Open AccessArticle

Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019

Bruna Alves Oliveira Destéfani

Micael Fernando Broggio

and

Carlos Alberto Eiras Garcia

Sistema de Monitoramento da Costa Brasileira (SiMCosta), Instituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande 19600-390, RS, Brazil

Author to whom correspondence should be addressed.

Atmosphere 2025, 16(1), 92; https://doi.org/10.3390/atmos16010092

Submission received: 22 November 2024 / Revised: 20 December 2024 / Accepted: 23 December 2024 / Published: 16 January 2025

(This article belongs to the Section Climatology)

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Figure 1
Map of the study region with cyclone genesis areas indicated by rectangles. Area A1 covers the continental region near the discharge of the Rio de la Plata, spanning Uruguay, Argentina, and southern Brazil (~30° latitude). Area A2 includes the oceanic region between the Rio de la Plata and the Brazil–Malvinas confluence. Area A3 encompasses the southeastern coast of Argentina, particularly near and south of the Gulf of San Matías. "> Figure 2
Trajectory and cyclogenesis densities calculated for P01 ((A) and (D), respectively) and P02 ((B) and (E), respectively). The trajectory bias (C) and cyclogenesis bias (F) are also presented in the figure. "> Figure 3
Time series of trajectory density (A), cyclogenesis (B), and distribution of mean vorticity values (C) for identified cyclones. "> Figure 4
Statistically significant annual trends (Mann-Kendall test) of trajectory and cyclogenesis density in P01 ((A) and (C), respectively) and P02 ((B) and (D), respectively). The white areas do not show trends. "> Figure 5
Seasonal cyclogenesis density from P01 and P02 during the positive and negative phase of the Antarctic Oscillation Climate Index (AAO). Summer patterns are shown in (A–D), fall in (E–H), winter in (I–L), and spring in (M–P). Cyclone densities for P01 and P02 during positive phase are represented in (A,E,I,M) and (B,F,J,N), respectively. Cyclogenesis densities for P01 and P02 during negative phase are shown in (C,G,K,O) and (D,H,L,P), respectively. "> Figure 6
Trajectory density and cyclogenesis of cyclones separated by directions for P01 and P02. The trajectory density and cyclogenesis from south to north for P01 are shown in (A) and (C), respectively. For P02, they are shown in (B) and (D), respectively. The trajectory density and cyclogenesis from north to south for P01 are shown in (E) and (G), respectively, while for P02, they are shown in (F) and (H), respectively. "> Figure 7
Trajectory density and seasonal cyclogenesis of identified cyclones from the south to north (S to N). On the left (right) panel is seasonal track (cyclogenesis) density. Summer patterns are shown in (A–D), fall in (E–H), winter in (I–L), and spring in (M–P). Track densities for P01 and P02 are represented in (A,E,I,M) and (B,F,J,N), respectively. Cyclogenesis densities for P01 and P02 are shown in (C,G,K,O) and (D,H,L,P), respectively. ">

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Abstract

This study investigates cyclone dynamics and impacts in the Southwestern Atlantic, with a focus on their effects on southern Brazil. As climate change intensifies coastal vulnerability, understanding cyclone behavior has become essential. Using the TRACK and cycloTRACK algorithms, we examined cyclone trajectories and cyclogenesis densities from 1979 to 2019 to analyze seasonal and spatial patterns shaped by large-scale atmospheric circulations, including the Antarctic Oscillation (AAO). The analysis explores trends in cyclone activity across various temporal and spatial scales, identifying key regions of cyclogenesis and trajectory density. Results indicate that the cycloTRACK algorithm is more effective at tracking more intense and consistent cyclones, excluding weaker systems. Seasonal patterns suggest variability in cyclone formation, likely associated with atmospheric instability and ocean–atmosphere interactions. While trends reveal an increase in cyclone passages in southern Brazil, these systems are strongly associated with extreme climatic events in the region, including coastal storms, intense precipitation, strong winds, and high waves. By clarifying cyclone dynamics and seasonal patterns, this study enhances our understanding of cyclone behavior and contributes to improved assessments of regional climate resilience in southern Brazil.

