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Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps. [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]

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]

This paper presents a novel theoretical framework for incorporating climate risks and adaptation investments into infrastructure debt pricing. Utilizing the Capital Asset Pricing Model (CAPM), the framework extends the conventional modeling of infrastructure project revenues and costs to include climate risk considerations. It proposes three climate-informed revenue and cost formulations: adjustmentment of mean and standard deviation, incorporation of extreme climate events via Pareto and Poisson distributions, and a climate-informed cost model that includes adaptation investment. The paper demonstrates the application of this model in pricing a loan for a Light Rail Transit project in Costa Rica, introducing the concepts of "flood risk premium" and "adaptation curves". This study not only offers a novel lens through which to view infrastructure investment under climate uncertainty but also sets the stage for transformative policy and practice in financial risk assessment, encouraging a shift towards more sustainable and resilient infrastructure development. [ABSTRACT FROM AUTHOR]

The increasing intensity and frequency of extreme climate events have made improving the adaptability to extreme climate events a strategic imperative for manufacturing companies. This paper investigates whether manufacturing enterprises increase green technology innovation affected by different extreme climate events. Based on panel data of Chinese listed manufacturing enterprises, we show that extreme precipitation events can positively promote green technology innovation, yet extreme temperature events do not. Heterogeneity analyses suggest that the effect of extreme precipitation events on green technology innovation is more significant for non-state-owned enterprises, poor performance enterprises, and high R&D intensity enterprises than other enterprises. Furthermore, the facilitating effect of extreme precipitation events on green technology innovation is merely temporary. [ABSTRACT FROM AUTHOR]

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]

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]

The diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates. Plain Language Summary: The future flood hazard status over the major UK urban region of Bristol (domain size ∼746 km2) is evaluated using the latest 12‐member UK Climate Projections Local ensemble rainfall data set at the hourly and kilometer scale. A total of 30,098 rainfall events from an equivalent 720 years of climate data are identified and used for flood modeling. Impact of changing precipitation pattern and soil moisture dynamics on surface water floods are evaluated, and the interaction between precipitation, inundation, infiltration, and soil wetness are handled simultaneously at a spatial resolution of ∼30 m or less using an improved hydrodynamic model (LISFLOOD‐FP). Our findings highlight a shift toward a later seasonal occurrence of extreme flooding under global warming. Specifically, a large proportion of future extreme flood hazards is projected to manifest in December, contrasting with historical trends where most extreme events occurred in November. Extreme precipitation can be magnified in rainy seasons due to amplified moisture convergence, while in dry periods limited moisture availability may offset precipitation increases. Despite an overall increase in the total annual rainfall volume, the future summer is anticipated to be much drier due to limited rainfall availability, leading to a further reduction in soil moisture availability. Key Points: Shifts to a later seasonal occurrence of extreme flooding is identified under global warming in the UKIncrease in annual total rainfall leads to a disproportionate rise in the resultant flood volumeImpact of the antecedent soil moisture on flood generation shows seasonality [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]

Average monthly air temperatures in Bosnia and Herzegovina (BiH) exhibit a notable rise during summer, ranging from 0.4 to 0.8 °C per decade, while precipitation experiences a significant decrease of up to 8 mm per decade. Climate models, across various RCP scenarios, project an increase in air temperature, that is most pronounced in the summer season. Additionally, there is a projected frequency and intensity of heavy precipitation during autumn. In BiH, agricultural production faces substantial risks, including droughts, spring and autumn frosts, hail, and floods. Recent years have witnessed extreme hydrological events, notably the 2012 drought and the 2014 floods. Strategic documents highlight the critical importance of addressing floods and droughts for agriculture, as well as their implications for the environment, households, and industry. To assess the severity of extreme hydrological events and their impact on agriculture, with a specific emphasis on autumn and summer in Bosnia and Herzegovina, average and peak values of the Standardized precipitation evapotranspiration index (SPEI) were calculated separately for the periods 1961-1990 and 1991-2020, focusing on October and August. Compared to the reference climatic period the current climate is characterized by shifts between intense wet and dry periods, with very few years exhibiting stable and expected weather conditions. Identified as extremely wet and flood-prone years, SPEI2 October values for 1974 (2.42), 1996 (2.13), 2001 (2.24), and 2014 (2.05) stand out, with only one extremely dry year in 1985 (-2.21). SPEI2 August indicates extremely dry years, notably 2012 (-2.35) and 2017 (-2.25). [ABSTRACT FROM AUTHOR]

