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Aim: As global climate changes, there is a clear mismatch between the temporal and spatial characteristics of body temperature and environmental temperature, confounding the assessment of thermal stress for organisms in many ecological studies. Here, we hindcast the hourly body temperatures of intertidal molluscs to explore the differences between them and environmental temperatures (air and water temperatures) in multiple metrics of thermal stress. Location: Intertidal shores in East Asia (0°–45°N, 100°E–140°E). Time Period: 40 years, 1980 to 2019. Major Taxa Studied: Mollusca. Methods: We collected habitat zonation data and measured the morphological characteristics of 25 intertidal molluscs living in East Asia. For three different types of intertidal molluscs (i.e., bivalves, limpets and snails), we built corresponding heat budget models (HBMs) to hindcast the hourly body temperatures from 1980 to 2019. We analysed the thermal stress of intertidal species faced in three metrics, annual extreme high temperatures (T99), seasonal daily maximum temperatures (DMT) and heatwaves, and compared them with environmental temperatures. Results: We found that T99 of body temperatures and their interannual warming rates are significantly higher than those of environmental temperatures. Moreover, there were non‐negligible mismatches between the seasonal thermal pattern and heatwaves of body temperatures and environmental temperatures, suggesting that the deleterious impacts of global warming on intertidal species are underestimated and cannot be directly revealed by environmental temperatures. Main Conclusions: Thermal stress patterns of body temperature were significantly different from those of environmental temperature, and the thermal stress faced by intertidal species had been persistently underestimated. These results emphasise that body temperature should be used as the appropriate metric for evaluating and predicting the impacts of global warming and weather extremes in the intertidal biological system. [ABSTRACT FROM AUTHOR]

Summary: Two functional responses largely guide woody plants' survival to winter conditions: cold hardiness and dormancy. Dormancy affects budbreak timing based on chill accumulation. Effects of warming on dormancy may appear time‐shifted: fall and winter warming events decrease chill accumulation, delaying budbreak observed in spring. The same warming events also affect cold hardiness dynamics, having immediate implications. As cold deacclimation rates increase with dormancy progression, the same amount of warming has greater damage risk the later it occurs in the season, depending on return of low temperatures. Should frequency of erratic weather increase with climate change, more instances of risk are expected. However, understanding how plants fare through seasons now and in future climates still requires better knowledge of winter physiology. [ABSTRACT FROM AUTHOR]

The scientific discourse on climate change throughout the US has primarily revolved around changes in mean climate and/or climate extremes. However, little is known about the impacts of climate change on mild weather conditions despite its significant relevance to quality of life. Here, we adopt the concept of "outdoor days" defined as those relatively pleasant days when most people may enjoy outdoor activities (Choi et al., 2024). We project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, annual outdoor days are projected to change slightly, with notable exceptions. We project relatively large drops in southeast (−23%), south (−19%), and Ohio Valley (−19%), and a significant increase in northwest (14%) toward the end of the century. Our findings have implications for quality of life in different regions, and for nation‐wide travel and tourism. Plain Language Summary: Here, we contribute to the understanding of how climate change will influence quality of life in the US by applying the concept of outdoor days—thermal comfort conditions allowing for outdoor activities, such as walking, jogging, and cycling by most people. We project using state‐of‐the‐art global climate models that climate change will shift the seasonality of outdoor days, resulting in less frequent outdoor days in summer and more frequent outdoor days in the other seasons across the country. Our results highlight specific regional hotspots in the US where annual outdoor days could significantly decrease or increase with important implication for quality of life in different climate regions of the US. Key Points: We project how climate change reshapes seasonality of US outdoor daysFuture climate change will likely result in a northwest‐southeast disparity in the projected change of annual outdoor days in the USWe provide new evidence of the impact of global warming on the quality of human life, travel, and tourism in the US [ABSTRACT FROM AUTHOR]

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]

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]

Global warming and extreme weather events (e.g., heat waves and droughts) have caused widespread adverse impacts across regions and sectors, including shrimp farming. However, the availability of in situ water temperature data typically only represents a small portion of shrimp farms' thermal profiles, and this often leads to limitations in investigating the impacts of global warming and climate change on shrimp farming. For this aim, satellite-derived water surface temperature (WST) at shrimp farms in Mong Cai City, Quang Ninh Province, Vietnam, was investigated by using Landsat data in the Google Earth Engine. Results showed that satellite-derived WST at shrimp farms in Mong Cai City ranged from 11.5 °C to 37.6 °C in 2000–2003, mainly concentrated at 15–35 °C (around 95% retrieved WST from Landsat data). There was a seasonal variation of WST at shrimp farms, with monthly mean WST ranged from 18.4 °C (January) to 29.9 °C (July). The mean annual WST at shrimp farms in Mong Cai City showed an increasing trend from 2000 to 2022. A remarkable warming trend of WST was detected during the shrimp-growing season (from April to October), and this may lead to a high risk for shrimp health. The findings of this study indicated that greater awareness of water temperature monitoring at shrimp farms is required if this activity is to be sustainable in the climate change context. [ABSTRACT FROM AUTHOR]

