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Arctic warming has been substantially greater than that in the rest of the world and has had an important influence on the global climate. This study first explores the temporal and spatial evolutionary characteristics of marine heatwaves (MHWs) over the Arctic Ocean in multiyear ice (MYI), first‐year ice (FYI), and open‐water (OPW) regions from 1982 to 2020. MHWs in the Arctic Ocean show obvious spatial and seasonal variations, mainly occurring over the FYI region in the JAS (July–August–September, JAS), and their occurrences have a significant increasing trend in recent decades, accompanied by an abrupt increase since 2010. Furthermore, a multivariable network‐based method is adopted to delineate the relationship between different climatic factors and MHWs in the Arctic Ocean and the climatic impacts of MHWs. The results show that the correlations between different climatic factors and MHWs in JAS in 2010–2020 are generally stronger than those in 1982–2009, and the main influencing factors of MHWs in different ice covers are different. MHWs in the MYI region are mainly affected by freshwater dilution processes, such as sea‐ice concentrations (SIC), precipitation, and mixed‐layer salinity. For the FYI region, the 2‐m air temperature and total heat flux mainly affect MHWs by thermodynamic processes, and the 500‐hPa geopotential height affects MHWs mainly by large‐scale atmospheric circulation. The MHWs in the OPW region are mainly related to the SIC, 850‐hPa geopotential height, and 10‐m v‐wind, indicating that they are correlated with atmospheric processes and wind fields. MHWs in JAS are also revealed to reduce or delay the formation of sea ice in OND (October–November–December, OND) by storing more abnormal heat, indicating that unfrozen ocean surfaces may lead to enhanced Arctic amplification in the following seasons. [ABSTRACT FROM AUTHOR]

Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management. [ABSTRACT FROM AUTHOR]

The recovery rates and assembly processes of stream bryophyte communities after severe disturbances are not well known. Breaking of an ice dam caused an extreme flood that completely removed bryophyte cover along several hundreds of metres of a boreal stream (Stream Uopajanpuro, Koillismaa, NE Finland). We monitored recolonization rates and successional processes of stream bryophytes in the disturbed stream section over 8 years. In the first two summers after the disturbance, the disturbed section remained largely unvegetated. The initial recovery of the bryophyte community resulted mainly from colonization of vegetative moss fragments of the dominant, perennial species present in the undisturbed upstream section, whereas typical early successional colonist species remained scarce. The recovery of total cover, richness and community composition of stream bryophytes took 5–6 years after the disturbance event, even though bryophyte vegetation supplying fragments was located in the immediate vicinity upstream of the disturbance site. Bryophyte species showed differences in the colonization–recovery rates. Mean spatial segregation among species showed no trend during recovery, whereas several species pairs showed both segregation and aggregation trends. Our results indicate that extreme flooding can have devastating effects on bryophyte cover, and the recovery of bryophytes is slow compared to other stream organisms, with full recovery taking up to several years. This recovery time estimate may only apply if there is an immediate upstream source of vegetative propagules from undisturbed populations. Where such populations are absent, recovery of bryophyte communities can take decades. [ABSTRACT FROM AUTHOR]

Global temperatures were exceptionally high in 2023/24. Every month from June 2023 to June 2024 set a new record, and September shattered the previous record by 0.5 $0.5$°C. The 2023 annual average approached 1.5 $1.5$°C above pre‐industrial levels. This results from both long‐term warming and internal variability, with the occurrence of an El Niño episode. However the amplitude of the 2023/24 anomalies was remarkable and surprised the scientific community. Here we analyze the rarity of 2023/24 global temperatures from a climate perspective. We show that a 'normal' year 2023 would have roughly equaled the previous annual record, and that the most extreme events of 2023/24 rank among the most extreme since 1940. Our analysis suggests that the 2023/24 event can be reconciled with the long‐term trend and an intense, but not implausible, peak of internal variability. Plain Language Summary: 2023 was the warmest year on record at global scale, and early 2024 has continued to break records. This remarkable episode has received a great deal of attention from the general public and the scientific community. It is well established that it is linked to the long‐term global warming and the occurrence of an El Niño episode, but some temperature anomalies appeared so high, shattering previous records, that several scientists suggested that global warming may have been underestimated, which would have serious implications for future projections. Here we take a step back from the 2023/24 event, precisely quantify its rarity and compare it with other hot years. Using climate monitoring and extreme event attribution methods, we first show that at the current rate of warming, a 'normal' year 2023 would have equaled the 'old' record of 2016, even without any help of El Niño. We also find that the most extreme events of 2023/24 are among the most extreme of the entire record, but remain comparable with some past events. Our analysis thus suggests that the 2023/24 event is extreme but not incompatible with current estimates of global warming. Key Points: At the current rate of global warming, a normal year 2023 would have equaled the record of 2016, without any help of El NinoThe most extreme anomalies of 2023/24 rank among the most extreme of the entire record since 1940The 2023/24 heat can be reconciled with current estimates of global warming and an extreme but not implausible peak of internal variability [ABSTRACT FROM AUTHOR]

