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To feed a rapidly growing human population in an era of climate change and increasingly uncertain weather—such as drought and flood—humanity will need crops with increased yields, greater resiliency to extreme weather, and more resistance to disease. Appropriately enough in the Century of Biology, that means turning to genetic tools such as CRISPR. [ABSTRACT FROM AUTHOR]

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

INTRODUCTION: Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. OBJECTIVES: The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. METHODS: Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. RESULTS AND DISCUSSION: The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. CONCLUSION: This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

The continuous growth in global energy demand has led to extensive burning and overconsumption of fossil fuels, resulting in a rapid increase in greenhouse gas emissions, primarily carbon dioxide. On one hand, the frequency and intensity of extreme weather events have increased dramatically due to global climate change. On the other hand, renewable energy sources are rapidly developing. Therefore, CO 2 electrochemical reduction technology has attracted considerable attention as a sustainable solution for converting CO 2 into valuable chemicals. Copper-based catalysts can efficiently reduce CO 2 directly to high-value chemicals, making them one of the focal points in CO 2 electrochemical reduction (eCO 2 RR) research. Many factors influence the activity and selectivity of eCO 2 RR, and this paper systematically reviews the recent advances in CO 2 electrochemical reduction using Cu-based catalysts. Firstly, the fundamental principles and reaction mechanisms of CO 2 electrochemical reduction are outlined. Subsequently, the discussion delves into Cu catalyst structures and composition synergistic control strategies, covering monometallic copper catalysts, multimetallic copper-based catalysts, copper oxides and their derived catalysts, and copper-organic composite catalysts. Finally, we also analyzed the challenges faced by Cu-based catalyst research and provided an outlook on the prospects of CO 2 electroreduction technology in conjunction with emerging AI technology. • Summarized the mechanisms of C 1 and C 2+ products in eCO 2 RR. • Summarized the application of five types of copper-based catalysts in eCO 2 RR. • Discussed the development prospects and strategies of eCO 2 RR in combination with AI. [ABSTRACT FROM AUTHOR]

Summary: Societal Impact Statement: Climate change continues to intensify the challenges of food production as agricultural systems face more variable and extreme weather. Coupled with increasing human population, growers must balance increasing crop yields with nutrient content to prevent global malnutrition. Photosynthetic diversity may permit some crops to tolerate climate change and elevated CO2 whilst maintaining both crop yield quantity and quality. This review examines how photosynthetic diversity interacts with crop production and nutritional stability under elevated CO2 and climate change, and highlights opportunities for photosynthetic diversity to inspire agricultural solutions. Summary: Innovative agricultural solutions are desperately needed to achieve food security for a growing human population amidst the imminent pressures of climate change that threaten more variable and extreme weather, placing additional pressures on already precarious agricultural systems. Not only are crop yields at risk under climate change but rising global atmospheric CO2 concentrations are concurrently driving a carbon dilution effect that threatens to reduce the nutritional quality of our crops to further global malnutrition. Plants using different photosynthetic metabolisms, however, experience these negative impacts to yield and nutrition to different degrees. Thus, photosynthetic diversity may offer solutions to combat malnutrition under climate change and elevated CO2 concentrations, whether that be through targeting existing resilient species for agricultural programmes or applying agricultural biotechnology to engineer photosynthetic diversity into existing crops. Here, we discuss how each major photosynthetic type is predicted to fare under elevated CO2 concentrations and climate change and explore agricultural opportunities to maintain both yield and nutrient stability. [ABSTRACT FROM AUTHOR]

The article discusses the landslides triggered by an extreme rainstorm in the Haihe River Basin, China, in July 2023. The heavy rainfall led to widespread landslides, causing casualties and significant property losses in the Beijing-Tianjin-Hebei region. The study highlights the increasing frequency of extreme weather events and emphasizes the need for vigilance in monitoring and early warning systems for rain-induced geological disasters to prevent future losses. [Extracted from the article]

Aim: As global climate changes, there is a clear mismatch between the temporal and spatial characteristics of body temperature and environmental temperature, confounding the assessment of thermal stress for organisms in many ecological studies. Here, we hindcast the hourly body temperatures of intertidal molluscs to explore the differences between them and environmental temperatures (air and water temperatures) in multiple metrics of thermal stress. Location: Intertidal shores in East Asia (0°–45°N, 100°E–140°E). Time Period: 40 years, 1980 to 2019. Major Taxa Studied: Mollusca. Methods: We collected habitat zonation data and measured the morphological characteristics of 25 intertidal molluscs living in East Asia. For three different types of intertidal molluscs (i.e., bivalves, limpets and snails), we built corresponding heat budget models (HBMs) to hindcast the hourly body temperatures from 1980 to 2019. We analysed the thermal stress of intertidal species faced in three metrics, annual extreme high temperatures (T99), seasonal daily maximum temperatures (DMT) and heatwaves, and compared them with environmental temperatures. Results: We found that T99 of body temperatures and their interannual warming rates are significantly higher than those of environmental temperatures. Moreover, there were non‐negligible mismatches between the seasonal thermal pattern and heatwaves of body temperatures and environmental temperatures, suggesting that the deleterious impacts of global warming on intertidal species are underestimated and cannot be directly revealed by environmental temperatures. Main Conclusions: Thermal stress patterns of body temperature were significantly different from those of environmental temperature, and the thermal stress faced by intertidal species had been persistently underestimated. These results emphasise that body temperature should be used as the appropriate metric for evaluating and predicting the impacts of global warming and weather extremes in the intertidal biological system. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for accurate lightning prediction in northeastern India, a region frequently impacted by severe convective storms. Despite advancements in forecasting, there remains a gap in predicting lightning activity, especially during extreme weather. This research evaluates the Weather Research and Forecasting (WRF) model's performance in simulating lightning events on June 16 and 17, 2022. The study employs the Morrison double-moment 6-class microphysics scheme and the Price and Rind A simple lightning parameterization for calculating global lightning distributions lightning parameterization method to model cloud-to-ground (CG) and intra-cloud (IC) lightning. Model predictions are validated against data from the Indian Institute of Tropical Meteorology Lightning Location Network (IITM-LLN) and the World-Wide Lightning Location Network (WWLLN), alongside INSAT-3D cloud brightness temperature and NASA-IMERG rainfall data. The research findings show that the lightning parameterization scheme with a 20 dBZ top threshold provides accurate predictions, with spatial autocorrelation metrics of 67.41% and 85.75% for June 16 and 17, respectively. Further analysis through skill scores, such as the Equitable Threat Score and Probability of Detection, supports the model's reliability. Hotspot analysis identifies regions of heightened lightning activity, offering valuable insights into areas prone to frequent strikes. This research emphasizes the importance of advanced modeling techniques and comprehensive data integration in enhancing weather forecasts, contributing to improved safety and mitigation strategies for extreme weather events in northeastern India. [ABSTRACT FROM AUTHOR]

