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The development and utilization of wind energy is of great significance to the sustainable development of China's economy and the realization of the "dual carbon" goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system's wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) distribution networks that are spread through larger territory and threatened by extreme weather conditions are sometimes formed by very long underground cable lines. In such circumstances, a significant amount of capacitive reactive power flow can be generated. If, concurrently, there is low power demand in the network, it can result in significant reverse reactive power flows and voltage rise issues. This paper proposes a general approach for analyzing and mitigating voltage rise issues and demonstrates it using an example of a real distribution network that operates under the described conditions. Previous studies that dealt with this problem did not include the allocation of multiple shunt reactors in a larger distribution network, modeling a high number of lines that create reverse reactive power flows, and modeling the main distribution transformers, which are the locations where voltage rise predominantly occurs. In this paper, we demonstrate that precise allocation and placement of multiple shunt reactors in a fully modeled, larger distribution system, including transformer models, can reduce reverse reactive power flows, thereby improving voltage in the distribution system. If hourly control of the power factor from the distributed generation unit is also implemented, the voltage can be further improved. [ABSTRACT FROM AUTHOR]

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

In the dark of a July night in 2018, a 5-billion-cubic-metre torrent of muddy water crashed through rooftops and ripped through the downstream villages of southeast Lao People's Democratic Republic (Lao PDR). An auxiliary "saddle" dam had collapsed in the US$1.02 billion Xe Pian-Xe Namnoy (XPXN) Hydropower Project that was still under construction and had just reached financial contractual close five years prior, in 2013. In the aftermath of the collapse, official state narratives pointed to extreme weather conditions and "unforeseen" construction and engineering miscalculations, viewing soil conditions as the primary culprit. This paper examines the financial dimensions of dam failure and introduces the term "fast finance": financier-driven timelines that have drastically expedited and shortened the legal, social, and pre-construction processes involved in hydropower dam projects to the detriment of dam safety, due diligence, and local participatory input. Extreme weather and anthropogenic climate change are not sole explanatory factors in the XPXN dam disaster. This paper highlights the also significant role of financial and political interests as contributing factors in dam safety and failure alongside extreme weather. The paper challenges conventional "natural disaster" framing of dam collapse by bringing into focus ex-ante political decision-making, financial engineering, and construction planning prior to dam construction to highlight the ways in which the XPXN catastrophe also had anthropogenic and "unnatural" contributing factors. Fast finance encompasses the role of temporality and the responsibility of state-business actors in ex-ante financial and infrastructure decisions that conclude with catastrophic outcomes. The article examines the re-engineering of contemporary dam finance through a case study of Lao PDR and argues that issues of financial engineering should be examined alongside other forms of civil, mechanical, structural, and hydrological engineering in the analysis of dam disasters. The temporal logics of financial actors-particularly the financialized logic of fast finance-has displaced the public-good-producing logic of patient capital. Financial logics shape and condition other forms of engineering and construction and are central to considerations of dam safety and accountability. Naturalizing discourses around extreme weather and aging dams deflect from the financial decisions and policy action, or inaction, of state-business actors to prevent dam collapse. [ABSTRACT FROM AUTHOR]

Context: This paper introduces the special issue of Pacific Conservation Biology devoted to the natural history of Two Peoples Bay Nature Reserve on the south coast of Western Australia. Methods: This paper provides the background to the special issue. Key results: Two Peoples Bay Nature Reserve was gazetted in 1967 for the conservation of the native biota, including two species; the Djimaalap/noisy scrub-bird (Atrichornis clamosus) and Ngilgaitch/Gilbert's potoroo (Potorous gilbertii), both believed extinct for over 100 years before being rediscovered on the Reserve. The Reserve is 4774.7 ha in area, with wetlands, heathlands, granite outcrops, sand dunes, beaches, cliffs, and islands. Since it was established, mean annual rainfall has decreased by 16.8%, mean annual maximum temperature has increased by 0.2°C, and mean annual minimum temperature has increased by 0.7°C. Conclusions: The paper poses the question: what do the changes of drier winters, hotter summers, and more extreme weather events mean for managers of conservation areas such as Two Peoples Bay? Implications: Changing climate will pose problems for the managers of Two Peoples Bay Nature Reserve in ensuring the conservation of the Djimaalap/noisy scrub-bird and Ngilgaitch/Gilbert's potoroo. Two Peoples Bay Nature Reserve comprises ~5000 ha of wetlands, heathlands, granite outcrops, sand dunes, beaches, cliffs, and islands. Two species, the Djimaalap/noisy scrub-bird and Ngilgaitch/Gilbert's potoroo, were believed extinct for over 100 years before being rediscovered on the Reserve. Since the Reserve was established in 1967, rainfall has decreased, while minimum and maximum temperatures have increased. We ask what the climate change means for managers of conservation areas such as Two Peoples Bay. This article belongs to the Collection The Natural History of Two Peoples Bay Nature Reserve, Western Australia. [ABSTRACT FROM AUTHOR]

