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Recently it was proposed that a sudden drainage of accumulated Arctic sea ice could have caused cold climate events during the last deglaciation. To explore this mechanism, we performed numerical experiments with an atmosphere–ocean–sea ice model. In these experiments, the impact of a large flush of Arctic sea ice was compared to a reference glacial state of the climate. In our results, the sea ice flush produces a major surface freshening of the North Atlantic Ocean and a 17% weakening of the strength of the Atlantic Meridional Overturning Circulation. Together with an increase in surface albedo, this weakening in ocean circulation leads to a cooling over the North Atlantic, extending to the downwind continents. Compared to our reference glacial state, the cooling reaches 5°C and lasts about 80 years. This climate anomaly is similar in magnitude and duration to relatively short cooling events during the last deglaciation, such as the Older Dryas, the Inter‐Allerød cold period and the Preboreal Oscillation. We thus conclude that the sea‐ice flush mechanism is consistent with the occurrence of such cooling events in the North Atlantic region. However, longer cooling events such as the Younger Dryas would require additional mechanisms. [ABSTRACT FROM AUTHOR]

The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively. [ABSTRACT FROM AUTHOR]

The stability of future carbon sinks is crucial for accurately predicting the global carbon cycle. However, the future dynamics and stability of carbon sinks remain largely unknown, especially in China, a significant global carbon sink region. Here, we examined the dynamics and stability of carbon sinks in China's terrestrial ecosystems from 2015 to 2,100 under two CMIP6 scenarios (SSP245 and SSP585), using XGBoost and SHAP models to quantify the impact of climatic drivers on carbon sink stability. China's future terrestrial ecosystems will act as a "carbon sink" (0.27–0.33 PgC/yr), with an initial increase that levels off over time. Although the carbon sink capacity increases, its stability does not consistently improve. Specifically, the stability of carbon sinks in future China's terrestrial ecosystems transitions from strengthening to weakening, primarily occurring in areas with higher carbon sink capacity. Further analysis revealed that atmospheric vapor pressure deficit (VPD) and temperature (Tas) are the two primary factors influencing carbon sink stability, with significant differences in their impacts across different scenarios. Under the SSP245 scenario, variations in VPD (VPD.CV) regulate water availability through stomatal conductance, making it the key driver of changes in carbon sink stability. In contrast, under the SSP585 scenario, although VPD.CV still plays an important role, temperature variability (Tas.CV) becomes the dominant factor, with more frequent extreme climate events exacerbating carbon cycle instability. The study highlights the differences in driving factors of carbon sink stability under different scenarios and stresses the importance of considering these differences, along with the scale and stability of carbon sinks, when developing long-term carbon management policies to effectively support carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Extreme climate events have increased in terms of their amplitudes, frequency and severity, greatly affecting ecosystem functions and the balance of the global carbon cycle. However, there are still uncertainties about how extreme climate change will affect tree growth. This study characterized the responses of tree growth to extreme climate on the northeastern Tibetan Plateau from 2000 to 2020. Meanwhile, a back propagation neural network was used to predict tree growth trends under two future emission scenarios from 2020 to 2050. This study revealed that: (1) the tree-ring width index (RWI) showed a decreasing trend (− 0.04/decade) in the eastern region, but the enhanced vegetation index (EVI) showed an increasing trend (0.05/decade) from 2000 to 2020. While both RWI and EVI in the middle and western regions showed increasing trends. (2) The responses of RWI and EVI to extreme climate were regionally asymmetric. In the eastern region, extreme precipitation inhibited tree radial growth, while extreme warm nights promoted tree canopy growth. In two other regions, both extreme precipitation and extreme warm nights promoted tree growth. (3) The model predicts that there was no significant change in RWI and EVI in the western region, but both RWI and EVI showed an increasing trend in the middle and eastern regions under the low emission scenario. Under the high emission scenario, the growth of tree stem and canopy in all three regions shows a general decreasing trend. The results of this study both improved the understanding of the differences in carbon allocation between tree stem (RWI) and canopy (EVI) and identified vulnerability thresholds for tree populations. [ABSTRACT FROM AUTHOR]

