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Humanity is facing a polycrisis, including climate change, loss of biodiversity, and economic inequalities. This commentary examines the role of physical activity (PA) research in the context of climate change, highlighting 3 perspectives: PA as a victim, as a perpetrator, and as part of the solution. The first perspective conceptualizes PA as a victim of climate change, as heat waves, extreme weather events, and high ozone levels pose a threat to health-enhancing PA and sports; adaptation strategies are needed and potentially lifesaving. The second perspective describes PA as a perpetrator to climate change due to the significant carbon footprint of some forms of PA and sport. The third perspective focuses on PA as a solution to climate change, as some forms of PA—such as active transport and urban gardening—have both individual and planetary health benefits. In addition, PA provides health benefits in extreme situations by reducing stress and maintaining health in times of crisis. Each of these 3 perspectives can guide future research in the field of PA and health. [ABSTRACT FROM AUTHOR]

The Journal of Physical Activity and Health has evolved over the past 20 years to address global challenges such as climate change, social inequalities, and pandemics. A special issue titled "Physical Activity as a Necessary Solution to Current Global Health Challenges" explores the multifaceted relationship between physical activity and various global issues. The issue covers topics ranging from climate change to equity, highlighting the importance of promoting physical activity for mental and social health benefits. Moving forward, the journal advocates for a mission-oriented research paradigm that emphasizes collaboration, equity, advocacy, and implementation of policies to address the complexities of physical inactivity in the modern era. [Extracted from the article]

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]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Food insecurity in Ethiopia is an immediate humanitarian crisis that is expected to worsen due to population growth and climate change. This study applied GIS-based approaches to evaluate the feasibility of deploying an emergent type of single-cell protein (SCP) technology to supplement the nutritional needs of Ethiopian citizens who are most vulnerable to drought. The technology—power-to-protein (PtP)—uses H2 and O2 from water electrolysis and CO2 from woody biomass combustion in a two-stage bioprocess to produce nutrient-rich protein powder for human consumption. Population density, land use, and other geographical data were used to identify optimal site locations for these PtP systems based on two deployment strategies: large centralized plants vs. small decentralized units. The model also accounted for biomass availability, collection, and distribution logistics. The analysis revealed three sites that are both (highly) vulnerable/food-insecure and accessible within walking distance. The identified sites are proximate to the urban areas of Mekele in northern Ethiopia, Addis Ababa in central Ethiopia, and Hawasa south of Addis Ababa. If centralized PtP were deployed, the protein requirements of these populations could be sustained for several months, assuming a modest biomass collection radius of 35 km. Decentralized PtP deployment was similarly effective, requiring a distribution density of 5.4–11.0 PtP units per km2 under conservative estimates and 0.76–1.1 units per km2 under optimistic estimates. Lastly, a theoretical comparison showed that PtP is more efficient than conventional agricultural food production regarding biomass-to-protein conversion yields. Overall, our study suggests that PtP technology would be a feasible approach to supplement the nutritional needs of Ethiopian people in times of drought-related emergencies. However, given logistical limitations and considering social preference factors, it would be more practical to implement PtP in conjunction with standard emergency food aid measures. [ABSTRACT FROM AUTHOR]

As the rising extreme weather events occurring in the Philippines, the extreme heat index during El Niño and consecutive tropical typhoons during La Niña created construct consciousness of the Filipinos with regards to climate change that is also increasing over the period; this consciousness may be labeled as a psychological term to "Ecoanxiety" which may create an adverse impact in terms of emotional and physical aspects. Thus, this study aims to investigate awareness, explore factors, and propose programs regarding eco- anxiety inside the urban locale area and was done in collaboration with the City Environment Service Department (CESD)- Bacoor (formerly known as City Environment and Natural Resources Office (CENRO). The research was conducted in the three areas of Bacoor, namely San Nicolas III, Molino III, and Molino IV, which employed mixed- methods design, incorporated both qualitative and quantitative approaches, and used validated instruments for interviews and survey questionnaires. There were 400 survey respondents, and 15 participants were interviewed. The data was analyzed using the following statistical treatment of frequency count and percentage and weighted mean for descriptive interpretation; the Colaizzi method was utilized for qualitative analysis. As such, results indicated that participants experienced eco-anxiety at a relatively low level. Coping mechanisms included relaxation techniques, optimism, and engaging in recreational activities. Physical manifestations of eco-anxiety included sleeping difficulties and fatigue. While it generally had a minor impact on social relationships, some respondents reported occasional disruptions and conflicts, especially related to differing environmental perspectives. The study recommends considering areas that are directly impacted by climate change, such as flood-prone areas. Additionally, the higher level of the term awareness may create significant contributions towards environmental sustainability in communities. [ABSTRACT FROM AUTHOR]

In challenging visibility conditions, the reliability of existing port lighting systems is significantly affected by abrupt changes in environmental factors (primarily stemming from ocean weather). This study proposes a cloud-edge collaborative dimming model that integrates a combined filter, enabling dynamic adaptation to these weather variations to ensure the stability of the lighting system. Additionally, the application of edge computing not only alleviates computational pressure but also facilitates the model's ability to achieve effective regional adaptive dimming in accordance with environmental regulations. Experimental results indicate that this method is suitable for scenarios with unknown mutations under extreme conditions, providing a more reliable and intelligent solution for port lighting systems within the Internet of Things (IoT) framework. [ABSTRACT FROM AUTHOR]

Traditional mid‐term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, we propose to leverage openly accessible weather reports for electricity demand and meteorological time series prediction problems. Our experiments on French and British load data show that the considered textual sources allow to improve overall accuracy of the reference model, particularly during extreme weather events such as storms or abnormal temperatures. Additionally, we apply our approach to the problem of imputation of missing values in meteorological time series, and we show that our text‐based approach beats standard methods. Furthermore, the influence of words on the time series' predictions can be interpreted for the considered encoding schemes of the text, leading to a greater confidence in our results. [ABSTRACT FROM AUTHOR]

Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, a common limitation of these ML models is their tendency to generate increasingly smooth predictions as forecast lead times increase, which often results in the underestimation of intensities of extreme weather events. To address this challenge, we developed the FuXi-Extreme model, which employs a denoising diffusion probabilistic model (DDPM) to enhance finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. An evaluation of extreme total precipitation (TP), 10-meter wind speed (WS10), and 2-meter temperature (T2M) illustrates the superior performance of FuXi-Extreme over both FuXi and HRES. Moreover, when evaluating tropical cyclone (TC) forecasts based on International Best Track Archive for Climate Stewardship (IBTrACS) dataset, both FuXi and FuXi-Extreme shows superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES. [ABSTRACT FROM AUTHOR]

Subsidized insurance against extreme weather events improves affordability among households in at‐risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at‐risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood‐prone properties. This fully offsets the negative direct effects of flooding on property prices, with high‐income areas and high‐value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution. [ABSTRACT FROM AUTHOR]

One of society's most pressing challenges in the twenty-first century is that of climate change. In fact, climate change is seen as the most defining issue of our time as we are witness to an anthropogenic perturbation in geology and earth sciences of global scale. To move forward in this new era, solutions will be sought to both mitigate the effects of climate change (e.g., reduce greenhouse gasses) as well as adapt and build resilience (e.g., improve infrastructure and agriculture to resist damage from extreme weather or floods). The immediacy of the needed solutions dictates that the response must use the full force of society's current knowledge base, science, technology, and innovation. Nanotechnology, an enabling technology that has matured over the past few decades and now considered for general-purpose and mass use, is ideal for addressing climate change and its impacts. To position nanotechnology to address such complex challenges, this Perspective integrates collective insights from a broad range of viewpoints and presents recommendations for how research can be motivated and scoped, organized, and implemented to achieve beneficial outcomes and innovations in the most efficient ways. While this Perspective was created with a focus on the research landscape within the United States, the findings are also relevant in other international contexts. Research that can effectively advance nanotechnology solutions will be use-inspired basic research, incorporate systems-level thinking, apply a convergence research approach, engage stakeholders, and require advanced nanotechnology infrastructure. By illuminating this compelling and complex research topic, this Perspective aims to direct, inform, and accelerate needed actions in the research community to advance nanotechnology solutions for addressing climate change. [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]

Climate change has caused devastating extreme weather in East and South Asia during the summer of 2022. The Ooty muon telescope records short-term variations in muon intensity during major thunderstorms, termed thunderstorm-induced muon events (TIMEs). As part of the GRAPES-3 experiment, the telescope detects approximately 370 billion muons every summer. Its excellent angular resolution validates the TIMEs with small directional variations. In the summer of 2022, the telescope detected 18 TIMEs, compared to eight in the previous summer. A sudden increase in TIMEs is possibly a harbinger of climate change. [ABSTRACT FROM AUTHOR]

In extremely cold regions with frequent extreme weather conditions, coupled factors make concrete corrosion by garbage landfill leachate become more complicated. Although previous studies have characterized the effects of individual factors, the mechanisms of coupled effects under specific conditions require further research. Therefore, this study analyzed the individual and coupled effects of landfill leachate, salt solution, and freeze–thaw cycles. The corrosion mechanism of landfill leachate was explained by analyzing the mechanical properties, surface morphology, and microstructure of the concrete. The individual factor tests showed that landfill leachate affected the performance of concrete more significantly than salt solution in the short term. In the immersion tests, the strength of concrete specimens corroded by landfill leachate was always minimized throughout the process, and the compressive and flexural strengths of concrete in landfill leachate decreased by 6.40% and 7.83%, respectively. In the coupled tests, freeze–thaw cycles amplified the effect of landfill leachate on concrete, exhibiting more severe damage than that with only landfill leachate. After four test cycles, the compressive strength of the concrete in the coupled test group decreased by 22.5%, 19.7%, and 31.3% and the flexural strength decreased by 40.2%, 26.7%, and 46.3%, respectively. Through surface morphology and microstructure analyses, we found that the inorganic salt ions in the landfill leachate reacted to form expansive ettringite and gypsum, which initially optimized the internal structure of the concrete but caused severe damage in the later stage. Concrete corrosion by landfill leachate was significantly enhanced under the influence of freeze–thaw cycles. The experimental results of this study provide basic data and theoretical support for designing and protecting concrete buildings in extremely cold environments. Practical Applications: The deterioration of concrete buildings in landfills by landfill leachate corrosion has received widespread attention, and this problem is pronounced in cold regions. This study selected landfill leachate, salt solution, and freeze–thaw cycles as the corrosion conditions for the concrete corrosion test under one-factor conditions. At the same time, landfill leachate and salt solution were coupled with freeze–thaw cycles for the concrete corrosion test. The severity of concrete damage was compared by the changes in mechanical properties and microscopic morphology of the concrete during the test. The results of the one-factor corrosion test showed that the landfill leachate would cause more damage to the concrete in the short term. Meanwhile, the coupled-factor corrosion test results showed that freeze–thaw cycles amplify concrete corrosion by landfill leachate, resulting in a significant reduction in the mechanical properties of concrete and damage to the internal structure. In addition, the study characterized the mechanism of action of landfill leachate corrosion on concrete. This could also provide theoretical support for constructing and protecting concrete structures in landfills in cold regions. [ABSTRACT FROM AUTHOR]

Extreme cold events, which have become more frequent, can revert the direction of long-term responses to climate change. In 2021, record snowstorms swept the United States, causing wildlife die-offs that may have been associated with rapid natural selection. Our objective was to determine whether the snowstorms caused natural selection in Eastern Bluebirds (Sialia sialis). To test which mechanism most influenced their survival, we measured the morphology and coloration of fatalities and survivors at three sites. Survival was associated with a longer tarsus and with a wider, longer, and deeper beak, in support of the starvation and thermal endurance hypotheses. Additionally, bluebirds with more-ornamented plumage were less likely to have survived, perhaps because of an early energy investment in mate and site acquisition. As bluebirds encounter increasingly warm summer conditions, the longer extremities favored during the snowstorms may continue to be favored through their thermoregulatory benefits. However, the dull plumage coloration favored by natural selection during the snowstorms may be opposed by sexual selection benefits of more-ornamented plumage. Overall, responses to extreme events are difficult to predict from responses to long-term climate change, and responses to one event, such as the 2021 snowstorms, may not predict responses to a future extreme event. [ABSTRACT FROM AUTHOR]

