This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year. These indices are constructed using two distinct approaches: the linear Empirical Orthogonal Functions (EOF) method and the unsupervised machine-learning Self-Organizing Maps (SOM) technique. Both methods are applied to Outgoing Longwave Radiation (OLR) and precipitation-filtered anomalies in the 30-90-day band over the South American domain. Results demonstrate that regional indices provide valuable insights into intraseasonal South American rainfall variability, including phase and strength, compared to global indices of the Madden-Julian Oscillation (MJO). Despite being computed using only the South American domain, the regional indices capture the tropical-tropical MJO teleconnection through the zonal wavenumber-1 structure. The diversity in amplitude and evolution of precipitation, primarily influenced by tropical-extratropical teleconnections through Rossby wave trains, is more evident when using the non-linear SOM index. The regional indices also accurately measure the impacts of intraseasonal variability on extreme precipitation events over South America. Case studies, such as the 2013/2014 summer drought episode, highlight this ability, when a deficient rainy season severely affected the Southeast Region of Brazil, impacting agricultural production and hydroelectric power generation. During this episode, the regional indices show agreement between drought periods and suppressed precipitation phases, while global indices indicate an inactive MJO phase. These findings underscore the effectiveness of regional indices in capturing intraseasonal variability, offering significant implications for extreme weather prediction and their impacts on South American water resources and socio-economic activities.

The impact of the model-output vertical resolution on the moist static energy (MSE) budget associated with the Madden-Julian Oscillation (MJO) is examined. To achieve this objective, we perform the MSE budget analysis employing reanalysis data at different vertical resolutions: raw output layers, six standard layers as stipulated by the CMIP6 model requirements, high resolution in the upper troposphere, and high resolution in the lower troposphere. Results show that the data with only six layers results in a significant budget residual, consistent with the CMIP6 models observed. Moreover, the data with high resolution in the upper troposphere capture more realistic moist processes than data with high resolution in the lower troposphere. The budget residuals amplify with the rainfall anomalies and correspond to an overestimated vertical MSE advection. The leading bias source is associated with a strong vertical gradient of mean-state dry static energy (DSE) in the upper troposphere, which interacts with anomalous vertical velocity and thus leads to more MSE transport. Unrealistic vertical profiles of mean-state DSE and convection are also observed in the CMIP6 models. Examination of the same analysis using the DYNAMO field campaign shows that budget residuals are evident in low vertical resolution data, yet they correspond to an underestimated vertical MSE advection. The results from these datasets indicate that existing MSE budget residuals in the CMIP6 models are partially attributed to the low vertical resolution data.

The moist processes of the Madden-Julian Oscillation (MJO) in the Coupled Model Intercomparison Project Phase 6 models are assessed using moisture mode theory-based diagnostics over the Indian Ocean (10°S-10°N, 75°E-100°E). Results show that no model can capture all the moisture mode properties relative to the reanalysis. Most models satisfy weak temperature gradient balance but have unrealistically fast MJO propagation and a lower moisture-precipitation correlation. Models that satisfy the most moisture mode criteria reliably simulate the background moist static energy (MSE) and low-level zonal winds compared to models that satisfy the least amount of criteria. The MSE budget associated with the MJO is also well-represented in the good models rather than in the poor models. Our results show that capturing the MJO's moisture mode properties over the Indian Ocean is associated with a more realistic representation of the MJO simulation and thus can be employed to diagnose MJO performance.

The governing thermodynamics of the Madden-Julian Oscillation (MJO) are examined using sounding and reanalysis data. On the basis of four objective criteria, results suggest that the MJO behaves like a moisture mode –a system whose thermodynamics is governed by moisture– only over the Indian Ocean. Over this basin, the small effective gross moist stability, i.e., a slow moist static energy (MSE) export by convection, and slow convective adjustment timescale allows moisture modes to exist for all zonal wavenumbers except 1. Elsewhere, the faster-propagating wavenumber 1-2 components are more prominent and the effective gross moist stability is higher, preventing weak temperature gradient (WTG) balance to be established and causing temperature and moisture to play similar roles in the MJO’s thermodynamics.

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.

A westward-propagating Rossby-like wave signal is found to explain a large fraction of the intraseasonal variance in cloud brightness over the western hemisphere. A series of diagnostic criteria suggest that this wave is a moisture mode: its moisture anomalies dominate the distribution of moist static energy (MSE) and are in phase with the precipitation anomalies; and the thermodynamic equation obeys the weak temperature gradient approximation. The wave propagates westward due to zonal moisture advection by the mean flow and is maintained by radiative heating and meridional moisture advection. These properties compare favorably with the westward propagating Rossby mode in an equatorial beta-plane model with prognostic moisture, mean meridional moisture gradient, and mean zonal wind. These results underscore the importance of water vapor in the dynamics of slowly evolving tropical systems, and the limitations of dry shallow water theory that rely on a “reduced equivalent depth” to represent moist dynamics.

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.