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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 observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning-based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events. [ABSTRACT FROM AUTHOR]

The impacts of climate change are real and in many parts of the world testify to its harsh reality, including rampant extreme weather events, droughts, heat, wildfires, and flooding which have recorded in places which have not experienced them in recent memory. In the quest to avert such events, there is a growing awareness and demand for sustainable processes and operations. Today, sustainability encompasses a balance between ecological footprint and human development index, taking into consideration economics, the green environment, safety, quality, ethics, diversity and inclusion (D&I), and communities. This article presents some steps that have been taken by Algeria to balance energetic autonomy and sustainable development, and a case study on green hydrogen production employing membrane processes. Algeria's objective to join the global fight against climate change is to develop its green hydrogen base. Given its resources, including available solar and wind power, seawater desalination plants, building capacity, and its favorable location, it is developing its green hydrogen economy to supply hydrogen, especially to Europe. This presents an opportunity for other developing nations, especially in Africa, to gain from this experience. [ABSTRACT FROM AUTHOR]

Introduction: The precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing. Methods: This paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the SWH in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The prediction ability of the model is evaluated by mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and Pearson correlation coefficient (PCC). Results: The experimental results show that the optimal input sequence length for the model is 12. Starting from 12 hours, the SAC-ConvLSTM model consistently outperforms other models in predictive performance. For the 24-hour prediction, this model achieves RMSE, MAE, and PCC values of 0.2117 m, 0.1083 m, and 0.9630, respectively. In addition, the introduction of wind can improve the accuracy of wave prediction. The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas. Discussion: This paper presents a 24-hour prediction of SWH in the South China Sea. Through comparative validation, the SAC-ConvLSTM model outperforms other models. The inclusion of wind data enhances the model's predictive capability. This model also performs well under extreme weather conditions. In physical oceanography, variables related to SWH include not only wind but also other factors such as mean wave period and sea surface air pressure. In the future, additional variables can be incorporated to further improve the model's predictive performance. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems' resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2-4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40-70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20-50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5-15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

Climate and land‐use change pose unprecedented threats to ecosystems, economies, and communities worldwide. To help mitigate the climate crisis, restoration is a rapidly growing industry used to offset carbon emissions. The most common approach is to plant fast‐growing monocultures with the aim of sequestering as much carbon as possible in the shortest time. However, there has been little economic analysis of planting options that explicitly address short and long‐term ecological risks such as fire, disease, and environmental change. Here we develop a method for quantifying ecological risks from fire to sequestration investments and show how these risks can be factored into an analysis of long‐term financial returns relative to opportunity costs. In the case study presented, we find that the apparent advantage of fast‐growing monoculture plantations is likely to be outweighed by the long‐term fire risks to the carbon stored in them. Our analytical framework provides a widely applicable approach to comparing planting options against each other and other land uses, considering key uncertainties. With climate change already manifesting through extreme weather events, rising sea levels, and shifting wildlife populations, our framework can be used to make informed decisions about the best solutions to increase carbon sequestration, reduce ecological risks, and reduce climate impacts with greater certainty. [ABSTRACT FROM AUTHOR]

Large‐scale topographies affect global extreme weather and climate though dynamic and thermal forcing. However, the impacts of typical topographies on heat waves in different drylands globally remain unclear. In this study, we find that heat waves are mainly occurred in global drylands during 1940–2022. The frequencies and intensities of heat waves in global drylands have significantly increased after 1980s. Multiple numerical model simulations reveal that the impact range of Asian topography on heat waves in global drylands is widest, not only in East Asia, Central, and west Asia locally, but also can reach as far as North Africa and North America. While the impact ranges of topographies in Africa, Arabian Peninsula, North America, and South America on heat waves in drylands are relatively narrow, which are concentrated in localized and their surroundings. These conclusions could provide clues to understand the influence of topography on global weather and climate extremes. Plain Language Summary: Topographies have important impacts on heat waves in global drylands. Through numerical model simulations, this study reveals that Asian topography contributes significantly to heat waves in global drylands, with the widest impact ranges which can cover drylands of East Asia, Central and west Asia, North Africa, even North America. The impacts of the East African Plateau, the Arabian Plateau, the Sierra Madre in North America and the Andes in South America on heat waves are mainly in the local and surrounding regions. Key Points: Heat waves are mainly occurred in global drylands and experience significant increases after 1980sThe impact range of Asian topography on heat waves in global drylands is widest, which can reach as far as North Africa and North AmericaThe topographies in Africa, Arabian Peninsula, North America, and South America can only affect heat waves in localized and surroundings [ABSTRACT FROM AUTHOR]

Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases. Plain Language Summary: The sudden drop in temperature and rapid spread of aerosols during cold waves resulted in a higher prevalence of infectious diseases. At the same time, low temperatures resulted in a higher risk of death due to infectious diseases and a variety of other factors during the epidemic. Key Points: The rapid dispersion of aerosols during cold waves hastens the transmission of infectious diseasesLow temperatures contribute to higher risk of death during infectious disease outbreaks [ABSTRACT FROM AUTHOR]

