The role of the Madden–Julian oscillation on the Amazon Basin intraseasonal rainfall variability

This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year, focusing on Brazil. 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 Brazil. 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.

Observational evidences linking tropical and extratropical wave disturbances in organizing precipitation over the Amazon basin (AB) are presented. The time scale of the wave disturbances considered in this thesis are from intraseasonal (low-and high-frequency intraseasonal) to subseasonal. The results are based on observed, satellite, and reanalysis datasets. The results suggest that on 30-70-day low-frequency, the Madden-Julian oscillation (MJO) is one of the main atmospheric mechanisms modulating intraseasonal rainfall variability over the Amazon region, playing an important role, particularly during the austral winter. Despite of the fact that the AB region extends over tropical region, the 30-70-day low-frequency intraseasonal are not strictly associated with the forcing produced by the equatorially propagating MJO. In addition, it is suggested that convective-based MJO indices are able to better account for the influence of the MJO on intraseasonal precipitation. The 10-30-day high-frequency intraseasonal (HFIS) activity account for about 22% of the total Amazon low-frequency intraseasonal rainfall events and it is part of large-scale pattern observed over South America during the onset of austral rainfall season (October-November) and during the dry season (May-September). During the peak of the wet season (December-April), the Northwest-Southeast orientation of the precipitation seems to be influenced by Rossby wave trains different to previously documented, with a southeastern South America (SESA)-South America convergence zone (SACZ) pattern.

The spatial and temporal structures of the leading modes of intraseasonal atmospheric variability over Central Africa using outgoing longwave radiation (OLR) and one-degree daily Global Precipitation Climatology Project (1DD GPCP) data are compared. A spectral analysis indicates that in the two datasets, the intraseasonal variability is dominated by 20–70-day period bands with center near 40–50 days. Results from empirical orthogonal function (EOF) analysis have shown that three main spatial structures characterize the leading modes of 20–70-day intraseasonal oscillations (ISO), but they differ in the two data by the amount of variance explained by each mode. For both time series, the power spectra of all the three principal components peak around 40–50 days, indicating Madden–Julian oscillation (MJO) signal. Moreover, the cross correlation computed among the principal components indicates that there exists a relatively high positive correlation between the EOFs of similar spatial loadings. For the two datasets, an index of MJO strength was built by averaging the 30–50-day power for each day and each EOF mode. A plot of the two indices revealed a coherent onset, peak, and decay of ISO activity but with different amplitudes. The correlation coefficients computed between the ISO indices corresponding to each mode revealed that they are highly correlated, especially for the annual mean time series where the correlation coefficient reaches up to 0.88 for the Tanzanian mode. Overall, the analysis showed that the leading modes are similar, but the 1DD GPCP loadings have better spatial localization, when compared with those of OLR datasets.

Rainfall over Africa varies across timescales of a few days to several weeks due to several tropical and extratropical modes of variability. Excessive rains or prolonged drought regularly result in natural disasters and have thus a severe impact on the local economy, agriculture, spread of diseases, and entire ecosystems. The dynamical nature of the atmosphere allows the existence of planetary balanced modes, which are called Rossby waves, and smaller-scale unbalanced inertio-gravity (IG) waves. The former, which are more rotational, arise from the horizontal pressure gradient force, while for the latter gravity acts as the restoring force, making their flow pattern more divergent. The main source of variability in the extratropics stems from Rossby waves. At the equator, further types of convectively coupled equatorial waves (CCEWs) exist, namely Kelvin and mixed Rossby-gravity (MRG) waves. As the slowest intraseasonal tropical mode, the Madden–Julian Oscillation (MJO), which is related to Kelvin and Rossby waves, acts on a timescale of 30 to 90 days. Although it is primarily a planetary mode, the MJO has a specific “flavor” over the African continent. On the short intraseasonal timescale of 10 to 25 days, equatorial Rossby (ER) waves and the internal modes of the West African monsoon, the quasi-biweekly zonal dipole (QBZD) and the Sahel mode, modulate rainfall. On the synoptic timescale of a few days to a week, African easterly waves (AEWs) are a dominant mode over West Africa, whereas Kelvin waves predominantly modulate rainfall over equatorial Africa. Extratropical influences on northern and southern Africa manifest themselves in Rossby wave trains, which modulate synoptic to intraseasonal rainfall through tropical rainfall plumes, cold air surges, and upper-tropospheric dry air intrusions. Furthermore, the Saharan heat low (SHL) acts as a link between the northern hemispheric extratropics and tropics. Finally, the Indian monsoon, the Atlantic, Indian, and the Pacific Oceans can remotely affect the intraseasonal variability of African rainfall. Forecasting synoptic to intraseasonal rainfall variability is an integral part of seamless prediction between the weather and climate regimes. In the early 21st century, numerical weather prediction (NWP) systems can forecast larger intraseasonal signals such as the MJO several weeks into the future, but they still struggle to forecast shorter scale features reliably. Besides NWP, statistical models can successfully forecast intraseasonal variability of rainfall. Due to the relevance of synoptic to intraseasonal rainfall variability for African societies, early warning systems (EWSs) have been developed to mitigate impacts.

