Search Results (11)

The assessment of bioclimatic conditions and meteorological parameters—such as air temperature and precipitation—helps identify optimal periods for various activities, considering regional and individual factors. Climatic and bioclimatic conditions are major factors influencing human health and daily activity. These factors are instrumental in determining the quality of life, the state of health, and the overall well-being of individuals. The analysis of meteorological parameters, including air temperature, humidity, and precipitation, facilitates the evaluation of climatic comfort across diverse regions. Bioclimatic studies are instrumental in identifying zones with favorable or unfavorable living conditions, a matter of particular importance in the planning of urban development and the formulation of landscaping and gardening measures. The study aims to assess the bioclimatic conditions prevailing in the Almaty region. It focuses on the Kapshagay Reservoir during the period 1990–2020, applying commonly used biometeorological indices. The software product ClimPACT2, which was developed for the analysis of extreme phenomena and weather changes, was utilized for the calculations. The primary meteorological indicators, specifically temperature and precipitation, were selected for the calculation of climatic indices. The observed spatial and temporal trends of climate change in the study area were analyzed. The findings indicated a substantial increase in the frequency of warm days and nights, concurrently accompanied by a decline in the occurrence of cold days and nights. The identified trends indicate a marked warming of the climate, which may have serious consequences for ecosystems and human activities. The analysis also revealed a significant increase in total annual precipitation in coastal zones. Full article

As cities grow, natural surfaces are replaced by heat-retaining materials, raising urban temperatures and intensifying heatwave impacts. The present study investigates the effectiveness of urban greening strategies, including green roofs, street vegetation and metropolitan parks, in enhancing climate resilience in Athens, a coastal Mediterranean city characterized by complex heatwave dynamics. The strategies were evaluated through simulations using the WRF model coupled with the BEP-BEM urban canopy model and a detailed land-cover map that is uses the 11 urban Local Climate Zones (LCZ) categories (CLIMPACT) tailored for Athens. Simulations focused on a significant heatwave event that affected the region in 2021 assessed the thermal impacts of the different greening scenarios. Results show that expanding green areas reduces peak temperatures and modifies local thermal circulations, highlighting the potential of greening in mitigating urban heat island effects. Full article

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts. Full article

Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins. Full article

Heatwave (HW) number (HWN), frequency (HWF), duration (HWD), magnitude (HWM), and amplitude (HWA) are key aspects for interpreting and understanding HW characteristics worldwide. Most previous HW studies over the Kingdom of Saudi Arabia (KSA) focused only on the temperature extremes, so this study aims to assess the decadal changes, anomalies, and spatiotemporal variations in the five HW aspects over KSA during the last four decades (1982–2021) using the ClimPACT2 software. Daily gridded (0.25° × 0.25°) maximum (TX) and minimum (TN) temperatures from the ECMWF-ERA5 reanalysis dataset were used to compute these heat wave (HW) aspects. The HW aspects were derived in ClimPACT2 using the Excess Heat Factor (EHF), the 90th percentile of TX (TX90), and the 90th percentile of TN (TN90), all based on the reference climate period of 1982–2011. The results showed that the decadal sum and anomaly of the five HW aspects increased gradually during the last four decades (1982–2021). The three indices showed that the maximum decadal sum of HWN (42 events), HWF (255 days), and HWD (145 days) occurred in the last decade. Additionally, the last decade has the maximum decadal sum of HWM (175–463 °C) and HWA (189–471 °C) as derived from TX90 and TN90, which is confirmed by EHF, with ranges of 7–58 and 15–185 °C², respectively. Finally, the periods 2015–2021 and 1984–1986 recorded the highest and lowest values of annual HW aspects, respectively, across the study period. Full article

The State of Veracruz (Mexico) is highly vulnerable to climate change. Therefore, it is necessary to identify and analyze local climate extreme trends and explore potential relationships between climate indices and maize. The objectives of this research were (1) to describe recent trends of climate indices (1979–2018) and (2) to compare these climate indices with maize yields produced in Veracruz, Mexico, under rainfed conditions. The methodology calculated and analyzed the sector-specific climate indices (Rx5day, PRCPTOT, SPI6, R20mm, TXx, TNn, TXgt50p, and TXge35) in 18 observation sites using Climpact. Climate indices were calculated over the spring-summer agricultural cycle and correlated with rainfed maize yields. Results show increasing trends for Rx5day, TXx, TXgt50p, and TXge35 indices in 65%, 56%, 89%, and 67% of the analyzed sites, respectively, whereas decreasing trends in PRCPTOT and R20mm indices were detected in 59% and 47% of the sites. Significant correlations (p < 0.05) between climate indices and maize yield were found in eight municipalities, of which 62% were positive. In conclusion, extreme temperature and precipitation local events are increasing in frequency, duration, and intensity, and depending on the site’s local climate, these might positively or negatively impact maize yields. Full article

