La temperatura es un parámetro climático con un patrón y distribución complejos. Para comprender a fondo este patrón y su complejidad, se requiere un análisis preciso. Este estudio analizó la variabilidad de la temperatura del aire superficial utilizando el método de microestados propios (EMA) y el método de funciones ortogonales empíricas (EOF), basados en datos NCEP-NCAR. Los resultados muestran que la distribución de la varianza de EOF se concentra en el modo inicial (EOF 1 al 88,42%), mientras que la varianza de EMA se encuentra más dispersa, con contribuciones superiores al 1% hasta el sexto modo. El patrón espacial resultante es consistente con estudios previos, aunque presenta diferencias de fase y una mayor varianza en EMA. El análisis espectral de Fourier muestra que EOF enfatiza los patrones anuales, mientras que EMA puede separar señales multiescala en periodos semianuales, anuales e interanuales. Además, EMA detectó con éxito importantes impactos globales vinculados a fenómenos climáticos regionales como los monzones, la Oscilación del Sur de El Niño (ENSO), ENSO Modoki y el Dipolo del Océano Índico, que son menos evidentes en los modos EOF superiores debido a su varianza relativamente pequeña. Por lo tanto, EOF es eficaz para identificar los patrones globales más dominantes, mientras que EMA es superior para capturar la variabilidad multiescala y los impactos globales vinculados a los fenómenos climáticos regionales.
La energía potencial convectiva disponible (CAPE) y la inhibición convectiva (CIN) son indicadores termodinámicos ampliamente utilizados para diagnosticar entornos convectivos y respaldar la predicción a corto plazo del potencial de tormentas eléctricas. Debido a que los productos de reanálisis proporcionan una cobertura espacial continua y consistencia a largo plazo, se aplican cada vez más para evaluaciones regionales de la inestabilidad convectiva. Sin embargo, su fiabilidad depende de qué tan bien reproducen la estructura termodinámica vertical observada. Este estudio evalúa la CAPE y la CIN derivadas de ERA5 comparándolas con observaciones de radiosonda para la temporada premonzónica (marzo-mayo) en el este de la India, durante 1987-2016 en Bhubaneswar y Calcuta, y 1994-2016 en Ranchi. El análisis muestra que los sesgos de la CAPE de ERA5 dependen en gran medida de la estación, el mes y la hora de lanzamiento. A las 00:00 UTC, el CAPE de ERA5 se subestima ligeramente en Bhubaneswar en marzo, pero se sobreestima en abril y mayo, mientras que Calcuta muestra una sobreestimación constante del CAPE de marzo a mayo; Ranchi muestra una sobreestimación significativa a las 00:00 UTC, intensificándose hacia mayo. A las 12:00 UTC, el CAPE de ERA5 subestimó los valores en marzo en Bhubaneswar y Calcuta, pero cambió a valores significativamente sobreestimados en abril-mayo. Para el CIN, ERA5 sobreestima consistentemente los valores a las 00:00 UTC en las tres estaciones (excepto Calcuta en mayo), mientras que a las 12:00 UTC, generalmente se sobreestima en Bhubaneswar y Ranchi, y muestra un cambio de signo en Calcuta (negativo en marzo, positivo en abril-mayo). Las correlaciones entre el CAPE y el CIN de ERA5 y la radiosonda son débiles, lo que sugiere una capacidad limitada para reproducir la variabilidad diaria. Los análisis de perfil indican además que la temperatura está bien representada por ERA5, mientras que los errores de humedad relativa (más pronunciados en Ranchi) dominan los sesgos de inestabilidad.
