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To feed a rapidly growing human population in an era of climate change and increasingly uncertain weather—such as drought and flood—humanity will need crops with increased yields, greater resiliency to extreme weather, and more resistance to disease. Appropriately enough in the Century of Biology, that means turning to genetic tools such as CRISPR. [ABSTRACT FROM AUTHOR]

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

INTRODUCTION: Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. OBJECTIVES: The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. METHODS: Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. RESULTS AND DISCUSSION: The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. CONCLUSION: This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

Climate change‐induced extreme weather and related drought and flood conditions are heterogeneous across space and time. The variability in location, timing, and magnitude of rainfall can alter how species respond to the drought and flood disturbances. To further complicate this matter, when droughts end they are often followed by extreme flooding, which are rarely considered as a disturbance (Humphries et al. 2024), let alone assessed with its own heterogeneity. Consequently, it is difficult to quantify impacts on ecological communities across large spatiotemporal scales without considering flood‐drought disturbance characteristics in sequence (Burton et al. 2020). We hypothesized that native organisms have evolved resistance to withstand repeated cycles of drought‐flood disturbances, and that established non‐native species have adapted to persist in novel conditions. To test this, we fit spatiotemporal models of species occurrence with local rainfall patterns as covariates in the drought and flood impacted Murray‐Darling basin in Australia during the decade long Millenium Drought, and its recovery period. During these drought conditions, river‐floodplain organisms in the Murray‐Darling became localized in refugia that limited longitudinal and lateral connectivity (Bond et al. 2008), and following flooding the same organisms were exposed to dispersal and recruitment opportunities (Humphries et al. 2020), as well as to hypoxic blackwater events that lead to the mortality of aquatic organisms (Small et al. 2014). At the basin‐scale we found that the range size of most native and non‐native fishes were highly resistant to the extreme drought and post‐flood conditions. At local scales, species richness, or detection, actually increased under drought conditions. Both findings highlight the resistance of species to climate change driven extreme weather, which opens new questions on community adaptations. [ABSTRACT FROM AUTHOR]

A global terrestrial precipitable water vapor (PWV) dataset has been developed using observations from the MicroWave Radiation Imager (MWRI) aboard the FY-3 satellite series (FY-3B, FY-3C and FY-3D) spanning 2012 to 2020. The dataset offers twice-daily PWV records at a spatial resolution of 0.25° × 0.25°, aligned with the ascending and descending orbits of the FY-3 satellites. The dataset was generated using an automated machine learning (ML) model that leverages MWRI-based features characterizing surface conditions and an enhanced Global Position System (GPS) PWV dataset as a reference. Trained on over one million sampling points from more than ten thousand stations worldwide, the model ensures a robust representation of global PWV variations. Independent evaluations against SuomiNet GPS and Integrated Global Radiosonde Archive Version 2 (IGRA2) PWV products yielded root mean square error (RMSE) of 4.47 mm and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across various surface conditions. The dataset effectively captures both spatial and temporal PWV variations, allowing for precise examination of localized and abrupt changes in water vapor induced by extreme weather events. Representing a significant advancement in global terrestrial PWV monitoring, the MWRI PWV dataset provides an all-weather, high-precision data record that bridges gaps in global coverage of passive microwave-based terrestrial PWV observations. It is a valuable resource for atmospheric research, climate modeling, water cycle studies, and beyond. The dataset is available at: https://doi.org/10.6084/m9.figshare.26712709 (Xia et al., 2024). [ABSTRACT FROM AUTHOR]

This commentary explores the intersection between climate, conflict, and child nutrition, highlighting the severe child nutrition crisis in Sub-Saharan Africa exacerbated by climate change and ongoing conflicts. Shifting climate patterns disrupt agricultural productivity and food security, while persistent conflicts displace populations and destroy infrastructure, significantly increasing food insecurity and malnutrition among children. Recent UNICEF data indicates that 1 in 4 children globally live in severe food poverty, with those affected up to 50% more likely to suffer from life-threatening malnutrition. Over half a billion children reside in areas prone to climate-related extreme weather events, challenging food production, distribution, and access. Extreme weather events have led to crop failures, food shortages, and price spikes, disproportionately affecting vulnerable populations. Changes in temperature and precipitation patterns also alter the nutritional content of crops, worsening nutritional challenges. Conflicts in SSA have led to a food crisis of unprecedented scale, with over 80% of the 137 million Africans facing acute food insecurity located in conflict-affected countries. The conflict between Russia and Ukraine has further disrupted global food and fertilizer supply chains, exacerbating food shortages and inflation in many African countries. Addressing this crisis requires a multifaceted approach integrating evidence-based, cost-effective strategies. This commentary advocates for the adoption of the 3 C approach—climate-smart school feeding programs, cultivation of edible insects, and community-based food hubs—as solutions to enhance child nutrition and build climate resilience. [ABSTRACT FROM AUTHOR]

Using observation data and numerical simulations, we have demonstrated that the stratospheric Quasi-Biennial Oscillation (QBO) can predict extreme high temperatures (EHTs) in South Asia in June. The vertical structure of the QBO plays a crucial role in this prediction. When the QBO in June shows easterlies (westerlies) at 50 hPa and westerlies (easterlies) at 70 hPa, more (fewer) EHT events occur. This likely results from the QBO's vertical structure causing positive (negative) temperature anomalies in the lower stratosphere and negative (positive) static stability anomalies near the tropical tropopause. These anomalies enhance (weaken) convective activity over the equatorial Indian Ocean, leading to anomalous circulation with ascending (descending) air over the equatorial Indian Ocean and descending (ascending) air over northern and central South Asia. This suppresses (promotes) convection over northern and central South Asia, affecting cloud formation and precipitation. Consequently, more (less) solar radiation reaches the region, along with weaker (stronger) evaporative cooling effects, warming (cooling) the surface and creating a background state conducive to (against) EHT events. Additionally, the opposite zonal winds at 30 hPa and 50 hPa in April may serve as a reference factor for predicting the probability of EHT events in northern and central South Asia. This study provides a potential approach for forecasting tropospheric extreme weather events based on stratospheric signals. [ABSTRACT FROM AUTHOR]