This paper explores hydroponic vertical systems as a sustainable solution to modern agricultural challenges, particularly those posed by climate change. Hydroponics, a method of growing plants without soil using nutrient-rich water solutions, offers significant advantages over traditional farming. Vertical systems maximize space efficiency by growing plants in stacked layers, making them ideal for urban environments with limited space. These systems provide a controlled environment that mitigates the impacts of extreme weather, ensuring consistent crop production. The paper reviews various hydroponic techniques, including deep water culture, nutrient film technique, flood and drain, and drip irrigation. It highlights the efficiency of water use in hydroponics, crucial for areas facing water scarcity. Advanced technologies, such as sensors, automated nutrient delivery, and LED lighting, are employed to optimise growing conditions, enhance resource use efficiency, and improve crop yields. LED lights, in particular, offer energy efficiency, customizable spectra, and low heat output. Mathematical models are used to maximize plant development and resource efficiency, providing a framework for understanding plant-environment interactions. Despite high initial setup costs and the need for technical expertise, hydroponic systems present long-term economic and environmental benefits. This paper underscores hydroponic vertical systems' potential to revolutionize urban agriculture, ensuring food security and sustainability amidst climate change challenges. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of extreme weather and severe environmental pollution, food security and human survival are at risk. Silicon, a key element in plant nutrition and environmental remediation, has a variety of applications in combatting various forms of abiotic and biotic stress and fostering healthy plants and soils. To establish a silicon-based defense barrier for plants, we firstly summarized and grouped the mechanisms of silicon functioning in plants: 1) molecular regulation and overall strengthening, 2) physical barrier and apoplastic obstruction, and 3) energy conservation. Additionally, the types and applications of silicon-based materials as soil remediation materials and fertilizers were discussed. Then, the challenges to build up such a barrier were analyzed in terms of silicon absorption and transportation, deposition and aggregation, and perception and regulation. An "external + internal" strategy was proposed to accelerate the establishment of a silicon-based barrier to enhance plants' resistance to extreme weather and environmental pollution, including high temperatures, drought, ultraviolet radiation, salt, heavy metals, nutrient deficiencies, new pollutants, and biotic stress. Finally, this paper emphasized the contribution of silicon barrier to healthy Soil-Plant-Atmosphere system that eventually benefit human health. This paper highlights the current understanding and future perspectives of silicon-related research in Soil-Plant-Atmosphere system, providing a reference for the application of silicon-based materials in environmental and agricultural fields. [ABSTRACT FROM AUTHOR]

Climate change is a financial risk to companies, and those that disregard this will have to deal with losses due to extreme weather events, new regulations, or the disruption of the supply chain. Companies must disclose climate-related financial implications to enable informed decision-making by investors and other stakeholders. Any indifference or disregard for climate-related disclosures will harm the investors who may trade based on insufficient information and prices of securities that do not account for climate risks. In India, disclosures are mandated for companies under the Companies Act 2013 and various Securities and Exchange Board of India Regulations. This paper will explore the adequacy of the current disclosure regime concerning climate change and argue that, although information regarding climate risks falls within the ambit of the current set of mandated disclosures, it is insufficient. The paper concludes by making a case for mandatory climate disclosures under the Companies Act. [ABSTRACT FROM AUTHOR]

In this paper, we investigated Cold Air Outbreaks (CAOs) in Hungary using temperature data from ten weather stations located near populous Hungarian cities. Our main motivation for performing this research was the fact that in this rapidly changing climate, these events continue to represent a threat to infrastructure and human life, such as the outbreaks experienced in early 2021 (e.g., Texas, USA) and late 2022 (Winter Storm Elliott). In addition, no comprehensive study of CAOs in Hungary has been conducted using station data. The definition of CAO used in this paper is that the daily mean temperature had to be in the lower 10th percentile of the daily climatology for five consecutive days, and we allowed a maximum two-day gap between periods matching the criteria above, after which we merged events together. We found that the number of CAOs in Hungary decreased considerably in recent decades (due to increasing mean temperatures), and the climates of the investigated stations became increasingly homogenous. Developing our understanding of CAOs around the world is important because, due to climate change, their seasonal distribution may change in a way that negatively impacts our life and economy. [ABSTRACT FROM AUTHOR]

