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Understanding the impact of various climate forcing agents, such as aerosols, on extreme precipitation is socially and scientifically vital. While anthropogenic absorbing aerosols influence Earth's energy balance and atmospheric convection, their role in extreme events remains unclear. This paper uses convective‐resolving radiative‐convective‐equilibrium simulations, with fixed solar radiation, to investigate the influence of absorbing aerosols on extreme precipitation comprehensively. Our findings reveal an underappreciated mechanism through which absorbing aerosols can, under certain conditions, strongly intensify extreme precipitation. Notably, we demonstrate that a mechanism previously reported for much warmer (hothouse) climates, where intense rainfall alternates with multi‐day dry spells, can operate under current realistic conditions due to absorbing aerosol influence. This mechanism operates when an aerosol perturbation shifts the lower tropospheric radiative heating rate to positive values, generating a strong inhibition layer. Our work highlights an additional potential effect of absorbing aerosols, with implications for climate change mitigation and disaster risk management. Plain Language Summary: Aerosols, particles suspended in the atmosphere, can interact with the incoming solar radiation by scattering or absorbing it. Aerosol species that absorb solar radiation generate local warming of the atmosphere. This local warming changes the vertical profile of temperature and by that affects cloud and precipitation development. In this paper we use idealized computer simulations to investigate the effect of absorbing aerosols on precipitation, and specifically on extreme precipitation events in the tropics. We demonstrate that under certain conditions, absorbing aerosols can strongly enhance extreme precipitation even despite reducing the mean. We show that this trend can be explained by a mechanism previously reported for much warmer climate conditions than currently found on Earth, involving heating by radiation of the lower part of the troposphere. These results have implications for climate change mitigation and disaster risk management. Key Points: The effect of absorbing aerosol on extreme precipitation is examined in idealized convective‐resolving radiative‐convective‐equilibrium simulationsAerosol perturbation that shifts the lower tropospheric radiative heating rate to positive values strongly enhances extreme precipitationThis trend is explained by a mechanism reported before for hothouse climate conditions involving a shift into an "episodic deluge" regime [ABSTRACT FROM AUTHOR]

Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Niño–Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden–Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña. Significance Statement: Forecasts of El Niño–Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Niño and La Niña do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context—the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes. [ABSTRACT FROM AUTHOR]

Many of the implications of climate change for Aotearoa (New Zealand) remain unclear. To identify so-far unseen or understudied threats and opportunities related to climate change we applied a horizon-scanning process. First, we collated 171 threats and opportunities across our diverse fields of research. We then scored each item for novelty and potential impact and finally reduced the list to ten threats and ten opportunities through a prioritisation process. Within the 20 items presented in this paper, we uncover a range of climate-related costs and benefits. Unexpected opportunities evolve from economic reorganisation and changes to perspectives. The threats we highlight include the overall failure to interconnect siloed policy responses, as well as those relating to extreme events and feedbacks, as well as pressures that undermine the coherence of society. A major theme of our work is that climate change effects in Aotearoa are likely to transgress the boundaries of research disciplines, industry sectors and policy systems, emphasising the importance of developing transdisciplinary methods and approaches. We use this insight to connect potential responses to climate change with Aotearoa's culture and geography. [ABSTRACT FROM AUTHOR]

Freak or rogue waves are a danger to ships, offshore infrastructure, and other maritime equipment. Reliable rogue wave forecasts could mitigate this risk for operations at sea. While the occurrence of oceanic rogue waves at sea is generally acknowledged, reliable rogue wave forecasts are unavailable. In this paper, the authors seek to overcome this shortcoming by demonstrating how rogue waves can be predicted from field measurements. An extensive buoy data set consisting of billions of waves is utilized to parameterize neural networks. This network is trained to distinguish waves prior to an extreme wave from waves which are not followed by an extreme wave. With this approach, three out of four rogue waves are correctly predicted 1 min ahead of time. When the advance warning time is extended to 5 min, it is found that the ratio of accurate predictions is reduced to seven out of ten rogue waves. Another strength of the trained neural networks is their capabilities to extrapolate. This aspect is verified by obtaining forecasts for a buoy location that is not included in the networks' training set. Furthermore, the performance of the trained neural network carries over to realistic scenarios where rogue waves are extremely rare. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. [ABSTRACT FROM AUTHOR]

De‐aliasing products are used in the estimation process of satellite‐based gravity field computation to reduce errors from high‐frequency mass variations that alias into monthly gravity fields. The latest official product is AOD1B RL07 and describes non‐tidal atmosphere and oceanic mass variations at 3‐hourly resolution. However, the model‐based de‐aliasing products are inevitably incomplete and prone to temporally and spatially correlated errors that substantially contribute to errors in the estimated gravity fields. Here, we investigate possible enhancement of current de‐aliasing products by nesting a regional high‐resolution atmospheric reanalysis over Europe into a global reanalysis. As further novelty we include almost mass consistent terrestrial water storage variability from a regional hydrological model nested into a global model as additional component of the de‐aliasing product. While we find in agreement with earlier studies only minor contributions from increasing the temporal resolution beyond 3‐hourly data, our investigations suggest that contributions from continental hydrology and from regional non‐hydrostatic atmospheric modeling to sub‐monthly mass variations could be relevant already for gravity fields estimated from current gravity missions. Moreover, in the context of extreme events, we find regionally contributions from additional moisture fields, such as cloud liquid water, in the order of a few mm over Europe. We suggest this needs to be taken into account when preparing data analysis schemes for future space gravimetric missions. Plain Language Summary: Observing temporal variations in the Earth's gravity field with satellite gravimetry plays an essential role for monitoring mass transports on and underneath the Earth's surface. This is crucial for understanding the evolution of floods and droughts, the role of groundwater pumping, and to quantify the melting of ice sheets and glaciers and the resulting sea level rise. In order to isolate the target variable (e.g., terrestrial water storage changes) unwanted signals (e.g., fast mass variations in the atmosphere) need to be removed in the gravity field estimation process using background models, so‐called de‐aliasing models. This paper focuses on improving background models by incorporating regional high‐resolution models, which more specifically resolve certain processes in the atmosphere. Our hypothesis is that this will lead to better gravity fields with increased spatial resolution and less noise. Moreover, we find that considering fast hydrological variations as additional background model could improve gravity fields from the current satellite mission GRACE‐FO. For the first time, we quantify contributions from so far neglected atmospheric moisture fields, such as cloud liquid water, to enhance background models in the context of extreme events—which, however, will likely be in particular relevant for more sensitive gravity missions in the future. Key Points: We provide an atmosphere‐hydrology de‐aliasing product with regional mass‐consistent refinement over EuropeUsing non‐hydrostatic as opposed to hydrostatic numerical weather prediction model output significantly impacts the de‐aliasing productWe found that for extreme events additional moisture fields unaccounted in present Atmosphere and Ocean De‐aliasing (AOD) models can reach magnitudes relevant for de‐aliasing [ABSTRACT FROM AUTHOR]