Annual maxima of daily precipitation sums can be typically described well with a stationary generalized extreme value (GEV) distribution. In many regions of the world, such a description does also work well for monthly maxima for a given month of the year. However, the description of seasonal and interannual variations requires the use of non-stationary models. Therefore, in this paper we propose a non-stationary modeling strategy applied to long time series from rain gauges in Germany. Seasonal variations in the GEV parameters are modeled with a series of harmonic functions and interannual variations with higher-order orthogonal polynomials. By including interactions between the terms, we allow for the seasonal cycle to change with time. Frequently, the shape parameter ξ of the GEV is estimated as a constant value also in otherwise instationary models. Here, we allow for seasonal–interannual variations and find that this is beneficial. A suitable model for each time series is selected with a stepwise forward regression method using the Bayesian information criterion (BIC). A cross-validated verification with the quantile skill score (QSS) and its decomposition reveals a performance gain of seasonally–interannually varying return levels with respect to a model allowing for seasonal variations only. Some evidence can be found that the impact of climate change on extreme precipitation in Germany can be detected, whereas changes are regionally very different. In general, an increase in return levels is more prevalent than a decrease. The median of the extreme precipitation distribution (2-year return level) generally increases during spring and autumn and is shifted to later times in the year; heavy precipitation (100-year return level) rises mainly in summer and occurs earlier in the year. [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]

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the " Hollowing " scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community's microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment. [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]

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]

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [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]

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]

Aims: We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods: We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results: The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions: These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications: The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia. This paper explores the relationships between the major forces influencing Australian weather and climate, and the chance of severe fire seasons. The findings could be valuable in decision making and preparation for upcoming fire seasons to avoid more seasons with devastating outcomes such as the 2019–2020 Black Summer. This article belongs to the Collection Fire and Climate. [ABSTRACT FROM AUTHOR]

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Extreme climate change is an existential threat to humanity and infrastructure development. At the same time, the construction and operation of carbon-intense public–private partnership (PPP) infrastructure such as road transport, water, public houses, energy supply and sanitation unleash most of the greenhouse gas emissions that impacts negatively on the climate. Increasingly, there is a heightened interests in the development and financing of climate-smart PPP solutions to promote resilient and sustainable public infrastructures. Therefore, this article aims at identifying the critical solutions to the provision of climate-smart PPP infrastructure projects together with the driving factors and challenges of its implementation in public facilities. The paper utilized a systematic literature review method where data were sourced from prominent academic databases of Scopus, Web of Science, Google Scholar, and PubMed. The outcomes of the review demonstrate that the adoption of climate finance, renewable energy, and maintaining resilient infrastructures are prominent solutions to attain low-carbon infrastructure development. Key drivers such as the global call to reduce huge emissions from construction projects and transition to sustainable green construction management account for the shift toward climate-smart PPP projects. The barriers identified include poor and unconcerted practice and policy directions to resolve emission problems in the construction industry. The outcomes of this article provide incentives for the development and management of climate-smart public projects. Researchers can harness the results to investigate and develop adaptation and mitigation strategies for low-carbon PPP projects. [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]

Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia. [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]

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]

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]

Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic. Moist, warm air from mid-latitudes lead to a speed-up of glacial ice loss in Novaya Zemlya, High Russian Arctic. Foehn winds enable especially high melt rates. These conditions became more frequent over the past decade. [ABSTRACT FROM AUTHOR]

Global warming has increased the probability of extreme climate events, with compound extreme events having more severe impacts on socioeconomics and the environment than individual extremes. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6), we predicted the spatiotemporal variations of compound extreme precipitation-high temperature events in China under three Shared Socioeconomic Pathways (SSPs) across two future periods, and analyzed the changes in exposed populations and identified influencing factors. From the result, we can see that, the CMIP6 effectively reproduces precipitation patterns but exhibits biases. The frequency of compound event rises across SSPs, especially under high radiative forcing, with a stronger long-term upward trend. Furthermore, the economically developed areas, notably China's southeastern coast and North China Plain, will be hotspots for frequent and intense compound extreme events, while other regions will see reduced exposure. Finally, in the long-term future (2070–2100), there is a noteworthy shift in population exposure to compound events, emphasizing the increasing influence of population factors over climate factors. This highlights the growing importance of interactions between population and climate in shaping exposure patterns. [ABSTRACT FROM AUTHOR]

