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Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

This paper compares the ideas contained in the main papers published on climate change since World War II to arrive at a suggested consensus of our present knowledge regarding climatic changes and their causes. Atmospheric carbon dioxide is only suggested as a cause in one theory, which, despite its wide acceptance by Politicians, the media, and the Public, ignores the findings in other studies, including the ideas found in the Milankovitch Cycles. It also does not explain the well-known NASA map of the changes between the global 1951–1978 and the 2010–2019 mean annual temperatures. The other theories by Oceanographers, Earth scientists, and Geographers fit together to indicate that the variations in climate are the result of differential solar heating of the Earth, resulting in a series of processes redistributing the heat to produce a more uniform range of climates around the surface of the Earth. Key factors are the shape of the Earth and the Milankovitch Cycles, the distribution of land and water bodies, the differences between heating land and water, ocean currents and gateways, air masses, and hurricanes. Low atmospheric carbon dioxide levels during cold events could result in too little of this gas to support photosynthesis in plants, resulting in the extermination of most life on Earth as we know it. The 23 ka Milankovitch cycle has begun to reduce the winter insolation received at the surface of the atmosphere in the mid-latitudes of the Northern Hemisphere starting in 2020. This results in extreme weather as the winter insolation reaching the surface of the atmosphere in the higher latitudes of the Northern Hemisphere decreases while the summer air temperatures increase. It heralds the start of the next glaciation. A brief outline is given of some of the climatic changes and consequences that may be expected in western Canada during the next 11.5 ka. [ABSTRACT FROM AUTHOR]

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model's accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA's capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. [ABSTRACT FROM AUTHOR]

With climate change, more intense weather phenomena can be expected, including pressure drops related to the arrival of an atmospheric front. Such drops of pressure are the main reason for gas emissions from closed mines to the surface, and a closed, empty mine shaft is the most likely route of this emission. Among the gases emitted, the most important are carbon dioxide and methane, creating a twofold problem—greenhouse gas emissions and gas hazards. The work presented in this paper simulated the spread of the mentioned gases near such an abandoned shaft for four variants: model validation, the most dangerous situations found during measurements with or without wind, and a forecast variant for a possible future pressure drop. It was found that a momentary CO2 emission of 0.69 m3/s and a momentary CH4 emission of 0.29 m3/s are possible, which for one hour of the appropriate drop would give hypothetically 2484 m3 CO2 and 1044 m3 CH4. In terms of gas hazards, the area that should be monitored and protected may exceed 25 m from a closed shaft in the absence of wind influence. The wind spreads the emitted gases to distances exceeding 50 m but dilutes them significantly. [ABSTRACT FROM AUTHOR]

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

Background: Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000–2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world's most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study. Methods: We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000–2020. Results: We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks. Conclusions: The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using two NDVI datasets (GIMMS and MODIS) originated from satellite remote sensing, the variability characteristics of NDVI over the TP on the interannual time scale and associated local climatic factors were investigated. The results show that two primary patterns of NDVI governed TP during the main growing season (June–September, JJAS) for the period 1982–2020. The first one is a uniform pattern, with a consistent spatial variation over the entire TP, and the second is a dipole pattern, with an out-of-phase spatial variation of NDVI between the northern and southern TP. Interannual variations of the different climatic factors regulate the NDVI variability over the different regions of the TP. The interannual variability of the uniform NDVI pattern is mainly affected by the two local climatic factors, the preceding May–August precipitation and simultaneous JJAS sunshine duration. Specifically, NDVIs over the southern and eastern TP have a more significant response to the preceding precipitation and simultaneous sunshine duration, respectively. The variability of the dipole NDVI pattern is primarily modulated by the preceding May–August precipitation and simultaneous surface air temperature, ground surface temperature, and sunshine duration. However, NDVIs over the northern and southern TP have different degrees of response to the four climatic factors, with the most significant response being to preceding precipitation. The combined effect of these factors contributes to the formation of the interannual variability in the uniform and dipole patterns. This paper may shed light on deeply understanding the reasons for the inconsistency in variations of vegetation over the different regions of the TP under climate change. In addition to the effect of local climatic factors that this study focuses on, the influence of external climatic factors on the variability of the TP NDVI deserves further research in the future. [ABSTRACT FROM AUTHOR]