Keywords:

extratropical cyclones; atmospheric circulation; climatic variability

1. Introduction

Climate change is intensifying the vulnerability and impacts on coastal regions, primarily through sea level rise and oceanic extremes, such as coastal storms, extreme storms (strong winds accompanied by highly high waves), extreme tropical and extratropical cyclones, marine heatwaves, extreme deoxygenation events, and acidification [1,2]. The increasing rise of greenhouse gasses in the atmosphere also significantly affects marine ecosystems at global, regional, and local scales [3].

A significant portion of global economic activity relies on coastal and oceanic areas, encompassing sectors such as fishing, agriculture, and natural resource extraction [4]. Brazil’s extensive coastline features many environments and ecosystems shaped by diverse regional climates and oceanographic dynamics, resulting in varying levels of exposure to climate change across different areas [5].

In this context, large-scale atmospheric circulation plays a crucial role in driving extreme events. Understanding these circulation patterns is essential for comprehending such events. The literature identifies three primary regions of cyclogenesis in the South Atlantic: (1) the area near the discharge of the Rio de la Plata, particularly between Uruguay and southern Brazil (~30° latitude), and (2) the southeastern coast of Argentina, near the Gulf of San Matías, spanning from 40° to 55° S. These regions are associated with baroclinic instability, driven by the influence of the subtropical jet, frequent occurrences of potential vorticity streamers, and the topographic impact of the Andes [6,7,8,9,10,11]. Additionally, (3) the southern–southeastern coast of Brazil is associated with baroclinic instability and the dynamics of the upper-level subtropical jet [8,9,10,12].

Gan and Rao [7] observed that cyclone genesis frequency over South America peaks in winter, particularly in May, while December experiences the lowest frequency. A detailed analysis is essential to fully understand the processes driving cyclone genesis in regions that influence Brazil, which may require the application of advanced algorithms and atmospheric models to generate valuable insights [8,9,13,14,15,16]. Occasionally, extratropical cyclones occurring in the South Atlantic Ocean near the Brazilian coast strengthen along the Uruguayan and Argentine coasts, generating extreme winds that eventually reach Brazil’s southern and southeastern coast [17].

The Antarctic Oscillation (AAO) climate index significantly influences the intensity and position of subtropical jets, thereby impacting the development and movement of cyclones and anticyclones due to its pressure variability across mid- and high-latitude atmospheric masses [18]. Also known as the Southern Annular Mode (SAM) [19], the AAO represents the principal mode of extratropical circulation variability in the Southern Hemisphere, characterized by zonally symmetric or annular structures. It features opposite-signed geopotential height anomalies in Antarctica and the surrounding zonal ring near 45° latitude. Several studies [20,21,22,23] have investigated the relationship between the AAO and meteorological patterns in South America.

A positive AAO index is typically associated with stronger, more zonally oriented winds in extratropical regions. Conversely, a negative AAO index is associated with reduced zonal winds and increased incursions of Antarctic air into the Southern Hemisphere [21]. During the positive phase of the AAO, Antarctica experiences negative anomalies in both temperature and geopotential height, while mid-latitude regions exhibit positive anomalies in these parameters. This phase leads to warmer temperatures in the Antarctic Peninsula, intensified cyclones over the Southern Ocean, and stronger easterly winds around 60° S. In contrast, the negative phase, characterized by low polarity indices, is associated with the opposite anomalies [24,25].

Murray and Simmonds [26] were the first to develop and implement an automatic scheme for tracking cyclones in the Southern Hemisphere. Given the limitations and continuous evolution of mathematical models, ongoing research in this field is crucial. Accurate cyclone identification and tracking are essential for issuing timely weather warnings and understanding future trends in extreme events and other climatic processes [27,28]. Various researchers have extensively documented the impacts of extratropical cyclones [29,30,31]. The key consequences include intensified extreme precipitation, strong winds, freezing events, and coastal flooding, all directly tied to changes in the trajectories of these cyclones [32].

Later, Hodges [33,34] developed the TRACK algorithm, designed for detecting and tracking cyclones and other meteorological features in atmospheric data. TRACK identifies cyclones using relative vorticity calculated from spherical coordinates of the zonal and meridional wind components at 850 hPa. This approach is effective for detecting both weak and fast-moving systems and cyclones in their early stages [35]. TRACK employs a fully automatic method that integrates a feature detection scheme based on cubic interpolation for a unit sphere and local maximization. This technique effectively identifies and links points to form cyclone trajectories. Hodges’ TRACK algorithm is a widely used tool in climate research for tracking cyclone trajectories and analyzing their behavior.

