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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]

Unprecedented climate extremes will likely become a new 'normal'. Urban political ecology is thus confronted with the challenge of exploring and theorising emerging geographies of unprecedented climate change. This raises questions on the extent and ways in which past socionatures can be mobilised for this task. It also urges a critical reflection on what forms of knowledge are needed to meet political ecology's normative aspirations of transforming emerging and future socionatures of climate change. I argue that a critique of past and present socionatures alone is not enough to meet these theoretical and normative goals. The main objective of this paper, therefore, is to lay the foundations of a future-oriented urban political ecology that approaches geographies of climate change more experimentally and speculatively. To this aim, I examine the explanatory potential of two (of many possible) experiments, collaboratively developed by a team of hydrologists, climatologists, and political ecologists. Both experiments depart from past and present logics of colonial violence, racial capitalism, and climate change, but explore competing notions of unprecedented futures. The first experiment consists of a critical-realist scenario approach that examines how power and variability in the exercise of agency might shape outcomes of future unprecedented climate extremes. The second involves a model that speculatively brings about a better world in response to climate extremes and lays the foundation of a political ecology of possibility. The paper thereby serves as a demonstration of how critique can be mobilised to explore and reimagine urban futures. [ABSTRACT FROM AUTHOR]

Taking China's provincial panel data from 2010 to 2020 as an example, this paper verifies that climate change, especially extreme climate change, can destabilize China's financial stability and explores the transmission path of climate risks with meteorological disaster losses as the channel variable. Our empirical results demonstrate that: (a) only after the extreme climate change breaks through a certain threshold, thus causing a significant impact on the financial system; (b) we cannot ignore the improvement of disaster prevention and reduction ability brought by the improvement of social and economic development level; (c) the impact of extreme climate change will spread to the financial system through the economic losses caused by meteorological disasters; (d) there is obvious regional heterogeneity in the spread of meteorological disaster losses to the financial system. Our results are robust to a wider battery of checks and have important policy implications. • This paper empirically tests the impact of climate change on financial stability. • Extreme climate change will damage China's financial stability. • The economic losses caused by climate disasters will spread to the finance. • The transmission of such losses has obvious regional heterogeneity. • This paper provides policy implication for coping with climate risks. [ABSTRACT FROM AUTHOR]

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]

The mechanism and effectiveness of urban greenery in mitigating urban heat islands, regulating microclimate, and enhancing thermal comfort has been extensively studied during the last decades. While sporadic empirical evidence has been generated, the trends and patterns of existing scholarship pertinent to urban greenery's cooling effect have been rarely summarized and synthesized. To bridge this knowledge gap, the present paper systematically reviewed 310 relevant publications in the Web of Science database (1998–2022) and conducted a bibliometric analysis to depict a comprehensive profile of urban greenery's cooling effect, focusing on global research trends, prevalent research topics, and future prospects. Our analytical results reveal (1) a steady increase in publications, active journals, and knowledge-generating institutions since 2008 that might be attributed to the free accessibility of diverse remote sensing data; (2) a significant increasing trend of transdisciplinarity and interdisciplinarity, expanding from Environmental Science and Ecology to various subjects such as Engineering, Remote Sensing, Construction & Building Technology, Urban Forestry, and Urban Studies; (3) four influential publication outlets including Urban Forestry & Urban Greening , Science of the Total Environment ; Building and Environment , and Sustainable Cities and Society ; (4) core research themes focusing on the association of urban greenery's biophysical characteristics with cognate cooling effect, urban heat island mitigation, and land surface temperature; and (5) several new research themes that have not yet well-developed in the extant literature, including the integration of various analytical approaches to up-scale empirical studies from micro-scale to meso- and global scales, extending urban greening-thermal comfort to public health and social thermal justice, and coupling urban greenery's cooling effect with other environmental/ecological benefits to inform the design of urban greenery for biodiverse, climate-resilient and sustainable cities. Findings of this synthetic review offer a reference for the research focusing on urban greenery's cooling effect, and provide clear direction for further development of cognate scholarship that is urgently needed facing more frequent urban climate extremes along with global warming. [ABSTRACT FROM AUTHOR]

With the accelerating deployment of renewable energy, photovoltaic (PV) and battery energy storage systems (BESS) have gained increasing research attention in extremely cold regions. However, the extreme low temperatures pose significant challenges to the performance and reliability of such systems. This paper reviews the current progress in PV-BESS technologies and applications in extreme cold climates. The major technical challenges are discussed, including the impacts of low temperatures on PV and battery efficiency, snow and icing effects, and difficulties in system modeling and optimization. Recent solutions are analyzed, such as advanced PV module designs, battery thermal controls, anti-icing coatings, and sensors. Case studies of existing PV-BESS demonstration projects in cold regions are presented, and key lessons learned from real-world operation data are summarized. The economic feasibility and policy incentives for promoting PV-BESS in cold areas are also examined. Finally, recommendations for further research and development are provided to overcome the limitations and knowledge gaps. This review covers the current state-of-the-art in PV-BESS systems suited for extreme cold environments, providing insights for researchers and engineers working on advancing renewable energy in these challenging climates. Key findings indicate that despite technical hurdles, PV-BESS systems show promising potential in extreme cold regions through continued system optimization, cost reduction, and supportive policies. • We summarize the state-of-the-art in PV-BESS technologies suited for extreme cold climates. • Field experiences and lessons learned from existing PV-BESS demonstration projects and installations in extreme cold regions are analyzed. • We provide recommendations for further research and development towards advancement. [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]

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]

Coffee (Coffea arabica L.) is a climate-sensitive crop; rainfalls may trigger flowering event occurrences, and extreme rainfall during a flowering day can cause considerable yield reductions. Multiple flowering events can occur in the span of 12 months; the number varies from year to year. This paper introduces a Bayesian network model capable of inferring coffee flowering events in coffee areas in the Pacific Region of Central America based on observed data for coffee flowering and precipitation. The model structure was determined based on expert knowledge, and the model parametrization was learned from 53 years of data registered in the region. Data from four farms in the region were used for model validation. The model's performance in the inference of flowering intensity was good (spherical payoff of 0.78 out of maximal 1.00), and the model was able to depict expected behaviors for single and multiple flowerings. Further, comprehensive new details on the dynamics of multiple flowerings within a crop season were obtained, e.g., that a large flowering event tends to occur more quickly (8 to 10 days) after rain than a small flowering (10 to 13 days). We believe that this Bayesian network model has the potential to evolve and support the development of agricultural index-based insurance to deal with yield losses due to extreme rainfall during flowering. The use of longer farm records for model building may also serve to increase farmers' trust in the reliability of the tool. • Coffee flowerings are affected by extreme climatic conditions. • 53 years of farmers' data were used to build a Bayesian Network flowering model. • Multiple flowering events can be inferred in a crop year. • The model was validated and used to learn about the flowering dynamic. • Model suitable for Pacific Central American areas or others with unimodal rain patterns. • The model can serve as a basis for index-based insurance against rainfall on flowering. [ABSTRACT FROM AUTHOR]