The research, led by Melek Candan İpek Şenbek of the General Directorate of State Hydraulic Works and Istanbul Technical University, together with Mikdat Kadıoğlu and Elçin Tan, examined thirty years of extreme precipitation events between 1995 and 2024. Using the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service, the team identified 637 days on which rainfall exceeded the 90th-percentile threshold, a statistical marker that separates genuinely extreme days from ordinary wet weather. Every one of those days was then assigned to a circulation class through an objective, machine-learning-driven classification rather than subjective judgment.

The method rested on two well-established statistical techniques working in tandem. First, principal component analysis compressed a suite of dynamic atmospheric variables, including mean sea-level pressure, 500-hPa geopotential height, and wind components at both the 850-hPa and 500-hPa levels, into a small number of orthogonal patterns. By deliberately excluding thermodynamic fields such as temperature and humidity from the clustering input, the authors reduced the influence of seasonal heating cycles and forced the algorithm to focus on the shape of the circulation itself. The K-means algorithm was then applied to the principal component scores, partitioning the 637 extreme days into groups of meteorologically similar situations.

Choosing the right number of clusters is the central judgment call in any such classification, and the authors retained a five-cluster solution after weighing statistical metrics, the robustness of the result to different initializations, and, crucially, whether each cluster could be interpreted as a coherent physical mechanism. The payoff was a set of five regimes that read like a field guide to Black Sea flooding: a Dynamic Convective regime, a Thermal Convective regime, a Northerly Orographic regime, a Cyclonic-Frontal regime, and a Southwesterly Orographic regime.

The standout performer was Cluster 4, the Dynamic Convective regime. It was the most frequent class, produced the highest mean precipitation intensity at 9.59 millimeters per day, and delivered the single wettest event in the entire record at 37.29 millimeters per day. Its events clustered overwhelmingly in summer and autumn, when strong dynamic lifting combined with the region’s abundant moisture supply to squeeze extraordinary volumes of water from the air. The August 2021 floods that devastated parts of the Western Black Sea region, examined in earlier atmospheric studies, fall squarely within the kind of warm-season, dynamically driven environment this regime represents.

Close behind in intensity was Cluster 3, the Thermal Convective regime, with a mean of 9.26 millimeters per day and a similar concentration in the warm season. What set this class apart was its thermodynamic character: enhanced moisture availability and relatively high convective available potential energy, or CAPE, the measure of the fuel available to rising air parcels. Notably, the authors found that the highest CAPE values were spatially confined rather than representative of the whole domain, a reminder that convective instability in this region can be a localized phenomenon, igniting over warm coastal waters or heated inland terrain while the broader atmosphere remains comparatively modest.

The remaining three regimes demonstrate a finding with real consequences for flood forecasting: extreme rainfall does not require an unstable atmosphere. Cluster 1, the Northerly Orographic regime, averaged 8.57 millimeters per day and peaked in autumn, when moist northerly flow off the Black Sea collided with the coastal topography and was forced upward. Cluster 5, the Southwesterly Orographic regime, operated on the same principle from the opposite direction, transporting moisture on southwesterly winds during autumn and winter and wringing it out through terrain-induced ascent. Cluster 2, the Cyclonic-Frontal regime, was the weakest at 7.83 millimeters per day and concentrated in winter and spring, with large-scale ascent and frontal lifting doing the work that convection performs in summer. In all three, persistent moisture supply combined with mechanical or synoptic lifting proved sufficient to generate extremes even under weakly unstable conditions.

Statistical testing confirmed that these differences were not noise. Precipitation intensity differed significantly among the five clusters, with a Kruskal-Wallis H statistic of 28.20 and a p-value below 0.001. Yet when the authors examined the year-to-year frequency of each regime across the full 1995 to 2024 period, they found considerable interannual variability but no statistically significant long-term trend in any of the five classes. In other words, the atmospheric recipes for Black Sea extremes have remained broadly stable in their occurrence over three decades, even as individual events have continued to strike with damaging force.

That stability finding carries an important caveat for climate interpretation. The absence of a trend in regime frequency does not mean the hazards themselves are frozen in place; a warmer atmosphere holds more water vapor, and the intensity of rainfall within a given regime can change even if the regime appears just as often. The authors argue that their results should guide how regional climate projections are read: rather than treating extreme precipitation as a single aggregate variable, projections and seasonal assessments should consider each regime’s contrasting physical mechanisms separately, because a future with more frequent Dynamic Convective setups would pose very different risks than one with more Cyclonic-Frontal events.

