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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]

• Staying can be an alternate climate change adaptation strategy. • Staying and mobility are re-imagined with ideas of resilience and evolution. • Four key research areas linking climate change, staying and resilience. • Historical context and translocal networks shape immobility. • Equity and governance identify population differentials and choice of im(mobility). This paper brings work on mobility and 'staying' together with theoretical ideas of resilience to consider responses to climate change. To date, the majority of work that has explored the impacts of climate change on human populations has taken a migration-centred perspective, with an emphasis on mobility as a key response in crises, including extreme climatic events and civil conflict. However, evidence suggests that people may alternatively – and pro-actively – adopt a different approach involving "staying" as a climate change adaptation strategy. This is important as recent evolutionary approaches to resilience have highlighted how resilience is an on-going process of adaptation which emphasises the temporal, fluid and open-ended aspects of individuals' experiences and practices in shaping everyday lives. In turn, this means that individuals' experiences and practices can lead to different strategies of staying (as well as moving) in the face of climate change. Consequently, the paper highlights four key areas where more research is required in order to explore the links between climate change, 'staying' and resilience. These include the importance of historical context in disentangling and contextualising the "multicausal" nature of individuals' mobility decisions; translocal networks in shaping mobility or immobility; the influence of equity, diversity and gendered social expectations on staying; and the importance of governance responses in facilitating resilience, adaptation and subsequent decisions by individuals to stay or move. [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]

Weather extremes have been responsible for widespread economic damage at global scale in the last decades. Agriculture alone absorbed 26% of the overall impact caused by natural hazards in low- and middle-income countries and even in high-income countries yield losses due to extreme weather are relevant. Vulnerability curves are traditionally used to quickly estimate the damage due to extreme events. This study maps the articles published from January 2000 to May 2022 implementing crop vulnerability curves to weather-related extreme events and climate change. Fifty-two articles have been identified through the use of Scopus, Web of Science, Google Scholar and the references of the selected papers. The selected papers have been analysed to determine for which extreme events vulnerability curves have been proposed, which crops have been studied, which explanatory variables have been used to create the curves, which functions are used to develop vulnerability curves and the number of parameters on which the proposed functions rely. Comparisons among the vulnerability curves for the various extremes are proposed, as well as indications of the main drawback of the developed vulnerability curves. Finally, areas where further research is needed are proposed together with recommendations on which elements should be included in vulnerability curve development. [ABSTRACT FROM AUTHOR]

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

Urban flood resilience can critically diminish the negative effects of extreme climatic conditions. In recent decades, green infrastructure has been gaining attention among researchers and authorities in terms of its use in urban contexts to enhance urban resilience. This paper tries to provide knowledge on how urban flood resilience has been recently approached through green infrastructure. To do this, the distribution of the topics of interest, authors, and sources/regions of publication are investigated through a systematic review of recent articles. Additionally, the methodological approaches and green infrastructure typologies are examined. Findings show an agglomeration of publications in developed countries. It was also observed that there is a predominance of quantitative methodological approaches and a low connectivity for some hot topics within this field of research (e.g., biodiversity). The most common green infrastructure typologies used in urban flood resilience research are also discussed. It is noticeable that more than half of the papers used general terms (e.g., urban park/open space) to describe green infrastructure rather than using technical typologies providing more information on water flow management characteristics. The outcomes are discussed to give an overview of the latest hotspots and gaps in this field of research, which gives some future directions/expectations to be followed in forthcoming investigations. [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]

Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures. Therefore, it causes an insulating layer on solar panels and the inability to produce electrical energy. The detection of snow-covered solar panels is crucial, as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation. This paper presents five deep learning models, VGG-16, VGG-19, ESNET-18, ESNET-50, and ESNET-101, which are used for the recognition and classification of solar panel images. In this paper, two different cases were applied; the first case is performed on the original dataset without trying any kind of preprocessing, and the second case is extreme climate conditions and simulated by generating motion noise. Furthermore, the dataset was replicated using the up sampling technique in order to handle the unbalancing issue. The conducted dataset is divided into three different categories, namely; all_snow, no_snow, and partial snow. The five models are trained, validated, and tested on this dataset under the same conditions 60% training, 20% validation, and testing 20% for both cases. The accuracy of the models has been compared and verified to distinguish and classify the processed dataset. The accuracy results in the first case show that the compared models VGG-16, VGG-19, ESNET-18, and ESNET-50 give 0.9592, while ESNET-101 gives 0.9694. In the second case, the models outperformed their counterparts in the first case by evaluating performance, where the accuracy results reached 1.00, 0.9545, 0.9888, 1.00. and 1.00 for VGG-16, VGG-19, ESNET-18 and ESNET-50, respectively. Consequently, we conclude that the second case models outperformed their peers. [ABSTRACT FROM AUTHOR]

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future. [ABSTRACT FROM AUTHOR]

Nature-based Solutions (NbS) were introduced by the IUCN for the first time, but today have different definitions in the literature. NbS are deemed the key to urban sustainability and aim to enhance the built environment through ecological and environmental interventions to support the built environment for future extremes of climate change and related hazards. NbS include blue and green infrastructures, ecological engineering, ecosystem services and ecosystem-based adaptation. Various frameworks defined different key considerations and the literature suggests plenty of frameworks towards successful NbS applications. Current debates critique the extent to which innovative and adaptive the solutions are, whether they are implemented by considering social values and social equity, and the financial burden they often bring which strengthens the disparities between the world cities. Uncontrolled urbanization often causes cities to become an environmental problem. This paper conducts a literature review to lay out the current debates and to highlight the multidimensionality of NbS. It focuses on the potential of NbS in disaster risk reduction and so the paper draws a framework to successfully implement and provide improvements for NbS based on the theoretical ground. NbS are investments in the life quality of the residents and preventive tools in the risk management of cities. The paper attempted to frame the NbS clearer for scholars interested in the subject. [ABSTRACT FROM AUTHOR]

The African Sahel countries are inherently fragile, environmentally insecure and economically weak. This paper underscores the compounded impacts brought about by the COVID-19 pandemic on resource supply security and, hence, the long-term development of the region. It outlines the Sahel-specific COVID-19 scenario by firstly highlighting the underlying vulnerabilities and later linking the health sector outcomes to increased political instability and environmental insecurity, particularly the deterioration of food security. In this sense, this paper shows from a region-wide perspective how COVID-19 in the Sahel is associated with enlarged sociopolitical developmental perils. Lower remittance sent by expatriates, violent conflicts, increased cross-border terrorism and migration, discriminant mobility restrictions of people and goods, weak national healthcare infrastructures, bottlenecks in international aid, pressures on the education system and recent climate extremes are some revealing examples of aggravators of the impacts on the supply of vital resources, such as food. This paper also shows the importance of considering the close interlinks between health, food and political stability in the Sahel. There is a paramount need for more comprehensive approaches linking human health to other sectors, and for re-considering local sustainable agriculture. To avoid prolonged or recurrent humanitarian crises, the Sahel countries need to strengthen response capacities through public sector-led responses. Examples of these responses include reinforced national disaster programs for the vulnerable, support to sustainable agriculture and food markets, improved performance and communication of public sector relief, state-based cooperation, building of regional alliances and peacemaking efforts. [ABSTRACT FROM AUTHOR]

Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze-thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable the time-lapse ERT surveys in Antarctica, however, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it in the Crater Lake CALM-S site at Deception Island, Antarctica to collect quasi-continuous ERT measurements. To efficiently process a large number of obtained A-ERT datasets, we developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted modeling results shows that the A-ERT system can detect the active-layer freezing and thawing events with very high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost one decade at this site. The evolution of thaw depth exhibits also a similar pattern in both years, with the active layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT, combined with the new processing workflow that we developed, is an efficient tool for studying permafrost and active layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the real-time progression of thaw depth, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and work towards establishing an early warning system for the consequences of climate change. [ABSTRACT FROM AUTHOR]