The Yangtze River Basin has always been one of China's most densely populated regions and one of the most active in agricultural activities. Although previous studies have examined extreme climate events in the Yangtze River Basin, the main factors influencing the long-term changes in the region’s key agricultural frost days (FD) and summer days (SU) are unclear. In this study, we analyze the variations of FD and SU in the Yangtze River Basin and explore the dominant factors driving the long-term changes in these two climate indices. The key findings are as follows: FD and SU show a downward and upward trend, with rates of − 0.88 days/decade and 0.65 days/decade in the Yangtze River Basin during 1961 − 2020, respectively. The significant weakening of the Asian Polar Vortex and the strengthening of the Northwest Pacific Subtropical High Pressure reduce FD and increase SU, respectively. In addition, the less FD in winter is closely linked to more SU in summer. We highlight that Kuroshio Current Sea Surface Temperature serves as a crucial connection between the less FD in winter and the more SU in summer over the Yangtze River Basin. Furthermore, global warming will significantly amplified the extreme temperature changes observed during the winter and summer. [ABSTRACT FROM AUTHOR]

China's terrestrial ecosystem carbon sink (TCS) is crucial for the global carbon budget. However, little is known how the enhanced human disturbances and increased extreme climate events may potentially destabilize TCS under warming climate. Using three process-based ecosystem models, we simulated the spatiotemporal variations of China's terrestrial net ecosystem productivity (NEP) from 2000 to 2020. We found that 26.7 % of the land area exhibit simultaneous increases in NEP temporal variability and autocorrelation during this period, indicating an increasing risk of TCS destabilization. Particularly, the southeastern subtropical monsoon region in China emerged as a hot-spot of potentially increasing NEP instability, despite its high carbon sink capacity, both NEP temporal variability and autocorrelation in this area exhibit a notable upward trend. Climate change, notably increasing precipitation and its temporal variation, appeared to be the primary driver of this instability. This harbinger implies that a regime shift in carbon sink capacity may occur as the warming climate continues to push it to the verge of stability. [Display omitted] Potential hot-spots of increased NEP instability in China indicated by the concomitant increase or decrease in both NEP PV and NEP AR1 during 2000–2020. (a) Regions with concomitant increase (red shading) and decrease (blue shading) in NEP PV and NEP AR1. Blank regions show contrasting trends in NBP PV and NBP AR1. (b) Comparison of NEP aggregated trend of NEP PV and NEP AR1 across different climatic zones, error bars indicate one standard error. Non-parametric Kruskal–Wallis rank sum test comparing the differences between each two groups, different letters represent significant difference (p < 0.05). Roman letter denote different climatic zones: I = temperate continental, II = temperate monsoonal, III = high-cold Tibetan Plateau, and IV = subtropical-tropical monsoonal. (c, d) Spatial relationship of NEP and trend of NEP with the aggregated trend of NEP PV and NEP AR1. Error bars indicate one standard error, and different letters represent significant differences (P < 0.05). Coefficients are standardized and include the 95 % credible interval. Rectangles represent forests, circles represent grasslands, and stars represent crop. [ABSTRACT FROM AUTHOR]

The emerging process of CO2 capture and sequestration will likely require large volumes of caustic. The fossil fuel demand and carbon footprint of transporting liquid caustic is self-defeating, and hence, there is a need for energy-efficient, on-site caustic production for carbon capture projects. Caustic production is dominated by the well-established “chlor-alkali” process. This process requires highly concentrated (∼25% w/v) and pure (>99.5 wt%) NaCl feed brines, uses high-cost ion-exchange membranes and high operating temperatures (90 °C), and generates a highly-concentrated (>33% w/v) caustic stream that can be further concentrated using thermal evaporation. This highly concentrated caustic is then shipped to customers, where it is typically diluted to the required level. We have developed a flow-through membrane/cathode electrolysis process that produces a caustic solution (pH 10.22–12.26) at a specific energy consumption (SEC) of 1.71 kW he kg−1 NaOH at room temperature using a 3.5% w/v NaCl solution as feed, while achieving pure H2 generation without the use of ion exchange membranes. We demonstrate that the SEC is strongly dependent on the flow rate through the cathode, reaching a minimum at a high rate of 1200 L m−2 h−1. Electrochemical impedance spectroscopy, confocal microscopy, and finite element modeling show that the SEC is lowered through a combination of enhanced mass transport (of H+ and OH− ions) to and from the cathode surface and H2 gas stripping, both facilitated by the high flow rates. This technology offers the opportunity for the on-site production of dilute caustic streams (potentially from softened seawater) at a significantly reduced energy cost (compared to conventional chlor-alkali processes that consume >2.1 kW he kg−1 NaOH). [ABSTRACT FROM AUTHOR]