For disaster-risk managers in one of Türkiye’s most flood-prone regions, the practical message is that the season and the synoptic signature together tell you what kind of threat is coming. A summer event flagged by high CAPE and dynamic lifting demands rapid-response flash-flood warning systems for steep catchments; an autumn northerly flow regime points to orographic enhancement along the coast and prolonged orographic rainfall on windward slopes; a winter frontal regime suggests broader-scale, longer-duration accumulation. By converting three decades of chaotic weather into five physically interpretable archetypes, the study offers forecasters and planners a structured vocabulary for a hazard that has too often seemed to arrive out of nowhere, and it demonstrates how objective clustering of reanalysis data can turn decades of atmospheric history into actionable knowledge.

Subject of Research: Synoptic classification of extreme precipitation events in the Western Black Sea Region of Türkiye

Article Title: Synoptic drivers of extreme precipitation in the western black Sea Region: a cluster-based classification (1995–2024)

Article References: İpek Şenbek, M. C., Kadıoğlu, M., & Tan, E. (2026). Synoptic drivers of extreme precipitation in the western black Sea Region: a cluster-based classification (1995–2024). Theoretical and Applied Climatology, 157(9), Article 598. https://doi.org/10.1007/s00704-026-06527-x

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06527-x

Keywords: extreme precipitation, Black Sea, synoptic climatology, K-means clustering, ERA5 reanalysis, orographic rainfall, convective storms, flood risk, Türkiye, climate variability, principal component analysis, atmospheric circulation

The core of the approach is a machine learning technique called a self-organizing map, or SOM, an unsupervised neural network first introduced by Teuvo Kohonen in the 1980s. SOMs have long been a workhorse of synoptic climatology, the branch of meteorology that classifies daily weather into recurring circulation patterns. Instead of asking whether a rainfall gauge has crossed some magic number, the method asks a more fundamental question: which large-scale atmospheric pattern is sitting over the region today? By grouping thousands of daily weather maps into a grid of representative patterns, the SOM turns the messy continuum of atmospheric states into an ordered catalogue of circulation regimes.

Islam and Sheridan trained separate 9 by 9 SOMs—one for each of five near-surface atmospheric variables drawn from the NCEP/NCAR Reanalysis, a widely used gridded reconstruction of historical weather. The five variables were sea-level pressure, 2-meter air temperature, 2-meter relative humidity, near-surface winds, and precipitation. Each of the 81 nodes in a given SOM represents a distinct, characteristic pattern of that variable across the South Asian domain. Every day in the reanalysis record is then mapped to the node whose pattern it most closely resembles, effectively assigning each day a circulation fingerprint.

The crucial innovation comes next. Rather than eyeballing the map grid to decide which patterns belong to which season, the researchers objectively assigned nodes to the four meteorological seasons recognized in the region—winter, pre-monsoon, monsoon, and post-monsoon. Once every node carries a seasonal label, the transition dates fall out naturally: the monsoon begins on the day the atmosphere settles into monsoon-labeled patterns and ends when it departs them. Because the assignment is systematic, the same procedure can be repeated for any variable, any region, or any sub-period, producing onset and withdrawal dates that are reproducible rather than dependent on the judgment of individual analysts.

The results reveal a striking and previously underappreciated picture of how differently the seasons announce themselves depending on what you measure. The study found no single, distinct correlation between the onset timings derived from the five variables, meaning that pressure, temperature, humidity, winds, and rainfall each tell their own story about when the seasons turn. Monsoon season, when defined by sea-level pressure, relative humidity, or temperature, tends to run longer than when it is defined by precipitation alone—a sign that the large-scale circulation reorganizes well before the rains become sustained.

That gap is not a curiosity; it is physically meaningful. The analysis shows that precipitation-based monsoon onset arrives roughly one week later than circulation-based onset, and precipitation-based withdrawal comes roughly one week earlier than the circulation suggests. In other words, the atmosphere shifts into its summer monsoon configuration before the rain fully commits, and it begins to relax out of that configuration before the rain fully quits. This is consistent with the well-established requirement for preconditioning: the large-scale flow must build up moisture convergence and low-level winds before sustained rainfall can develop, and it lingers in a summer-like state as the rains taper off.

The framework also captured a long-term trend in the winter season that will interest climate monitors. Winter length has increased over the record, driven by onset shifting earlier and withdrawal shifting later. Because the method is threshold-free, such trends emerge directly from the circulation record rather than from any chosen definition of what counts as a winter day, making them easier to compare across studies and across variables. The authors note that the same procedure can be applied regionally as well as across the whole domain, and their results demonstrate transferability across sub-regions within the same climate regime—meaning a forecaster in Bangladesh and one in northwestern India can apply the identical recipe and get internally consistent answers.