Purpose: This research paper aims to empirically explore how stock market investors' perceptions are affected by extreme climatic events like El Nino and floods in Malaysia. Design/methodology/approach: This study uses structural equation modelling (SEM) to analyse the empirical data gathered through a questionnaire survey involving 273 individual investors from Bursa Malaysia between January and June 2019. Findings: Results reveal that companies' efforts, especially for agriculture and plantation-based industries, to adapt to climate change risk at the production, business and stock market levels significantly impact investors' behaviour and investment decisions. Moreover, stock market investors' climate change knowledge shows a significant moderating effect on corporate climate change adaptation initiatives and investors' decisions to invest in Malaysian agricultural and plantation industry stocks. Practical implications: This research has significant implications for practice and policy, as it measures the stock market investors' level of awareness about climate change events and explores the companies' strategies to reduce climatic risks to their business model. Social implications: This study shows the way to adjust the climate change information in the stock market investment decision to improve market efficiency and sustainable stock exchanges initiative. Originality/value: To the best of the authors' knowledge, this paper is the pioneer one to provide a comprehensive link between climate change events and business performances at production level, business level and stock market levels by drawing inferences from empirical data on investors' behaviours. This study also added value in investment theories and financial literature by observing the climate change as an important factor to determine the investors' decisions in the stock market. [ABSTRACT FROM AUTHOR]

Disasters, whether natural or man-made, pose inevitable global challenges. Events such as COVID-19, earthquakes, extreme climatic conditions, and conflicts underscore the urgent demand for effective temporary housing solutions. These temporary housing units (THUs) serve as an aid in assisting displaced people to rebuild their lives as the recovery process unfolds. However, numerous temporary housing units present environmental, economic, and social issues that hinder their sustainability. This paper investigates the underlying causes of these issues, defines the essential requirements that temporary housing units must meet, and proposes an initial design to fulfill these requirements. The methodology comprises three key phases: case study analysis, requirement identification, and the integration of these requirements into the design process. The main findings highlight that the construction of sustainable temporary housing units necessitates a meticulous consideration of various parameters to achieve a balanced equilibrium between economic, social, and environmental impacts. Possible future research directions are emphasized, including the use of digital tools and BIM models to promote the adoption of circular economy practices and the validation of the design solution through value analysis. Possible improvements in the user's well-being are also taken into consideration. [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]

Settlements operate across a wide range of densities and do so for every socio-economic mode of life from those based on hunter-gatherer economies to those which are based on industrial production. Human beings also live across a range of residential densities from very high to very low. Why they do so is a function of many factors, especially differing socio-cultural ways of managing interaction and communication and the associated social and political practices of the communities. Settlement forms are seen as a derivative of many factors because they are. But they are not thereby an epiphenomenon - especially as they become larger, more durable, and bulkier. That gives them inertia and, as a consequence, they become an agency in their own right which produces outcomes with consequences for the communities, which inhabit them. They are not a neutral background. Instead, their materiality, their sizes, and their densities have an impact on the viability of social life. This paper considers the outcomes generated by the regional networks of low-density, urban settlements larger than 100 sq km in extent. The implications of what happened to agrarian-based low-density urban settlements, like Greater Angkor and the Classic Maya settlements, such as Caracol, are of consequence for the risk faced by the regional networks of present-day, low-density urban giants - the megalopoleis and desa-kota. A further perspective is provided by placing these great cities of the past and the present in the larger context of the trajectories and outcomes of smaller low-density settlements over the previous six millennia. The concern is the implications for the viability of low-density urbanism in contexts of the rapid, extreme climate change we are now beginning to experience. The implications are ominous, yet the past also indicates that social and cultural systems are robust, that human beings can survive, and that they retain and continue to remake their social traditions as they adjust to seriously changing circumstances. [ABSTRACT FROM AUTHOR]