El chorro de bajo nivel del Caribe (CLLJ) es un rasgo atmosférico crucial que controla la variabilidad climática y el transporte de humedad en la región del Caribe. Este estudio analiza las tendencias históricas y las proyecciones futuras del CLLJ utilizando datos de observación, productos de reanálisis y modelos climáticos de la Fase 6 del Proyecto de Intercomparación de Modelos Acoplados (CMIP6). El análisis histórico (1981-2020) revela un debilitamiento del CLLJ, respaldado por tres de los cuatro productos de reanálisis que muestran disminuciones de la velocidad del viento de -0,02 a -0,05 m/s a 925 hPa , consistentes con las reducciones observadas del gradiente de presión entre el anticiclón subtropical del Atlántico Norte (NASH) y la baja presión de Panamá. La variabilidad multidecadal natural, incluyendo la Oscilación Multidecadal del Atlántico (AMO) y la Oscilación Decadal del Pacífico (PDO), probablemente contribuyó a este debilitamiento. Los modelos CMIP6 demuestran una capacidad limitada para reproducir las características históricas del CLLJ, con solo un 18 % que captura las tendencias de debilitamiento observadas y un 82 % que no alcanza un RMSE* < 1, lo que indica una capacidad inferior a la de la climatología. Las proyecciones futuras (2015-2099) muestran una clara dependencia del escenario: el escenario de bajas emisiones (SSP1-2.6) mantiene las condiciones actuales, mientras que los escenarios de altas emisiones (SSP3-7.0 y SSP5-8.5) impulsan un fortalecimiento progresivo a través de gradientes de presión mejorados. Los cambios estacionales redistribuyen la intensidad del CLLJ, debilitando los máximos invernales y fortaleciendo los mínimos otoñales, lo que podría eliminar la estructura bimodal actual bajo altas emisiones. Estos hallazgos tienen profundas implicaciones para los recursos hídricos regionales y la energía renovable, lo que requiere estrategias de gestión adaptativa que tengan en cuenta las incertidumbres de las proyecciones.
Crop wild relatives underpin crop improvement yet remain under-conserved as climate change accelerates. We developed a climate-robust, uncertainty-aware framework to prioritize in situ conservation of bean's wild relatives (Phaseolus) in Mexico, a global center of origin and diversification. We compiled occurrence data for 44 taxa and modeled climatic suitability for 36 taxa using an ensemble of seven algorithms; sparsely sampled, range-restricted taxa were incorporated using buffered occurrences. Projections spanned current climate and mid-century futures under RCP4.5 and RCP8.5 scenarios, using contrasting "storyline" GCMs. We mapped climate stability (macro-refugia) and high-risk areas, and implemented systematic conservation planning with zonation for each climate zone, explicitly accounting for model uncertainty and favoring connectivity between present and future suitable areas. Spatial prioritizations were statistically invariant across GCMs, scenarios, and timeframes; therefore, summaries focus on RCP 8.5 (2040-2070). Macro-refugia concentrate in the Eje Neovolcanico Transmexicano, Sierra Madre Oriental, Sierra Madre del Sur, western Oaxaca, and Chiapas; high-risk priorities cluster in Sierra Madre del Sur, Balsas Depression, Baja California Sur, and parts of Chiapas and the Sierra Madre Occidental. Efficiency curves show strong complementarity: prioritizing similar to 5% of national territory captures similar to 30% of species' ranges; 17% captures similar to 50%; 30% captures similar to 70%. Many priorities intersect existing protected areas, yet substantial opportunities remain outside them. Our workflow yields transparent, reproducible maps to guide climate-informed connectivity, community-based reserves, and targeted seed-bank enrichment. More broadly, this transferable approach supports safeguarding Crop wild relatives in other centers of origin, aligning biodiversity conservation with long-term food security under rapid climatic change.
This study presents a comprehensive comparative analysis of conventional and artificial intelligence (AI)-based methods for estimating Weibull distribution parameters using 10 years (2013-2023) of hourly wind speed data from six sites in northwestern Iraq: AlKaam, AlShirgat, Baiji, Nenwa, Sinjar, and Haweja. Three traditional methods, the method of moments (MOM), the maximum likelihood method (MLM), and the least squares method (LSM), are compared against three AI algorithms: particle swarm optimization (PSO), differential evolution (DE), and genetic algorithm (GA). Model performance is evaluated using statistical indicators, including RMSE, MAE, R2, and chi2. Results show that PSO consistently outperforms all other methods, achieving the highest coefficient of determination (R2) of 0.998 and the lowest error values across all locations. Among conventional techniques, MLM achieved superior accuracy, with an average RMSE of 0.073 and an R2 of 0.57. Wind resource assessment revealed Haweja as the most promising site, with a mean wind speed of 5.69 m s-1, followed by AlKaam (5.59 m s-1) and Baiji (5.57 m s-1). Economic evaluation showed that using PSO-estimated parameters significantly improves energy forecasting accuracy and reduces the levelized cost of energy (LCOE) to as low as $0.03/kWh at Haweja. The 2.5 MW WT5 turbine achieved optimal performance, generating over 8000 MWh year-1 with capacity factors exceeding 39% at high-potential sites. This work provides a robust framework for accurate wind energy potential assessment in Iraq and similar arid regions, demonstrating the critical advantage of AI-driven parameter estimation in enhancing both technical and economic feasibility of wind power projects.