Climate change and mental health are inextricably linked crises that demand urgent responses within the health sector and beyond. Mental health challenges associated with climate change are wide-ranging. They include depression, anxiety and post-traumatic stress resulting from increased exposure to extreme weather events, generalized climate anxiety and indirect impacts. However, there is a significant adaptation gap when it comes to addressing the mental health risks posed by climate change. Lack of capacity is frequently cited as a barrier to adaptation, yet 'capacity' covers many facets. This article examines the capacities of policy systems to design and implement adaptation initiatives for addressing the increasing risks to mental health posed by climate change. Focusing on England (UK) as an illustrative case study, this article deploys a policy capacity framework and draws on semi-structured interviews and policy document analysis. It identifies the ways that analytical, operational and political policy (in)capacities manifest across relevant policy areas, which include health, flood and coastal erosion risk management, spatial planning, natural environment and emergency management. Our analysis reveals that despite some strengths in analytical and political capacity, strained operational capacity is exacerbating and reinforcing adaptation gaps. We also demonstrate some of the complex interactions between different types of capacities that both enable and hamper adaptation. This article demonstrates the value of analysing policy capacity, and its potential in identifying and designing the necessary interventions to help circumvent a growing mental health crisis under climate change. Key policy insights In England, efforts to adapt to risks to mental health under climate change are supported through strong analytical capacity, involving research and knowledge generation, education and training, particularly in the mental health sector and Flood and Coastal Erosion Risk Management (FCERM). There is, however, room for improvement. Relatively strong political capacity, including advocacy and the role of policy champions, have led to increasing legitimation of the issue and fostered a growing policy landscape. However, some discernible gaps remain. There are significant weaknesses in operational capacity, restricting coordinated, preventative adaptation. This is partly due to significant resource constraints, and historic and current fragmentation within and between the health sector and others. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are extreme weather events that have major impacts on the structure and functioning of marine ecosystems worldwide. Due to anthropogenic climate change, the occurrence of MHWs is predicted to increase in future. There is already evidence linking MHWs with reductions in biodiversity and incidence of mass mortality events in coastal ecosystems. However, because MHWs are unpredictable, the quantification of their effects on communities is challenging.Here, we use the Helgoland Roads long‐term time series (German Bight, North Sea), one of the richest marine time series in the world, and implement a modified before‐after control‐impact (BACI) design to evaluate MHW effect on mesozooplankton communities. Mesozooplankton play an essential role in connecting primary producers to higher trophic levels, and any changes in their community structure could have far‐reaching impacts on the entire ecosystem.The responses of mesozooplankton community to MHWs in terms of community structure and densities occurred mainly in spring and autumn. Abundances of seven taxa, including some of the most abundant groups (e.g. copepods), were affected either positively or negatively in response to MHWs. In contrast, we observed no clear evidence of an impact of summer and winter MHWs; instead, the density of the most common taxa remained unchanged.Our results highlight the seasonally dependent impacts of MHWs on mesozooplankton communities and the challenges in evaluating those impacts. Long‐term monitoring is an important contributor to the quantification of effects of MHWs on natural populations. [ABSTRACT FROM AUTHOR]

The intersection of the COVID-19 pandemic with other crises can amplify vulnerabilities and push communities further into poverty. In low-income countries, the dual impacts of COVID-19 and extreme weather events, along with multidimensional poverty and structural vulnerabilities in agriculture can decimate farmer livelihoods. This study aims to understand the effects of individual and compounding crises (COVID-19, cyclones, and vanilla price collapse) on smallholder vanilla farmers and local coping strategies in Madagascar, one of the world's largest vanilla producers and poorest countries. We used semi-structured and scenario-based interviews across two case study villages with contrasting enforcement of forest regulations. We found that the impact of the pandemic, combined with the cyclone event, disrupted livelihoods, resulting in income losses and food security challenges that exacerbated farmer vulnerabilities. Sixty eight percent of households reported crop losses due to strong winds and heavy rainfall brought by cyclone Enawo in 2017. The COVID-19 outbreak struck the region just as the residents were recovering from the effects of the cyclone. COVID-19-related travel restrictions in the aftermath of the cyclone took a substantial economic toll, with 54.1% of respondents experiencing a decline in earnings, and 17% facing a total loss of income due to the imposed lockdown. The decline in vanilla prices at the onset of 2020 had a far-reaching additional impact, affecting not only farmers but also residents who rely on other sources of income. Local communities reported using the forest resources more frequently as a safety net during crises in the village with more lenient regulations. This study underscores the importance of understanding the interconnectedness and compounding impacts of cascading crises on food security and natural resource use. We highlight the need for a comprehensive approach to increasing farmer resilience, particularly for those reliant on global market crops such as vanilla. [ABSTRACT FROM AUTHOR]

Background: Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods: In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results: Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only. Conclusion: The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs), typically comprising 5–30 amino acids, can deliver impermeable cargo molecules across membranes into the cytosol of eukaryotic cells. CPPs can transport a broad spectrum of molecules, including peptides, proteins, nucleic acids, and small molecules. Delivery can be accomplished by covalently attaching a CPP to the target cargo or by the formation of a noncovalent complex between the CPP and cargo. CPPs penetrate cells via two distinct mechanisms: energy-dependent endocytic pathways or energy-independent direct translocation across the plasma membrane. CPPs could potentially reduce dependence on chemical fertilizers and pesticides, boost crop yields and resilience to extreme weather conditions, improve nutrient uptake from the soil, and enhance nutrient contents in plants. Cell-penetrating peptides (CPPs) are short (typically 5–30 amino acids), cationic, amphipathic, or hydrophobic peptides that facilitate the cellular uptake of diverse cargo molecules by eukaryotic cells via direct translocation or endocytosis across the plasma membrane. CPPs can deliver a variety of bioactive cargos, including proteins, peptides, nucleic acids, and small molecules into the cell. Once inside, the delivered cargo may function in the cytosol, nucleus, or other subcellular compartments. Numerous CPPs have been used for studies and drug delivery in mammalian systems. Although CPPs have many potential uses in plant research and agriculture, the application of CPPs in plants remains limited. Here we review the structures and mechanisms of CPPs and highlight their potential applications for sustainable agriculture. [ABSTRACT FROM AUTHOR]

This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods. [ABSTRACT FROM AUTHOR]

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI S ) and anomalous temperatures (HWI E ), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI S , SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types. [ABSTRACT FROM AUTHOR]

The frequency of extreme climatic and weather events has increased since 1950 due to global changes induced by human influence. These events can have significant impacts at the individual, population, and community levels across various taxonomic groups. They can be particularly detrimental to bird populations during their breeding season, affecting specific breeding parameters. This study originated from an exceptionally rare breeding season of a resident Great Tit population recorded in spring 2002 when an unusual mortality of nestlings was observed. We quantified weather conditions (temperature, rainfall) from the end of April, a few days before we started detecting failed nests, and compared them with previous and subsequent years. In early May, when many pairs were incubating or caring for newly hatched chicks, we detected unusually low ambient temperatures and unusually high rainfall. This event likely caused that many first clutches failed completely, and that, compared with previous and subsequent years, fledging and breeding success was relatively low in those which were successful. There was also an increased proportion of replacement clutches that year. Anyway, the overall production of fledglings per breeding pair over the breeding season was 2.39, lower than other years. Although recruitment rate in 2003 was similar to other years, the lower production of fledglings in 2002 probably resulted in a decrease in the number of breeding pairs in the following years. [ABSTRACT FROM AUTHOR]

An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed. [ABSTRACT FROM AUTHOR]

The importance of strengthening grid resilience has grown with the increase in environmental destruction and modern power grid complexity, as a consequence of power outages inflicted by human intrusion and extreme weather events. Micro-grids (MGs) have proven to be a viable alternative in such circumstances. However, these occurrences are highly unpredictable, resulting in unintended islands of MGs with negative consequences. As a response, alerting its distributed generations about unintended island is indeed a crucial issue for enhancing grid resilience with MG. Therefore, it is essential to develop a technique for the efficient and accurate detection of unintended islands. There has been an increase in the use of micro-phasor measurement units (µ-PMUs) in MG. In the perspective of this, using an efficient µ-PMU, the research provides a method for finding unintended islands in a MG. The µ-PMU analyses the solar generator bus voltage and analyzes it with symmetrical components for island identification. This study introduces a µ-PMU based Fortescue-transform and random forest algorithm method for rapid detection of unintended islanding in distribution generation system. The approach monitors voltage phasor of zero and negative sequence, calculating angular sum over time to distinguish islanding event from other disturbance. Using Matlab/Simulink, the proposed method is evaluated on the IEEE-34 node distribution network. Multiple simulations provide validation for the method's resilient performance. The methodology proposed has a detection time of 20 ms. [ABSTRACT FROM AUTHOR]

