https://orcid.org/0000-0002-3474-6922
Figures
Abstract
Equatorial waves are key drivers of intraseasonal variability in the tropics, exerting a strong influence on convection and rainfall over northern South America. This variability arises from a broad spectrum of quasi-periodic and aperiodic modes operating on synoptic to global scales. Although each wave exhibits distinct dynamics and seasonality, isolating the individual effects of these waves on precipitation remains challenging. Here, we employ a local index to estimate precipitation anomalies associated with the enhancing and inhibiting phases of multiple convectively coupled equatorial waves (CCEWs): easterly waves (EW), Kelvin waves (KW), mixed Rossby–gravity waves (MRG), the Madden–Julian Oscillation (MJO), eastward inertio-gravity waves (EIG), and equatorial Rossby waves (ER). Our results are broadly consistent with previous studies for northern South America and with local indices applied elsewhere, but provide novel evidence of the effects of EIG and ER waves, which had not been previously documented in the region. Positive (negative) precipitation anomalies are systematically associated with enhancing (inhibiting) phases, and their spatial distribution highlights particularly affected areas, including the Andes, the Pacific coast, and the eastern plains. This work presents advances in understanding of the precipitation anomalies during the transit of tropical waves, which is crucial for improving rainfall forecasting systems in the region.
Citation: Builes-Jaramillo A, Jaramillo-Moreno A, Salas HD (2026) Isolating the influence of intraseasonal oscillations on northern South American precipitation. PLOS Clim 5(5): e0000923. https://doi.org/10.1371/journal.pclm.0000923
Editor: Ahmed Kenawy, Mansoura University, EGYPT
Received: October 17, 2025; Accepted: April 21, 2026; Published: May 20, 2026
Copyright: © 2026 Builes-Jaramillo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The minimal dataset required to replicate the findings of this study consists of ERA5 hourly data on single levels, Daily Interpolated Outgoing Longwave Radiation (OLR) data, and CHIRPS precipitation data. ERA5 hourly data on single levels are freely and publicly available through the Copernicus Climate Data Store (CDS) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF): https://cds.climate.copernicus.eu/datasets. Specifically, the dataset used corresponds to the ERA5 reanalysis single-level atmospheric variables described in the Methods section. Access requires free registration with the Copernicus Climate Data Store. Daily Interpolated Outgoing Longwave Radiation (OLR) data used for the filtering approach are freely and publicly available from the NOAA Physical Sciences Laboratory (PSL): https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html. The specific dataset corresponds to the NOAA Interpolated OLR dataset described in the Methods section. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data) rainfall estimates, derived from rain gauge and satellite observations, are freely and publicly available through the Climate Hazards Center of the University of California, Santa Barbara: https://www.chc.ucsb.edu/data/chirps. The specific dataset used corresponds to the CHIRPS precipitation product described in the Methods section. All datasets used in this study are publicly accessible without restriction for research purposes, and interested researchers can fully replicate the study findings by directly downloading these datasets from the indicated sources and following the methodological procedures described in the Methods section, including data preprocessing, filtering, and diagnostic calculations. The authors did not have any special access privileges to these third-party datasets that other researchers would not have. All data were obtained through standard public access provided by the respective data repositories.
Funding: The work of A.B-J. was supported by Institución Universitaria Colegio Mayor de Antioquia by the project FAI149 and Convocatoria de Pasantías 2024. The work of A.J-M. was supported by UNAM-PAPIIT IN104426. The work of H.D.S. was supported by Instituto Tecnológico Metropolitano as contribution to the project FAI149. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare that no competing interests exist.
Introduction
Convectively Coupled Equatorial Waves (CCEWs) are fundamental drivers of tropical atmospheric variability, with timescales ranging from a few days to several weeks [1–4]. These waves play an important role in modulating precipitation [5], extreme weather events [6], and influencing moisture transport patterns and other hydroclimatic features over the tropics [7–9].
Due to its climatic diversity and heavy reliance on rain-fed agriculture and hydroelectric power, northern South America is especially susceptible to intraseasonal rainfall variability [10]. Several studies have examined the influence of CCEWs in the region. For example, studies by Hollis et al. [11], Dominguez et al. [12], Giraldo-Cardenas et al. [13], Valencia et al. [14], and Builes-Jaramillo et al. [15] have quantified the contribution of different CCEWs to precipitation and explored their interactions with processes such as low-level jets. Other works have focused on the role of specific wave types such as Kelvin waves, the Madden–Julian Oscillation (MJO), and equatorial Rossby (ER) waves in modulating rainfall over the Amazon basin and southern South America, as well as their interactions with low-frequency modes such as the El Niño-Southern Oscillation (ENSO) (e.g., Refs [10,16,17]). The identification and tracking of these intraseasonal disturbances commonly rely on methodologies such as space-time filtering, spectral projection, and empirical orthogonal function (EOF) analysis [3].