The impact of climate change on the physical environment, ecosystems, and human societies is increasingly recognized as the most important global challenge. Climate change may alter, among others, the thermal environment, the occurrence of extreme weather events, and the human exposure to physical, chemical, and biological pollutants, thus affecting human health with several potential outcomes. The impact of climate change on occupational health and safety has been receiving increasing attention in last years. In the European Union, the health and safety of workers is under the rule of Directive 89/391 and its daughters. In a changing climate, compliance with all requirements of the existing EU regulation entails an additional effort to implement preventive and protective measures. A central role in workers' health protection is played by proper workers' information and training, which is partly in charge of the occupational physicians. This paper provides a basic proposal on topics related to climate change to update workers' information and training and to integrate the curricula of occupational physicians. Importantly, suitable information and training may contribute to promoting workers' health and to implement adaptation measures, which are part of the individual, societal, and global responses to climate change. [ABSTRACT FROM AUTHOR]

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation. [ABSTRACT FROM AUTHOR]

Climate emulators are models calibrated on Earth System Models (ESMs) to replicate their behaviour. 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 realisations 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 calculate how much do the quantiles of the emulations deviate from those of the ESMs, resulting in good performances. Moreover, we argue that this framework can be expanded to further variables, given that it works over a large range of annual indicators. 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]

In this paper, we first review the research advancements in blocking dynamics and highlight the merits and drawbacks of the previous theories of atmospheric blocking. Then, the dynamical mechanisms of atmospheric blocking are presented based on a nonlinear multi-scale interaction (NMI) model. Previous studies suggested that the eddy deformation (e.g., eddy straining, wave breaking, and eddy merging) might lead to the formation and maintenance of atmospheric blocking. However, the results were speculative and problematic because the previous studies, based on the time-mean eddy-mean flow interaction model, cannot identify the causal relationship between the evolution of atmospheric blocking and the eddy deformation. Based on the NMI model, we indicate that the onset, growth, maintenance, and decay of atmospheric blocking is mainly produced by the spatiotemporal evolution of pre-existing upstream synoptic-scale eddies, whereas the eddy deformation is a concomitant phenomenon of the blocking formation. The lifetime of blocking is mainly determined by the meridional background potential vorticity gradient (PVy) because a small PVy favors weak energy dispersion and strong nonlinearity to sustain the blocking. But the zonal movement of atmospheric blocking is associated with the background westerly wind, PVy, and the blocking amplitude. Using this NMI model, a bridge from the climate change to sub-seasonal atmospheric blocking and weather extremes might be established via examining the effect of climate change on PVy. Thus, it is expected that using the NMI model to explore the dynamics of atmospheric blocking and its change is a new direction in the future. [ABSTRACT FROM AUTHOR]

In addition to compromising food security, climate change-related extreme weather lowers urban residents' incomes and access to food. Unexpected effects of climate change on food availability are possible. This review paper aims to investigate how climate change affects the availability of food. Climate change can have a wide range of effects on agricultural systems, making it more challenging to sustainably produce enough food and inexpensive, nutrient-dense food. We must broaden our narrow focus from increasing yield and plant productivity to take into account the impact of climate change on the nutritional content of food crops in order to fully comprehend how climate change is affecting our world's food supply. Proactive adaptation can boost capacity to manage climate change by integrating climate change in long-term decision-making and eliminating disincentives to adjust behaviour in response to climate change. [ABSTRACT FROM AUTHOR]

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. [ABSTRACT FROM AUTHOR]