Atmospheric Rivers (ARs) are long and narrow bands of strong horizontal water vapour transport concentrated in the lower troposphere. ARs play an important role in producing some high-impact weather events such as extreme precipitation and flooding, damaging winds, and temperature anomalies. To facilitate various studies on the short- and long-term variability of ARs and their impacts, I compiled a multi-decade global dataset containing 12 relevant meteorological variables for AR analysis. These variables were derived from the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis version 5 (ERA5). They are available at 6-hour intervals from 1940 to present. Also included in the dataset is an interactive web browser-based graphical tool for visualising the AR evolution on regional (North America) and global scales. This ERA5-based Dataset for Atmospheric River Analysis (EDARA) may serve as a valuable resource for many AR-related research and applications. [ABSTRACT FROM AUTHOR]

The Mediterranean region is experiencing pronounced aridification and in certain areas higher occurrence of intense precipitation. In this work, we analyze the evolution of the precipitation probability distribution in terms of precipitating days (or "wet‐days") and all‐days quantile trends, in Europe and the Mediterranean, using the ERA5 reanalysis. Looking at the form of wet‐days quantile trends curves, we identify four regimes. Two are predominant: in most of northern Europe the precipitation quantiles all intensify, while in the Mediterranean the low‐medium quantiles are mostly decreasing as extremes intensify or decrease. The wet‐days distribution is then modeled by a Weibull law with two parameters, whose changes capture the four regimes. Assessing the significance of the parameters' changes over 1950–2020 shows that a signal on wet‐days distribution has already emerged in northern Europe (where the distribution shifts to more intense precipitation), but not yet in the Mediterranean, where the natural variability is stronger. We extend the results by describing the all‐days distribution change as the wet‐days' change plus a contribution from the dry‐days frequency change, and study their relative contribution. In northern Europe, the wet‐days distribution change is the dominant driver, and the contribution of dry‐days frequency change can be neglected for wet‐days percentiles above about 50%. In the Mediterranean, however, the change of precipitation distribution comes from the significant increase of dry‐days frequency instead of an intensity change during wet‐days. Therefore, in the Mediterranean the increase of dry‐days frequency is crucial for all‐days trends, even for heavy precipitation. Plain Language Summary: The Mediterranean region is facing increased dryness alongside more intense rainfall in certain areas. We delved into the change of precipitation frequency and intensity across Europe and the Mediterranean using ERA5 reanalysis data from 1950 to 2020. Our analysis revealed four distinct patterns in how rainfall is evolving. In much of northern Europe, heavy rainfall events are becoming more frequent, while in the Mediterranean, lighter precipitation is decreasing while heavy rain is either increasing or decreasing. To understand these changes better, we used a mathematical model with two parameters. This model helped us track the types of changes in precipitation patterns. We found that in northern Europe, the increase in rainfall intensity is the primary driver of change, while in the Mediterranean, it's more about the increase of the dry‐days frequency. These findings underscore the importance of considering both the frequency and intensity of precipitation when studying climate change impacts. By understanding these shifts, we can better prepare for future weather extremes in these regions and adapt to the changing climate. Key Points: Four regimes of change for daily precipitation distribution are identified, and are captured by a two parameters analytical modelIn northern Europe, a signal of increasing mean and extreme precipitation has emergedIn the Mediterranean, the changes of precipitation appears dominated by changes in dry‐days frequency [ABSTRACT FROM AUTHOR]

The quantitative characterization and prediction of localized severe weather events that emerge as coherences generated by the highly non-linear interacting multivariate dynamics of global weather systems poses a significant challenge whose solution is increasingly important in the face of climate change where weather extremes are on the rise. As weather measurement systems (multiband satellite, radar, etc) continue to dramatically improve, increasingly complex time-dependent multivariate 3D datasets offer the potential to inform such problems but pose an increasingly daunting computational challenge. Here we describe the application to global weather systems of a novel computational method called the Entropy Field Decomposition (EFD) capable of efficiently characterizing coherent spatiotemporal structures in non-linear multivariate interacting physical systems. Using the EFD derived system configurations, we demonstrate the application of a second novel computational method called Space-Time Information Trajectories (STITs) that reveal how spatiotemporal coherences are dynamically connected. The method is demonstrated on the specific phenomenon known as atmospheric rivers (ARs) which are a prime example of a highly coherent, in both space and time, severe weather phenomenon whose generation and persistence are influenced by weather dynamics on a wide range of spatial and temporal scales. The EFD reveals how the interacting wind vector field and humidity scalar field couple to produce ARs, while the resulting STITS reveal the linkage between ARs and large-scale planetary circulations. The focus on ARs is also motivated by their devastating social and economic effects that have made them the subject of increasing scientific investigation to which the EFD may offer new insights. The application of EFD and STITs to the broader range of severe weather events is discussed. [ABSTRACT FROM AUTHOR]