Different diagnostic indices have been developed to characterize the phase and amplitude of CCEWs, each suited to specific wave types and regions. Among the regional measures, the Maritime Continent Index [18] highlights the modulation of MJO, Kelvin, and ER waves on the diurnal cycle of convection. At a broader scale, the Bimodal Intraseasonal Oscillation Index [19,20] employs extended EOF analysis of outgoing longwave radiation (OLR) to represent both the MJO and the Boreal Summer Intraseasonal Oscillation (BSISO), whereas other OLR-based indices, such as the MJO Index (OMI) [21], focus more narrowly on the convective component of the MJO. Multivariate approaches include the widely used Real-time Multivariate MJO (RMM) Index [22], which combines OLR with upper- and lower- level winds. More recently, the Multivariate Index for Tropical Intraseasonal Oscillations (MII) [23], which refines the RMM framework by applying a sliding-window EOF to capture seasonal changes and the vertical structure of ISO. In addition, regional diagnostics have also been proposed, such as the ISO Index for Central Africa [24], the SOM-based Local PCA for monsoon ISOs [25], and the Kelvin Convectively Coupled Index [8], which reconstructs OLR anomalies associated with specific equatorial waves.
More recently, Sapucci et al. [10] proposed regional indices based on Empirical Orthogonal Functions (EOFs) and artificial neural networks. Their approach employs Self-Organizing Maps (SOM), an unsupervised machine learning technique that generates a nonlinear projection of the input data. Unlike traditional methods, SOM is not constrained by orthogonality requirements, enabling the identification of non-orthogonal patterns that may be more physically relevant than those obtained by conventional statistical techniques [10,26].
Despite these advances, there remains a need for regional indices that can capture wave–convection interactions across multiple intraseasonal modes, allowing for improved isolation of CCEWs and minimizing interference among overlapping signals. This challenge is especially critical in regions with complex climatological and topographic conditions, such as northern South America. In this study, we build upon previous work on intraseasonal variability in the region [10,15,16] by developing a local index of intraseasonal activity centered on northern South America (specifically over Colombia). This approach allows, for the first time, the simultaneous isolation of multiple CCEW modes and the quantification of their contribution to regional hydroclimate variability. Specifically, we: (i) identify tropical waves— including Equatorial Rossby (ER), eastward inertio-gravity (EIG), Tropical Easterly Waves (EWs), Kelvin waves (KWs), the Madden–Julian Oscillation (MJO), and Mixed Rossby–Gravity (MRG) waves-based on observed OLR data, along with an assessment of their influence on the region’s hydroclimate. Other CCEW modes are not considered because their characteristic periods are substantially shorter than the intraseasonal variability targeted in this analysis, which focuses on timescales between 2 and 90 days; (ii) characterize the annual cycle of CCEWs, distinguishing between their enhancement phases (associated with negative OLR anomalies) and inhibition phases (associated with positive OLR anomalies); and (iii) analyze the synoptic atmospheric environment during periods of active wave influence, with a focus on their potential to modulate precipitation patterns across the region.
Datasets and methodology
To investigate the role of intraseasonal variability in modulating rainfall over northern South America, we focus on a domain centered over Colombia. The study region, shown in Fig 1, is bounded by 70°–80°W and 0°–12°N, which is used to construct a local intraseasonal oscillation index. Most of the region is characterized by a bimodal precipitation regime, with two rainy seasons (March–April–May and September–October–November) and two drier seasons (December–January–February and June–July–August) [27,28]. This area encompasses a zone of deep tropical convection influenced by the Andes, the Intertropical Convergence Zone (ITCZ), and ocean–atmosphere interactions, making it a key region for diagnosing intraseasonal disturbances that modulate rainfall in northern South America [29].
Mean daily precipitation (mm/day) from CHIRPS is displayed in the background, highlighting the spatial distribution of rainfall. The dashed box (70°–80°W, 0°–12°N) delineates the domain used to construct the local intraseasonal oscillation index centered over Colombia. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
Datasets
Outgoing longwave radiation (OLR).