FSO is the best communication medium for data transmission because of its high bandwidth, effective deployability and maximum transmission data rates with very high security. In this research paper an optical network model has been designed in the optisystem for different modulation schemes with WDM to enhance the capacity of the system. Attenuations for different weather conditions in Dehradun have been calculated and the performance has been evaluated accordingly. The performance is analysed in terms of BER (Bit Error Rate), maximum Q-factor, and eye height for each of the modulation schemes at different transmission distances and different weather attenuations. Analysis comprehends that the polarization shift keying is the best modulation technique for the WDM-FSO communication network among all. Thus, we suggest a model for an optical communication network using WDM and PolSK modulation scheme for Dehradun (Uttarakhand), where internet service is not very easy due to its extreme weather variations and got the excellent performance. [ABSTRACT FROM AUTHOR]

The occurrence of extreme hot and dry summer conditions in the Pacific Northwest region of North America (PNW) has been known to be influenced by climate modes of variability such as the El Niño-Southern Oscillation and other variations in tropospheric circulation such as stationary waves and blocking. However, the extent to which the subseasonal remote tropical driver influences summer heat extremes and fire weather conditions across the PNW remains elusive. Our investigation reveals that the occurrence of heat extremes and associated fire-conducive weather conditions in the PNW is significantly heightened during the boreal summer intraseasonal oscillation (BSISO) phases 6-7, by ~50–120% relative to the seasonal probability. The promotion of these heat extremes is primarily attributed to the enhanced diabatic heating over the tropical central-to-eastern North Pacific, which generates a wave train traveling downstream toward North America, resulting in a prominent high-pressure system over the PNW. The ridge, subsequently, promotes surface warming over the region primarily through increased surface radiative heating and enhanced adiabatic warming. The results suggest a potential pathway to improving subseasonal-to-seasonal predictions of heatwaves and wildfire risks in the PNW by improving the representation of BSISO heating over the tropical-to-eastern North Pacific. [ABSTRACT FROM AUTHOR]

The analysis of climatic drivers is of utmost importance for defining new hydrological parameters. The territory of Serbia covers the area of 88,361 km2 and all its rivers belong to three big basins, the Aegean, the Black sea and the Adriatic. Extreme weather conditions have been determining the properties of drainage basins within the last sixty year (1953–2023). This research, conducted for the first time in the territory of Serbia, has shown comparative data on the changes of drainage basins and the changes in the type of river networks for the period of last sixty years. The research also includes the changes of the topography, the terrain and the surface of the basins. The main methodology is based on GIS methods, geostatistical method and remote sensing. Adjusted Strahler's order was also used for the purpose of this research. The obtained results on the topology of river networks were classified into six different types, them being parallel, dendritic, mixed, radial, fan-shaped and rectangular. There has been an increase by 8% in parallel, mixed and dendritic type. The most significant changes in the last sixty years were observed within the Aegean and Adriatic sea basin. The surface of the Black sea basin increased by 1.2%, with the addition of a higher number of tributaries, whereas the basins of Adriatic and the Aegean sea had a decrease by 0.3% and 0.9% respectively. The number of tributaries decreased by 5.5% within the Adriatic sea basin, and by 8.8% within the Aegean sea basin. [ABSTRACT FROM AUTHOR]

More than half of Japan's land area experiences significant snowfall during winter, and the damage caused by various snow and ice disasters remains a dire issue, which also leads to decreased living standards. Simultaneously, the nature of snow and ice disasters has been transformed due to climate change and the increasing occurrence of extreme weather conditions. The National Research Institute for Earth Science and Disaster Resilience (NIED) has been continuously conducting research to address these problems in relation to snow and ice disasters. This study presents the results of the project "Research on Combining Risk Monitoring and Forecasting Technologies for Mitigation of Increasingly Diverse Snow Disaster" conducted by the NIED over a seven-year period from April 2016 to March 2023. This project developed technology for conducting accurate observations of snowfall and snow cover conditions over wide areas as well as technology for areal prediction of snow and ice disasters through simulations. Based on collaboration with stakeholders, such as local governments, our study investigated how to optimize the use of our information products for snow and ice disaster mitigation. Through these insights, the NIED provides information for prompt and appropriate responses to snow and ice disasters, thus supporting safe and comfortable living in both snowy and non-snowy areas. [ABSTRACT FROM AUTHOR]

The widespread utilization of reanalysis-based precipitation (RP) data has significantly facilitated hydrometeorological studies in areas where observations are scarce. However, assessing the applicability of the RP data before being applied in any basin is necessary considering that the inherited errors of the RP data vary with surface conditions, seasonal cycles, and different climatic zones. In this study, the Ili River Basin (IRB) was selected as the study area to evaluate the accuracy and impact of ERA5 reanalysis precipitation data, a type of RP data, on the hydrological cycling in the IRB. Four methods, linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM), were tested to correct the bias of the ERA5 reanalysis precipitation. We found the following results. (1) The ERA5 precipitation exhibits a significant overestimation of gauge-observed precipitation within the IRB, indicating poor accuracy. Compared to the gauge-observed precipitation, the comprehensive performance of ERA5 precipitation in the dry season is better than that in the entire year, while the one in the entire year is better than that in the rainy season. The main error of the ERA5 precipitation lies in its insufficient ability to distinguish between no rain (0–0.1 mm) and light rain (0.1–4 mm) events, (2) All bias correction methods effectively address the biases in the ERA5 precipitation, although the extent of correction varies. The LS and DM methods outperformed the others regarding the time-series-based indices, while the DM method outperformed the others regarding the exceedance probability, and (3) In evaluating runoff simulation accuracy indices, hydrologic simulations driven by the corrected ERA5 precipitation using the DM method markedly outperform those driven by the ERA5 precipitation alone and even those corrected by the PT and LS methods. Specifically, the DM method exhibits the best correction of the flow duration curve and peak flow, with Nash-Sutcliffe efficiency coefficient (NSE) at 0.83 and relative deviation (RD) at 9.23% in the calibration period, and NSE at 0.85 and RD at 1.29% in the verification period. Corrected ERA5 precipitation data can fill in the gaps left by meteorological stations, effectively addressing the issue of inaccurate peak values in runoff simulations caused by using uncorrected ERA5 precipitation data. Practical Applications: Due to the constraints of complex terrain and natural conditions, the hydrometeorological monitoring stations are sparse and unevenly distributed in western China's Ili River Basin (IRB). The precipitation data obtained from the monitoring sites can hardly meet the needs of hydrological simulations. Furthermore, it challenges us to manage the IRB better when facing extreme weather conditions (e.g., drought and flooding). The ERA5 reanalysis precipitation data with comprehensive coverage and high resolution is a promising alternative to observing precipitation at the basin level. However, verifying the feasibility of using ERA5 precipitation data in IRB is necessary. In this study, we first noticed that the ERA5 precipitation data has a significant deviation and low accuracy in IRB by comparing it with available observed precipitation. Thus, we corrected ERA5 reanalysis precipitation using four different methods: linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). We found that the DM method can better capture extreme precipitation events, and the corresponding simulated runoff has the highest agreement with the observed runoff. [ABSTRACT FROM AUTHOR]

