An alternative approach to assess the South America intraseasonal variability is presented. In this study, we use a normal-mode decomposition method to decompose the South American 30-90-day Low-Frequency Intraseasonal (LFI) and 10-30-day High-Frequency Intraseasonal (HFI) variability systematically into rotational (ROT) and inertio-gravity (IGW) components in the reanalysis data. The seasonal cycle of the LFI and HFI convective and dynamical structure is well-described by the first leading pattern (EOF1). The LFI EOF1 spatial structure during the rainy season is the dipole-like between the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), influenced by the large-scale Madden-Julian Oscillation (MJO). During the dry season, alternating periods of enhanced and suppressed convection over South America is primarily controlled by extratropical wave disturbances. The HFI spatial pattern also resembles the SESA–SACZ structure, in response to the Rossby wave trains. Results based on normal-mode decomposition of reanalysis data and the LFI and HFI indices show that the tropospheric circulation and SESA–SACZ convective structure observed over South America are dominated by ROT modes (Rossby). A considerable portion of the LFI variability is also associated with the inertio-gravity (IGW) modes (Kelvin mode), prevailing mainly during the wet season. The proposed decomposition methodology provides insights into the dynamic of the South America intraseasonal variability, giving a powerful tool for diagnosing circulation model issues in order to improve the prediction of precipitation.

Considering that the instrumental climate record covers a period of about a century, it becomes necessary to use paleoclimatic records/models to explore the stability of the climatic variability and investigate the robustness of the teleconnections of the past. It is important to identify if the observed patterns in the current period persist over the last millennium when the changes in the orbital induced climate variations are negligible. In several studies on climatic causality crosscorrelation functions are used and the analysis is based on the relationship between atmospheric structures in pairs, a procedure that has several limitations in the elucidation of the network of possible connections. To mitigate these barriers, this work uses Partial Directed Coherence (PDC) and kernel nonlinear Partial Directed Coherence (knPDC) to allow the inference of the linear or nonlinear couplings between the climatological patterns, respectively. Connections between the two groups of climatic indicators in the last millennium were observed from 850 to 1850. The first group comprises the El Nino-Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO) and Atlantic Interhemispheric SST Gradient (GTA) and the second, Antarctic Oscillation (AAO), El Nino-Southern Oscillation (ENSO), Pacific-South American (PSA1, EOF2) and QuasiBiennial Oscillation (QBO). The climate indices were computed from a weighted average set of climate model simulations of PMIP3, which represent simulations oriented to the past climate in the climate projection models of CMIP5. For the first group, no significant results were observed on the low-frequency band, observing only linear relationships between the Pacific and Atlantic Oceans. For the second group, the causal analysis point to linear relationships between ENSO↔AAO, and nonlinear between ENSO↔PSA in the low and high band and QBO↔AAO, QBO↔ENSO and QBO↔PSA in the low-frequency band. In summary, the results indicate a higher nonlinear connection between low-frequency phenomena.

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.

Instead of using the traditional space-time Fourier analysis of filtered specific atmospheric fields, a normal-mode decomposition method is used to analyze the South American intraseasonal variability. Intraseasonal variability was separate into the 30-90-day Low-Frequency Intraseasonal (LFI) and 10-30-day High-Frequency Intraseasonal (HFI) variability, and analyzed the contribution of the rotational (ROT) and inertio-gravity (IGW) components to the observed convective and circulation features. The seasonal cycle of the LFI and HFI convective and dynamical structure is well-described by the first leading pattern (EOF1). The LFI EOF1 spatial structure during the rainy season is the dipole-like between the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), influenced by the large-scale Madden-Julian Oscillation (MJO). During the dry season, alternating periods of enhanced and suppressed convection over South America are primarily controlled by extratropical wave disturbances. The HFI spatial pattern also resembles the SESA–SACZ structure, in response to the Rossby wave trains. Results based on normal-mode decomposition of reanalysis data and the LFI and HFI indices show that the tropospheric circulation and SESA–SACZ convective structure observed over South America are dominated by ROT modes (e.g., Rossby). A considerable portion of the LFI variability is also associated with the inertio-gravity (IGW) modes (e.g., Kelvin mode), prevailing mainly during the rainy season. The proposed decomposition methodology provides new insights into the dynamics of the South American intraseasonal variability, giving a powerful tool for diagnosing circulation model issues in order to improve the prediction of precipitation.