For convection diagnostics, we use on the NOAA Interpolated Outgoing Longwave Radiation (OLR) dataset [30], available from 1981 to 2022 at 2.5° resolution. This widely used gridded product, distributed by the NOAA Physical Science Laboratory (https://psl.noaa.gov/data/gridded/index.html), serves as a robust statistical proxy for deep tropical convection [1].
Precipitation.
Rainfall variability was characterized using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), a quasi-global dataset extending over four decades. Covering 50°S–50°N at 0.05° resolution, CHIRPS merges the in-house climatology CHPclim, high-resolution satellite imagery, and in-situ station records to produce gridded precipitation fields suitable for long-term analyses and drought monitoring [31].
ERA5 reanalysis.
To examine the large-scale circulation and moisture budget, we employed ERA5 reanalysis products [32]. Surface winds and specific humidity fields were analyzed to compute horizontal water vapor transport and to construct composites of the synoptic environment during active ISO phases, while vertically integrated moisture divergence provided insights into the background moisture balance.
Methodology
Wave number-frequency power spectra and fourier filters.
To isolate the contribution of tropical waves to convective variability over the study region, we applied wavenumber–frequency filtering to the NOAA OLR dataset to extract the components associated with specific wave types. Following Wheeler and Kiladis [33], the OLR data were first preprocessed by removing the mean, linear trend, and annual cycle. The wavenumber–frequency filtering was then applied to isolate the components associated with specific CCEWs, using the corresponding spectral windows defined in that study. This approach allows the filtered OLR anomalies to retain contributions from both symmetric and antisymmetric equatorial wave structures. The filtering procedure extracts the characteristic spatiotemporal signatures of convectively coupled equatorial wave modes from the global tropical OLR field. The analysis targeted convectively coupled Kelvin (KW), mixed Rossby–gravity (MRG), tropical easterly (EW), equatorial Rossby (ER), and eastward inertio–gravity (EIG) waves, as well as the Madden–Julian Oscillation (MJO). Each signal was isolated by applying an inverse Fourier transform to retain only the spectral components within the corresponding wave-specific domain.
Spatial EOF analysis.
To define a local index for isolating tropical wave activity and after the Wheeler and Kiladis space–time filtering that isolates the OLR variability for each intraseasonal mode, we follow the procedure proposed by Mounier et al. [8] and recently applied by Builes-Jaramillo et al. [15]. This method synthesizes the activity of each wave type using Spatial Empirical Orthogonal Functions (SEOFs) over the local domain 70°–80°W; 0°–12°N. Each filtered OLR field is first decomposed via spatial EOF analysis for the corresponding wave type (S1 Fig in Supporting information). The dimensionality of the field is then reduced by retaining only the first two SEOF modes, which account for the largest fraction of variance. A reconstructed OLR anomaly field is obtained by combining the first and second SEOF modes (SEOF12). From this reconstructed field, time series are extracted for all grid cells within the target region (70°–80°W, 0°–12°N as previously mentioned). These time series are subsequently averaged and standardized to produce the SEOF12 index, which characterizes the regional activity of each tropical wave mode.
We define the Enhancing (Inhibiting) phase (EP/IP) of each wave as the period when the reconstructed SEOF12 index is negative (positive), indicating stronger (weaker) convective activity over the base region. Specifically, EP (IP) are identified when the standardized SEOF12 index falls below –1.5 (above +1.5). The ± 1.5 standardized threshold follows previous applications of local intraseasonal indices [8,15] and represents a balance between selecting sufficiently strong wave activity and retaining an adequate number of events for composite analysis.
Isolation of ISO events and their contribution to rainfall anomalies.
After computing the SEOF12 index for each of the intraseasonal oscillations (ISOs)—namely KW, MRG, ER, EIG, EW, and MJO—we derived six time series of standardized mean OLR anomalies averaged over the corresponding index regions (Fig 1). To isolate the individual effect of each ISO, during the EP (IP) of one particular ISO, we further required that all other ISOs’ indices remain within a neutral range of minus 1.5 to plus 1.5 standard deviations during these events. Although ISOs coexist and interact continuously in the tropical atmosphere, this filtering procedure reduces the impact of overlapping signals and highlights periods when a single ISO is most dominant, thereby enabling a more robust attribution of its effects on convection and rainfall anomalies. The selected dates corresponding to these isolated phases were then used to compute composites of deseasonalized precipitation fields for each wave type, with statistical significance assessed against climatology using a t-test.
To quantify the influence of each ISO on daily precipitation anomalies, we first identified days corresponding to its enhancing and inhibiting phases (denoted as PD). From this set, we selected the subset of days that coincided with positive (negative) precipitation anomalies, defined relative to a 30-day running mean of the precipitation data set. These days are referred to as Anomalous Precipitation Days (APD).