This paper investigates changes in political participation following a disaster. Drawing on the electoral results and flood declarations after the 2010 flood in Slovakia, a dataset was constructed that illustrates when and how often each municipality was affected by disastrous conditions before the elections. The analysis revealed that experiencing a flood significantly increased the level of political participation in a municipality. However, the effect of flooding on elections is conditional. First, significantly higher turnout occurs only when a flood affects the municipality on election day. Second, repeated flooding before the elections does not change the overall participation rate. In contrast, it is the one‐time, continuous extreme weather event that most mobilises voters. Third, only severely affected municipalities demonstrated a significant turnout effect. Lastly, the timing variable is crucial in determining the impact of repetition and severity. This paper distinguishes between different kinds of flood events that can influence disaster behaviour. [ABSTRACT FROM AUTHOR]

This document is a discussion of the original paper titled "Coastal Defense Megaprojects in an Era of Sea-Level Rise: Politically Feasible Strategies or Army Corps Fantasies?" by D. J. Rasmussen, Robert E. Kopp, and Michael Oppenheimer. The authors review coastal defense megaprojects with storm surge barriers in the United States and discuss their feasibility. They compare the findings of the original paper with experiences from the implementation of coastal megaprojects in the Netherlands and identify key factors crucial for their feasibility and realization. The document concludes that coastal megaprojects with barriers are feasible and will be needed for future coastal protection and adaptation. [Extracted from the article]

Road surface condition (RSC) is an important indicator in road safety studies, enabling transportation departments to employ it for conducting surveys, inspections, cleaning, and maintenance, ultimately contributing to improved performance in road upkeep. However, traditional recognition methods can be easily affected when extreme weather frequently occurs such as winter seasonal changes. To achieve real-time and automatic RSC monitoring, this paper proposes an improved Mask–region-based convolutional neural network (RCNN) based on Swin Transformer-PAFPN and a dynamic head detection network. Meanwhile, transfer learning is used to reduce training time, and data enhancement and multiscale training are applied to achieve better performance. The experimental results show that the proposed model achieves an outstanding mean average precision at 0.5 (mAP@0.5) score of 89.8 under favorable weather conditions characterized by clear visibility, surpassing other popular methods. Notably, the proposed model exhibits lower parameters and GigaFLOPS (GFLOPs) (72.41 and 158.35, respectively) compared to other popular methods, thus demanding fewer computational resources. Furthermore, in challenging weather conditions characterized by poor visibility, such as foggy and nighttime scenarios, the proposed model achieves mAP@0.5 scores of 78.50 and 82.40, respectively. These scores not only outperform those of other popular methods but also underscore the robustness of the proposed model in extreme weather conditions. This exceptional performance demonstrates the proposed model's effectiveness in addressing complex road conditions under various meteorological circumstances, providing reliable technical support for practical traffic monitoring and road maintenance. [ABSTRACT FROM AUTHOR]

This paper attempts to evaluate the change in the behavior of the acoustic waves associated with flexure cracks developed in rubberized concretes in a subfreezing environment. Seven normal and rubberized concrete mixtures were developed with different compositions. Prism samples from each mixture were tested at two temperatures (25°C and −20°C) under a four-point monotonic flexure test while being monitored via two attached acoustic emission (AE) sensors to collect the emitted AEs till failure. The AE signal characteristics such as signal amplitudes, number of hits, and cumulative signal strength (CSS) were collected and used for three AE parameter-based analyses: b-value, intensity, and rise time–amplitude (RA) analysis. Analyzing the acoustic activity revealed micro- and macrocracks nucleation, which were found to be associated with a noticeable spike in CSS, historic index [H(t)], severity (Sr) values, and a significant dip in the b-values. In addition, cold temperature was found to increase the micro- and macrocracking onset load and time regardless of mixture composition. Besides, mixtures with a lower C/F, less crumb rubber (CR) content, and/or smaller rubber particle size witnessed higher micro- and macrocrack load and time thresholds. Noticeably, the AE signal attenuation effect caused by the high CR content (up to 30%) at 25°C was significantly relieved when samples were tested at −20°C. Three charts were developed to classify the cracking level based on the values of the intensity analysis parameters [H(t) and S] and RA analysis. Practical Applications: Infrastructure failures can cause severe economic losses and fatalities, but luckily they can be avoided through regular inspections with the aid of nondestructive testing and subsequent repairs. Aging structures need more inspections to detect potential deficiencies, whereas newly constructed ones can safely undergo fewer inspections and preserve resources. Data from nondestructive testing programs can optimize inspection schedules. When it comes to hard-to-reach structures in extreme weather, acoustic emission (AE) has the potential to be a more suitable nondestructive testing technique over other conventional methods. Some concerns were raised regarding the effectiveness of the AE technique for applications involving novel construction materials such as rubberized concrete, because rubber particles have noticeable acoustic absorption capacities that may affect the parameters of the AE waves and impact the posttesting analysis. Another concern was the influence of cold temperature on the AE wave characteristics due to the change in concrete microstructure at low temperatures. This study aimed at addressing these concerns by utilizing AE analysis to highlight the onset of micro- or macrocracks in rubberized concrete mixtures exposed to cold temperatures. Three user-friendly charts are presented that can advise on inspection decisions based on whether deterioration has reached a certain level. [ABSTRACT FROM AUTHOR]