Pacific Islands, many relatively remote and small, have been occupied by people for more than 3000 years during which time they experienced climate‐driven environmental changes (both slow and rapid onset) that challenged human survival and led to the evolution of place‐based coping strategies expressed through traditional knowledge (TK). In today's globalized Pacific Islands region, into which western worldviews and global adaptation strategies have made significant inroads, most plans for coping with climate‐changed futures are founded in science‐based understandings of the world that undervalue and sideline TK. Many such plans have proved difficult to implement as a consequence. This paper reviews the nature of extant Pacific TK for coping with climate change, something that includes TK for anticipating climate change (including climate variability and climate extremes) as well as ancillary TK associated with food and water security, traditional ecological knowledge, environmental conservation, and settlement and house construction that represent coping strategies. Much of this TK can be demonstrated as being effective with precedents in other (traditional) contexts and a compelling plausible scientific basis. This study demonstrates that Pacific Islands TK for coping with climate change has value and, especially because of its place‐based nature, should be central to future climate‐change adaptation strategies to enhance their uptake, effectiveness and sustainability. To this end, this paper proposes specific ways forward to optimize the utility of TK and ensure it has a realistic role in sustaining Pacific Island communities into the future. This article is categorized under:Climate, History, Society, Culture > Ideas and KnowledgePaleoclimates and Current Trends > Modern Climate ChangeAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Nowadays, frequent climate extremes exert a serious impact on agricultural production and social development, which is seldom studied in the previous literature. Production–Living–Ecological (PLE) is a useful analysis framework, and China is a suitable model for such study. This paper takes the Huai River Eco-Economic Belt (HREB), an important agricultural zone in China, to study the relationship among agricultural production (P), society (L), and climate change (E), which is referred to as APLE. This paper constructs a coupled coordination evaluation index system for the APLE system and uses coupling coordination degree models and geographic detector to study the spatial and temporal evolution of the coordinated development of 34 counties (cities) in the HREB from 2009 to 2018. The results show the following: (1) The development of the agricultural subsystem and the social subsystem formed a "scissors difference" from 2009 to 2014, and the three subsystems showed a slight upward trend during 2014–2018. (2) The coupling and coordinated development of the APLE system in the HREB was generally stable, and the coupling coordination degree was improved from low-grade and slightly uncoordinated to barely and primarily coordinated. Furthermore, the spatial differentiation of the coupling coordination degree shows a clear pattern of being high in the southeast and low in the northwest. (3) The main influencing factors are the drought and flood protection rate, the effective irrigation rate, the per capita electricity consumption in agriculture, the number of beds in healthcare facilities per 10,000 people, the per capita disposable income of urban residents, the annual average temperature, and the annual precipitation. (4) The spatial–temporal evolution of the coupling and coordinated development of the APLE system is the result of the comprehensive effect of internal driving forces such as food security, the consumption level of rural residents, and the development level of urbanization construction, and external driving forces such as government public welfare and natural conditions. [ABSTRACT FROM AUTHOR]

Ongoing climate change is known to cause an increase in the frequency and amplitude of local temperature and precipitation extremes in many regions of the Earth. While gradual changes in the climatological conditions are known to strongly influence plant flowering dates, the question arises if and how extremes specifically impact the timing of this important phenological phase. In this study, we systematically quantify simultaneities between meteorological extremes and the timing of flowering of four shrub species across Germany by means of event coincidence analysis, a novel statistical tool that allows assessing whether or not two types of events exhibit similar sequences of occurrences. Our systematic investigation supports previous findings of experimental studies by highlighting the impact of early spring temperatures on the flowering of wildlife plants. In addition, we find statistically significant indications for some long-term relations reaching back to the previous year. [ABSTRACT FROM AUTHOR]