The degree of association between ISO phases and precipitation anomalies was then quantified as the proportion of coincident days relative to the total number of APDs in a given month. This metric, expressed as a percentage, is defined as AISO:
where n is the number of APDs in the month, and the index i corresponds to the ISO phase (EP or IP). Thus, AISO represents the fraction of positive (negative) precipitation anomalies potentially attributable to ISO activity, providing a quantitative measure of how intraseasonal variability modulates rainfall at regional scales.
This approach allows us to better isolate the influence of individual ISOs although it does not represent a complete separation among wave modes. Treating the remaining ISOs as “neutral” when their indices lie within ±1.5 standard deviations reduces overlap, but residual sub-threshold variability and interactions among waves may still influence the local convective environment. Therefore, the composites should be interpreted as precipitation responses during periods when one ISO is dominant, rather than as strictly isolated effects.
Results and discussion
To characterize the intraseasonal variability of ISOs, we first examined the annual cycle of the number of days classified in the enhancing (EP) and inhibiting (IP) phases of each ISO. Fig 2 shows these results as monthly boxplots for the entire study period. EW and MRG (Fig 2a and 2d) waves exhibit a similar activity pattern from April to December, with a pronounced peak in July and August, corresponding to the boreal summer. This suggests that these waves may play a key role during the peak months of the rainy season in the region (Fig 3 blue dashed line). In contrast, KW and EIG (Fig 2c and 2e) waves, although EP events occur throughout the year, exhibit their highest activity from boreal autumn to spring, indicating a possible influence on rainfall events during the dry season. The MJO (Fig 2b) displays greater activity and variability from April to October, with a marked decrease in August and enhanced variability during the transition from spring to summer, pointing to its potential role in shaping the onset of the rainy season in the region. ER (Fig 2f) waves remain active throughout the year, but exhibit a bimodal pattern, with peaks in April–May and October–November. These peaks align with the two main rainfall maxima observed over the Colombian Andes (Fig 3, blue dashed line), suggesting that ER waves may contribute to the bimodal precipitation feature of the region.
Monthly boxplots show the count of days in Enhancing (blue) and Inhibiting (red) phases for each wave type. These phases represent days when intraseasonal oscillations tend to favor or suppress convection over the study region.
Solid black lines show days classified as enhancing convection, while dashed black lines show days classified as inhibiting convection. The blue dashed line indicates the climatological mean daily precipitation for the region.
Regarding the phase classification, the annual cycles for most wave types, including EWs, EIG, MRG, and ER, exhibit similar behavior in EP and IP phases. Particularly, the ISOs EP are around 986–1046 days, and the Isolated EP for each ISO is around 40% to 51% (see S1 Table in Supporting information). KW shows greater variability in EP events during summer and reduced IP activity. In comparison, the MJO exhibits an inverse pattern in May, with higher EP variability, and in June, with higher IP variability, as well as a predominance of EP in July.
The similarities in the annual cycles of the ISOs suggest that two or more wave types (or even all of them) may be relevant for understanding precipitation events in the region during specific months. For instance, both EWs and MRGs exhibit their highest activity from May to September, suggesting potential reinforcement of convective conditions during this season. In contrast, the seasonal cycles of EWs and KWs often diverge, raising the possibility of antiphase interactions that could dampen or modulate precipitation signals. ISOs can co-occur in phase, or even in antiphase. These overlaps and contrasts underscore the complexity of ISO interactions and their combined influence on regional rainfall variability. Therefore, isolating events is essential to disentangle the specific contribution of each ISO to precipitation and related atmospheric fields and to avoid misleading interpretations caused by the superposition of multiple wave types. Nevertheless, isolating the ISOs activity does not fully guarantee that other processes that may affect OLR in the region and that might be influencing this variable along with the wave transit.
Fig 3 shows the mean annual cycle of daily precipitation for the study region (blue dashed line), along with the mean annual cycle calculated for days when the EP (black solid line) and IP (black dashed line) phases were active. As mentioned earlier, the annual cycle displays a clear bimodal pattern, with a rainy season characterized by two prominent peaks in May and October. Mean precipitation during EP episodes for all ISOs is consistently higher than the climatological mean throughout the year. In contrast, precipitation during IP episodes tends to decrease relative to the mean, indicating environments more favorable to convection during EP and less favorable during IP, as expected. Although the mean precipitation during EP generally resembles the shape of the annual cycle, some ISOs show enhanced values compared to the mean, particularly in the decreasing phases of the annual cycle. For example, EIG (Fig 3e) exhibits higher precipitation from March to June, KW (Fig 3c) from May to June, and MRG (Fig 3d) from October to November.