Traditional engineering approaches are often inefficient and vulnerable, and cities face increasing challenges due to population growth, consumption rates and persistent inequalities. This paper aims to identify Nature-based Solutions (NbS) for water-sensitive European cities, categorising interventions according to their essential ecological functions for urban resilience. Through analysis of 77 projects in 46 cities, with interventions at three spatial scales, it examined a variety of actions that impact urban water management, addressing key challenges and their environmental impacts at the urban scale. The study concluded the importance of four categories of blue infrastructure and three categories of green infrastructure, highlighting their contribution to flood protection, water quality improvement and stormwater management. In Mediterranean regions facing water scarcity and extreme weather events, NbS represent a paradigm shift towards urban resilience and sustainability. [ABSTRACT FROM AUTHOR]

Virtual reconstruction of ancient buildings often has incomplete records of the original design and construction details, and can only be reconstructed based on limited data, drawings and photography, which is different from the actual conditions. The unique overhanging structure of the corridor gable building makes it vulnerable to damage in extreme weather conditions. In order to ensure that the virtual reconstruction results can not only reproduce the original appearance of history, but also ensure that the reconstructed model maintains structural stability in the long term. This paper proposes a reconstruction method of the original appearance of the corridor gable building remains based on structural dynamics analysis. This method comprehensively uses three-dimensional reconstruction, structural engineering, dynamic analysis, and computer simulation technology to ensure the structural accuracy and historical authenticity of the virtually reconstructed corridor gable building. First, through data collection and analysis, combined with ancient architectural construction techniques, a preliminary three-dimensional model was created, which included all structural elements and details. Several groups of reconstruction schemes are determined based on material properties. Then, using finite element analysis software, perform dynamic analysis on the three-dimensional model. Evaluate the stability of the reconstructed structure and optimize the material selection plan to ensure the feasibility and accuracy of the virtual reconstruction. Taking the virtual reconstruction of the eaves in front of Leiyin Cave as an example, it shows that this method is effective and feasible to achieve the virtual reconstruction of corridor gable buildings. It provides new ideas for virtual reconstruction of ancient buildings and has important practical application value. [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]

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]

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]

Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models. Plain Language Summary: A machine learning model is employed to bias correct the large‐scale dynamical and thermodynamical fields simulated by a low‐resolution global climate model. The impact of the machine learning model on the large‐scale storm environment associated with tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) was evaluated. It is found that the ML bias correction can effectively reduce the mean climate biases in large‐scale wind, temperature, and humidity fields associated with the three types of high‐impact weather systems. For storms such as ETCs and ARs that can be partly resolved by the low‐resolution climate models, the machine learning bias correction shows skills in improving the long‐term statistics of these weather events. For storms such as TCs that can not be well resolved in the low‐resolution climate models, the machine learning approach produces more realistic statistics of the tropical cyclone tracks by providing more realistic large‐scale steering winds for downscaling approaches. By reducing model biases without affecting the climate change signals in large‐scale storm environments derived from the low‐resolution climate model simulations, machine learning bias correction has the potential to provide more reliable projections for assessing future changes in extreme weather events. Key Points: A machine learning approach with a long short‐term memory network is used to bias correct climate simulations from the E3SM atmosphere modelThe approach effectively reduces biases in the simulated large‐scale model states while preserving their responses to climate changeImproving large‐scale storm environment with bias correction facilitates modeling of high‐impact weather systems and their future changes [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]

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]

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse. [ABSTRACT FROM AUTHOR]