The Canadian Prairies are associated with high natural hydroclimatic variability including the frequent periodic occurrence of droughts and pluvials. These extremes carry various risks including significant damage to the economy, environment and society. The well-documented level of damage necessitates further risk assessment and planned reductions to vulnerability, particularly in light of a warming climate. A logical starting point involves awareness and information about the changing characteristics of such climate extremes. We focus on the compound occurrence of droughts and pluvials as the risks from this type of event are magnified compared to the hydroclimatic extremes in isolation. Compound droughts and pluvials (CDP) are drought and pluvial events that occur in close succession in time or in close proximity in area. Also, research on CDP is limited even for the worldwide literature. Therefore, the purposes of this paper are to synthesize recent literature concerning the risks of CDP, and to provide examples of past occurrences, with a focus on the Canadian Prairies. Since literature from the Prairies is limited, global work is also reviewed. That literature indicates increasing concern and interest in CDP. Relationships between drought and pluvials are also characterized using the SPEI Global Monitor for the Prairies, emphasizing the recent past. Research mostly considers drought and pluvials as separate events in the Prairies, but is integrated here to characterize the relationships of these extremes. The spatiotemporal patterns showed that several of the extreme to record pluvials were found to be closely associated with extreme droughts in the Prairies. The intensities of the extremes and their dry to wet boundaries were described. This is the first research to explore the concept of and to provide examples of CDP for the Prairies and for Canada. Examples of CDP provide insights into the regional hydroclimatic variability. Furthermore, most literature on future projections strongly suggests that this variability is likely to increase, mainly driven by anthropogenic climate change. Therefore, improved methods to characterize and to quantify CDP are required. These findings suggest means of decreasing vulnerability and associated damages. Although the study area is the Canadian Prairies, the work is relevant to other regions that are becoming more vulnerable to increasing risks of and vulnerabilities to such compound extremes. [ABSTRACT FROM AUTHOR]

Even if online and distance learning options were accessible before COVID-19 it wasn't appreciated and incorporated properly within educational process. The unwelcomed situation created by COVID-19 pandemic it has brought a lot of uncertainties, challenges and set a milestone for online educational process. In the context of suspended face-to-face activities, teachers had to solve a great challenge: to teach online experimental activities at science disciplines. Hence, everyone had to adapt and to found in a short period of time the best solutions. If delivering theoretical aspects was easier to implement, experimental activities became quite provocative at that moment. This paper presents solutions that we found and implemented in our science classes during COVID-19 pandemic period and the new perspectives that arose from this experience. Considering that online learning represents a powerful educational solution and having in view possible future emerging situations (pandemic, extreme climatic conditions etc) that may affect face to face learning, we intend to develop and implement in our science disciplines a virtual laboratory under the name "Hybrid Environmental Engineering Laboratory for exercising practical skills". [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]

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

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]

For building environments, meteorological factors such as daily mean temperature, extreme temperature and seasonal temperature changes, are essential, as they impact building structures significantly. Due to the importance of detailed and accurate temperature data, and taking Beijing, China, as an example, this paper developed a fast and effective interpolation method to extract hourly meteorological data, based on 30 years' raw meteorological data. With the interpolated data, this paper defined the extreme weather for buildings. Moreover, a temperature model based on probability and statistical analysis was constructed, and the general climate standard for days and extreme climate for typical days with different return periods were obtained. Furthermore, meteorological models for standard annual temperature were also achieved, reflecting the daily variation and annual variation of temperature, and can provide continuous-numerical-simulation parameters for analyzing daily and annual temperature. According to the daily temperature difference obeys the Gumble Distribution, the daily temperature difference in different return periods and extreme climates is obtained by analysis. Therefore, annual temperature ranges of different recurrence intervals and extreme climate are also achieved, and the annual temperature ranges can be used to analyze the effect of different recurrence intervals and extreme weather on building structures. [ABSTRACT FROM AUTHOR]

In the pursuit of more efficient vehicles on the world's roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the 'Octovalve', is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve's performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life. [ABSTRACT FROM AUTHOR]

Oil refineries are among industrial installations that are vulnerable to climate extreme events, whose frequency and intensity have been increasing over the last decades. Building resilience in resources to withstand climate-related hazards and to recover fast at low human and material cost, for changing climate conditions, is required. In this paper, we present an action research effort for the design of a viable decentralized climate-resilience-providing virtual organization in an oil refinery in Greece using the Viable System Model. The VIPLAN method was employed for the methodological design of a distributed Climate Resilience Providing Organisation for the case of a refinery facility in Greece. The paper presents the process and the results of this effort. [ABSTRACT FROM AUTHOR]