Climate change exhibits a clear trend of escalating frequency and intensity of extreme weather events, posing heightened risks to drainage systems along the existing road networks. However, very few studies to date have investigated the consequences of projected future changes in rainfall on main road drainage and the resulting risk of road flooding. The work presented in this paper builds on the limited research by introducing a probabilistic model for assessing the impact of climate change on road drainage systems, incorporating climate uncertainty and drainage system variation. The probabilistic scenario-based model and associated framework offer a practical and innovative method for estimating the impact of short-duration storms under future climates for 2071–2100, in the absence of fine-resolution spatio-temporal data. The model also facilitates the assessment of the effectiveness of a climate adaptation strategy. An illustrative case-study of a road drainage system located in the south of Ireland is presented. It was found that the probability of road flooding during intense rainfall is projected to surpass the current acceptable limits set by Irish standards. Assessment of a proactive climate adaptation strategy implemented in 2015 indicated it may need to be adjusted to further reduce climate change impacts and optimise adaptation costs. • Climate change can impact extreme rainfall patterns, and resulting road flooding. • A flooding model is developed incorporating climate and drainage system uncertainty. • A quantitative climate risk analysis framework is proposed for road drainage. • The effectiveness of a climate adaptation strategy adopted in Ireland is quantified. [ABSTRACT FROM AUTHOR]

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Jeju Island, South Korea is a biodiversity hotspot, but the vulnerability of Jeju Island fauna to climate change has not been thoroughly investigated. In this paper, we tested the adaptability of the Korean large brown frog Rana uenoi to future climate change. While increased temperatures did not affect the hatchability of frog eggs, hatched tadpoles in greenhouse treatment with less exposure to sunlight (less maximum temperature) were significantly (14%) shorter in length after three weeks of growth, indicating increased temperature of early season helps growth of tadpoles. However, right after three weeks, all tadpoles in a higher-temperature treatment (outside) died after the experiment logged a maximum temperature of 34.3°C. In the field, the average temperature of puddles without tadpoles (puddles in the middle of the rivulet with all day exposure to sunlight) was 33.2°C. During an indoor temperature elevation experiment, tadpoles suddenly died at 32.4°C. Therefore, the expected maximum survivable temperature for R. uenoi is around 32°C. Our results therefore suggest that R. uenoi tadpoles are vulnerable to elevated temperatures and could suffer mortality from climate change. Climate change will continue to cause extreme weather events like this (sudden elevation of temperature in spring), and frogs, especially tadpoles with restricted movement, will be particularly vulnerable to these events. Identifying how adaptable these amphibian species are to the weather events predicted by future climate change scenarios will be key for their conservation on Jeju Island. [ABSTRACT FROM AUTHOR]

As concerns around climate change and global warming intensify, extreme weather events such as heavy rain, blizzards, and smog-induced haze have greatly impacted the commuting travel mode selection of urban residents. Such behavioral shifts have in turn led to changes of the carbon emissions generated from these residents. This paper constructs a "extreme weather (W)–travel behavior (B)–carbon emissions (C)" research framework. Using a multiple logistic regression model, the transportation mode shift model, and the econometric model of urban resident's travel behavior under the influence of extreme weather conditions were constructed. The marginal effects of weather on residents' commuter behavior, through the use of transportation type and distance of travel were also obtained. The study found that the overall carbon dioxide emission levels of daily commuting has gradually decreased due to the influence of extreme weather. However, as some travelers still adopted high-emission commuting modes through the use of taxis or ride-sharing services, there was still a slight increase in CCDE levels in certain extreme weather contexts. In particular, when haze was prevalent, vehicle restriction policies only reduced CCDE by 2.18%, while the remaining 77.83% of total CCDE remaining unchanged. This research provides a key reference point for governmental departments in urban transportation management and environmental protection to formulate policies. [ABSTRACT FROM AUTHOR]

Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper. [ABSTRACT FROM AUTHOR]