Monoculture systems in South East Asia are facing challenges due to climate change-induced extreme weather conditions, leading to significant annual production losses in rice and oil palm. To ensure the stability of these crops, innovative strategies like resilient agroforestry systems need to be explored. Converting oil palm (Elaeis guineensis) monocultures to rice (Oryza sativa)-based intercropping systems shows promise, but achieving optimal yields requires adjusting palm density and identifying rice varieties adapted to changes in light quantity and diurnal fluctuation. This paper proposes a methodology that combines a model of light interception with indoor experiments to assess the feasibility of rice–oil palm agroforestry systems. Using a functional–structural plant model of oil palm, the planting design was optimized to maximize transmitted light for rice. Simulation results estimated the potential impact on oil palm carbon assimilation and transpiration. In growth chambers, simulated light conditions were replicated with adjustments to intensity and daily fluctuation. Three light treatments independently evaluated the effects of light intensity and fluctuation on different rice accessions. The simulation study revealed intercropping designs that significantly increased light transmission for rice cultivation with minimal decrease in oil palm densities compared with conventional designs. The results estimated a loss in oil palm productivity of less than 10%, attributed to improved carbon assimilation and water use efficiency. Changes in rice plant architecture were primarily influenced by light quantity, while variations in yield components were attributed to light fluctuations. Different rice accessions exhibited diverse responses to light fluctuations, indicating the potential for selecting genotypes suitable for agroforestry systems. [ABSTRACT FROM AUTHOR]

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event. [ABSTRACT FROM AUTHOR]

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions. [ABSTRACT FROM AUTHOR]

Climate change and the variable extreme weather conditions that today's agricultural producers are dealing with represent some of the most important issues in food production. Nowadays, weather patterns are increasingly unpredictable, characterized by frequent fluctuations in temperature, precipitation, and extreme weather events. As a result, there is an increasing demand for scientists to develop more resilient and tolerant crop cultivars. Plant breeders must become creative and utilize all available resources to create modern high-yielding and widely adapted cultivars to help agriculture grow and thrive amidst the emerging changes. Forage legumes, due to their beneficial characteristics, are among the crops that can contribute to mitigating the consequences of climate change. Furthermore, what certainly does not contribute to weather conditions is the erosion of plant genetic material, which has been caused by the modernization of agriculture and the selection of the best cultivars with desirable traits over many years. Crop wild relatives (CWRs) and landraces represent plant genetic materials rich in novel gene variants that contain traits for resistance and tolerance to different climatic conditions. To expand the genetic base of cultivars and mitigate the consequences of climate change, breeders are increasingly utilizing pre-breeding methods. These methods include all the activities connected to the identification of desirable genes and traits from un-adapted materials, such as CWRs, and the transformation of these traits to an intermediate set of materials that can be used for creating new cultivars. This review paper will cover the pre-breeding process, including its components, and the resistance and/or tolerance of the CWRs and landraces of forage legumes to different extreme environmental conditions. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

In the mid–high latitude atmosphere, the instability of planetary waves characterizes enhanced planetary wave activity or amplified large‐scale waviness leading to increased regional weather extremes. In this paper, a nonlinear Schrödinger equation is derived to describe the evolution of planetary waves. Then the consequences of Arctic amplification (AA)‐induced meridional background potential vorticity (PVy) changes on the modulational instability of planetary waves are examined. It is found that the modulational instability of uniform planetary wave trains mainly results from the presence of high‐order dispersion and nonlinearity, even though such an instability depends on the amplitude, vertical structure and zonal wavenumber of uniform planetary waves and the atmospheric stratification. Because the nonlinearity and high‐order dispersion depend on the magnitude of PVy, the modulational instability of planetary waves is significantly influenced by the variation of PVy associated with AA. It is also revealed that stronger modulational instability of planetary waves tends to occur in the smaller PVy region or in higher latitudes due to both stronger nonlinearity and weaker high‐order dispersion for fixed background and planetary wave parameters, which is conducive to more intense large‐scale waviness. However, because AA can reduce PVy in the mid–high latitudes mainly in the lower troposphere via reductions of winter zonal winds and meridional temperature gradients, the reduced PVy under AA can significantly enhance the modulational instability. Thus, the role of AA is to amplify planetary wave activity in mid–high latitudes through strengthening the modulational instability of planetary waves due to reduced PVy, which further enhances large‐scale waviness. [ABSTRACT FROM AUTHOR]

Climatic and geographic factors characterizing local conditions, as well as the ecological state of the external environment, have a direct effect on human health, morbidity, and life expectancy. The paper considers the basic concepts of medical ecology, a pathogenetic role of environmental atmospheric pollution in the development of environmentally induced and environmentally dependent diseases associated with exposure to meteorological factors. In addition to the major hygienic risk factors affecting the public health (a level of air, water, and soil pollution), the levels of acoustic and electromagnetic background, the type of human nutrition, architectural and planning conditions of human life are of significant importance. Global climate warming causes numerous environmental changes, primarily an increase in surface air temperature, which in turn entails global changes in weather conditions that lead to various weather anomalies. Human health is directly dependent on environmental conditions and a degree of adaptation to them. The paper highlights the issues of diagnosing environmentally induced diseases and measures to counteract their spread. Based on the system diagnosis using physiological indicators of human health and the integration of the adaptation and nosological approaches, a hardware-software complex has been developed and introduced into medical practice. Corrective technologies of regenerative medicine that are used for ecologically dependent pathologies are described. Their application in the process of the complex sanatorium and resort treatment is most effective. [ABSTRACT FROM AUTHOR]