Fig 4 shows the mean anomalies of daily precipitation associated with the composite days classified as EP and IP for each isolated ISO. For the EP (IP) composites, the region bounded by the coordinates 5°N-10°N; 70°W-78°W exhibits predominant positive (negative) precipitation anomalies for all analyzed ISOs. During EP, the EW, MJO, and MRG (Fig 4a, 4c and 4d) show positive precipitation anomalies over the plains between Colombia and Venezuela (Orinoco basin). KWs (Fig 4b) show positive anomalies to the south, with the strongest magnitudes in the central–northern Amazon, consistent with Mayta et al. [16]. Furthermore, the EWs and MRGs (Fig 4a and 4d)) depict negative anomalies over the north-eastern Amazon. Finally, EIGs and ERs show positive precipitation anomalies over northeastern Colombia (Fig 4e and 4f)). During IP, the KW, EIG, and ER (Fig 4h, 4k and 4i) show negative precipitation anomalies over the Orinoco basin (dry Orinoco), whereas MRG (Fig 4j) shows positive anomalies in this zone (Orinoco). Furthermore, KW and EIG (Fig 4h and 4k) exhibit positive anomalies over the north-eastern and western Amazon basin, respectively. The EW, MJO and MRG (Fig 4g, 4i and 4j) exhibit negative anomalies over the Colombian and Ecuadorian Andes.
Colored areas denote anomalies statistically significant at the 95% confidence level. The label nobs denotes the number of events contributing to each composite. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
These spatial patterns, particularly for ER, KW, and MJO, deserve further research to assess their role and relative importance across key regions of Colombia but a detailed examination is beyond the scope of this study. Moreover, precipitation anomalies exhibit variability in terms of spatial patterns, magnitude, and sign, which also deserve further research. For instance, MJOs in their IP (EP) are linked to negative (no significant) anomalies south of 5°S. Another example is that rainfall anomalies during ERs show significance only between 0° and 12°N.
Fig 5a shows AISO results during EP for each isolated ISO and month during the study period (1981–2022). Fig 5b presents the total number of days with positive precipitation anomalies per month. Similar to Fig 3, the largest number of positive precipitation anomalies occurs from April to November, with peaks in May and October. It is important to note that the AISO represents the percentage of events in which the influence of a specific ISO was isolated, meaning that the contribution of other waves is comparatively weaker.
a) Mean AISO (%) for each ISO and month, b) study zone mean of the positive anomaly days for each month in the study region, c) examples of the spatial distribution of AISO for the waves in selected months. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
From May to October (rainy season), the distribution of events highlights the strong influence of EWs, which ranges 2–7% of positive precipitation anomalies, while other waves contribute between 2% and 6%. From November to April (dry season), the behavior is markedly different: the MJO explains about 11% of positive precipitation anomaly events in February, while KWs and ERs each contribute around 5% in January and February. This emphasizes the relevance of these waves for precipitation during the dry season, particularly when MJO activity peaks in the boreal winter [34,35].
Spatially, the strongest AISO signals appear over the Andes. Fig 5c illustrates the spatial distribution of AISO for each ISO for the month of maximum AISO values. For example, the MJO reaches its highest AISO in February (11%), with localized maxima in central Colombia, the Andes and the northern Amazon. KWs peak between February and April (5– 6%), with a uniform distribution over the region but enhanced signals along the Colombia-Panama Pacific coast and isolated spots over the Caribbean. MRGs exhibit a continuous influence from May to September (∼4%), also uniformly distributed. EIGs show their maximum influence in November–December (6%), with slightly higher values in southern Colombia. Finally, ERs exhibit continuous activity from December to April (4–5%), with localized maxima above 12% in northern central Colombia during January.
Fig 6a shows the AISO results for the IP. Fig 6b presents the total number of days with negative precipitation anomalies per month. Similarly to Fig 3, the largest number of negative precipitation anomalies occurs between December and March, with a peak in January. We note the overlap between the EWs and MRGs during July and August, which accounts for approximately 11% of the negative precipitation anomalies.
a) Mean AISO (%) for each ISO and month, b) study zone mean of the negative anomaly days for each month in the study region c) examples of the spatial distribution of AISO for the waves in selected months. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
From May to October (rainy season), the distribution of events highlights the strong influence of negative precipitation anomalies of EWs, which ranges 2–7%, the MJOs and MRGs from 4–6%, while other ISOs contribute up to 2%. From November to April (dry season), the behavior is markedly different: KW coincide with 6% of negative precipitation anomaly events in April, while EIGs and ERs each coincide around 5% in November. Our results confirm the importance of local indices in evaluating the influence of ISOs on extreme drought events [10].