The impacts of climate change on human health are often underestimated or perceived to be in a distant future. Here, we present the projected impacts of climate change in the context of COVID-19, a recent human health catastrophe. We compared projected heat mortality with COVID-19 deaths in 38 cities worldwide and found that in half of these cities, heat-related deaths could exceed annual COVID-19 deaths in less than ten years (at + 3.0 °C increase in global warming relative to preindustrial). In seven of these cities, heat mortality could exceed COVID-19 deaths in less than five years. Our results underscore the crucial need for climate action and for the integration of climate change into public health discourse and policy. [ABSTRACT FROM AUTHOR]

Karst small towns globally face challenges due to limited disaster-resilient resources, making it difficult to handle increasingly severe disaster environments. Improving the efficiency of disaster-resilient resource utilization and maintaining a tight balance state of disaster-resilient resources (TBS) are crucial for enhancing disaster adaptability and resilience. This study used urban and disaster data from a representative karst region in China (2017–2021) to conduct a quantitative analysis of TBS in karst small towns, exploring the mechanisms and interactions within this state and identifying obstacle factors. Results show an average TBS of 0.355, indicating a low level with growing disparities among towns. The spatial pattern of TBS has shifted from "high in the south, low in the north" to a multi-center structure, with central towns exhibiting stronger resource siphoning compared to radiating and driving abilities. The Coupling Coordination Degree model shows high coupling but low coordination, with an average value of 0.56, suggesting moderate coordination. Enhancing subsystem coupling and coordination is essential for improving TBS and disaster adaptability. Obstacle factor diagnosis identifies Driving Forces as the main constraint, followed by Responses, States, Pressures, and Impact. Lack of disaster monitoring and early warning technologies increases socio-economic losses caused by disasters. [ABSTRACT FROM AUTHOR]

This study explores carbon sequestration in South Korea's riverine wetlands, focusing on the four major rivers: Han, Yeongsan, Geum, and Nakdong. Field data from the Yeongsan River wetland, including 3D topography surveys, grainsize analyses, and loss-on-ignition measurements, were used to assess carbon stocks and their environmental drivers. The Yeongsan River was selected as a representative site due to its geomorphological, hydrological, and climatic similarities with the other three major rivers, which influence sediment transport and carbon dynamics. Carbon stocks of 3.31 megagrams (Mg) per hectare, observed in the Dam-Yang Stream Wetland, suggest that the four major rivers collectively have the potential to store approximately 23.42 million metric tons of carbon annually, accounting for 3.9% of South Korea's carbon budget. Geomorphological features at different elevations significantly influence soil carbon storage, with finer sediments contributing to higher carbon retention in low-energy environments. Seasonal variations in stream geomorphology, driven by floods and tropical cyclones, are dominant factors regulating sediment transport and organic matter deposition. Our findings suggest that controlled discharge events could enhance sediment and organic material retention, boosting carbon sequestration across riverine wetlands. This study highlights the critical role of geomorphological and hydrological processes in enhancing wetland carbon storage and mitigating carbon emissions. [ABSTRACT FROM AUTHOR]