Estuaries are the important interface between the land and sea, providing significant environmental, economic, cultural and social values. However, they face unprecedented pressures including eutrophication, harmful algal blooms, habitat loss, and extreme weather due to climate change. Here we present an open access, quality-controlled water quality dataset collected from twelve diverse estuaries spanning 1000 km along the southeastern Australian coastline. Water depth, temperature and salinity data were collected across two years (2018–2021) capturing drought, wildfire and flood periods, using high accuracy Seabird MicroCAT field sensors located within oyster leases. These fully autonomous instruments collected and transmitted data every 10 minutes before downstream quality checking and uploading onto a public website. Simultaneous, high-resolution, longitudinal environmental data collected across multiple estuaries throughout a range of extreme weather events are exceptionally rare in the Southern Hemisphere, yet provide an invaluable resource for the aquaculture industry, researchers and environmental regulators alike. [ABSTRACT FROM AUTHOR]

More intense fire weather due to climate change is implicated as a key driver of recent extreme wildfire events. As fuel stock, the role of vegetation and its phenology changes in wildfire dynamics, however is not fully appreciated. Using long-term satellite-based burned areas and photosynthesis observations, we reveal that an earlier peak photosynthesis timing (PPT) potentially acts to escalate subsequent wildfires, with an increase in the global average burned fraction of 0.021% (∼2.20 Mha) for every additional day of PPT advancement. Satellite observations and Earth System modeling consistently show that this fire escalation is likely due to intensified drought conditions and increased fuel availability associated with the climate feedback arising from earlier PPT. Current fire-enabled dynamic global vegetation models can reproduce the observed negative correlation between PPT and burned area but underestimate the strength of the relationship notably. Given the continued PPT advancement owing to climate change, the bioclimatic effects of vegetation phenology change suggest a potentially pervasive upward pressure on future wildfires. [ABSTRACT FROM AUTHOR]

We develop a three-timescale framework for modeling climate change and introduce a space-heterogeneous one-dimensional energy balance model. This model, addressing temperature fluctuations from rising carbon dioxide levels and the super-greenhouse effect in tropical regions, fits within the setting of stochastic reaction–diffusion equations. Our results show how both mean and variance of temperature increase, without the system going through a bifurcation point. This study aims to advance the conceptual understanding of the extreme weather events frequency increase due to climate change. [ABSTRACT FROM AUTHOR]

Near real-time (NRT) crop type mapping plays a crucial role in modeling crop development, managing food supply chains, and supporting sustainable agriculture. The low-latency updates on crop type distribution also help assess the impacts of weather extremes and climate change on agricultural production in a timely fashion, aiding in identification of early risks in food insecurity as well as rapid assessments of the damage. Yet NRT crop type mapping is challenging due to the obstacle in acquiring timely crop type reference labels during the current season for crop mapping model building. Meanwhile, the crop mapping models constructed with historical crop type labels and corresponding satellite imagery may not be applicable to the current season in NRT due to spatiotemporal variability of crop phenology. The difficulty in characterizing crop phenology in NRT remains a significant hurdle in NRT crop type mapping. To tackle these issues, a novel emergence-based thermal phenological framework (EMET) is proposed in this study for field-level NRT crop type mapping. The EMET framework comprises three key components: hybrid deep learning spatiotemporal image fusion, NRT thermal-based crop phenology normalization, and NRT crop type characterization. The hybrid fusion model integrates super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM) to generate daily satellite observations with a high spatial resolution in NRT. The NRT thermal-based crop phenology normalization innovatively synthesizes within-season crop emergence (WISE) model and thermal time accumulation throughout the growing season, to timely normalize crop phenological progress derived from temporally dense fusion imagery. The NRT normalized fusion time series are then fed into an advanced deep learning classifier, the self-attention based LSTM (SAtLSTM) model, to identify crop types. Results in Illinois and Minnesota of the U.S. Corn Belt suggest that the EMET framework significantly enhances the model scalability with crop phenology normalized in NRT for timely crop mapping. A consistently higher overall accuracy is yielded by the EMET framework throughout the growing season compared to the calendar-based and WISE-based benchmark scenarios. When transferred to different study sites and testing years, EMET maintains an advantage of over 5% in overall accuracy during early- to mid-season. Moreover, EMET reaches an overall accuracy of 85% a month earlier than the benchmarks, and it can accurately characterize crop types with an overall accuracy of 90% as early as in late July. F1 scores for both corn and soybeans also achieve 90% around late July. The EMET framework paves the way for large-scale satellite-based NRT crop type mapping at the field level, which can largely help reduce food market volatility to enhance food security, as well as benefit a variety of agricultural applications to optimize crop management towards more sustainable agricultural production. [ABSTRACT FROM AUTHOR]