Climate change has led to extreme weather events and food security issues, which have become urgent global problems. Crop models and hydrological models play important roles in analyzing these issues, but crop models have limitations in large-scale simulations, and hydrological models have shortcomings in depicting underlying surface details. Jointly using the two models to complement each other's shortcomings is becoming an important method, playing an important role in improving the quantitative characterization ability of different facets of agricultural production and is crucial to improving the efficiency of water resources utilization. This paper reviews the development process of crop and hydrological models and summarizes the widely used models at present, providing reference for researchers to select crop and hydrological models; secondly, it summarizes the types, methods, and examples of joint use of models, and analyzes possible problems in model combination; finally, some suggestions are put forward for the joint use of crop and hydrological models, providing a reference for researchers to combine models. • Retrospect on the development process of crop and hydrological models. • Summarized case studies on the combination of crop and hydrological models. • Provide an overview of cases where crop and hydrological models are used in conjunction. • General considerations and suggestions for future development. [ABSTRACT FROM AUTHOR]

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

Due to global climate change, the effect of extreme weather on power systems has attracted extensive attention. In the prior-art grid resilience studies, the hurricanes or wildfires are mainly defended in terms of expected line damages, while they are prone to trigger short-circuit fault (SCF) evolved with dynamic influence in reality. In this paper, a fragile model is developed to evaluate the nodal SCF probability considering the insulation aging of equipment and extreme weather condition. Then, a response framework for extreme weather events is developed for a transmission system to defend the cascading impacts of expected SCFs. Specifically, switches are shifted to restrain the out-of-range short-circuit currents (SCCs) so that to ensure the SCFs can be removed by circuit breakers, generation rescheduling and load shedding are arranged to maintain the post-fault system transient stability. The above measures are optimized simultaneously by an integrated Mixed-Integer Nonlinear Programming (MINLP). Considering the error or uncertainty of weather event forecasts, a multi-state model is established to provide the most cost-effective grid resilience enhancement scheme, in which the expected urgent adaptions of the initial scheme subject to weather state transition is included in the overall cost. The proposed model and techniques are validated using the IEEE 39-bus New-England test system and realistic meteorological data. [ABSTRACT FROM AUTHOR]

Access to adequate and reliable electricity is paramount for the adaptation and resilience of typhoon-prone coastal communities, particularly in the face of intensifying challenges posed by climate change. Climate-related extreme weather events, such as typhoons and their consequent flooding and storm surges, exert significant pressure on electricity systems in these vulnerable regions, affecting every aspect from generation to transmission, distribution, and end-user utilization. Therefore, urgent action is required to fortify these communities with electric systems that are resilient enough to withstand such meteorological extremes. This review paper examines the far-reaching impacts of extreme weather events on electricity systems and showcases illuminating case studies that highlight innovative strategies to bolster these systems against such extremes. The review emphasizes the pivotal role of meticulous energy planning, strong support from local governments, and the significance of infrastructure hardening in building resilient energy systems. Future research opportunities exist in integrating renewable energy, engaging community members, assessing climate risks, and cross-sectoral resilience. [ABSTRACT FROM AUTHOR]

The global consensus is to reduce greenhouse gas emissions and actively respond to climate change (CC). Global warming has irreversibly altered the Earth's ecosystems. Unpredictable extreme weather events caused by CC are posing new risks to urban infrastructure. Infrastructure is one of the primary guarantees to maintain the stable operation of the city. Therefore, it is imperative to strengthen the climate resilience of infrastructure to avoid the loss of life and property caused by climate risks. This paper uses CiteSpace to analyze data in the field of climate resilience infrastructure (CRI) over the past 25 years. We find that global CRI research has transitioned through three stages. According to the geographic spatial distribution map drawn by ArcGIS, it can be found that developed countries account for a relatively large number of documents. The research institution is dominated by institutions of higher learning, with limited cooperation between institutions and loose organizational collaboration. CRI is composed of multi-disciplinary collaborative development, from a single discipline of environmental ecology or water resources to a research field integrating engineering, meteorology, sustainability, and energy. Urban resilience and Nature-based solutions are research hotspots. Small Island Developing States are major objects in the future. The research emphasis has shifted from addressing the multiple problems caused by CC to increasing the climate resilience of infrastructure to enhance the resistance of urban systems. Renewable energy and climate models are applied to infrastructure construction. In general, CRI is a effective measure that can help reduce environmental pollution, carbon emissions, and global climate regulation. In addition, we suggest taking cities as pilot projects in the future, increasing CRI projects and providing policy guidance for urban planning and construction. • Research on global climate resilience infrastructure has gone through three stages. • Multi-field collaboration is the future trend of climate resilience infrastructure. • Urban resilience and Nature-based solutions are research hotspots. • Climate resilient infrastructure is a sustainable solution. • Climate resilient infrastructure can improve urban resilience. [ABSTRACT FROM AUTHOR]