Large-scale atmospheric circulations are a significant driver of rainfall extremes. However, little attention has so far been devoted to understanding how large-scale circulation patterns influence sub-daily rainfall extremes. Using a gauge-based sub-daily rainfall dataset, we investigate the relationship between large-scale circulations and 3-hour extremes (defined here as ≥ 40 mm rainfall in 3 h) for western Europe. A set of 30 weather patterns (WPs) developed by the UK Met Office and reanalysis data of geopotential height at 500 hPa (z500) are used to represent large-scale atmospheric conditions. Strong associations with 3-hour extremes are found for a small number of WPs: over 50% of 3-hour rainfall extremes across Western Europe occur with just 5 WPs. Composites of z500 reveal the WPs resulting in southerly or south-westerly flow along the leading edge of a trough, accompanied by a ridge to the east or northeast, are most favourable for sub-daily rainfall extremes, with a statistically significant difference between the atmospheric conditions on WP days with a 3-hour extreme rainfall event compared to WP non-event days. Given that large-scale circulations are predictable much further in advance than individual extreme rainfall events, these identified relationships could therefore have important implications for forecasting, aiding in the early identification of periods with increased risk of short-duration rainfall extremes. [ABSTRACT FROM AUTHOR]

Earth's temperature has risen by an average of 0.11°F per decade since 1850 and experts predict continued global warming. Studies have shown that exposure to extreme temperatures is associated with adverse health outcomes. Missed primary care visits can lead to incomplete preventive health screenings and unmanaged chronic diseases. This study examines the associations between extreme temperature conditions and primary care utilization among adult Philadelphians. A total of 1,048,575 appointments from 91,580 patients age ≥ 18 years enrolled in the study at thirteen university-based outpatient clinics in Philadelphia from January 1, 2009 to December 31, 2019. Statistical analysis was performed from June to December 2023. Data on attended and missed appointments was linked with measures of daily maximum temperature and precipitation, stratified by warm and cold seasons. Sociodemographic variables and associations with chronic disease status were explored. Rates of missed appointments increased by 0.72% for every 1°F decrease in daily maximum temperatures below 39°F and increased by 0.64% for every 1°F increase above 89°F. Individuals ≥ 65 years and those with chronic conditions had stronger associations with an increased rate of missed appointments. Temperature extremes were associated with higher rates of missed primary care appointments. Individuals with chronic diseases were more likely to have missed appointments associated with extreme temperatures. The findings suggest the need for primary care physicians to explore different modes of care delivery to support vulnerable populations, such as making telemedicine during extreme weather events a viable and affordable option. [ABSTRACT FROM AUTHOR]

[Display omitted] Vegetation phenology serves as a crucial indicator of ecosystem dynamics and its response to environmental cues. Against the backdrop of global climate warming, it plays a pivotal role in delving into global climate change, terrestrial ecosystem dynamics, and guiding agricultural production. Ground-based field observations of vegetation phenology are increasingly challenged by rapid global ecological changes. Since the 1970 s, the development and application of remote sensing technology have offered a novel approach to address these challenges. Utilizing satellite remote sensing to acquire phenological parameters has been widely applied in monitoring vegetation phenology, significantly advancing phenological research. This paper describes commonly used vegetation indices, smoothing methods, and extraction techniques in monitoring vegetation phenology using satellite remote sensing. It systematically summarizes the applications and progress of vegetation phenology remote sensing at a global scale in recent years and analyzes the challenges of vegetation phenology remote sensing: These challenges include the need for higher spatiotemporal resolution data to capture vegetation changes, the necessity to compare remote sensing monitoring methods with direct field observations, the requirement to compare different remote sensing techniques to ensure accuracy, and the importance of incorporating seasonal variations and differences into phenology extraction models. It delves into the key issues and challenges existing in current vegetation phenology remote sensing, including the limitations of existing vegetation indices, the impact of spatiotemporal scale effects on phenology parameter extraction, uncertainties in phenology algorithms and machine learning, and the relationship between vegetation phenology and global climate change. Based on these discussions, the it proposes several opportunities and future prospects, containing improving the temporal and spatial resolution of data sources, using multiple datasets to monitor vegetation phenology dynamics, quantifying uncertainties in the algorithm and machine learning processes for phenology parameter extraction, clarifying the adaptive mechanisms of vegetation phenology to environmental changes, focusing on the impact of extreme weather, and establishing an integrated "sky-space-ground" vegetation phenology monitoring network. These developments aim to enhance the accuracy of phenology extraction, explore and understand the mechanisms of surface phenology changes, and impart more biophysical significance to vegetation phenology parameters. [ABSTRACT FROM AUTHOR]

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events. [ABSTRACT FROM AUTHOR]

Reliable performance of the electricity distribution networks is critical to avoid outages and disruptions which may lead to substantial economic losses. Some of the important components of the electricity distribution networks include overhead cables, utility poles, cross-arms, substations, and transformers. These components are subjected to deterioration with aging and pose risks of failure creating safety concerns, specially under extreme weather conditions. Efficient maintenance and the use of accurate condition assessment techniques to monitor the integrity of the aforementioned components are key factors to be considered in managing the electricity distribution assets. This article presents a review of different condition assessment techniques employed for monitoring the electricity distribution infrastructure. The conventional inspection techniques and advancements in non-destructive testing methods are discussed outlining the relative advantages and disadvantages. Other than the normal failures of distribution infrastructure due to component ageing, unexpected and less frequent extreme weather events can have significant impacts on the resilience of the electrical power infrastructure. Thus, the effects of extreme weather conditions on the reliability of the electricity distribution networks and the failure rates of assets are reviewed in this study. Further, the maintenance and decision-making approaches implemented in the power distribution networks are also discussed. [ABSTRACT FROM AUTHOR]

Considering the projected impacts of climate change in upcoming decades, innovative educational approaches should encourage inventive problem-solving techniques and societal change, fostering transformative climate adaptation. The value of drama in climate adaptation education remains a novel area in the environmental education research literature and requires further exploration of its potential benefits to Climate Change Education (CCE). This article presents a proposal for CCE to include various elements in a drama workshop by evaluating a methodological framework. Participants in the workshop studied the vulnerabilities that arose from flooding and droughts while dramatizing different social conflicts to develop building adaptation scenarios. Through the exploration of problems via playful activities, participants collaboratively construct narratives and texts rich with meaning, based on a critical and creative perception of themes, needs, desires, and overlapping ideologies. This short-term experience manifests efficacy in elucidating the underpinnings of social systems structures, human values, and motivations. This article analyzes workshop results, providing a pedagogical structure and theoretical foundation, contributing to a better comprehension of drama in education and the creation of capacities towards CCE. [ABSTRACT FROM AUTHOR]