The space–time structure of intraseasonal (10–90 day) rainfall variability in the western tropical Pacific is studied using daily 3B42 TRMM and ERA-Interim reanalysis data for the period 1998–2014. Empirical orthogonal function (EOF) analysis of 10–90-day filtered daily rainfall anomalies identifies two leading modes in both May–October and November–April; together these modes explain about 11%–12% of the total intraseasonal variance over the domain in both seasons and up to 60% over large areas of the western Pacific in both climatological periods. The two leading modes in May–October are linearly related to each other and both are well correlated with the Madden–Julian oscillation (MJO) indices. Although the two leading EOF modes in November–April are linearly independent of each other, both show statistically significant correlations with the MJO. The phase composites of 30–80-day filtered data show that the two leading modes are associated with strong eastward and northward propagation of rainfall anomalies in May–October, and eastward and southward propagation of rainfall anomalies in November–April. The eastward propagation of rainfall anomalies in both seasons and southeastward propagation related with EOF2 in November–April is linked to the development of low-level moisture flux convergence ahead of the active convection. Similarly, the northward propagation in May–October is also connected with low-level moisture flux convergence, but surface wind and evaporation variations are also important. The wind–evaporation–SST feedback mechanism drives the southeastward propagation of rainfall anomalies associated with EOF1 in November–April. The different mechanisms for southeastward propagation associated with two leading modes in November–April suggest dynamically different relations with the MJO.

The summer monsoon rainfall over India exhibits strong intraseasonal variability. Earlier studies have identified Madden Julian Oscillation (MJO) as one of the most influencing factors of the intraseasonal variability of the monsoon rainfall. In this study, using India Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler–Hendon MJO indices, the intra-seasonal variation of daily rainfall distribution over India associated with various Phases of eastward propagating MJO life cycle was examined to understand the mechanism linking the MJO to the intraseasonal variability. During MJO Phases of 1 and 2, formation of MJO associated positive convective anomaly over the equatorial Indian Ocean activated the oceanic tropical convergence zone (OTCZ) and the resultant changes in the monsoon circulation caused break monsoon type rainfall distribution. Associated with this, negative convective anomalies over monsoon trough zone region extended eastwards to date line indicating weaker than normal northern hemisphere inter tropical convergence zone (ITCZ). The positive convective anomalies over OTCZ and negative convective anomalies over ITCZ formed a dipole like pattern. Subsequently, as the MJO propagated eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the OTCZ was observed and negative convective anomalies started appearing over equatorial Indian Ocean. During Phase 4, while the eastwards propagating MJO linked positive convective anomalies activated the eastern part of the ITCZ, the northward propagating OTCZ merged with monsoon trough (western part of the ITCZ) and induced positive convective anomalies over the region. During Phases 5 and 6, the dipole pattern in convective anomalies was reversed compared to that during Phases 1 and 2. This resulted active monsoon type rainfall distribution over India. During the subsequent Phases (7 and 8), the convective and lower tropospheric anomaly patterns were very similar to that during Phase 1 and 2 except for above normal convective anomalies over equatorial Indian Ocean. A general decrease in the rainfall was also observed over most parts of the country. The associated dry conditions extended up to northwest Pacific. Thus the impact of the MJO on the monsoon was not limited to the Indian region. The impact was rather felt over larger spatial scale extending up to Pacific. This study also revealed that the onset of break and active events over India and the duration of these events are strongly related to the Phase and strength of the MJO. The break events were relatively better associated with the strong MJO Phases than the active events. About 83% of the break events were found to be set in during the Phases 7, 8, 1 and 2 of MJO with maximum during Phase 1 (40%). On the other hand, about 70% of the active events were set in during the MJO Phases of 3 to 6 with maximum during Phase 4 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO Phases for the prediction of break and active conditions which are very crucial for agriculture decisions.