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation. [ABSTRACT FROM AUTHOR]

High-resolution numerical ocean models can be used to help interpret sparse observations in the nearshore and to help understand the impacts of climate change and extreme events on these dynamically complex coastal areas. However, these high-resolution ocean models require inputs with comparably high resolution, which is particularly difficult to achieve for freshwater discharge. Here, we explored a simple rain-based hydrological model as input into a high-resolution (≳ 13 m) model of Quatsino Sound – a fjord system located on the northwestern coast of Vancouver Island, British Columbia, Canada. Through a series of sensitivity tests using an application of the Finite Volume Community Ocean Model (FVCOM version 4.1), we found that model performance was hindered by the lack of knowledge of ungauged rivers and streams. In this case study, including the only major gauged river implied ignoring 538 other watersheds of various sizes and accounted for only about a quarter of the total estimated freshwater discharge. We found that including at least 60 % and ideally closer to 75 %–80 % of total freshwater fluxes gave similar model performance to including all possible 539 freshwater sources; in our model simulations, this percentage of freshwater flux meant including rivers with watersheds greater than 20–50 km2 , corresponding to 7–19 total rivers. Further sensitivity tests also indicated that knowing the main outpour locations into the nearshore ocean is an important factor, but it is not as important as the total freshwater discharge included. Overall, this study illustrates the complexities of studying the land–ocean connection and offers a simple and accessible tool to help address a common problem in nearshore modelling. [ABSTRACT FROM AUTHOR]

This article explores the challenges posed by extreme weather and climate-related events and the inadequacy of current resilience and adaptation frameworks. It discusses the "Exploring Unprecedented Extremes" workshop held in Montreal, Canada, which aimed to address the research gap in understanding and preparing for these events. The workshop emphasized the need for transformational changes in governance and the integration of diverse perspectives to effectively respond to climate extremes. It also highlighted the importance of effective communication and coproduction of knowledge in bridging the gap between scientific research and decision-making. The article provides examples of recent extreme events and emphasizes the urgency of addressing these challenges. It discusses the importance of understanding different ways of knowing and establishing trust in research collaborations, as well as exploring various modeling approaches used to study climate change and extreme weather events. The text concludes by emphasizing the need for a shift in perspective and the adoption of transdisciplinary approaches to effectively address climate extremes. The workshop served as a foundation for examining the impacts of climate change and identifying strategies for adaptation and transformation, and the text emphasizes the urgency of taking action and promoting understanding among individuals, communities, and organizations. [Extracted from the article]

Objective: To investigate the impact of extreme climate on geological disasters in Shanxi and propose effective disaster prevention and mitigation strategies. Methods: Using daily temperature and precipitation data from 27 meteorological stations in Shanxi Province from 1975 to 2020, 32 extreme climate indices were calculated. Combined with geological disaster site data, the distribution characteristics of extreme climates and their relationship with geological disasters were analyzed, and a regression model for geological disaster risk zones was constructed. Results: Sixteen extreme climate indices in Shanxi Province showed significant changes, especially TMAXmean (100% significant). Indices related to negative precipitation effects showed a declining trend, with 77.78% being significant, while 96.3% of positive temperature effect indices showed an increasing trend, with 73.6% being significant. Geological disaster hotspots were concentrated in the mid-altitude (500–1500 m) hilly and low mountain areas along the central north–south axis and on Q and Pz strata. Extreme high-temperature indices were significantly positively correlated with geological disaster hotspots, while extreme low-temperature indices were negatively correlated. Indices related to extreme heavy precipitation (e.g., R99p.Slope, RX5day.Slope) were associated with an increase in geological disaster hotspots, whereas higher total precipitation and frequent heavy precipitation events were associated with a decrease in disaster hotspots. The grey relational degree between the Z-score and TXn.Slope, TXx.Slope, GSL.Slope, and TX90P.Slope was greater than 0.8. The random forest model performed best in evaluation metrics such as MAE, RMSE, and R2. Conclusions: Shanxi is likely to experience more extreme high-temperature and precipitation events in the future. The low-altitude hilly and terraced areas in Zones III and VII are key regions for geological disaster prevention and control. High temperatures and extreme rainfall events generally increase the disaster risk, while higher total precipitation reduces it. The random forest model is the optimal tool for predicting geological disaster risks in Shanxi Province. [ABSTRACT FROM AUTHOR]