Extreme weather events are expected to increase in frequency and intensity in South America, likely causing decreased plant productivity and altering species distributions. Yerba mate (Ilex paraguariensis) is a tree species native to South America and has an ecological, social, and economic importance in several countries. Natural forests and cultivated areas of yerba mate will be endangered by climate change because of the expected reduction in water availability. Here we determined how clonal genotypes of yerba mate (BRS BLD Yari, BRS BLD Aupaba, BRS 408, and EC40) respond to four levels of soil water holding capacity (100%, 80%, 60%, and 40% WHC) over 60 days, by evaluating the plants' morphophysiological and biochemical characteristics. We observed a reduction in plant height and biomass accumulation related to the decrease in water availability; physiological and biochemical parameters indicated that water-deficit stress reduced the plants' C assimilation and increased their production of bioactive compounds. BRS BLD Yari had a higher tolerance to low water availability, with greater biomass accumulation and photosynthetic rates that indicate greater water use efficiency. Understanding how different yerba mate genotypes respond to water deficit is essential for species conservation and developing climate-adapted breeding programs. [ABSTRACT FROM AUTHOR]

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This article explores the historical impact of droughts in the early modern Caribbean and their effects on the region's environment and population. The author examines a letter from 1726 written by Colonel John Hart, the Governor of the British colony of the Leeward Islands, which describes the devastating consequences of a prolonged drought in Antigua. The letter raises questions about climate change, water security, and environmental racism, highlighting the need for further research on how colonialism altered the region's climate and affected the lives of African, Indigenous, and European residents. The article emphasizes the importance of understanding the historical context of droughts in order to address present-day challenges of water insecurity and climate change in drought-prone regions worldwide. [Extracted from the article]

This study proposes a novel framework for the prediction of structural damages caused by extreme weather and climate events. In current practice, following a weather event, inspectors manually evaluate damaged structures and assign a damage state classification according to FEMA guidelines. The application of machine learning methods to postevent damage classification has received significant attention in the past decade. Current state-of-the-art applications in automating the assigning of damage states have focused on postevent unmanned aerial system (UAS)-driven image classification. These works have achieved moderate success using damage classes with varying similarities to established FEMA guidelines. This work proposes a framework for predicting FEMA damage states at a single structure level prior to an event. Using a precurated data set of structural characteristics and predicted best track storm data, a novel approach can be used to optimize postevent response efforts. The methodology was validated using a data set of structural features and best track storm data gathered following Hurricanes Harvey, Michael, Irma, and Dorian. The trained model achieved a 48.08% single damage state classification accuracy and an 84.24%±1 class damage state classification accuracy. These results show that the proposed framework can perform pre-event damage prediction with performance on par with the current postevent damage classification methods. Practical Applications: This study provides a framework for the prediction of hurricane-induced structural damage states prior to an event. Based on a lightweight artificial intelligence model, the framework is designed to be accessible to homeowners, municipalities, and relief organizations that do not have access to sophisticated hardware. The framework utilizes structural characteristics and storm information to generate structure-by-structure damage predictions based on FEMA hurricane damage states. A data set consisting of structures impacted by Hurricanes Harvey, Michael, Irma, and Dorian was used to validate the framework and provide an estimate of its capabilities. The trained model achieved an overall single state classification accuracy of 48.08% and a ±1 class accuracy of 84.24%. These results show that the proposed framework can provide homeowners with prehurricane predictions of the damage state their unique home is likely to suffer with the same level of performance as current state-of-the-art image-based postevent damage classification artificial intelligence models. [ABSTRACT FROM AUTHOR]

Introduction: Extreme weather becomes increasingly frequent and severe under climate change, causing unexpected damage to trees. Among them, sleet damage is particularly harmful to evergreen trees in subtropical area. Camphor trees (Cinnamomum camphora), as dominant street trees in central China, are prone to sleet damage, resulting in loss of valuable ecosystem functions. Methods: By measuring tree size characteristics of 118 camphor trees before and after a record-breaking sleet event in Wuhan, a mega-city in central China, we built allometric equations between size and volume of broken branches and used the random forest regression to model sleet damage to camphor trees. Results: We identified that larger trees with intermediate bole height suffered more than smaller trees with tall bole height from the sleet event. We estimated the volume of broken branches of a camphor tree with DBH at 35.0 cm as 106.4 dm3, equivalent to 55.3 kg biomass loss, from the sleet event. Discussion: We suggest that pruning the branches instead of topping the main stems of small camphor trees would reduce the sleet hazard. To mitigate the negative impacts of climate change, regular pruning should be practiced on street camphor trees to protect them from future heavy sleet events. [ABSTRACT FROM AUTHOR]

This paper examines the impact of rain on traffic sign recognition system, addressing one of the challenges posed by harsh environmental conditions like low lighting, extreme weather (rain,fog, snow) and reduced sign visibility. A novel system is proposed in this work, which is capable of handling three different rain types (drizzle, torrential, and heavy). This work explores how different rain types affect training and testing of three customized convolutional neural networks for traffic sign recognition. Results show that the system's performance is dependent on the rain type during training and testing. To address this variability, a multistage classifier is proposed: level 1 classifies rain type, and level 2 selects an appropriate traffic sign classifier based on output of level1. This work also analyzes the effect of augmenting three different types of rain for developing noise robust traffic sign recognition system. Experiments were conducted using publicly available German Traffic Sign Recognition Benchmark dataset. The proposed system attains overall classification accuracy of 95.10% measured through the accuracy score metric, which has not been claimed till now in any previous work in addressing the challenges of recognizing traffic signs in presence of different types of rain. [ABSTRACT FROM AUTHOR]

Extreme events in the Mediterranean have increased in frequency and intensity during the last decade, and this trend is expected to continue in the future. Characterising the features of extremes using observations and well-established datasets is critical for understanding the processes and development of extreme weather, as well as improving forecast. This study uses observational data collected during the Mediterranean Experiment for Sea Salt And Dust Ice Nuclei (MESSA-DIN) from July to September 2021 to characterise the synoptic analysis of the severe summer of 2021. The analysis focuses primarily on water vapour fluxes, humidity, convective parameters, and the role of aerosols in cloud formation. Furthermore, we compare the findings to the widely known atmospheric reanalysis ERA5, pointing out agreements and inconsistencies with observations, as well as discussing aspects that can improve modelling activities for addressing and forecasting extreme weather, with a focus on the extreme flooding that affected Central Europe in July 2021. The findings show the crucial role of water vapour fluxes in regional climate events, emphasizing the need for high- resolution data from microwave radiometers and atmospheric profilers to verify and improve predictions under complex atmospheric conditions. ERA5 performed rather well in synoptic representation, but it exhibited a dry bias in RH values, which affected the accurate representation of water vapour fluxes. By comparing the ERA5 RH bias with upper-air measurements available during the campaign period at the Potenza GRUAN (GCOS Reference Upper-Air Network) site, in South Italy, the bias was further examined, showing to exhibit an irregular behavior at different sites. The findings also emphasize the need for improving reanalysis model performance in complex terrain conditions, particularly near coastal areas, as well as the use of km-scale models for mesoscale research and dealing with extreme weather. [ABSTRACT FROM AUTHOR]