Background: Sudden temperature drops, resulting from extreme weather events, often occur during the boll-setting period of cotton in Xinjiang, China, causing decreased expression of Bacillus thuringiensis (Bt) insecticidal proteins in cotton bolls. The precise threshold temperatures and durations that lead to significant changes in Cry1Ac endotoxin levels under low temperatures remain unclear. To address this, we investigated the effects of different temperatures and stress durations on Cry1Ac endotoxin levels in cotton bolls. In 2020–2021, two Bt transgenic cotton varieties, conventional Sikang1 and hybrid Sikang3, were selected as experimental materials. Various low temperatures (ranging from 16 to 20 °C) with different durations (12 h, 24 h and 48 h) were applied during the peak boll-setting period. Results: As the temperature decreased, the Cry1Ac endotoxin content in the boll shell, fiber, and seed exhibited a declining trend. Moreover, the threshold temperature which caused a significant reduction in Cry1Ac endotoxin content increased with the prolonged duration of low-temperature stress. Among the components of cotton bolls, seeds were most affected by low-temperature stress, with the threshold temperature for a significant reduction in Cry1Ac endotoxin content ranging from 17 °C to 19 °C. Correlation analysis indicated that low temperatures led to a decrease in protein synthesis capacity and an increase in degradation ability, resulting in reduced Cry1Ac endotoxin content. Pathway analysis revealed that both free amino acid and peptidase had significant negative effects on Cry1Ac endotoxin content. Conclusion: In summary, when the daily average temperature was ≤ 19 °C, implementing cultural practices to reduce free amino acid content and peptidase activity could serve as effective cold defense strategies for Bt cotton production. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions Shocks to the food system (such as extreme weather events, wars, and pandemics) are felt by institutional food systems. For hospitals, these shocks affect the quantity, quality, and variety of foods that can be offered to patients. One strategy to buffer the hospital food supply from external threats is to prioritise ingredients produced locally. Thus, the aim of the current research is to describe the country of origin of all foods purchased by a large, metropolitan healthcare organisation and to identify opportunities for improving the locality of the food supply.This study was of a cross‐sectional, observational design. The country of origin for all foods procured over a 1‐year period by a large, urban healthcare organisation was determined by proportion of food budget spend. State of origin was identified for fresh fruit, vegetables, and meat. The organisation was in Queensland, Australia and utilised a cook‐fresh, room‐service foodservice model. Descriptive analysis was used to to determine the number of items and the proportion of budget spend on all foods produced in Australia, and by food category. Similar descriptive statistics were generated to determine the proportion of the budget spend on fresh fruits, vegetables and meats produced in Queensland.Over the 1‐year period, 659 individual food items were purchased by the hospital foodservice, and 502 food items were included in the anlaysis. In total, 53% of the food budget was spent on Australian foods (100% Australian ingredients) and almost all fruit (73%) and vegetables (91%) were Australian grown. Procuring fresh fruit (28%), vegetables (35%), and meat (46%) from within the state was less common, and this may reflect the primary states of production across Australia, and seasonal variability of the food supply.Findings offer priority areas for improving the locality of the food supply. Future research to determine if procuring more foods locally has benefits to consistency of the food supply is warranted. [ABSTRACT FROM AUTHOR]

The future survival of coral reefs in the Anthropocene depends on the capacity of corals to adapt as oceans warm and extreme weather events become more frequent. Targeted interventions designed to assist evolutionary processes in corals require a comprehensive understanding of the distribution and structure of standing variation, however, efforts to map genomic variation in corals have so far focussed almost exclusively on SNPs, overlooking structural variants that have been shown to drive adaptive processes in other taxa. Here, we show that the reef‐building coral, Acropora kenti, harbours at least five large, highly polymorphic structural variants, all of which exhibit signatures of strongly suppressed recombination in heterokaryotypes, a feature commonly associated with chromosomal inversions. Based on their high minor allele frequency, uniform distribution across habitats and elevated genetic load, we propose that these inversions in A. kenti are likely to be under balancing selection. An excess of SNPs with high impact on protein‐coding genes within these loci elevates their importance both as potential targets for adaptive selection and as contributors to genetic decline if coral populations become fragmented or inbred in future. [ABSTRACT FROM AUTHOR]

Climate change poses a substantial threat to agricultural sustainability globally. Agriculture is a vital component of the gross domestic production of developing countries. The multifaceted impacts of climate change on agriculture, highlighting how extreme weather events such as water stress, heatwaves, erratic rainfall, storms, floods, and emerging pest infestations are disrupting agricultural productivity. The socioeconomic status of farmers is particularly vulnerable to climatic extremes with future projections indicating significant increment in ambient air temperatures and unpredictable, intense rainfall patterns. Agriculture has historically relied on the extensive use of synthetic fertilizers, herbicides, and insecticides, combined with advancements in irrigation and biotechnological approaches to boost productivity. It encompasses a range of practices designed to enhance the resilience of agricultural systems, improve productivity, and reduce greenhouse gas emissions. By adopting climate‐smart practices, farmers can better adapt to changing climatic conditions, thereby ensuring more sustainable and secure food production. Furthermore, it identifies key areas for future research, focusing on the development of innovative adaptation and mitigation strategies. These strategies are essential for minimizing the detrimental impacts of climate change on agriculture and for promoting the long‐term sustainability of food systems. This article underscores the importance of interdisciplinary approaches and the integration of advanced technologies to address the challenges posed by climate change. By fostering a deeper understanding of these issues to inform policymakers, researchers, and practitioners about effective strategies to safeguard agricultural productivity and food security in the face of changing climate. [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record‐breaking 43‐day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience‐building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA. [ABSTRACT FROM AUTHOR]