Purpose: Climate change-induced weather changes are severe and frequent, making it difficult to predict apparel sales. The primary goal of this study was to assess consumers' responses to winter apparel searches when external stimuli, such as weather, calendars and promotions arise and to develop a decision-making tool that allows apparel retailers to establish sales strategies according to external stimuli. Design/methodology/approach: The theoretical framework of this study was the effect of external stimuli, such as calendar, promotion and weather, on seasonal apparel search in a consumer's decision-making process. Using weather observation data and Google Trends over the past 12 years, from 2008 to 2020, consumers' responses to external stimuli were analyzed using a classification and regression tree to gain consumer insights into the decision process. The relative importance of the factors in the model was determined, a tree model was developed and the model was tested. Findings: Winter apparel searches increased when the average, maximum and minimum temperatures, windchill, and the previous day's windchill decreased. The month of the year varies depending on weather factors, and promotional sales events do not increase search activities for seasonal apparel. However, sales events during the higher-than-normal temperature season triggered search activity for seasonal apparel. Originality/value: Consumer responses to external stimuli were analyzed through classification and regression trees to discover consumer insights into the decision-making process to improve stock management because climate change-induced weather changes are unpredictable. [ABSTRACT FROM AUTHOR]

Despite significant advances in technology and flood risk management, as well as the countless risk prevention initiatives undertaken by governments and institutions in recent decades, flood hazards persist in threatening human life and health, especially under the effects of climate change. To assess the effectiveness of the various programs or measures devised to protect human life and health from floods, it is crucial to measure and understand its impacts on society, establishing the capability to track indicators or metrics that reflect the spatial distribution and temporal progress of floods and their impacts. In this context, this study uses disaster loss data derived from international disaster databases adapted in regional context following the Nomenclature of Territorial Units for Statistics level 2 (or NUTS2), to examine the spatial distribution and temporal evolution of deaths, directly attributable to flood disasters. In addition, we explore the potential of currently available datasets in understanding and monitoring flood-related mortality, an important standardized progress indicator of flood disaster impacts. This study is framed within the United Nations' Sustainable Development Goals (SDGs), recently adopted by the European Union, and is focused on the Union's territories in the Mediterranean region, an area particularly sensitive to climate change. Results show interesting spatial patterns, and generally inconclusive temporal trends, although locally we see evidence of both an increase and a decline in flood mortality. In addition, this work discusses the currently available datasets potential, weaknesses and limitations, as well as the importance of tracking flood impacts on human life in a future increasingly influenced by extreme weather events and climate change. [ABSTRACT FROM AUTHOR]

Increasing extreme weather events and climate change can significantly affect soil moisture regimes, particularly soil suction, leading to additional challenges associated with unsaturated soils, including the collapse phenomenon. The collapsibility of soils poses significant engineering and geotechnical risks globally, necessitating urgent attention from engineers. This work establishes a numerical model of a shallow foundation subjected to rainfall and load using COMSOL Multiphysics. A hydromechanical model (H-M) is introduced which incorporates The Richards' module and the Extended Basic Barcelona Model (EBBM) as a constitutive model to predict settlements in shallow foundations influenced by climate change and intense rainfall. The validation of the model is conducted through experimental tests, ensuring its accuracy. Additionally, in the practical application, the hydromechanical model is applied to anticipate the effect of infiltration on settlements of shallow foundations. The simulation results show that infiltration leads to an increase in the pressure head above the water table, decreasing soil suction, which induces additional settlement due to wetting-induced collapse. The maximum settlement happened at the corners of the footing due to increased exposure to infiltration and a greater reduction in suction. The collapse potential calculated from the numerical simulation was found to be consistent with the predictions established via analytical models, validating the accuracy of the numerical approach. [ABSTRACT FROM AUTHOR]