Background: Increasing concern has recently been highlighted regarding crop damage due to extreme weather events caused by global warming and the increased production of ground-level ozone. Several studies have investigated rice growth in response to fertilization conditions under various environmental stress conditions; however, studies on growth development in response to fertilization conditions under combined high-temperature/ozone treatment conditions are scarce. In this study, we aimed investigate the growth and physiological development of rice under combined high temperature and ozone treatment conditions and to reveal the damage-mitigation effects of NPK fertilization treatments. Results: The plants were treated with varying levels of NPK [N2 (N-P-K: 9.0-4.5-4.0 kg/a), P2 (4.5-9.0-4.0 kg/a), K2 (4.5-4.5-8.0 kg/a), and control (4.5-4.5-4.0 kg/10a).] under combined high-temperature (35 ℃) and ozone (150 pb) treatment conditions. Analysis of the growth metrics, including plant height, leaf age, dry weight, and the plant height/leaf age (PH/L) ratio were revealed that combined high-temperature/ozone treatment promoted the phenological development indicated by increasing leaf age but decreased the plant height and dry weight indicating its negative effect on quantitative growth. The effects of this combined high-temperature/ozone treatment on growth were alleviated by NPK fertilization, particularly in K2 treatment but worsened in N2 treatment. Visible damage symptoms in rice leaves induced by exposure to the combined stressors was also alleviated by the K2 treatment. At the physiological level, K2 treatment reduced the expression of OsF3H2, which is associated with antioxidant activity, suggesting that potassium improved stress tolerance. Additionally, expression of genes related to abscisic acid (ABA) metabolism showed increased OsNECD (ABA synthesis) and decreased OsCYP707A3 (ABA degradation) in the K2 treatment, promoting a stronger adaptive stress response. Stomatal conductance measurements indicated a slight increase under K2 treatment, reflecting enhanced regulation of stomatal function during stress. Conclusion: The study highlights the potential of potassium fertilization to mitigate combined high-temperature and ozone stress in rice, suggesting it as a strategy to improve crop resilience and optimize fertilization. The findings offer insights into fertilization treatments and can guide future research on stress tolerance in crops. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Results Conclusion Implications Impact Reporting Method Patient or Public Contribution The aim of this study is to identify the factors associated with nurses' perceptions and behaviours related to climate change and health (PBCH) according to their PBCH levels.A cross‐sectional study was used.This study included a sample of 499 Korean nurses and adhered to the STROBE checklist. Data were collected from March 23 to May 10, 2023. Quantile regression analysis was performed, and PBCH levels were measured using the Korean version of the Climate Health and Nursing Tool.Across all quantile groups, the experience of extreme weather events and awareness of climate change‐coping facilitators were associated with PBCH. Differences were observed in factors associated with PBCH levels. Significant associations with PBCH were observed within the 75th percentile group, for having a religion, household income, and workplace climate friendliness. In the 25th percentile group, having a child, the number of sources for climate change–health‐related information, and experience in setting climate change–health goals and strategies significantly influenced PBCH.We propose a differentiated strategy by elucidating the factors associated with high and low quantiles of PBCH levels.By verifying specific factors associated with PBCH levels, nurses can enhance their preparedness to respond to the health risks posed by climate change in their clients.Identifying common factors associated with all quantiles of nurses is important for establishing universal PBCH characteristics. Recognising the distinctions between high and low PBCH levels can aid in developing tailored nursing strategies to enhance PBCH among nurses.This study adhered to the STROBE guidelines.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Cover crops are multifunctional tools that mitigate environmental impacts of agriculture, enhance resilience to weather extremes and suppress weeds and arthropod pests. Cover crops provide non‐crop food and habitat resources that attract natural enemies of pests, but their outcomes for pest management are less clear in regions where keystone mutualisms between red imported fire ants and aphids dominate. Here, we manipulate ant exclusion treatments and cover crop treatments (living mulches and terminated cover crops) that vary in food/habitat resources and examine responses of ants, aphids, other herbivores and predators in a cotton agroecosystem. Living mulches reduced both ants and aphids in the crop canopy by 97% and 93%, respectively, relative to bare soil treatments, and terminated cover crops reduced them as well by a lesser degree (~50%). Non‐aphid herbivores occurred in low densities system‐wide and increased in living mulches, whilst native predators had variable responses to cover crops and ant exclusion. Cover crops had no effect on prey removal in the crop canopy, but living mulches tripled rates of weed seed biocontrol, relative to bare soils. Cover crops elicited a shift in fire ant foraging from cotton foliage downward to the soil‐surface, preventing competitive exclusion by keystone ant/aphid mutualists that dominate crop monocultures. Cover crops altered the system‐wide impacts of fire ants: reducing ecosystem disservices (i.e. aphid tending) and enhancing ecosystem services (i.e. weed seed biocontrol). These results provide incentives for cover crop adoption as a regenerative practice in large‐scale commercial agriculture. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is characterized by persistent anticyclones that "block" the midlatitude jet stream, causing temperature and precipitation extremes. The traffic jam theory posits that blocking events occur when the Local Wave Activity flux, a measure of storm activity, exceeds the carrying capacity of the jet stream, leading to a pile up. The theory's efficacy for prediction is tested with atmospheric reanalysis by defining "exceedance events", the time and location where wave activity first exceeds flow capacity. The theory captures the Northern Hemisphere winter blocking climatology, with strong spatial correlation between exceedance and blocking events. Both events are favored not only by low carrying capacity (narrow roads), but also a downstream reduction in capacity (lane closures causing a bottleneck). The theory fails, however, to accurately predict blocking events in time. Exceedance events are not a useful predictor of an imminent block, suggesting that confounding factors explain their shared climatological structure. Plain Language Summary: An atmospheric block is a large, high pressure weather pattern that blocks the jet stream, affecting many regions in the midlatitudes including North America and Europe. Blocks are notable for their persistence, driving extreme weather conditions for up to a week or longer. Despite their significant societal impact, we don't fully understand the mechanism(s) that generate blocks. A traffic jam theory was proposed, which suggested that the onset of a block is caused by having too much "storm activity flux", which leads to a pile up of storm activity, just as a traffic jam is precipitated by conditions where the vehicular flux exceeding the road capacity, blocking traffic. This analogy is useful for understanding the preferred locations of atmospheric blocks in the time‐mean sense, but is not predictive in terms of individual blocking events. We further propose to incorporate additional regional constraints on flux capacity, analogous to "traffic bottlenecks", to improve our understanding of preferred blocking locations. Key Points: Flow capacity exceedance events, predictors of blocking onset in the traffic jam theory, are defined and evaluated in climate reanalysesA downstream reduction in flow capacity is ubiquitous for both exceedance and blocking events: lane closures favor traffic jamsBlocks are co‐located with exceedance events in space but not in time, limiting the utility of the traffic jam theory for prediction [ABSTRACT FROM AUTHOR]

