Search Results (1,573)

Extreme heat, a leading cause of weather-related morbidity and mortality, particularly affects vulnerable populations such as older people, increasing their risk of stroke. There is a gap between scientific knowledge and policy implementation, particularly regarding climatic risk factors for stroke. This study aims to identify knowledge barriers and enablers and formulate recommendations. We held eight focus groups of participants aged ≥ 60 years (N = 56), a workshop with 36 public health policy experts and stakeholders, and six in-depth interviews with experts. Three main themes emerged: (1) risk perception and responsibility attribution, revealing varying awareness of climate change risk for stroke and complex personal, cultural, and institutional responsibilities; (2) barriers to climate change adaptation, including knowledge gaps, environmental maladaptation, and insufficient governmental resources; and (3) enabling factors and adaptive solutions, highlighting individual coping strategies, education, and collaborative policy interventions. Focus group participants demonstrated diverse adaptive behaviors, while policymakers emphasized interagency collaboration and targeted knowledge dissemination. Older individuals demonstrated limited knowledge about climate change and its health risks. National policies lack effective communication. There is a critical need for knowledge dissemination, coping tools, and solutions for healthcare providers and at-risk groups, particularly regarding the health implications of climate change. Full article

The North American Monsoon (NAM) in southern Arizona continues to be a topic of interest to many ecologists studying the triggers and characteristics of plant growth and reproduction in relation to the onset of the monsoon. The purpose of this article is to report interannual variation in the timing of NAM onset found while researching the phenology of Saguaro cactus (Carnegiea gigantea). Using a daily rainfall dataset from 33 stations located in Pima and Pinal Counties, Arizona, from 1990–2022, we analyzed monsoon onset, monsoon precipitation, annual precipitation, and the proportion of annual station precipitation received during the monsoon season. Onset was measured by the first day from 1 June to 30 September with precipitation ≥ 10 mm counted from the day of the vernal equinox of the year. Generalized Additive Models (GAMs) identified sinusoidal waves with a period of 8.6 years and amplitudes of 14–29 days, providing frequency and amplitude estimates for Sinusoidal Regression Models (SRMs). Sinusoidal wave patterns found in the monsoon onset dataset are suggested in monsoon, annual, and proportion of monsoon in station-averaged annual precipitation although in and approximately mirror-image. These unexpected findings may have important implications for forecasters as well as ecologists interested in plant phenology. Full article

Natural disaster events caused by extreme precipitation have far-reaching and widespread impacts on society, the economy, and ecosystems. However, understanding the long-term trends of extreme precipitation indices and their spatiotemporal correlations with disaster events remains limited. This is especially true given the diverse factors influencing their relationship in China, which makes their spatial linkage highly complex. This study aims to detect recent spatial trends in extreme precipitation indices in China and link them with related natural disaster events, as well as with the spatial evolution of land use and land cover and Gross Domestic Product (GDP). Daily precipitation data from 1274 rain gauge stations spanning the period from 1961 to 2021 were used to analyze the spatial distribution characteristics of extreme precipitation index climate trends in China. The results revealed a significant increasing trend of the intensity of extreme precipitation in eastern China, but a decreasing trend of amount, frequency, and duration of extreme precipitation in southwest China, accompanied by a significant increase in consecutive dry days. Natural disaster records related to extreme precipitation trends indicated a significant increase at an annual rate of 1.3 times in the frequency of flood, storm, drought, and landslide occurrences nationwide, with substantial regional dependence in disaster types. Furthermore, the spatial evolution of land use and GDP levels showed a close association with the spatial distribution of natural disaster events induced by extreme precipitation. Although the number of deaths caused by extreme precipitation-related disasters in China is decreasing (by 51 people per year), the economic losses are increasing annually at an annual rate of USD 530,991, particularly due to floods and storms. This study holds the potential to inform decision-making processes, facilitate the implementation of mitigation and adaptation measures, and contribute to reducing the impacts of natural disasters across diverse regions worldwide. Full article

The long instrumental series of precipitation in Pisa, the earliest one in Italy, has been reconstructed after the careful recovery and critical analysis of its history, data, and metadata. Precipitation amounts have been recovered from May 1707 to December 2024, but there are gaps due to lost data. The recovered dataset includes 47.4% of the total daily, 65.0% of monthly, and 77.4% of yearly values. Original observation registers and metadata are scarce or even missing, so a thorough investigation of contemporary sources has been performed to recover as much information as possible concerning observers, instruments, locations, exposures, measuring protocols, and ancient local units. The main features of the precipitation regime in Pisa have been investigated, and the variability in the amount and frequency at different time scales, as well as extreme events, have been analysed. Pisa is characterized by intense precipitation in autumn due to the penetration of Atlantic perturbations, and the most extreme daily events occur mainly in the transition period between the end of summer and the onset of autumn. A small decreasing trend has been found in the anomaly of the yearly amount in the 1867–2024 unbroken period, with the most remarkable month anomalies in summer. The time series of the Standard Precipitation Index indicates that the period around 1945 was particularly dry, and also indicates a slight increase in arid conditions over time, mainly in spring. The most extreme yearly amounts were found in the 18th century, and the series of the daily 90th and 95th percentiles show a small decreasing trend in the 1884–2004 period. The comparison with other contemporary Italian series made it possible to identify the peculiarity of the precipitation regime in Pisa, adding an important piece to the historical research on the climate of the Italian peninsula from a long-term perspective. Full article