Modern agriculture's goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation. [ABSTRACT FROM AUTHOR]

Working equids play a central role in mountainous communities, but their work often goes unnoticed by the wider world, with sparse documentation of their role, value, or welfare -- a state which often extends to their human counterparts. Communities living in the remote Manaslu Valley, Nepal, face a number of uncertainties, including extreme weather events due to the seasonal monsoon and, more recently, the construction of a new road network. Using semi-structured interviews, questionnaires, and Equine Assessment Research and Scoping (EARS) welfare assessments, we outline the specific role of pack mules in supporting the lives of local people, explain the nuanced links between human experience and mule welfare, and gain insight into how people living in this volatile environment manage uncertainty and risk. Mule work was felt to be the 'only option' for a sustainable livelihood for most mule owners although, in some cases, mules had enabled respondents to diversify their income. Mule owners with more husbandry experience did not own mules in more positive behavioural states, which may suggest a lack of generational knowledge and support networks. Short-term ongoing risks, such as the monsoon or unstable tracks, had a larger impact both financially and emotionally than the long-term but distant implications of the road construction. Mule owners must constantly balance the risk of working during the monsoon season, when conditions are treacherous but pay was higher, with losing valuable income but keeping themselves and their mules safe; they do, however, have a more mobile option for employment than non-owners. Mules enable a level of resilience and agility for communities living with constant uncertainty and change, which is only beginning to be recognised formally within the sustainable development sphere. Integration of animal welfare into the SDGs would allow humanitarian aid initiatives to strengthen support networks around working equids, which would greatly benefit the mules and humans alike. [ABSTRACT FROM AUTHOR]

Extreme weather events such as floods, bushfires, cyclones, and drought, are projected to increase in eastern Australia. Understanding how these events influence the combined, sustainable well-being of humans, animals, and ecosystems - that is One Health - will enable development of transdisciplinary and ultimately more effective interventions. A scoping review was conducted to explore the research associated with the effects of extreme weather events in eastern Australia using a One Health lens, specifically identifying the type of extreme weather events studied, the research conducted in the context of One Health, and gaps to inform improved One Health implementation. The review followed JBI guidelines (based on PRISMA). Eligible research was peer-reviewed, in English, and published since 2007, in which primary research studies investigated the impact of extreme weather events in eastern Australia on at least two of ecosystems, human health, and animal health. Using structured search terms, six databases were searched. Following removal of duplicates, 870 records were screened by two reviewers. Eleven records were eligible for data extraction and charting. The scope of extreme weather events studied was relatively limited, with studies in flood and bushfire settings predominating, but relatively little research on cyclones. Major health themes included more than the impact of extreme weather events on physical health (zoonotic and vector-borne diseases) through investigation of social well-being and mental health in the context of the human-animal bond in evacuation behaviors and drought. Research gaps include studies across a broader range of extreme weather events and health topics, as well as a more comprehensive approach to including the impacts of extreme weather events on all three domains of One Health. The limited research focus inevitably translates to limited recommendations for policy, planning and response to manage extreme weather event emergencies. Given the expected increase in frequency of these events, there is a critical need for more comprehensive primary research to better identify strategies and facilitate implementation of One Health promotion for improved outcomes in extreme weather event emergencies. [ABSTRACT FROM AUTHOR]

AbstractHuman-induced climate change will likely cause more intense and frequent cyclones and typhoons, as noted in the Intergovernmental Panel on Climate Change (IPCC) report Climate Change 2021: The Physical Science Basis. Within the Asia-Pacific region, regional security discussions at the leadership level focus on the imminent threats to states and societies of more frequent and intense extreme weather events and sea-level rise as it is the world’s most exposed region. This article examines recent developments and discourse regarding climate change in the Asia-Pacific security architecture using the Association of Southeast Asian Nations (ASEAN) as a case study. It finds increasing recognition of climate change and its implications for peace and security within the regional security architecture. Debates over how best to address it to overcome an atomised approach to climate change impacts on peace and security in the region remain unresolved, but recent pronouncements at the leadership level signal an emerging mindset shift in understanding climate change within the regional security discourse. However, the substantive discussions largely take place in hybrid forums, albeit with official recognition in more traditional platforms, which may undermine efforts for an inclusive regional discourse on the climate change impacts on peace and security in ASEAN. [ABSTRACT FROM AUTHOR]

Rapid climate changes bear significant consequences on various aspects of our lives, notably by deteriorating living conditions in certain areas to such extent that inhabitants have no choice but flee. Despite recognition of this issue, the dynamics of the relationship between the environmental factors and the human mobility have yet to be thoroughly investigated. This study aims to explore the application of advanced remote sensing analytics for developing detailed climate indicators at a micro (district) level, and to examine the relationship between climate factors and internally displaced persons. After detailing our data sources and the analytics employed for indicator development, we discuss various types of events and their repercussions. Our findings corroborate that slow-onset and rapid-onset climate events differently impact society, and the responses hinge on the urgency precipitated by the detrimental aftermath of the extreme weather event and, most crucially, on people's capabilities. We also underscore the importance of data quality and availability for the socio-economic indicators to enhance future studies, given the intertwined associations between climate change, economic deprivation, and violent conflict. [ABSTRACT FROM AUTHOR]