Fig 6c shows the spatial distribution of AISO for the IP. EWs and MRGs reach their higher AISO in August around 10% each over eastern Colombia. KWs and ERs reach their peaks in April (4%), with a uniform distribution over the region but enhanced signals along the Andes mountain range. MJOs exhibit a continuous influence from April to September (3–6%) with a spatial local maximum at eastern Colombia, northern Colombia, and the Caribbean. EIGs show their maximum influence in November(5%), with higher values scattered around the Andes and northern Colombia.
Composites of water vapor transport at 850 hPa (vectors) and vertically integrated moisture divergence reveal contrasting patterns between the Enhancing (EP, left) and Inhibiting Phases (IP, right) of the ISOs (Fig 7). During EP (IP), negative (positive) divergence anomalies prevail over the study region, consistent with the precipitation increase (decrease) shown in Fig 4. The water vapor transport within the study domain (highlighted in lime) is generally easterly across the northern Caribbean within the corridor of the Caribbean Low-Level Jet, veering to northerly flow across the Panama Isthmus toward the Pacific Ocean. In addition, a strong east–west pathway extends through the Orinoco plains between Colombia and Venezuela, associated with the Orinoco Low-Level Jet [36,37]. During EWs and MRGs, the dominant Orinoco moisture pathway weakens, accompanied by a slight increase in moisture transport across the Andes to the south. In contrast, KWs and ERs are characterized by enhanced moisture transport from the Caribbean toward the Pacific through Panama.
During EP (left panels a) to f)) and IP (right panels g) to l)) of the ISOs, respectively. Region in color are the ones with statistical significance at the 95% (t-test). The label nobs denotes the number of events contributing to each composite. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
Taken together, these results reveal clear contrasts in the relative importance of different ISOs across regions and seasons. Summarizing the influence of waves by region and phase (Fig 4), we find that during the EP, there are statistically significant positive precipitation anomalies in northwestern Colombia and Central America influenced by MRG, EIG, ER, and EW waves; in the Andes, influenced by KW, MJO, EIG, and ER waves; in the Orinoco River basin, influenced by MRG and EW waves; and in the Amazon River basin, influenced by KW waves. During the IP, we find negative precipitation anomalies in northwestern Colombia and Central America associated with EIG waves; in the Andes, associated with EW, MJO, and MRG waves; in the Orinoco River basin, associated with KW, EIG, and ER waves; and in the Amazon River basin, associated with KW and EIG waves. For the IP phase, a counterintuitive result of positive precipitation anomalies associated with the EW waves is found in the Orinoco River basin. In this sense, the resulting precipitation patterns are not symmetrical from one phase to the other over the study region. Regarding the spatial disaggregation of the seasonal influence over positive or negative precipitation anomalies (Figs 5 and 6), our results highlight the spatial heterogeneity over the index region for the highest AISO values. We consider that this result deserves further investigation to unlock the potential of the local index approach in a decision making level.
Conclusions
In this study, we examined the influence of intraseasonal oscillations (ISOs) on precipitation in northern South America. Using observed Outgoing Longwave Radiation (OLR) data and a local index designed to isolate tropical waves centered over the region, we identified Equatorial Rossby (ER), eastward inertio-gravity (EIG), Tropical Easterly Waves (TEWs), Kelvin waves (KWs), the Madden–Julian Oscillation (MJO), and Mixed Rossby–Gravity (MRG) waves. Each wave was further characterized according to its Enhancing and Inhibiting phases (EP and IP).
Our analysis shows that precipitation generally increases during the Enhancing Phase (EP) and decreases during the Inhibiting Phase (IP) across all wave types. In particular, KWs enhance rainfall by up to 50% between March and July, around 0° and 70°W, which helps explain the asymmetry between the first and second rainy seasons in the region. While some of our results are consistent with previous studies [15,16], we emphasize here the added value of filtering overlapping ISOs to isolate their individual contributions better.
By isolating the activity of each ISO, we quantified the percentage of positive precipitation anomalies associated with the EP, which in some cases reached 11% (e.g., MJOs in February). For most ISOs during their active season, the influence was around 5% between positive precipitation and EP. Negative precipitation anomalies were more pronounced from April to August. For example, in August, EW, MRG, and MJO are related to 16% of negative precipitation anomalies in the region. These precipitation responses were consistent with the patterns of low-level moisture transport and divergence.
