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.

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.

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.