Flaounas et al. [36] developed and applied the cycloTRACK algorithm to identify and track cyclones. This method leverages patterns of closed contours derived from filtered relative vorticity values at the 850 hPa level to pinpoint and monitor cyclones accurately. Concurrently, the algorithm generates all possible cyclone tracks and selects the one with the smallest differences in relative vorticity between consecutive points, ensuring precise tracking. To validate this approach, the authors applied the algorithm to the Northern Hemisphere during winters from 1989 to 2009. They conducted three integrations of the algorithm, each with varying filtering intensities, to assess its sensitivity to the relative vorticity field. The results indicated that while filtering affects only weak cyclones, the algorithm successfully detected and tracked most strong cyclones, demonstrating its effectiveness as a powerful tool for cyclone identification.

Gramcianinov et al. [37] used the TRACK algorithm to investigate cyclone climatology in the southwestern South Atlantic Ocean. Their study focused on the spatial distribution of extreme waves generated by extratropical cyclones and the atmospheric conditions leading to the most severe events in the region. The authors generated and publicized their cyclone climatology database.

This research uses the TRACK and cycloTRACK algorithms to analyze cyclone trajectories and cyclogenesis densities in the Southwestern Atlantic Ocean and their regional impacts from 1979 to 2019. We will use the TRACK algorithm [37] and the cycloTRACK algorithm [36] to conduct a comprehensive climatological study of cyclone behavior. Our objectives are to (a) investigate the densities of cyclone trajectories and genesis events in and around southern Brazil, utilizing data from both TRACK and cycloTRACK algorithms; (b) examine time series data for trajectory density, cyclogenesis and mean cyclone vorticity, providing insights into long-term trends and variations; (c) analyze spatial annual trends in trajectory and cyclogenesis densities to identify significant changes and patterns over the years; (d) explore seasonal variations in cyclogenesis density in relation to the phases of the Antarctic Oscillation (AAO), assessing how these phases influence cyclone activity; and (e) assess the impacts of cyclone activity on the southern Brazilian region, including potential effects on weather patterns and regional climate. The findings provide valuable insights into cyclone dynamics and their critical role in shaping extreme weather events in southern Brazil.

2. Materials and Methods

2.1. Study Area

Figure 1 illustrates the study region, the primary cyclone genesis areas, and the trajectories we analyze in this study. Area 1 (A1), a significant cyclone genesis zone in South America, includes the continental region around the discharge of the Rio de la Plata, spanning Uruguay, Argentina, and southern Brazil (~30° latitude). Area 2 (A2) covers the oceanic region between the Rio de la Plata and the Brazil–Malvinas confluence, an area noted for significant thermal variability that significantly affects climate and cyclone formation [38]. Area 3 (A3), another principal cyclone genesis region, encompasses the southeastern coast of Argentina, particularly near and south of the Gulf of San Matías.

2.2. The Dataset Derived from the Algorithm cycloTRACK

We utilized hourly 850 hPa vorticity data from the ERA5 atmospheric reanalysis—ECMWF (European Centre for Medium-Range Weather Forecasts) to apply the cycloTRACK algorithm and obtain the cyclone climatology for the study area. Our analysis spanned 41 years, from 1979 to 2019, within the region between latitudes 25° S and 65° S and longitudes 75° W and 20° E. We established the period from 1979 to 2019 based on the study by Gramcianinov et al. [37], which presents a cyclone climatology for this interval, allowing for a comparison between the climatologies generated by different methodologies.

ERA5 provides publicly available products covering data from 1979 to the present, including real-time updates up to 5 days. The reanalysis offers hourly estimates of a wide range of atmospheric, terrestrial, and oceanic variables on a 30 km grid, with atmospheric profiles extending from the surface to an altitude of 80 km across 137 levels.

For configuring the cycloTRACK algorithm, we employed a filter value 10, which was the most effective after testing various options. To further refine the results and reduce potential artifacts, we applied an additional filter to exclude cyclones with durations under 24 h and displacements of less than 1000 km. This step was crucial for eliminating spurious trajectories and cyclones that could result from numerical artifacts or minor computational errors. The resulting dataset includes cyclone tracks and genesis densities, and we designate it as ‘Product 01’ (P01).