Public parks and other green areas are crucial components of urban development. Urban management in emerging countries such as Bangladesh faces major challenges, especially because of the socio-environmental impacts of urbanization. Urban management initiatives in developing countries sometimes neglect crucial services for university students, such as study environments and recreational facilities. This study aimed to investigate students' perceptions of urban green space (UGS) and its potential benefits to our daily lives at Shahjalal University of Science and Technology (SUST) in Sylhet, Bangladesh, We collected data from 438 respondents by using a survey questionnaire-based stated preference approach as a methodological tool, using non-monetary assertions as the basis for the method. The survey included questions about respondents' social background, the frequency with which they visited green spaces, the benefits of urban green spaces and their perception of urban green spaces. We performed statistical analysis both descriptive and inferential statistics. Our findings suggest that 71.7% of students primarily use street trees and peace gardens as their main sites for urban green spaces. The study suggests that the advantages of urban green spaces, which include physical, mental, and environmental benefits, are strongly associated with criteria such as gender, academic level, and vulnerable to home locality for climate change (CC) or extreme weather events (EWEs). The Binary Logistic regression analysis identified urban life as the most influential factor. The correct classification rate was approximately 74.7%, indicating the model's strong accuracy in classification. Students who have lived in urban areas for more than 20 years have a reduced awareness of urban green spaces at 5% level of significance. Planning and policymaking for the creation and administration of urban green spaces, considering aspects like land use and environmental sustainability, could benefit from this study. [ABSTRACT FROM AUTHOR]

AbstractSudden outbreaks of agricultural pests are increasing in India due to the growing prominence of environmental issues like global warming, the occurrence of weather extremes, and hydrological extremes. Climate change impacts the developmental rates and population dynamics of agricultural pests. As rice is a staple food widely used in India, it often suffers intensive damage by various pests, leading to a considerable decrease in crop yield. One of the most common pests in rice fields in India and neighboring countries is the rice thrips (Stenchaetothrips biformis). However, the degree of pest infestation of rice crops under climate change scenarios is poorly known. Therefore, this study aimed to understand the potential geographical distribution of the rice thrips in India for future scenarios using the MaxEnt modeling framework. Future projections from global climate models (GCMs), such as HadGEM3-GC31 and MPI-ESMI-2, are used to predict the most affected regions in 2050 under three greenhouse gas emission scenarios: SSP1-2.6, SSP3-4.5, and SSP5-8.5. The results from the MaxEnt indicate that the southern and northeastern parts of India will be highly affected by rice thrips in 2050. The affected number of districts are found to be 256, 330 and 313 under SSP1-2.6, SSP3-4.5, and SSP5-8.5 scenarios, respectively. It has been observed that the maximum temperature is the most influential climatic parameter for the geographical distribution of rice thrips, followed by the annual mean and minimum temperatures. It can be concluded from these results that the elevated temperature enhances the growth and distribution of rice thrips. [ABSTRACT FROM AUTHOR]

Current human population growth along Earth's coasts is on a collision path with anticipated consequences of increasing natural and anthropogenic induced coastal hazards. Using recently-available ambient, dasymetric data, we developed methods to estimate annual continental and global coastal populations from (2000–2018) measured horizontally from the shoreline inward. We found: (1) large concentrations of population in relatively small bands and regions along the coast (~ 2 billion within 50 km and ~ 1 billion within 10 km); (2) higher growth rates of coastal population than inland population (an addition of 463 million within 50 km and 233 million within 10 km); (3) strong influence of distance from the coast to predict population distribution; and (4) that macro population patterns and growth could be expressed and modeled as a power function at continental and global levels. Findings point to emerging macro population patterns along the coast as contributing to increasing anthropogenic effects on Earth systems and increasing human risks associated with sea-level rise, land subsidence, extreme weather, and public health. Reliable data tracking of the magnitude, spatial distribution and change of human populations in the coastal regions is essential for comprehensive coastal monitoring. [ABSTRACT FROM AUTHOR]

In December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation. Plain Language Summary: In December 2022, North America was hit by an exceptionally severe cold event. Scientists have been trying to understand the reasons behind this cold wave. Using detailed weather data and climate models, researchers found that natural climate variability played a major role, causing temperatures to drop by about 5.14 degrees Celsius. On the other hand, external factors like human‐induced climate change had a minor effect, slightly reducing the severity of the cold by 0.42 degrees Celsius. The study identified an atmospheric wave train pattern, originating from the North Pacific and moving toward North America, which played a crucial role in triggering this extreme weather. Further investigations showed that this wave train was influenced by specific large‐scale weather patterns in the Pacific region, namely the Pacific‐North American (PNA) and North Pacific Oscillation (NPO) patterns, which contributed to the cold temperatures by approximately 2.18 and 2.12 degrees Celsius, respectively. Additionally, colder‐than‐normal sea surface temperatures in the tropical central Pacific, associated with La Niña, strengthened the wave train. Internal thermodynamical processes, such as increased snow cover, also slightly intensified the cold wave by reflecting more sunlight and reducing the amount of solar energy reaching the surface. Key Points: Internal variability triggers 2022 December extreme cold wave in North AmericaPNA and NPO are two key teleconnections responsible for this extreme cold waveSnow cover‐SAT feedback intensifies this extreme cold wave [ABSTRACT FROM AUTHOR]

The interannual variation of the South China Sea (SCS) summer monsoon onset (SMO) may bring extreme weather and climate disasters in East Asia. However, its skillful forecast still remains challenging. This study investigates the intraseasonal ocean‐atmosphere interaction that affects the SCSSMO through diagnostic analysis and numerical experiments. It reveals that the cold sea surface temperature in the Southern SCS during winter (referred as cold tongue, CT) is the key pathway controlling the propagation of the 30–60 days intraseasonal oscillation (ISO) convective system from the Bay of Bengal (BOB) to the SCS. The CT variations affect the interannual variation of the SCSSMO. Specifically, the strong (weak) CT after the peak of La Niña (El Niño) years suppresses (enhances) the propagating ISO from the BOB to the SCS, resulting in a delayed (advanced) SCSSMO. This finding offers the new scientific insights for improving the forecasting of the SCSSMO. Plain Language Summary: The occurrence of a strong cold tongue (CT) in winter, commonly detected in the southern South China Sea (SCS) during La Niña decay years, plays a significant role in delaying the SCS summer monsoon onset (SCSSMO). It adversely affects the northeastward progression of the 30‐60‐day convective Intraseasonal Oscillation (ISO) from the equatorial Indian Ocean to the southern SCS. This interference leads to the delayed onset of the SCSSMO. It highlights the crucial influence of the CT sea surface temperatures (SST) on the linkage between Bay of Bengal and SCS at the intraseasonal scale, thus significantly impacting the timing of the SCSSMO. Key Points: The winter cold tongue in South China Sea (SCS) is a crucial oceanic precursor for the interannual variation of the SCS summer monsoon onset (SCSSMO)Strong cold tongue in the SCS can suppress the propagation of the 30‐60‐day intraseasonal oscillation into the SCSStrong SCS cold tongue during La Niña years tends to delay the SCSSMO [ABSTRACT FROM AUTHOR]

