Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Local climate influences of inland water bodies, complex topography, and surrounding seas cause temperate, arid, and continental climate properties to prevail with local variations in different parts of Turkey. The intra-regional variability of environmental factors creates uncertainties and challenges in climate modeling. Multi-model ensemble analysis is suggested to be used to characterize the uncertainties and minimize the generalization error in projections. This study is part of a research on climate change impacts in Turkey, focusing on the impacts on surface air temperature through a multi-model ensemble analysis of high-resolution climate models. The ensemble set comprises 12 EURO-CORDEX RCMs and two models from the Japan Meteorological Research Institute (MRI). Firstly, historical model data are validated with temperature records from 59 meteorological stations. Furthermore, changes in temperature climatology in the future in short- (2020-2030), medium- (2031-2050), and long-term (2051-2100) horizons are analyzed and compared with the precipitation changes. In the ensemble, two MRI models (MRI-AGCM, NHRCM) and two CORDEX RCMs nested in the HadGEM2-ES (RCA4 and CCLM4-8-17) perform best to replicate the spatial variability of climatology. The 14-member ensemble projects a gradual increase in the temperature up to 4.5 and 6.6 ºC under RCP4.5 and RCP8.5 scenarios, respectively. The projections agree on an inverse relationship between temperature and precipitation changes. More substantial impacts are projected in inland compared to coastal regions.

The objectives of the present study are to analyze: (1) drought events with large coverage and their possible response to El Niño Southern Oscillation (ENSO) and (2) extreme daily precipitation (EDP) events, both during the 1980-2019 period considering daily precipitation data from climatological stations during the summer months (July-September) in the state of Jalisco, Mexico. For the first objective, a drought analysis was performed using the Standardized Precipitation Index (SPI) at time scales of three (SPI-3) and 12 months (SPI-12), calculating seasonal (July-September) and annual (January-December) series. For the second objective, an EDP event was defined by filtering records greater than 30 mm from the selected stations, then adjusting them to a probability distribution to obtain the 99th percentile (P99) of each series. Values above P99 were identified as EDP events. The results indicate that drought events with large coverage (SPI-12) occurred under La Niña conditions in the 1989-1990 and 2011-2012 periods, affecting 71.4 and 64.3% of the state, respectively, where the coastal region was the least affected. A total of 57 EDP events were identified, but no particular ENSO pattern was determined. The most frequent peak activity occurred in 1987, 1999, 2010, and 2013, representing 31.6%, concentrated in 11 out of 28 climatological stations. While ENSO influences are weaker in these regions, other drivers, such as tropical cyclones, need adaptive disaster preparedness measures. Strengthening early warning systems, improving urban drainage infrastructure, and updating zoning regulations can mitigate flood impacts, reduce economic losses, and protect lives.

This study aims to improve the understanding of the interaction between El Niño-Southern Oscillation (ENSO) events and the phases of the Southern Annular Mode (SAM), as well as their influence on the western Antarctic Peninsula (WAP). Monthly series of air temperature (Ta), sea surface temperature (SST), and sea ice coverage (SIC) from 1981 to 2020 were analyzed at six representative points within the study area. Ta, SIC, and SST data were obtained from the ERA5 database. The Southern Oscillation Index (SOI) and Antarctic Oscillation Index (AAOI) were sourced from NOAA’s Climate Prediction Center. The spectral density of the anomaly series for each variable was calculated, and the characteristic cycles of SAM and ENSO were extracted. The most influential components driving the oscillations of the studied series were identified. A cross-correlation analysis was conducted between the anomaly components of Ta, SIC, and SST and those of SOI and AAOI. The results indicate that Ta series exhibit moderate positive correlations with SAM, particularly between the 5.2-year quasi-period of AAOI and the 4.4-year quasi period of Ta in the Drake Passage and Bransfield Strait. The SST series in Bransfield correlate with AAOI5.2, while the quasi-periods of 4.6 and 5.2 years in the Drake Passage correlate with SOI3.5. The SIC series show positive correlations with the 3.5-year quasi-period of SOI for lag times greater than one year, except in the Bransfield Strait. Extreme absolute Ta and SST events in the WAP region are strongly influenced by SAM and ENSO. It is concluded that the coupling of ENSO and SAM phases amplifies their effects on meteo-marine variables.

Drought is a complex phenomenon that includes meteorological, agricultural, and hydrological aspects. It is characterized by an extended period of insufficient rainfall that adversely impacts civilization‘s economic, social, and environmental aspects. This study compares four meteorological indices, including the Standardized Precipitation Index (SPI), China-Z index (CZI), Modified China Z Index (MCZI), and Z-Score Index (ZSI), to identify the most suitable drought indices (DIs) for assessing drought in the Tafna watershed. Monthly rainfall data from 14 stations (1970-2019) were used to calculate drought events on 1-, 3-, and 12-month time scales. On the 1- and 3-month time scales, the frequency of total drought for SPI-1 and CZI-1 is higher than for MCZI-1 and ZSI-1. At the 12-month time scale, all DIs showed the same frequency of total drought for most stations. The highest Pearson correlation coefficient is observed between SPI-1 and CZI-1 on a 1-month scale, with values exceeding 0.939 across all stations. Additionally, 57 and 71% of stations exhibit the highest correlation between SPI and CZI, with coefficients exceeding 0.963 and 0.999 at 3- and 12-month scales, respectively. Most stations do not show any trend (increase or decrease) using the Mann-Kendall trend test in all index values at 1- and 3-month scales. On a 12-month scale, most stations showed an increase in the values of all DIs. The results reported in this study provide valuable insights that can enhance the management of water resources and improve preparedness for drought events in the Tafna watershed, particularly in the context of climate change.

