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The North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale. Plain Language Summary: A significant correlation between the North Atlantic Oscillation (NAO) and the North Atlantic tripole sea surface temperature (SST_tri) is widely recognized. However, this study found that the correlation between the two was significantly weakened in late winter during 1980–2001 while no such change was detected in early winter. Therefore, this paper will focus on the late winter and discuss the role of NAO spatial structure in the above correlation changes. When the NAO south center is located over the North Atlantic, both observations and the climate models indicate that the corresponding North Atlantic SST shows a significant tripole pattern. However, during 1980–2001, the NAO south center shift significantly eastward from the North Atlantic toward Western Europe (up to 20° longitude). At the same time, the NAO north center expanded eastward significantly. The eastward shift of the NAO results in the significant eastward shift of the turbulent heat flux and wind stress anomalies. Shift in the region of strong air‐sea interaction led to the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale and also provide implications for subseasonal‐seasonal predictions of Eurasian climate extremes. Key Points: The link between NAO and North Atlantic tripole SST weakened obviously during 1980–2001 late winter, which was not detected in early winterThe eastward shift of ∼20° longitude in NAO south center forced the strong air‐sea interactions region to shift in observations and CMIP6NAO's spatial shift in late winter caused the changes in the link between NAO and North Atlantic tripole SST [ABSTRACT FROM AUTHOR]

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

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

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

Rodents are one of the most abundant and diverse groups of mammals in arid zones. Their population and community dynamics are closely linked to climatic factors and pulses of primary productivity activation. In the future, climatic conditions in arid ecosystems could become more extreme as a consequence of climate change, which could affect the species that inhabit these regions. In this paper, we review the literature on the ecological response of rodents in the world's arid zones, at the population and community levels, to climatological factors (temperature and precipitation), climatic events and the possible impact of climate change. We used the PRISMA protocol to systematically search the literature, and the “vote‐counting” method to count positive, null or negative responses of the rodents. At the population level, rodent density and abundance responded positively to precipitation, while biomass responded negatively and reproduction responded positively to increasing temperature. At the community level, rodent density, biomass and diversity increased after rainfall. El Niño events were positively related to rodent population increases. In contrast, intense storms affected the survival of some species. Rodents with physiological adaptations suited to living in arid areas, for example, the heteromyids, responded positively to precipitation and could suffer fewer negative ecological consequences in the future when exposed to increased temperature and changes in precipitation. [ABSTRACT FROM AUTHOR]

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

This study investigated food security governance in relation to taming the endemic starvation for peace and sustainable development in Africa. Food and nutrition security remain a multi-dimensional concept to ensure sustainability of the food system by 2030. It is a prerequisite to meaningful development as starving people are not capable of learning, innovating, nor prioritizing sustainable development. The COVID-19 preventive measures such as lockdowns and curfews, and persistent conflict in Africa, plunged deteriorating food insecurity into steep descent that necessitates collaborative efforts and partnerships to change the trajectory. The partnerships will restore livelihoods, and eliminate starvation, disease, abject poverty, wars and eventually restore peace. Data for the study were collected through systematic review of scholarly reviewed publications obtained from Access to Global Online Research in Agriculture (AGORA) database, University of Nairobi and Google search engines. The objectives of this paper, therefore, are to analyse underlying causes of persistent starvation in Africa, examine the factors that contribute to food security issues degenerating into conflict, and highlight the role of governance in finding lasting solutions to persistent starvation in the continent. However, it was revealed that, there is congruence that conflict and disease (COVID-19) may require more attention, with unanimous calls for the adoption of multilateralism in food governance as strategy to handle the nexus issues on food, water, energy, climate, economy, conflict and disease. Attention is also required to be focused on innovations supporting small-scale farmers, especially women farmers who are the most vulnerable, to benefit from autonomous measures (such as climate-smart agriculture practices) and adaptation to climate extremes. Conclusions are drawn on the best possible strategies available to mitigate endemic starvation in Africa, the urgent need for joint efforts to eliminate conflict – induced food insecurity, and emphasis on a systems approach to tackle nexus issues (Food-Water-Energy-Climate-Economy-Conflict and Disease) to ensure sustainable development. [ABSTRACT FROM AUTHOR]