Over the past twelve years the number of papers that explore the impacts of climate change on biodiversity in the conservation literature has grown on average by 20% annually. By categorising these papers on their primary research questions, we show that the vast majority of these articles (88.6%) focus only on those impacts that arise directly as a result of climate change, ignoring the potentially significant indirect threats that arise from human adaptation responses. This pattern has remained fairly consistent throughout the review period (2000-2012), with a trend towards more articles considering both direct and indirect impacts towards the end of the period. We also find a bias in the time-frames considered by published articles that project future impacts of climate change on biodiversity, with more than three-quarters (77.9%) of papers only considering impacts after 2031, and almost half (49.1%) only considering impacts after 2051. This focus on long-term, direct impacts creates a mismatch, not only with the life-cycles of species and timescales of many ecological processes, but also with most management and policy timelines and the short-term nature of human decision making processes. The focus on studying the long-term, direct impacts of climate change on biodiversity is likely a function of the lack of availability of climate projections on shorter temporal scales; a perception that short-term impacts will be minor; and, insufficient integration with the social and political sciences. While the direct impact of changes in mean climatic conditions will significantly change the biosphere by the end of the century, near term changes in seasonality and extreme events coupled with human adaptation responses are likely to have substantial impacts much sooner, threatening the survival of species and ecosystems. It is therefore essential that we balance our research efforts to facilitate a better understanding of these more imminent threats. [ABSTRACT FROM AUTHOR]

The climate trend today is reflected in the national, regional and sectoral socio-economic strategies of both developed and developing countries. Most often, the climate trend is presented in the form of predicted values of temperature increase on the Earth's surface by 1.5-2.0 degrees and the facts of the occurrence of the most probable weather risks. Practice shows that when making forecasts of socio-economic development, adverse hydrometeorological phenomena are not included in the system of significant and naturally occurring factors. Therefore, they do not find the necessary reflection in strategic documents. At the same time, weather anomalies systematically cause material and economic damage. The authors put forward a hypothesis about the significance of the factor of dangerous weather phenomena in the system of climatic factors that determine the level of agricultural production development and structural changes in the branches of the agro-industrial complex. The scientific task, to which the study is directed, is to establish a causal relationship between climatic parameters and production and economic indicators of the efficiency of the crop sub-sector. For this, a unique database was collected, which included values systematized by types of weather anomalies over the past 30 years in the context of the constituent entities of the Russian Federation. This made it possible to determine the most typical dangerous weather phenomena for different regions that cause material damage. In the sub-sector of crop production, these are wind, heavy rains, extreme fire hazard and a combination of these phenomena. A clear relationship has been established between their number and the dynamics of crop production. The most vulnerable categories of producers to each type of hazardous weather phenomena have been identified. The relationship between climatic variables and economic efficiency of the crop production sub-sector is determined. The results of the analysis will serve as a justification for the need to take into account dangerous weather phenomena in the system of the most meaningful trends when predicting the impact of climate change on agricultural production and food security of the country. This will contribute to the development of relevant sectoral and regional adaptation strategies in the face of the onset of the most probable natural and climatic risks. [ABSTRACT FROM AUTHOR]

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events. [ABSTRACT FROM AUTHOR]