The last two decades have seen an increase in the intensity and frequency of extreme weather events, placing complex demands on police and emergency services in the UK. Despite the growing recognition of the cascading and multidimensional threats, there is limited understanding of how policing and local governance actors are preparing for the climate crisis. In this paper, we argue that polycentric governance approaches, such as the Local Resilience Forums (LRFs), provide an essential focal point for emergency planning and disaster response activity that is rooted in localised contexts. However, while the LRFs are designed to benefit from the inclusion of a broad range of actors, dominant mentalities, knowledge hierarchies and powerful actors may limit their epistemic potential. We propose that a pathway towards a resilient and adaptive policing agenda for the Anthropocene necessitates acknowledging the limits of policing knowledge, expertise and traditional command-and-control mentalities. We draw on the notion of epistocracy to propose a policy and research agenda for police and partner agencies to harness the epistemic potential of LRFs. By adopting approaches to leverage pluralised forms of knowledge and identifying strategies for co-opting community-based actors in emergency planning and response activities, police and partner agencies can strengthen local capacity-building and better promote local resilience and adaptation efforts. [ABSTRACT FROM AUTHOR]

Climate change poses significant challenges to the grapevine cultivation for wine production. In the last years, the occurrence of extreme weather events such as intense heat waves and prolonged period of drought increased sunburn damage with negative consequences to yield and berry composition. Short-term adaptation strategies are urgently needed to mitigate these effects. In this light, our study aimed to evaluate the efficacy of zeolite application on the canopy and irrigation during ripening on sunburn damage, yield, and berry composition in cv. Sangiovese (Vitis vinifera L.). Over 3 years (2021-2023), canopy management and irrigation treatments were arranged in a strip-plot design. Canopy management treatments included leaf removal on the cluster zone, with and without zeolite application, and no defoliated control; irrigation treatments comprised irrigation from berry softening and no irrigation. Both zeolite application and irrigation reduced sunburn damage, thus mitigating yield loss. Regarding berry composition, zeolite application did not alter the main parameters whereas irrigation led to reductions in sugar and anthocyanin concentrations. These findings suggest that zeolite application and irrigation during ripening represent promising and cost-effective solutions for grape growers facing climate changeinduced challenges. However, further studies are necessary to optimize timing of irrigation to avoid detrimental effects on anthocyanin accumulation. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

The accelerating impacts of climate change present significant challenges to sustainable urban development, testing the resilience of current governance frameworks and stakeholder responsibilities. In alignment with the EU's Green Deal, robust adaptation strategies and proactive climate risk anticipation are essential. Traditional discussions emphasize overcoming technological, financial and institutional barriers; however, social and individual factors also significantly hinder adaptation. This study explores the pivotal role of citizen engagement in climate risk management and adaptation, focusing on the Lithuanian context and comparing the results with a survey in Sweden. The research evaluates both external influences, such as experiences with climate‐related hazards and adaptation actions, and internal factors, including beliefs, values and individual adaptive capacity. Key findings reveal that adaptation behaviour in Lithuania and in Sweden is deeply influenced by past experiences with extreme weather events and economic considerations, with a significant portion of the population having faced such events recently. Factors such as economic considerations (low costs, financial benefits) are identified as critical motivators for future adaptation actions in Lithuania. On the other hand, in Sweden, respondents first preferred ecological factors (such as contributing to climate change mitigation) when considering future adaptation actions. The study underscores the need for targeted educational interventions to enhance community resilience, highlighting the importance of socio‐cultural contexts in shaping adaptation strategies. It emphasizes the necessity for comprehensive, inclusive educational programmes that address local climate impacts and promote proactive community involvement. The findings advocate for further comparative studies across diverse socio‐cultural settings to deepen insights into effective adaptation measures and to support the development of resilient communities worldwide. [ABSTRACT FROM AUTHOR]

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE. [ABSTRACT FROM AUTHOR]

Heat stress is a prevalent factor that significantly damages crops, especially with the ongoing global warming and increasing frequency of extreme weather events. Tobacco is particularly sensitive to temperature fluctuations, experiencing reduced yield and quality under high temperatures. However, the underlying molecular mechanisms of heat resistance in tobacco remain poorly understood. This study comprehensively analyzed biochemical, transcriptomic, and metabolomic responses to heat stress on the root and shoot of the tobacco cultivar K326 compared to control conditions. Heat stress significantly increased the activities of antioxidant enzymes (CAT, POD, and SOD) and levels of osmotic mediators (soluble sugars, sucrose, and proline) in the shoot. Furthermore, transcriptome analysis identified 13,176 differentially expressed genes (DEGs) in the root (6,129 up-regulated and 7,047 down-regulated) and 12,283 DEGs (6,621 up-regulated and 5,662 down-regulated) in the shoot. The root had 24 enriched KEGG pathways, including phenylpropanoid metabolism, while the shoot had 32 significant pathways, such as galactose metabolism and MAPK signaling. The metabolomic data identified 647 metabolites in the root and 932 in the shoot, with carbohydrates and amino acids being the main categories. The root had 116 differentially abundant metabolites (DAMs) (107 up-regulated and 9 downregulated), and the shoot contained 256 DAMs (251 up-regulated and 5 downregulated). Joint transcriptome and metabolome analysis showed that galactose metabolism and starch and sucrose metabolism were co-enriched in both tissues. In contrast, amino sugar and nucleotide sugar metabolism was enriched in the root, and purine metabolism in the shoot. The purine metabolic pathway in the shoot can modulate the expression of MYB transcription factors by influencing ABA synthesis and signaling, thereby controlling the accumulation of HSPs, raffinose, sucrose, and trehalose to enhance heat tolerance. Furthermore, NtMYB78, an MYB transcription factor, enhances tolerance for heat stress in tobacco. This research offers a foundational framework for investigating and implementing heat-resistant genes and metabolic pathways in the root and shoot of tobacco seedlings. [ABSTRACT FROM AUTHOR]