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study's findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region. [ABSTRACT FROM AUTHOR]

Changing climatic conditions threaten forest ecosystems. Drought, disease and infestation, are leading to forest die-offs which cause substantial economic and ecological losses. In central Europe, this is especially relevant for commercially important coniferous tree species. This study uses climate envelope exceedance (CEE) to approximate species risk under different future climate scenarios. To achieve this, we used current species presence-absence and historical climate data, coupled with future climate scenarios from various Earth System Models. Climate scenarios tended towards drier and warmer conditions, causing strong CEEs especially for spruce. However, we show that annual averages of temperature and precipitation obscure climate extremes. Including climate extremes reveals a broader increase in CEEs across all tree species. Our study shows that the consideration of climate extremes, which cannot be adequately reflected in annual averages, leads to a different assessment of the risk of forests and thus the options for adapting to climate change. [ABSTRACT FROM AUTHOR]

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons. [ABSTRACT FROM AUTHOR]

Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes' early warning. Skillful prediction of EISOs opens a pathway for heat extremes' prediction by extending it from the weather to the subseasonal timescales. [ABSTRACT FROM AUTHOR]

The increasing global population and the challenges faced by the food production sector, including urbanization, reduction of arable land, and climatic extremes, necessitate innovative solutions for sustainable agriculture. This comprehensive review examines advancements in improving the energy performance of agricultural greenhouses, highlighting innovations in thermal and energy efficiency, particularly in heating and cooling systems. The methods include a systematic analysis of current technologies and their applications in optimizing greenhouse design and functionality. Key findings reveal significant progress in materials and techniques that enhance energy efficiency and operational sustainability. The review identifies gaps in the current knowledge, such as the need for more research on the economic viability of new materials and the development of predictive models for various climatic conditions. The conclusions emphasize the importance of integrating renewable energy technologies and advanced control systems to achieve energy-efficient and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project. Plain Language Summary: The projected loss of Arctic sea‐ice under 2°C global warming will cause large warming in the Arctic region and climate and weather anomalies outside the Arctic. The warming in the Arctic will mean warmer airmasses coming from the Arctic and also more moisture from the open Arctic Ocean. Furthermore, it will also change atmospheric circulation. These effects together will determine the impacts of Arctic warming. In this study, we introduce a novel sub‐sampling method to isolate atmospheric circulation change in response to the Arctic warming. The method involves selecting members of simulations from the experiment with future Arctic sea‐ice conditions, the average of which is equal to the average of the members of simulations in the experiment with present‐day Arctic sea‐ice conditions. We found that atmospheric circulation change in European regions is relatively weak so that warming effects will dominate the climate and weather response there. On the other hand, atmospheric circulation change will dominate the climate and weather response in East Eurasia. We also found that stronger atmospheric circulation changes will generally increase the response to the Arctic warming. We suggest caution when assessing whether different responses in different models can be interpreted as true differences in model physics. Key Points: A novel sub‐sampling method is introduced to isolate the role of dynamics in the response to projected Arctic sea‐ice lossA dynamical Siberian High response dominates the temperature response over East Eurasia while that of the North Atlantic Oscillation is weakInter‐model differences in Polar Amplification Model Intercomparison Project likely contain a large fraction of internal variability due to the unconstrained dynamic effects [ABSTRACT FROM AUTHOR]