This paper introduces and summarises the main outcomes of the 19th workshop of the International Association for Phytoplankton Taxonomy and Ecology held in Tiszafüred, Hungary, Tisza Balneum Hotel, from 23 to 30 September 2022. The selected theme of the workshop was "Trait-Based Approaches in Micro-Algal Ecology". The discussions presented during the workshop sessions resulted in the 18 articles contained in this Special Issue. There are 6 main thematic aspects developed by the participants: 1. Shape and size: are these traits easy to measure? 2. Spatial scales: when and where to look for microalgae? 3. Climate and extremes of ecological gradients: hot topics of this century. 4. Metaphyton and metaphytic habitats: life beyond plankton and benthos. 5. Microalgae in water management: phycology in practice. 6. Traditional and new methods: perspectives and comments. Trait-based approaches in microalgae ecology, although requiring further investigation and methodological development, represent a valid tool for refining the analysis of environmental variability in aquatic ecosystems. The papers presented in this Special Issue demonstrate that these approaches are extremely useful not only in the study of planktic algae but constitute a thoughtful method for the analysis of benthic and metaphytic microalgae in a wide variety of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks). [ABSTRACT FROM AUTHOR]

The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery. [ABSTRACT FROM AUTHOR]

Green roofing technologies have been developed and implemented worldwide as they bring with them thermal, hydrological, and environmental benefits, especially in urbanized areas. Many studies have been conducted to investigate the thermal performance of green roofs over reinforced cement concrete (RCC) structural bases but there is a lacuna of experimental evaluation of the thermal performance of green roofs on sloping galvanized iron (GI) sheet truss type roof structures. In this paper, the thermal performance of a retrofitted extensive green roof is quantified; this is challenging in subtropical climates because of extreme seasonal and diurnal variations. The thermal performance of the green roof system was assessed using a life-size experiment setup followed by a whole-building simulation using Design Builder, which runs on the EnergyPlus simulation engine. Two prototype roofs were analyzed and the performance of the retrofitted extensive green roof was compared with that of a conventional RCC roof solution. To validate the results, two prototype buildings were constructed at a university campus and their thermal and energy performance analyzed for two observation periods, one during a typical summer week and the other during a typical winter week. The extensive retrofitted green roof displayed enhanced thermal and energy performance at a nominal additional cost. The findings of this study can be deployed in the development of a retrofitted extensive green roof module for sloped GI sheet roofs for better thermal performance, and hence reduced energy loads, and meet the sustainable development goals. [ABSTRACT FROM AUTHOR]

Emerging economies (EMEs) often ignore effective mitigation strategies for persistent climate changes to prioritise growth acceleration. This paper shows that they cannot sustain their economic growth due to the adverse impact of temperature rise on total factor productivity (TFP). Using a standard growth model, it demonstrates how temperature rise and variation for growing carbon emissions reduce capital productivity along with the damage to ecosystem services and labour productivity, adversely impacting total factor productivity (TFP) of an individual economy. A cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses the issues of endogeneity, heterogeneity and cross-sectional dependence with stochastic trends, has been applied to 21 EMEs over the period from 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. Although EMEs have heterogeneous impacts across the countries depending upon their climatic zones and income levels, one degree increase in temperature, on average, has decreased the TFP by approximately 3.22 per cent. It is much higher in the extreme climatic zones and less developed economies. • Temperature rise and its variation for raising carbon emission reduce capital productivity along with ecosystem services and labour productivity, adversely impacting total factor productivity (TFP). • The cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses endogeneity, cross-sectional dependence and heterogeneity, has been applied to 21 emerging markets and developing economies for 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. • One-degree increase in temperature, on average, reduces the TFP by approximately 3 per cent. • The temperature rise has heterogeneous and non-linear impacts on TFP across countries in geo-climatic locations. • Two extre-climatic zones are worse-affected than the moderate-climatic region. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that, as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme look more and more like one another: they converge towards a typical, i.e. most probable, trajectory called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics to understand the physical mechanisms driving extreme events in climate models. [ABSTRACT FROM AUTHOR]