Hainan medaka (Oryzias curvinotus) is a small euryhaline fish species native to the northern margin of the South China Sea. Our long-term field observations indicate a concerning decline in its wild resources. Climate change, an uncontrollable factor, has altered the species' distribution pattern. In this study, we simulated the shifts in the species range of O. curvinotus during the Last Glacial Maximum (LGM), current, and the next one hundred year, and analyzed its habitat attributes. The results demonstrate that bio2 (mean diurnal range of temperature) is a crucial factor in shaping the species range of O. curvinotus. The simulation results reveal that the current habitats are located in the coastal areas of northern Vietnam, the northeastern Hainan Province, the coastal areas of Guangdong Province and Guangxi Zhuang Autonomous Region, and a few areas in Taiwan Province of China, covering a total area of 17.82×104 km2. Highly suitable habitats are mainly concentrated in the coastal areas of Hai Phong, Nam Dinh, and Thanh Hoa in northern Vietnam, the central part of Leizhou Peninsula, and the west coast of the Pearl River Estuary. For the tropical species Hainan medaka, the impact of the LGM was relatively minor, and there were extensive suitable habitats during historical times, including three refugia. Currently, only Refugium 2 near Guanghai Town, Taishan County, Jiangmen City, Guangdong Province in China remains, while the other two refugia have submerged below sea level. Future climate warming under different carbon emission levels is projected to cause a short-term expansion, followed by a relief in expansion. By 2100, the potential habitat area of O. curvinotus is slightly larger than the current scenario. It is noteworthy that under future climate warming scenarios, the highly suitable habitats will not migrate northward but will expand near the 21°N latitude. Overall, Hainan medaka is not expected to be threatened in the future. Our study provides long-term dynamic distribution data, which provides a theoretical basis for the long-term development and conservation management of Hainan medaka. [ABSTRACT FROM AUTHOR]

Introduction: This study explores the economic and environmental impacts of water resources in the context of achieving climate neutrality in the EU Strategy for the Danube Region (EUSDR). The research is focused on eight EUSDR member states: Bulgaria, Czechia, Germany, Croatia, Hungary, Austria, Romania, and Slovakia, with the aim of analyzing the dynamic relationship between water resources, economic growth, and environmental sustainability. Given the ambitious goals of the European Green Deal, this study examines how water management can support the transition towards climate neutrality. Methods: A dynamic econometric model was developed using a consolidated database of relevant climate neutrality indicators from 2010 to 2021. The selected indicators included GDP per capita, water use in agriculture, gross water abstraction, sewage sludge production, and water productivity. The econometric model was validated using statistical tools such as the One-Sample Kolmogorov-Smirnov Test, and analysis was conducted using Gretl and IBM-SPSS 25 software. Results: The findings reveal that water resources management significantly contributes to the achievement of climate neutrality targets. The model achieved over 86% significance in explaining the financial efforts needed to achieve the Green Deal's 2030 and 2050 goals. Moreover, water productivity and investments in water-related infrastructure were found to play critical roles in ensuring the success of these climate policies. Discussion: The results highlight the importance of policy coordination across the EUSDR member states and the need for increased investment in sustainable water resource management. Achieving climate neutrality requires addressing economic disparities between states and enhancing regional cooperation. Conclusion: The proposed dynamic model offers a valuable tool for monitoring progress towards climate neutrality, particularly in relation to water resource management. This research underlines the need for targeted policy interventions and investments to meet the Green Deal's goals while safeguarding the region's water resources. [ABSTRACT FROM AUTHOR]

Introduction: In a world where one in four individuals lacks assured access to water, this article investigates how the experiences of Traditional Peoples and Communities (TPCs) can serve as a guiding light in the search for solutions to water disparities, exacerbated by global warming. The study focuses on the microterritory known as Carapitanga in Paraty, Rio de Janeiro, Brazil, where the presence of indigenous, quilombola and caiçara communities highlights the significance of territorially-based and nature-focused solutions. Methods: The research utilises the Ecology of Knowledge and Action Research as methodological pillars to investigate the Water Culture of TPCs and its impact on the Hydrosocial Cycle. Results: The study reveals that the Water Culture of TPCs stimulates actions that propel balance in the Hydrosocial Cycle. This knowledge casts new light on the governance of water and sanitation. Discussion: The findings offer alternatives anchored in the concept of Buen Vivir and the recognition of water as a fundamental source of life for cities worldwide. This not only contributes to the understanding of water resource management but also presents potential solutions for global water access disparities. [ABSTRACT FROM AUTHOR]