Temperature is a climate parameter with a complex pattern and distribution. A thorough understanding of this pattern and complexity requires precise analysis. This study analyzed surface air temperature variability using the eigen microstate approach (EMA) and empirical orthogonal function (EOF) methods based on NCEP-NCAR data. The results show that the EOF variance distribution is concentrated in the initial mode (EOF 1 at 88.42%), whereas the EMA variance is more spread out, with contributions > 1% up to the sixth mode. The resulting spatial pattern is consistent with previous studies, although it exhibits phase differences and higher variance in EMA. Fourier spectral analysis shows that EOF emphasizes annual patterns, whereas EMA can separate multiscale signals across semi-annual, annual, and interannual periods. Furthermore, EMA successfully detected important global impacts linked to regional climate phenomena such as monsoons, El Ni & ntilde;o Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole, which are less clear in higher EOF modes due to their relatively small variance. Thus, EOF is effective for identifying the global most dominant patterns, while EMA is superior in capturing multiscale variability and global impacts linked to regional climate phenomena.
The thermodynamic effect of the Saharan Air Layer (SAL) on convective stability in the Caribbean Basin is investigated through statistical analysis of aerosol optical depth (AOD), water vapor, and convective available potential energy (CAPE) over Barbados, Guadeloupe, Puerto Rico, and the Cayman Islands. This work quantifies the hierarchical controls on atmospheric instability by looking at correlations under dusty and low-dust regimes and percentage changes in CAPE under combined dust-water vapor forcing. The results show that water vapor, which has strong and often significant positive relationships with CAPE, is the primary driver of convection. In contrast, AOD-CAPE correlations are usually insignificant, indicating that dust loading by itself is not a good linear predictor of convective potential. Conversely, dust serves as an important secondary modulator; when combined with water vapor, it continuously reduces CAPE at every site, with average decreases ranging from-0.76 to-18.11%. The largest reductions occur at night in Barbados, highlighting the significance of dust proximity and the absence of daytime thermal forcing. Furthermore, dust acts as a "decoupler", introducing non-linearity and, in some situations, statistically obscuring the relationship between moisture and CAPE, thereby altering the moisture-convection relationship. These results demonstrate that the convective potential offered by ambient moisture is counteracted by the unidirectional stabilizing effect of Saharan dust on the tropical atmosphere. The findings highlight the necessity of using dust-aware parameterizations in regional climate models and vertically resolved aerosol measurements to enhance forecasts of rainfall, drought, and tropical storm activity throughout the Caribbean.
The Jagodina landspout of May 24, 2016, is one of the most comprehensively documented tornado events in Serbia and provides valuable insights for the broader European context, where non-mesocyclone tornadoes are still underreported and insufficiently understood. Thanks to the unique contribution of an eyewitness from Jagodina, who recorded the entire event with photographs and video footage, it was possible to perform a detailed synoptic forensic analysis. Complementary documentation by the European Severe Weather Database (ESWD) further validated the occurrence and timing of the landspout (11:30 UTC +/- 15 min). The event unfolded within a complex synoptic backdrop marked by a large-scale upper-level low and the support of an aloft jet streak, combined with a thermodynamically unstable mesoscale environment. Numerical values derived from radiosonde and model data showed moderate convective available potential energy (CAPE) (similar to 405 J kg(-1)), a negative lifted index (-3), dew point temperatures of 12-15 degrees C, and temperatures near 20 degrees C at the surface, pointing to significant instability. In addition, the presence of abundant low-to mid-level moisture, steep lapse rates, and small-scale convergence zones near the surface provided the critical boundary-layer forcing required for vortex initiation. This synergy of factors demonstrates that even in the absence of mesocyclone dynamics, relatively modest instability, when coupled with favorable surface processes, can produce a tornadic vortex. The Jagodina case emphasizes the necessity of integrating eyewitness documentation, synoptic-scale analysis, and numerical guidance to advance understanding and recognition of landspouts across Europe.