In dynamical systems, events that deviate significantly from usual or expected behavior are referred to as extreme events. This paper investigates the mechanism of extreme event generation in a 3D jerk system based on a generalized memristive device. In addition, regions of coexisting parallel bifurcation branches are explored as a way of investigating the multistability of the memristive system. The system is examined using bifurcation diagrams, Lyapunov exponents, time series, probability density functions of events, and inter-event intervals. It is found that extreme events occur via a period-doubling route and are due to an interior crisis that manifests itself as a sudden shift from low-amplitude to high-amplitude oscillations. Multistability is also identified when both control parameters and initial values are modified. Finally, an analog circuit based on the memristive jerk system is designed and experimentally realized. To our knowledge, this is the first time that extreme events have been reported in a memristive jerk system in particular and in jerk systems in general. [ABSTRACT FROM AUTHOR]

Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) shifts in phytoplankton communities associated with four pulse disturbances (Tax Day Flood in 2016, Hurricane Harvey in 2017, Tropical Storm Imelda in 2019, and Winter Storm Uri in 2021) that occurred in Galveston Bay (TX, USA). Water samples collected daily were processed using an Imaging FlowCytobot (IFCB), along with concurrent measurements of temperature, salinity, and chlorophyll-a. Stronger storm events with localized heavy precipitation and flooding had greater impacts on community composition, increasing diversity (Shannon–Weiner and Simpson Indices) while a cold wave event lowered it. Diatoms and dinoflagellates accounted for the largest fraction of the community, cyanobacteria and chlorophytes varied mostly with salinity, while euglenoids, cryptophytes, and raphidophytes, albeit at lower densities, fluctuated greatly. The unconstrained variance of the redundancy analysis models pointed to additional environmental processes than those measured being responsible for the changes observed. These findings provide insights into the impact of pulse disturbances of different magnitudes, durations, and timings on phytoplankton communities. [ABSTRACT FROM AUTHOR]

AbstractExtreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations of precipitation from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river basins (Danube and Mississippi). We rely on observed precipitation extremes while exploiting a physics-based average-temperature covariate, enabling us to project future precipitation extents based on projected temperatures. We include the covariate into newly developed time-varying r-Pareto processes using suitably chosen spatial risk functionals r. This model captures temporal non-stationarity in the spatial dependence structure of precipitation extremes by linking it to the temperature covariate, derived from reanalysis data (ERA5-Land) for model calibration and from bias-corrected climate simulations (CMIP6) for projections. Our results show an increasing trend in the margins, with both significantly positive or negative trend coefficients depending on season and river (sub-)basin. During major rainy seasons, the significant trends indicate that future spatial extreme events will become relatively more intense and localized in several sub-basins. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

This study investigates the behavior of the Izhikevich discrete neuron model across various parameter configurations. Bifurcation diagrams and Lyapunov exponents are utilized to examine the impact of these parameters on the behavior of the system. The study specifically identifies important parameter ranges in which the attractor undergoes a sudden expansion, displaying characteristics of extreme events. Within the system, two distinct categories of extreme events can be identified: rare occurrences of small probability events located in the tail of the probability distribution and Dragon-King (DK) events, which possess a high probability amplitude. DK events are verified through the use of the DK test. The research concludes by examining the practical ramifications of these findings. The significance of forecasting and controlling extreme events in intricate systems is underscored, along with the cruciality of identifying their happening. [ABSTRACT FROM AUTHOR]

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change. [ABSTRACT FROM AUTHOR]

Effective cash management is key in banking operations and has implications for cost control, customer service, and risk management. As transactions become more diverse, manual forecasting methods have become inadequate for accurate vault cash forecasting, which involves extensive data analysis. To address this challenge, the banking industry has adopted FinTech tools based on big data and deep learning for various client services. These methods are generally accurate but perform poorly in cases with extreme events, for which data are scarce. In this study, we propose a time series prediction model with long short-term memory and an attention mechanism that effectively predicts the presence of extreme values. We applied extreme value theory to define the extreme value loss for extreme situations and use a sliding window to process time series data. The enhanced extreme value loss function in our model yields improved prediction accuracy for time series data. We evaluated the proposed model against previous methods in evaluation experiments on data from three branches of a commercial bank in Taiwan, where the vault cash data of each exhibited extreme values. The proposed model was highly accurate: it had a lower mean absolute percentage error and higher trend accuracy than competing methods on a majority of time series, and it was also more accurate in predicting extreme values in time series data. [ABSTRACT FROM AUTHOR]