The Qilian Mountains are a climate-sensitive area in northwest China, and extreme precipitation events have an important impact on its ecological environment. Therefore, considering the global warming scenario, it is highly important to project the extreme precipitation indices over the Qilian Mountains in the future. This study is based on three CMIP6 models (CESM2, EC-Earth3, and KACE-1-0-G). A bias correction algorithm (QDM) was used to correct the precipitation outputs of the models. The eight extreme precipitation indices over the Qilian Mountains during the historical period and in the future were calculated using meteorological software (ClimPACT2), and the performance of the CMIP6 models to simulate the extreme precipitation indices of the Qilian Mountains in the historical period was evaluated. Results revealed that: (1) The corrected CMIP6 models could simulate the changes in extreme precipitation indices over the Qilian Mountains in the historical period relatively well, and the corrected CESM2 displayed better simulation as compared to the other two CMIP6 models. The CMIP6 models performed well while simulating R10mm (CC is higher than 0.71) and PRCPTOT (CC is higher than 0.84). (2) The changes in the eight extreme precipitation indices were greater with the enhancement of the SSP scenario. The growth rate of precipitation in the Qilian Mountains during the 21st century under SSP585 is significantly higher than the other two SSP scenarios. The increment of precipitation in the Qilian Mountains mainly comes from the increase in heavy precipitation. (3) The Qilian Mountains will become wetter in the 21st century, especially in the central and eastern regions. The largest increase in precipitation intensity will be observed in the western Qilian Mountains. Additionally, total precipitation will also increase in the middle and end of the 21st century under SSP585. Furthermore, the precipitation increment of the Qilian Mountains will increase with the altitude in the middle and end of the 21st century. This study aims to provide a reference for the changes in extreme precipitation events, glacier mass balance, and water resources in the Qilian Mountains during the 21st century. Full article

The increase in the intensity and frequency of climate extremes threatens socioeconomic development. This study examines variability of mean and extreme climate, farmers’ perception of the changes, and impacts in the Awash River Basin. Daily rainfall and temperature data were used to analyze 23 extreme climate indices. The Mann–Kendall test was used to assess the magnitude and significance of the changes. Results show an increase in minimum (0.019–0.055 °C/year) and maximum temperatures (0.049–0.09 °C/year), while total rainfall is on a downward trend (from −3.84 mm/year to −10.26 mm/year). Warm extreme temperature indicators, including warmest day (TXx), warmest night (TNx), warm day (TX90p), warm night (TN90p), and warm spell duration indicator (WSDI), show a significant increasing trend (p < 0.05). Nevertheless, except the tepid–cool humid agroecology zone, cold extreme temperature indicators in cool days (TN10p), cool nights (TX10p), and cold spell duration (CSDI) are declining. Extreme precipitation indices, including maximum 1-day precipitation amount (RX1day), count of days when precipitation ≥10 mm (R10 mm), maximum 5-day precipitation amount (RX5day), count of days when precipitation ≥20 mm (R20mm), very wet days (R95p), extreme wet days (R99p), and total precipitation (PRCPTOT), show a decreasing trend. The perception of most farmers’ on climate change and climate extremes agreed with climate records. The major impacts perceived and asserted over all agroecologies are food price inflation, crop productivity decline, crop pests and diseases spread, livestock disease increase, and the emergence of pests and weeds. The increasing trend in extreme warm temperatures, decreasing trend in the cold extreme, and declining trend in precipitation indicators affected agricultural productivity and farmers whose livelihood depends on rainfed agriculture. This agroecology-specific study provides critical information to policymakers, decision makers, and farmers about the potential impacts of climate change and extreme events, leading to the development of agroecology-based adaptation measures. Full article

Climate Data Science (CDS) Toolbox Species Distribution Model (SDM) aims identifying the suitable areas for species, community of species and landscape units. This model is based on the use of 23 variables available over the Internet, for which any assumptions are formulated about their relationships with the spatial distribution of species. The application of CDS Toolbox SDM on the assessment of the potential impact of two scenarios of climate change (Representative Concentration Pathways RCP4.5 and RCP6.0) on the suitability of grapevine crops in France shows a general decrease of the most suitable areas for grapevine crops between 41% and 83% towards 2070 according to the current location of the vineyard parcels. The results underline a potential shift of the suitable areas in northern part of the French territory. They also show a potential shift of the most suitable areas in altitude (60 m in average) for RCP6.0 scenario. Finally, the model shows that RCP4.5 scenario should be more drastic than RCP6.0 scenario by 2050 and 2070. In effect, the model underlines a significant potential decrease of cultivated crops in the areas of high probably of suitable areas, according to the baseline scenario. This decrease would be of 630,000 ha for 2070 RCP4.5 scenario and 330,000 ha for 2070 RCP6.0 scenario. Full article

Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen’s slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of −1.01 °C, −3.75 days, −5.67 days, −11.40 nights, and −2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months. Full article

Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical observations, or reanalysis data products are scarce. This study introduces a new high-resolution global gridded dataset of CEIs based on sub-daily temperature and precipitation data from the Global Land Data Assimilation System (GLDAS). The dataset called “CEI_0p25_1970_2016” includes 71 annual (and in some cases monthly) CEIs at 0.25 ${}^{\circ }$ × 0.25 ${}^{\circ }$ gridded resolution, covering 47 years over the period 1970–2016. The data of individual indices are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Potential applications of CEI_0p25_1970_2016 presented here include the assessment of sectoral impacts (e.g., Agriculture, Health, Energy, and Hydrology), as well as the identification of hot spots (clusters) showing similar historical spatial patterns of high/low temperature and precipitation extremes. CEI_0p25_1970_2016 fills gaps in existing CEI datasets by encompassing not only more indices, but also by being the only comprehensive global gridded CEI data available at high spatial resolution. Full article