• Treat a problem of predicting the demand for staple food and feed grain. • A novel fractional discrete dynamic multivariate grey model is proposed. • The model considers the effects of linear correction term & random disturbance term. • Sailfish optimizer is used to optimize the fractional order accumulation parameter. • Empirical analysis shows the proposed method performs better than similar methods. Increasing geopolitical conflicts, extreme weather and other emergencies are continuously affecting global agriculture and food systems, threatening national and regional food security. Therefore, it is particularly important to accurately predict national or regional grain demand. In this paper, a novel fractional discrete dynamic multivariate grey model is proposed to predict grain demand. Based on the discrete modeling idea, the model considers the impacts of the change trend of the related factor sequences, adds the linear correction term and the random disturbance term, and applies the fractional order accumulation strategy, which improves the accuracy and robustness. An algorithm based on sailfish optimizer is introduced to optimize the fractional order accumulation parameter of the model. Taking the Yangtze River Economic Belt as an example, the fitting and prediction results of the novel and the existing three models are compared. The novel model can better fit and predict the demand for staple and feed grain, which is superior compared to other models. The predictions show that demand for staple food and urban feed grain in the Yangtze River Economic Belt will increase to varying degrees, while rural feed grain demand will remain stable overall. This paper will help the government to better grasp the changes in the structure and quantity of staple food and feed grain demand in the Yangtze River Economic Belt and formulate efforts to ensure food security. [ABSTRACT FROM AUTHOR]

The ecosystems of the Danube River basin exhibit remarkable biodiversity, housing over 2,000 plant species and more than 5,000 animal species. This places the Danube among Europe's most valuable rivers in terms of aquatic life diversity, with a total of 103 identified species. The Danube is increasingly affected by climate change, impacting the hydrologic cycle and the availability of food and water resources. The effects of climate change are already observable and projected to intensify, notably in increased frequency and intensity of extreme weather events such as heatwaves, drought periods, and floods. In this paper, we aim to present the impact of climate change on economically valuable fish within the Danube River. We have determined the abundance of these species and analyzed the correlation between recorded temperatures and the river's water level. Given the Danube's unique variety of species, we will highlight the importance of protecting and conserving this valuable ecosystem amidst the escalating climate threats. [ABSTRACT FROM AUTHOR]

The accumulation of carbon dioxide in the atmosphere due to fossil fuel burning and deforestation has caused global warming and an increase in extreme weather events. To complement the shift towards clean energy, it is crucial to adopt methods for carbon dioxide removal, known as negative emission technologies. Enhanced weathering is one such approach that involves accelerating the natural process of rock weathering by spreading finely ground rocks over large areas, such as agricultural land or coastal areas. This exploratory review paper provides an overview of the fundamental mechanisms behind enhanced weathering, and outlines the techniques for its implementation. The environmental benefits of enhanced weathering are highlighted, including carbon dioxide removal, and improvement of soil fertility. Furthermore, potential impacts on ecosystems and biodiversity are examined, along with the effects on water, soil and air quality. The paper also considers the risks and challenges associated with large-scale implementation and long-term stability of enhanced weathering. Additionally, the integration of enhanced weathering with Sustainable Development Goals is explored, along with the potential co-benefits and trade-offs with other sustainability objectives. To conclude, this exploratory review paper summarizes the key findings and proposes avenues for further research in this field of enhanced weathering. [ABSTRACT FROM AUTHOR]

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaussian processing regression, and the proposed 6-gene genetic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly improved the correlation and reduced errors. However, MGGP showed the best results among the three methods. [ABSTRACT FROM AUTHOR]

Southeast Asia’s coastal communities are the frontliners of climate change impacts. Traditional livelihoods and fish landings are already declining due to changes in sea temperatures and increasingly frequent extreme weather. A human security lens should be used to examine the consequences of climate change impacts on the marginalised communities that suffer directly from it. This study adapts a 2001 Intergovernmental Panel on Climate Change vulnerability assessment for human and food security and assessment of potential conflicts, merging it with a tool to assess community resilience and adaptability to threats, as a framework to analyse the predicament of Malaysia’s artisanal fishermen. A case study based on 16 years of ethnographic and participant observation in a fishing community that depends on the maritime boundary between Malaysia and Singapore is used to illustrate the above. This paper demonstrates how local knowledge and experience in overcoming change should be used for better climate change adaptation policies and implementation. [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