Climate extremes, such as severe drought, heat waves and periods of heavy rainfall, can have profound consequences for ecological systems and for human welfare. Global climate change is expected to increase both the frequency and the intensity of climate extremes and there is an urgent need to understand their ecological consequences. Major challenges for advancing our understanding of the ecological consequences of climate extremes include setting a climatic baseline to facilitate the statistical determination of when climate conditions are extreme, having sufficient knowledge of ecological systems so that extreme ecological responses can be identified, and finally, being able to attribute a climate extreme as the driver of an extreme ecological response, defined as an extreme climatic event (ECE). The papers in this issue represent a cross-section of the emerging field of climate extremes research, including an examination of the palaeo-ecological record to assess patterns and drivers of extreme ecological responses in the late Quaternary, experiments in grasslands assessing a range of ecological responses and the role of ecotypic variation in determining responses to climate extremes, and the quantification of the ecological consequences of a recent ECE in the desert Southwest of the USA. Synthesis. The papers in this Special Feature suggest that although the occurrence of ECEs may be common in palaeo-ecological and observational studies, studies in which climate extremes have been experimentally imposed often do not result in ecological responses outside the bounds of normal variability of a system. Thus, ECEs occur much less frequently than their potential drivers and even less frequently than observational studies suggest. Future research is needed to identify the types and time-scales of climate extremes that result in ECEs, the potential for interactions among different types of climate changes and extremes, and the role of genetic, species and trait diversity in determining ecological responses and their evolutionary consequences. These research priorities require the development of alternative research approaches to impose realistic climate extremes on a broad range of organisms and ecosystems. [ABSTRACT FROM AUTHOR]

Against an academic and policy backdrop of interest in (and concerns about) the issue, this paper draws on a range of academic writing in various disciplines to explore visual strategies of climate change communication. The geographic scope of the investigation is the United Kingdom, with particular attention to recognizable icons of climate change in UK media and the images used in political campaigns. The paper is in two parts. The first part concentrates on various efforts to put a 'face' on the climate change issue, while part two suggests that weather and renewable energy are the dominant alternative motifs. The paper draws a basic distinction between fear-laden representations of climate change and a variety of visual efforts to use so-called inspirational imagery. All of the images reviewed suggest an affirmative answer to the question in the title, there are multiple efforts underway to move beyond polar bears and represent climate change in more creative and meaningful ways. The bigger question addressed is one raised already by photographers as well as academics, i.e. whether documentary photography (rather than particular types of images) is the more fundamental issue. The answer in the paper is that photographs are no different from other visual images in their capacity to draw attention to messages. The challenge is to use visuals creatively, in ways that prompt positive engagement with climate change without enhancing public disengagement and fatalism. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Although the use of anomaly fields in the forecast process has been shown to be useful and has caught forecasters' attention, current short-range (1-3 days) weather analyses and forecasts are still predominantly total-field based. This paper systematically examines the pros and cons of anomaly- versus total-field-based approaches in weather analysis using a case from 1 July 1991 (showcase) and 41 cases from 1998 (statistics) of heavy rain events that occurred in China. The comparison is done for both basic atmospheric variables (height, temperature, wind, and humidity) and diagnostic parameters (divergence, vorticity, and potential vorticity). Generally, anomaly fields show a more enhanced and concentrated signal (pattern) directly related to surface anomalous weather events, while total fields can obscure the visualization of anomalous features due to the climatic background. The advantage is noticeable in basic atmospheric variables, but is marginal in nonconservative diagnostic parameters and is lost in conservative diagnostic parameters. Sometimes a mix of total and anomaly fields works the best; for example, in the moist vorticity when anomalous vorticity combines with total moisture, it can depict the heavy rain area the best when comparing to either the purely total or purely anomalous moist vorticity. Based on this study, it is recommended that anomaly-based weather analysis could be a valuable supplement to the commonly used total-field-based approach. Anomalies can help a forecaster to more quickly identify where an abnormal weather event might occur as well as more easily pinpoint possible meteorological causes than a total field. However, one should not use the anomaly structure approach alone to explain the underlying dynamics without a total field. [ABSTRACT FROM AUTHOR]