Wicked problems of climate change, extreme weather events, pandemics, and rising inequality are pushing businesses to explore the next frontiers of resilience, innovation, and sustainability. Although previous studies have examined diverse aspects of resilience, innovation, and sustainability independently, attempts to integrate all three are limited. Further, in the literature, there is a growing need to adopt an interdisciplinary approach to regeneration for solving wicked problems. We aim to address this gap by combining a systematic literature review with practice review methodology to unearth evidence and research themes that integrate resilience, innovation, sustainability, circularity, and regeneration. In this paper, we present 10 cases, including diverse businesses and Net Zero Cities that have adopted business practices for regenerative, sustainable, and circular value creation. In doing so, we extend the discourse on sustainable or circular business models by proposing a definition of regenerative innovation and developing four archetypes of regenerative innovation (urban transformation innovation, technological innovation, responsible innovation, and business model innovation). We draw from the theoretical lens of socio-ecological systems to present nature as the key stakeholder for regenerative businesses. We make novel contributions to theory, practice, and policy by highlighting a new system-based paradigm of regenerative innovation for future resilient products and services. [ABSTRACT FROM AUTHOR]

In recent years, climate change has significantly impacted European river systems, reshaping streamflow patterns with far-reaching consequences for ecosystems and water resources. This study investigates hydrological variability across 16 major European rivers. Cross-correlation analysis reveals strong climatic influences, with the Danube showing robust positive correlations with rivers such as the Elbe, Oder, Rhine, and Vistula, suggesting shared climatic drivers. In contrast, negative correlations between Central European rivers, like the Danube and Drava, and Southern European and UK rivers highlight the influence of distinct climatic regimes and geographical factors. To assess trends and identify abrupt changes in streamflow, the study applies the Mann-Kendall (MK), Seasonal Kendall (SK), and Regional Kendall (RK) tests, along with the Bayesian Estimator of Abrupt Change, Seasonal Change, and Trend (BEAST) algorithm. Hierarchical clustering analysis identifies four distinct groups: one cluster dominated by a strong negative trend in the Ebro River, another with a positive trend in the Thames, and two additional clusters representing Eastern and Western European rivers, with a few exceptions. The findings from the MK and SK tests reveal declining trends in rivers such as the Danube, Drava, and Nemunas, likely driven by long-term climatic shifts. In contrast, the Thames exhibits significant increases in streamflow, likely reflecting unique climatic conditions in Southern UK. The RK test further corroborates these observations, showing an overall pattern of declining streamflow across the studied rivers. BEAST analysis uncovers historical variability by identifying important transitions on long-term trends and abrupt changes in streamflow. Rivers like Danube, Loire, Drava, Nemunas and Vistula transitioned from positive to negative trends starting in the 1970s to 1990s. BEAST reveals also more complex dynamics in rivers like Rio Ebro and Thames, where both upward and downward abrupt changes reflect the impact of extreme weather events. [ABSTRACT FROM AUTHOR]

The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather events. Initially, a grey relational analysis is conducted to identify key environmental factors influencing photovoltaic power generation. Subsequently, GMM clustering is utilized to classify meteorological data points based on their probabilities within different Gaussian distributions, enabling comprehensive meteorological clustering and extraction of significant extreme weather data. The data are decomposed using VMD to Fourier transform, followed by smoothing processing and signal reconstruction using KELM to forecast photovoltaic power output under major extreme weather conditions. The proposed prediction scheme is validated by establishing three prediction models, and the predicted photovoltaic output under four major extreme weather conditions is analyzed to assess the impact of severe weather on photovoltaic power station output. The experimental results show that the photovoltaic power output under conditions of dust storms, thunderstorms, solid hail precipitation, and snowstorms is reduced by 68.84%, 42.70%, 61.86%, and 49.92%, respectively, compared to that under clear day conditions. The photovoltaic power prediction accuracies, in descending order, are dust storms, solid hail precipitation, thunderstorms, and snowstorms. [ABSTRACT FROM AUTHOR]

Although an extensive body of research examines the implications of climate change for various aspects of human health, little conceptual or empirical work has focused on how climate change may affect addiction recovery. This paper addresses this gap by identifying probable pathways through which climate change may impact recovery, drawing on environmental, public health, and recovery research. Applying the theory of recovery capital, I outline the potential effects of climate change on social, physical, human, and cultural recovery domains. For example, in the domain of social capital, extreme weather and climate-related displacement are likely to make it more challenging for people in recovery to attend social events and support groups, though the use of digital support services may partially mitigate negative impacts. I synthesize this research in a model of probable pathways through which climate change will affect recovery capital and recovery outcomes, highlighting the ways in which marginalization related to factors such as race, income, and age is likely to shape the magnitude of effects. Building from this model, I discuss research implications, including the need for qualitative studies that examine how people engage as resilient actors pursuing recovery in the face of this ongoing global crisis, as well as quantitative studies that test hypotheses about the specific links between climate change and recovery capital. This paper concludes with implications for practice at individual, organizational, and societal levels, describing actions that service providers and people in recovery can take to build recovery capital while pursuing climate justice activism. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is a phenomenon that can lead to extreme weather events over a large region, yet its causes are not fully understood. Global climate models show limitations in representing Northern Hemisphere blocking, especially its frequency, and decadal variability in Greenland blocking in summer in the recent decades. In this study we evaluate the ability of high-resolution (HighResMIP) Earth System Models (ESMs) to simulate summer blocking over the Greenland area, using different but complementary methods to describe the characteristics of blocking. We find that the HighResMIP ensemble can reproduce the spatial pattern of Greenland blocking events, albeit with systematic biases, and capture the relative frequencies of the main blocking patterns: namely the wave breaking structure, North Atlantic ridge, and omega-type blocking. However, the HighResMIP ensemble fails to simulate the observed temporal variations of Greenland blocking index (GB2) and the extremely high values of daily GB2 observed in recent decades. In addition, we do not find clearly superior representation of blocking features from higher-resolution in HighResMIP models compared with lower-resolution models. We also find large sea surface temperature (SST) biases over the North Atlantic and seas surrounding Greenland, and biases in moisture transport over the North Atlantic toward Greenland, especially over the western flank of blocking areas, which might together contribute to model biases in the representation of blocking magnitude. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Agricultural Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pacific Island Countries (PICs) are vulnerable to climate change impacts, including sea level rise, extreme weather events and other environmental changes. Planned relocation can be an adaptive response to climatic threats. In Fiji, six communities have already relocated. While there is growing interest in planned relocation, there are few empirical case studies from which to learn. Narikoso village, in the Kadavu Province of Fiji, undertook partial relocation in 2020. Drawing on qualitative research (interviews, group discussions, observation), informed by Vanua methodology in 2022, this study examines the impacts of partial planned relocation on people's lives and livelihoods. Seven sustainable livelihood assets – or forms of 'capital' – are explored: natural, social, financial, human, physical and cultural, with the addition of spiritual. Our research found that planned relocation altered forms of capital that underpin sustainable livelihoods, leading to both benefits and problems. We argue that planned relocation must not only reduce exposure to climatic and environmental risk, but promote and preserve the integrity of local ecosystems, value continuity of culture and sustain and develop diverse assets that support sustainable livelihoods. This demands deep engagement with climate change‐affected communities to ensure that planned relocations sustain people's livelihoods, dignity and survival. [ABSTRACT FROM AUTHOR]