With the increasing prominence of extreme climate events and the construction and development of scenic areas, strengthening the management of rainwater resources in scenic areas has become an urgent need to enhance the capability to resist rain-induced flooding disasters and utilize rainwater resources for building safe, ecological, and sustainable scenic areas. In this paper, the Yesanpo scenic area was taken as the research object. Based on topographic and hydrometeorological data, the soil conservation service (SCS) model and ArcGIS spatial analysis tools were used to simulate the inundation areas resulting from an extreme daily rainfall event that 100-year return period within the research region, and comprehensively analyze the inundation areas. Drawing inspiration from the concept of resilience in ecology, this study proposed the construction of rainwater resource management strategies at three levels of ecological resilience, facility resilience, and social resilience. These strategies aimed to facilitate the transformation of stormwater from disaster to resource, thereby enhancing the systemic resilience of the Yesanpo scenic area against floods and water scarcity issues. At the same time, these strategies promoted the construction of resource-saving and environmentally friendly scenic spots, committed to treat rainwater as a valuable resource, and achieved the goal of rainwater recycling, providing a new perspective for promoting the sustainable development of scenic spots. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Climate extremes are threatening transportation infrastructures and hence require new methods to address their vulnerability and improve their resilience. However, existing studies have yet to examine the climate impacts on transportation networks systematically rather than independently assessing the infrastructures at a component level. Therefore, it is crucial to configure alternative shipping routes from a systematic perspective to reduce climate vulnerabilities and optimise the resilience of the whole shipping network. This paper aims to assess the global shipping network focusing on climate resilience by a methodology that combines climate risk indicators, centrality analysis and ship routing optimisation. The methodology is designed for overviewing the climate vulnerability of the current and future scenarios for comparison. First, a multi-centrality assessment defines the global shipping hubs and network vulnerabilities. Secondly, a shipping model is built for finding the optimal shipping route between ports, considering the port disruption days caused by climate change (e.g. extreme weather) based on the climate vulnerability analysis result from the first step. It contributes to a new framework combining the global and local seaport climate vulnerabilities. Furthermore, it recommends changing shipping routes by a foreseeable increase in port disruptions caused by extreme weather for climate adaptation. [ABSTRACT FROM AUTHOR]

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models. Plain Language Summary: In this paper we propose a new data‐driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co‐occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Key Points: Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak‐over‐threshold models in most regions [ABSTRACT FROM AUTHOR]

The increasing global temperature has increased awareness of enhancing Outdoor Thermal Comfort (OTC). OTC has become a primary indicator for the effectiveness of urban environments, which directly affects inhabitants' health, comfort, and quality of life. Consequently, many thermal comfort indices were developed to quantify outdoor thermal comfort; among them was the Universal Thermal Climate Index (UTCI), which is one of the broadly used thermal indexes in evaluating OTC. As another axis of this awareness, several computer-aided design (CAD) software have worked on developing their techniques to increase the accuracy of their outdoor simulations and climate measurements. The paper concerns exploring the potential of the Ladybug as a tool for measuring UTCI through an algorithmic script designed with the Ladybug (ver. 1.6.0) in the Grasshopper interface within Rhinoceros 3D software. The script was applied to two urban neighbourhoods in different climate zones of Egypt, Cairo (an inland city and the capital of Egypt) and New Damietta (a coastal city on the Mediterranean Sea), to investigate its capabilities in simulating and measuring the UTCI of different climate zones in the extreme hot week of the year as an examination period. Results showed that the maximum UTCI value in the coastal neighbourhood decreased from 32.93°C to 31.64°C, while its minimum value decreased from 28.79°C to 27.28 °C compared to the inland neighbourhood. The maximum and minimum UTCI values, hourly data, and the percentages of UTCI heat stress categories for six test locations in each neighborhood were also calculated and compared. [ABSTRACT FROM AUTHOR]

In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions. [ABSTRACT FROM AUTHOR]

This article is a response to comments made by researchers from the Universidad de Sevilla regarding a paper on extending climate justice to animals. The authors of the response agree with the commenters that the Iberian Lynx population has increased due to conservation efforts, but they clarify that their paper was not intended as a critique of these measures. The purpose of their paper is to discuss how to help wild animals cope with climate change, using the Iberian Lynx as an illustrative case. They argue that while the population has increased, the species is still at risk of extinction due to climate change. The authors also address the commenters' mention of the lack of originality in their proposed conservation measures, stating that their intention was to examine the moral implications of intervention strategies. Overall, the authors emphasize the importance of climate justice for animals, including the Iberian Lynx. [Extracted from the article]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [ABSTRACT FROM AUTHOR]

Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side. [ABSTRACT FROM AUTHOR]

Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes identified beforehand. An efficient alternative forecasting approach is based on the data-driven models. Despite their potential ability in modeling spatio-temporal ocean data, they ignore the fact that the ocean variables in different spatial regions and time periods typically have ever changing influences on each other, thus cannot yield satisfactory prediction results. In this paper, we develop a novel attention based dynamic graph for the ocean variable forecasting problem, which captures both the spatial and temporal dependencies. Specifically, we employ joint self-attention to incorporate information from the spatial graph over the target region, and model the graph evolution across long-range time steps. The performance of the proposed prediction model has been examined in the Indian Ocean based on ocean grid data products datasets. Experimental results demonstrate that this model has significant forecasting capability within 12 months, compared with the numerical methods and the state-of-the-art spatio-temporal embedding baselines. [ABSTRACT FROM AUTHOR]

• The complex interconnectedness of climate change, energy demand, and health and long-term care poses current and future cascading challenges for the resilience of older people. • Key interconnections include a concern for the impact of frequent and intense weather events parallel to a global increase in life expectancy and social justice issues connected with strained resources and public infrastructure. • Comprehensive, targeted policies are needed on the national and global level that incentivize sustainable practices, enhance climate resilience, and support vulnerable populations in the Global South, and that mitigate the social justice implications of climate change for older adults. Challenges faced by many countries are energy insecurity, climate change, and the health and long-term care of growing numbers of older people. These challenges are increasingly intersecting with rising energy prices, aging populations, and an increased frequency and intensity of extreme climate events. This paper gives a deeper understanding of the current and predicted interconnections among these challenges through narrative-driven content and thematic analysis from workshops with a diverse group of international stakeholders from the Global North and Global South. Narratives emerged highlighting a complex nexus of interconnections and presenting critical action areas. Targeted local and global policies and interventions are needed to alleviate stress on health systems, encourage the integrated uptake of clean energy sources, and uphold social justice across all economies. Professionals can use this work to inform the design and implementation of effective interventions and increase the resilience of older adults by better preparing for systemic risks. [ABSTRACT FROM AUTHOR]

The vadose zone serves as a crucial link for the mutual transformation of atmospheric, surface, ecological, and groundwater systems. Infiltration recharge in the vadose zone is a key step in the Earth's water cycle and plays an extremely important role in the sustainable development of groundwater resources, particularly in arid and semi-arid regions. However, under the influence of extreme climatic conditions and intense human activity, the vadose zone has thickened in many places globally. Changes in the vadose zone structure lead to alterations in the infiltration process. Researchers have attempted to quantify this process using various methods. However, it has been found that conventional monitoring methods are inadequate to effectively describe the complex infiltration recharge process under the multifactorial influence of a deep vadose zone. Through an analysis of relevant literature published from 2000 to 2023 regarding deep vadose zone infiltration recharge, this paper identifies four contentious bottlenecks: (1) effective monitoring and simulation of deep vadose zone infiltration recharge, (2) modes of deep infiltration recharge, (3) issues related to the quantity and recharge period of precipitation and irrigation infiltration recharge, and (4) quantification of spatial variations and scale effects of infiltration recharge. After reviewing the latest developments in infiltration recharge monitoring and simulation and systematically analyzing the influencing factors and mechanisms of deep vadose zone infiltration recharge, this study provides answers to the aforementioned issues. The combined use of monitoring and numerical simulation methods, taking into account infiltration recharge scenarios and scales, can enhance the reliability and accuracy of the calculation results. Additionally, piston flow may not be the primary mode of water movement in the deep vadose zones. Understanding the modes and characteristics of water movement, as well as the differences in suction and desorption processes, is fundamental for accurately describing nonlinear infiltration recharge processes. Furthermore, the measured average vertical infiltration rates of the deep vadose zone vary widely from 0.14 to 500 mm/d globally. In the North China Plain, vertical infiltration recharge rates range from 133 to 300 mm/a. These significant differences are related to the research scale, external conditions, and internal soil structure within the vadose zone. Finally, a systematic analysis of the driving factors of nonlinear infiltration recharge in the deep vadose zone is a prerequisite for quantifying spatial variations and scale effects. Only by fully considering the interactions and contributions of various driving factors can the spatiotemporal variations in soil infiltration be effectively quantified. Therefore, our research team suggests that future studies on deep vadose zone infiltration recharge should focus on establishing a unified layout for large-scale, multi-point, synchronous, in situ, and long-term monitoring; constructing relationships between the vadose zone structure and hydraulic characteristics; and conducting comprehensive studies on the overall water cycle in the Earth's surface layers, with the deep vadose zone as the core. These will help build a research system for the spatiotemporal infiltration recharge of water in the deep vadose zone at multiple layers and scales, achieving the closest approximation to a realistic description of the deep vadose zone infiltration recharge. [ABSTRACT FROM AUTHOR]