2.3. The Dataset Derived from Algorithm TRACK

The TRACK algorithm [33,34] was used by Gramcianinov et al. [37] to derive the cyclone climatology in the South Atlantic. The authors also used hourly 850 hPa vorticity data from the ERA5 atmospheric reanalysis by ECMWF to apply the TRACK algorithm. Therefore, both cyclone climatologies analyzed in this study share the same input data. The authors generated and publicized the climatology database in https://data.mendeley.com/datasets/kwcvfr52hp/3 (accessed on 10 January 2021), which we will define as ‘Product 02’ (P02).

2.4. Comparisons Between the CycloTRACK and TRACK Methods

Both methods have similarities, such as using 850 hPa vorticity values and connecting grid points (according to vorticity values) to create cyclone trajectories. However, the main difference lies in the mathematical method used by each technique. CycloTRACK constructs all possible tracks and finally selects the one with minor differences in relative vorticity between neighboring points. At the same time, TRACK uses a cubic approach, considering a unit sphere for the calculation.

2.5. Characterization of Cyclones by Direction and Seasonal Analysis

All cyclones identified in products P01 and P02 were categorized based on their displacement direction. Cyclones moving from south to north (S to N) were grouped into various directions, such as S, SE, and SW, while those moving from north to south (N to S) were classified into directions, including N, NE, and NW.

To analyze the cyclones impacting the southern Brazilian area, we categorized them based on their direction of movement: from the north (N/NE/NW) and the south (S/SE/SW). We selected those whose trajectories intersected at least 25% within the region bounded by latitudes −22° S to 42° S and longitudes −60° W to −33° W.

We performed seasonal analyses based on the monthly averages for each season in the Southern Hemisphere, considering the following periods: autumn (March, April, and May), winter (June, July, and August), spring (September, October, and November), and summer (December, January, and February).

2.6. Statistical Analysis

We computed basic statistical metrics for products P01 (cycloTRACK) and P02 (TRACK). Before performing the analysis, we calculated trajectory and genesis densities to visualize the results, defining density as the number of cyclones recorded per unit area (e.g., square kilometers) and per unit time (e.g., months). This approach quantitatively measures cyclone occurrence in each region throughout the study period. Then, we constructed time series analyses for trajectory density, cyclogenesis, and mean cyclone vorticity values. These time series were generated by summing all densities across the investigated area at each time step. To smooth intra-annual fluctuations, we applied annual moving averages (using a 12-month window) to the data. We also calculated the bias, which represents the difference between the annual moving averages obtained from P01 and P02 (see Section 3.1). We performed a linear regression on those time series of tracks and cyclogenesis densities to evaluate annual trends, calculating their intercepts, slopes, and the corresponding standard errors of the estimates and the correlation between them using Pearson’s correlation coefficient (see Section 3.2). Finally, we applied the Mann–Kendall test, a non-parametric method suitable for detecting monotonic trends in time series with seasonal variations, to identify potential regional spatial annual trends (see Section 3.3) related to climate changes in climatological datasets [39,40]. In this case, areas on the map with no trends were masked blank.

3. Results and Discussion

3.1. Densities of Cyclone Trajectories and Genesis Events in and Around Southern Brazil

The analysis reveals that the primary path for cyclones reaching southern Brazil originates from the southwest region of the South Atlantic. Both datasets show higher trajectory densities (Figure 2A,B) in Area A2 (the Brazil–Malvinas confluence region, east of the La Plata River mouth), with Product P02 indicating an even greater density (Figure 2B). According to Hoskins and Hodges [8], the South American region is dynamically complex, with the Brazil–Malvinas confluence area experiencing frequent frontal systems and extratropical cyclones [41]. Research also highlights that the discharge from the La Plata Plume, driven by winds, affects southern Brazil’s marine ecosystems by enhancing biological productivity due to its nutrient richness. This discharge can lead to harmful algal blooms and subsequent coastal eutrophication [42,43]. The La Plata River Plume travels along the Uruguayan coast to southern Brazil, influenced by annual and seasonal variations in river discharge and wind patterns [44,45].