Madden–Julian Oscillation (MJO) impact on the Amazon intraseasonal precipitation assessed by different MJO indices is investigated through an analysis of composite events and observed case studies. The MJO indices diagnosing skill is described in detail using reanalysis, satellite, and gauge-based gridded rainfall data. Three types of existing MJO indices are considered: (1) OLR-based MJO (OMI index); (2) dynamically- or circulation-based MJO (VPM index); and (3) combined convectively- and dynamically-based MJO (Wheeler–Hendon RMM index). Our results suggest that, in the large-scale (i.e., around the global tropics), even in the regional domain, the MJO OLR-only index well-represent the dynamical and convective features associated with the intraseasonal variability. On the other hand, each index gives diverging results on rainfall characterization over the Amazon Basin (AB). For instance, the cumulative distribution of precipitation for each MJO phase and index depicts considerable differences in the main climatic regions of the AB, indicating a diverging intraseasonal representation for extreme rainfall values. In addition, when event-by-event is assessed, details as well as the identification of events themselves can differ among indices. This characteristic is particularly observed during extreme rainfall events in the AB. A significant percentage of MJO activity is detected only by the MJO OLR-based index. Because the large-scale zonal circulation dominates the dynamically-based indices (RMM and VPM), the MJO impact in these indices is not an appropriate measure of convective MJO activity. Since the convective component of the MJO is our primary objective, the results presented in this study show that the OLR-based MJO index is able to better account for the MJO impacts over the AB. The indices considered in this study are often used for monitoring and forecasting the MJO activity over South America. However, given the dissimilarity of the representation of precipitation in the AB, our findings also support the consideration of a regional index for monitoring and forecasting the MJO impacts.

The influence of Madden–Julian oscillation (MJO) is examined on intraseasonal rainfall variability during the wet season (November–April) by using the real-time multivariate (RMM) MJO index, ERA5 reanalysis, and daily observed rainfall dataset from 26 stations in Saudi Arabia for the period 1985–2021. The MJO 8 phases are categorized into wet (phases 1, 2, 7 and 8) and dry (phases 3, 4, 5, and 6) based on the Saudi Arabian intraseasonal rainfall characteristics associated with MJO phases. It is observed that 41% (46%) of total (extreme) rainfall events occur during the MJO wet phases, while only 23% (18%) of such events occur during MJO dry phases. The intraseasonal variability signals are isolated from daily dataset by applying a 30- to 90-day period bandpass filter. The analyses are validated by constructing composites of daily filtered precipitation anomalies during MJO 8 phases. The physical mechanism indicates that the significant intraseasonal wetter conditions are linked with enhanced easterly and southeasterly moisture convergence over Saudi Arabia from the Arabian Sea. The atmospheric cyclonic circulation anomalies during the wet phases favor more moisture convergence and vertical moisture advection, which may lead to enhanced convection and rainfall. However, during the dry phases, anticyclonic circulation anomalies enhance moisture divergence and reduce vertical moisture advection and consequently suppress the convection and rainfall activity over Saudi Arabia. The analyses show that the intraseasonal rainfall variability over Saudi Arabia is significantly influenced by the MJO during the wet season. These findings have important implications for sub-seasonal rainfall forecasting in Saudi Arabia.

The Madden–Julian Oscillation (MJO) is the major mode of intraseasonal variability (30–60 days) in the tropics, having large rainfall impacts globally, and possibly on southern Africa. However, the latter impact is not well understood and needs to be further explored. The life cycle of the MJO, known to be asymmetric, has been nevertheless analyzed usually through methods constrained by both linearity and orthogonality, such as empirical orthogonal function analysis. Here we explore a non-linear classification method, the self-organizing map (SOM), a type of artificial neural network used to produce a low-dimensional representation of high-dimensional datasets, to capture more accurately the life cycle of the MJO and its global impacts. The classification is applied on intraseasonal anomalies of outgoing longwave radiation within the tropical region over the 1980–2009 period. Using the SOM to describe the MJO is a new approach, complimentary to the usual real-time multivariate MJO index. It efficiently captures this propagative phenomenon and its seasonality, and is shown to provide additional temporal and spatial information on MJO activity. For each node, the subtropical convection is analyzed, with a particular focus on the southern Africa region. Results show that the convection activity over the central tropical Indian Ocean is a key factor influencing the intraseasonal convective activity over the southern African region. Enhanced (suppressed) convection over the central Indian Ocean tends to suppress (enhance) convection over the southern African region with a 10-day lag by modulating the moisture transport.