As the global deployment of renewable technologies accelerate, finding efficient ways to store energy will aid in responding to shifting energy demands. A prospective option not only in harvesting solar energy but also in emission‐free heating is MOlecular Solar Thermal (MOST) energy storage. A central part of MOST applications is to develop methods to release the stored energy. Herein, the Quadricyclane (QC)‐to‐Norbornadiene catalyzed back reaction is explored in a specially designed packed‐bed reactor. Four distinctly sized and purposely synthesized platinum on activated carbon catalysts are studied to trigger the heat release from the energy‐dense QC isomer. The catalysts are fully characterized using a variety of structural, surface, and spectroscopic techniques. Parameters to optimize catalytic conversion and heat release in flow conditions are explored including particle size and packing behavior, flow rates, and molecular residence times. Moreover, using CO pulse chemisorption technique, site time yield values and a turnover number are reported. Complementary to the flow reactions, computational fluid dynamic simulations applying lattice Boltzmann methods to two catalytic packed beds of different size ranges are done to evaluate fluid‐dynamic behavior within the reactor bed to ascertain the ideal particle size and packing density for catalysis in MOST applications. [ABSTRACT FROM AUTHOR]

Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future. This study assesses the projected changes in flood characteristics based on eight downscaled and bias-corrected General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6. The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. These analyses are carried out using the hierarchical Archimedean copula in three structures, and their accuracy in estimating the flood frequencies is ultimately compared. The results show that a heterogeneous asymmetric copula offers more flexibility to capture varying degrees of asymmetry across different parts of the distribution, leading to more accurate modeling results compared to homogeneous asymmetric and symmetric copulas. Also it has been found that climate change can influence the trivariate joint return periods, particularly in the far future. In other words, flood frequency may increase by approximately 50% in some cases in the far future compared to the mid-term future and historical period. This demonstrates that flood characteristics are expected to show nonstationary behavior in the future as a result of climate change. The results provide insightful information for managing and accessing flood risk in a dynamic environment. [ABSTRACT FROM AUTHOR]

Marine climate significantly influences the spatial morphology of coastal village's streets. However, research on coastal villages lacks spatial parameterization analysis that can cope with the complex climatic environment. Focusing on the coastal village's street in Fuzhou City, China, this paper studies the relationship between street space morphology and the impact of extreme heat and wind conditions. Thermal comfort degree and the average wind speed are main optimization objectives. By using parameterization techniques to establish a dynamic model and conducting multi-objective optimization driven by genetic algorithms, the degree of influence of morphological indicators and climate indicators is revealed. The conclusions of the study indicate that the morphological indicators of the main space have a more significant impact on the climatic environment than the morphological indicators of the interfaces on either side of the street. The influence of thermal comfort indicators on the street space within the climate environment is greater than that of wind speed indicators. This study concludes by proposing a ranking of key morphological indicators along with their optimal intervals for coastal villages adapted to the marine climate. The key morphological indicators, listed in order of importance, include appropriate street widths (2.3–4.3 m), eave height for single-storey buildings (2.0–4.6 m), eave height for double-storey buildings (5.4–7.7 m), eave depth (0.8–1.1 m for brick-timber dwellings and 0.3–0.6 m for masonry dwellings), roof slope (20–28°), entrance space depth (0.8–1.3 m), balcony overhang depth (0.6–0.7 m), colonnade depth (1.5–2.4 m), street orientation (NE-SW) and building depth (3.2–5.0 m). This study provides an empirical reference for climate-adapted village design and renewal. [ABSTRACT FROM AUTHOR]

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions. [ABSTRACT FROM AUTHOR]

Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets. Results and Discussion: The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion: Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management. [ABSTRACT FROM AUTHOR]

Food security is crucial for national stability and public welfare. Since the 21st century, China's grain production has been significantly influenced by the rapid process of urbanization. In this context, this paper systematically measures the multidimensional coupling patterns and dynamic coupling processes between urbanization and grain production from 2000 to 2022, and preliminarily summarizes the complex coupling mechanisms within the Chinese context. The goal is to provide scientific references for achieving high-quality coordinated development of urbanization and grain production in China. The study reveals the following key findings: (1) The coupling relationship between urbanization and grain production exhibits both regional heterogeneity and temporal variability, demonstrating specific levels of coupling and dynamic processes under distinct spatiotemporal conditions. (2) Between 2000 and 2022, both urbanization and grain production patterns in China underwent significant reconstruction, with the coupling coordination level displaying a long-term spatial pattern of "high in the north, low in the south; high in the east, low in the west." Although there is an overall upward trend in coupling coordination states, spatial imbalances and dimensional heterogeneity persist. (3) Since the beginning of the 21st century, the dynamic coupling processes between provincial urbanization and grain production have primarily manifested as two types: simultaneous increase (with urbanization outpacing grain production) and urban increase accompanied by grain production decrease. Various dynamic coupling types exhibit significant spatial clustering, and the multidimensional dynamic coupling processes reveal notable similarities. (4) The evolution of coupling states demonstrates an overall trend of optimization, with clear bidirectional migration trends observed in coupling dynamics, primarily transitioning from simultaneous increase (urbanization outpacing grain production) to urban increase with grain production decrease, and vice versa. (5) The formation of the complex coupling relationship between urbanization and grain production in the Chinese context is fundamentally influenced by changes in population quantity and structure between urban and rural areas, shifts in land use, economic transformation, regional specialization, technological interactions, and factor mobility. These influences exhibit significant negative effects in the domains of population, land, and economy, while showcasing notable positive effects in terms of technology and factor mobility. [ABSTRACT FROM AUTHOR]