In this study, a prediction model was developed that considers the rainfall characteristics and damage characteristics of heavy rainfall disasters in Korea using machine learning models. Considering the damage characteristics of heavy rainfall disasters that occurred from 1999 to 2019 in 228 administrative districts in Korea, four types of total rainfall and five types of damage costs were selected to predict the total damage cost. The machine learning models selected for this study were Random Forest, K-Nearest Neighbors, Decision Tree, and eXtreme Gradient Boosting, and their accuracy was evaluated using $R^2$, EVS, and MAPE. The training period spanned from 1999 to 2015, while the evaluation period extended from 2016 to 2019. The Random Forest model emerged as the most effective model for predicting the total damage costs associated with heavy rainfall disasters, exhibiting an accuracy of 0.95 for $R^2$, 0.95 for EVS, and 0.05 for MAPE. It was observed that when the total damage costs are minimal, all models demonstrate high prediction capability. However, as the damage costs escalate, the prediction power experiences a decline due to the presence of errors. The machine learning prediction model for heavy rainfall disasters developed in this study has the potential to contribute to national efforts aimed at preventing and preparing for heavy rainfall disasters. Full article

(1) Background: This study investigated seasonal variations in particulate matter (PM) ratios (PM₁/PM_2.5, PM_2.5/PM₁₀, and PM₁/PM₁₀) and their relationship with the meteorological conditions in Rio Grande, Brazil. (2) Methods: PM₁, PM_2.5, and PM₁₀ levels were collected using low-cost Gaia Air Quality Monitors, which measured PM concentrations at high temporal resolution. Meteorological variables, including atmospheric pressure, temperature, relative humidity, wind speed, and precipitation, were obtained from the National Institute of Meteorology (INMET). The data were analyzed through multiple linear regression to assess the influence of meteorological factors on PM ratios. (3) Results: The results show that the highest PM ratios occurred in winter, indicating a predominance of fine and ultrafine particles, while the lowest ratios were observed in spring and summer. Multiple linear regression analysis identified atmospheric pressure, wind speed, and maximum temperature as the key drivers of PM distribution. (4) Conclusions: This study highlights the importance of continuous monitoring of PM ratios, particularly PM₁, which remains underexplored in Brazil. The findings underscore the need for targeted air quality policies emphasizing seasonal mitigation strategies and improved pollution control to minimize the health risks associated with fine and ultrafine PM exposure. Full article

Regular meteorological observations in Padua started in 1725 and have continued unbroken up to the present, making the series one of the longest in the world. Daily mean temperatures and precipitation amounts have recently been homogenized for the entire 1725–2024 period, making it possible to add new measurements without further work. Starting from the temperature series, the trends of cold and warm spells are investigated in this paper. The ongoing warming that started in the 1970s is extensively analyzed on the basis of the variability of the mean values and a magnitude index that captures both the duration and intensity of a spell and by investigating the frequency of extreme events by means of Intensity–Duration–Frequency curves. The periods with the greatest deviation from the climatological average are analyzed in detail: February 1740 and April 1755, the months with the largest negative and positive temperature anomalies, respectively, in the 300-year-long series. Moreover, the analysis of snow occurrences extracted from the original logs, together with the pressure observations from the long series of London and Uppsala, made it possible to evaluate the most typical synoptic situations leading to snow events in Padua for the whole period. Full article

Effective climate change communication is central to public engagement, self-efficacy, and support for mitigation and adaptation policies. Yet, communicators face numerous challenges, such as misinformation, climate despair, and the complexity of climate science. This study examines the views of 29 climate change communicators from Australia and Aotearoa New Zealand to evaluate the alignment between practitioner insights and research-backed strategies. Findings confirm the significance of audience segmentation, localized, and solution-focused messaging and collaborative approaches. A four-pillar framework, consisting of simplicity and local relevance, audience segmentation, storytelling, and actionable steps, emerges as a strategic model for enhancing the communication impact. This study offers a structured guide for improving climate communication effectiveness within diverse sociopolitical contexts by integrating theoretical insights with practitioner perspectives. Full article