<p><span>We investigate the impact of air-sea coupling on the simulation of the intraseasonal variability of rainfall over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled simulations forced with sea surface temperature (SST) climatology and daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific Convergence Zone (SPCZ) is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST. The coupled model best simulates the key characteristics of the two intraseasonal rainfall modes of variability in the South Pacific, as identified by an Empirical Orthogonal Function (EOF) analysis. The spatial structure of the two EOF modes in all three simulations is very similar, suggesting these modes are independent of air-sea coupling and primarily generated by the dynamics of the atmosphere. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating </span><span>Madden-Julian Oscillation (</span><span>MJO</span><span>)</span><span> signals over the Indian Ocean and western Pacific. Air-sea interaction seems crucial for such propagation as both eastward and southeastward propagations substantially reduced in the uncoupled model forced with SST climatology. Prescribing daily SST from the coupled model improves the simulation of both eastward and southeastward propagations in the uncoupled model forced with daily SST, showing the role of SST variability on the propagation of the intraseasonal variability, but the periodicity differs from the coupled model. The change in the periodicity is attributed to a weaker SST-rainfall relationship that shifts from SST leading rainfall to a nearly in-phase relationship in the uncoupled model forced with daily SST.</span></p>

Based on 12 years of daily satellite precipitation data and reanalysis winds, intraseasonal (30–90 days) variability in Caribbean precipitation is linked to phases of the Madden–Julian oscillation (MJO). Intraseasonal variability is largest during September–November (SON), but some modulation of precipitation by the MJO appears throughout all seasons. Precipitation anomalies up to 50% above (below) the annual mean are observed in phases 1 and 2 (5 and 6) of the MJO. The changes in Caribbean precipitation associated with the MJO are shown to be related to changes in the low-level (925 hPa) winds. When precipitation anomalies are above (below) average in phases 1 and 2 (5 and 6), wind anomalies act to decrease (increase) the strength of the prevailing easterly trade winds. The changes in the low-level winds are most apparent in the region of the Caribbean low-level jet (CLLJ), and divergence anomalies associated with the entrance and exit regions of the CLLJ precede the precipitation anomalies. The CLLJ itself is also shown to be subject to intraseasonal variability, and its magnitude varies with the phase of the MJO. Again, intraseasonal variability in the CLLJ associated with the MJO is observed in all seasons and shows a significant coherence with intraseasonal variability in the precipitation. Extreme rainfall events over islands in the Caribbean show a strong relationship with the MJO phase, with extreme events being most common in phases 1 and 2 of an MJO event. This relationship between the MJO and extreme events has important implications for the predictability of precipitation extremes in the Caribbean.

During boreal summer (June–October), interactions between intraseasonal variability in the Eastern Hemisphere and east Pacific warm pool are often described as a local amplification of the Madden–Julian oscillation (MJO), the dominant mode of tropical intraseasonal variability. The MJO in the Eastern Hemisphere emits eastward-propagating dry Kelvin waves that are a source of rapid communication with the east Pacific. However, the precise mechanism by and degree to which intraseasonal variability in the Eastern Hemisphere interacts with the east Pacific are not well understood. To quantify the relationship, sensitivity tests in two separate models are used: the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM) and the International Pacific Research Center Regional Atmosphere Model (IRAM). Different methods are employed to isolate the east Pacific from outside intraseasonal signals in each model. When isolated from Kelvin wave fronts associated with the MJO, the CAM produces similar east Pacific intraseasonal variability to observations. In the CAM, the communication of intraseasonal signals by Kelvin waves does not appear necessary to the initiation and maintenance of east Pacific intraseasonal variability, suggesting that such events can be independent of the MJO. However, communication by MJO-initiated Kelvin waves provides a possible phase locking mechanism between hemispheres. When the east Pacific is isolated from all remote intraseasonal signals in the IRAM, intraseasonal events there are weak and incoherent. In the IRAM communication across the Pacific appears necessary to the representation of east Pacific intraseasonal variability. However, the IRAM contains an important bias in the climatological low-level winds that may suppress east Pacific intraseasonal events.