Introduction: Climate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking. Methods: This paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN). Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method. Results: As a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity. Discussion: The developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability. [ABSTRACT FROM AUTHOR]

Background: Exposure to extreme weatherly events potentially develops mental disorders among affected individuals. Aim: To synthesize the burden of mental disorders following impact of extreme weather events in South-east Asian (SEA) countries. Methods: Proposal was registered in PROSPERO register [CRD42023469788] and reported as per PRISMA-2020 guidelines. Studies reporting prevalence of mental health disorders following extreme weather events from SEA countries during 1990 and 2023 were searched on Embase, PubMed, and Scopus databases. Study quality was assessed using Appraisal tool for Cross-Sectional Studies. Overall pooled prevalence was computed using DerSimonian-Laird method for random-effects model and reported as 95% confidence intervals. Results: On screening 12,046 records, we included 155 studies (2,04,474 participants) for analysis. Overall burden of mental disorders was 35.31% (95%CI: 30.42%-40.21%). In subgroup analysis, studies on cyclones, India, local residents, children and adolescents, rural settings, and community settings reported higher burden of mental disorders. Depression (28.58%; 95%CI: 24.05%-33.1%) and PTSD (29.36%; 95%CI: 26.26%-32.46%) had similar prevalence. Visiting tourists to SEA region experienced fear, fear of recurrence of tsunami, nightmares, and sense of helplessness. Mental health outcomes were relatively higher in studies conducted within 1 year of events. Heterogeneity and possibility of publication bias exists among the reported studies. Conclusion: With the significant rise in episodes of extreme weather events in SEA region over the last three decades, mental disorders are documented in different proportions. We suggest prioritizing well-informed policies to formulate inclusive and resilient strategies on effectively identifying and treating mental health concerns among victims of extreme weather events. [ABSTRACT FROM AUTHOR]

Accurate precipitation nowcasting is crucial for daily life, but the current forecasting models need further improvement in terms of accuracy. This paper proposed an innovative forecasting model called BTPN to address this issue. The model introduced bidirectional Transformer to extract features from both the forward and backward directions of spatiotemporal sequences, capturing key information and reducing spatiotemporal feature loss. It combined the convolutional Transformer block with the local encoding of convolution and the global encoding of Transformer to enhance spatiotemporal information extraction and correlation, alleviating the problem of long-term spatiotemporal information loss. The model also incorporated a detail extraction module to reduce the loss of local details and mitigate the issue of dissipation in high-value areas. Evaluation on the HKO-7 dataset shows that the BTPN model surpasses other advanced models in terms of MAE, SSIM, and CSI metrics, demonstrating excellent predictive capability in large-scale precipitation and extreme weather scenarios. The experiments demonstrate that the BTPN model possesses higher forecasting accuracy and promising applications. [ABSTRACT FROM AUTHOR]

The American Inflation Reduction Act (IRA) of 2022 contains climate-related provisions that may have noteworthy implications for cancer control and prevention. This commentary assesses the potential co-benefits of the IRA for cancer control efforts, specifically policies and programs to reduce carcinogen exposure via air quality monitoring and air pollution reduction. Allocations through the IRA for air quality improvement, paired with its environmental justice provisions, hold promise for advancing cancer prevention by targeting resources to communities most susceptible to environmental hazards. Moreover, climate resilience measures dictated by the IRA are crucial for oncology professionals grappling with the dual challenges of climate change and cancer care. Climate-driven extreme weather events can exacerbate carcinogen exposure and disrupt access to cancer care, underscoring the need for resilient health-care infrastructure. The IRA's provisions for clean energy incentives and infrastructure upgrades offer oncology care institutions opportunities to mitigate emissions and bolster resilience against climate-related disruptions, ultimately improving cancer outcomes. Climate-related initiatives funded by the IRA present a unique and timely avenue to advance equitable cancer control efforts. This commentary underscores the critical intersection between climate resilience policy and oncology care, highlighting the potential to promote a healthier and more resilient future for all. [ABSTRACT FROM AUTHOR]