Regarding cyclone genesis, we found that P02 (Figure 2E) exhibits a higher density than P01 (Figure 2D), likely due to the filtering effects of the cycloTRACK algorithm, which enhances the detection of intense cyclones while reducing the identification of weaker ones. The region with the highest cyclogenesis density was identified in Area A1, located to the east of the Andes and spanning Argentine, Uruguayan, and southern Brazilian territories. This area is influenced by baroclinic instability linked to the Lee effect caused by the Andes [35,46]. The cycloTRACK output indicates that the most energetic cyclogenesis region is situated further southwest than previously reported by Gramcianinov et al. [37], near the La Plata estuary, as confirmed by the bias analysis (Figure 2F). Despite these differences, both methods reveal similar cyclone genesis areas. The bias analysis shows that P02 (Figure 2C) has a higher density of trajectories near the South American continent, extending southeastward into the South Atlantic (in blue). In comparison, P01 displays a higher density of trajectories between longitudes 42° and 12° W and latitudes 42° and 62° S (in red).

3.2. Temporal Evolution of Trajectory Density, Cyclogenesis, and Mean Cyclone Vorticity

The time series analysis of trajectory density for P01 and P02 indicates an overall increase from 1979 to 2019 (Figure 3A). For clarity and simplicity, the density unit (DU) is defined as [n (10⁶ km²)⁻¹ (month)⁻¹] and will be used from this point forward. The standard deviations for trajectory density were 36.0 DU for P01 and 32.8 DU for P02. The correlation between the two products was 0.54 (p < 0.05), reflecting a moderate-to-high correlation according to Hopkins [47]. Both products exhibited positive and significant annual trend rates in trajectory density, with an annual trend rate of 0.59% per year (p < 0.05) for P01 and 0.41% per year (p < 0.05) for P02. The intercepts were 221.17 DU (standard error (SE) = 2.95 DU) for P01 and 265.13 DU (SE = 2.72 DU) for P02, with slopes of 0.1085 du/year (SE = 0.0104 DU/year) and 0.0916 DU/year (SE = 0.0096 DU/year), respectively, with P01 showing a slightly steeper rate of increase. The standard errors (SEs) of the slopes for both P01 and P02 are much smaller than their respective slopes, indicating high confidence (>95%) in the trend estimates.

The cyclogenesis densities time series also yielded statistically satisfactory results, with positive and significant annual trend rates of 0.25% and 0.17% per year in P01 and P02, respectively (Figure 3B). The intercepts were 19.51 DU (SE = 0.23 DU) for P01 and 23.91 (SE = 0.21 DU) for P02, with slopes of 0.0041 DU/year (SE = 0.0008 DU/year) and 0.0034 (SE = 0.0007 DU/year), respectively. These findings indicate a small but statistically significant (>95%) upward trend, with P01 exhibiting a slightly higher annual increase than P02. A slight correlation [47] was found (R = 0.27, p < 0.05) between the two times series (P01 and P02).

The histograms of mean cyclone vorticity values (Figure 3C) reveal that P01 exhibits higher vorticity values than P02. Both datasets show a peak frequency of around 4 × 10⁻⁵ Hz, with 36% for P01 and 33% for P02. Notably, P01 does not display vorticity values below 3 × 10⁻⁵ Hz, whereas P02 includes 27% cyclone vorticity near 2 × 10⁻⁵ Hz, which suggests that cycloTRACK prioritizes the identification of more intense and consistent cyclones while minimizing or excluding weaker, less reliable systems. Flaounas et al. [27] emphasized that filtering out weaker cyclones is essential, as high-confidence methods are specifically designed to represent well-defined and organized systems accurately. This strategy reduces false or poorly identified tracks, improving climatological datasets’ robustness. This approach helps reduce the presence of false or poorly identified tracks, thereby enhancing the robustness of climatological datasets.

3.3. Spatial Annual Trends

The spatial annual trends in trajectory and cyclogenesis density, derived from the P01 and P02 climatologies, reveal statistically significant increases in cyclone trajectories (Figure 4). Both datasets show a predominant positive trend in trajectory density, with a notable negative trend at around 32° W longitude and above 32° S latitude, which is more pronounced in P02 (Figure 4A,B). Numerous studies [11,14,16,48] have documented an intensification in cyclone activity in the South Atlantic. This increase may also be linked to changes in chlorophyll concentrations in the La Plata region, as the dispersion of La Plata waters, influenced by regional winds, affects these concentrations [44].