This paper presents projected changes in temperature and precipitation extremes in China by the end of the twenty-first century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. The temporal changes and their spatial patterns in the Expert Team on Climate Change Detection and Indices (ETCCDI) indices under the RCP4.5 and RCP8.5 emission scenarios are analyzed. Compared to the reference period 1986-2005, substantial changes are projected in temperature and precipitation extremes under both emission scenarios. These changes include a decrease in cold extremes, an increase in warm extremes, and an intensification of precipitation extremes. The intermodel spread in the projection increases with time, with wider spread under RCP8.5 than RCP4.5 for most indices, especially at the subregional scale. The difference in the projected changes under the two RCPs begins to emerge in the 2040s. Analyses based on the mixed-effects analysis of variance (ANOVA) model indicate that by the end of the twenty-first century, at the national scale, the dominant contributor to the projection uncertainty of most temperature-based indices, and some precipitation extremes [including maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day), and total extremely wet day total amount (R95p)], is the difference in emission scenarios. By the end of the twenty-first century, model uncertainty is the dominant factor at the regional scale and for the other indices. Natural variability can also play very important role. [ABSTRACT FROM AUTHOR]

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change. [ABSTRACT FROM AUTHOR]

Extreme weather exerts a huge impact on human beings and it is of vital importance to study the regular pattern of meteorological and hydrological factors. In this paper, a selection of seven extreme indices is used to analyze the trend of precipitation extremes of 18 meteorological stations located in Zhejiang Province, east China using the Mann-Kendall test. Then the precipitation trends in the plum season (from May to July) and typhoon season (from August to October) are studied separately. The results show that the precipitation trend varies from east to west. There is a positive trend in the east and a negative one in the west. The largest part of Zhejiang Province shows a positive trend in heavy precipitation and the most significant upward trend is detected in Dinghai with 3.4 mm/year for precipitation on very wet days. Although the upward trend of extreme precipitation is not prevailing, the range of increase in specific areas is apparent, like Dinghai with 1.3 mm/year. Precipitation intensity exhibits an upward trend in most areas and a typical upward trend can be found in Dachendao, Tianmushan, and Yuhuan with 0.04, 0.02, and 0.05 mm/year respectively. Precipitation intensity in both plum and typhoon seasons has increased too, especially for the coastal stations. [ABSTRACT FROM AUTHOR]

This paper, prepared by the Climate Change Technology Subcommittee, a subcommittee of the Power and Energy Society Energy Development and Power Generation Committee, identifies key technical issues facing the electric power industry, related to global climate change. The technical challenges arise from: 1) impacts on system operating strategies, configuration, and expansion plans of emission-reducing technologies; 2) power infrastructure response to extreme weather events; 3) effects of government policies including an expanded use of renewable and alternative energy technologies; and 4) impacts of market rules on power system operation. Possible lessons from other industries' responses to climate change are explored. [ABSTRACT FROM AUTHOR]

Extreme weather events and associated damages to the electricity system are increasing. Actions are being taken to enhance resilience, but the pace of these enhancements do not meet the challenges faced by communities. This paper reviews federal funding programs that support community resilience efforts for electricity resilience, provides case studies, and highlights opportunities to reduce electricity system vulnerabilities including increasing awareness of federal funding opportunities, and adopting hazard-based design standards. [ABSTRACT FROM AUTHOR]