A detailed analysis is made of horizontal‐component geomagnetic‐disturbance data acquired at the Colaba observatory in India recording the Carrington magnetic storm of September 1859. Prior to attaining its maximum absolute value, disturbance at Colaba increased with an e‐folding timescale of 0.46 hr (28 min). Following its maximum, absolute disturbance at Colaba decreased as a trend having an e‐folding timescale of 0.31 hr (19 min). Both of these timescales are much shorter than those characterizing the drift period of ring‐current ions. Furthermore, over one 28‐min interval when absolute disturbance was increasing, the data indicate an absolute rate of change of ≥2,436 nT/hr. If this is representative of disturbance generated by a symmetric magnetospheric ring current, then, assuming a standard and widely used parameterization, an interplanetary electric field of ≥451 mV/m is indicated. An idealized and extreme solar‐wind dynamic pressure could, conceivably, reduce this bound on the interplanetary electric field to ≥202 mV/m. If the parameterization for electric‐field extrapolation is accurate, but the field strengths obtained are deemed implausible, then it can be concluded that the Colaba disturbance data were significantly affected by partial‐ring, field‐aligned, or ionospheric currents. The same conclusion is supported by the shortness of the e‐folding timescales characterizing the Colaba data. Several prominent studies of the Carrington event need to be reconsidered. Key Points: Before (after) maximum, the absolute value of disturbance at Colaba increased (decreased) with an exponential timescale of 0.46 hr (0.31 hr)If Colaba disturbance was due to a symmetric ring current, standard extrapolations require an interplanetary electric field of >451 mV/mResults suggest significant contributions to Colaba disturbance by partial‐ring, dayside field‐aligned, or dayside ionospheric currents [ABSTRACT FROM AUTHOR]

The protection against a storm event provided by nourishment to Costa da Caparica beaches near Lisbon, Portugal, is investigated numerically with a two-dimensional-horizontal morphodynamic model able to generate and propagate the longer infragravity waves. The beach has a groyne field and a multi-typology backshore. The nourishment of 106 m3 of sand was placed at the beach face and backshore. Pre- and post-nourishment topo-bathymetric surveys of the beach, which suffers from chronic erosion, were performed under a monitoring program. The morphodynamics of the pre- and post-nourished beach when exposed to a simulated historically damaging storm event and the post-storm morphologies were compared to evaluate the efficacy of the nourishment. Results indicate that the lower surface level of the beach face and backshore of the pre-nourished beach induces a larger erosion volume. The nourishment prevented the extreme retreat of the shoreline that occurred during the storm in the pre-nourished beach and reduced the storm-induced erosion volume by 20%, thus protecting the beach effectively against the storm. The beach backshore typology (seawall vs. dune) exerts differential influences on the sandy bottom. As a result, multi-typology backshores induce alongshore variability in cross-shore dynamics. The backshore seawalls exposed to direct wave action cause higher erosion volumes and a larger cross-shore extension of the active zone. The most vulnerable alongshore sectors of the beach were identified and related to the mechanisms responsible for the erosion phenomenon. These findings strengthen the importance of sand nourishment for the protection and sustainability of beaches, particularly those with a seawall at the backshore, where storm events cause higher erosion. [ABSTRACT FROM AUTHOR]

We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate analysis. The construction of our new coefficient is based on the framework of multivariate regular variation and transformed-linear algebra operations. We show how this coefficient allows identifying pairs of variables that have partially uncorrelated tails given some other variables in a random vector. Unlike other recently introduced conditional independence frameworks for extremes, our approach requires minimal modeling assumptions and can thus be used in exploratory analyses to learn the structure of extremal graphical models. Similarly to traditional Gaussian graphical models where edges correspond to the nonzero entries of the precision matrix, we can exploit classical inference methods for high-dimensional data, such as the graphical Lasso with Laplacian spectral constraints, to efficiently learn the extremal network structure via the PTCC. We apply our new method to study extreme risk networks in two different datasets (extreme river discharges and historical global currency exchange data) and show that we can extract interesting extremal structures with meaningful domain-specific interpretations. [ABSTRACT FROM AUTHOR]

The aim of this research is to introduce and investigate a novel entropy-based approach to market efficiency assessment that utilizes symbolic encoding procedure including volatility estimates. The proposed new method is compared with the existing symbolic time series procedures with two thresholds. Statistical analyses of symbol-sequence histograms are conducted. The main research question is whether extreme events influence stock market returns and informational efficiency measured by entropy. The findings of empirical experiments for real-data from selected financial markets confirm that the new approach is especially useful in assessing stock market informational efficiency as it allows to capture extreme changes in market returns, specifically during turbulent periods. [ABSTRACT FROM AUTHOR]