The annual trend analysis of cyclogenesis density reveals that rates are generally lower in P01 (Figure 4C) compared to P02 (Figure 4D). In P01, there is an increase in cyclone formation in Area A1, encompassing the continental region of Uruguay at latitude 32° S, and in Area A2, between the mouth of the La Plata River and the Brazil-Malvinas Confluence. Conversely, a decrease in cyclone formation is observed south of A1, near latitude 32° and the Uruguay-Argentina border.

In P02, positive annual trends are evident in the oceanic region near the Gulf of San Matías and southern Argentina (Area A3) and in Area A2 near the mouth of the La Plata River (Figure 4D). Wang et al. [49] observed regional and seasonal variations in decadal trends in larger-scale cyclones from 1951 to 2010. Pezza and Ambrizzi [16] noted an increase in more intense cyclones (with central pressures below 980 hPa), typically occurring at mid-latitudes and associated with strong winds and heavy precipitation. Reboita et al. [15] found similar results, reporting positive trends in the southwest Atlantic Ocean between 1980 and 2012 during summer and winter. However, they did not identify statistically significant trends in this region. Pezza et al. [49] also pointed out that standard significance tests for such analyses may be unreliable due to the high variability associated with cyclones.

A recent study by De Jesus et al. [11] used multiple models to investigate key aspects of cyclogenesis along the east coast of South America. Their projections for a distant future scenario indicate a decreasing trend in cyclogenetic activity in Area A3 while showing an increase in cyclonic activity over a broad offshore region near the coasts of Uruguay and Argentina, particularly in Area A2. This observed trend in Area A2 is consistent with findings from previous research [50,51,52,53]. The increase in cyclonic activity in this region may result from intensified low-level temperature advection and increased moisture availability [52]. Reboita et al. [52] also reported that stronger cyclones over Uruguay and southern Brazil are closely associated with increased precipitation in these areas. Sea surface temperature plays a crucial role in temperature advection and moisture availability, with warming contributing to baroclinic instability, which is vital for cyclone formation and intensification [54].

3.4. Seasonal Cyclogenesis Density During AAO Phases

We conducted seasonal analyses of cyclogenesis for cyclones identified in P01 and P02 during the positive and negative phases of the AAO climatic index. These analyses focus on the southwestern Atlantic, spanning latitudes 20° S to 57° S and longitudes 75° W to 31° W (Figure 5).

Both methods reveal minor spatial differences in cyclone density, with the most notable discrepancy observed in P02. During summer (Figure 5A–D), high cyclone density is evident in Area A1 near 32° S, extending to Area A3 south of the Gulf of San Matías. Winter and spring had the highest cyclogenesis density during positive AAO phases (Figure 5J,N).

Spring and summer consistently show the highest cyclogenesis density over oceanic regions near southern Brazil. Elevated sea surface temperatures drive this increase by providing additional energy and moisture to the atmosphere, thereby facilitating the formation of extratropical cyclones. Various atmospheric systems, including frontal systems, mesoscale convective formations, mid-level cyclonic structures, high-altitude vortices, and cyclones, shape weather patterns in southern Brazil [55]. The Gulf of San Matías and the region between Argentina and Uruguay are particularly active cyclogenetic areas [6,7].

In autumn, cyclone genesis area and density values decrease, especially during the positive phase of the AAO (Figure 5E,F). This reduction is linked to the southward shift of the polar jet stream, increased atmospheric stability at mid-latitudes, and reduced convergence of air masses during this season under the positive AAO phase [56].

Reboita et al. [20] examined the relationship between the AAO climate index and cyclone density in the South Atlantic. Their findings indicated that cyclones’ most significant density variations occur around the Antarctic continent, which can indirectly influence cyclones affecting South America. During the negative phase of the AAO, positive precipitation anomalies were observed throughout southern South America in autumn, with more concentrated anomalies around southern Brazil in summer. Conversely, during the positive phase of the AAO, southern Brazil experienced negative precipitation anomalies in autumn, while the southeast showed weak positive anomalies [20]. These findings align with our results, which show decreased cyclogenesis densities and reduced rainfall in southern Brazil during the positive phase of the AAO in autumn.