This study presents a synoptic classification of heavy rainfall events in the western and southwestern mesoregions of Paran & aacute; State between 2000 and 2022. A total of 178 cases were identified, characterized by daily precipitation accumulations exceeding 32.8 mm. The classification revealed four principal synoptic patterns associated with heavy rainfall events in the study region. Two of these patterns were linked to the passage of a cold front, while the remaining two were tied to cyclogenetic processes. All four patterns exhibited northwesterly winds at the surface level beginning two days prior to the occurrence of the heavy rainfall event. The Northwestern Argentinean Low and Chaco Low were identified as the main meteorological systems that advect warm, moist airflow into the study region. All four synoptic patterns showed the development of convective systems starting one day before the occurrence of a heavy rainfall event in the study region. Among them, the synoptic pattern associated with a cold front featuring a zonal displacement exhibited the most intense convective activity around the study region, as indicated by brightness temperatures below 240 K. The synoptic pattern associated with cyclogenesis and with ablocking configuration showed surface cyclonic circulation as early as the day before the occurrence of the heavy rainfall events, leading to increased surface mass convergence. This mechanism contributed to intensified rainfall in the study region, underscoring the role of low-level vorticity in sustaining heavy precipitation.
Convective available potential energy (CAPE) and convective inhibition (CIN) are widely used thermodynamic indicators for diagnosing convective environments and supporting nowcasting of thunderstorm potential. Because reanalysis products provide continuous spatial coverage and long-term consistency, they are increasingly applied for regional assessments of convective instability. However, their reliability depends on how well they reproduce the observed vertical thermodynamic structure. This study evaluates ERA5-derived CAPE and CIN against radiosonde observations for the pre-monsoon season (March-May) over eastern India, during 1987-2016 at Bhubaneswar and Kolkata, and 1994-2016 at Ranchi. The analysis shows that ERA5 CAPE biases are strongly dependent on station, month, and launch time. At 00:00 UTC, ERA5 CAPE is slightly underestimated at Bhubaneswar in March but becomes overestimated in April and May, while Kolkata exhibits consistent CAPE overestimation across March to May; Ranchi shows a significant overestimation at 00:00 UTC, intensifying toward May. At 12:00 UTC, ERA5 CAPE underestimated values in March at Bhubaneswar and Kolkata but shifted to significantly overestimated values in April-May. For CIN, ERA5 consistently overestimates values at 00:00 UTC at all three stations (except for Kolkata in May), while at 12:00 UTC, it is generally overestimated at Bhubaneswar and Ranchi, and it shows a sign change at Kolkata (negative in March, positive in April-May). Correlations between ERA5 and radiosonde CAPE and CIN are weak, suggesting limited skill in reproducing day-to-day variability. Profile diagnostics further indicate that temperature is well represented by ERA5, whereas relative humidity errors (most pronounced at Ranchi) dominate the instability biases.
The Caribbean Low-Level Jet (CLLJ) is a crucial atmospheric feature controlling climate variability and moisture transport in the Caribbean region. This study analyzes historical trends and future projections of the CLLJ using observational data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. Historical analysis (1981-2020) reveals CLLJ weakening, supported by three of the four reanalysis products showing wind-speed decreases of-0.02 to-0.05 m s-1 y-1 at 925 hPa, consistent with observed pressure-gradient reductions between the North Atlantic Subtropical High (NASH) and Panama Low. Natural multidecadal variability, including the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), likely contributed to this weakening. CMIP6 models demonstrate limited skill in reproducing historical CLLJ characteristics, with only 18% capturing observed weakening trends and 82% failing to achieve RMSE* < 1, indicating skill inferior to climatology. Future projections (2015-2099) show clear scenario dependence: the low emission scenario (SSP1-2.6) maintains current conditions while high emission scenarios (SSP3-7.0 and SSP5-8.5) drive progressive strengthening through enhanced pressure gradients. Seasonal changes redistribute CLLJ intensity, weakening winter maxima while strengthening autumn minima, potentially eliminating the current bimodal structure under high emissions. These findings have profound implications for regional water resources and renewable energy, requiring adaptive manage-ment strategies that account for projection uncertainties.