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health. • Frequency of hot-dry events and hot-humidity events will increase and decrease, respectively, in the future. • There are abrupt points and periodicity in the future time series of compound temperature and precipitation extremes. • Asia is projected to have the greatest exposure and climate change contributes the most to exposure. [ABSTRACT FROM AUTHOR]

[Display omitted] • NDVI shows an overall increasing trend from 2000 to 2020. • The Hexi region shows a warming and wetting trend. • Extreme precipitation is more important for NDVI. • The lag effect of NDVI on climate extremes varies by indicators and spaces. Climate extremes significantly affects vegetation dynamics, and understanding the relationship between dryland vegetation and climate extremes is crucial for regional ecological security and disaster warning. According to meteorological and satellite remote sensing data, the paper analyses the dynamics of normalized difference vegetation index (NDVI) and climate extremes in the Hexi region from 2000 to 2020 using Theil-Sen Median, Mann-Kendall and linear regression methods; the relationship between climate extremes and vegetation dynamics is revealed using Pearson correlation and boosted regression trees (BRT) models. On this basis, the time-lag effect of NDVI in response to climate extremes was further investigated. The results show that the NDVI in the Hexi region increased from 2000 to 2020, with significant greening (67.72 %) and a small amount of degradation concentrated in the desert in the northwest and the oasis in the southeast. The correlation between climate extremes indicators and NDVI shows a large spatial heterogeneity, with extreme precipitation showing a high importance for all regions except oasis. NDVI has no lag or 1 month lag for extreme temperature, no lag for max 1-day precipitation amount (Rx1day) for areas other than mountainous areas, and a 3-month lag for max 5-day precipitation amount (Rx5day) for extensive areas. The results of this study could serve as a reference for the study of the relationship between vegetation dynamics and climate extremes in arid and semi-arid regions, and provide a theoretical basis for the establishment of scientific and reasonable strategies for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

Understanding climate extremes and indices is crucial for addressing climate change impacts, including the increased variability in local weather patterns and extreme events. Previous studies on the Arabian Peninsula (AP) have been limited by sparse station data and restricted spatial coverage. To overcome this limitation, this paper aims to assess the spatial distribution and temporal trends of temperature and precipitation indices suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI) using ERA5 reanalysis data from 1951 to 2020 for the AP. Additionally, this study investigated the changes relative to the reference period (1951–1980). The modified Mann-Kendall test and Sen's Slope estimator are utilized to detect significant trends and estimate their magnitude. Results revealed that ERA5 was consistent with previous studies that utilized in-situ station data, which offers a more detailed picture of the affected areas. The findings indicated a significant warming trend in temperature indices, with an increase of over 1 °C per decade observed across several areas, including the northern AP, and certain regions experiencing a discrepant change of 3 °C increase compared to the reference period. Changes in rainfall indices indicate a shift in rainfall patterns from the AP's fertile southwest regions towards more intense patterns in specific eastern regions, including Oman, Kuwait, Saudi Arabia, and Yemen. However, the precipitation temporal trends are weak in magnitude and variable spatially, with dominant decreases in both intensity (-10 mm per decade) and frequency indices (-5 days per decade). Some locations are subject to the combined effects of most heatwave indicators simultaneously, while flood indicators and yet others by drought indicators influence others. All these locations have been accurately identified in this study. The findings provide valuable information regarding the region's climate change vulnerability and adaptation needs. [Display omitted] • Prior studies relied on limited station data, short period, and MK trend test. • Extremes are assessed using ERA5, ETCCDI, Modified-MK, and Sen's Slope. • Affected areas are identified, and valuable insights for policymakers are set. • Significant warming trend: >1 °C/decade and > 2 °C change from reference period. • Spatiotemporal trends of precipitation are weak in magnitude and variable spatially. [ABSTRACT FROM AUTHOR]