In January 2022, the strongest Arctic cyclone on record resulted in a record weekly loss in sea ice cover in the Barents‐Kara‐Laptev seas. While ECMWF operational forecasts skillfully predicted the cyclone, the loss in sea ice was poorly predicted. We explore the ocean's response to the cyclone using observations from an Argo float that was profiling in the region, and investigate model biases in simulating the observed sea ice loss in a fully coupled GCM. The observations showed changes over the whole ocean column in the Barents Sea after the passage of the storm, cooling and mixing with enough implied heat release to melt roughly 1 m of sea ice. We replicate the observed cyclone in the GCM by nudging the model's winds to observations above the boundary layer. In these simulations, the associated loss of sea ice is only about 10%–15% of the observed loss, and the ocean exhibits very small changes in response to the cyclone. With the use of a simple 1‐D ice‐ocean model, we find that the overly strong ocean stratification in the GCM may be a significant source of model bias in its simulated response to the cyclone. However, even initialized with observed stratification profiles, the 1‐D model also underestimated mixing and sea ice melt relative to the observations. Plain Language Summary: Extreme storms in the Arctic can significantly impact the ocean and sea ice state. In January 2022, the strongest Arctic storm on record resulted in a record loss of sea ice. The storm was well predicted by the ECMWF operational forecasts, yet the loss of sea ice was not. Here we further study the impact that the storm had on the ocean, and how well a fully coupled global climate model simulates the observed response in sea ice and ocean to the storm. We do this by nudging the winds in the model to observations. In observations, the ocean responded to the storm by cooling and mixing to full depth in the Barents Sea, releasing enough heat to melt a significant amount of sea ice. In contrast, the model's simulated sea ice and ocean response to the storm is much smaller than estimated in observations. The model's ocean stratification prior to the storm is significantly stronger than observed and is likely a source of bias, which we confirm with the use of a simple one dimensional model. Key Points: An Argo float showed cooling and mixing in the Barents Sea during a record Arctic cyclone, accounting for the associated record sea ice lossA coupled GCM with winds nudged to observations shows much smaller changes in sea ice and ocean structure with the passage of the cycloneA 1‐D ocean model shows that too‐strong stratification in the GCM is a main source of bias in its sea ice and ocean response to the cyclone [ABSTRACT FROM AUTHOR]

The storm Daniel and subsequent floods hit the Region of Thessaly (Greece) in early September 2023, causing extensive damage to the built environment (buildings, networks, and infrastructure), the natural environment (water bodies and soil), and the population (fatalities, injured, homeless, and displaced people). Additionally, the conditions and factors favorable for indirect public health impact (infectious diseases) emerged in the flood-affected communities. The factors had to do with infectious diseases from rodents and vectors, injuries, respiratory infections, water contamination, flood waste and their disposal sites as well as structural damage to buildings and the failures of infrastructure. The conditions that evolved necessitated the mobilization of the Civil Protection and Public Health agencies not only to cope with the storm and subsequent floods but also to avoid and manage indirect public health impact. The instructions provided to affected residents, health experts, and Civil Protection staff were consistent with the best practices and lessons learned from previous disasters. The emphasis should be on training actions for competent agencies, as well as education and increasing the awareness of the general population. Non-structural and structural measures should be implemented for increasing the climate resilience of infrastructures including the health care systems within a One Health approach. [ABSTRACT FROM AUTHOR]

The studies on progressive collapse have primarily focused on threat-independent methods, wherein a sudden column removal is suggested in codes. However, a real collapse scenario is necessarily threat-dependent. Focusing on blast- and impact-induced progressive collapses, the current study considers cases in which damage is concentrated in a single member, without resulting in complete column loss. It is demonstrated that the progressive collapse performance under specific threats can be better or worse compared to that of sudden column removal. Thus, dynamic column removal does not necessarily guarantee the most critical scenario, as the response in a damaged system can sometimes exceed expectations. A simple analytical model is proposed to describe in detail the observed phenomena and emphasizes the development of catenary forces in the column under lateral extreme loading scenarios. The results provide a deeper insight into the progressive collapse performance of frame systems and the involved member-level resisting mechanisms.

Abstract This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Abstract A Mediterranean cyclone is a weather phenomenon capable of producing extremely severe conditions, including heavy rainfall and strong winds. Between March 24 and 26, 2023, a cyclone passed along the western Egyptian Mediterranean coast, spanning three days. This paper aims to investigate the cyclone's impact on wave characteristics, focusing particularly on simulating changes in the energy transported from wind to waves during its passage, which constitutes the core objective of this study. The research methodology involved collecting meteorological and hydrodynamic data over five days from March 23 to 27, 2023, utilizing databases of the Bologna Limited Area Model (BOLAM) and the General Bathymetric Chart of the Oceans (GEBCO). This data, combined with field data for model calibration and validation, was analyzed using the Simulating the WAves Nearshore (SWAN) model packaged within the Delft 3D hydrodynamical model, integrated with other data manipulation tools. (SWAN) demonstrated the ability to simulate energy transport during extreme weather events along the coastal area with high resolution, up to 500 m. The results indicate a significant increase in significant wave height, reaching up to 2.5 m, and disturbances in wind direction, with velocities exceeding 10 m per second. These conditions pose risks to the infrastructure in some cities along the study area and have severe impacts on coastal communities. A notable finding from the simulations is the excess energy transport, which reached up to 12,000 watts per meter over the sea surface during the cyclone. Furthermore, calibration and validation results affirm the (SWAN) model's capability to accurately study wave characteristics.