The objective is to prove that the nexus of "information disorder" campaigns, through rumours, conspiracy theories, and natural hazards/extreme events, may allow powerful and frequent foreign influence campaigns against communities in their most vulnerable situation. It can be a form of hybrid threat. In other words, this paper aims to understand how social media platforms can be weaponised through "information disorder" campaigns, particularly during extreme events. It was considered two examples where information disorder had a solid social and political impact, with security consequences - during natural hazards and extreme public health events. "Information disorder" campaigns, during extreme events and disasters, through social media platforms can immediately impact political, geopolitical and security dynamics. It is information whose veracity is indisputable but leads to distorted conclusions and can subvert the current political environment. Information is multiplied at high speed and low cost, allowing the dissemination of false information to cement political and social division and influence different decision-making procedures. This type of operation could be more than a question of strategic communication. It is a threat to democracies that may place communication at the heart of a geopolitical and security strategy. The nexus, "information disorder," campaigns for natural hazards and extreme events favour influence campaigns against communities in their most vulnerable situation. [ABSTRACT FROM AUTHOR]

The recently developed regula-falsi profile likelihood (RF-PL) method has potential for quantifying uncertainty in nonstationary hydrological frequency analysis. However, its applicability to diverse distributions is constrained and lacks comprehensive evaluation. This paper extends the RF-PL method to multiple distributions (Gumbel, Generalized Logistic (GLO), Generalized Normal (GNO), Log-Normal, Log-Pearson type III, and Pearson type III (PE3)) by introducing their reparametrized log-likelihood functions. The extended multi-distribution RF-PL (MD-RF-PL) method is systematically assessed in a simulation study and practical applications, covering diverse nonstationary scenarios, distributions, L-skewness ( τ 3 ), and return periods, and compared with the bootstrap and/or the conventional PL as benchmarks. The findings indicate that the MD-RF-PL method is computationally comparable to the bootstrap method but overall superior in capturing the true quantiles with reasonably wide confidence intervals when τ 3 = 0.1 (moderate tails) and 0.3 (heavy tails). However, when τ 3 = 0.5 (very heavy tails), although the MD-RF-PL method is superior and roughly equivalent to the bootstrap method for GEV and GLO distributions, respectively, it is inferior for GNO and PE3 distributions, albeit avoiding the occasionally extremely wide confidence intervals of the bootstrap method. This low performance of the MD-RF-PL method is associated with numerical instability. Moreover, the MD-RF-PL method reduces the computational demand of the PL method by 94–99% without degrading its accuracy. Both the simulation study and the practical applications consistently support the preference of the MD-RF-PL method for distributions with moderate to heavy tails and highlight the need for improving its numerical stability for very heavy tails. [ABSTRACT FROM AUTHOR]

An unprecedented heat wave occurred over the Pacific Northwest and southwest Canada on 25–30 June 2021, resulting in all-time temperature records that greatly exceeded previous record maximum temperatures. The impacts were substantial, including several hundred deaths, thousands of hospitalizations, a major wildfire in Lytton, British Columbia, Canada, and severe damage to regional vegetation. Several factors came together to produce this extreme event: a record-breaking midtropospheric ridge over British Columbia in the optimal location, record-breaking midtropospheric temperatures, strong subsidence in the lower atmosphere, low-level easterly flow that produced downslope warming on regional terrain and the removal of cooler marine air, an approaching low-level trough that enhanced downslope flow, the occurrence at a time of maximum insolation, and drier-than-normal soil moisture. It is shown that all-time-record temperatures have not become more frequent and that annual high temperatures only increased at the rate of baseline global warming. Although anthropogenic warming may have contributed as much as 1°C to the event, there is little evidence of further amplification from increasing greenhouse gases. Weather forecasts were excellent for this event, with highly accurate predictions of the extreme temperatures. Significance Statement: This paper describes the atmospheric evolution that produced an extreme heat wave over the Pacific Northwest during June 2021 and puts this event into historical perspective. [ABSTRACT FROM AUTHOR]

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

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

Energy prices are a widely discussed topic and their development influences everyday life of every person. This paper deals with the development of electricity and natural gas prices in the years 2017 - 2022 with the aim to evaluate this development and determine the impact of extreme events on this development. For the purposes of describing the development, the descriptive analysis of electricity and natural gas time series was used. The impact of extreme events represented by the COVID-19 pandemic was determined using the correlation analysis. The results show that in the monitored period, the development of electricity prices was more stable that the prices of natural gas. However, the correlation between the average increment in the number of patients with COVID-19 and the prices of energy commodities could not be confirmed. To prove the influence the above extreme events, a more complex analysis would be necessary, which would include other factors that could have an impact on energy prices. [ABSTRACT FROM AUTHOR]

Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change. [ABSTRACT FROM AUTHOR]

The development of clean energy has made the green finance market attractive to investors, but existing studies lack an analysis of the impact of uncertain environmental shocks on the volatility of green finance stocks. This paper focuses on the impact of major events on clean energy stock returns by using a mixed-frequency model. Specifically, to identify asymmetric effects and extreme information spillovers, we construct extreme positive returns/positive returns and extreme negative returns/negative returns, respectively. The in-sample findings suggest that asymmetric effects and extreme shocks contain valid information about future clean energy stock price volatility. The out-of-sample analysis shows that the extended model can successfully predict the volatility of clean energy stock markets. More importantly, the extended model can generate predictive gains from both statistical and economic aspects. This study can provide a reference for implementing and adjusting energy policies, which is important for optimizing the energy structure and developing the green energy industry. • This paper investigates the performance of the extended GARCH-MIDAS model. • Extreme shocks have different effects on the clean energy stock markets volatility. • The model with long and short-term extreme shocks has better predictive power. • The predictability of extreme shocks is robust by several robustness checks. [ABSTRACT FROM AUTHOR]

Water is a crucial and invaluable natural resource essential for humanity to sustain on Earth. Around 70% of the Earth’s surface is covered by salt water, which has the largest water volume, and just about 2.5% of fresh water is available for human consumption. The factors that control the spatio-temporal variability of these water resources are envisaged to be of importance. Hydrometeorology is the branch of science that deals with water resource management and understanding water availability by simultaneously using the principles of hydrology and meteorology. Extreme hydro-meteorological events like floods, droughts, and other hydro-meteorological calamities are impacting the region’s water resources. For a big state such as Madhya Pradesh, where the availability of hydro-meteorological data is critical in dealing with the management of water resources not only for the state but for the other neighbouring states, those aquifers and rivers are fed by the cross-boundary rivers of the state. Several research activities that have been carried out in Madhya Pradesh in hydrometeorology and allied disciplines by various researchers are reviewed and presented in this paper. This research paper also discussed the analysis of hydrometeorology services and highlighted the significance of hydrometeorology research at regional level. Apart from this, the major challenges faced in hydro-meteorological research in Madhya Pradesh are also highlighted in the paper. [ABSTRACT FROM AUTHOR]