Critical review of temperature criteria for controlling cracking due to concrete hardening Various limits for the concrete temperature are specified for the production of concrete on site. These are primarily to ensure the workability of the fresh concrete in extreme weather conditions and to ensure proper hydration. There are also additional concrete temperature requirements for certain applications to reduce the risk of cracking or crack intensity due to hardening‐induced restraint stresses. In this context, the maximum member temperature, the maximum fresh concrete temperature or the maximum temperature gradient across the cross‐section are generally limited. However, these limits are defined independently of the ambient temperature. The extent to which these temperature limits are justified in terms of the risk of cracking due to concrete hardening is investigated using thermo‐mechanical simulations on 3D volume models. In detail, the temperature and stress evolution due to concrete hardening is simulated for different member types and ambient boundary conditions and evaluated with respect to the associated risk of cracking. The systematic evaluation of the results shows that the general limits of maximum member temperature and fresh concrete temperature are only pragmatic solutions for controlling the risk of cracking due to concrete hardening. These limits should always be considered with regard to the ambient temperature. [ABSTRACT FROM AUTHOR]

Phenol–formaldehyde (PF) resins remain the preferred adhesive for exterior plywood, as they confer these boards their extreme weather resistance. However, their high price and toxicity has made phenol alternatives, such as technical lignins, increasingly more attractive. While many works report the use of kraft lignin, the most commercially available form are lignosulphonates (LS). However, these lack industrial success and are associated with low moisture resistance. In the current study, lignosulphonate–phenol–formaldehyde (LPF) resoles were synthesized considering a phenol replacement of 30% (w/w). Two LS samples of softwood (SLS) and hardwood (HLS) origin were compared. These samples were previously methylolated to increase their reactivity. The effectiveness of the treatment was confirmed through the Automated Bonding Evaluation System. Plywood was manufactured and tested according to EN 314 class 3 for exterior conditions, which is seldom found in the literature. Although a 35% increase in shear strength is still necessary to comply with the standard, methylolated SLS was the most promising substitute, as it resulted in the highest board performance. Notably, when this sample was used without previous methylolation, the plywood boards suffered delamination during immersion in boiling water prior to shear testing. These results reinforce the need for the methylolation of LS to increase the weather resistance of plywood. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979. [ABSTRACT FROM AUTHOR]

The Mediterranean region contains some of the areas with the highest urban density in the world, and these areas keep expanding, making this region a "hotspot" of climate change. Life in the Mediterranean unfolds predominantly outdoors throughout the year, exposing its inhabitants to an evolving climate that is progressively harsher and punctuated by increasingly frequent extreme weather events. More and more people are going to be exposed to severe heat waves, droughts, water shortage, dust storms, forest fires and poor air quality on the one hand, and torrential rains and floods on the other hand. The urban heat island further increases thermal stress of city dwellers and plays a key role in citizens' health and well‐being. In this exhaustive review, based on state‐of‐the‐art studies we delve into the realm of climate change and extreme weather phenomena as they intersect with urban populations in Mediterranean cities, both in the present and in the foreseeable future. Our focus lies on identifying knowledge gaps, inconsistencies in observed climatic hazards and shortcomings in assessing the associated risks and their societal and environmental ramifications. Moreover, we undertake a comprehensive survey of future predictions exploring the variables thermal stress, air pollution, air quality and characteristics of the hydro‐climatic systems, that is, droughts, fires and floods. Yet, critical knowledge gaps persist in understanding the science, the coping mechanisms, the strategies for preparedness and adaptation and the intricate interplay between these facets and societal dynamics. The developing countries in the Mediterranean region stand exceptionally vulnerable. It is imperative for more affluent nations to share their expertise and extend assistance to less developed counterparts, aiding them in navigating climate‐related challenges, devising adaptive strategies and facilitating their implementation. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large‐scale drivers in the agro‐ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation‐dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade−1) and cold spells (−1.28 days·decade−1). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation‐dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper‐level jet by a high‐pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea‐surface temperature, low sea‐level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate‐induced disasters occurring in various AEZs of Pakistan. [ABSTRACT FROM AUTHOR]

Summary Societal Impact Statement Summary 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.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]

Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km2) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events. [ABSTRACT FROM AUTHOR]

Global warming has led to more frequent extreme weather events, such as heavy summer rains, in the Huang‐Huai‐Hai region. These events significantly impede the growth and development of waxy maize in the area and disrupt the stable progression of the industry. However, there is a lack of effective agricultural measures to mitigate the impact of waterlogging, and the underlying regulation mechanisms remain unclear. To fill this knowledge gap, we conducted a two‐year experiment to assess whether exogenous EDAH (a mixture of ethephon and diethyl aminoethyl hexanoate (DA‐6), ethephon: DA‐6 = 27%: 3%) application during the waxy maize V6 stage, combined with 10 days of waterlogging treatment at the V6, VT and R2 growth stages. The results indicate that exogenous EDAH mitigates the adverse effects of waterlogging stress to a certain extent. It is noteworthy that exogenous EDAH increases the leaf area index and photosynthetic parameters of waxy maize, enhances the activity of catalase in ear leaves at the R3 stage, inhibits the accumulation of malondialdehyde and delays premature aging of plants. Furthermore, exogenous EDAH delays premature ripening of grains caused by waterlogging, increases the moisture content of fresh waxy maize grains during the fresh edible period, but does not effectively mitigate the yield losses caused by waterlogging. However, exogenous EDAH effectively improves grain quality under waterlogging stress, increasing the soluble sugar content and total protein content while reducing starch content, ultimately enhancing the edibility of fresh ears. Through TOPSIS comprehensive evaluation, it can be inferred that exogenous EDAH effectively mitigates the overall impact of waterlogging on waxy maize at both the V6 and VT stages. This research sheds light on potential strategies to mitigate the adverse effects of waterlogging on agricultural productivity and grain quality. [ABSTRACT FROM AUTHOR]