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges. [ABSTRACT FROM AUTHOR]

The present study investigates different combinations and methods for estimating the extreme Total Water Level (TWL) and its implications for predicting flood extension caused by coastal storms. This study analyses various TWL components and approaches and assesses how different methodologies alter flood predictions, with implications for warning systems and emergency responses. Using different combinations of individual TWL components, flood extension simulations were conducted using a hydrodynamic model in the Volano Beach area (Emilia-Romagna, Italy). A real coastal storm event was used as a reference for comparison. The findings indicate that the selection of individual TWL components and calculation methods significantly impacts flood extension predictions. The approaches, which involve calculating extreme values from a combined time series or the water level time series plus the extreme value of wave setup, yield the most realistic results, excluding the runup component. In comparison, the other combinations overestimate the flood. Incorporating hydromorphological models like XBeach could enhance the accuracy of runup estimations and improve the overall method reliability. Despite limitations such as runup estimation and the use of generic regional parameters, this study underscores the importance of the TWL combination selection in accurately predicting flood extents, emphasising the need for context-specific adaptations in environmental contexts. [ABSTRACT FROM AUTHOR]

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio‐economic loss. In this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short‐range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short‐range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm‐associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon‐based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy‐precipitating storms. Plain Language Summary: Earth system models are immensely useful for understanding how the climate system works. However, it is important to recognize that they have limitations including wet or dry precipitation biases caused by complicated factors. On the other hand, different storm types contribute to regional precipitation differently under varying conditions. Attributing precipitation to sourced storm types is a new approach to understanding model precipitation biases. Here we build a new data set to study precipitation from several storm types including tropical cyclones, extratropical cyclones, atmospheric rivers, and mesoscales convection systems. We find that these four storm types explain 67% of global mean precipitation and 82% of extreme precipitation. We also demonstrate the application of this tool for understanding biases in modeled precipitation. The future application of this new tool will shed light on the causes of modeled precipitation biases and underlying model problems. Key Points: A global observational database for tracking four major heavy‐rain storm systems is established for the 2006–2020These four storm systems contribute to over 67% of global annual mean precipitation and over 80% of top 1% precipitation extremesClimate model short‐range hindcasts underestimate the storm‐associated precipitation, especially for heavy precipitation extremes [ABSTRACT FROM AUTHOR]

The study of extreme events (EEs) in photonics has expanded significantly due to straightforward implementation conditions. EEs have not been discussed systematically, to the best of our knowledge, in the chaotic dynamics of a Fabry–Perot laser with optical feedback, so we address this in the current contribution. Herein, we not only find EEs in all modes but also divide the EEs in total output into two categories for further discussion. The two types of EEs have similar statistical features to conventional rogue waves. The occurrence probability of EEs undergoes a saturation effect as the feedback strength increases. Additionally, we analyze the influence of feedback strength, feedback delay, and pump current on the probability of EEs defined by two criteria of EEs and find similar trends. We hope that this work contributes to a deep understanding and serves as inspiration for further research into various multimode semiconductor laser systems. [ABSTRACT FROM AUTHOR]

Abstract Global temperatures were exceptionally high in 2023/24. Every month from June 2023 to June 2024 set a new record, and September shattered the previous record by 0.5°C. The 2023 annual average approached 1.5°C above pre‐industrial levels. This results from both long‐term warming and internal variability, with the occurrence of an El Niño episode. However the amplitude of the 2023/24 anomalies was remarkable and surprised the scientific community. Here we analyze the rarity of 2023/24 global temperatures from a climate perspective. We show that a ‘normal’ year 2023 would have roughly equaled the previous annual record, and that the most extreme events of 2023/24 rank among the most extreme since 1940. Our analysis suggests that the 2023/24 event can be reconciled with the long‐term trend and an intense, but not implausible, peak of internal variability.

The Yangtze River basin experienced record-breaking high temperatures in July–August 2022, leading the China Meteorological Administration to issue its first ever “red heat warning”. We use simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) of the Coupled Model Intercomparison Project 6 (CMIP6) to investigate the role of anthropogenic drivers in this extreme event. We have demonstrated that the strong Western Pacific Subtropical High (WPSH), attributed to internal variability, serves as the clear proximate driver for such extreme event, whether in the factual world or in the counterfactual world. When considering similar circulation patterns in 2022, the results show that anthropogenic forcing has contributed to the 2022-like heatwave by a factor about 7 compared to natural forcing under the present climate of the past 30 years. Specifically, the anthropogenic greenhouse gases made the event about 10 times more likely, while anthropogenic aerosols had negative effect. The results were similar but differed in exact contribution values when specific circulation regimes of 2022 were not considered. In general, global warming caused by anthropogenic activities has made extreme summer heatwaves far more frequent, especially in recent decades.