Saline lakes contributions to the carbon cycle is crucial to the Qinghai‐Tibetan Plateau (QTP) carbon budget. Here, based on the 8‐year direct measurement of CO2 flux over the Qinghai Lake (QHL) and 83 collected CO2 flux data estimated by pCO2 sampling from 45 lakes over the QTP, we identified the interannual variations of CO2 flux and its response to the extreme climate events. Results showed: (a) the QHL CO2 absorption weakened in the spring, autumn and winter and turn to CO2 emissions in the summer during 2013–2020; (b) with higher Ts and less precipitation, coupling of positive Pacific Decadal Oscillation (PDO) and El Niño enhanced the summer CO2 emissions; and (c) the PDO and ENSO had obvious superposition effect on the decrease of CO2 absorption in autumn. Our results show the potential mechanism of lake CO2 flux responses to extreme climate and further defines the significance of the QTP carbon budget and cycling. Plain Language Summary: The CO2 flux at the water‐air interface is especially important since it directly affects the accurate evaluation of the global carbon budget. However, lacking of long‐term continuous observation data left an undeniable gap on the interannual variations of CO2 flux and its respond to extreme climate events (the El Niño‐Southern Oscillation and Pacific Decadal Oscillation: ENSO and PDO) in saline lakes, although they are globally significant. Here, based on the 8‐year direct measurement of CO2 flux by eddy covariance system over the QHL and 83 collected CO2 flux data estimated by pCO2 sampling from 45 lakes over the QTP. This study found a weakening of CO2 absorption in the spring, autumn and winter, and an enhancing of CO2 emissions in the summer in QHL during 2013–2020, and first reported a notable transformation of carbon sink to source of saline lakes for responding to the extreme climate events. Moreover, the enhancing of CO2 emissions may be stronger in saline lakes than that in fresh lakes over QTP. The results firstly show the potential mechanism of lake CO2 flux responses to extreme climate and further defines the significance of the QTP carbon budget and cycling. Key Points: The QHL CO2 absorption weakened in spring, autumn and winter, and even turn to CO2 emissions in summer during 2013–2020In‐phase PDO and El Niño events enhanced the summer CO2 emissions in saline lake over QTPRising in Ts and decreasing in precipitation dominated the CO2 variations [ABSTRACT FROM AUTHOR]

The consumption of cassava, a vital staple food for more than 1 billion people worldwide, holds particular significance in sub‐Saharan Africa (SSA). Expansion in cassava production in SSA is driven by increasing market demand, local consumption, and adaptability to diverse environments. However, cyanide concentration in cassava tubers and products across SSA ranges from 9 to 1148 ppm – most exceed the World Health Organisation's recommended safe level of 10 ppm. Such variation and high cyanide concentrations in cassava products are expected to be exacerbated by climate‐induced increases in the frequency, intensity and occurrence of drought, heat waves and biotic stresses, further jeopardising regional food security. Thus, it is essential to examine cassava production and cyanide toxicity under climate change and their implications for food security in SSA. In this review, we look at the drivers of cassava production and spatial variation in cyanide concentrations across SSA, impacts of climate variability and biotic stresses on cassava cyanide concentrations in SSA, and crop management practices for reducing cyanogenic glucosides in cassava tubers. We surmise that urgent actions are required to adopt improved cassava varieties and management strategies that reduce cassava cyanide toxicity amid climate‐induced challenges in SSA. [ABSTRACT FROM AUTHOR]

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events. [ABSTRACT FROM AUTHOR]

Climate change impacts have the potential to alter the design rainfall estimates around the world. Decreasing trends in the summer and winter rainfall in New South Wales (NSW), Australia have already been observed due to climate variability and change. The derivation of design rainfall from historical rainfall, which is required for the design of stormwater management infrastructure, may be ineffective and costly. It is essential to consider climate change impacts in estimating design rainfall for the successful design of stormwater management infrastructure. In this study, the probability of the occurrence of daily extreme rainfall has been assessed under climate change conditions. The assessment was performed using data from 29 meteorological stations in NSW, Australia. For the evaluation of future design rainfall, the probability of the occurrence of extreme rainfall for different recurrence intervals was developed from daily extreme rainfall for the periods of 2020 to 2099 and compared with the current Australian Bureau of Meteorology (BoM) design rainfall estimates. The historical mean extreme rainfall across NSW varied from 37.71 mm to 147.3 mm, indicating the topographic and climatic influences on extreme rainfall. The outcomes of the study suggested that the future design rainfall will be significantly different from the current BoM estimates for most of the studied stations. The comparison of the results showed that future rainfall in NSW will change from −4.7% to +60% for a 100-year recurrence interval. However, for a 2-year recurrence interval, the potential design rainfall change varies from an approximately 8% increase to a 40% decrease. This study revealed that the currently designed stormwater management infrastructure will be idle in the changing climate. [ABSTRACT FROM AUTHOR]