Single‐model initial‐condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI‐GE CMIP6 goes beyond its predecessor ensemble MPI‐GE by providing high‐frequency output, the full range of emission scenarios including the highly policy‐relevant low emission scenarios SSP1‐1.9 and SSP1‐2.6, and the opportunity to compare the ensemble to complementary high‐resolution simulations. First, we describe MPI‐GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI‐GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high‐resolution simulations, and that 3‐hourly output projects a decreasing activity of storms in mid‐latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence. Plain Language Summary: Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power. Key Points: The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenariosThe ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limitsMPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations [ABSTRACT FROM AUTHOR]

There is an urgent need to better understand and predict crop yield responses to weather disturbances, in particular, of extreme nature, such as heavy precipitation events, droughts, and heat waves, to improve future crop production projections under weather variability, extreme events, and climate change. In this paper, we develop quantile regression models for estimating crop yield probability distributions depending on monthly temperature and precipitation values and soil quality characteristics, which can be made available for different climate change projections. Crop yields, historical and those simulated by the EPIC model, are analyzed and distinguished according to their levels, i.e., mean and critical quantiles. Then, the crop yield quantiles are approximated by fitting separate quantile-based regression models. The developed statistical crop yield meta-model enables the analysis of crop yields and respective probabilities of their occurrence as a function of the exogenous parameters such as temperature and precipitation and endogenous, in general, decision-dependent parameters (such as soil characteristics), which can be altered by land use practices. Statistical and machine learning models can be used as reduced form scenario generators (meta-models) of stochastic events (scenarios), as a submodel of more complex models, e.g., Integrated Assessment model (IAM) GLOBIOM. [ABSTRACT FROM AUTHOR]

Studying temperature probability distributions and the physical processes that shape them is important for understanding extreme temperature events. Previous work has used a conditional mean temperature framework to reveal whether horizontal temperature advection drives temperature to extreme or median values at a specific location as a method to dynamically interpret temperature probability distributions. In this paper, we generalize this method to study how other processes shape temperature probability distributions and explore the diverse effects of horizontal temperature advection on temperature probability distributions at different locations and different temperature percentiles. We apply this generalized method to several representative regions to demonstrate its use. We find that temperature advection drives temperatures toward more extreme values over most land in the midlatitudes (i.e., cold air advection occurs during cold anomalies and warm air advection occurs during warm anomalies). In contrast, we find that horizontal temperature advection dampens temperature anomalies in some coastal summer monsoon regions, where extreme temperatures result from other processes, such as horizontal humidity advection and vertical temperature advection. By calculating the mean of processes conditioned on the temperature percentile, this method enables composite analysis of processes that contribute to events for all percentiles and a range of processes. We show examples of composites at different percentiles for certain processes and regions to illustrate the conditional mean analysis. This general approach may benefit future studies related to temperature probability distributions and extreme events. [ABSTRACT FROM AUTHOR]

The nexus between climate change and military security is essentially non-existent in Pakistan's security discourse. The paper argues for the connection between these two sectors - from the Copenhagen School framework of security - and presents how threats from climate change threaten Pakistan's military security. Most notable threats, in severity and frequency, are sea level rise, cyclone risks, drastic floods, warming patterns, and glacial melting. Sea level rise and cyclone risks impact naval assets and infrastructure while warming patterns and glacial melting affect troop movement, deployments, and logistics. Resultantly, military training, force capacity, and operational readiness is affected. Threats are more potent in strategically significant locations (Siachen, Sindh, and Punjab) which house important forward military bases. The paper provides actionable recommendations that create foundations for future steps as well. [ABSTRACT FROM AUTHOR]

The large-scale flows of the oceans and the atmosphere are driven by a non-uniform surface heating over latitude, and rotation. For many years scientists try to understand these flows by doing laboratory experiments. In the present paper we discuss two rather new laboratory experiments designed to study certain aspects of the atmospheric circulation. One of the experiments, the differentially heated rotating annulus at the Brandenburg University of Technology (BTU) Cottbus, has a cooled inner cylinder and a heated outer wall. However, the structure of the atmospheric meridional circulation motivates a variation of this "classical" design. In the second experiment described, operational at the Institute of Continuous Media Mechanics (ICMM) in Perm, heating and cooling is performed at different vertical levels that resembles more the atmospheric situation. Recent results of both experiments are presented and discussed. Differences and consistencies are highlighted. Though many issues are still open we conclude that both setups have their merits. The variation with heating and cooling at different levels might be more suited to study processes in the transition zone between pure rotating convection and the zone of westerly winds. On the other hand, the simpler boundary conditions of the BTU experiment make this experiment easier to control. [ABSTRACT FROM AUTHOR]

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatterplot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moisture in the atmosphere is that there is a higher chance of occurrence rather than an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm (6 h)−1 to a lesser extent. The result is derived from an analysis of 33 local rain gauge station data and a shared sounding over Friuli Venezia Giulia, northeast Italy. Significance Statement: Moisture is the source of clouds. Clouds, in turn, are source of rain. So we may expect that more moisture in the atmosphere causes more intense rains. We may further speculate that with more moisture in the atmosphere as a consequence of the global warning, we must face more catastrophic rain events and floods. However, this paper, by analyzing data over Friuli Venezia Giulia, northeast Italy, suggests otherwise: more moisture indeed increases frequencies of intense rains, but not their magnitudes as much. [ABSTRACT FROM AUTHOR]

Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies. Plain Language Summary: Understanding the occurrence and propagation of regional heatwaves is of vital importance to mitigate the consequence of heat extremes. A low‐cost, accurate, and timely prediction algorithm for regional heatwaves is desirable. In this study, we use the measurement data set collected from the ground weather stations, together with an advanced deep learning algorithm, to predict the occurrence of regional heatwaves in the lower 48 states of the U.S. The prediction model is trained by the daily weather observations from 91 weather stations and achieves over 90% accuracy in validation. In addition, we summarize the spatiotemporal patterns of the climate dynamics learned by the deep learning model. Our results align with the previous findings while providing a much finer resolution. The proposed modeling framework can be applied to predict other types of extreme events, such as extreme precipitation, drought, and compounded events. The analysis of the model structure will also enhance our understanding of the causal inference between climate regions in the U.S. from a brand‐new deep learning perspective. Key Points: Trained by the daily weather data from 91 ground stations over the CONUS, the Graph Neural Network (GNN) model can predict regional heatwaves with 90% accuracyWith the interpretable structure of GNN, we analyze the interplays among meteorological features and the connections between climate zonesFrom a deep‐learning perspective, the spatiotemporal patterns of climate dynamics are reproduced with a finer granularity [ABSTRACT FROM AUTHOR]