ABSTRACT Attributions of rainfall anomalies to weather systems and their spatio‐temporal variability in Victoria, southeast Australia are investigated with a multimethod weather type dataset and two popularly used gridded daily rainfall datasets for the period 1979–2015. The rainfall anomalies before, during and after the Millennium Drought (1997–2009) are compared to quantify the temporal variability of rainfall responses to weather type changes. The results show: (1) Three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall; (2) Contributions of weather types to rainfall vary from month to month with winter season rainfall coming from more diverse weather types than summer rainfall; (3) The contributions of weather types to rainfall in three periods show temporal variabilities and there is a clear shift of contribution pattern after the Millennium Drought, such as front‐thunderstorm (FT) is now the largest contributor to rainfall compared with cyclone‐frontal‐thunderstorm (CFT) before and during the Millennium Drought; (4) A seasonal shift in the post‐drought period is found with higher rainfall in February and March and lower rainfall in September and October. The increased rainfall in February mainly results from Front–Thunderstorm (FT) and Thunderstorm‐only (TO), while rainfall declines in September from all weather types; (5) Several rainfall characteristics that are important for streamflow generation, such as rainfall intensity, probability of rainfall occurrence, number of rainfall days and the maximum daily rainfall, do depend on the weather types; (6) The results are similar with different rainfall datasets, but differences do exist, especially at the local scale. The conclusions of this study are drawn from an Australian case study but have implications for other regions to investigate the attributions of rainfall characteristic changes to weather systems. [ABSTRACT FROM AUTHOR]

The extreme weather and climate occur frequently attracted high attention from society, which brings high uncertainty risks to enterprises. This study uses a fixed effects model to investigate the impact of corporate climate-related risk perceptions on their behavior. We found that increased climate-related risk perception catalyzes corporate green and low-carbon transitions (GLCT). From the perspectives of internal governance and external supervision, it is found that executive risk aversion, government environmental regulation, and investor attention can all contribute to the positive impact of climate-related risk perception on GLCT. Additionally, corporate climate-related risk perceptions can contribute to transition outcomes and lead to favorable capital market responses by improving GLCT actions, and that the contribution of climate-related risk perceptions to GLCT is stronger among non-state-owned enterprises and high-polluting industries. This study provides strategic insights for organizations to effectively manage climate risk. [ABSTRACT FROM AUTHOR]

Teleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively. Plain Language Summary: Teleconnections are usually manifested as recurring patterns which link weather and climate anomalies over large distances across the globe. Therefore, they play an important role in shaping climate variability and regional climate change. The limited observational records make it difficult to draw robust conclusions. To avoid this concern, climate model simulations are widely used as they can provide a larger sample size. However, climate models do not always agree with the observed teleconnections. When this happens, scientific judgment is needed to assess the suitability of models for studying teleconnections. Here, based on an example, we introduce a forensic approach to assessing the fidelity of teleconnections in models, where a physically‐based analysis is required to understand the origin of the model‐observations discrepancy. By "forensic" we mean a scientific approach that considers various explanations for a phenomenon and attempts to disprove them through physical evidence, thereby narrowing down the possibilities. In this way, one can better understand the reason for the difference between model and observations and make an informed use of the climate model outputs. Key Points: Although the model shows an opposite‐signed teleconnection to observations, it successfully captures the known underlying causal pathwayThe existence of an additional pathway explains the discrepancy, with no strong observational evidence for or against itA bias adjustment of this additional pathway aligns the modeled relationship with observations and adjusts the related climate impacts [ABSTRACT FROM AUTHOR]

Climate change‐induced extreme weather and related drought and flood conditions are heterogeneous across space and time. The variability in location, timing, and magnitude of rainfall can alter how species respond to the drought and flood disturbances. To further complicate this matter, when droughts end they are often followed by extreme flooding, which are rarely considered as a disturbance (Humphries et al. 2024), let alone assessed with its own heterogeneity. Consequently, it is difficult to quantify impacts on ecological communities across large spatiotemporal scales without considering flood‐drought disturbance characteristics in sequence (Burton et al. 2020). We hypothesized that native organisms have evolved resistance to withstand repeated cycles of drought‐flood disturbances, and that established non‐native species have adapted to persist in novel conditions. To test this, we fit spatiotemporal models of species occurrence with local rainfall patterns as covariates in the drought and flood impacted Murray‐Darling basin in Australia during the decade long Millenium Drought, and its recovery period. During these drought conditions, river‐floodplain organisms in the Murray‐Darling became localized in refugia that limited longitudinal and lateral connectivity (Bond et al. 2008), and following flooding the same organisms were exposed to dispersal and recruitment opportunities (Humphries et al. 2020), as well as to hypoxic blackwater events that lead to the mortality of aquatic organisms (Small et al. 2014). At the basin‐scale we found that the range size of most native and non‐native fishes were highly resistant to the extreme drought and post‐flood conditions. At local scales, species richness, or detection, actually increased under drought conditions. Both findings highlight the resistance of species to climate change driven extreme weather, which opens new questions on community adaptations. [ABSTRACT FROM AUTHOR]

Typhoons are one of the most important weather systems that can cause severe ozone (O3) pollution in eastern China. While the effects of individual typhoons on O3 concentrations have been extensively studied, the effects of consecutive northward typhoons and the underlying mechanisms remain unclear, partly due to the complex processes involved. Here, Typhoons Maysak and Haishen, two consecutive northward typhoons in 2020, are selected to investigate their impact on the O3 pollution in eastern China. The results show that consecutive northward typhoons not only produced and maintained meteorological conditions conducive to O3 generation (e.g., elevated temperatures and intensified solar radiation), but also facilitated local accumulation and cross-regional transport of O3. These factors jointly led to a 30% increase in O3 concentration in eastern China with a prolonged period of O3 pollution. Our work underscores the significance of complex meteorological conditions in O3 pollution occurrences during extreme weather events, advancing our understanding of how consecutive northward typhoons affect air quality. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme weather events, such as hurricanes and rising sea levels due to climate change, presents significant challenges to coastal infrastructure. With 42% of bridges in the United States exceeding 50 years of age, which surpasses their intended service life, there is a pressing need to assess their structural integrity, especially in coastal regions. This study focuses on evaluating the structural performance of bearing pads in coastal bridges under hurricane-induced wave loadings. Utilizing a combination of physics-based models (PBM), nonlinear modal time history analysis, and numerical validation, the research examines the hysteresis response of eight distinct bearing pad configurations. The findings indicate that reinforced circular bearing pads exhibit significantly greater shear displacements compared to plain elastomeric pads, underscoring the critical influence of shape factors on shear response. Fragility functions developed in the study illustrate the probability of shear failure, with reinforced circular pads showing a 28% higher likelihood of exceedance compared to rectangular plain pads. These results highlight the necessity for advanced design methodologies specifically for bearing pads to enhance the resilience of coastal infrastructure against severe hydrodynamic forces induced by hurricanes. [ABSTRACT FROM AUTHOR]