This study administered 379 questionnaires to smallholder farming households to determine livestock ownership, climate change, and its impacts on livestock production, including measures to adapt livestock production to climate change. Data were analyzed using ordinary least squares regression model, Two sample Z proportions test, and percentages. Results indicate that few livestock were owned by households, commonly cattle, goats, pigs and chickens. Joint ownership dominated, followed by ownership by household heads, be they male or female, then spouses. Non-household heads rarely owned livestock. Joint livestock ownership significantly increases the total livestock units a household owns. The respondents overwhelmingly reported that the climate had changed, with a shorter rainy season, droughts, floods, and higher temperatures. There were slight variations in the perception of climate change across the study sites and by gender. Livestock production had been affected by increased incidences of disease, water, and fodder shortages, ultimately reducing livestock productivity across all the sites. Livestock households have adapted to climate change and other production constraints more broadly by addressing livestock health through administering vaccines, consulting with veterinary officials, and using traditional remedies for livestock diseases. Some respondents supplemented fodder and water, while others resorted to selling off the livestock. More men reported the provision of water as they more likely deal with large livestock that need bigger quantities of water. Similarly, while more commonly reported by men, the provision of fodder shows variability across chiefdoms, suggesting that fodder scarcity is a region-specific constraint. The use of traditional remedies such as herbs remained an important adaptive measure, especially among women. The study concludes that livestock production has high potential to contribute to inclusive climate change adaptation in the study region and more broadly but there is an urgent need to increase the percentage of livestock owning households and the size of the herds for livestock to make meaningful contributions to household welfare and climate resilience. [ABSTRACT FROM AUTHOR]

Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases. This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization. Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior labels, which is useful for area segmentation and pest detection. Automation of irrigation and fertilization systems employs reinforcement learning algorithms to maximize efficiency. This multidisciplinary review discusses recent approaches to 1) Modeling Soil health and plant-soil interaction, 2) Yield prediction in tropical fruit orchards, 3) Integrating meteorological models for enhanced tropical fruit production, and 4) Economics of tropical fruit business through modeling. Furthermore, this review illustrates the complexity and multidisciplinary research on models for tropical fruit and platforms using agricultural models. Further opportunities to advance fruit modeling frameworks are indicated, requiring technical knowledge about the fruit crop requirements with user-friendly platforms to collect and access fruit tree data and site-specific agroecological conditions. [ABSTRACT FROM AUTHOR]