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40‐year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6‐hourly TC snapshots in the control and find a corresponding "twin" in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture‐related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short‐term intensity variability. Plain Language Summary: We examined how tropical cyclones (TCs) near the United States might change due to warmer climates. First, we recreated past weather conditions from 1980 to 2019 using a specialized climate model. Then, we predicted future changes in the same meteorology by modifying the model to include warmer temperatures and running four additional simulations. We tracked and analyzed over 4,000 instances of TCs from the past 40 years, ensuring that these TCs were all matched between all five simulations, and evaluated their changes under future warming conditions. Our findings suggest that TCs will become stronger on average as the world warms, with the most notable increases in TC moisture. Not every TC in our study becomes more intense with warming, however, highlighting the complexity of understanding how extreme weather will change in the future. We found that the chances of a TC rapidly intensifying or suddenly weakening both rise with higher levels of warming. These trends could complicate predictions of TC intensity in the future, presenting additional challenges for forecasting and preparation. Key Points: Thermodynamic global warming simulations provide a unique large‐sample size view into paired counterfactual tropical cyclones12 km simulations indicate future near‐United States cyclones will, on average, be wetter, slightly more intense, but similarly sizedPeriods of rapid intensification and weakening indicate increased tropical cyclone intensity variability in warmer climates [ABSTRACT FROM AUTHOR]

This study explores the mechanisms contributing to heavy precipitation associated with landfalling atmospheric rivers (ARs) and orography, focusing on a high‐profile case study of an extratropical cyclone, storm Bronagh, which caused flooding and travel disruption in the UK. While prior research has established the connection between ARs, orography, and precipitation, the specific mechanisms leading to intense rainfall remain unclear. A novel methodology is introduced that quantifies the relative contribution to the total precipitation from cyclone‐induced processes and orographic processes using modifications to the Met Office Unified Model orography and microphysics parameterization scheme. Results show that the majority (63%) $(63\%)$ of storm Bronagh 's rainfall over land is attributed to cyclone‐related processes (frontal ascent and embedded convection). However, interaction of the AR associated with storm Bronagh and orography focuses more of the rain over the hills, enhancing precipitation in this region by a further 50% $50\%$. Further analysis reveals that the seeder‐feeder mechanism plays a predominant role in orographically enhanced rainfall. Accretion and riming processes occur as rain from the cyclone induced (seeder) cloud falls through the orographically induced (feeder) cloud enhancing precipitation rates. The feeder cloud formation is related to forced ascent of air within the AR over the orography. In conclusion, this study sheds light on the intricate interplay between ARs, orography, and cyclone precipitation during the landfall of extratropical cyclones. By separating the mechanisms behind heavy rainfall in the context of storm Bronagh, we quantify for the first time the effect of the seeder‐feeder effect at different heights over orography. Plain Language Summary: This study investigates why we experience heavy rainfall during storms in the UK. We focus on storm Bronagh which reached the national news because of the flooding and travel disruption it caused. While there is an established statistical link between atmospheric rivers, mountains, and rain, the details of how atmospheric rivers interact with mountains leading to intense rainfall were not well‐understood. In this research, a new approach which made adjustments to a weather model is used to determine these complex interactions. The results showed that most of the heavy rain during storm Bronagh came from the storm itself, but that it was enhanced via interaction with the mountains. The moisture in an atmospheric river turns into cloud droplets as it is forced to rise over the mountains. A specific process, allowing small raindrops to grow rapidly as they fall through orographic cloud, caused the localized rainfall enhancement. Understanding these processes helps us predict and prepare for extreme weather events better, enhancing our ability to respond to their impacts effectively. Key Points: The largest contribution to observed rainfall totals (63%) was associated with frontal ascent and convection in storm BronaghInteraction of storm Bronagh with orography enhanced rainfall totals by a further 50%Orographic rainfall enhancement in storm Bronagh is dominated by the seeder‐feeder process [ABSTRACT FROM AUTHOR]

The Indus River Basin (IRB), one of the major river basins in the Hindu Kush Himalaya, is primarily sustained by meltwater from the cryosphere. It caters to diverse sectors, including intense irrigation-supported agriculture, energy production, tourism and biodiversity. The cryosphere of the Upper Indus Basin (UIB) is mainly influenced by the western disturbances with subordinate contribution from the Indian Summer Monsoon. Increase in ambient temperature, shrinking cryospheric reserves, fluctuating surface run-off and enhanced frequency of extreme weather events are some of the noticeable indicators of climate change in the UIB. These changes will most likely adversely affect the water-dependent sectors in the upstream and downstream of IRB, posing serious threat to food security and livelihoods. Although the region has gained significant attention in recent years, there remains a noticeable knowledge gap pertaining to certain key issues with serious implications for the natural environment and the people. A national workshop was organized for stimulated deliberations to identify the major knowledge gaps and suggest a policy framework for climate change mitigation in the UIB. The workshop underscores the urgent need of multi-institutional, multidisciplinary, comprehensive, coordinated and time-bound collaboration to study the interplay of complex drivers on water resources of the UIB. [ABSTRACT FROM AUTHOR]

High-amplitude quasi-stationary atmospheric Rossby waves with zonal wave numbers 6–8 associated with the phenomenon of quasi-resonant amplification (QRA) have been linked to persistent summer extreme weather events in the Northern Hemisphere. QRA is not well-resolved in current generation climate models, therefore, necessitating an alternative approach to assessing their behavior. Using a previously-developed fingerprint-based semi-empirical approach, we project future occurrence of QRA events based on a QRA index derived from the zonally averaged surface temperature field, comparing results from CMIP 5 and 6 (Coupled Model Intercomparison Project). There is a general agreement among models, with most simulations projecting substantial increase in QRA index. Larger increases are found among CMIP6-SSP5-8.5 (42 models, 46 realizations), with 85% of models displaying a positive trend, as compared with 60% of CMIP5-RCP8.5 (33 models, 75 realizations), with a reduced spread among CMIP6-SSP5-8.5 models. CMIP6-SSP3-7.0 (23 models, 26 realizations) simulations display qualitatively similar behavior to CMIP6-SSP5-8.5, indicating a substantial increase in QRA events under business-as-usual emissions scenarios, and the results hold regardless of the increase in climate sensitivity in CMIP6. Projected aerosol reductions in CMIP6-SSP3-7.0-lowNTCF (5 models, 16 realizations) lead to halting effect in QRA index and Arctic Amplification during the 1st half of the twenty-first century. Our analysis suggests that anthropogenic warming will likely lead to an even more substantial increase in QRA events (and associated summer weather extremes) than indicated by past analyses. [ABSTRACT FROM AUTHOR]

Research on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private‐sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research. [ABSTRACT FROM AUTHOR]