A global terrestrial precipitable water vapor (PWV) dataset has been developed using observations from the MicroWave Radiation Imager (MWRI) aboard the FY-3 satellite series (FY-3B, FY-3C and FY-3D) spanning 2012 to 2020. The dataset offers twice-daily PWV records at a spatial resolution of 0.25° × 0.25°, aligned with the ascending and descending orbits of the FY-3 satellites. The dataset was generated using an automated machine learning (ML) model that leverages MWRI-based features characterizing surface conditions and an enhanced Global Position System (GPS) PWV dataset as a reference. Trained on over one million sampling points from more than ten thousand stations worldwide, the model ensures a robust representation of global PWV variations. Independent evaluations against SuomiNet GPS and Integrated Global Radiosonde Archive Version 2 (IGRA2) PWV products yielded root mean square error (RMSE) of 4.47 mm and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across various surface conditions. The dataset effectively captures both spatial and temporal PWV variations, allowing for precise examination of localized and abrupt changes in water vapor induced by extreme weather events. Representing a significant advancement in global terrestrial PWV monitoring, the MWRI PWV dataset provides an all-weather, high-precision data record that bridges gaps in global coverage of passive microwave-based terrestrial PWV observations. It is a valuable resource for atmospheric research, climate modeling, water cycle studies, and beyond. The dataset is available at: https://doi.org/10.6084/m9.figshare.26712709 (Xia et al., 2024). [ABSTRACT FROM AUTHOR]

This commentary explores the intersection between climate, conflict, and child nutrition, highlighting the severe child nutrition crisis in Sub-Saharan Africa exacerbated by climate change and ongoing conflicts. Shifting climate patterns disrupt agricultural productivity and food security, while persistent conflicts displace populations and destroy infrastructure, significantly increasing food insecurity and malnutrition among children. Recent UNICEF data indicates that 1 in 4 children globally live in severe food poverty, with those affected up to 50% more likely to suffer from life-threatening malnutrition. Over half a billion children reside in areas prone to climate-related extreme weather events, challenging food production, distribution, and access. Extreme weather events have led to crop failures, food shortages, and price spikes, disproportionately affecting vulnerable populations. Changes in temperature and precipitation patterns also alter the nutritional content of crops, worsening nutritional challenges. Conflicts in SSA have led to a food crisis of unprecedented scale, with over 80% of the 137 million Africans facing acute food insecurity located in conflict-affected countries. The conflict between Russia and Ukraine has further disrupted global food and fertilizer supply chains, exacerbating food shortages and inflation in many African countries. Addressing this crisis requires a multifaceted approach integrating evidence-based, cost-effective strategies. This commentary advocates for the adoption of the 3 C approach—climate-smart school feeding programs, cultivation of edible insects, and community-based food hubs—as solutions to enhance child nutrition and build climate resilience. [ABSTRACT FROM AUTHOR]

Using observation data and numerical simulations, we have demonstrated that the stratospheric Quasi-Biennial Oscillation (QBO) can predict extreme high temperatures (EHTs) in South Asia in June. The vertical structure of the QBO plays a crucial role in this prediction. When the QBO in June shows easterlies (westerlies) at 50 hPa and westerlies (easterlies) at 70 hPa, more (fewer) EHT events occur. This likely results from the QBO's vertical structure causing positive (negative) temperature anomalies in the lower stratosphere and negative (positive) static stability anomalies near the tropical tropopause. These anomalies enhance (weaken) convective activity over the equatorial Indian Ocean, leading to anomalous circulation with ascending (descending) air over the equatorial Indian Ocean and descending (ascending) air over northern and central South Asia. This suppresses (promotes) convection over northern and central South Asia, affecting cloud formation and precipitation. Consequently, more (less) solar radiation reaches the region, along with weaker (stronger) evaporative cooling effects, warming (cooling) the surface and creating a background state conducive to (against) EHT events. Additionally, the opposite zonal winds at 30 hPa and 50 hPa in April may serve as a reference factor for predicting the probability of EHT events in northern and central South Asia. This study provides a potential approach for forecasting tropospheric extreme weather events based on stratospheric signals. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme weather and national conflicts has highlighted the necessity of enhancing the robustness of the grain trade network. In this paper, we study the evolution of major trading countries, and the network robustness under random and different attack strategies. The results show that the heterogeneity of the Global Soybean Trade Network (GSTN) and trade connections among countries are increasing. The soybean trade network has strong resistance to random attacks, while subjected to targeted attacks, the stability of the network is significantly affected and damaged. [ABSTRACT FROM AUTHOR]

Tidal flats are valuable ecosystems that depend on complex biogeomorphic processes between organisms and sediment transport. Climate change has led to a rise in extreme weather events, such as storms. This, in turn, has increased sediment dynamics and created risks for the benthic communities inhabiting tidal flats. However, replicating sediment disturbances caused by extreme weather is difficult. To overcome this, we used the plow rake to enhance the natural tidal currents and wave conditions to simulate intensified sediment dynamics. The raking disturbance was repeated on two intertidal zones with different inundation frequencies and wind fetch levels to simulate the increasing frequency of storm impact on sediments due to climate change. We compared the measurements of sediment dynamics and macrofauna between plots that were raked and the control plots that were only influenced by natural hydrodynamics. Results showed that tidal flat sediments experienced erosion by 10–20 mm after six times biweekly raking treatments, depending on the site‐specific hydrodynamic conditions. Sediment dynamics served as a helpful tool for monitoring the species distribution regarding mobility traits: the high dynamic exposed sites were inhabited by mobile species, while the low dynamic sheltered sites were characterized by less‐mobile species. Moreover, the raking treatment decreased the abundance of species with immobile traits, yet the species composition did not experience significant change. Overall, the present findings indicate that tidal flats with low sediment dynamics and immobile macrofauna are at higher risk of declining abundance under intensified sediment disturbances than areas with high sediment dynamics and mobile macrofauna. [ABSTRACT FROM AUTHOR]

Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain. In order to effectively identify freezing damages of winter wheat in spring, this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province, which was an important winter wheat planting area in China. The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological, crop, and disaster data from 99 meteorological stations from 1981 to 2020 were explored. The results indicated that: (1) the optimized LSTM model effectively identified freezing damages of winter wheat in spring. The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error (MAPE) of 8.73% and a goodness of fit (R2 ) with 0.90 compared to the actual minimum temperature. Based on the disaster data and freezing damage index for winter wheat in spring, the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model. (2) From 1981 to 2020, the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened, and the high frequency area of mild freezing damage moved from the Northeast to the East. The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan, and lower in the southwestern and southeastern Henan, with an average frequency of 0-1.75 times·10y-1 during the past 40 years. The frequency of mild freezing damage of winter wheat in spring decreased per decade, gradually decreasing from 0.843 times per decade to 0.157 times per decade. The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East. (3) From 1981 to 2020, the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing. The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East. The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West, and higher in the North than in the South. The average frequency of severe freezing damage in the 40a period was 0-2.75times·10y-1 . The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade. The high frequency area of severe freezing damage moved from the Northeast to the East. The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing, but the frequency of extreme weather events under climate warming was increasing. Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses. Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved, and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences. Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change, and also have certain reference values for other agricultural meteorological disaster identification. [ABSTRACT FROM AUTHOR]

Simple Summary: The Andean Altiplano, known for its extreme weather and high biodiversity, is an ideal place to study how insects adapt to their environment. This research focuses on the butterfly species Phulia nymphula, which is common in the high-altitudinal Andes Mountains, to identify how their wing shapes vary across six locations in the Northern Chilean Altiplano. By analyzing the wings of 77 butterflies, the study found significant differences in wing shape, likely due to local environmental conditions. These differences suggest that the butterflies have adapted to their specific habitats. The findings showed how the wing shape differentiate between localities across the Northern Altiplano and provide insights into how high-altitude species evolve and adapt through changes in their morphology, highlighting the role of ecological and evolutionary processes in shaping biodiversity in extreme environments. The Andean Altiplano, characterized by its extreme climatic conditions and high levels of biodiversity, provides a unique environment for studying ecological and evolutionary adaptations in insect morphology. Butterflies, due their large wing surface compared to body surface, and wide distribution among a geographical area given the flight capabilities provided by their wings, constitute a good biological model to study morphological adaptations following extreme weathers. This study focuses on Phulia nymphula, a butterfly species widely distributed in the Andes, to evaluate wing shape variation across six localities in the Northern Chilean Altiplano. The geometric morphometrics analysis of 77 specimens from six locations from the Chilean Altiplano (Caquena, Sorapata Lake, Chungará, Casiri Macho Lake, Surire Salt Flat, and Visviri) revealed significant differences in wing shape among populations. According to the presented results, variations are likely influenced by local environmental conditions and selective pressures, suggesting specific adaptations to the microhabitats of the Altiplano. The first three principal components represented 60.92% of the total wing shape variation. The detected morphological differences indicate adaptive divergence among populations, reflecting evolutionary responses to the extreme and fragmented conditions of the Altiplano. This study gives insights into the understanding of how high-altitude species can diversify and adapt through morphological variation, providing evidence of ecological and evolutionary processes shaping biodiversity in extreme environments. [ABSTRACT FROM AUTHOR]

Interspecific crop diversity (e.g., intercropping) has been documented to promote sustainability in agroecological systems with benefits for pollination services and insect pollinators. These benefits may also be extended to intraspecific crop diversity (e.g., cultivation of multiple genotypes or cultivars in a production space), but no review to date has examined the benefits of intraspecific crop diversity for pollination and pollinator communities. While mixing cultivars is necessary and a widespread practice for pollination of self-incompatible or male-sterile crops, it is not as widespread for other crop species. However, many other crops have shown reduced yield quantity or quality with self-fertilization due to partial self-sterility, early acting inbreeding depression, and xenia. These crops could thus experience increased production in diverse cultivar mixtures. Cultivar mixtures could also benefit pollinator communities through providing complementary and temporally consistent floral resources, with cascading effects on pollination services. However, successfully implementing cultivar mixtures requires an understanding of how cultivar identity and arrangement affect successful cross-pollination. In this review, we describe the potential benefits of increased intraspecific crop diversity for optimal pollination and pollinator populations across insect-pollinated crops. Additionally, we explore how research advances in cultivar characteristics and insect pollinator behavior and movement, as well as crop pollen flow, can inform cultivar mixtures and spatial arrangements. We find evidence that mixing cultivars, even in self-compatible crops, improves pollination outcomes and yields. Additionally, given insect pollinator behavior and pollen flow, such mixing must occur at relatively small spatial scales. Furthermore, cultivar diversity could ensure successful pollination and resource production for pollinators under extreme weather events. We also discuss costs and benefits of diverse cultivar mixtures from a grower’s perspective and offer suggestions for future research including translating findings within the context of farming systems so that recommendations are practical and achievable. [ABSTRACT FROM AUTHOR]

The European Community has prioritized reducing energy consumption and improving energy efficiency in the building sector, along with ensuring increasingly high standards of thermal comfort, as key goals over recent decades. Given the impact of climate change, the rising frequency of extreme weather events, and the rapid shifts in peak demand during both winter and summer, buildings must efficiently respond to sudden and extreme temperature fluctuations while maintaining optimal indoor comfort. Phase-change materials (PCMs), which can adapt their thermophysical properties in response to external conditions, may offer a solution for enhancing building resilience to climate change. This paper evaluates the benefits of integrating various PCMs with plasterboard in the energy retrofit of a multi-family complex in a Mediterranean climate. The study examines the application of a PCM with a melting temperature of 25 °C at three different thicknesses (74.2 mm, 37.1 mm, and 20.8 mm) to external walls, ceilings, and both walls and ceilings simultaneously. Among the various applications, using the PCM on walls alone maximized heating savings as thickness increased (26.6%), while ceiling application maximized cooling energy savings (17.5%). Combined solutions offered the most balanced seasonal benefits, leading to the greatest overall energy reductions (24.1%). [ABSTRACT FROM AUTHOR]

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system's resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries. [ABSTRACT FROM AUTHOR]

Extreme weather events are increasingly recognized as major stress factors for forest ecosystems, causing both immediate and long-term effects. This study focuses on the impacts experienced by the forests of Valdisotto, Valfurva, and Sondalo (28% of the total area is covered by forests) in Upper Valtellina (Italy) due to the Vaia storm that occurred in October 2018. To define the immediate impacts of Vaia, we assess the economic value of forest ecosystem services (ESs), particularly those provided by timber production and carbon sequestration, pre- and post-Vaia and during the emergency period. We used the market price method to assess the economic values of timber production and carbon sequestration, as these are considered to be marketable goods. Based on data processed from Sentinel-2 satellite images (with a spatial resolution of 10 m), our results show that, despite the reduction in forest area (−2.02%) and timber stock (−2.38%), the economic value of the timber production increased after Vaia due to higher timber prices (i.e., from a total of €124.97 million to €130.72 million). However, considering the whole emergency period (2019–2020), the total losses are equal to €5.10 million for Valdisotto, €0.32 million for Valfurva, and €0.43 million for Sondalo. Instead, an economic loss of 2.88% is experienced for carbon sequestration, with Valdisotto being the more affected municipality (−4.48% of the pre-Vaia economic value). In terms of long-term impacts, we discuss the enhanced impacts due to the spread of the bark beetle Ips typopgraphus. [ABSTRACT FROM AUTHOR]