In the last 20 years, research on the observed and projected impacts of climate change on cultural heritage has led to significant developments regarding damage quantification and risk assessment, which unfortunately are not yet exhaustively transferred to practical applications and to the sector of policy and decision making. One of the major reasons for this still lacking alignment remains with the inadequate handover of quantitative data, which is a prerequisite for the development of measures and strategies for the mitigation of the impacts and risk reduction. In this paper, we focus on the methods and approaches put in place for the production of projections providing quantitative assessments of climate change-induced impacts in the near and far future (up to the 21st century) on outdoor built heritage mainly constituted by stone and stone-like materials. Our critical study found that different approaches have been applied for quantifying slow cumulative damage due to the ongoing variations of climate and air pollution parameters and to risk assessment caused by hydrometeorological extreme events induced by variations of temperature and precipitation. There is clear evidence that efforts are still needed for directing research to provide concrete solutions and tools addressed to meet the requirements of stakeholders and to solve the existing challenges in the field: selected effective models and tools are illustrated. The discussion is structured in order to highlight the driving role of research in supporting the definition of priorities for heritage managers and the development of strategies by decision and policy makers for the prevention and safeguarding of cultural heritage at risk. [ABSTRACT FROM AUTHOR]

Meteorological extremes on the seasonal time scale have received increased attention due to their relevance for society and economy. A recently developed approach to identify seasonal extremes is applied here to ERA5 reanalyses from 1950 to 2020 to identify hot and cold, wet and dry, and stormy and calm extreme seasons globally. The approach consists of (i) fitting a statistical model to seasonal mean values (of temperature, precipitation, and wind speed) at each grid point, (ii) selecting a local return period threshold above which seasonal mean values are deemed extreme, and (iii) forming spatially coherent extreme season objects. The paper introduces the ERA5 extreme season explorer, an open-access web portal enabling researchers to visualize and download extreme season objects of any of the six types in their region of interest, for further investigating their underlying dynamics, statistical properties, and impacts. To illustrate the potential of our extreme season objects, we first discuss the top 10 cold winters in ERA5 globally and then focus on an unusual triple-compound extreme season in winter 1953/54 in Europe, which was simultaneously extremely cold, dry, and calm. We show that detailed analysis of weather system dynamics, including cyclones, blocks, jets, and Rossby waves, provides important insight into the processes leading to extreme seasons. In summary, this study presents for the first time a catalogue of objectively identified extreme seasons in the last decades, shows exemplarily how large-scale dynamics can lead to such seasons, and with the help of the explorer supports the community in accelerating research in this important area at the interface of weather and climate dynamics. [ABSTRACT FROM AUTHOR]

According to the final report of the European Union OMC expert group on strengthening cultural heritage resilience for anthropogenic climate change, the impacts of climate change, particularly extreme weather events, on cultural heritage in Europe have become increasingly evident in recent years and are progressing at an unprecedented speed and scale. Archaeological sites, museum collections, and historical buildings and structures are affected, among others, by rising temperatures or by heavy storms and precipitation events. Deep scientific knowledge about future climate projections is required to develop appropriate preservation strategies and measures to protect and adapt cultural heritage. In this paper we present the first set of results of the KERES project. The project focuses on the impacts of future extreme climate events on the built heritage and historic gardens. An ensemble of climate simulations is used to analyze changes in both climatology and extreme events for several climate variables at two cultural heritage sites in Germany. In this study, a methodology was developed to guide climate scientists on how to better tailor climate information for the needs of stakeholders in the cultural heritage sector. It would help the stakeholders to integrate the results of climate projections into the prevention and emergency management, in particular for the risk assessment of extreme events. The effects of interpolation from a model grid to a location of cultural heritage site and advantages of an ensemble approach have been demonstrated in the study. [ABSTRACT FROM AUTHOR]

This paper aims to present simple regressive equations to estimate the parameters of the three-parameter depth–duration–frequency (DDF) curve (3p-DDF), which accurately expresses, for a preassigned return period, the relationship between the rainfall depth and the rainfall duration over large duration ranges, from below 1 h (i.e., tens of minutes) to above 1 h (up to 24 h). These equations are developed to relate their parameters to those of the two-parameter DDF curve (2p-DDF), which can be estimated more easily being based on more readily available data related to rainfall durations above 1 h. In the applications, the regressive equations are first calibrated using recent pluviographic data in northern Italy, Germany, and Sweden. Two validation steps are then carried out to test the equations in terms of estimated rainfall depths using the same data as those used in the calibration step and data of stations from other geographic areas, i.e., Sicily in southern Italy, and from the past century, respectively. The results obtained prove this methodology capable of providing reliable estimation of short-duration rainfalls with various return periods in the absence of measurements with fine temporal resolution. [ABSTRACT FROM AUTHOR]

This paper proposes a novel repair rule set (RRS) for scheduling the power system infrastructure repair after the occurrence of extreme events. RRS is made up of multiple repair rules, each of them can be applied in arbitrary post-disaster failure scenarios to rank the repair actions by priority. A learning-to-rank technique called AdaRank is used to train the repair rules by combining the weak learners derived from the dynamic repair scheduling model. Then, RRS is constructed by iteratively clustering the training cases and retraining the repair rule for each cluster. Increasing the number of repair rules within RRS allows it to differentiate various types of failure scenarios, thereby improving its performance. Further combined with multi-label K nearest neighbor (ML-KNN) technique, RRS is able to schedule the repair without the full knowledge of real-time failure information, such as the estimated repair time. The results of case studies on IEEE-118 test systems show that the proposed method has a desirable performance compared to the exact mathematical optimization model. Moreover, it reduces the requirement for failure information while significantly improving the computational efficiency. [ABSTRACT FROM AUTHOR]

The relationship between public policy and extreme weather events is of critical importance for disaster risk management. Institutions can facilitate adaptation through effective resource allocation. In Europe, initiatives such as the Next Generation EU offer funding to address adaptation challenges. Italy’s National Resilience and Recovery Plan (PNRR) aims, among other goals, to enhance disaster preparedness. This paper assesses the distribution of PNRR funding across Italian provinces, examining its geographical convergence with climate-related extreme events. With this analysis, useful insights can inform policymakers on PNRR resource allocation for disaster preparedness across Italy. [ABSTRACT FROM AUTHOR]