Global warming, driven by human activities since the Industrial Revolution, has significantly elevated atmospheric carbon dioxide (CO2) levels, leading to higher global temperatures and a rise in extreme weather events. The ocean, as a major carbon sink, has absorbed about 30% of human-induced carbon emissions, helping mitigate global warming's impacts. This study examines the spatiotemporal distribution of air-sea CO2 flux in the Northwest Pacific from 1982 to 2021 and its response to El Niño-Southern Oscillation, using Empirical Orthogonal Function and composite analysis. The seasonal patterns of air-sea CO2 flux and the influence of environmental factors were further evaluated. The results show that air-sea CO2 flux in the Northwest Pacific exhibits clear seasonal fluctuations. In winter, high-latitude areas act as significant carbon sources. Strong winds deepen the mixed layer, promoting CO2 release from the ocean into the atmosphere. In contrast, in summer, longer daylight hours, rising SST, and melting sea ice lead to upwelling, which brings nutrients to the surface and stimulates phytoplankton growth. This process turns the region into a carbon sink as phytoplankton growth, driven by intense sunlight, enhances the ocean's CO2 absorption. The mid-latitude region consistently acts as a carbon sink yearround. During El Niño events, more negative air-sea CO2 flux anomalies appear in the eastern Northwest Pacific, enhancing carbon uptake. La Niña events have the opposite effect in the eastern regions. These findings highlight the Northwest Pacific's critical role in modulating regional and global carbon cycles under varying climatic conditions. Understanding these dynamics is crucial for improving predictions of future climate impacts and for developing effective strategies to mitigate global warming. [ABSTRACT FROM AUTHOR]

Transmission towers serve as crucial safety pillars within the power transmission system, and their damage can lead to severe consequences. The structural failure of a tower undergoes a process from the initiation of local damage to overall failure, emphasizing the importance of conducting detailed local safety research. This paper introduces a nonlinear damage analysis method rooted in the continuous damage theory, specifically designed for critical areas of transmission towers. A material subroutine for elastic‐plastic‐damage constitutive equations is developed using commercial software, and thorough verification ensures the accuracy of both the subroutine and the algorithm. The proposed algorithm is then applied to analyze the damage in critical areas of a tower, simulating the plasticity‐damage coupling evolution of the main leg during the collapse of the transmission tower. Regarding the treatment of bolt connections in the local model, it indicates that there is a small difference between the contact model and the rigid‐joint model results. Taking computational efficiency into consideration, it is recommended to employ rigid‐joint model to simulate the evolution of damage. The presented example illustrates damage occurring on the outer side of the main leg, ultimately leading to lateral damage under the combined influence of bending and torsion. This research offers a novel method for investigating the failure mechanisms of transmission towers under extreme weather conditions and proposes precise reinforcement strategies. [ABSTRACT FROM AUTHOR]

Drought is one of the most serious challenges facing agriculture and ecosystems around the world. With more frequent and more extreme weather events, the effects of drought are becoming more severe, leading to yield losses, soil depletion and environmental degradation. In this work, we present an analysis of the impact of a marine algae biostimulanat andits ability to offset the effects of drought stress in blueberry cultivation. The aim of the research was to evaluate various fertilisation programs in increasing plant resistance to abiotic stress such as drought. It was tested whether the algal biostimulator provides the same tolerance to drought stress in highbush blueberry plants as regular fertilisers without biostimulation. The research was conducted in 2022 in a greenhouse in controlled drought conditions. Three-year-old highbush blueberry bushes (12 pieces) were used in the experiment. Highbush blueberry bushes (Vaccinium corymbosum) 'Brigitta Blue' varieties were planted in plastic pots with a capacity of 10 dm3 containing an acidic substrate and placed in a greenhouse. Controlled lighting conditions were maintained using sodium lamps and a temperature of 25°C/20°C day/night. The substrate in pots was maintained at 80% of field water capacity by manual watering and weekly supply of nutrient solution for 5 weeks until water deficit occurred. Half of the plants were sprayed weekly with biostimulant at a concentration of 1%, three times 1 week apart (1 application per week). The biostimulant was evenly applied to the entire plant. Seven days after the third application of the product, half of the unsprayed and sprayed plants were subjected to water deficit stress by holding thewatering until 40% of the field water capacity (FC) was reached. The experimental layout included four combinations: C—Control—no biostimulation, no water deficit; CS—Stress control—water deficit up to 40% FC, no biostimulation; B—Biostimulator—no water deficit, biostimulation; BS—Stress plus biostimulator—water deficit up to 40% FC, biostimulation. Fertilisers with seaweed extracts show the ability to reduce the adverse effects of stress, promoting plant resilience, including tolerance to drought stress. The following were evaluated in the experiment: catalase activity, peroxidase activity, free malondialdehyde content, photosynthetic activity and leaf mineral content. The biostimulant used in experiment increased the oxidative activity of the enzymes pe-roxidase and catalase under simulated drought stress conditions. The algal biostimulant increased the average value of catalase activity by 20% in comparison to the control plants, in both combinatinations. The tested biostimulator had no effect on the chlorophyll content in the leaves or the concentration of nutrients in the leaves. The effect of marine algae products on the yield quantity and high quality is related among other to bioactive substances which helps to prevent drought stress. [ABSTRACT FROM AUTHOR]

An accurate assessment of frost damage in coffee plantations can help develop effective agronomic practices to cope with extreme weather events. Remotely piloted aircrafts (RPA) have emerged as promising tools to evaluate the impacts caused by frost on coffee production. The objective was to evaluate the impact of frost on coffee plants, using vegetation indices, in plantations of different ages and areas of climatic risks. We evaluated two coffee plantations located in Brazil, aged one and two years on the date of frost occurrence. Multispectral images were collected by a remotely piloted aircraft, three days after the occurrence of frost in July 2021. The relationship between frost damage and these vegetation indices was estimated by Pearson's correlation using simple and multiple linear regression. The results showed that variations in frost damage were observed based on planting age and topography conditions. The use of PRA was efficient in evaluating frost damage in both young and adult plants, indicating its potential and application in different situations. The vegetation index MSR and MCARI2 indices were effective in assessing damage in one-year-old coffee plantations, whereas the SAVI, MCARI1, and MCARI2 indices were more suitable for visualizing frost damage in two-year-old coffee plantations. [ABSTRACT FROM AUTHOR]

With global warming in recent years, extreme weather conditions have increased in frequency and intensity, exacerbating the challenges for waterproofing materials. The current stages of SBS asphalt waterproofing membrane aging research mainly focus on the raw materials and modifiers in a single factor; multifactor-coupled aging research is less studied. This study focused on the coupled aging characteristics of SBS-modified asphalt waterproofing membranes, aiming to reveal the mechanism of its influence on the material's performance under the environmental effects of high temperature and freeze–thaw. Through the accelerated aging test, we simulated the environmental conditions of high temperature in summer and freeze–thaw in winter to observe the mechanical properties of waterproofing membranes, low-temperature flexibility, and apparent phenomena. Then, Fourier transform infrared spectroscopy (FTIR) evaluated the performance and chemical structure of SBS-modified asphalt waterproofing membranes after aging by the coupled aging of the thermo-oxidative freeze–thaw cycle. The results showed that the low-temperature flexibility of the waterproofing membranes was significantly reduced after the coupled aging effect, and, at the same time, their tensile strength was also reduced. However, despite the tensile properties being impaired, the membrane maintained good ductility, and its elongation at break did not fall below 47%. [ABSTRACT FROM AUTHOR]