This study built truck productivity prediction models incorporating real-site weather conditions at varying temporal resolutions. The best models were combined with SHapley Additive exPlanations to offer quantitative and qualitative analysis for input variables' effect on the model outputs. The results showed that mining engineers can make more accurate predictions of truck productivity at the weekly resolution compared with other resolutions. The three most influential input parameters were haul distance, empty speed, and ambient temperature. Extreme weather, such as strong wind speed, heavy precipitation, and extreme relative humidity, had a certain effect on truck-shovel allocation. Meanwhile, a unified graphical user interface was developed to predict mine truck productivity. [ABSTRACT FROM AUTHOR]

In the Earth's general circulation, polar jets act as baroclinic pumps of angular momentum and heat. Reanalysis datasets indicate that shear changes near jets induce upward displacements of the jet cores, suggesting a weakening thermodynamic pumping over the last 50 years. From secondary flow theory, a well‐established principle in fluid dynamics, an increased frequency of heatwaves and persistent winter storms is expected. The ageostrophic wind shear between 700 and 50 hPa indicates the strength of this secondary circulation. The weakening tendency during the reanalysis period also exists in multimodel simulations under the RCP8.5 emissions scenario for the 21st century. The reduction between the periods 2005–2025 and 2081–2100 reaches 18%, 5.3% and 19%, respectively, for the North America, Mid‐Europe and East Asia sectors of the Northern Hemisphere polar jet. Within this background, cold‐surge events are the result of synergic co‐working of several factors. The occurrence trend for transitional season winter extreme events also is examined. The winter extremes seemingly have larger temperature drops. However, in a warming climate, they emerge more rapidly from extreme cold states. The storm tracks, especially over North America, have equatorward extensions, indicating that winter storms can reach lower latitudes. Due to the temperature‐dependence of air viscosity, secondary flows decrease more slowly than the main zonal flow. This imposes an important adjustment to the traditional polar amplification effects on midlatitude winter extremes. During a warmer winter (the primary manifestation of a warmer climate), spatially uniform positive trends in cold extreme events are not expected. There are, however, regions experiencing more winter extremes. These regions show consistent patterns in both the reanalysis period and the remainder of the 21st century. [ABSTRACT FROM AUTHOR]

Arctic Oscillation (AO) is the dominant mode of atmospheric circulation in the extratropical Northern Hemisphere regions. The spatial pattern of the AO Arctic center affects the extent of polar cold air outbreaks southward. However, the underlying nature and causes of its interdecadal variation remain unclear. Utilizing ERA5 reanalysis data, this study identifies two distinct spatial patterns of the wintertime AO Arctic center through the K‐means clustering method, which alternate over different decade periods. The Double‐trough pattern generates a tripolar temperature pattern of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau" through a Rossby wave train during 1960–1997/2013–2024. While the Single‐trough pattern leads to a dipolar temperature pattern in 1998–2012. Furthermore, interdecadal variations in North Atlantic sea surface temperature meridional gradient act as an influencing factor in shaping the spatial pattern of the AO Arctic center. This research aids the understanding and prediction of climate anomalies using AO signals within various decadal contexts. Plain Language Summary: Arctic Oscillation (AO) is a key extratropical atmospheric variability mode in the Northern Hemisphere, significantly influencing weather and climate anomalies in the mid‐latitudes. The shape of AO's trough in the Arctic center governs the southward advancement of Arctic cold air. Using the K‐means clustering method, the spatial patterns of the wintertime AO Arctic center are primarily divided into two distinct categories, which alternate across different decades. Specifically, from 1960 to 1997 and 2013 to 2024, the AO Arctic center exhibits dual troughs over the Ural Mountains and North America, resulting in a distribution of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau." However, during the period from 1998 to 2012, only the North American trough is present, and its temperature connection with Eurasia weakens. Notably, interdecadal variations in the AO Arctic center are affected by North Atlantic sea surface temperature meridional gradient. This study enhances our understanding of the physical significance of AO and assists us in better utilizing it to predict winter weather and climate anomalies. Key Points: The AO Arctic center shows two spatial patterns alternating between Double‐ and Single‐trough configurations on an interdecadal timescaleThe two spatial patterns of the AO Arctic center are modulated by the interdecadal variations of North Atlantic SST meridional gradientThe Double‐trough pattern of the AO Arctic center can cause a unique tripolar pattern of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau" [ABSTRACT FROM AUTHOR]

Solar power will become the largest renewable energy source, contributing to global carbon neutrality. In addition to the well‐recognized temporal intermittency of solar energy supply, the local energy demand to cope with extreme weathers can further stress the energy grid; both the supply and demand can be greatly influenced by future climate change. Here, we redefine solar drought events by considering supply demand imbalance in solar power. Observation and multi‐model simulations reveal an anthropogenic exacerbation of global solar drought frequency in the past three decades. Moreover, compared to the pathway SSP2‐4.5, the carbon neutrality pathway SSP1‐2.6 can mitigate the increasing frequency and severity of solar droughts by 60% and 63% in the 2090s, respectively. Our study suggests a co‐benefit of solar energy stability and security toward carbon neutrality, especially in developing nations where the local supply demand imbalance is a major issue. Plain Language Summary: Solar power is a key renewable source to support the formidable target of carbon neutrality in the coming decades. However, the supply and demand of solar power are both influenced by future climate change. To our knowledge, no studies have provided an integrated perspective of both the supply and the demand side and the potential imbalance lasting for 3 days due to the more impactful compound events. In this study, we first develop an index (SDI) to describe the dynamic changes of Supply Demand Imbalance relationship in solar power and then quantify the potential risk of solar droughts at the global scale. Based on observation and simulations, we reveal an anthropogenic exacerbation of global solar drought frequency in the past three decades. Furthermore, we project notable regional inequality in the possible energy imbalance and highlight the mitigating effect of the carbon neutrality policy on increasing frequency and severity of solar droughts toward the end of the 21st century. Key Points: The frequency of global solar droughts shows large spatial heterogeneityGHG forcing dominates the increasing solar droughts worldwide in the past three decadesThe increasing frequency and severity of solar droughts can be greatly mitigated by a carbon neutrality policy toward the end of the 21st century [ABSTRACT FROM AUTHOR]