A thermodynamic model is developed in which the dynamics of Venusian atmospheric superrotation is subordinated to thermal forcing. The key mechanism is a diurnal thermal tide, generated by simplified heating based on radiative balance between solar insolation and long-wave emission. This heating produces horizontal temperature gradients (day-night and equator-pole), which, through hydrostatic equilibrium, translate into geopotential gradients. These gradients act as the primary force in the equations of motion, increasing specific angular momentum with altitude and generating a superrotating zonal wind in the direction of planetary rotation, but opposed to the slow propagation of the thermal tide. Numerical integration across 24 tropospheric layers, coupled with a regolith thermal conductivity model (down to 19.1 m depth) to compute surface heat flux, demonstrates that thermally induced vertical circulation transports angular momentum from the surface to the cloud level. It is concluded that the diurnal thermal tide, driven by insolation gradients, is the fundamental mechanism generating and maintaining superrotation in Venus' atmosphere. This novel thermodynamic approach provides a unified framework for understanding this phenomenon on slowly rotating planets.
The effects of drought on non-irrigated maize yields in the Colombian Atlantic and in the Pacific of Nicaragua were analyzed within the scope of ENSO. The Standardized Precipitation Index (SPI-12m) was calculated for 2000-2024 and correlated with crop yields, resulting in a coefficient of determination (R2) of 0.52 between yields and SPI in the Pacific, a moderate correlation (R = 0.66) between regional yields (abs values)-although no correlation after removing series trend-, and a strong correlation (R = 0.80) between SPI values. ENSO affected rainfall regional patterns differently. In the Atlantic, more annual SPI values (10) were outside the normal range than in the Pacific, 60% due to drought, occurring twice consecutively (2001-2002 and 2014-2015). In the Pacific, three of the nine years outside the normal range were due to drought, while the other six were due to abnormally wet conditions, compared to four in the Atlantic. In both regions, abnormally wet conditions occurred consecutively from 2010-2011, although with greater intensity in the Atlantic (1.94, 1.32) than in the Pacific (1.64, 1.13), with 2022 being an extremely wet year (2.02 and 2.09). Even with an upward yield trend, rainfed maize productivity was vulnerable to extreme weather conditions, caused by drought in the Pacific, with the greatest decreases in 2009 and 2015 (-0.47 and-0.22), as well as excessive rainfall in the Atlantic in 2021 (-0.42). The Atlantic showed greater variability in yields (var = 0.097 vs. 0.03 for the Pacific), where prolonged ENSO of any intensity has a greater impact on rainfall patterns and yields.
This study addresses the climatological data gap in southwestern Mexico by compiling a historical record of temperature and precipitation in Puerto & Aacute;ngel, Oaxaca. The analysis used processed in situ data from CONA-GUA (1941-1978), supplemented with recent satellite-derived atmospheric and sea surface temperature (SST) data to cover periods with sparse records. Methodologically, the research employed principal component analysis (PCA) and descriptive statistics. Sixteen relevant climate change indices for tropical regions were calculated using RClimDex, excluding frost-related phenomena. The satellite time series were decomposed into trend, seasonality, and residuals using the STD method, with analyses primarily conducted in Octave using the M language. Key findings reveal a temperature increase during the 1940s, aligning with national and global patterns. A strong correlation was identified between abundant rainfall and El Ni & ntilde;o events, while the coldest years coincided with La Ni & ntilde;a. Satellite data show a clear warming trend in both atmospheric temperature and SST from the 2010s onward. This warming is evidenced by the disappearance of historical cold records and an increase in recent maximum temperatures. Consequently, the region experiences more frequent extreme events, evidenced by a high number of intense hurricanes making landfall on the Oaxacan coast since the 1990s, with a notable concentration from the 2010s (e.g., Frank, Carlotta, Barbara, Agatha). The study concludes that the Oaxacan coast is highly sensitive to global and regional climate anomalies.