The increase in the frequency of extreme precipitation in different parts of the world is well documented and a cause for concern in terms of global climate change. Although clouds are the only source of precipitation, there is a lack of knowledge about the type of clouds involved in extreme precipitation events. Satellite- and ground-based observations show that over the central Indian region (19°–26°N, 75°–85°E), about 60% of the extreme precipitation comes from deep convective clouds (DCCs), which have a cloud-top pressure (CTP) of less than 440 hPa and a cloud optical thickness (COT) of over 23. It was also observed that cloud liquid water (CLW) and COT show the highest correlation with extreme precipitation. Furthermore, CLW and COT show the highest contrast between extreme and nonextreme precipitation events. Simulations by a 12-km Global Forecast System (GFS, spectral) model show that the model underestimates the extreme precipitation threshold with increased forecast lead time. The simulation of associated cloud optical parameters is also poor at all lead times in different parts of India. The model also fails to capture the observed relationship between the frequency of extreme precipitation and deep convective clouds without showing any correlation between them at all lead times. This is possibly because the model simulates the observed vertical structure of the apparent heat source poorly at all lead times which leads to poor simulation of the observed relationship between cloud optical properties and extreme rainfall. Significance Statement: The occurrence of extreme rainfall events is on the rise across the globe, and India is no exception. Clouds are the only source of rainfall; however, there is a lack of understanding about what type of clouds is causing them. Therefore, it is necessary to understand the relationship between cloud, convection, and extreme rainfall events during the Indian summer monsoon months. Furthermore, it is also important to investigate the skill of the current-generation global models to simulate them. This study highlights how cloud optical properties, convection, and extreme rainfall events are related to each other and also finds that a 12-km GFS model simulates these observed relationships poorly at all lead times leading to the poor simulation of extreme rainfall events. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the implementation and impact of climate-sensitive training programs tailored to the specific climate risks and business needs of Dickson County, Tennessee. It aims to emphasize the importance of integrating climate adaptation strategies into business training and to propose a replicable model that can be adapted to regions facing similar challenges, contributing to global sustainability efforts. Design/methodology/approach: The research uses Dickson County's climate profile as a case study, analyzing its impact on local businesses in sectors such as agriculture, manufacturing and tourism. This paper integrates theoretical frameworks, including organizational learning and adaptive capacity, to provide practical strategies for climate-sensitive training. It further incorporates insights from local institutions, industry partnerships and public data, using a qualitative approach to analyze key themes related to training effectiveness and sustainability. Findings: The findings demonstrate that climate-sensitive training significantly enhances the resilience and sustainability of businesses by preparing them to address local climate challenges. Critical factors for successful implementation include stakeholder engagement, industry-specific customization and the continuous evaluation of training outcomes. The model presented shows promise for broader applicability in other regions, especially when localized climate data and collaborative efforts are integrated. Originality/value: This paper provides a novel, region-specific approach to climate-sensitive training, distinguishing itself by combining theoretical insights with practical strategies for businesses. By focusing on localized adaptation and stakeholder collaboration, the model addresses an existing gap in the literature on climate adaptation training for smaller, climate-vulnerable regions. This work offers a replicable and scalable framework that contributes to the global discourse on sustainable development and climate resilience in business practices. [ABSTRACT FROM AUTHOR]

Understanding the causes of past atmospheric methane (CH4) variability is important for characterizing the relationship between CH4, global climate and terrestrial biogeochemical cycling. Ice core records of atmospheric CH4 contain rapid variations linked to abrupt climate changes of the last glacial period known as Dansgaard–Oeschger (DO) events and Heinrich events (HE)1,2. The drivers of these CH4 variations remain unknown but can be constrained with ice core measurements of the stable isotopic composition of atmospheric CH4, which is sensitive to the strength of different isotopically distinguishable emission categories (microbial, pyrogenic and geologic)3, 4–5. Here we present multi-decadal-scale measurements of δ13C–CH4 and δD–CH4 from the WAIS Divide and Talos Dome ice cores and identify abrupt 1‰ enrichments in δ13C–CH4 synchronous with HE CH4 pulses and 0.5‰ δ13C–CH4 enrichments synchronous with DO CH4 increases. δD–CH4 varied little across the abrupt CH4 changes. Using box models to interpret these isotopic shifts6 and assuming a constant δ13C–CH4 of microbial emissions, we propose that abrupt shifts in tropical rainfall associated with HEs and DO events enhanced 13C-enriched pyrogenic CH4 emissions, and by extension global wildfire extent, by 90–150%. Carbon cycle box modelling experiments7 suggest that the resulting released terrestrial carbon could have caused from one-third to all of the abrupt CO2 increases associated with HEs. These findings suggest that fire regimes and the terrestrial carbon cycle varied contemporaneously and substantially with past abrupt climate changes of the last glacial period.An increase in wildfire extent and related greenhouse gas emissions can be linked to abrupt climatic changes during the last glacial period. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the potential of machine learning in advancing the Sustainable Development Goals, particularly Goal 11, which focuses on sustainable urban and community development. To reduce the impacts of increasing urbanization on the environment, it is necessary to prioritize the sustainable development of smart cities. Smart cities use information and communication technology techniques to enhance sustainability by improving resource management and reducing environmental impact. In this context, the use of artificial intelligence enhances the overall quality of life, which is a critical component of sustainable smart cities. Machine learning, a subset of artificial intelligence, is crucial in promoting the development of sustainable smart cities. This study focuses on the application of machine learning in sustainable smart cities, ranging from energy management, transportation efficiency, waste management, and public safety. It highlights the role of machine learning algorithms to improve operational efficiency, minimize expenses, and reduce environmental impact. The practical use of ML in smart cities across several countries demonstrates its ability to handle urban challenges and increase sustainability. This paper discusses a variety of real-world initiatives that have successfully employed machine learning to develop sustainable smart cities, as well as in-depth studies of the ML algorithms used and the obtained results. The paper also covers the challenges of implementing machine learning into smart city projects, such as data quality, model interpretability, scalability, and ethical considerations. It emphasizes the importance of high-quality data, clear models, and the right use of machine learning tools. [ABSTRACT FROM AUTHOR]