Low production potential of arid regions heightens vulnerability of farms to market shocks and extreme weather events. Here we examine African smallholder farmer perceptions of climate change, including perceived (intended) and actual adaptation strategies. We invoke survey questionnaires, focus group discussions, interviews and meteorological data to compare smallholder perceptions with actual weather events realised. We showed that most communities perceived climate change through the lens of perturbations to rainfall and temperature. Perceived increases in precipitation, indicated by 62% of respondents, and increased temperature, indicated by 77% of participants, aligned well with evidence shown by meteorological data. Around 88% of respondents identified prolonged drought as the most frequent extreme weather, followed by unseasonal rainfall (86% of respondents). Diversification of pasture fodder species and access to technology enabling timely weather forecasts were preferred actual and intended adaptation strategies, respectively. Recurrent and prolonged drought, spurious seasonal weather patterns, and lack of access to timely weather prognostics were the primary constraints to adoption of practices aimed at climate change adaptation. While farm size and practitioner experience were not associated with adaptive capacity, awareness of climate change impact potential and household income significantly influenced the rate and extent of adoption. We revealed a marked influence of gender in adaptation to the changing climate, with households where males made decisions exhibiting 76% adoption, compared with 34% of households adopting climate adaptations where decisions were made by females. Taken together, our study narrates critical roles of knowledge, finances, and gender in enabling or inhibiting adaptation to the climate crisis. [ABSTRACT FROM AUTHOR]

Globally, increasing occurrences of forest fires are major threats to the ecosystem. The rich forests in the Himalayas also suffer from high incidents of forest fires in the pre‐monsoon summer months, March to June. Research studies are limited in identifying the meteorological factors governing the spatiotemporal distribution of forest fires. Using three satellite‐based data sets of monthly burned area (BA) for March to June, we found higher BA in the Eastern Himalayas (EH) compared to the Central (CH) and Western Himalayas (WH). Using statistical methods, we found Vapor Pressure Deficit (VPD) to be the most dominating variable controlling BA in the Himalayas. Precipitation, soil moisture and temperature, with their relative variability, control VPD that governs the interannual and intraseasonal variations of BA. Our results imply that a good forecast of VPD will facilitate alert generation for the Himalayan forest fires. Plain Language Summary: The Intergovernmental Panel on Climate Change's sixth assessment report warns of a substantial rise in global forest fires due to climate change. This threatens biodiversity, pollution, soil erosion, human health, and exacerbates global warming. In India, 54.4% of forests are fire‐prone, with the Himalayas, a critical biodiversity hotspot, hosting 13% of the nation's forests. Forest fires here can significantly impact global and regional socio‐economies. While global literature discusses meteorological factors influencing forest fires, the Himalayan region remains underexplored in this context. The present study, the first of its kind, focuses on the Himalayas during peak fire months (March–June) and finds an increasing trend in burnt areas over the Himalayas. The eastern Himalayas were found to be more prone to forest fires, which peak in March–April because of a substantial number of dry days. We found that precipitation, temperature, and soil moisture govern the variability of vapor pressure deficit (VPD), which controls BA. Emphasizing VPD as the primary driver of Himalayan forest fires, the study highlights the necessity for extensive monitoring to develop effective management and mitigation strategies in this unique biodiversity hotspot. Key Points: The spatial extent of the forest burned area is increasing across the entire HimalayasThe Eastern Himalayas show a higher forest burned area than the western and central HimalayasVapor pressure deficit governs the interannual and intraseasonal variations in the forest fire extent [ABSTRACT FROM AUTHOR]

Mangroves protect coasts from extreme weather and erosion but can be destroyed by climate change and harvesting. However, there is no consistent formulation of protective capacity that integrates key factors such as area, width, height, and health. Here, we quantified and analyzed a process-based measure of mangrove coastal protection index (MCPI) incorporating cross-shore width, canopy height, and the normalized difference vegetation index (health index). Width/area generally declined at low rates but width increases in some regions reduced MCPI. Cluster exchange network analysis from 2007 to 2019 showed an 800% increase in mangrove forests with characteristically low height, width, and MCPI. Globally, this suggests a 25% decrease in MCPI from 2007 to 2019, primarily from height/biomass change, compared to a 2% decrease in area. Relatively sheltered low-latitude high mangrove strands (>20 m) of high-MCPI appear to be resilient to destruction from cyclones. In contrast, our results highlight an alarming, widespread decline in low MCPI, particularly along coasts exposed to deep water, possibly in concert with human destruction, cyclones, and intensifying oceanic boundary currents. This study assesses the protective capacity of mangroves by incorporating their width, height, and health. It reveals that while mangrove area remained relatively stable, the coastal protection capacity declined by 25% from 2007 to 2019 due to significant decreases in height and biomass. This decline poses risks for coastal regions, particularly those vulnerable to climate change and human activities. These findings underscore the urgent need for enhanced conservation efforts to preserve mangrove ecosystems and their protective functions. [ABSTRACT FROM AUTHOR]