This study investigates the role of the Indian Ocean High Pressure (IOHP) system in modulating surface tur-bulent heat fluxes-specifically latent heat flux (LHF) and sensible heat flux (SHF)-across the southeastern Indian Ocean during the austral summer (DJF) over the 1988-2017 period. The IOHP is characterized using a center-of-action (COA) framework to quantify its intensity, latitudinal extent, and pressure magnitude. Our findings show that LHF exhibits a statistically significant long-term decline during 1988-2017, whereas SHF demonstrates only weak interannual variability with no consistent trend. This contrasting behavior highlights that evaporative cooling dominates the long-term flux changes, while SHF remains secondary.Long-term analysis reveals a statistically significant downward trend in LHF, closely linked to reductions in IOHP pressure and spatial coverage. In contrast, SHF exhibits weaker variability with no consistent trend. Composite diagnostics based on high and low IOHP phases show suppressed LHF and SHF across 15-30 degrees S and 65-95 degrees E during intensified IOHP years, indicating enhanced atmospheric stability and reduced surface moisture flux. These results are supported by Monte Carlo significance testing. Further, scatterplots illustrate strong inverse relationships between IOHP pressure and LHF (r approximate to-0.93), underscoring the IOHP's control on evaporation processes. Detrended correlation analyses with dominant teleconnections-including El Ni & ntilde;o-Southern Oscillation, the Indian Ocean Dipole, the Southern Annular Mode, and the South Pacific High-highlight mode-specific interactions, with the IOHP acting as a regional mediator of broader climate variability. This work demonstrates that a weakening and contraction of the IOHP could exacerbate surface warming and reduce ocean-atmosphere coupling, with potential implications for regional hydroclimate and energy balance over western Australia.
As the center of origin and diversification of maize, Mexico heavily depends on its production, making climate change a significant risk to food security. Implementing adaptive measures is essential to mitigate production losses. To evaluate farmers' perspectives on climate change and the alignment between adaptive measures in practice, those proposed in scientific literature, and public policy, we conducted 30 semi-structured interviews in a highly productive area in Mexico (between August 2022 and January 2023) where rainfed and irrigated agriculture coexist (La Piedad). We then reviewed the scientific literature and analyzed all applicable policy instruments for the area. Our findings indicate that farmers recognize climate change as a significant threat and implement adaptive measures empirically. While scientific literature and public policies promote specific strategies, these are rarely implemented. Instead, practices such as leasing land to agave producers (which is not a good climate change adaptation measure) are becoming more common. The most widespread practice to retain moisture is leaving crop residues, though some farmers have abandoned it due to a lack of technical support. Despite acknowledging climate change, 87% of farmers lack access to reliable climate information or early warnings for extreme events like La Ni & ntilde;a. This difference between practices highlights the need for vigorous efforts to bridge scientific recommendations with on-field practices. In conclusion, while farmers perceive climate change as a risk, effective implementation of science-based adaptation measures requires substantial support through policy and technical assistance.
The Mediterranean region is highly vulnerable to climate variability, with profound implications for water security. This study assesses the impact of four decades (1983-2023) of climatic fluctuations on land use and groundwater resources in the Wadi Fekan sub-watershed, northwest Algeria. Using standardized indices (SPI, CMI) and break detection tests on data from 11 rainfall stations, we identified a pivotal climatic shift between 1999 and 2006. This break initiated a wetter regime, leading to a 31.7% increase in rainfall. The resulting increase in runoff led to a measurable expansion of the main riverbed (+0.045% in land-use class). Concurrently, the alluvial aquifer experienced substantial recharge, with volumes rising from 17.997 km3 (2003-2013) to 25.615 km3 (2013-2023). However, spatial analysis revealed a paradox: despite overall wetter conditions, aridity intensified in the basin's center due to a Foehn effect, and the aquifer's net water balance remained negative over the study period. This indicates that groundwater overexploitation during prior droughts created a hydrologic deficit that recent rainfall has not fully offset. These findings demonstrate the persistent vulnerability of semi-arid aquifers to climatic stress and anthropogenic pressure, underscoring the critical need for sustainable management strategies that address both climate variability and historical overuse to mitigate future water crises.