Background: Air pollution is increasingly recognized as a risk factor for acute exacerbation of chronic obstructive pulmonary disease (COPD). Changing climate and weather patterns can modify the levels and types of air pollutants. For example, dust outbreaks increase particulate air pollution. Objective: This paper examines the effect of Saharan dust storms on the concentration of coarse particulate matter in Miami, and its association with the risk of acute exacerbation of COPD (AECOPD). Methods: In this prospective cohort study, 296 COPD patients (with 313 events) were followed between 2013 and 2016. We used Light Detection and Ranging (LIDAR) and satellite-based Aerosol Optical Depth (AOD) to identify dust events and quantify particulate matter (PM) exposure, respectively. Exacerbation events were modeled with respect to location- and time-lagged dust and PM exposures, using multivariate logistic regressions. Measurements and main results: Dust duration and intensity increased yearly during the study period. During dust events, AOD increased by 51% and particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) increased by 25%. Adjusting for confounders, ambient temperature and local PM2.5 exposure, one-day lagged dust exposure was associated with 4.9 times higher odds of two or more (2+ hereto after) AECOPD events (odds ratio = 4.9; 95% CI = 1.8–13.4; p < 0.001). Ambient temperature exposure also showed a significant association with 2+ and 3+ AECOPD events. The risk of AECOPD lasted up to 15 days after dust exposure, declining from 10× higher on day 0 to 20% higher on day 15. Conclusions: Saharan dust outbreaks observed in Miami elevate the concentration of PM and increase the risk of AECOPD in COPD patients with recurring exacerbations. [ABSTRACT FROM AUTHOR]

This paper describes management practices of a model resilient electric utility that can serve as a framework for advancing planning and preparation for extreme weather and climate hazards. The framework focuses on practices grouped into eight domains progressing through five levels of maturity. For each domain, a discussion of resilience management practices is provided along with examples. By assessing its maturity level and taking steps to increase it, a utility can realize increased resilience benefits. [ABSTRACT FROM AUTHOR]

Extreme weather events and associated damages have been increasing and these trends are expected to continue. Actions are being taken to enhance electricity system resilience. However, the justification for capital investments on resilience requires utilities to justify that the economic benefits outweigh the costs. This paper reviews the types of resilience measures being analyzed in cost-benefit analyses and addresses opportunities for improvement in characterizing the benefits for investments that enhance the resilience of electricity systems. [ABSTRACT FROM AUTHOR]

Background: Globally, the response to climate change is gradually gaining momentum as the impacts of climate change unfold. In South Africa, it is increasingly apparent that delays in responding to climate change over the past decades have jeopardized human life and livelihoods. While slow progress with mitigation, especially in the energy sector, has garnered much attention, focus is now shifting to developing plans and systems to adapt to the impacts of climate change.Methods: We applied systematic review methods to assess progress with climate change adaptation in the health sector in South Africa. This case study provides useful lessons which could be applied in other countries in the African region, or globally. We reviewed the literature indexed in PubMed and Web of Science, together with relevant grey literature. We included articles describing adaptation interventions to reduce the impact of climate change on health in South Africa. All study designs were eligible. Data from included articles and grey literature were summed thematically.Results: Of the 820 publications screened, 21 were included, together with an additional xx papers. Very few studies presented findings of an intervention or used high-quality research designs. Several policy frameworks for climate change have been developed at national and local government levels. These, however, pay little attention to health concerns and the specific needs of vulnerable groups. Systems for forecasting extreme weather, and tracking malaria and other infections appear well established. Yet, there is little evidence about the country's preparedness for extreme weather events, or the ability of the already strained health system to respond to these events. Seemingly, few adaptation measures have taken place in occupational and other settings. To date, little attention has been given to climate change in training curricula for health workers.Conclusions: Overall, the volume and quality of research is disappointing, and disproportionate to the threat posed by climate change in South Africa. This is surprising given that the requisite expertise for policy advocacy, identifying effective interventions and implementing systems-based approaches rests within the health sector. More effective use of data, a traditional strength of health professionals, could support adaptation and promote accountability of the state. With increased health-sector leadership, climate change could be reframed as predominately a health issue, one necessitating an urgent, adequately-resourced response. Such a shift in South Africa, but also beyond the country, may play a key role in accelerating climate change adaptation and mitigation. [ABSTRACT FROM AUTHOR]