• Ethiopia's food system sustainability was estimated at 0.46 on a 0–1 scale in 2020. • Ethiopia's sustainability falls short of the 0.75 satisfactory benchmark. • Social and Environmental dimensions exceed a discrete sustainability level of 0.5. • Economic, and food and nutrition security dimensions receive low scores, below 0.5. In the face of continuous population growth, environmental degradation, climate extremes and enduring abject poverty, ensuring sustainability of the food system is of crucial importance. As such, developing a comprehensive measure of sustainability has been of a great interest for both researchers and policy makers so as to measure progresses and evaluate the outcomes overtime. This paper attempts to assess the food system sustainability (FSSI) in Ethiopia over the past two decades from 2001 to 2020. A composite index was constructed by aggregating 24 selected food system indicators in the four food system dimensions: environmental, economic, social, and food and nutrition security. 'Distance to a Reference' method was employed for data normalization, whereas equal weighting and linear aggregation methods were used for aggregating the indicators in each dimension. The result showed that in a scale from 0 to 1, better score was achieved in the social dimension (0.66), followed by environmental dimension (0.52), and food and nutrition dimension (0.43), with the least recorded in the economic dimension (0.31) in 2020. The overall Food System Sustainability Index for the most recent year (2020) was estimated to be 0.46 which is far below a satisfactory level of food system sustainability (0.75) as per OECD Classification of Sustainability Indices. The result calls for policy interventions aimed at improving the overall food system sustainability in light of the four food system dimensions, in general, and the weak dimensions, in particular. Given the predictable need for more food production in the face of growing populace, interventions that enhance synergies and limit the potential trade-off among the food system outcomes, as well as striking the right balance should be the policy direction for Ethiopia. [ABSTRACT FROM AUTHOR]

Floods are a great threat to Pakistan with increasing concern. As the consequences of increased extreme weather related to climate change, Pakistan experiences severe floods almost every year. This study aims to explore and analysis the actual inundated situation, magnitude, the possible causes of the 2022 devastating floods, and future trends. We presented an enhanced nationwide flood mapping method and compared with other pixel-based image processing techniques including active contours and change detection. These algorithms were applied to Sentinel-1 Ground Range Detected (GRD) Synthetic Aperture Radar (SAR) imagery (10 m spatial resolution) with various land types and inundation scenarios in Pakistan, and were evaluated using other reference flood products. Accuracy evaluation analysis demonstrated that our algorithm has high robustness and accuracy, with the overall accuracy (OA) higher than 0.83 and critical success index (CSI) up to 0.91, and is suitable for automated flood monitoring in near real time. Nearly one-third of the lands were flooded in 2022, and more than half were inundated croplands. Punjab and Sindh provinces were the most severely affected regions, with the proportions of inundated area in 2022 (21.26% and 20.55%) nearly twice of that in 2010 (11.40% and 12.70%), indicating an intensified flooding trend. Analysis of possible influential factors showed that the intense and cumulative rainfall during the monsoon season (June to August) was the major cause of the 2022 flood event. Although the snow melted rapidly in June (the average change in snow depth is ∼10 mm), the overall ablation contributed insignificant amount to the flood water. The glacial lake outburst floods (GLOFs) induced by abnormal April–May heatwave provide water flowed into the tributaries of the Indus River, but are difficult to spread for thousands of kilometers from mountains to the plain downstream. The combination of the intrinsic arid climate and extreme floods exacerbate the already severe situation. • This paper presents an automated method based on tile thresholding and fuzzy logic. • The inundation in Punjab and Sindh in 2022 are nearly twice of that in 2010. • The cumulative rainfall is the major cause of the 2022 flood event. • The snow melting and GLOFs are likely small relative to the flood water gains. • These observations imply an intensified flooding and water cycle in Pakistan. [ABSTRACT FROM AUTHOR]

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed. [ABSTRACT FROM AUTHOR]

The editorial discusses the importance of adopting passive housing strategies to enhance energy efficiency and reduce the reliance on traditional heating and cooling systems in response to climate change and population growth. It also highlights the need for agroforestry systems to promote carbon storage and nitrogen levels in the soil. The editorial emphasizes the negative impacts of climate change on coastal and ocean environments, particularly ocean warming and acidification, and calls for ethical data gathering methods and standardized communication protocols to address these challenges. The editorial concludes by mentioning that these topics are covered in the seven papers included in the issue. [Extracted from the article]

