The fog in Padua, Italy, is the result of a complex interplay between local climate, pollution and synoptic-scale meteorological conditions. The modern definition of “fog” was adopted by the World Meteorological Organization, founded in 1950. Prior to that, no precise visibility threshold had been established, and early meteorological observers recorded its occurrence based on subjective criteria. The meteorological observations made in Padua since the mid-18th century include sky conditions and fog, but the distinction between fog and mist was undefined, making it difficult to compare records from different observers. Caution is therefore needed when analyzing fog occurrence to distinguish the climate signal from observational artifacts. For instance, at the Specola Observatory in Padua from 1773 to 1913, the fog that appeared only on the horizon—but not at the zenith—was often disregarded, since vertical visibility was crucial for astronomical observations. Starting from 1920, other manned stations began providing systematic fog records, which have continued to the present. The aim of this study is to reconstruct the occurrence of fog in Padua since the late 18th century. As available datasets —observational and instrumental—partly overlap in time, and data from nearby locations are available, it is possible to assess their mutual consistency, to evaluate the reliability of historical visual observations, and to investigate fog variability and trend over time.

Coastal areas are rich in diverse resources and are ideal locations for developing the tourism industry. Thus, in coastal tourist centers, the growth rate is high, although often disorganized and unsustainable. In Mexico, tourist centers have fostered poverty belts where inhabitants live in conditions of high vulnerability due to hydrometeorological and geological phenomena in regular and irregular settlements. Thus, various coastal tourist areas in Mexico have been impacted by these types of phenomena, causing deaths, a high number of victims, and significant economic losses. Previous studies have confirmed that tropical cyclones can trigger landslides resulting from intense rainfall; however, risk estimation models and their components are presented separately. This paper presents a model based on the Intergovernmental Panel on Climate Change (IPCC) framework to estimate vulnerability to tropical cyclones and landslides in the context of climate change. The integration of both disruptive phenomena and climate change was carried out in the exposure sub-index. The socioeconomic situation of the inhabitants was included in the sensitivity sub-index. Vulnerability was modeled for the near, medium, and distant future, with population growth projections for the towns of Cabo San Lucas and San José del Cabo, Mexico. Climate change associated with urban expansion will increase exposure from 121.27 to 956.74 km2, while the vulnerable population is expected to increase from 133,266 to 250,386 by 2100. The model proved to be an effective tool for determining the combined vulnerability of both phenomena, allowing for the generation of strategies for decision-makers to implement actions focused on reducing vulnerability and building resilience.

The macro-economic growth rate of Peru is analyzed for sensitivity to climatic conditions. Year-on-year fluctuations in the inflation-adjusted gross domestic product (GDP) per capita over the period 1970–2024 are subjected to correlation and composite statistical methods. Upturns relate to cool east Pacific La Niña, downturns with warm El Niño. Composites are analyzed by subtracting upper and lower terciles, representing a difference of ~USD 40 B at current value. These reveal how the regional climate exerts a partial influence among external factors. During the austral summer with southeasterly winds over the east Pacific, sea temperatures undergo a 2.5 °C cooling. Consequently, atmospheric subsidence draws humid air from the Amazon toward the Peruvian highlands, improving crop production. Dry weather along the coast sustains transportation networks and urban infrastructure, ensuring good economic performance over the year. The opposing influence of El Niño is built into the statistics. A multi-variate algorithm is developed to predict changes in the Peru growth rate. Austral summer winds and subsurface temperatures over the tropical east Pacific account for a modest 23% of year-on-year variance. Although external factors and the varied landscape weaken macro-economic links with climate, our predictors significantly improve on traditional indices: SOI and Nino3. Adaptive measures are suggested to take advantage of Southern Oscillation’s influence on Peru’s economy.

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions.

Climate change has been occurring due to global warming since the 1950s, causing impacts on natural and social systems, including health. This review article involves the One Health approach as a holistic approach that integrates environmental, human, and animal health, since there is a significant gap in knowledge about the impacts of climate change on health. The questions that guide this research are as follows: What is the state of the art in studies on climate change and One Health? What are the main topics addressed in studies on climate change and One Health at a global level? The main objective is to conduct a systematic review of studies on climate change and its relationship with One Health to assess the main topics studied, involving climate change and health at a global level, and identify the gaps and challenges of these studies. The review demonstrated the exponential growth of studies that relate climate change to One Health, especially in the last three decades, with more records of studies that address infectious diseases such as arboviruses. Furthermore, studies on climate and its impact on mental health were detected, causing depression, anxiety, post-traumatic stress disorder (PTSD), solastalgia, and eco-anxiety, especially in vulnerable populations such as indigenous communities, women, children, family farmers, and the elderly. The One Health approach was shown to be restricted to health-related issues. Thus, theoretical and experimental studies are still needed to assess the real impact of climate change on the various axes involving human health and its relationship with anthropogenic activities, environmental health, and animal health.

The Gravity Recovery and Climate Experiment (GRACE) mission, operational from 2002 to 2017, provided critical measurements of Earth’s gravity field anomalies which have been extensively used to study groundwater and terrestrial water storage (TWS) dynamics. In this research, we utilize GRACE data to identify, model, and analyze potential climate parameters contributing to the reconstruction of TWS variability in the Aral Sea Basin region (ASB). We assess the impact of climate change and anthropogenic nature management on TWS change using a quantitative method. Our analysis reveals a significant decline in the TWS at a rate of 0.44 cm year−1 during the 2005–2009 period, primarily attributed to the prevailing drought conditions in the region. Notably, the estimated impact of anthropogenic influence on TWS during the same period of −1.39 cm year−1 is higher than the influence of climatic variables, indicating that anthropogenic activity was the dominant factor in water resource depletion. In contrast, we observed an increase in TWS at a rate of 0.82 cm year−1 during the 2013–2017 period, which can be attributed to the implementation of more effective water resource management practices in the ASB.

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies.

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change.

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a low-climate-prone village in the land-locked district of Paschim Bardhaman, West Bengal, India, with no extreme climate events. A total of 85 participants (44 in Brajaballavpur, 41 in Jemua) were selected through purposive sampling. Using a comparative qualitative research design grounded in ethnographic fieldwork, data were collected through household interviews, Participatory Rural Appraisals (PRAs), Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs), and analyzed manually using inductive thematic analysis. Findings reveal that bonding and bridging social capital were more prominent in Brajaballavpur, where dense horizontal ties supported collective action during extreme weather events. Conversely, linking social capital was more visible in Jemua, where participants more frequently accessed formal institutions such as the Gram Panchayat, local NGOs, and government functionaries that facilitated grievance redressal and information access, but these networks were concentrated among more politically connected individuals. The study concludes that climate vulnerability shapes the type, strength, and strategic use of social capital in village communities. While bonding and bridging ties are crucial in high-risk contexts, linking capital plays a critical role in enabling long-term social structures in lower-risk settings. The study contributes to both academic literature and policy design by offering a relational and place-based understanding of climate vulnerability and social capital.

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests.