Psychosocial stressors related to extreme weather events and multiple resource insecurities: qualitative insights from refugee youth in an Ugandan humanitarian setting

Associations Between the Experience of Extreme Weather Events and Perceived Health Status Among U.S. Adults in the Health Information National Trends Survey, 2024.

Vulnerability assessment of urban road networks under extreme weather events

Extreme weather events are becoming more frequent in Alberta, Canada, with growing implications for electricity demand. This study examined how extreme hot and cold temperature events influence electricity consumption across eight major population centers in the province. Using daily maximum and minimum temperature, we identified extreme days based on percentile-based thresholds and evaluated their temporal trends from 1961 to 2023. The trend analysis revealed a significant increase in hot days frequency, particularly since 1991, with northern and central regions (0.50 – 0.67 days/year) experiencing the most pronounced warming. Conversely, cold days have declined significantly across all locations. Daily electricity usage showed a U-shaped relationship with temperature as consumption increased during both hot and cold extremes. Except for Lethbridge in the south, where demand peaked on hot days, all other locations had higher loads on cold days, followed by hot days, and the lowest on normal days. While cold days generally drive the greatest usage, several southern and central cities (e.g., Calgary, Medicine Hat) also showed elevated demand during heat events. Moreover, the relationship between temperature extremes and electricity use intensified over time, reflected by steeper slopes and stronger correlation coefficients in recent years. For instance, in Calgary, the additional demand per degree Celsius of maximum temperature on hot days rose from 0.29 GWh/°C in 2011–2012 to 0.49 GWh/°C in 2018–2019. These results underscore the escalating influence of climate extremes on Alberta’s electricity system and reinforce the need for adaptive, climate-resilient energy strategies.

Multi external event probabilistic safety assessment framework

Hurricanes have significant consequences for ecosystems, potentially disrupting the carbon cycle at both local and regional scales and releasing carbon back into the atmosphere through storm-associated impacts on vegetation and agricultural areas. The present work analyzes the interactions amongst terrestrial carbon fluxes, rainfall, and land cover for three significant hurricanes: Harvey (Texas), Irma (Florida), and Maria (Puerto Rico). This study utilized net ecosystem exchange (NEE) data derived from the Soil Moisture Active Passive (SMAP) NASA satellite mission, which provides global estimates of soil moisture and carbon flux, and analyzed these data for coastal climate zones during the hurricane season. The results were validated using eddy covariance tower-based in-situ CO 2 flux observations during hurricane landfall. Results showed that southern Texas (Harvey) experienced the highest amount of carbon release (0.33 megatons), followed by Florida (Irma) (0.03 megatons) and Puerto Rico (Maria) (0.02 megatons). The land cover products, such as the National Land Cover Dataset (NLCD) and the Copernicus Global Land Service (CGLS), showed overall reductions in land cover in Florida (-1.02%), Texas (-0.97%), and Puerto Rico (-0.46%). Furthermore, vegetation cover changes were estimated using MODIS-derived enhanced vegetation index (EVI), showing major changes over Puerto Rico (-3.81%) and southeast Texas (-2.94%), while normalized difference vegetation index (NDVI) showed more moderate reductions over Puerto Rico (-3.06%), southeast Texas (-1.12%), and Florida (-0.16%). These reductions indicate short-term vegetation stress and decreased photosynthetic activity, which may temporarily reduce carbon uptake, leading affected regions to transition from carbon sinks to temporary carbon sources. These findings highlight hurricanes as significant drivers of short-term carbon emissions and vegetation change. This study enhances understanding of hurricane-associated disturbances in the carbon cycle by examining spatial and temporal variations in carbon fluxes during extreme weather events. • The impacts of hurricanes Harvey, Irma, and Maria (2017) on terrestrial carbon fluxes were assessed. • Carbon release during and after landfall was measured using SMAP-derived NEE and eddy covariance CO 2 flux data. • Hurricane Harvey resulted in the largest carbon emission (0.33 megatons), followed by Irma (0.03 megatons) and Maria (0.02 megatons). • Vegetation loss, derived from MODIS NDVI and land cover change products, was greatest in Texas (6,199.3 km 2 ), then Florida (492.92 km 2 ), and Puerto Rico (14.53 km 2 ). • Vegetation declines led to reduced photosynthetic activity, temporarily turning affected areas from carbon sinks into carbon sources. • Findings highlight hurricanes as significant short-term drivers of carbon emissions and ecosystem disturbance across coastal regions.

Temporal dynamics of crustacean zooplankton community structure and functional traits in a temperate island marine ecosystem: A case study of the adjacent waters of the Miaodao Archipelago, China.

Compound atmospheric and marine hot extremes coupled with droughts in Madagascar

Solar irradiation variability in the high Andean region of Amazonas-Peru: Spatiotemporal climatic perspective to evaluate the potential for solar energy implementation

Privacy-preserving clearing strategies in joint local energy and flexibility markets under uncertain extreme weather event

Blood-sucking arthropods in the Anthropocene: climate change thermotolerance, and global disease risks.

Scenario separation-based optimal planning method for renewable electric energy system considering extreme weather event risks

Atmospheric blocking events are quasi-stationary high-pressure systems that disrupt the typical paths of polar and subtropical air currents, often producing prolonged extreme weather events such as summer heat waves or winter cold spells. Despite their critical role in shaping mid-latitude weather, accurately modeling and analyzing blocking events in long meteorological records remains a significant challenge. To address this challenge, we present an uncertainty visualization framework for detecting and characterizing atmospheric blocking events. First, we introduce a geometry-based detection and tracking method, evaluated on both pre-industrial climate model simulations (UKESM) and reanalysis data (ERA5), which represent historical Earth observations assimilated from satellite and station measurements onto regular numerical grids using weather models. Second, we propose a suite of uncertainty-aware summaries: contour boxplots that capture representative boundaries and their variability, frequency heatmaps that encode occurrences, and 3D temporal stacks that situate these patterns in time. Third, we demonstrate our framework in a case study of the 2003 European heatwave, mapping the spatiotemporal occurrences of blocking events using these summaries. Collectively, these uncertainty visualizations reveal where blocking events are most likely to occur and how their spatial footprints evolve over time. Weenvision our framework as a valuable tool for climate scientists and meteorologists: by analyzing how blocking frequency, duration, and intensity vary across regions and climate scenarios, it supports both the study of historical blocking events and the assessment of scenario-dependent climate risks associated with changes in extreme weather linked to blocking.

Tidal wetlands are critical ecosystems for coastal sustainability, yet despite growing regulatory protection, they continue to decline globally. Their long-term resilience to interacting chronic stressors and extreme events remains uncertain, in part because comprehensive, high-frequency monitoring has been lacking. While direct land-use conversion has been substantially restricted in the United States, the true trajectory of these protected habitats has remained unclear. Here, we use four decades of high-resolution satellite records to analyze the shifting dynamics of US tidal wetlands. We reveal a widespread and previously unquantified acceleration in the rate of tidal wetland loss,amounting to a net loss of -1640 km2 at the rate of -40.53 km2 year-1, accelerating by -0.73 km2 year-2, of which tidal marsh contributed the majority of this loss with a cumulative decline of 1567 km2. Furthermore, we show that the drivers of this decline are shifting: while chronic stressors like relativesea level rise have caused the largest cumulative loss(~60% of the total area loss), acute shocks from extreme weather now dominate(1.4 timesthat ofthe chronic stressors) the acceleration of that loss. By contrast, direct human activities were a minor driver, accounting for only 4% of total observed losses. These findings indicate that the resilience of these protected ecosystems is declining. It provides an urgent warning that existing conservation strategies, initially concerned with direct human impacts and increasingly focused on relativesea level rise as a slow-moving pressure, are ill-equipped for a future of increasing extreme weather events and highlights the need to redesign adaptation policies.

Association Between Extreme Climate Exposures and Bodily Pain: Evidence from the Chinese Health and Retirement Longitudinal Study.

With the global increase in extreme weather events, understanding the effects of consecutive extreme PM2.5(EPM) and cold spells (CS) events on specific mortality is vital. Daily meteorological, air pollution, and mortality data were collected in Zigong. Using the Distributed Lag Nonlinear Model (DLNM), we defined the lag as 14 days and quantified the risk effect of EPM-CS events (P95 for EPM, P7.5 for CS) on resident mortality and explored the potential amplification of damage resulting from different patterns of sequential extreme events. Additionally, we calculated the attributable fraction (AF) of extreme events and conducted stratified analyses based on age, gender, marital status, etc. RESULTS: Exposure to cold spells, PM2.5, and compound events was statistically associated with an increased risk of mortality. The cumulative rate ratios (CRRs) of EPM-CS events for total non-accidental mortality was 1.56(1.44,1.69). The mortality risks of EPM-CS events in females, elderly people ≥ 65 years, low level of education, and widowed, divorced, and never married were higher, with AF were 6.64%(95%CI: 5.42%, 7.89%),6.51%(95%CI: 5.52%, 7.51%), 6.10%(95%CI: 5.17%, 7.06%) and 7.73%(95%CI: 6.07%, 8.51%), respectively. The attributable fraction of specific mortality due to the EPM-CS events was the highest for cerebrovascular disease. Exposure to combined events was associated with a substantial increase in mortality risk, and the damaging effect of combined events occurring in the short term was more significant. Our findings demonstrated synergistic mortality risks from compound cold and pollution exposure, highlighting a disproportionate impact on vulnerable populations. This evidence supports the rationale for developing integrated early warning systems as a targeted intervention.

The ERA5 reanalysis dataset, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), provides high-resolution, hourly global climate and weather data from 1950 to the present. However, its massive volume poses substantial storage and distribution challenges. To address this, we introduce CRA5, a highly compressed version of ERA5 generated by the neural network framework Aeolus. CRA5 reduces the 400 TB uncompressed float32 dataset to just 0.85 TB, achieving a 470×compression ratio. Notably, it offers over 100 times higher compression than the lossless GRIB files from the Copernicus Climate Data Store (CDS). Extensive experiments validate its numerical accuracy: CRA5 maintains consistent climatology and comparable power spectral density, yielding a mean absolute error of only 0.17 K for temperature across 37 vertical levels. Furthermore, it faithfully reconstructs extreme weather events and large-scale climatological patterns. By significantly lowering infrastructure barriers, CRA5 accelerates data access and facilitates broader collaboration in large-scale atmospheric research.

ABSTRACT While extreme weather events may make climate change feel more immediate and personally relevant, this potential depends largely on how such events are framed in the media. This article examines how Italian prime-time television news covered the May 2023 floods in Emilia-Romagna, focusing on the salience of climate change, the framing of causal attribution, the visibility of political and scientific actors, and the presence of obstructionist narratives. Drawing on a mixed-methods content analysis informed by framing theory, the study analyzes 1,017 television news reports broadcast on Italy’s main channels in 2023. The findings show that climate change was rarely mentioned in coverage of the floods and even less frequently framed as a causal factor. Explicit discussions of causality were generally limited and, when present, were more likely to attribute responsibility to failures in land or emergency management than to climate change. Political actors dominated the coverage, whereas scientists appeared mainly in segments explicitly addressing climate change. A qualitative analysis of the 29 news items that identified climate change as a cause of the floods further reveals the presence of obstructionist frames that downplay systemic responsibility and discourage climate action. The study contributes to research on climate communication, climate and attribution framing, and climate delay discourse, while also providing new evidence from a media context that remains highly influential but has received relatively limited scholarly attention.

A field-deployable immunofluorescence chip for adenovirus monitoring in groundwater systems.

To address the increasing prevalence of extreme weather events, there has been a notable shift in research priorities toward the development of smart textiles capable of adaptively regulating the microclimate temperature of the human body. This study uses electrospinning technology to create multifunctional polyurethane nanofiber membranes (PUBPs) with a sandwich structure. The outer layer contains thermosensitive color-changing microcapsules (RT-BCMs) incorporated into the PU nanofiber membrane, and the middle layer contains phase-change microcapsules (RT-PCMs) incorporated into the PU nanofiber membrane. The PUBPs nanofiber membrane exhibits a reversible color change within a temperature range of 26-40 °C. At high temperatures, the membrane appears white, increasing solar reflectance to 87.4% while maintaining mid-infrared emissivity of 95.3%, thereby achieving radiative cooling. At low temperatures, the membrane appears blue, with solar reflectance reduced to 69.1% while maintaining mid-infrared emissivity of 95.1%, thereby achieving solar heating. The PUBPs nanofiber membrane is capable of acting as a temperature buffer zone, with a phase change enthalpy of up to 106.8 J/g. This property makes PUBPs nanofiber membrane effective in mitigating temperature fluctuations caused by external temperature changes. After 20 days of UV aging and 100 thermal cycles, optical property retention and phase-change performance remain essentially unchanged, showing great weather resistance ability and cycling stability. Outdoor experiments show that under low solar irradiance of 163.2 W/m2, the maximum temperature rise is 5.4 °C; under high solar irradiance of 958.2 W/m2, the maximum temperature drop reaches 8.8 °C. This PUBPs nanofiber membrane, prepared through the synergistic mechanism of thermochromic and phase-change functions, effectively achieves all-season adaptive thermal management and provides a strategy for developing smart, temperature-controlled textiles.