A gridded daily rainfall dataset prepared from observations at 3700 stations is used to analyze the intraseasonal and interannual variability of the summer monsoon rainfall over India. It is found that the major drought years are characterized by large-scale negative rainfall anomalies covering nearly all of India and persisting for the entire monsoon season. The intraseasonal variability of rainfall during a monsoon season is characterized by the occurrence of active and break phases. During the active phase, the rainfall is above normal over central India and below normal over northern India (foothills of the Himalaya) and southern India. This pattern is reversed during the break phase. It is found that the nature of the intraseasonal variability is not different during the years of major droughts or major floods. This suggests that a simple conceptual model to explain the interannual variability of the Indian monsoon rainfall should consist of a linear combination of a large-scale persistent s...

The Indo-Gangetic Plains (IGPs) are densely populated and agriculturally productive areas with strong interannual and intraseasonal rainfall variability. The intraseasonal rainfall variability over IGPs is due to variation in sea surface temperature in the equatorial Indian Ocean. The intense rainfall activity over IGPs is mainly convection-driven, which may be linked with Madden–Julian Oscillation (MJO). The threshold for exceptionally heavy rainfall during the period 1979–2012 is based on the analysis of heavy rainfall episodes (percentage departure in daily rainfall (PDR ≥ 700%). The thresholds for extremely strong MJO events show highest departure in MJO amplitude (PDA ≤ 200%). The present study aims to find the simultaneous relationship between 60 and 30-day cycles of rainfall variability over IGPs and linked with MJO amplitude variability for the period 1979–2012. Further, the 30-day cycle of rainfall variability is elaborately studied for different phases of MJO. The monthly and daily variability of IGPs rainfall as well as MJO amplitude is analysed to find important intense rainfall and MJO events. The results suggest that the monthly rainfall variability is caused due to synoptic scale weather systems like monsoon trough oscillation and corresponding pressure fluctuations over IGPs. The exceptionally intense rainfall activity during onset and retreat phases is observed to be associated with MJO phases 6–8. The intense rainfall activity during active-break phase is observed to be associated with MJO phases 3–5. The intense rainfall events during break phase are observed along foothills of Himalaya. The day-to-day rainfall variability is due to interaction between monsoon circulation and MJO.

The spatial and temporal structures of the intraseasonal atmospheric variability over central Africa is investigated using 2.5° × 2.5° daily outgoing longwave radiation (OLR) and National Centers for Environmental Prediction (NCEP) Reanalysis zonal winds for the period 1980–2010. The study begins with an overview of the Central African rainfall regime, noting in particular the contrast amongst Western and Eastern parts, with different topography and surface conditions features. The annual mean rainfall and OLR over the region revealed a zone of intense convective activity centered on the equator near 30°E, which extends southward and covers almost all the Congo forest. The annual cycle of rainfall reflects the classical bi-annual shift of Inter-Tropical Convergence Zone across the equatorial belt, between 10°S and 10°N. The result of the empirical orthogonal functions (EOFs) analysis has shown that the three leading EOF modes explain about 45 % of total intraseasonal variability. The power spectra of all the three corresponding principal components (PCs) peak around 45–50 days, indicating a Madden–Julian Oscillation (MJO) signal. The first mode exhibits high positive loadings over Northern Congo, the second over Southern Ethiopia and the third over Southwestern Tanzania. The PCs time series revealed less interannual modulation of intraseasonal oscillations for the Congo mode, while Ethiopian and Tanzanian modes exhibit strong interannual variations. Hovmoller plots of OLR, 200 and 850 hPa NCEP zonal winds found the eastward propagating features to be the dominant pattern in all the three times series, but this propagation is less pronounced in the OLR than in the 850 and 200 hpa zonal wind anomalies. An index of MJO strength was built by averaging the 30–50 day power for each day. A plot of MJO indices and El Nino Southern Oscillation (ENSO) cycle confirm a strong interannual modulation of MJO over Eastern central Africa partially linked with the ENSO events (El Nino and La Nina). Strong MJO activity is observed during La Nina years or during ENSO-neutral years, while weak or absent MJO activity is typically associated with strong El Nino episodes.