Rapid urbanization and climate impacts have raised concerns about the emergence and aggravation of urban heat island effects. In Africa, studies have focused more on big cities due to their growing populations and high climate impact, while mid-sized cities remain under-studied, with limited comparative insights into their distinct characteristics. This study therefore provided a spatiotemporal analysis of land use land cover change (LULCC) and surface urban heat islands (SUHI) effects in the Nigerian mid-sized cities of Akure and Osogbo from 2014 to 2023. This study used Landsat 8 and 9 imagery (2014 and 2023) and analyzed data via Google Earth Engine and ArcGIS Pro 3.4. Results showed that Akure’s built areas increased significantly from 164.026 km² to 224.191 km² while Osogbo witnessed a smaller expansion from 41.808 km² to 58.315 km² in built areas. This study identified Normalized Difference Vegetation Index (NDVI) and emissivity patterns associated with vegetation and thermal emissions and a positive association between LST and urbanization. The findings across Akure and Osogbo cities established that LULCC has different impacts on SUHI effects. As a result, evidence from a mid-sized city might not be extended to other cities of similar size and socioeconomic characteristics without caution. Full article

The study of the effects of climate change and air pollution on human health is an interesting topic for wellbeing projects in urban areas. We present a method for highlighting how adverse weather and environmental conditions affect human health and influence emergency room admissions during the summer in an urban area. Daily apparent temperature, a biometeorological index, was used to characterize thermal discomfort while atmospheric concentrations of PM10 and NOX were used as indicators of unfavorable environmental conditions. We analyzed how the above parameters influence the emergency room access, considering all the different pathologies. Over the four years analyzed, we identified the periods during which environmental conditions (both thermal discomfort and pollutant concentrations) were unfavorable, the persistence of these conditions, and verified that during these days, the average daily number of emergency room visits increased. Visits for ENT and dermatological disorders also showed significant increases. Our analysis showed that emergency room access is useful in evaluating the impact of unfavorable climatic and environmental conditions on human health during the summer period; vice versa, our results could be used to optimize resource management in emergency rooms during this specific period of the year. Full article

The ongoing and projected future climate change impacts the heating, cooling, and air-conditioning sectors both directly and indirectly. The consideration of heating, cooling, and energy degree-days is a consistent, robust, and widely used approach for quantitatively estimating the energy demand of closed environments based on outdoor thermal conditions. Hence, the spatial distribution and the long-term changes in this demand depend on on the quantity of final users for such services; it is essential to consider demographic data in the assessment. The paper presents a comprehensive analysis of the population-weighted degree-days for the near past and the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) scenario-driven future over Southeast Europe for all four ‘Tier 1’ Shared Socioeconomic Pathways (SSPs) based on the methodology of the United Kingdom Meteorological Office and performed using large NEX-GDDP CMIP6 ensemble of global circulation models (GCMs) and up to date population dynamics data from the NASA’s SEDAC. As an expression of regional warming tendencies, the study reveals an overall reduction in heating and an increase in cooling degree-days, confirming the leading role of the climate. We also provide evidences for the influence of the population factor, which significantly alters the region’s degree-day climatology in both space and time. The resulting overall picture on country-wide and regional level is complex; in some cases, the population dynamics is projected to outbalance the thermal-induced changes. Full article

Extreme climate events are increasing in severity and frequency and affecting the livelihood of pastoralists. Understanding these impacts is crucial for developing effective management strategies. Thus, this study examines the effects of drought on livestock production and market dynamics in semi-arid Ethiopia and explores the adaptation strategies employed by Borana pastoralists. Both the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to calculate indicators of drought severity between 1993 and 2022. Surveys were also conducted in 244 selected households. In addition, focus group discussions and field observations were conducted to investigate the adaptation practices of Borana pastoralists to drought. A line graph was used to illustrate the relationship between the Standardized Precipitation Index (SPI) and livestock market prices. The study found extreme drought in 1985, 2000, and 2011, with the most severe to moderate dryness occurring in the Arero, Elwaya, Dubuluk, Guchi, and Yabelo areas. The study found that severe droughts are increasing, affecting pastoralists’ livelihoods. The recurring drought led to a shortage of feed and water, which resulted in the starvation and death of livestock and jeopardized the livelihoods of pastoralists. In addition, the decline in milk production and falling market prices are said to have had a negative impact. Diversification of livelihood sources, mobility of livestock to seek out forage and water resources, and diversification of herd composition to take advantage of varying drought tolerance have been the usual long-term adaptation strategies of Borana pastoralists. Given the multiple negative impacts of climate change, development interventions in pastoral and agro-pastoral areas of Ethiopia should focus on proactive measures to reduce the impacts of climate change on livestock production. Full article