Global Historical Climate Network (GHCN) data are used to characterize changes in large-scale fall extreme precipitation in the mid-Atlantic and the northeast United States. Days with the highest regional extreme precipitation total [extreme precipitation (EP) days] are sorted into weather types based on tropical cyclone (TC), atmospheric river (AR), and extreme integrated water vapor transport (IVT) influences. Increased cumulative precipitation from EP days is attributed primarily to three sources. First, over the mid-Atlantic states, Pennsylvania, and eastern New England, large increasing trends are found in precipitation occurring on EP days attributed to TC-related weather types. These increases are due to a late-1990s increase in TC frequency, which manifests primarily as increased TC remnants in the mid-Atlantic area. Second, over New York State and central and northern New England, there are increasing trends in EP day precipitation from AR-related weather types. Finally, there is evidence of increasing extreme IVT-related precipitation in the absence of ARs and TCs. However, when taking into account prior TC influences, it is found that a combination of TC-related weather types accounts for much of the increasing EP day precipitation trend. These trends are then compared to EP day synoptic changes relating to atmospheric moisture content and transport. Results indicate that fall EP days have become warmer and moister, but that this does not necessarily translate to higher IVT because wind speeds have stayed the same or slowed. This is consistent with fall climatological changes during the 1979–2019 analysis period, including higher atmospheric water content and slowed westerlies in the vicinity of the mid-Atlantic. Significance Statement: This paper sorts days with high extreme precipitation totals in the mid-Atlantic and northeast United States fall into categories based on the presence of factors such as tropical cyclones (TCs), atmospheric rivers (ARs), and other extreme moisture transport. These days have become more frequent between 1979 and 2019, in part due to events with TC influences. However, the synoptic environment on extreme days has also changed, becoming warmer and moister. Many of the most extreme events involve combined AR and TC influences. This is important because it shows that 1) large-scale extreme precipitation has become more common and has changed synoptically and 2) interactions between tropical and non-tropical systems are a key factor for extreme events in this part of the country. Future work might investigate how changes in TC frequency, moisture transport, and jet stream patterns will affect these events in a future climate. [ABSTRACT FROM AUTHOR]

THIS PAPER AIMED TO INVESTIGATE COINTEGRATION AND CAUSALITY EFFECT WITH BASE TO RUSSIAN FINANCIAL MARKET OVER UKRAINE, HUNGARY AND ROMANIA, (2) WHETHER IS THERE ANY OPPORTUNITY FOR HEDGE THE INVESTMENT IN ONE MARKET BY INVESTING IN ANOTHER MARKET. FOR THIS PURPOSE WE EMPLOY VECTOR ERROR CORRELATION MODEL (VECM) AND CCC GARCH ON THE SAMPLE DATA FROM JANUARY 2013 TO AUGUST 2022 COLLECTED FROM BLOOMBERG. AIC CRITERIA HAS BEEN USED TO DETERMINE THE IDEAL LAG DURATION, WHICH IS DETERMINED TO BE 8 DAYS FOR PFTS - UKRAINE AND MOEX - RUSSIA, 9 DAYS FOR BET - ROMANIA AND MOEX - RUSSIA, AND 8 DAYS FOR BUX - HUNGARY AND MOEX - RUSSIA. THIS IS BECAUSE COINTEGRATION RESULTS ARE SENSITIVE TO LAG LENGTH. CAUSALITY MUST EXIST IF TWO PARAMETERS (EXTRINSIC AND INTRINSIC) COINTEGRATE IN AT LEAST ONE DIRECTION (GRANGER, 1986). ADDITIONALLY, IT WAS ASSERTED BY GHOSH (1993), LIEN AND LUO (1994), AND LIEN (1996) THAT IF THE TWO PRICE SERIES ARE DISCOVERED TO BE COINTEGRATED, THEN THERE ARE VIABLE ERROR CORRECTION REPRESENTATIONS OF THE PRICE SERIES THAT CONTAIN BOTH SHORT-TERM DYNAMICS AND LONG-TERM INFORMATION. THE STUDY USES THE VECTOR ERROR CORRECTION MODEL (VECM) BECAUSE SPOT AND FUTURES PRICES ARE COINTEGRATED IN ORDER TO EXAMINE THE LEAD-LAG RELATIONSHIP, THE LONG- AND SHORT-RUN SPEED ALTERATION TOWARDS EQUILIBRIUM, OR THE LONG-RUN STEADY STATE (CAUSALITY), BETWEEN PFTS INDEX (UKRAINE) AND MOEX INDEX - (RUSSIA) STOCK MARKET PRICES OF SAMPLE VARIABLES. [ABSTRACT FROM AUTHOR]

Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years. • We proposed a Markov-Switching Autoregressive model with a conditional hidden seasonal Markov component. • The proposed model accurately captures and represents extreme river flow events. • Deep machine learning neural networks were used to forecast river flow. • River flow forecasts improve when the training period increases. [ABSTRACT FROM AUTHOR]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme events, exogenous and endogenous, poses challenges for our societies. The current pandemic is a case in point; but "once-in-a-century" weather events are also becoming more common, leading to erosion, wildfire and even volcanic events that change ecosystems and disturbance regimes, threaten the sustainability of our life-support systems, and challenge the robustness and resilience of societies. Dealing with extremes will require new approaches and large-scale collective action. Preemptive measures can increase general resilience, a first line of protection, while more specific reactive responses are developed. Preemptive measures also can minimize the negative effects of events that cannot be avoided. In this paper, we first explore approaches to prevention, mitigation and adaptation, drawing inspiration from how evolutionary challenges have made biological systems robust and resilient, and from the general theory of complex adaptive systems. We argue further that proactive steps that go beyond will be necessary to reduce unacceptable consequences. [ABSTRACT FROM AUTHOR]