As the global community shifts towards a clean energy paradigm, the understanding of energy security has witnessed significant evolution. Historical interpretations centered on the supply reliability and pricing stability. Contemporarily, terms such as "disruption", "risk", "threat", and "vulnerability" have become central in shaping our perspective on energy security. This is particularly in light of external shocks including geopolitical conflicts, wars, acts of terrorism, extreme climatic occurrences, and other unpredictable challenges. In the face of these external pressures, conventional indicator-based methodologies exhibit inherent challenges in adequately quantifying national energy security. This study introduces a variance-based weighting approach within the classical composite indicator framework and characterize the influence of such external shocks into the weights of individual energy security indicators. As a result, this approach can provide a holistic, intuitive, and communicable tool in the integration of external shock factors into the assessment of energy security performance, all while staying true to the fundamental essence of energy security. Through a re-examination of the energy security risk in major energy-consuming countries from the 2020 GEI, this paper underscores the effectiveness and applicability of our approach. • Novel variance-based weighting method introduced. • Enables inclusion of external factors in energy security performance. • Bridge subjective and objective weighting in composite indicators. • Reassesses energy security risk in major consumers using 2020 GEI. [ABSTRACT FROM AUTHOR]

• Drought followed by extreme rain/flooding exposed vulnerabilities in Ceará, Brazil. • Adaptative strategies in dry periods may become maladaptive during extreme rain. • Impacts of and responses to drought, extreme rain, and flooding are interrelated. • Hydrometereological extremes should be considered together in semi-arid regions. As climates continue to change, semi-arid rainfall regimes are projected to become increasingly more extreme, with longer dry periods and fewer, but more intense, heavy rainfall events. This process of hydroclimatic intensification increases the risk associated with successive extreme climate hazards, such as drought and then heavy rainfall, in the same area. However, the development of effective adaptation strategies to drought, heavy rainfall, and flood hazards is hampered by the lack of integrated analyses that consider the interactions among these hazards and responses to them. To increase understanding of these interactions, this paper examines impacts of and adaptive actions taken in response to prolonged drought (2012–2018) and subsequent extreme rainfall and flooding (March and April 2023) in the semi-arid region of Ceará, Northeast Brazil. We find that policies and adaptive measures focus primarily on drought hazard, while preparation for extreme rainfall events and flooding is scant. Management plans and long-term adaptive strategies are being developed for drought and not other extreme hydrometeorological events. Additionally, emergency measures for extreme rainfall and flooding are much less structured than those for drought. As such, the paradigm "Convivência com o semiárido" ("Living with the semi-arid region") remains limited to adapting to drought hazard, ultimately hindering adequate extreme rainfall and drought responses. To promote more effective adaptive strategies in the context of hydroclimatic intensification in semi-arid regions, drought, heavy rainfall, and flood responses must be considered in a more integrated manner to create management plans to prepare for the potential impacts of these hazards into the future. [ABSTRACT FROM AUTHOR]

Unlike previous studies solely focused on the influences of low or mid-latitudes signals on regional extreme events, this paper used the three-pattern decomposition of global atmospheric circulation (3P-DGAC), taking the 2018 hot summer in East Asia for example, to quantitatively reveal the dynamic and synergistic effects of low and mid-latitudes signals on regional extreme events. By analyzing the anomalous three-dimensional structure of atmospheric circulations in 2018 hot summer, we discovered that the local meridional circulation at low latitudes and zonal circulation at mid-latitudes both produced abnormal downdrafts in East Asia, which were consistent with the anomalous anticyclone in this region. Then, we investigated the relative vorticity advection combined with 3P-DGAC to uncover the dominant circulation patterns, and found the horizontal circulation at mid-latitudes and local meridional circulation at low latitudes were the major contributor to the anomalous anticyclone, accounting for 56% and 44% respectively. This result indicated the 2018 hot summer might be influenced by the synergistic effect of low and mid-latitudes signals. Finally, by investigating sea surface temperature anomaly (SSTA) and mid-latitudes wave train, we considered the tripole-like SSTA pattern in the North Atlantic and positive SSTA in Kuroshio and its extension area (K-KE) as the main causes of horizontal circulation anomalies, and the anomalous meridional circulation was related to the extremely late onset of South China Sea summer monsoon (SCSSM). This study provided a novel way to quantitatively study synergistic effects of low and mid-latitudes signals on regional extreme events. • East Asia was hit by extreme heat event in the summer of 2018. • Horizontal circulations at mid-latitudes and meridional circulations at low latitudes contributed greatly to this event. • The combination of 3P-DGAC and dynamic variables can be applied to the study of dynamic causes of extreme climate events. [ABSTRACT FROM AUTHOR]