This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks. Plain Language Summary: In the summer of 2023, North China experienced four consecutive extreme high‐temperature events, which are called heatwaves. This study investigates the main factors that cause the four events and shows how well the operational numerical models can predict the heatwaves. The first three heatwaves shared similar circulation, with high‐pressure systems controlling North China. This local circulation anomaly was related to an upstream quasi‐stationary wave train along the polar front jet. The fourth heatwave was associated with a low‐pressure system over North China and an upstream quasi‐stationary wave train along the subtropical jet. Moreover, predictions from operational models demonstrate their capability to forecast the occurrence of high temperatures 2–5 days ahead, with an underestimation in the intensity. However, models are not reliable when it comes to predicting heatwaves 2 weeks in advance. Key Points: North China witnessed record‐breaking heatwaves in June and July 2023, consisting of four sequential synoptic‐scale eventsThe first three and the last heatwaves are controlled by distinct local circulations induced by atmospheric wave trains across EurasiaS2S models capture the heatwave occurrences 2–5 days in advance, but predictive skill notably diminishes beyond 2 weeks [ABSTRACT FROM AUTHOR]

Dual-polarization radar often detects columnar regions of enhanced differential reflectivity (ZDR) extending vertically above the environmental 0 °C level. Indicative of supercooled liquid drops and wet ice particles lofted by strong updrafts, these ZDR columns are increasingly understood to be of use in predicting extreme rainfall. With the aim of achieving practical application of ZDR column measurements, this paper focuses on the relationship between the height of ZDR columns and rainfall intensity near the ground. All the data on ZDR columns analyzed in this study was collected from weather radar stations in Japan. The height of each column and rainfall rates at low levels were analyzed using an automated algorithm. A regression analysis result reveals peak column height to be positively correlated with maximum rainfall rate near ground level, and that rainfall intensity on the ground is likely to exceed 50 mm h-1 when radar identifies a ZDR column. Furthermore, extreme rainfall with an intensity of 180 mm h-1 or more is likely associated with a column over 3 km tall from the 0 °C level. These findings suggest that surveillance of ZDR columns can contribute to the reliability of very short-range forecasts or nowcasts as well as assist with the issue of early warnings of extreme rainfall and flash floods. [ABSTRACT FROM AUTHOR]

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

Distribution systems are facing challenges in serving lifeline loads after extreme events. Network reconfiguration is a traditional and practical method for power supply restoration, which has strong but inflexible power transfer capabilities influenced by network topology. Multiple failures of utility power under extreme events will further limit the efficiency of network reconfiguration. Electric buses (EBs) can be utilized to achieve power supply considering their discharging capabilities as mobile storage devices. However, the mobility of EBs and the influences of transport systems must be carefully considered to enhance the resilience of distribution systems. Reconfiguration and EBs are complementary in terms of recovery capabilities and location flexibility, and more important loads can be recovered by the coordination between EBs and network reconfiguration. This paper proposes a coordinated restoration method for EBs and reconfigurations considering the influences of transport systems. The post-disaster restoration problem is formulated as a bi-level model, in which the network topology is optimized in the upper-level aiming at maximizing restoration loads through the main grid and EBs, while the traffic paths of all EBs are optimized with the goal of maximizing the restoration loads by the EBs in the lower-level considering time consumption and energy consumption during movement. The PSO and a genetic algorithm are used to solve the proposed bi-level optimization problem. Simulation studies are performed to verify the superiority of the proposed method.

Climate change is altering the severity and intensity of extreme weather events. Occupying microhabitats that buffer extreme weather may help species avoid harsh environmental conditions. We describe the thermal microclimate of Atlantic Puffin (Fratercula arctica) and Leach’s Storm-petrel (Hydrobates leucorhous) burrows and quantify whether burrows are thermal refuges during extreme cold weather events. We further test for the effect of weather conditions and burrow characteristics on nest microclimate and buffering capacity during extreme cold weather. We find that both species actively breed in burrow microclimates that are below their lower critical temperatures, which may impose significant thermoregulatory costs. However, burrows do act as thermal refuges because nests are kept 7.4–8.0 °C warmer than ambient temperatures during extreme cold weather events. Overall, external temperature and wind speed were strong drivers of burrow temperature, but burrow and habitat characteristics did not explain the variability in burrow buffering capacity during extreme cold weather. Our results suggest that burrows may provide a direct line of defence for seabird chicks against cold events. Given the complex responses of burrow microclimates to extreme events, quantifying how changes in environmental conditions will impact burrow-nesting seabirds in the future is key.