Sustainable construction has become a growing trend among researchers and stakeholders. Simultaneously, resilience and risk assessments for civil Infrastructures have flourished in terms of importance among researchers, economic sectors, and society. Nevertheless, there is no abundant research that correspond to both approaches, despite that, there are massive similarities and shared characteristics between both investigation branches. Distinctively, this year has demonstrated that sustainable development is directly obstructed by different extreme events that trigger risks and vulnerabilities in civil Infrastructures. These extreme events require a deep and complex study to minimize the impacts they may cause in society and economy, two main factors considered in the study of sustainability. Therefore, when a risk and resilience assessments are conducted, it is already analyzed as a sizable part of sustainability. Consequently, there exists a possibility to create a methodology that examines and assesses four categories of civil infrastructure sustainability: Technical, environmental, social, and economical. The aim of this paper is to demonstrate the need of a comprehensive approach between sustainability, risk, and resilience assessment, compiling and comparing the existing methodologies for assessing the impacts on civil infrastructures, showing that both present resemblances and none can be omitted, being necessary for the decision-making. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that culminated with the development of numerous long-track, violent tornadoes over the southeastern United States during the afternoon of 27 April. This notorious afternoon supercell outbreak was preceded by two quasi-linear convective systems (hereinafter QLCS1 and QLCS2), the first of which was an anomalously severe nocturnal system that rapidly grew upscale during the previous evening. Here in Part II, we use a series of RUC 1-h forecasts and output from convection-permitting WRF-ARW simulations configured both with and without latent heat release to investigate how environmental modifications and upscale feedbacks produced by the two QLCSs contributed to the evolution and exceptional severity of this multiepisode outbreak. QLCS1 was primarily responsible for amplifying the large-scale flow pattern, inducing two upper-level jet streaks, and promoting secondary surface cyclogenesis downstream from the primary baroclinic system. Upper-level divergence markedly increased after QLCS1 developed, which yielded strong isallobaric forcing that rapidly strengthened the low-level jet (LLJ) and vertical wind shear over the warm sector and contributed to the system's upscale growth and notable severity. Moreover, QLCS2 modified the mesoscale environment prior to the supercell outbreak by promoting the downstream formation of a pronounced upper-level jet streak, altering the midlevel jet structure, and furthering the development of a highly ageostrophic LLJ over the Southeast. Collectively, the flow modifications produced by both QLCSs contributed to the notably favorable shear profiles present during the afternoon supercell outbreak. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part II of a study that describes the meteorological factors supporting such a prolific event. Herein we investigate the convectively forced environmental modifications that occurred during a 36-h period encompassing three successive convective episodes. The first two episodes collectively altered the upper-level flow pattern and markedly enhanced low-level winds throughout the warm sector. These modifications served as upscale feedbacks that contributed to the first episode's exceptional severity and to the remarkable vertical shear profiles that supported numerous long-track and violent tornadoes during the final episode on the afternoon of 27 April. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that primarily impacted the southeastern United States, including two quasi-linear convective systems (hereinafter QLCS1 and QLCS2) that preceded the notorious outbreak of long-track, violent tornadoes spawned by numerous supercells on the afternoon of 27 April. The ∼36-h period encompassing these three episodes was part of a longer multiday outbreak that occurred ahead of a slowly moving upper-level trough over the Rocky Mountains. Here in Part I, we detail how the environment evolved to support this extended outbreak, with particular attention given to the three successive systems that each exhibited a different morphology and severity. The amplifying upper-level trough and attendant jet streak resulted from a Rossby wave–breaking event that yielded a complex tropopause structure and supported three prominent shortwave troughs that sequentially moved into the south-central United States. QLCS1 formed ahead of the second shortwave and was accompanied by rapid flow modifications, including considerable low-level jet (LLJ) intensification. The third shortwave moved into the lee of the Rockies early on 27 April to yield destabilization behind QLCS1 and support the formation of QLCS2, which was followed by further LLJ intensification and helped to establish favorable deep-layer shear profiles over the warm sector. The afternoon supercell outbreak commenced following the movement of this shortwave into the Mississippi Valley, which was attended by a deep tropopause fold, cold front aloft, and dryline that promoted two prominent bands of tornadic supercells over the Southeast. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part I of a study that describes the meteorological factors supporting such a prolific event. We focus on a 36-h period encompassing three convective episodes that impacted the Southeast and culminated in the widespread formation of long-track, violent tornadoes. Flow enhancements accompanying the first two episodes and the movement of a cold front aloft into the Southeast were found to contribute to the evolution of the final and most devastating episode. Part II describes how the first two episodes directly modified the environment to enhance the severity of this outbreak. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that culminated with the development of numerous long-track, violent tornadoes over the southeastern United States during the afternoon of 27 April. This notorious afternoon supercell outbreak was preceded by two quasi-linear convective systems (hereinafter QLCS1 and QLCS2), the first of which was an anomalously severe nocturnal system that rapidly grew upscale during the previous evening. Here in Part II, we use a series of RUC 1-h forecasts and output from convection-permitting WRF-ARW simulations configured both with and without latent heat release to investigate how environmental modifications and upscale feedbacks produced by the two QLCSs contributed to the evolution and exceptional severity of this multiepisode outbreak. QLCS1 was primarily responsible for amplifying the large-scale flow pattern, inducing two upper-level jet streaks, and promoting secondary surface cyclogenesis downstream from the primary baroclinic system. Upper-level divergence markedly increased after QLCS1 developed, which yielded strong isallobaric forcing that rapidly strengthened the low-level jet (LLJ) and vertical wind shear over the warm sector and contributed to the system's upscale growth and no table severity. Moreover, QLCS2 modified the mesoscale environment prior to the supercell outbreak by promoting the downstream formation of a pronounced upper-level jet streak, altering the midlevel jet structure, and furthering the development of a highly ageostrophic LLJ over the Southeast. Collectively, the flow modifications produced by both QLCSs contributed to the notably favorable shear profiles present during the afternoon supercell outbreak. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part II of a study that describes the meteorological factors supporting such a prolific event. Herein we investigate the convectively forced environmental modifications that occurred during a 36-h period encompassing three successive convective episodes. The first two episodes collectively altered the upper-level flow pattern and markedly enhanced low-level winds throughout the warm sector. These modifications served as upscale feedbacks that contributed to the first episode's exceptional severity and to the remarkable vertical shear profiles that supported numerous long-track and violent tornadoes during the final episode on the afternoon of 27 April. [ABSTRACT FROM AUTHOR]

One of the most prolific tornado outbreaks ever documented occurred on 26–27 April 2011 and comprised three successive episodes of tornadic convection that primarily impacted the southeastern United States, including two quasi-linear convective systems (hereinafter QLCS1 and QLCS2) that preceded the notorious outbreak of long-track, violent tornadoes spawned by numerous supercells on the afternoon of 27 April. The ∼36-h period encompassing these three episodes was part of a longer multiday outbreak that occurred ahead of a slowly moving upper-level trough over the Rocky Mountains. Here in Part I, we detail how the environment evolved to support this extended outbreak, with particular attention given to the three successive systems that each exhibited a different morphology and severity. The amplifying upper-level trough and attendant jet streak resulted from a Rossby wave–breaking event that yielded a complex tropopause structure and supported three prominent shortwave troughs that sequentially moved into the south-central United States. QLCS1 formed ahead of the second shortwave and was accompanied by rapid flow modifications, including considerable low-level jet (LLJ) intensification. The third shortwave moved into the lee of the Rockies early on 27 April to yield destabilization behind QLCS1 and support the formation of QLCS2, which was followed by further LLJ intensification and helped to establish favorable deep-layer shear profiles over the warm sector. The afternoon supercell outbreak commenced following the movement of this shortwave into the Mississippi Valley, which was attended by a deep tropopause fold, cold front aloft, and dryline that promoted two prominent bands of tornadic supercells over the Southeast. Significance Statement: The tornado outbreak that impacted the United States on 26–27 April 2011 was part of an extended outbreak that produced 343 tornadoes and numerous fatalities. This paper is Part I of a study that describes the meteorological factors supporting such a prolific event. We focus on a 36-h period encompassing three convective episodes that impacted the Southeast and culminated in the widespread formation of long-track, violent tornadoes. Flow enhancements accompanying the first two episodes and the movement of a cold front aloft into the Southeast were found to contribute to the evolution of the final and most devastating episode. Part II describes how the first two episodes directly modified the environment to enhance the severity of this outbreak. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