Central America and the Caribbean are regularly battered by megadroughts, heavy rainfall, heat waves, and tropical cyclones. Although 21st‐century climate change is expected to increase the frequency, intensity, and duration of these extreme weather events (EWEs), their incidence in regional protected areas (PAs) remains poorly explored. We examined historical and projected EWEs across the region based on 32 metrics that describe distinct dimensions (i.e., intensity, duration, and frequency) of heat waves, cyclones, droughts, and rainfall and compared trends in PAs with trends in unprotected lands. From the early 21st century onward, exposure to EWEs increased across the region, and PAs were predicted to be more exposed to climate extremes than unprotected areas (as shown by autoregressive model coefficients at p < 0.05 significance level). This was particularly true for heat waves, which were projected to have a significantly higher average (tested by Wilcoxon tests at p < 0.01) intensity and duration, and tropical cyclones, which affected PAs more severely in carbon‐intensive scenarios. PAs were also predicted to be significantly less exposed to droughts and heavy rainfall than unprotected areas (tested by Wilcoxon tests at p < 0.01). However, droughts that could threaten connectivity between PAs are increasingly common in this region. We estimated that approximately 65% of the study area will experience at least one drought episode that is more intense and longer lasting than previous droughts. Collectively, our results highlight that new conservation strategies adapted to threats associated with EWEs need to be tailored and implemented promptly. Unless urgent action is taken, significant damage may be inflicted on the unique biodiversity of the region. [ABSTRACT FROM AUTHOR]

Food and feed production worldwide heavily relies on wheat (Triticum aestivum). However, current agricultural practices face numerous challenges including a shortage of land for cultivation, a desire to reduce the use of chemical pesticides and fertilizers, and the development of resistance towards employed pesticides and virulence towards host resistance in the most widely grown varieties. In this paper, we demonstrate based on the literature that cultivating wheat variety mixtures generally leads to increased yield and yield stability across years and environments, reduced severity of multiple diseases, and a decreased risk of lodging before harvest compared to the cultivation of pure stands. Moreover, mixtures may delay fungicide resistance development and increase genetic diversity, ultimately prolonging the durability of resistance genes. Furthermore, growing mixtures may lower the risk of crop failure due to more extreme weather events and lead to better utilization of water and nutrients. We discuss a Danish case study advocating the cropping of wheat varieties in mixtures as an example of how variety mixtures can be utilized in integrated pest management strategies. The study shows that if all involved stakeholder groups promote the adoption of variety mixtures, a major uptake by farmers can be reached, potentially reducing the dependency on pesticides in current cropping schemes. [ABSTRACT FROM AUTHOR]

Arctic regions are warming significantly faster than other parts of the globe, leading to changes in snow, ice and weather conditions, ecosystems and local cultures. These changes have brought worry and concern and triggered feelings of loss among Arctic Indigenous Peoples and local communities. Recently, research has started to address emotional and social dimensions of climate change, framed through the concept of ecological grief. In this study, we examine sociocultural impacts of climate change and expressions of ecological grief among members of reindeer herding communities in the Sámi Homeland in Finland. Results indicate that ecological grief is felt in connection to major environmental concerns in the area: changes in winter weather and extreme weather events, Atlantic salmon decline and land use changes, which all have cultural and social consequences. Our results indicate that ecological grief is strongly associated with ecological losses, but also with political decisions regarding natural resource governance. [ABSTRACT FROM AUTHOR]

Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

Groundwater-level and rainfall measurements from 37 borewells in the Visakhapatnam district, Andhra Pradesh, India, from 2002 to 2021 were analyzed using Bayesian Neural Networks (BNNs) to comprehend the predictability of groundwater levels. We found chaotic dynamics in the groundwater and rainfall data, but a dominant trend component was seen in the groundwater-level data from phase plots. Dynamics suggest the presence of self-organized criticality/chaos in the groundwater dynamics over decadal time scales. We used BNN prediction models, (i) nonlinear autoregressive (NAR), (ii) nonlinear input–output (NIO) and (iii) nonlinear autoregressive exogenic Input (NARX), to predict the groundwater levels with rainfall and temperature as exogenic inputs. We noticed ~ 94 to 95% prediction accuracy with the NAR model with optimal inputs and ~ 1% improvement with added exogenic input. Interestingly, the study indicates that (i) the dynamics of the groundwater differ significantly from rainfall and temperature in the region, (ii) the nonlinear autoregressive model based on the self-organized dynamics of groundwater-level changes is robust in providing prediction accuracy up to ~ 95%, and (iii) the dynamics of remaining ~ 5% groundwater-level changes may be due to the presence of randomly varying extreme weather events and man-made/anthropogenic changes. [ABSTRACT FROM AUTHOR]