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

Purpose: The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces. Design/methodology/approach: This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis. Findings: Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively. Research limitations/implications: To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE. Practical implications: To the best of the authors' knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE. Originality/value: The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages. [ABSTRACT FROM AUTHOR]

Purpose: Climate risks are crucial for non-life insurers due to their significant exposure to both transition and physical risks. The aim of this study is to develop a multi-period model that represents climate risks in non-life insurance, encompassing the impacts of both physical and transition risks as well as their reinforcing dependence. Literature suggests that as physical climate risks increase, the urgency for climate policies intensifies, leading to higher climate transition risks. Design/methodology/approach: Our model includes a stochastic transition process affecting assets based on their exposure in climate policy-relevant sectors (green and brown investments) and a dependence structure between this process and liabilities, where the physical risks manifest as an increase in claims. Findings: Our simulation indicates that the choice of the transition process, as well as the consideration of dependencies, has a significant influence on the insurers' profit, but even more on the probability of ruin. The impact of green versus brown investment strategies varies considerably based on whether dependencies are taken into account or not. Originality/value: The results of this study are intended to deepen the understanding of the effects of climate risks on non-life insurers and provide a quantitative analysis of the impact of green and brown investing within this framework. [ABSTRACT FROM AUTHOR]

Systematic crises may disrupt well‐designed nutrition interventions. Continuing services requires understanding the intervention paths that have been disrupted and adapting as crises permit. Alive & Thrive developed an intervention to integrate nutrition services into urban antenatal care services in Dhaka, which started at the onset of COVID‐19 and encountered extraordinary disruption of services. We investigated the disruptions and adaptations that occurred to continue the delivery of services for women and children and elucidated how the intervention team made those adaptations. We examined the intervention components planned and those implemented annotating the disruptions and adaptations. Subsequently, we detailed the intervention paths (capacity building, supportive supervision, demand generation, counselling services, and reporting, data management and performance review). We sorted out processes at the system, organizational, service delivery and individual levels on how the intervention team made the adaptations. Disruptions included decreased client load and demand for services, attrition of providers and intervention staff, key intervention activities becoming unfeasible and clients and providers facing challenges affecting utilization and provision of services. Adaptations included incorporating new guidance for the continuity of services, managing workforce turnover and incorporating remote modalities for all intervention components. The intervention adapted to continue by incorporating hybrid modalities including both original activities that were feasible and adapted activities. Amidst health system crises, the adapted intervention was successfully delivered. This knowledge of how to identify disruptions and adapt interventions during major crises is critical as Bangladesh and other countries face new threats (conflict, climate, economic downturns, inequities and epidemics). Key messages: Well‐designed nutrition interventions may be disrupted by crises that affect the interventions themselves and the platforms on which they run.Combining contextualized expertise in operational settings with a data‐driven decision‐making process can facilitate the timely identification of intervention disruptions and enable swift adaptations.Continuity of nutrition services amidst crises is feasible by adopting hybrid modalities including both original and adapted implementation paths.Visualizing adaptations to the intervention paths sheds light on how to deliver nutrition services during major systematic disruptions.Knowledge of how to adapt nutrition interventions during crises is critical going forward to respond successfully in future disruptive events. [ABSTRACT FROM AUTHOR]