Transmission lines are affected by external environmental factors such as strong winds and ice cover. In recent years, extreme weather events have increased, leading to recurrent disturbances in transmission lines because of wind deflection. These incidents have resulted in significant financial losses and have disrupted regular industrial and domestic activities. In this paper, the ANSYS Workbench 2020 R2 finite element analysis platform was used to establish a transmission line-hanging insulator string system model. Calculations on transmission lines were conducted considering variations in different stall spacing, height differences, wind speed, and the wind attack angle. The impact of these diverse factors on the wind deflection of insulators was scrutinized, leading to the derivation of patterns describing how the wind deflection angle shifts in response to changes in stall spacing, height differences, wind speed, and the wind attack angle. Based on the generalized linear regression network and particle swarm improved support vector machine algorithm, a meteorological prediction-based early warning method for wind deflection of transmission lines was proposed, a transmission line wind deflection early warning model was established, and the practical effect of the model was evaluated. The outcomes of this study provide crucial data for the formulation and development of ultra-high voltage (UHV) and extra-high voltage (EHV) transmission networks. Furthermore, they can contribute to the advanced detection of wind deflection issues. [ABSTRACT FROM AUTHOR]

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Globally, rural conditions are in states of change. They are often highly vulnerable to climate and environmental change, extreme weather events, conflict, socio-economic changes, inequalities, and demographic changes. These changes are putting stress on rural areas, which rely upon agriculture and natural resources for their livelihoods and are often the foundation of national economies. Communication for development (C4D) has played an important role in addressing these challenges. Its thinking is broadly consistent with rural development goals—indeed, the roots of C4D come in part from rural development and agricultural extension. Communication for development (C4D) was defined by the World Congress on Communication for Development as "...a social process based on dialogue using a broad range of tools and methods. It also seeks change at different levels, including listening, building trust, sharing knowledge and skills, building policies, debating, and learning for sustained and meaningful change. It is not public relations or corporate communications". However, after decades of action to address these interrelated rural development challenges, much remains to be done. This paper critically considers the following: What does inclusive rural development mean now, in light of environmental change, and how does this affect the conceptualisation and practice of C4D? This was done by using three countries as case studies: Malawi, Ukraine, and the Philippines. Each of these countries represented contrasting challenges and opportunities for rural development and environmental change, with lessons from their experiences shedding insight into the communication for development thinking. [ABSTRACT FROM AUTHOR]

Despite ample evidence on the labor productivity impact of heat shocks, little is known about how heat shocks affect labor adjustment over time. This paper uses administrative employer–employee linked data from Brazil to examine margins of labor market adjustment to extreme heat. Tracking workers over time and across job spells, I find that quarterly heat shocks increase the probability of formal manufacturing layoffs. In the medium run, there is limited intersectoral and interregional reallocation, as well as a significant failure rate to reallocate back to formal sectors, indicating job transitional costs. Finally, the employment impact of heat shocks is larger for workers in more routine manual task intensive, less abstract occupations. [ABSTRACT FROM AUTHOR]

This document provides a summary of a hackathon event organized by the Ministry of Justice (MoJ) and Aon, a reinsurance company, to explore the social impacts of climate change. The hackathon focused on developing consistent scenarios for future climate risk, understanding uncertainties in European windstorm modeling, and providing real-time context for extreme weather events. The teams developed tools and methodologies to assess the severity and potential impact of extreme events on insured losses. The collaboration between the MoJ, Aon, and participants from various institutions aims to further research and develop papers and grant proposals in these areas. [Extracted from the article]

The far-reaching effects of climate change are among the leading global concerns today. The impacts of changing climate manifest in rising global temperatures (on land and in the ocean), escalating destructive extreme weather events, increasing biodiversity loss, shifting biomes, growing food insecurity, greater health risks (physical and mental), and involuntary migration of people, among other interconnected factors. The complexity of these problems individually and collectively is receiving extensive consideration in research and public arenas. Concerns about the influences of climate change have been increasing since the 1950s and through the last half of the twentieth century scientific understanding reached a consensus of the causes and numerous negative outcomes. The impacts have become clear in the first decades of the twenty-first century. Researchers in many disciplines are cautioning that the world is rapidly reaching a tipping point in the overall health of the planet, after which recovery will be very difficult. In addition, decision makers are grappling with how to evaluate multiple and sometimes competing calls for action and to decide how to address the issues best. In both research and decision-making settings, grey literature has been a prominent information genre about the multifaceted aspects of climate. Large quantities of grey literature on climate and climate change subjects have been produced. Academic institutions, governmental and intergovernmental bodies, non-governmental organizations, professional associations, think tanks, and news media have turned out grey literature ranging from brochures to hefty technical reports. These materials have been generated in print and digital formats to fulfil a suite of roles: 1) to report research findings, 2) to compile and synthesize literature on research and professional practice, 3) to inform policymakers and the public, 4) to advocate for policy development, 5) to implement policy, 6) to promote change in professional and individual practice, 7) to educate; 8) to broker information and promote networking, and 9) to counter misinformation. In this paper we draw on research about decision making in public policy and management practice about coastal and marine environments to illustrate how grey literature on climate subjects has been deployed in delivering on these roles. Grey literature is widely used in many decision-making contexts. Recognizing its numerous roles can inform citizens, managers, planners, and policy- and decision-makers in addressing the climate challenges facing society today. [ABSTRACT FROM AUTHOR]