Dust and high cloud interactions are critical for climate change, primarily due to the dominant roles of high cloud in the greenhouse effect and the continental precipitation. Nonetheless, disentangling the specific impacts of dust from the overlying meteorology influence on high clouds presents great challenges. In this study, we construct a meteorological pattern that successfully reveal the intricate connection between high cloud distribution and atmospheric conditions. Through this strong bounded relationship, we find that dust exhibits notable controls over the facilitation or inhibition of ice particle growth contingent upon the prevailing meteorological fields. More dust can increase precipitation rate and shorten high cloud lifetime particularly under favorable meteorology. These findings underscore the crucial role of dust concentration in mitigating global warming for future climate change. Plain Language Summary: Dust particles in the atmosphere play a crucial role in the formation and dissipation of high clouds, which can significantly impact global climate and weather patterns. By analyzing satellite observations and climate model data, we discovered that dust can either enhance or suppress the growth of ice crystals, depending on the atmospheric conditions. In moist environments with favorable weather, increased dust leads to larger ice crystals and more precipitation, accelerating the life cycle of high clouds. Conversely, in dry conditions with unfavorable weather, excessive dust competes for limited moisture, resulting in smaller ice crystals, although it can still increase rainfall during the dissipation stage of high clouds. As climate change progresses and dust storms become more frequent, the increased presence of dust in the atmosphere may lead to more extreme weather events and potentially mitigate some of the warming effects caused by high clouds. Understanding these complex interactions between dust, high clouds, and weather conditions is essential for improving climate models and predicting future changes in Earth's water cycle and energy balance. Key Points: Extracted first principal component, highly correlated with high cloud fraction, isolating meteorological effects on cloudsDust facilitates ice crystal formation and precipitation, catalyzing cloud lifecycle and ultimately modifying radiative effectsIncreased dust accelerates high cloud lifecycle, causing more extreme weather and mitigating climate warming [ABSTRACT FROM AUTHOR]

Compound warm extremes exert profound impacts on environment, health, and socioeconomics. Yang et al. (2024) indicated a shift or transition from warm‐dry extremes (WDEs), common in non‐ice‐covered areas, to warm‐wet extremes (WWEs) in ice‐covered zones. Utilizing ERA5 reanalysis data, this study determined the duration and frequency of WDEs and WWEs across ice‐covered and non‐ice‐covered regions. A comprehensive analysis uncovers the physical mechanisms responsible for the paradigm differences and attributes them to the weakening of land‐atmosphere interaction caused by ice‐cover, which inhibits soil moisture feedback and reduces the intensity and duration of warm events in ice‐covered areas. Both WDEs and WWEs are associated with high‐pressure systems (HPs). WDEs, situated directly beneath HPs, intensify due to adiabatic warming from subsidence motions. Conversely, WWEs, located beneath the poleward fringes of HPs, emerge from advective warming and moistening associated with poleward intrusions of warm‐moist air. Plain Language Summary: Significantly rising temperatures can coincide with extreme weather events such as droughts or rainstorms to form compound warm‐dry extremes (WDEs) or warm‐wet extremes (WWEs), leading to more severe consequences than just hot weather alone. Recent findings indicate that WDEs are more prevalent in non‐ice‐covered regions where people reside and crops are cultivated, while WWEs are more common in ice‐covered areas. This study seeks to comprehend the reasons behind the paradigm differences of extreme events by analyzing the conditions during hot summers. Our research has identified two primary causes for the paradigm differences. First, in non‐ice‐covered regions, the ground warms the overlying air, which in turn dries out the soil, leading to even higher temperatures. This explains why more WDEs are observed in these areas. However, in ice‐covered regions, this process is inhibited by ice. Second, the high‐pressure systems accompanying these extreme events behave differently based on the presence or absence of ice. In non‐ice‐covered regions, these systems are located directly above the regions, exacerbating the hot‐dry conditions by pushing air downwards. Conversely, hot‐wet conditions occur in ice‐covered regions because these areas are situated beneath the poleward fringes of the systems, which transport warm and moist air from the lower latitudes. Key Points: In contrast to midlatitude regions where warm‐dry extremes are common, warm‐wet extremes prevail over polar regionsPoleward circulation associated with high pressure systems, rather than subsidence, produces warm‐wet conditionsSnow‐ and ice‐covered surfaces neutralize the land‐air interaction feedback loop that reinforces hot‐dry spells in midlatitude regions [ABSTRACT FROM AUTHOR]

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression‐based Bayesian Optimisation (G‐BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. Plain Language Summary: This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression‐based Bayesian Optimisation (G‐BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G‐BO, we identified more effective values that substantially improved the model's simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions. Key Points: Our study optimizes WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulationG‐BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidityResults suggest updating WRF model's default settings for better extreme heat event simulations [ABSTRACT FROM AUTHOR]

The mid‐latitude extreme weather disasters are often associated with explosively developing cyclones (ECs). Based on different vertical development characteristics, 4,608 ECs identified over the North Pacific in the cold season of 44 years of NCEP‐CFSR reanalyzes are divided into four types of upward development and four types of downward development categories. ECs with vertical upward (downward) development follow a northeastward (nearly eastward) path, mainly explosively developing over the Northwest Pacific (Asia continent and Pacific). Furthermore, utilizing the piecewise potential vorticity inversion method reveals the synergetic forcing of the turbulent heat transport and baroclinicity in the lower troposphere, the latent heat release in the middle levels, the upper‐level jet stream, and the downward intrusion of stratospheric potential vorticity on the ECs. Different configurations of these influences from the troposphere to the stratosphere result in the occurrences of eight types of ECs in the cold season over the North Pacific. Plain Language Summary: Since the late twentieth century, there has been a significant variation in the frequency of explosively developing cyclones (ECs), which bring extreme weather disasters to the mid‐latitudes. This study classifies winter North Pacific ECs into four upward and four downward developing categories. The upward developing ECs include those enhanced only in the lower troposphere (UL‐EC), those developing upward to the middle troposphere (UM‐EC), those developing upward to the upper troposphere (UU‐EC), and those developing upward into the stratosphere (US‐EC). In contrast, the downward developing ECs can be classified as those intensifying only in the upper troposphere (DU‐EC), those developing downward to the middle troposphere (DM‐EC), those developing downward to the lower troposphere (DL‐EC), and those developing downward from the stratosphere (DS‐EC) to the lower troposphere. Furthermore, the piecewise potential vorticity inversion results show that the explosive development of UL‐EC and UM‐EC are dominated by the thermodynamical processes in the middle‐to‐lower troposphere, exhibiting a baroclinic structure with opposite anomalous geopotential height between the troposphere and stratosphere. However, the other types have a consistent stratospheric‐to‐tropospheric negative anomalies, which is determined by the upper‐layer dynamical processes. The downward intrusion of stratospheric potential vorticity dominates the explosive development for the US‐EC and DS‐EC. Key Points: The North Pacific explosively developing cyclones during cold season are divided into eight up‐ and down‐ward vertically developing typesThe explosively developing cyclones are proved to be an important link between the stratosphere and troposphere in the mid‐high latitudesUsing the piecewise potential vorticity inversion method, the quantitative forcings on the explosively developing cyclones are explored [ABSTRACT FROM AUTHOR]