Interest in co-benefits—the multiple benefits from mitigating climate change while addressing other sustainability challenges—has grown as policymakers seek to lower the costs of decarbonization. Much of this interest stems from data-driven models that quantify how much improved air quality, better health, and other co-benefits can offset those costs. However, co-benefits research often features transport, residential energy, and other solutions that face greater social and institutional barriers than economic barriers to achieving estimated gains. Few studies have assessed the costs of overcoming these barriers. The main objective of this study was to develop and apply methods for estimating these costs. Toward that end, this study developed a mixed method approach that used original survey and budgetary data to estimate the costs of clearing social and institutional barriers to implementing transport and residential energy solutions in Thailand. The results revealed that the costs of overcoming key social and institutional barriers were approximately USD 170–270 million per year from 2022 to 2032 for the transport sector in Thailand. The costs of overcoming social and institutional barriers for residential energy solutions are approximately USD 0.07–0.1 million per year over a comparable period. The results suggested that the costs of overcoming barriers were likely lower than the benefits for all solutions and greater for transportation (driven by the implementation of inspection and maintenance programs) than residential energy in Thailand. More generally, the results underlined a need for greater integration between work on co-benefits and transaction costs to assist policymakers in understanding how much investing in institutional capacity building, coordination, awareness raising, and other enabling reforms can help align a healthier climate with other development priorities. Full article

The Darfur conflict, which emerged in the early 21st century, represents a multifaceted crisis driven by socio-political and environmental factors, with resource scarcity, exacerbated by climate change, playing a pivotal role in intensifying tensions between agricultural and pastoral communities. While climate change is typically associated with adverse environmental outcomes, an analysis of data spanning four decades (1980–2023) reveals a contrasting trend of increased precipitation, enhanced vegetation, and decreased drought frequency in recent years. This research explores the potential of these positive environmental changes to mitigate resource-based conflicts and foster political stability in Darfur as improved environmental conditions are posited to create a foundation for conflict resolution and sustainable peacebuilding. The present study integrates trends in the Enhanced Vegetation Index (EVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to examine these shifts. EVI data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250 m resolution, was used to assess large-scale vegetation patterns in arid and semi-arid landscapes. The Autoregressive Integrated Moving Average (ARIMA) model was employed to forecast future precipitation scenarios up to the year 2034, enhancing the understanding of long-term climatic trends. Data processing and analysis utilized advanced tools, including Google Earth Engine (GEE), ArcGIS Pro (version 3.4), and R software (version 4.3.2). The findings reveal a significant (33.19%) improvement in natural vegetation cover between 2000 and 2023, with degraded and unchanged areas accounting for 1.95% and 64.86%, respectively. This finding aligns with a marked increase in annual precipitation and a reduction in drought intensity over the study period. Historical SPEI analysis showed persistent drought events between 1980 and 2012, followed by a notable decline in drought frequency and severity from 2013 to 2024. Precipitation projections suggest a stable trend, potentially supporting further vegetation recovery in the region. These environmental improvements are preliminarily linked to climate-change-induced increases in precipitation and reductions in drought severity. This study’s findings contribute to a nuanced understanding of the interplay between environmental dynamics and socio-political stability in Darfur, offering actionable insights for policy interventions aimed at fostering sustainable peace and resilience in the region. Full article

Bangladesh, one of the most vulnerable countries to climate change, has been experiencing significant climate change-induced risks. Particularly, the northwest region of the country has been severely affected by climate extremes, including droughts and heat waves. Therefore, proper understanding and assessment of future climate change scenarios is crucial for the adaptive management of water resources. The current study used the statistical downscaling model (SDSM) to downscale and analyze climate change-induced future changes in temperature and precipitation based on multiple global climate models (GCMs), including HadCM3, CanESM2, and CanESM5. A quantitative approach was adopted for both calibration and validation, showing that the SDSM is well-suited for downscaling mean temperature and precipitation. Furthermore, bias correction was applied to enhance the accuracy of the downscaled climate variables. The downscaled projections revealed an upward trend in mean annual temperatures, while precipitation exhibited a declining trend up to the end of the century for all scenarios. The observed data periods for the CanESM5, CanESM2, and HadCM3 GCMs used in SDSM were 1985–2014, 1975–2005, and 1975–2001, respectively. Based on the aforementioned periods, the projections for the next century indicate that under the CanESM5 (SSP5-8.5 scenario), temperature is projected to increase by 0.98 °C, with a 12.4% decrease in precipitation. For CanESM2 (RCP8.5 scenario), temperature is expected to rise by 0.94 °C, and precipitation is projected to decrease by 10.3%. Similarly, under HadCM3 (A2 scenario), temperature is projected to increase by 0.67 °C, with a 7.0% decrease in precipitation. These downscaled pathways provide a strong basis for assessing the potential impacts of future climate change across the northwestern region of Bangladesh. Full article