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post‐fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post‐fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long‐term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large‐scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post‐fire hillslope erosion even across 100‐km2 scales under conditions without debris flows. Plain Language Summary: In a warming climate, more wildfire and more extreme rain will cause more erosion, producing more sediment that will be carried downstream by rivers. Understanding how much sediment a landscape produces after fire and extreme rain is essential for managing water resources and infrastructure, because sediment fills up storage space in reservoirs and can interfere with proper functioning of water systems and roads. We measured how much sediment was produced in a watershed due to wildfire followed by extreme rain and flooding, studying the Carmel River on the central California coast, USA. We evaluated how much sediment this watershed produced by measuring the volume of sediment deposited within a reservoir that the watershed drains into. We determined that the rate of sediment exported from the study watershed due to a large fire and post‐fire floods was much greater than the long‐term rate there. Having compared our results with the amount of sediment predicted by a model called the Water Erosion Prediction Project model, we found that the model performed well and realistically predicted the amount of sediment that this landscape would shed. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean‐type climate. Key Points: A 116‐km2 central California watershed produced sediment yield of 854–1,100 t/km2/yr in the first year after a fire and extreme rainPost‐fire sediment yield in an extremely wet year without debris flows was 3.5–4.6 times greater than the long‐term rateThe Water Erosion Prediction Project model predicted 81%–106% of the measured post‐fire sediment eroded from hillslopes [ABSTRACT FROM AUTHOR]

Fungi have always posed an unquestionable threat to heritage collections worldwide. Now, in a future of climate change, biological risk factors may have to be considered even more than before. Models and simulations to assess possible impacts a changing outdoor climate will have on indoor environments and, in turn, on biodeterioration are still underdeveloped and require a more substantial data basis. This study aimed at filling some of these knowledge gaps through a broad-based approach combining microclimatic and microbiological monitoring in four historic libraries in Austria with an uncontrolled indoor climate: Altenburg Abbey, Melk Abbey, Klosterneuburg Monastery and the Capuchin Monastery in Vienna. Data were generated from thermohygrometric sensors, cultivation-dependent air- and surface sampling and further surface dust sampling for cultivation-independent analyses. Results gave insights on the status quo of microbiological loads in the libraries and outdoor–indoor relationships. Influences of the geographic location and room-use on corresponding indoor fungal profiles were identified. Lower fungal diversities were found at the most rural site with the strongest climatic fluctuations and extreme values than in the most urban, sheltered library with a very stable climate. Further, the humidity-stabilizing potential of large collections of hygroscopic materials, such as books, was also examined. Implications for a sustainable approach to prevent future biodeterioration are discussed, supporting the long-term preservation of these valuable historic collections. [ABSTRACT FROM AUTHOR]

The present era has witnessed an unprecedented scenario with extreme climate changes, depleting natural resources and rising global food demands and its widespread societal impact. From providing bio-based resources to fulfilling socio-economic necessities, tackling environmental challenges, and ecosystem restoration, microbes exist as integral members of the ecosystem and influence human lives. Microbes demonstrate remarkable potential to adapt and thrive in climatic variations and extreme niches and promote environmental sustainability. It is important to mention that advances in fungal biotechnologies have opened new avenues and significantly contributed to improving human lives through addressing socio-economic challenges. Microbe-based sustainable innovations would likely contribute to the United Nations sustainable development goals (SDGs) by providing affordable energy (use of agro-industrial waste by microbial conversions), reducing economic burdens/affordable living conditions (new opportunities by the creation of bio-based industries for a sustainable living), tackling climatic changes (use of sustainable alternative fuels for reducing carbon footprints), conserving marine life (production of microbe-based bioplastics for safer marine life) and poverty reduction (microbial products), among other microbe-mediated approaches. The article highlights the emerging trends and future directions into how fungal biotechnologies can provide feasible and sustainable solutions to achieve SDGs and address global issues. [ABSTRACT FROM AUTHOR]