Climate change and mental health are inextricably linked crises that demand urgent responses within the health sector and beyond. Mental health challenges associated with climate change are wide-ranging. They include depression, anxiety and post-traumatic stress resulting from increased exposure to extreme weather events, generalized climate anxiety and indirect impacts. However, there is a significant adaptation gap when it comes to addressing the mental health risks posed by climate change. Lack of capacity is frequently cited as a barrier to adaptation, yet 'capacity' covers many facets. This article examines the capacities of policy systems to design and implement adaptation initiatives for addressing the increasing risks to mental health posed by climate change. Focusing on England (UK) as an illustrative case study, this article deploys a policy capacity framework and draws on semi-structured interviews and policy document analysis. It identifies the ways that analytical, operational and political policy (in)capacities manifest across relevant policy areas, which include health, flood and coastal erosion risk management, spatial planning, natural environment and emergency management. Our analysis reveals that despite some strengths in analytical and political capacity, strained operational capacity is exacerbating and reinforcing adaptation gaps. We also demonstrate some of the complex interactions between different types of capacities that both enable and hamper adaptation. This article demonstrates the value of analysing policy capacity, and its potential in identifying and designing the necessary interventions to help circumvent a growing mental health crisis under climate change. Key policy insights In England, efforts to adapt to risks to mental health under climate change are supported through strong analytical capacity, involving research and knowledge generation, education and training, particularly in the mental health sector and Flood and Coastal Erosion Risk Management (FCERM). There is, however, room for improvement. Relatively strong political capacity, including advocacy and the role of policy champions, have led to increasing legitimation of the issue and fostered a growing policy landscape. However, some discernible gaps remain. There are significant weaknesses in operational capacity, restricting coordinated, preventative adaptation. This is partly due to significant resource constraints, and historic and current fragmentation within and between the health sector and others. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are extreme weather events that have major impacts on the structure and functioning of marine ecosystems worldwide. Due to anthropogenic climate change, the occurrence of MHWs is predicted to increase in future. There is already evidence linking MHWs with reductions in biodiversity and incidence of mass mortality events in coastal ecosystems. However, because MHWs are unpredictable, the quantification of their effects on communities is challenging.Here, we use the Helgoland Roads long‐term time series (German Bight, North Sea), one of the richest marine time series in the world, and implement a modified before‐after control‐impact (BACI) design to evaluate MHW effect on mesozooplankton communities. Mesozooplankton play an essential role in connecting primary producers to higher trophic levels, and any changes in their community structure could have far‐reaching impacts on the entire ecosystem.The responses of mesozooplankton community to MHWs in terms of community structure and densities occurred mainly in spring and autumn. Abundances of seven taxa, including some of the most abundant groups (e.g. copepods), were affected either positively or negatively in response to MHWs. In contrast, we observed no clear evidence of an impact of summer and winter MHWs; instead, the density of the most common taxa remained unchanged.Our results highlight the seasonally dependent impacts of MHWs on mesozooplankton communities and the challenges in evaluating those impacts. Long‐term monitoring is an important contributor to the quantification of effects of MHWs on natural populations. [ABSTRACT FROM AUTHOR]

The intersection of the COVID-19 pandemic with other crises can amplify vulnerabilities and push communities further into poverty. In low-income countries, the dual impacts of COVID-19 and extreme weather events, along with multidimensional poverty and structural vulnerabilities in agriculture can decimate farmer livelihoods. This study aims to understand the effects of individual and compounding crises (COVID-19, cyclones, and vanilla price collapse) on smallholder vanilla farmers and local coping strategies in Madagascar, one of the world's largest vanilla producers and poorest countries. We used semi-structured and scenario-based interviews across two case study villages with contrasting enforcement of forest regulations. We found that the impact of the pandemic, combined with the cyclone event, disrupted livelihoods, resulting in income losses and food security challenges that exacerbated farmer vulnerabilities. Sixty eight percent of households reported crop losses due to strong winds and heavy rainfall brought by cyclone Enawo in 2017. The COVID-19 outbreak struck the region just as the residents were recovering from the effects of the cyclone. COVID-19-related travel restrictions in the aftermath of the cyclone took a substantial economic toll, with 54.1% of respondents experiencing a decline in earnings, and 17% facing a total loss of income due to the imposed lockdown. The decline in vanilla prices at the onset of 2020 had a far-reaching additional impact, affecting not only farmers but also residents who rely on other sources of income. Local communities reported using the forest resources more frequently as a safety net during crises in the village with more lenient regulations. This study underscores the importance of understanding the interconnectedness and compounding impacts of cascading crises on food security and natural resource use. We highlight the need for a comprehensive approach to increasing farmer resilience, particularly for those reliant on global market crops such as vanilla. [ABSTRACT FROM AUTHOR]

Background: Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods: In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results: Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only. Conclusion: The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs), typically comprising 5–30 amino acids, can deliver impermeable cargo molecules across membranes into the cytosol of eukaryotic cells. CPPs can transport a broad spectrum of molecules, including peptides, proteins, nucleic acids, and small molecules. Delivery can be accomplished by covalently attaching a CPP to the target cargo or by the formation of a noncovalent complex between the CPP and cargo. CPPs penetrate cells via two distinct mechanisms: energy-dependent endocytic pathways or energy-independent direct translocation across the plasma membrane. CPPs could potentially reduce dependence on chemical fertilizers and pesticides, boost crop yields and resilience to extreme weather conditions, improve nutrient uptake from the soil, and enhance nutrient contents in plants. Cell-penetrating peptides (CPPs) are short (typically 5–30 amino acids), cationic, amphipathic, or hydrophobic peptides that facilitate the cellular uptake of diverse cargo molecules by eukaryotic cells via direct translocation or endocytosis across the plasma membrane. CPPs can deliver a variety of bioactive cargos, including proteins, peptides, nucleic acids, and small molecules into the cell. Once inside, the delivered cargo may function in the cytosol, nucleus, or other subcellular compartments. Numerous CPPs have been used for studies and drug delivery in mammalian systems. Although CPPs have many potential uses in plant research and agriculture, the application of CPPs in plants remains limited. Here we review the structures and mechanisms of CPPs and highlight their potential applications for sustainable agriculture. [ABSTRACT FROM AUTHOR]