Background: Frontline clinics - primary care clinics that predominantly serve low-income and socially disadvantaged communities - are facing increasing impacts from climate-related extreme events. This study evaluated staff perspectives at frontline clinics on the health risks of climate change, the impacts of extreme events on their clinics and patients, and their motivators to improve climate resiliency. Methods: A national, cross-sectional survey was conducted of staff working at frontline clinics including administrators, case workers, and providers across the United States. Survey questions assessed clinic and respondent attributes, knowledge of health risks of climate change, preferences for climate change educational and operational resources, and clinic and patient climate impacts and resilience. The survey was distributed electronically to a convenience sample of primary care clinics and to the National Association of Community Health Centers (NACHC) and National Association of Free and Charitable Clinics (NAFC) listservs. Data was collected from September to November of 2021. Respondents were current staff who consented to the survey. Responses were collected via Qualtrics, and the statistical analysis was completed using Stata. Results: A total of 430 surveys were completed representing clinics in 43 states. Most (82.0%) respondents reported human activities were causing climate change. Over half (52.8%) of respondents reported an operational disruption to their clinic from extreme events in the past 3 years, and another 54.4% had plans in place to address risks from extreme events. The most useful resources identified to improve operational resilience were checklists and planning guidance. Over half (52.0%) of respondents reported they were motivated to use these resources to improve clinic preparedness. Most (84.4%) providers and case workers reported that climate change impacted patient health, however only 36.2% discussed health risks with patients, with barriers including more pressing topics and time available. Another 55.7% of respondents reported they were motivated to learn ways to help patients prepare for extreme events. Conclusions: Climate-related extreme events were reported to impact patient health and disrupt frontline clinic operations. Overcoming gaps in knowledge about climate change impacts on health and providing climate educational resources can engender motivation to improve clinic and patient resilience to climate change. [ABSTRACT FROM AUTHOR]

Background: Extreme weather is a recognised risk factor for stillbirth and preterm birth, disrupts women's access to healthcare during pregnancy and childbirth, and negatively affects the care of newborns. Reliable and accessible heat and weather warning systems are key in alerting individuals to undertake protective measures. There is a notable gap in understanding how women and caregivers in rural East Africa perceive and utilize weather information. We investigated community members' heat and weather warning information-seeking behaviour, identified available sources, assessed their reliability and utility, and examined their influence on behaviour. Settings: Our research was conducted in rural Kilifi County in Kenya's coastal region. The area experiences temperatures exceeding 23°C throughout the year, with extended periods of extreme temperatures [> 40°C] and long and severe droughts. Methods: We conducted in-depth interviews [IDI] with pregnant and postpartum women [n = 21] and held six focus group discussions [FGDs] involving mothers-in-law and community health volunteers [CHVs]. The data were analysed in NVivo 12 using both inductive and deductive approaches. Results: We found significant gaps concerning pregnant and post-partum women, and their caregivers, having timely access to weather forecasts and heat information from health or meteorological authorities. Information on heat and weather warnings is disseminated through various channels, including television, radio, mobile phones, and word of mouth, which are facilitated by community influencers such as CHVs and local chiefs. Indigenous methods of weather forecasting, such as cloud observation, consulting local "rainmakers", and studying the behavioural patterns of amphibians, are employed in conjunction with warnings from the Kenyan Meteorological Department (KMD). Barriers to accessing weather information include the cost of television and smartphones and a lack of segmented information in local languages. Conclusions: National and county meteorological services need to enhance public participation, communication, and the delivery of heat and weather information to guide community-level response measures and individual behaviour change. They should also collaborate with health professionals to address heat risks for vulnerable groups. Further research is needed to empower indigenous weather predictors with modern weather information and revise national policies to deliver tailored messages to vulnerable populations like pregnant and postpartum women. [ABSTRACT FROM AUTHOR]

The article "ArboItaly: Leveraging open data for enhanced arbovirus surveillance in Italy" discusses the importance of arboviruses, such as West Nile, chikungunya, and dengue, and their impact on public health. It highlights the need for robust public data and genomic surveillance systems to monitor and respond to these diseases effectively. The creation of ArboItaly, a centralized repository for arboviral disease surveillance in Italy, aims to consolidate epidemiological data and facilitate targeted control measures. The article emphasizes the significance of international collaboration and information sharing to address the evolving threat of arboviruses globally, especially in the context of climate change and increasing disease transmission risks. [Extracted from the article]

Climate change's effect on agriculture is a severe problem in every society, particularly in West Africa. The rising temperatures and extreme weather events have significant implications for food security. In this study, we examine the effects of CO2 emissions, agricultural advancements, and food availability on economic growth in West Africa from 1990 to 2020, using a panel ARDL approach with data from 14 countries. The analysis evaluates both long- and short-term effects, with the Dynamic Fixed Effects (DFE) estimator chosen as the most robust model. Results show no significant link between CO2 emissions and economic growth, while agriculture plays a key role, and food availability has mixed effects. The findings underscore the importance of sustainable agriculture and food security in promoting economic resilience in West Africa, offering insights for policymakers. [ABSTRACT FROM AUTHOR]

Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales. Plain Language Summary: Global flash drought risks are prevailing currently and will continue to be an important concern under climate change. Skillful prediction of global flash drought onset and termination events can provide critical early warning information for agricultural preparations and water resources management. However, current weather forecasting models suffer from large errors in root‐zone soil moisture and flash drought predictions. Here, we develop a machine learning‐based framework that could predict 33% and 24% global flash drought onset and termination events at 7 day lead time, better than the state‐of‐the‐art dynamic model. The machine learning‐based framework can reduce soil moisture prediction errors in dynamic coupled models and thus enhances global flash drought prediction skills. The proposed machine learning forecasting approach may provide scientific advances for flash drought early warning at global scales. Key Points: Machine learning is used for global flash drought prediction up to 2 week lead time33% and 24% global flash drought onset and termination events can be correctly predicted at 7 day lead timeMachine learning improves global flash drought prediction over dynamic model [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm‐resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land‐ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation. Plain Language Summary: Mesoscale convective systems (MCSs) account for more than half of tropical total rainfall and are the primary drivers of extreme rainfall and flooding events. However, understanding how the frequency of these systems will change in a warming climate remains a challenge. This is due to both the incomplete representation of these disturbances in global climate models and a lack of fundamental theory to explain their dynamics. In this study, we employ a global storm resolving model to shed light on the response of tropical MCS occurrences to climate warming. What emerges is a striking contrast: MCSs over tropical land are projected to decrease in frequency, while their occurrence over tropical oceans is anticipated to rise. This difference is linked to the relative changes in convective available potential energy and convective inhibition. The divergent response in MCS occurrences has significant implications for both average and extreme precipitation patterns. As a result, it presents challenges for developing regional climate adaptation and mitigation strategies in the face of a warming climate. Key Points: A global storm resolving model is employed to study the response of global mesoscale convective systems (MCSs) to a warming climateTropical MCS occurrence exhibits divergent responses to increased SST, declining over land while increasing over the oceanThis land‐ocean contrast is attributed to the changes in convective available potential energy and convective inhibition, on MCSs [ABSTRACT FROM AUTHOR]