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation. Plain Language Summary: The relationship between the intensity and frequency of extremes and climate change as well as their attribution to human activities is fundamental for improving the assessment of risk and the elaboration of adaptation strategies. Temperature extremes are often reported and estimated using observations or model data using indices, which are widely adopted in the research community and by decision‐makers. However, the number of temperature extremes is quantified assuming input observations as perfect, whereas these are always affected by uncertainties due to instrumental noise and systematic effects that cannot be always properly accounted for. This also implies that climate extreme indices may under or over‐represent the number of temperature extremes. The advent of reference measurement networks, as well as the overall increase in observational data quality due to recent technological improvements, allows us to quantify measurement uncertainties in detail. In this paper, temperature extremes over the US are estimated from near‐surface temperature measurements provided by the USCRN network in the period 2006–2020 with related uncertainties. The use of uncertainty illustrates the range of values that climate extreme indices may assume. Possible sources of uncertainties and comparisons with data from atmospheric reanalysis are also discussed. Key Points: An extensive assessment of uncertainties for four climate extreme indices is provided using reference near‐surface temperaturesEstimate uncertainties of climate indices for reanalysis validation and quantification of extremes by propagating measurement uncertaintiesUSCRN traceable measurements with quantified uncertainties increase confidence in estimating extreme indices and decisions for adaptation [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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Climate extremes are threatening transportation infrastructures and hence require new methods to address their vulnerability and improve their resilience. However, existing studies have yet to examine the climate impacts on transportation networks systematically rather than independently assessing the infrastructures at a component level. Therefore, it is crucial to configure alternative shipping routes from a systematic perspective to reduce climate vulnerabilities and optimise the resilience of the whole shipping network. This paper aims to assess the global shipping network focusing on climate resilience by a methodology that combines climate risk indicators, centrality analysis and ship routing optimisation. The methodology is designed for overviewing the climate vulnerability of the current and future scenarios for comparison. First, a multi-centrality assessment defines the global shipping hubs and network vulnerabilities. Secondly, a shipping model is built for finding the optimal shipping route between ports, considering the port disruption days caused by climate change (e.g. extreme weather) based on the climate vulnerability analysis result from the first step. It contributes to a new framework combining the global and local seaport climate vulnerabilities. Furthermore, it recommends changing shipping routes by a foreseeable increase in port disruptions caused by extreme weather for climate adaptation. [ABSTRACT FROM AUTHOR]

The sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events that impact agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered as the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for years 2005–2020. [ABSTRACT FROM AUTHOR]

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies. [ABSTRACT FROM AUTHOR]

Quantifying the extent of change in climatic and hydrological variables in the past and the future is essential for climate change-resilient development, especially in the climate change sensitive region of Nepal. This paper analyzed future climatic trends and extremes, historical hydrological extremes and their linkage with historical precipitation extremes, and discussed the impacts of climate change on various sectors in the less-studied Small and Medium-sized Eastern River Basins (SMERB) of Nepal. Applying an ensemble of five Coupled Model Intercomparison Project phase 6 (CMIP6) global climate models (GCMs), we evaluated 10 precipitation and 13 temperature extreme indices using Climpact2 for the historical (1979–2020), near-future (2021–2045), mid-future (2046–2070) and far-future (2071–2100) under two Shared Socioeconomic Pathway (SSP), SSP245 and SSP585. Hydrological extreme indices were assessed using Indicators of Hydrologic Alteration tool. Projected climate revealed significant increase (9–73%) in annual and seasonal rainfall except winter; frequent intense rainfall extremes but prolonged dry spells; significant increasing minimum and maximum temperature trends (0.4–5 °C); rise in extreme heat events; increasing minimum and maximum discharge extreme trends in most hydrological stations; as well as strong association between maximum 1 day precipitation (Rx1day) and 1 day max flow for all stations. Growing dry periods but intense rainfall in few wet days, coupled with warming pattern all-over SMERB with frequent extreme events indicate high risk for future climate-related disasters. The harsher climate will potentially have damaging implications, especially in climate-induced disasters, food security, and water and sanitation infrastructure. [ABSTRACT FROM AUTHOR]

Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new "MESMER-X" version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts. [ABSTRACT FROM AUTHOR]