3.5. The Impacts over the Southern Brazilian Area

Cyclones originating from the south (Figure 6A,B) generally exhibit higher trajectory density near the South American continent than those from the north (Figure 6E,F). In terms of cyclogenesis, the density of cyclones from the south (Figure 6C,D) is lower than that of cyclones from the north (Figure 6G,H). This difference is significant because cyclones from the south frequently generate alongshore winds along Brazil’s coast, which play a crucial role in transporting energy, sediments, and nutrients, including the La Plata plume and chlorophyll. For example, Garcia and Garcia [44] identified a statistically significant correlation (r = 0.91, p < 0.05) between chlorophyll variability and alongshore wind anomalies near the mouth of the La Plata estuary.

We also observed that cyclones from the northern quadrant in P01 generally form around 37.5° S latitude, south of Area A1. In contrast, P02 shows maximum density around A1 at approximately 30° S latitude. Both categories exhibit southeastward trajectories towards the southwestern South Atlantic region.

Overall, the trajectory density suggests that cyclones originating from the south tend to move northeastward, forming in oceanic regions near the South American coast and following trajectories parallel to the continent. Additionally, the primary formation area for cyclones originating from the south is in Area A3, near the Gulf of San Matías in Argentina.

3.6. Seasonal Variability of Cyclones Moving Northward

Figure 7 illustrates the seasonal trajectory and cyclogenesis densities for cyclones from the south, as observed in the P01 and P02 datasets. In all seasons, the predominant trajectory shows cyclones moving northeastward from north of the Gulf of San Matias, following a path parallel to the South American coast. This trajectory pattern, noted by Gan and Rao [7], is linked to the passage of cold fronts across the region. The same study highlights that baroclinic mechanisms are South America’s primary drivers of cyclogenesis.

In P02, higher trajectory densities are evident during winter and spring (Figure 7J,N), whereas P01 displays a more even distribution, with lower densities observed in summer. Additionally, both datasets indicate a slight northward shift in cyclone trajectories during winter. This seasonal variability underscores the influence of baroclinic processes and cold fronts on regional cyclone patterns.

Regarding cyclogenesis, area A3 consistently emerges as the primary source of cyclones originating from the south. P01 and P02 data reveal higher cyclone densities in this region during winter and spring, with a peak observed in spring. This finding aligns with previous studies identifying A3 as one of South America’s most active cyclogenic areas [6,7]. Additionally, P02 data highlight a notable increase in cyclone density in area A1 during autumn and winter (Figure 7H,L). This seasonal variation further supports the role of A3 and A1 as critical regions for cyclone formation and underscores their importance in understanding regional cyclone dynamics.

4. Conclusions

This study aimed to evaluate the cyclone-tracking method cycloTRACK [36] and analyze cyclone trajectories and genesis, focusing on southern Brazil. The analysis utilized the cycloTRACK product (P01) and the dataset from Gramcianinov et al. [37] (P02). Both methods effectively identified key cyclone trajectories and genesis areas, with P02 showing higher trajectory density in the Brazil-Malvinas confluence region (east of the La Plata River mouth) compared to P01. The P01 product exhibited higher vorticity values than P02, indicating that the filtering and track selection criteria of cycloTRACK, which minimize vorticity differences between consecutive points, tend to favor more intense and consistent cyclones while excluding weaker systems.

Trajectory trend analysis revealed positive trends for both methodologies in areas A1, A2, and A3, indicating an increase in cyclone trajectories near southern Brazil. However, a negative trend was noted at a longitude of 32° W above 32° S latitude, more pronounced in the P02 data. For cyclogenesis, P01 showed a positive trend in cyclones forming in A2, particularly in oceanic regions near the mouth of the La Plata River (around 37° S latitude). Conversely, cyclones forming in Area A1 (the continental region of Uruguay at 32° S latitude) exhibited a negative trend. The analysis also indicated positive annual trends in cyclogenesis in A3, the oceanic region near the Gulf of San Mathias extending to southern Argentina. These trends highlight increasing extreme weather events in the region, often linked to coastal storms, such as heavy rains, strong winds, and high waves.

The Antarctic Oscillation (AAO) climate index analysis revealed high cyclone density in A1, near 33° S in Argentine territory, particularly during the summer, at the borders with Brazil and Uruguay. Generally, spring and summer exhibited higher cyclone genesis density in oceanic regions near southern Brazil, which is associated with atmospheric instability from ocean surface warming. In contrast, autumn showed reduced cyclone density, especially during the positive phase of the AAO.

