two groups of climatological indicators, from
1950 to 2018, available from the National Oceanic and Atmospheric
Administration (NOAA). The first group comprises the El Nino-Southern
Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), Pacific
Decadal Oscillation (PDO) and Atlantic Interhemispheric SST Gradient
(AITG) and the second, Antarctic Oscillation (AAO), PDO, Pacific-South
American (PSA) and Sunspot Number (SPI). Causality analysis suggests
that ENSO causes AMO and AITG causes PDO, highlighting the nonlinear
relations ENSO-PDO and ENSO-AITG. Furthermore, we observe the influences
PDO-AITG and PDO-AAO, evidencing the energy transfer from the Pacific to
the Atlantic Ocean. Also, PDC and knPDC techniques results suggest that
some indices have nonlinear interaction, emphasizing the use of
nonlinear machine learning techniques, e.g., deep learning, that can
capture these variations.%
\end{abstract}%



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