Abstract Background Forest ecosystems undergo significant transformations due to harvesting and climate fluctuations, emphasizing the critical role of seeding in natural regeneration and long-term structural preservation. Climate change further amplifies these dynamics, affecting phenology across species and regions. In Tierra del Fuego (Argentina), Nothofagus pumilio (lenga) forests represent the most important timber resource, and it is managed through different silvicultural strategies. This species demonstrates notable post-disturbance regeneration, yet seed fall exhibits significant variability, leading to variations in seed quality (e.g., viability). This study aims to assess fluctuations in N. pumilio seed quality, determine how it varies concerning forest management strategies, annual productivity, and the co-occurrence of climatic phenomena including El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Results Viable seeds represented 18.4% of the total, notably higher in unharvested than in managed areas. Conversely, empty seeds were more prevalent in harvested areas (> 75%). Seed quality exhibited significant differences across silvicultural treatments, except for insect-predated seeds, which had similar proportions across all areas, though dispersed retention showed higher predation. When considering years with varying production levels, high-production years favoured full and viable seeds, particularly in unharvested forests and aggregated retention, while low-production years saw reduced viability across all treatments. Quadratic models revealed that viability increased with seed production, where unharvested forests achieved the highest values. Climate variability influenced seed proportions, where ENSO+/SAM+ promoting more full and viable seeds, while ENSO–/SAM+ favoured non-predated seeds, especially in unharvested stands. Conclusions Seed quality varies among treatments and years with different levels of seeding. Variations in seed quality, linked to climatic events, influence seed viability. Seed quality plays a critical role in forest regeneration, ensuring a seedling bank for harvested stands to face climate variability. These findings are relevant for forest management and ecosystem services, considering the increasing climate variability and extreme events. Understanding these influences is crucial for Nothofagus pumilio forests' sustainability and global forest adaptation strategies.

The protection against a storm event provided by nourishment to Costa da Caparica beaches near Lisbon, Portugal, is investigated numerically with a two-dimensional-horizontal morphodynamic model able to generate and propagate the longer infragravity waves. The beach has a groyne field and a multi-typology backshore. The nourishment of 106 m3 of sand was placed at the beach face and backshore. Pre- and post-nourishment topo-bathymetric surveys of the beach, which suffers from chronic erosion, were performed under a monitoring program. The morphodynamics of the pre- and post-nourished beach when exposed to a simulated historically damaging storm event and the post-storm morphologies were compared to evaluate the efficacy of the nourishment. Results indicate that the lower surface level of the beach face and backshore of the pre-nourished beach induces a larger erosion volume. The nourishment prevented the extreme retreat of the shoreline that occurred during the storm in the pre-nourished beach and reduced the storm-induced erosion volume by 20%, thus protecting the beach effectively against the storm. The beach backshore typology (seawall vs. dune) exerts differential influences on the sandy bottom. As a result, multi-typology backshores induce alongshore variability in cross-shore dynamics. The backshore seawalls exposed to direct wave action cause higher erosion volumes and a larger cross-shore extension of the active zone. The most vulnerable alongshore sectors of the beach were identified and related to the mechanisms responsible for the erosion phenomenon. These findings strengthen the importance of sand nourishment for the protection and sustainability of beaches, particularly those with a seawall at the backshore, where storm events cause higher erosion.

Identifying the behavior of precipitation is one of the most important planning principles related to water resources. In this research, an attempt was made to analyze the trend of time changes in extreme rainfall profiles of the country by using the daily rainfall data of 3423 synoptic, climatology, and rain gauge stations for the period from 1970 to 2016 and by performing interpolation using the kriging method. Then, using percentile profiles (percentile less than 10, less than 25, 25 to 75, 75 to 90, and above 90) and regression analysis, changes in the frequency of member days of each of the percentile methods over time were calculated and mapped. The results showed that during the studied period, 86.6% of cells associated with days with the tenth percentile or less in the country had an increasing trend. On the other hand, the pixels associated with days with the 90th percentile and more have shown an increasing trend. Considering that the pixels with the 25th, 25th-75th percentiles (normal), and 75th percentile have shown a decreasing trend in terms of the number of days in their group, it can be concluded that the country's rainfall conditions and the days with rainfall are towards the limit values has moved and the possibility of drought or destructive floods has increased in the country.

Flood, drought, and frost may be disruptive events for agriculture. The subsidised crop insurance schemes are coping strategies that increase farms resilience to weather shocks and in fact the occurrence of extreme weather events and the level of subsidised crop insurance are correlated. Stronger evidence is found in Southern geographical areas, where drought (a major risking risk) is more frequent, and for spring-summer crops, that are less resilient to weather shocks. The article points at the need to reform extant policies to move toward a holistic approach for risk management.

Abstract This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks.