Subdaily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated subdaily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. The aim of this paper is as follows: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a microensemble of convection-permitting RCM simulations over Belgium against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e., hourly or 2 hourly), but not on 6-24-h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears to be robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. [ABSTRACT FROM AUTHOR]

The waveguidability of an upper-tropospheric zonal jet quantifies its propensity to duct Rossby waves in the zonal direction. This property has played a central role in previous attempts to explain large wave amplitudes and the subsequent occurrence of extreme weather. In these studies, waveguidability was diagnosed with the help of ray tracing arguments using the zonal average of the observed flow as the relevant background state. Here, it is argued that this method is problematic both conceptually and mathematically. The issue is investigated in the framework of the nondivergent barotropic model. This model allows the straightforward computation of an alternative "zonalized" background state, which is obtained through conservative symmetrization of potential vorticity contours and that is argued to be superior to the zonal average. Using an idealized prototypical flow configuration with large-amplitude eddies, it is shown that the two different choices for the background state yield very different results; in particular, the zonal-mean background state diagnoses a zonal waveguide, while the zonalized background state does not. This result suggests that the existence of a waveguide in the zonal-mean background state is a consequence of, rather than a precondition for, large wave amplitudes, and it would mean that the direction of causality is opposite to the usual argument. The analysis is applied to two heatwave episodes from summer 2003 and 2010, yielding essentially the same result. It is concluded that previous arguments about the role of waveguidability for extreme weather need to be carefully reevaluated to prevent misinterpretation in the future. Significance Statement: Understanding strong undulations of the midlatitude jet stream is important, because these undulations are sometimes associated with extreme weather. Several previous publications have suggested that episodes with strong undulations can be caused by a jet that acts as a circumglobal waveguide. In the current paper we argue that these results are likely to be based on a misinterpretation and that the causality may be almost the other way around: large jet undulations can render the traditional waveguide diagnostic inappropriate and, thereby, fake strong circumglobal waveguidability. We suggest a novel diagnostic that tries to circumvent the problem. Our work implies that the results from previous publications need to be carefully reevaluated in order to avoid misinterpretation in the future. [ABSTRACT FROM AUTHOR]

Accurate and efficient prediction of extreme ship responses continues to be a challenging problem in ship hydrodynamics. Probabilistic frameworks in conjunction with computationally efficient numerical hydrodynamic tools have been developed that allow researchers and designers to better understand extremes. However, the ability of these hydrodynamic tools to represent the physics quantitatively during extreme events is limited. Previous research successfully implemented the critical wave groups (CWG) probabilistic method with computational fluid dynamics (CFD). Although the CWG method allows for less simulation time than a Monte Carlo approach, the large quantity of simulations required is cost prohibitive. The objective of the present paper is to reduce the computational cost of implementing CWG with CFD, through the construction of long short-term memory (LSTM) neural networks. After training the models with a limited quantity of simulations, the models can provide a larger quantity of predictions to calculate the probability. The new framework is demonstrated with a 2-D midship section of the Office of Naval Research Tumblehome (ONRT) hull in Sea State 7 and beam seas at zero speed. The new framework is able to produce predictions that are representative of a purely CFD-driven CWG framework, with two orders of magnitude of computational cost savings. • The critical wave groups method is implemented with CFD and LSTM neural networks. • The LSTM neural network is able to represent a purely CFD-driven CWG implementation. • Combining CWG, CFD, and LSTM NN enables efficient observations of extreme events. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) into contemporary energy communities (ECs) has revolutionized power systems, fostering sustainable and clean energy infrastructures. This paper focuses on the effective load-carrying capability (ELCC) to enhance grid resilience in the presence of DERs. We introduce an innovative network topology-based optimization framework that seamlessly integrates economic and resilience metrics within ECs while reducing carbon emissions. The Pareto front of non-dominated solutions for the proposed three-objective optimization problems is extracted, providing a comprehensive visualization of the trade-off between economic, resilience, and emission objectives, enabling informed decision-making. Analytical results, validated on the IEEE 33-bus test system, demonstrate the effectiveness of DER-based ELCC quantification in managing load supply during emergencies. Case studies show how the synergy between economic and resilience-based metrics significantly enhances grid resilience. The proposed framework has diverse applications, including enhancing grid adaptability to climate change, promoting sustainable energy integration, optimizing demand response strategies, and supporting the transition to a decarbonized energy community. This work addresses the challenges and opportunities in the evolving energy landscape, emphasizing the importance of our approach in achieving a cleaner and more resilient energy future. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal graph neural network capable of effective representation learning of the spatiotemporal interrelationships and interdependencies of in-situ observation data from multiple locations for multivariate multi-step ahead time-series forecasting. The propose model is largely composed of graph learning, spatial encoder, and temporal decoder, and ablation studies on variants of the three modules and comparative experiments with state-of-the-art deep neural networks for sequence modeling were also performed extensively. The proposed model showed improved predictability than conventional numerical model-based approaches or state-of-the-art models by applying consecutive multi-step ahead time-series prediction of sea surface temperature at multiple locations along the coast. For more rigorous performance evaluation, not only the overall performance of the test data, but also the performance of extreme cases included in the test data based on historical records were separately assessed. The prediction rationales were also presented through quantified relative contributions between neighbor locations using the trained adjacency matrix obtained through graph learning. The results showed that it is well consistent with the ocean physics and geographical domain knowledge, demonstrating the feasibility and reliability of the proposed method. Therefore, the proposed method shows sufficient potential to be used as a scientific tool for decision-making in extreme events such as marine heat waves or for operational ocean forecasting. [ABSTRACT FROM AUTHOR]