The North Atlantic storm track plays a critical role in setting the regional weather and climate over Western Europe. In response to anthropogenic emissions, the summer North Atlantic storm track has weakened in recent decades and is projected to continue weakening by the end of this century. In light of growing efforts to mitigate climate change, it is crucial to assess how reversible the CO2‐induced storm track weakening is. Here, I show that under CO2 removal scenarios, the recovery timescale of the storm track is more than double its weakening period. It is found that due to a prolonged high‐latitude warming, postponing the execution of mitigation policies beyond doubling of CO2 concentrations would lead to a centennial‐scale recovery of the storms. Given the impacts of weakening summer storms on weather and extreme events, the delayed recovery of the storms might have broader regional consequences for Western Europe. Plain Language Summary: The summer North Atlantic storm track sets the regional weather and climate over Europe, by transferring heat, moisture and momentum across the North Atlantic basin. Anthropogenic emissions were found to weaken the storm track, which would greatly affect precipitation and hot‐dry extreme events over Europe. It is thus critical to assess the reversibility of the summer North Atlantic storm track weakening under a reduction in greenhouse gases. Here, using targeted numerical simulations, I show that the reversibility time‐scale of the storm track is more than double its weakening time‐scale. Thus, postponing the execution of mitigation strategies increases the reversibility time‐scales of the storm track. Specifically, avoiding centennial‐scale impacts of the weakening storm track can be achieved by executing mitigation strategies prior of reaching doubling of CO2 concentrations. Our analysis reveals that the prolonged recovery of the storm track stems from a prolonged warming of the high latitudes relative to the lower latitudes. Key Points: The reversibility time‐scale of the North Atlantic summer storm track is more than double its weakening time‐scalePostponing mitigation policies beyond a doubling of CO2 levels yields centennial‐scale recovery of the storm trackThe prolonged recovery of the storm track stems from a prolonged warming of the high latitudes relative to the lower latitudes [ABSTRACT FROM AUTHOR]

Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists' judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management. [ABSTRACT FROM AUTHOR]

In the Atacama Desert, one of the driest places on Earth, the persistent absence of water preserves the record of environmental change, making it an invaluable proxy for studying the evolution of life on Earth. Due to the scarcity of in situ measurements and difficulties in satellite remote sensing, information on precipitation characteristics is limited even for the present climate. Guided by a case study of extreme precipitation in late January 2019, we derive a conceptual framework to explain how moisture transport combined with the diurnal circulation produces rainfall. We found a synoptic‐scale weather pattern that we named "moist northerlies" (MNs) based on surface observations, reanalysis, and high‐resolution simulation. During an MN event, moisture transport from the tropical Pacific is observed in the lower free‐troposphere in the forefront of an 850 hPa low‐pressure offshore Atacama. The diurnal circulation along the western Andean slope transports the moist free tropospheric air above the marine boundary layer inland, triggering clouds and storms. A trough over the southeast Pacific and a southward displaced Bolivian High seem to drive the MNs dynamically. Long‐term observations (1960–2020) show that most of the rainy days in the hyperarid core (75%) are triggered by MNs, occurring more frequently during neutral/La Niña conditions and phases 7‐8‐1 of the Madden‐Julian oscillation (MJO). A trend analysis (1991–2020) reveals that summer water vapor along the west coast of South America has increased rapidly due to the MNs, enhancing summer rainfall in Atacama. The implications of climate change and other climate variability modes are discussed. Plain Language Summary: The Atacama Desert, known as one of the driest places on Earth, holds vital information about how life has evolved in an extremely dry environment. Despite the dryness, rainfall is observed with some recurrence without being completely understood. This study focuses on understanding the mechanisms behind summer rain in the driest part of the Atacama Desert. We started investigating a specific extreme rainfall event in January 2019, discovering a weather pattern called "moist northerlies" (MNs) that transport moisture hundreds and thousands of kilometers from the tropical Pacific to the desert. The moisture is transported inland during the day, triggering rain in the Atacama. Over several decades, we found that MNs cause most rainy days in the desert. These MN events seem to be influenced by larger weather patterns such as the El Niño–Southern oscillation and the Madden‐Julian oscillation. Additionally, a rapid increase in humidity has been observed along the west coast of South America in recent decades, leading to more summer rainfall in the Atacama and greening in the Andean Precordillera. This study also discusses how anthropogenic global warming and natural climate variability might affect the MNs. Key Points: Summer moisture transport, named moist northerlies (MNs), brings free troposphere humidity from the tropical Pacific to the Atacama75% of austral summer rainfall days in the hyperarid core of the Atacama result from MNsThe Hadley cell expansion and the poleward shift of the Bolivian High are likely driving an increase of the MN frequency in recent decades [ABSTRACT FROM AUTHOR]