The approach presented here, based on a local index, permits the analysis of the potential influence of different equatorial waves over a specific region of study and enables both simultaneous and lagged analyses using the same index. This framework has the potential to improve existing forecast systems and could be adapted by regional meteorological agencies. An additional advantage is that the method can be implemented elsewhere in the tropics.
Nonetheless, several questions remain for future research: (1) To what extent are ecosystems and river basins in the region sensitive to precipitation anomalies associated with equatorial waves? (2) Do areas of maximum AISO values spatially overlap across different ISOs? (3) Why do MRGs exhibit a contrasting pattern compared with other ISOs?; Overall, our findings provide new insight into the role of intraseasonal oscillations in modulating rainfall over northern South America. By disentangling the contributions of individual wave types, this study advances the understanding of tropical variability in the region. It provides a framework that can inform both scientific research and operational forecasting.
Supporting information
S1 Table. Percentage of total ISO events represented by isolated ISO events.
https://doi.org/10.1371/journal.pclm.0000923.s001
(XLSX)
S1 Fig. SEOF12 structures for each ISO.
The green polygon is the region for detection of ISOs activity. Base map source: Natural Earth free vector and raster map data @naturalearthdata.com.
https://doi.org/10.1371/journal.pclm.0000923.s002
(TIF)
References
- 1. Kiladis GN, Wheeler MC, Haertel PT, Straub KH, Roundy PE. Convectively coupled equatorial waves. Reviews of Geophysics. 2009;47(2).
- 2. Roundy PE, Frank WM. A Climatology of Waves in the Equatorial Region. J Atmos Sci. 2004;61(17):2105–32.
- 3. Knippertz P, Gehne M, Kiladis GN, Kikuchi K, Rasheeda Satheesh A, Roundy PE, et al. The intricacies of identifying equatorial waves. Quart J Royal Meteoro Soc. 2022;148(747):2814–52.
- 4. Bartana H, Garfinkel CI, Shamir O, Rao J. Projected future changes in equatorial wave spectrum in CMIP6. Clim Dyn. 2022;60(11–12):3277–89.
- 5. Okello PO, Tan G, Ongoma V, Nyandega I. Influence of convectively coupled equatorial Kelvin waves on March-May precipitation over East Africa. Geographica Pannonica. 2021;25(1):24–34.
- 6. Peyrillé P, Roehrig R, Sanogo S. Tropical Waves Are Key Drivers of Extreme Precipitation Events in the Central Sahel. Geophysical Research Letters. 2023;50(20).
- 7. Sobel AH, Maloney ED, Bellon G, Frierson DM. Surface Fluxes and Tropical Intraseasonal Variability: a Reassessment. J Adv Model Earth Syst. 2010;2(1).
- 8. Mounier F, Kiladis GN, Janicot S. Analysis of the Dominant Mode of Convectively Coupled Kelvin Waves in the West African Monsoon. Journal of Climate. 2007;20(8):1487–503.
- 9. Feng X, Yang G-Y, Hodges KI, Methven J. Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. Nat Commun. 2023;14(1):511. pmid:36720876
- 10. Sapucci CR, Mayta VC, da Silva Dias PL. South American Intraseasonal Oscillation: EOF and Neural Network Approaches. Journal of Geophysical Research: Atmospheres. 2025;130:e2024JD041988.
- 11. Hollis MA, Stachnik JP, Lewis-Merritt C, McCrary RR, Martin ER. Precipitation Characteristics of Easterly Waves Across the Global Tropics. JGR Atmospheres. 2024;129(7).
- 12. Dominguez C, Done JM, Bruyère CL. Easterly wave contributions to seasonal rainfall over the tropical Americas in observations and a regional climate model. Clim Dyn. 2019;54(1–2):191–209.
- 13. Giraldo-Cardenas S, Arias PA, Vieira SC, Zuluaga MD. Easterly waves and precipitation over northern South America and the Caribbean. Intl Journal of Climatology. 2021;42(3):1483–99.
- 14. Valencia J, Yepes J, Mejía JF, Builes-Jaramillo A, Salas HD. Influence of Tropical Easterly Waves on the ChocoJet during the 2019 OTREC Campaign. Journal of Hydrometeorology. 2024;25(2):325–37.