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. [ABSTRACT FROM AUTHOR]

This document is an editorial that focuses on the importance of addressing climate change in urban environments and the role of cities in mitigating greenhouse gas emissions. It emphasizes the need for adaptation strategies and highlights the interdependence between urban ecosystems and climate change. The document includes several articles that provide facts and figures on the changing climate and its impacts on the environment, society, finance, and health. These articles discuss various topics related to urbanization, climate change, and sustainable cities, such as the impact of urbanization on nighttime surface urban heat islands, the environmental effects of single-use plastic water bottles in hotels, the role of urban ecosystems in mitigating climate change, the challenges of smart city development, and the vulnerability of developing nations to climate change. The papers stress the importance of sustainable and resilient strategies to address these issues and promote the well-being of urban environments. [Extracted from the article]

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth's surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase. [ABSTRACT FROM AUTHOR]

Global water scarcity and extreme weather intensify drought stress, significantly reducing cotton yield and quality worldwide. Drought treatments are conducted using a population of chromosome segment substitution lines generated from E22 (G. hirsutum) and 3–79 (G. barbadense) as parental lines either show superior yields or fiber quality under both control and drought conditions. Fourteen datasets, covering 4 yields and 4 quality traits, are compiled and assessed for drought resistance using the drought resistance coefficient (DRC) and membership function value of drought resistance (MFVD). Genome‐wide association studies, linkage analysis, and bulked segregant analysis are combined to analyze the DR‐related QTL. A total of 121 significant QTL are identified by DRC and MFVD of the 8 traits. CRISPR/Cas9 and virus‐induced gene silencing techniques verified DRR1 and DRT1 as pivotal genes in regulating drought resistant of cotton, with hap3‐79 exhibiting greater drought resistance than hapE22 concerning DRR1 and DRT1. Moreover, 14 markers with superior yield and fiber quality are selected for drought treatment. This study offers valuable insights into yield and fiber quality variations between G. hirsutum and G. barbadense amid drought, providing crucial theoretical and technological backing for developing cotton varieties resilient to drought, with high yield and superior fiber quality. [ABSTRACT FROM AUTHOR]

Introduction: Ancient water harvesting systems, such as those from the Indus Valley Civilization (~3500 BCE), have been vital for irrigation and climate resilience, especially in arid regions. One such prominent system in South Asia, called tank irrigation, initially thrived through community management but declined post-independence due to colonial policies and neglect in Sri Lanka and India. This study evaluates current policy frameworks and rehabilitation programs to enhance the resilience of these systems in India, develop strategies for their protection and adaptation to climate change, and integrate global lessons for sustainable development. Methods: A systematic meta-analysis of grey literature was conducted to aggregate data on policy constraints. Policy analysis involved detailed investigations of relevant documents, regulations, and comparative analyses of frameworks at regional and national levels. Pilot projects on tank rehabilitation were assessed through reported case studies and field surveys to gauge impact. Thematic analysis was used to explore the global potential of these systems in climate resilience and overall environmental sustainability. Results: The analysis showed that pilot projects for tank rehabilitation had limited success in achieving sustainability under current climate conditions. Tank irrigation systems are crucial for adapting to extreme weather, including floods, droughts, and heat waves, replenishing groundwater, reducing soil erosion, and ensuring reliable water supplies. Traditional water harvesting technologies support 17 Sustainable Development Goals (SDGs), including clean water access, hunger reduction, gender equality, and climate action. Integrating AI and machine learning in water management benefits disaster response, while eco-tourism aids system maintenance and cultural awareness. Discussion: The study underscores the need for policy reforms to enhance tank rehabilitation and institutional arrangements. It calls for increased beneficiary participation and constitutional recognition of current practices. Strategic, national-scale assessments and resilience targets are recommended to improve the effectiveness of such water harvesting systems in mitigating natural hazards and enhancing environmental services. [ABSTRACT FROM AUTHOR]

Tidal flats are valuable ecosystems that depend on complex biogeomorphic processes between organisms and sediment transport. Climate change has led to a rise in extreme weather events, such as storms. This, in turn, has increased sediment dynamics and created risks for the benthic communities inhabiting tidal flats. However, replicating sediment disturbances caused by extreme weather is difficult. To overcome this, we used the plow rake to enhance the natural tidal currents and wave conditions to simulate intensified sediment dynamics. The raking disturbance was repeated on two intertidal zones with different inundation frequencies and wind fetch levels to simulate the increasing frequency of storm impact on sediments due to climate change. We compared the measurements of sediment dynamics and macrofauna between plots that were raked and the control plots that were only influenced by natural hydrodynamics. Results showed that tidal flat sediments experienced erosion by 10–20 mm after six times biweekly raking treatments, depending on the site‐specific hydrodynamic conditions. Sediment dynamics served as a helpful tool for monitoring the species distribution regarding mobility traits: the high dynamic exposed sites were inhabited by mobile species, while the low dynamic sheltered sites were characterized by less‐mobile species. Moreover, the raking treatment decreased the abundance of species with immobile traits, yet the species composition did not experience significant change. Overall, the present findings indicate that tidal flats with low sediment dynamics and immobile macrofauna are at higher risk of declining abundance under intensified sediment disturbances than areas with high sediment dynamics and mobile macrofauna. [ABSTRACT FROM AUTHOR]

Climate change and Artificial Intelligence (AI) are both attracting great interest across society. There is also substantial interest in merging the two sciences, with evidence already that AI can identify earlier precursors to extreme weather events. There are a range of AI algorithms, and selection of the most appropriate one maximizes the amount of additional understanding extractable for any dataset. However, most AI algorithms are statistically based and even with careful splitting between data for training and testing, they arguably remain as emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing and climate change is an example of this, requiring understanding responses to atmospheric Greenhouse Gas (GHG) concentrations that may be substantially higher than present or the recent past. Notable, though, is the emerging AI technique of "equation discovery". AI-derived equations from data also does not automatically guarantee good performance for new forcing regimes. However, access to equations rather than a statistical emulator guides system understanding, as their variables and parameters often have a better interpretation. Better process knowledge enables judgements as to whether equations are trusted under extrapolation. For many climate system attributes, descriptive equations are not yet fully available or may be unreliable. This uncertainty is hindering the development of Earth System Models (ESMs) which remain the main tool for projections of large-scale environmental change as GHGs rise. Here, we make the case for using AI-driven equation discovery in climate research, given that its outputs are more interpretable in terms of processes. As ESMs are based around the numerical discretisation of equations that describe climate components, equation discovery from new datasets provides a format amenable to direct inclusion into such models where representation of environmental systems is missing. We present three illustrative examples of how AI-led equation discovery may advance future climate science research. These are generating new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess Tipping Point (TP) risks. [ABSTRACT FROM AUTHOR]