Particulate matter (PM) concentrations in the Caribbean result from complex interactions between transboundary natural sources and local anthropogenic and natural emissions. This study presents a five-year analysis of PM10 and PM2.5 levels in Puerto Plata, a coastal tourist city that hosts significant port and cruise ship activity. Annual average PM10 and PM2.5 concentrations were 35.0 ± 16.4 and 8.8 ± 5.1 µg m–3, respectively, exceeding annual guidelines established by the World Health Organization (WHO) by factors of 2.3 and 1.8, respectively. PM2.5/PM10 ratios below 0.4 occurred on 88.6% of days during the study period, with a global average of 0.27, indicating a dominance of coarse particles driven by Saharan dust intrusions and sea spray emissions. The weak correlation between PM10 and PM2.5 (ρ = 0.61) suggests different emission sources and atmospheric behavior. Fine-particle episodes (PM2.5/PM10 ratio ≥ 0.6) were sporadic (2.3%) and primarily associated with localized combustion sources, such as fireworks displays during tourism-related events. Seasonal dynamics revealed marked PM10 peaks during summer associated with dust transport (June-August). Conversely, PM2.5 concentrations showed a limited seasonal variability, reflecting steady local emissions of fine PM. WHO’s 24-h thresholds were exceeded on 19% of days for PM10 and 7% for PM2.5, underscoring chronic exposure risks.
The Grijalva basin is of great relevance in southern Mexico because it receives the highest precipitation and has the most extensive hydroelectric system in the country. In addition, the lower basin has been impacted by extreme flooding in recent years. It is the source of water for several million people and the regional industry. For this study, the basin was divided into four sub-basins: Angostura, Chicoasén, Malpaso, and Peñitas. Each of these sub-basins has a dam that helps regulate the water flow and generate hydroelectric energy. To better understand the region’s climatology, this study uses long-term rainfall observations from sub-basins to describe precipitation patterns and their variability. Various statistics are computed to describe the annual precipitation cycle for each sub-basin. The results show that Angostura, Chicoasén, and Malpaso share a common climatology, with precipitation peaks in June and September and a mid-summer drought (MSD) in July. Peñitas receives considerably more precipitation throughout the year, with the highest values in October-November. In all sub-basins, La Niña (El Niño) years are characterized by increased (decreased) precipitation in the rainy season. The study demonstrates that the extreme precipitation observed during La Niña years in late summer and autumn is mainly due to an increased number of tropical cyclones over the western Caribbean Sea and the Gulf of Mexico. The interquartile range and other percentile values of monthly precipitation provide additional information that may be useful for dam management.
Climate change is expected to modify the current suitable areas for bean cultivation, driven by regional shifts in temperature and precipitation. Despite the economic and food security importance of beans in Colombia, there is a lack of knowledge about how ongoing and future climate change may reshape their agroclimatic suitability across the country. This study aimed to assess the potential future shifts in suitable areas for climbing bean (Phaseolus vulgaris L.) cultivation in the department of Cundinamarca under projected climate change scenarios. Suitable areas were categorized into A1 (best conditions), A2 (moderate constraints), A3 (strong restrictions), and N1 (not suitable), based on a decision tree with defined altitude, temperature, and precipitation intervals. The period 1981-2010 was utilized as the present climate, and the future periods 2011-2040, 2041-2070, and 2071-2100 were considered under the climate change scenarios RCP 4.5 and RCP 8.5. Information from the Global Climate Model CCSM4 from the National Center for Atmospheric Research was used to identify new potential areas and changes in optimal zones under future scenarios. The forecast for Cundinamarca indicates that under the RCP 4.5 scenario, the total suitable area decreases slightly by 3.8%, while the A1 zone expands, especially in cooler highland regions. In a high-emission future (the RCP 8.5 scenario), the total suitable area declines more sharply (by 14.8% between 2071 and 2100), while the unsuitable area increases by 6.5%. The expansion of A3 zones by up to 13.3% in the early and mid-21st century reflects the downgrading of currently optimal or moderate areas to low suitability due to rising temperatures, particularly in the Llanos foothills and the Magdalena slopes subregions.