In September 2020, Western North America was impacted by a highly anomalous meteorological event. Over the Pacific Northwest, strong and dry easterly winds exceeded historically observed values for the time of year and contributed to the rapid spread of several large wildfires. Nine lives were lost and over 5000 homes and businesses were destroyed in Oregon. The smoke from the fires enveloped the region for nearly two weeks after the event. Concurrently, the same weather system brought record-breaking cold, dramatic 24-h temperature falls, and early-season snowfall to parts of the Rocky Mountains. Here we use synoptic analysis and air parcel backward trajectories to build a process-based understanding of this extreme event and to put it in a climatological context. The primary atmospheric driver was the rapid development of a highly amplified 500 hPa tropospheric wave pattern that persisted for several days. A record-breaking ridge of high pressure characterized the western side of the wave pattern with a record-breaking trough of low pressure to the east. A notable anticyclonic Rossby wave breaking event occurred as the wave train amplified. Air parcel backward trajectories show that dry air over the Pacific Northwest, which exacerbated the fire danger, originated in the mid-troposphere and descended through subsidence to the surface. At the same time, dramatic temperature falls were recorded along the east side of the Rocky Mountains, driven by strong transport of high-latitude air near the surface.

In this paper, we explore extreme events in a current-controlled memristor-based second-order nonautonomous nonlinear dynamical system. This system is a nonhyperbolic system and is found to possess a pair of symmetrical center equilibrium points. It exhibits nonhyperbolic chaos, abnormally large-amplitude oscillations, leading to extreme events and multistability for different parametric regimes. These were studied numerically by drawing the two-parameter phase diagram and constructing the basins of attraction for a large range of initial conditions. We believe that it is for the first time that extreme events have been reported in a two-dimensional nonautonomous nonhyberbolic system. [ABSTRACT FROM AUTHOR]

Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations. Plain Language Summary: Wind energy is one of the key renewable energy sources and has been widely developed worldwide to mitigate global warming. Extreme wind can damage wind turbines and jeopardize the reliability of wind power generation, disturbing the stability of regional power supplies. Therefore, understanding the spatial and temporal variability of extreme wind speeds at wind turbine hub height under climate change is essential for the development of local wind power. This study examines the spatial and temporal patterns of 100‐m extreme wind speeds in China using empirical downscaling and Bayesian model averaging ensemble methods driven by several global climate models and reanalysis data. The results show that Inner Mongolia, northeastern China, western Tibet, and eastern coastal areas are the areas in China where high extreme wind speeds are most prevalent. This spatial pattern will typically persist in the future, but extreme wind speeds in Tibet, Qinghai, and Sichuan will drop dramatically by the end of the 21st century. Extreme wind speeds are considerably higher in winter and autumn than in the other two seasons. Local governments should take measures to prevent regional energy disparities due to the deterioration of wind turbines because of the anticipated wind extremes under climate change. Key Points: An empirical downscaling Bayesian ensemble projection model is constructed to investigate extreme wind speed variationsExtreme wind speeds in central and southeastern China are projected to increase in future periodsExtreme wind speeds are considerably higher in winter and autumn than in the other two seasons [ABSTRACT FROM AUTHOR]

In June 2020, a record-breaking Saharan dust storm, known as the "Godzilla" extreme event, caused significant dust transport from the Sahara Desert across the Atlantic Ocean to the United States. Based on satellite observations, the magnitude of aerosol optical depth (AOD) has consistently remained highest over the Atlantic Ocean for the past 18 years. This study uses satellite observations (including MODIS and CALIOP) and MERRA-2 reanalysis products to investigate the relationships between dust and marine clouds. During this extreme event, the concentration of AOD exhibits a synchronous anomaly with the cloud fraction (CF). Principal components analysis (PCA) results show that the enhanced temperature and specific humidity near the surface contribute the most to cloud development over the tropical Atlantic Ocean. Despite the reduced sensitivity of CF to aerosols, the semi-direct effect of dust can still play a crucial role during this extreme dust storm. We found that the presence of absorbing aerosols above the cloud layers warms the air, accompanied by an enhancement of surface moisture, thereby benefiting low-level cloud coverage. [ABSTRACT FROM AUTHOR]

Extreme winter temperatures govern the northern range limit of black mangroves (Avicennia germinans) in southeastern North America. There is a pressing need for studies that advance our understanding of how extreme cold temperature events affect mangroves near their range limits. However, such events are infrequent and challenging to study at regional scales. Here, we compared the damage to mangroves from extreme freeze events in 2018 and 2021, using local data from sites in USA (Florida, Louisiana, and Texas) and northeastern Mexico (Tamaulipas). In 2018, mangrove damage was concentrated in Louisiana and the upper Texas coast, where minimum temperatures ranged from -4 °C to -7 °C. In 2021, damage from a more severe freeze event was concentrated along the central to northern coasts of Texas, where minimum temperatures ranged from -4 °C to -10 °C. We used regional temperature and vegetation data from these events to quantify temperature thresholds for A. germinans leaf damage. Our results indicate that A. germinans leaf damage is likely to occur when temperatures are between -4 °C and -6 °C. These findings help refine temperature thresholds for A. germinans leaf damage and advance understanding of the effects of extreme freeze events on mangrove range expansion. This information is valuable for anticipating future range dynamics in a warming world. [ABSTRACT FROM AUTHOR]