The technical elegance of the SOM approach lies in what it removes. Traditional monsoon onset definitions—such as the classic criteria for onset over Kerala used by the India Meteorological Department—typically hinge on thresholds: so many millimeters of rain at so many stations for so many consecutive days, or a specified reversal of the upper-level zonal winds. Those criteria are operationally useful but inherently arbitrary in their cutoffs, and different thresholds yield different dates. Hydrological definitions based on vertically integrated moisture transport, dynamical definitions based on wind reversals, and station-based rainfall definitions have all coexisted for decades, each defensible yet none mutually comparable. By anchoring the seasonal transition in the identity of the circulation pattern itself, the SOM framework sidesteps the threshold problem entirely and produces what the authors describe as a robust platform for multi-variable, circulation-informed seasonal dates.

Why does this matter beyond the academic literature? Monsoon onset timing is among the most consequential pieces of seasonal information in the world. An early or late onset can shift sowing windows for rice, wheat, and cotton, alter reservoir management decisions, and modulate the risk of flooding during the pre-monsoon and post-monsoon transition periods. Forecast systems have made real progress in predicting onset—the study’s reference list includes work on coupled seasonal forecast models and extended-range prediction built on SOMs—but verification of those forecasts still depends on having a consistent, objective definition of what onset actually is. A framework that yields the same answer whether applied to pressure, humidity, temperature, winds, or rainfall, and at both continental and regional scales, gives forecasters and agrometeorologists a common yardstick for the first time.

The study also carries a broader lesson for climate science: synoptic climatology, the tradition of classifying weather maps into regimes, remains a powerful and underused lens for diagnosing seasonal transitions, not just daily weather. By demonstrating that a single unsupervised learning method can harmonize five different atmospheric variables into one coherent seasonal calendar, Islam and Sheridan have offered the monsoon research community something it has long lacked—a uniform, reproducible, and threshold-free declaration of when the seasons begin and end. As the climate continues to warm and the stakes of getting the monsoon calendar right continue to rise, frameworks like this one may become the standard against which both observations and forecasts are judged.

Subject of Research: A threshold-free self-organizing map framework for determining uniform seasonal onset and withdrawal dates in the South Asian monsoon

Article Title: Uniform seasonal onset declaration in the South Asian monsoon: a SOM-based circulation framework

Article References: Islam, M. R., & Sheridan, S. C. (2026). Uniform seasonal onset declaration in the South Asian monsoon: a SOM-based circulation framework. Theoretical and Applied Climatology, 157(10), Article 660. https://doi.org/10.1007/s00704-026-06598-w

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06598-w

Keywords: South Asian monsoon, monsoon onset, self-organizing maps, synoptic climatology, NCEP/NCAR Reanalysis, seasonal transitions, machine learning, precipitation, sea-level pressure, climate variability, monsoon withdrawal, meteorology

The team built their classification from the winds at the 850 hectopascal pressure level, roughly 1.5 kilometers above the surface, using the ERA5 reanalysis, the most comprehensive global atmospheric dataset produced by the Copernicus Climate Change Service. The 850 hPa level is a strategic choice for this part of the world because it captures the low-level circulation that steers moisture from the Amazon basin, the South Atlantic, and the South Pacific into the continent’s interior. By grouping thousands of daily wind fields into a small number of representative configurations, the researchers distilled the chaotic day-to-day weather of subtropical South America into a manageable catalogue of seven circulation patterns, or CPs, that together capture the full range of the region’s temporal variability, from individual storms to multi-decadal trends.

What makes the seven patterns compelling is that they are not statistical abstractions; each one corresponds to a physically recognizable feature of South American meteorology. The classification reproduces the behavior of the South American Low Level Jet, the narrow corridor of moist winds that races southward along the eastern flank of the Andes and feeds severe thunderstorms over the plains of Argentina. It captures the South Atlantic Convergence Zone, the vast northwest-to-southeast band of clouds and convection that anchors the summer monsoon. It also tracks the northward march of weather perturbations into the subtropics and the dominant circulation anomalies that set up over the adjacent Atlantic and Pacific Oceans, which act as the region’s great atmospheric switches.

The seasonal behavior of each pattern emerges clearly from the analysis. Certain configurations dominate the austral summer monsoon months, while others characterize the transitional seasons or the drier winter regime. This seasonality matters because it means the same circulation catalogue can be applied year-round without losing physical meaning, a limitation that has hampered previous pattern-based studies focused on single seasons. The authors verified that their seven CPs provide a faithful representation of both the synoptic features and their annual cycle, striking a balance between detail and parsimony that a larger or smaller set of classes failed to achieve.

Perhaps the most immediately useful finding is the tight link between the circulation patterns and rainfall. Each CP is associated with distinct rainfall anomalies and heavy precipitation events across different subregions of subtropical South America. In practical terms, knowing which pattern is in place tells forecasters and water managers which areas face elevated odds of extreme rain and which are likely to stay dry. Because the classification is built on daily data, it can flag the circulation setups that precede flooding episodes in the La Plata Basin, or the persistent blocking configurations that starve central Chile of the frontal rains its Mediterranean-style climate depends on.