- 15. Builes-Jaramillo A, Yepes J, Salas HD, Bedoya-Soto JM, Rivera P, Valencia J, et al. Intraseasonal Oscillations and Hydroclimate of Northern South America, Central America and Mexico. Intl Journal of Climatology. 2025;45(8).
- 16. Mayta VC, Kiladis GN, Dias J, Dias PLS, Gehne M. Convectively Coupled Kelvin Waves Over Tropical South America. Journal of Climate. 2021;:1–52.
- 17. Mayta VC, Lin Q-J, Adames Corraliza ÁF, Chavez Mayta E. Impact of tropical waves on extreme rainfall events during coastal El Niño. Environ Res Lett. 2024;19(9):094037.
- 18. Vincent CL, Lane TP, Wheeler MC. A local index of Maritime Continent intraseasonal variability based on rain rates over the land and sea. Geophysical Research Letters. 2016;43(17):9306–14.
- 19. Kikuchi K. Extension of the bimodal intraseasonal oscillation index using JRA-55 reanalysis. Clim Dyn. 2019;54(1–2):919–33.
- 20. Konda G, Vissa NK. Intraseasonal Convection and Air–Sea Fluxes Over the Indian Monsoon Region Revealed from the Bimodal ISO Index. Pure Appl Geophys. 2019;176(8):3665–80.
- 21. Kiladis GN, Dias J, Straub KH, Wheeler MC, Tulich SN, Kikuchi K, et al. A Comparison of OLR and Circulation-Based Indices for Tracking the MJO. Monthly Weather Review. 2014;142(5):1697–715.
- 22. Wheeler MC, Hendon HH. An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Mon Wea Rev. 2004;132(8):1917–32.
- 23. Wang S, Martin ZK, Sobel AH, Tippett MK, Dias J, Kiladis GN, et al. A Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally-Varying Modal Structures. JGR Atmospheres. 2022;127(4).
- 24. Sandjon AT, Tchinda CW, Vondou DA, Nzeukou A, Mba WP. Interannual variations in the amplitude of 25–70-day intraseasonal oscillations in Central Africa and relationship with ENSO. Bull of Atmos Sci& Technol. 2020;1(3–4):387–405.
- 25. Sahai AK, Chattopadhyay R, Joseph S, Abhilash S, Borah N, Goswami BN. A new method to compute the principal components from self-organizing maps: an application to monsoon intraseasonal oscillations. Int J Climatol. 2013;:n/a-n/a.
- 26. Gervais M, Atallah E, Gyakum JR, Tremblay LB. Arctic Air Masses in a Warming World. Journal of Climate. 2016;29(7):2359–73.
- 27.
Poveda G, Mejia JF, Arias PA, Zuluaga MD, Salazar JF, Carmona AM, et al. Climate of Northwestern South America. 2025.
- 28. Urrea V, Ochoa A, Mesa O. Seasonality of Rainfall in Colombia. Water Resources Research. 2019;55(5):4149–62.
- 29. Poveda G, Mesa OJ. Feedbacks between Hydrological Processes in Tropical South America and Large-Scale Ocean–Atmospheric Phenomena. J Climate. 1997;10(10):2690–702.
- 30. Liebmann B, Smith CA. Description of a Complete (Interpolated) Outgoing Longwave Radiation Dataset. Bulletin of the American Meteorological Society. 1996;77:1275–7.
- 31. Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, et al. The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes. Sci Data. 2015;2:150066. pmid:26646728
- 32. Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J. Quarterly Journal of the Royal Meteorological Society. 2020;146:1999–2049.
- 33. Wheeler M, Kiladis GN. Convectively Coupled Equatorial Waves: Analysis of Clouds and Temperature in the Wavenumber–Frequency Domain. J Atmos Sci. 1999;56(3):374–99.
- 34. Zhang C. Madden-Julian Oscillation. Reviews of Geophysics. 2005;43.
- 35. Tchinda CW, Sandjon AT, Tchotchou ALD, Siwe AN, Vondou DA, Nzeukou A. The influence of intraseasonal oscillations on rainfall variability over Central Africa: case of the 25–70 days variability. Scientific Reports. 2023;13:19842.
- 36. Builes-Jaramillo A, Yepes J, Salas HD. The Orinoco Low-Level Jet and Its Association with the Hydroclimatology of Northern South America. Journal of Hydrometeorology. 2022;23(2):209–23.
- 37. Jiménez-Sánchez G, Markowski PM, Jewtoukoff V, Young GS, Stensrud DJ. The Orinoco Low-Level Jet: An Investigation of Its Characteristics and Evolution Using the WRF Model. JGR Atmospheres. 2019;124(20):10696–711.