Overall, the results reaffirm that A3, located along the southern coast of Argentina near the Gulf of San Mathias, is one of the most active cyclogenic areas in South America. This region exhibits the highest cyclone density during winter and spring, with storms originating from the south and moving northeastward parallel to the coast.

Author Contributions

B.A.O.D.: conceptualization, methodology, investigation, data curation, writing—original draft, writing—review and editing; M.F.B.: conceptualization, methodology, investigation, data curation; C.A.E.G.: conceptualization, resources, supervision, funding, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Council for Scientific and Technological Development (CNPq), processes numbers 446401/2015-3 and 409666/2022-0.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Product 02, used in this study, is available from a public online database (https://data.mendeley.com/datasets/kwcvfr52hp/3, accessed on 10 January 2021). Product 01, which supports the conclusions of this article, will be made available by the authors upon request.

Acknowledgments

We express our gratitude to the team at the Brazilian Coastal Monitoring System (SiMCosta) for providing crucial data, and the National Council for Scientific and Technological Development (CNPq) for their financial support of the National Ocean Observation and Monitoring Network (ReNOMO).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of the study region with cyclone genesis areas indicated by rectangles. Area A1 covers the continental region near the discharge of the Rio de la Plata, spanning Uruguay, Argentina, and southern Brazil (~30° latitude). Area A2 includes the oceanic region between the Rio de la Plata and the Brazil–Malvinas confluence. Area A3 encompasses the southeastern coast of Argentina, particularly near and south of the Gulf of San Matías.

Figure 2. Trajectory and cyclogenesis densities calculated for P01 ((A) and (D), respectively) and P02 ((B) and (E), respectively). The trajectory bias (C) and cyclogenesis bias (F) are also presented in the figure.

Figure 3. Time series of trajectory density (A), cyclogenesis (B), and distribution of mean vorticity values (C) for identified cyclones.

Figure 4. Statistically significant annual trends (Mann-Kendall test) of trajectory and cyclogenesis density in P01 ((A) and (C), respectively) and P02 ((B) and (D), respectively). The white areas do not show trends.

Figure 5. Seasonal cyclogenesis density from P01 and P02 during the positive and negative phase of the Antarctic Oscillation Climate Index (AAO). Summer patterns are shown in (A–D), fall in (E–H), winter in (I–L), and spring in (M–P). Cyclone densities for P01 and P02 during positive phase are represented in (A,E,I,M) and (B,F,J,N), respectively. Cyclogenesis densities for P01 and P02 during negative phase are shown in (C,G,K,O) and (D,H,L,P), respectively.

Figure 6. Trajectory density and cyclogenesis of cyclones separated by directions for P01 and P02. The trajectory density and cyclogenesis from south to north for P01 are shown in (A) and (C), respectively. For P02, they are shown in (B) and (D), respectively. The trajectory density and cyclogenesis from north to south for P01 are shown in (E) and (G), respectively, while for P02, they are shown in (F) and (H), respectively.

Figure 7. Trajectory density and seasonal cyclogenesis of identified cyclones from the south to north (S to N). On the left (right) panel is seasonal track (cyclogenesis) density. Summer patterns are shown in (A–D), fall in (E–H), winter in (I–L), and spring in (M–P). Track densities for P01 and P02 are represented in (A,E,I,M) and (B,F,J,N), respectively. Cyclogenesis densities for P01 and P02 are shown in (C,G,K,O) and (D,H,L,P), respectively.

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Destéfani, B.A.O.; Broggio, M.F.; Garcia, C.A.E. Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019. Atmosphere 2025, 16, 92. https://doi.org/10.3390/atmos16010092

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Destéfani BAO, Broggio MF, Garcia CAE. Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019. Atmosphere. 2025; 16(1):92. https://doi.org/10.3390/atmos16010092

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Destéfani, Bruna Alves Oliveira, Micael Fernando Broggio, and Carlos Alberto Eiras Garcia. 2025. "Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019" Atmosphere 16, no. 1: 92. https://doi.org/10.3390/atmos16010092

APA Style

Destéfani, B. A. O., Broggio, M. F., & Garcia, C. A. E. (2025). Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019. Atmosphere, 16(1), 92. https://doi.org/10.3390/atmos16010092

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