In Peru, various studies on canopy urban heat islands (CUHI) show their effects on the health and habitability of cities. In the case of Lima, various effects have been analyzed. However, the analysis of the mitigation mechanisms of urban heat islands is still limited. Therefore, information on the mitigation potential of such measures is also limited. The main objective of this work is to evaluate the effect of green corridors on Arequipa Avenue in the city of Lima with respect to the mitigation of CUHI. The work was carried out by recording temperatures and relative humidity between August and September 2022, using fixed stations, through determined observation points, and numerical atmospheric modelling. The results show that the maximum temperatures reached in areas without a corridor are higher than those in areas with a green corridor by up to 5.4 ºC. On the other hand, there are significant differences in the intensity of CUHI between areas inside and outside the corridor. CUHI values within the corridor, ranging from 0.4 to 0.6 ºC, are lower than those found outside, indicating the positive effect of the green corridors in mitigating the CUHI impact.  The analysis of the data allows us to evaluate the effectiveness of this intervention proposed in the Strategic evaluation of measures to reduce the urban heat island in the Province of Lima, prepared by the Metropolitan Municipality of Lima.

The regionalization of precipitation is a vital tool for understanding hydrological phenomena, particularly in the context of the El Niño-Southern Oscillation (ENSO) phases (El Niño, La Niña, neutral, and climatology) in Guatemala. This study introduces a novel framework that defines previously diffuse regional boundaries, revealing the dynamic nature of precipitation patterns across the country. The findings demonstrate how regional boundaries shift in response to ENSO phases, as well as under climatology and neutral conditions. These insights highlight the importance of considering dynamic regionalization to accurately analyze climatic impacts and precipitation variability, providing a foundation for more effective climate adaptation strategies.

This study analyzes extreme precipitation in eastern China over the past 60 years through climatology and trend detection, with future projections using 10 indices. Data from 1310 meteorological stations and 20 CMIP6 models were employed to examine the spatiotemporal evolution of these indices from 1961 to 2020 during summer, both regionally and across eight major river basins. Key findings include: (1) the spatial distribution of extreme precipitation generally decreases from south to north and east to west. High-value centers for extreme precipitation are found south of the Yangtze River, while the maximum consecutive wet days decline from the southeast coast inland. (2) Extreme summer precipitation in eastern China increased over six decades, with intensity, 1-day max, heavy, extreme heavy precipitation, and very heavy rain days rising at 0.03, 0.11, 0.22, 0.13 mm yr–1, and 0.02 d yr–1, respectively. Trends show growth in the Yangtze River Basin and the northeast, but declines in the Hai and Yellow River basins, with decreases up to 0.50 mm yr–1. (3) Multi-model ensemble simulations reliably capture summer extreme precipitation trends, indicating that by 2060, heavy and extreme precipitation amounts will increase by up to 10% under SSP-1.26. Higher carbon emissions will further accelerate this increase, particularly in the southeast rivers, the Pearl River, and the Yangtze River basins. These results provide important references for predicting extreme precipitation in eastern China.

This paper examines the characteristics associated with the spatiotemporal evolution of extreme precipitation, assesses its recurrence frequency, and predicts future return levels over northern Algeria. The study employs extreme precipitation indices in conjunction with the application of extreme value theory to a rainfall dataset spanning from 1982 to 2022. The study focused on modeling the index that demonstrated the highest percentage of significant positive trends at the α = 0.05 significance level. This was accomplished through the utilization of the Mann-Kendall test and the generalized extreme value distribution. Subsequently, the model was validated using the Kolmogorov-Smirnov fit test. The results revealed that the northeastern region of the study area experienced a more pronounced increase in rainfall intensity compared to the southern and western regions. Significant trends in precipitation intensity were observed over time. Notably, the index of days with rainfall exceeding 20 mm demonstrated the highest percentage of positive trends, with 88% of meteorological stations exhibiting an upward trend. Furthermore, a strong correlation was identified between the index of days with rainfall exceeding 20 mm and the very wet days index, particularly in the high plateaus and western region. This finding supports the hypothesis that extreme rainfall patterns are becoming more frequent in the region.

As extreme weather phenomena, heatwaves bring severe risks to human health, society, and ecosystems. Over the past few years, Bangladesh has experienced heatwaves that are becoming more frequent and intense. Early warning systems (EWS) can help to minimize the potential damage from these events by providing sufficient time for thorough and effective preparation. Traditionally, numerical weather prediction (NWP) is employed for heatwave forecasting, but it is both expensive and time-consuming. This study explores the potential of using machine learning as a faster and more cost-effective alternative to NWP. Specifically, we focus on building an artificial neural network (ANN) to predict heatwaves three days in advance over Bangladesh. Our model utilizes 28 features to predict a binary target value (0 for no heatwave, 1 for heatwave). The results are promising, with the model achieving an accuracy of 91% in distinguishing heatwave and non-heatwave days. This suggests that machine learning can be a valuable tool for large-scale heatwave prediction in Bangladesh.

A new approach is presented in this work to measure the electrical charge carried by precipitation particles and their corresponding fall velocity. The instrument represents an improved version of our previous device, with the primary goal of increasing the sampling rate of charged droplets to improve and make the statistical analysis of charged raindrops more robust. Additionally, the instrument incorporates a computational program for detecting individual raindrop passages, enabling automatic calculation of its electrical charge and fall velocity. To test the new device’s performance, it was simultaneously used with our previous instrument during a storm in Córdoba on November 21, 2023. It was observed that the latest instrument increased the sampling rate nearly fivefold compared to the old one. The results demonstrate a high degree of consistency across different devices, validating the reliability and reproducibility of the new device.