Changes in climate and land‐use/land‐cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local‐scale hydrological models, with few studies accounting for climate and human drivers such as land‐use/land‐cover change. We used a data‐driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land‐cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land‐use/land‐cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data‐driven model and a process‐based model. We found that the scenario with the largest forest‐dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process‐based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest‐dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land‐use management policies for adaptation and water‐stress mitigation as well as strategies to prepare for future flood and drought events. Plain Language Summary: Climate change and land cover change, such as deforestation and urban growth, impact the surface water such as rivers, reservoirs, and lakes. Surface water is the most easily accessible resource for water use, such as irrigation, public water supply, and power generation. As the climate and land cover continue to change and our water demand increases, we need to understand possible changes in surface water availability to plan ahead. To get an idea of some of these possibilities, we projected SWAs for watersheds in the southeastern U.S. for eight different climate‐land cover change scenarios. We found that forested land cover was important in maintaining consistent SWAs. Our scenario where the study area lost a large amount of forested land cover had the watersheds with the most dramatic changes in SWA—both increases and decreases. The scenario where the amount of forested land cover was the most similar to today projected the least amount of change in SWA. Having these scenarios, and understanding the importance of forests in surface water dynamics can help decision‐makers plan for climate impacts on water supply and demand, including future floods and droughts. Key Points: Projected seasonal changes in surface water area (SWA) from 2006 to 2099 for eight spatially and temporally changing climate and land‐use scenariosScenario with the most forest‐loss had the largest watershed changes in SWA—positive and negative—and most increase overallChanges in surface water projected by our data‐driven model were supported by projections from a process‐based model with similar inputs [ABSTRACT FROM AUTHOR]

Heatwaves have significant effects on ecosystems and human health. Human habitability is impacted severely as human exposure to heatwaves is projected to increase, however, the contribution of soil moisture effects to the increased exposure is unknown. We use data from four climate models, in which two experiments are used to isolate soil moisture effects and in this way to examine projected changes of soil moisture contributions to projected increases in heatwave events. Contributions from soil moisture to future population exposure to heatwaves are also investigated. With soil moisture effects combined with global warming, the longest yearly heatwaves are found to increase by up to 20 days, intensify by up to 2°C in mean temperature, with an increasing of frequency by 15% (the percentage relative to the total number of days for a year) over most mid‐latitude land regions by 2040–2070 under the SSP585 high emissions scenario. Furthermore, soil moisture changes are found to have a significant role in projected increases of multiple heatwave characteristics regionally compared with the global land area and contribute to more global population exposed to heatwaves. Plain Language Summary: Heatwaves have great impacts on human health, agriculture, economy, and many other related societal factors. Existing research has not formed a comprehensive understanding of the influences from soil moisture and drought on population exposure to global heatwaves. This study aims to provide a better perception and quantification of soil moisture effects on changes of both heatwaves per se and population exposure to heatwaves, which will help to enhance further insights of climate change impacts on human society. Using outputs from dedicated climate model experiments, it is found detectable contributions from soil moisture changes on exacerbating heatwaves and increasing population exposure to them. This study can inform evidence‐based policies and potential land management techniques such as irrigation, land radiative management, and forestation, for mitigating the impacts of climate extremes. Key Points: We conducted the first global analysis of soil moisture effects on population exposure to heatwavesEffects of projected soil moisture changes constitute a strong component in the exacerbation of heatwaves over most land regionsPopulation exposure to heatwaves rises sharply in Asia, Australia, US, Europe, and central and southern Africa [ABSTRACT FROM AUTHOR]

Devising effective strategies to retard the negative impacts of extreme climatic events on water security in general and environmental water balance in specific is imperative, especially in semi-arid rural-Western-India, where climate change has markedly increased water scarcity in domestic and agricultural sectors. This exploratory research thus aimed at investigating the water budget against mismanagement of available water resources for remote-marginalized villages in rural-Nashik (West-India). Investigation entailed visual interpretation of LULC analysis (2008–2017) using fine-scale Google Earth imageries, rainfall-runoff analysis using SCS-CN method, determining the supply-demand potential of water storage structures using field-experimentations and PRA tools, and estimation of season-wise water-budget using hydrological (water-balance) equations. Results identified agriculture as the major land-use (~ 78%) followed by hilly regions (~ 16%) and water bodies (~ 3%). Surface runoff was found as the dominating hydrological phenomenon (33–89% of monsoonal rainfall) followed by surface storage (6–36% of annual rainfall) and groundwater flow (4–82% of annual rainfall). Deforestation over hills for firewood, which reduced forest cover by 52%, was observed as the primary concern for soil erosion, thereby siltation of water bodies and higher runoff against percolation. Erratic rainfall patterns (departure range − 42 to 83% during 1998–2017) coupled with sub-graded irrigational systems confined the agricultural practices to one season [South–West monsoon (June–September)]. High unmet drinking water demand (73% to 98%) forced women communities to fetch water by walking 4.5 ± 1.5 km. Considering the urgent response to collectively combat water-budget imbalance, integrated water security plans were proposed using decentralized participatory watershed development approaches. [ABSTRACT FROM AUTHOR]