Compound hazards involving tropical cyclones and extreme heat ("TC‐heat") have rarely been examined in prior research. Peripheral subsidence associated with cyclones can warm an area to varying degrees, depending on the cyclone's strength and position. Meanwhile, the peripheral airflow also overwhelmed the land‐sea breeze circulation system in coastal cities and favored downwind urbanized heat advection on the Southern China coastline. Here, we systematically applied ERA5 global reanalysis data, local surface‐level observations, MODIS‐based land‐cover data and HYSPLIT backward trajectories to evaluate how the position of distant TCs may divert the monsoon system and foster the surface heat advection from upwind urban agglomerate amplifying air temperature in downwind coastline. The analysis suggests when a distant TC is situated in a favorable area (the vicinity of Taiwan and Luzon Strait) at roughly 500–1,250 km to the east of Hong Kong, it forms a unique meteorological condition which allows the surface heat transport from the Greater Bay Area to Hong Kong, reflecting that regional build‐up of heat could be an important mechanism for producing local heat extremes. Plain Language Summary: Rapid urbanization and global climate change bring us new hazards through compound extreme weather events in urban society. Tropical cyclones (TC) are known to bring violent wind and heavy rain upon arrival, while in distant they can also send very hot weather conditions (called TC‐heat events) to regions under its subsidence airflow. This study shows the combined effect of heat from upwind urban areas in coincidence with the TC‐induced subsidence could bring even higher temperatures to the downstream cities like Hong Kong. Comprehensive analysis with global climate data, local weather observations, and backward simulation models were used to further understand how the location of TC may adverse the wind circulation patterns in inland areas and contribute to a larger extreme heat threat to the coastal cities. This can facilitate the early forecast and warning for the potential occurrence of such TC‐heat compound hazard, potentially benefitting public preparedness and response in combating extreme heat and cyclone influences. Key Points: High temperature events occur when distant Tropical Cyclone (DistTC) located at 500–1,250 km from Hong KongSurface heat can be built‐up in inland China cities, transport to Hong Kong as downwind footprint and contribute to the temperature enhancementProbability of high temperature events with DistTC is 15% higher than the normal summer days in Hong Kong [ABSTRACT FROM AUTHOR]

Amid escalating geopolitical conflicts and extreme weather, the transformation to a renewable‐dominated power system faces increasing supply security risks. The current literature lacks comprehensive multidimensional assessment index systems to quantify these risks. This paper first identifies the key factors influencing supply security in renewable‐dominated power system. Subsequently, it constructs a supply security risk assessment model based on a comprehensive evaluation method combining the analytic hierarchy process method and the entropy weight method. The effectiveness of the approach is then evaluated through simulations of supply security risks across 11 provinces, including Inner Mongolia and Sichuan. Finally, the analysis of supply security risks in the Sichuan power system is conducted at different stages, focusing on changes in supply security risk levels and risk transmission mechanisms during Sichuan power crunch events in 2022. The results show that during Sichuan's 2022 high‐temperature power rationing event, the comprehensive supply security risk score of the Sichuan power system dropped from 88.7 to 76.91. This decline was mainly due to a significant reduction in hydropower output, which lowered scores of the source‐side risk and consequently increased the system's supply security risk. [ABSTRACT FROM AUTHOR]

Extreme weather events are becoming more frequent, with poorly known consequences for wildlife. In December 2021, an atmospheric river brought record-shattering amounts of rain and snow to interior Alaska, creating conditions expected to cause mass mortality in grazing ungulate populations that need to access ground forage. We characterized snowpack conditions following the storm and used a 36-year monitoring dataset to quantify impacts on caribou (Rangifer tarandus) and their primary predator, wolves (Canis lupus). December precipitation was 7.3 SD above the 99-year mean and 2.5-fold higher than the prior record, with a return period of 333 years. However, ice thickness within the snowpack was highly variable across vegetation types, and caribou shifted to use higher elevations that can blow free of snow. Caribou and wolf mortality rates were 1.3–1.8 SD above normal and caribou population growth rates were low but similar to recent years, indicating a surprisingly weak demographic response. These findings indicate that landscape diversity may bolster resistance of wildlife populations to short-term, potentially devastating effects of extreme weather. Using weather data combined with multi-decadal caribou and wolf demographic data, heterogeneous habitat and topography are shown to mitigate the impact of an extreme precipitation event on wildlife populations in Alaska. [ABSTRACT FROM AUTHOR]

Featured Application: This article proposes a solution for reducing and even eliminating flood effects in Prahova County, Romania, by extending the length of a bridge and reducing its embankment. Recently, the frequency and intensity of extreme weather events have increased in many regions worldwide. Among them, floods, whose effects are devastating in many cases, have been recorded in Romania in the last few years. Built to reduce the flooding effects on the communities, structures such as embankments can sometimes accentuate the hazard. This article investigates such a situation and proposes solutions to reduce or even eliminate the flood impact on the community living in the Vărbilău Catchment in Romania. Recorded data series, field observations, GIS techniques, and hydraulic modeling were used to design the hazard maps and perform the 3D representations that illustrate the actual situation (when the small opening of the bridge favors the flooding) and the proposed solutions (extending the bridge opening and shortening the embankment). It is shown that adding seven pillars to the bridge would reduce the flooded surface by more than 1.5 times and the affected buildings' surface by more than 3.5 times compared to the current situation. [ABSTRACT FROM AUTHOR]

The unique structure of penguin feathers endows them with extremely high thermal insulation properties in extreme cold environments via sunlight absorption, heat insulation and infrared reflection. Herein, inspired by a thermal insulation strategy of the penguin feathers, the laminated wearable textile with coupled thermal insulation was fabricated for thermal management. The outermost layer, comprised of carbon nanotube-cellulose, simulates the black tip of the penguin's dorsal feather, exhibiting exceptional light-to-heat conversion as it can swiftly elevate its temperature by 5 ℃ upon exposure to simulated sunlight. The middle layer, constructed from polylactic acid, mimics the intermediate section of the penguin's feather, achieving effective heat insulation by trapping significant amounts of air. And the innermost layer MnO2-cellulose membrane is functionally similar to the fluff of the penguin closest to the skin, reflecting the infrared radiation of the body to reduce heat loss. Additionally, the textile underwent hydrophobic modification, endowing it with waterproof properties, resulting in an alteration of the exterior contact angle from 52.2 to 118.8°. Furthermore, the textile displayed anti-ultraviolet capabilities (with ultraviolet transmittance below 8%), excellent mechanical performance (with tensile strength is 3.22 MPa) and active deicing properties, enhancing the wearable comfort. This work represents a versatile and comprehensive textile learning from the bionic structure of penguin feathers for highly efficient thermal management applications, making it attractive for textiles to satisfy various wearable applications in cold environments and even extreme weather conditions. [ABSTRACT FROM AUTHOR]

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model's ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. [ABSTRACT FROM AUTHOR]

Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth's surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman's rank correlation coefficient (ρ) between this measure and the DCs from the Copernicus Emergency Management Service ( D C C E M S ), from MODIS + CHIRP ( D C 1 km ) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a ρ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. D C 1 km was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than D C C E M S and +300% than the temperature data. If a ρ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the D C 1 km values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in D C 1 km significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled "Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition" which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method. [ABSTRACT FROM AUTHOR]

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China's 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions. [ABSTRACT FROM AUTHOR]