• Managed vegetation represents a powerful tool for climate adaptation and mitigation, offering significant potential for positive environmental impact. • In the context of the Arabian Peninsula, managed vegetation has the remarkable capability to cool the surface temperature by over 5°Celsius, particularly during the hottest days of the year. • Achieving sustainability in water management remains a critical challenge that requires careful attention and proactive measures. The growth of vegetation in desert areas is expected to cause considerable changes to the local climate. Compared to bare ground desert, the balance of different processes related to vegetation such as reduced albedo and increased evapotranspiration can either warm up or cool down the surface climate. Quantifying these changes is essential for impact assessments of climate mitigation and adaptation measures, especially in regions increasingly hit by heat waves such as the Arabian Peninsula. In this respect, the analysis of in situ observations is challenging owing to limited data, uneven vegetation distribution, complex terrain, and other confounding factors. Thus, this study, using long-term satellite data at the daily temporal resolution, estimates the impact of vegetation on land surface temperature for several agricultural regions in the Kingdom of Saudi Arabia. Results demonstrate that managed vegetation has a significant cooling effect during the day and particularly during hot weather events, reducing the highest temperatures by > 5 °C. During heat waves, managed vegetation can provide an additional cooling effect of about 3 °C (2 to 4 °C depending on the location). This effect is robustly assessed though a tailored statistical indicator that is suitable for application over complex terrain that are found in the Saudi agricultural sites. However, a situation is identified where the vegetation vigor collapsed and the cooling effect vanished, which was possibly related to issues associated with water management sustainability. Thus, herein, this paper discusses several potential solutions to achieve sustainable irrigation among those that were recently proposed in the scientific literature, such as desalinization from renewable energy and land-use change options that could enhance local precipitation. This study supports the decision-making process of the green initiatives undertaken in the Middle East and similar efforts in other arid regions. [ABSTRACT FROM AUTHOR]

• The RF-PL method is extended for the MEV and SMEV models. • The MEV distribution is found prone to overfit the samples under nonstationarity. • The SMEV outperforms MEV and GEV for both fitting and prediction purposes. • The use of SMEV can advance the NS-HFA, especially by reducing the uncertainty. The implementation of nonstationary hydrological frequency analysis (NS-HFA) has often been hampered by the relatively short datasets and the resulting high uncertainty. Most recently, the non-asymptotic Metastatistical extreme value (MEV) and simplified MEV (SMEV) distributions, which rely on the ordinary events rather than the extremes only, have attracted attention in the HFA of both rainfall and streamflow. Despite their use for trend detection/attribution and producing future projections, their practical implementation for the NS-HFA is absent in the literature. This paper therefore implemented these models (called MEV and SMEV-based models) in the NS-HFA and comprehensively assessed their performance from the perspectives of fitting efficiency, accuracy, and uncertainty for both in-sample fitting and out-of-sample prediction purposes. The asymptotic models based on the generalized extreme value (GEV) distribution were used as the benchmark. The assessment employed synthetic and real rainfall datasets that exhibit stationarity in the number of events per year. All the nonstationary ordinary-event datasets followed the Weibull distribution with linearly changing parameters, while their standardized annual maximum series aligned with the GEV distribution. Thus, the MEV, SMEV- and GEV-based models could be fairly assessed and compared. The regula-falsi profile likelihood method was extended to quantify the uncertainty of the MEV and SMEV-based models. The results demonstrated that the MEV model was not advantageous over other models in terms of all three evaluation perspectives. Whereas the SMEV-based models demonstrated superiority due to their higher accuracy, equivalent or better fitting efficiency, as well as lower uncertainty compared to all other models. Therefore, this paper advocates the use of the SMEV distribution to advance the NS-HFA by harnessing the information from the ordinary events. [ABSTRACT FROM AUTHOR]

• A theoretical framework for the causal inference of delay propagation in the air traffic control system is proposed. • The theoretical framework discovers the delay dependencies from the airport delay time series. • The extreme delay propagation network is constructed, and the network is analyzed at the system level using complex network theory and related metrics. • Small airports play a more important role in the process of delay propagation. • A new critical airport identification metric is proposed. In the air traffic control (ATC) system, delays are tightly coupled among airports. Understanding the interactions of these delays is a prerequisite for accurate prediction. However, most researches in this field depend on correlation methods, and few studies apply causal discovery methods. The latest approach uses the cause-and-effect diagram discovery algorithm PCMCI to mine the causal dependencies between time series. In the application context of the ATC system, not all delays have the propagation effect because of a series of optimization measures by airports and airlines. This paper proposes an extreme event measurement mechanism to solve the above problem by setting the airport thresholds adaptively through a complete search of the parameter space. The improved method allows for a more accurate exploration of the time lag delay propagation relationship between airports. The experimental simulation results show that the algorithm in this paper can extract time lag causality in nonlinear, multidimensional time series considering the ATC system characteristics. In addition, we use the causality of airport delays to construct an extreme delay propagation network (EDPN), use complex network theory and related metrics to analyze the network at the system level, and propose a new critical airport identification metric. In the case study of the U.S. aviation system, we find that small airports have a weak ability to mitigate extreme delays and are more likely to propagate delays to other airports in addition to their own impacts; Larger airports have strong mitigation and robustness to extreme delays but suffer from overload operations. These findings are helpful to explain the interactions of delay processes, and can provide specific theoretical support in improving system operation efficiency and developing delay propagation mitigation measures. [ABSTRACT FROM AUTHOR]

In structural applications, uncertainties are unavoidable and play a significant role in risk assessment. Probabilistic risk assessment (PRA) is used to determine the level of risk associated with complex systems such as nuclear power plants, space missions, earthquakes, tornado and floods. The Bayesian inference is often regarded as an effective framework for analysing probabilistic risk and the prior probability and likelihood function are inferred from available data. The traditional approaches usually approximate likelihood functions using conventional moments (C-moments). If data are insufficient, approximations are inadequate, leading to inaccurate conclusions. Therefore in order to determine the amount of risk, it is desirable to develop a PRA framework that is distribution independent and less sensitive or insensitive to extremes in the scarce data. In this paper, L-moments are proposed to characterise or approximate likelihood functions in the Bayesian inference. L-moment ratio diagram is employed to select the appropriate distribution of conditional probabilities. The Bayesian approach is paired with L-moment approach to perform PRA and uncertainty quantification. The efficacy of the proposed approach is demonstrated on three engineering examples. In assessing risk from few samples in the presence of extremes, L-moments based Bayesian inference for PRA framework significantly outperformed its C-moment counterpart. • L-moments approximate likelihood functions in the Bayesian inference for PRA. • The approach is distribution-independent and less sensitive to extremes, if any. • The proposed approach is demonstrated to be effective on real life engineering examples. • L-moment based PRA outperforms C-moments for assessing risk from few samples includes extremes. [ABSTRACT FROM AUTHOR]