The study then connects these daily patterns to the planet’s great climate oscillations. The influence of the El Niño Southern Oscillation, the periodic warming and cooling of the tropical Pacific, is clearly reflected in the interannual variability of the CP frequencies: during El Niño or La Niña years, certain patterns appear more or less often, shifting the regional rainfall odds in characteristic ways. Just as importantly, the researchers documented cross-time-scale interactions, showing that subseasonal drivers such as the Madden Julian Oscillation modulate how ENSO’s influence plays out on the ground. This layered interaction, where a slowly evolving Pacific anomaly conditions the impact of a fast-moving equatorial wave, helps explain why the same ENSO event can produce different rainfall outcomes in different years, a puzzle that has long frustrated seasonal forecasters.

The long-term story is where the study turns from description to attribution. The team found that long-term trends in the Southern Annular Mode, the north-south see-saw of westerly winds around Antarctica, drive frequency changes in one of the circulation patterns typical of transitional seasons, and that this shift promotes a drying trend over central Chile. Meanwhile, the Pacific Decadal Oscillation, a slower oscillation of North Pacific sea surface temperatures with hemisphere-wide reach, alters the frequency of a summer pattern in ways that promote drying over the subtropical eastern Andes. In other words, two of the region’s most alarming climate trends, the central Chile megadrought and the drying of the Andean foothills, can be traced through the circulation patterns to specific remote climate drivers operating on decadal scales. The CP framework thus functions as an attribution tool, converting abstract global indices into concrete statements about which atmospheric configurations are becoming more or less common and what that means for regional water supplies.

This attribution capability arrives at a critical moment. Central Chile has endured one of the most severe multi-year droughts ever recorded in the Southern Hemisphere, with cascading effects on agriculture, hydropower, and the capital city of Santiago’s water security. The La Plata Basin, home to tens of millions of people and much of the continent’s grain production, swung from record floods to a punishing 2019 to 2021 drought within a single decade. Understanding which circulation patterns underpin these swings, and whether their frequencies are shifting under the combined pressure of natural variability and anthropogenic climate change, is essential for anticipating what the coming decades hold. The new classification provides a common vocabulary for those discussions, one grounded in daily weather rather than abstract seasonal averages.

Beyond diagnosis, the authors highlight a second practical application: model evaluation. Because the seven CPs are defined purely from large-scale wind fields, they can be diagnosed equally well in global and regional climate models. Comparing the simulated frequency, persistence, and rainfall associations of each pattern against the ERA5-based benchmark offers a rigorous test of how faithfully models represent the atmospheric engine of South American hydroclimate. Patterns that are missing, overrepresented, or incorrectly linked to precipitation in a model point directly to the physical processes that need improvement. Given that climate projections for the region carry substantial uncertainty, particularly for summertime rainfall, this reference classification gives model developers and downscaling studies a concrete target for validation, complementing earlier work by members of the same team on extreme precipitation and circulation types in southern South America.

The full methodology is transparent and reproducible. The ERA5 reanalysis data are openly available through the Copernicus Climate Data Store, daily precipitation comes from the gauge-based CPC global dataset maintained by NOAA, and all the climate indices used, from ENSO and the Indian Ocean Dipole to the Southern Annular Mode, the Pacific Decadal Oscillation, the Atlantic Multidecadal Oscillation, and the Madden Julian Oscillation indices, are publicly distributed. The R scripts that perform the classification and analysis have been released on GitHub, allowing any researcher to replicate the results, extend them to other regions, or apply the framework to model output. This openness, combined with the elegance of reducing a continent’s weather to seven archetypes, positions the study to become a standard reference for anyone studying, forecasting, or modeling rainfall across subtropical South America, from synoptic meteorologists tracking the next flood season to climate scientists weighing the fingerprints of a warming world on the winds above the Andes.

Subject of Research: Atmospheric circulation patterns and teleconnections controlling rainfall variability in subtropical South America

Article Title: Multi-temporal diagnostic of atmospheric circulation patterns and teleconnections in subtropical South America

Article References: Medina, F. D., Olmo, M. E., & Bettolli, M. L. (2026). Multi-temporal diagnostic of atmospheric circulation patterns and teleconnections in subtropical South America. Climate Dynamics, 64(10), Article 432. https://doi.org/10.1007/s00382-026-08393-9

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08393-9

Keywords: atmospheric circulation patterns, subtropical South America, synoptic climatology, ERA5 reanalysis, ENSO, Southern Annular Mode, Pacific Decadal Oscillation, South American Low Level Jet, South Atlantic Convergence Zone, rainfall variability, drought, climate dynamics