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain. [ABSTRACT FROM AUTHOR]

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index χ and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all χ ≤ 0.001 except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both χ = 0.3182 for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes. [ABSTRACT FROM AUTHOR]

The paper reports the first results of an experiment in northern Finland during winter 2022–2023 that aimed at studying seismic events caused by seasonal freezing in the so-called critical zone (CZ) of the Earth. These events have attracted public attention recently, as multiple reports about them from local inhabitants in Arctic and sub-Arctic areas appeared on social networks. To make an instrumental study of such events, to reveal the relationship between their occurrence and winter weather conditions, and to evaluate the possible hazard, we installed two high-resolution seismic arrays with co-located soil temperature stations at two sites in Finland, one of them being in the city of Oulu in the sub-Arctic area (65.04° N, 25.61° E) and the other one above the polar circle in the municipality of Sodankylä (67.36° N, 26.63° E). The equipment recorded continuous seismic and soil temperature data during November 2022–April 2023. Based on reports from the inhabitants of Talvikangas (Oulu) about the ground shaking and unusual noises on 6 January 2023 and their observations of new fractures on the roads there, we selected the time interval for the identification of frost quakes that originated during that day from continuous seismic records in Talvikangas and in Sodankylä. During the selected time interval, the extremely rapid air temperature drops of about -1.4 °C h -1 in Talvikangas and -0.88 °C h -1 in Sodankylä were observed. We identified and located two types of seismic events – namely, frost quakes with frequencies of about 10–20 Hz, with waveforms like those of tectonic events, and irregularly shaped frost tremors with frequencies of about 120–180 Hz. The sources of frost quakes in Talvikangas are mainly located on irrigated wetland, while in Sodankylä, about 50 % of registered frost quakes were caused by ice fracturing on the Kitinen River. However, several relatively strong events, with their origin in the wetlands, were also recorded. A significant number of sources of frost tremors are confined to wetland areas cut by irrigation channels and to roads cleaned from snow during winter both in Talvikangas and in Sodankylä. We calculated ground accelerations and ground velocities for the strongest events from both groups and compared them to equivalent properties of other seismic signals, like distant local earthquakes in the area, mining production blasts, and cargo train vibration. Our study shows that high-frequency frost tremors corresponding to surface fracture opening in the uppermost frozen surface layer of a thickness of up to 5 cm can directly damage infrastructure objects like roads and basements of buildings. Surface waves, produced by frost quakes and propagating inside the shallow soil layer, have large enough ground accelerations at epicentral distances of hundreds of metres. Therefore, frost quakes should be considered a phenomenon that can potentially damage infrastructures, and they have to be taken into account in seismic hazard assessments. Our research is the first instrumental study of seismic events originating from wetland areas. These events occur as a result of the interaction between the uppermost layer of the solid Earth's CZ and atmosphere processes that deserves further study. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor is a crucial factor in the Earth's water cycle. As an important greenhouse gas, changes in the spatio‐temporal distribution of atmospheric water vapor can contribute to the occurrence of various extreme weather phenomena. Lidar, with its high spatial and temporal resolutions, has great potential for applications in water vapor profile detection. Raman lidar and differential absorption lidar (DIAL) have been successfully used to detect atmospheric water vapor. System calibration is crucial to ensure that the measured profile accurately represents the concentration profile of atmospheric water vapor. Choosing an effective system calibration method can ensure the accuracy of long‐term lidar measurements. This paper reviews the latest progress and applications of atmospheric water vapor lidar calibration in recent years. The basic principles of Raman lidar and DIAL calibration are introduced. Various methods and benefits of system calibration are discussed. Raman lidar has three commonly used calibration methods: external calibration, internal calibration, and hybrid calibration methods. The most commonly used method is external calibration based on radiosondes. DIAL is usually implemented with an advantageous self‐calibration method. Finally, potential development directions for atmospheric water vapor lidar and calibration technology are discussed. This article is categorized under:Science of Water > MethodsEngineering Water > MethodsHuman Water > Value of Water [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]