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Agriculture is the most vulnerable sector to climate change and weather anomalies. In rainfed ecosystems, rainfall amount and distribution are the deciding factors for crop planning. Rainfall variability and trend analysis help to minimize weather related risk and enhance the productivity by proper crop planning. The study on rainfall distribution and its trend was assessed in a rainfed area of Ramanathapuram district of Tamil Nadu, India by using Simple descriptive statistics along with non-parametric analysis of Men-Kendall analysis, Sen's slope estimator; innovative trend analysis and sequential Men-Kendall test were carried out to understand the quantity of rainfall and rainy-day variability for the period of 1982 to 2022. The results indicated that the mean annual rainfall is 716 mm and spread over 56 rainy days with 21 percent of coefficient of variation. Seasonal analysis indicated that, there was no significant trend was observed among the seasons, but NEM showed a significant increase in trend (p-value = 0.048) with a positive magnitude of 3.2 mm/year of rainfall. An increasing trend in heavy rainfall events during the NEM season is evident with 92 percent. [ABSTRACT FROM AUTHOR]

The escalating impact of climate change on global weather patterns threatens the functionality and resilience of infrastructure systems. This paper presents a rigorous risk assessment protocol tailored to existing bridge infrastructure, integrating climate change projections, structural integrity, and socioeconomic factors. The protocol's application involves five sequential steps: selecting a bridge, disassembling the structure into components, calculating utilization factors for design and projected temperatures, evaluating severity factors encompassing structural and socioeconomic aspects, and ultimately determining an overall risk rating. To demonstrate the protocol's effectiveness, a case study was conducted on the Westminster Drive Underpass in London, Ontario. This study shows how the protocol systematically evaluates the vulnerability of each bridge component to projected temperatures under the Representative Concentration Pathway 6.0 model. The protocol provides a holistic risk assessment by incorporating both the structural response and socioeconomic implications of failure. The results rank the bridge's risk level and highlight the urgency of intervention. The protocol emerges as a robust tool for decision-makers, practitioners, and engineers, offering a comprehensive approach to strengthen bridge infrastructure against the challenges of climate change. [ABSTRACT FROM AUTHOR]

The timing of spring onset is a particularly sensitive climate change indicator that also allows the study of weather interannual variations and extremes. This indicator can be derived from a suite of phenological models called the Extended Spring Indices (SI-x). These models transform daily minimum and maximum temperatures into a set of consistent indices that track the timing of first leaf and first bloom for key indicator plant species. The SI-x also enables the calculation of the so-called frost damage index. Using new computational technologies and high spatial resolution gridded weather data, here we present and evaluate a multi-decadal 1 km spatial resolution version of the SI-x models, which cover North American (1980 to 2022) and European areas (1950 to 2020). Because of the generally good agreement with ground phenological observations, this continental-scale and high spatial resolution product can support both scientists and decision makers in their quest to hind- and forecast weather and climate change impacts. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems' resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2-4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40-70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20-50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5-15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas–Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability. [ABSTRACT FROM AUTHOR]

The Mediterranean region contains some of the areas with the highest urban density in the world, and these areas keep expanding, making this region a "hotspot" of climate change. Life in the Mediterranean unfolds predominantly outdoors throughout the year, exposing its inhabitants to an evolving climate that is progressively harsher and punctuated by increasingly frequent extreme weather events. More and more people are going to be exposed to severe heat waves, droughts, water shortage, dust storms, forest fires and poor air quality on the one hand, and torrential rains and floods on the other hand. The urban heat island further increases thermal stress of city dwellers and plays a key role in citizens' health and well‐being. In this exhaustive review, based on state‐of‐the‐art studies we delve into the realm of climate change and extreme weather phenomena as they intersect with urban populations in Mediterranean cities, both in the present and in the foreseeable future. Our focus lies on identifying knowledge gaps, inconsistencies in observed climatic hazards and shortcomings in assessing the associated risks and their societal and environmental ramifications. Moreover, we undertake a comprehensive survey of future predictions exploring the variables thermal stress, air pollution, air quality and characteristics of the hydro‐climatic systems, that is, droughts, fires and floods. Yet, critical knowledge gaps persist in understanding the science, the coping mechanisms, the strategies for preparedness and adaptation and the intricate interplay between these facets and societal dynamics. The developing countries in the Mediterranean region stand exceptionally vulnerable. It is imperative for more affluent nations to share their expertise and extend assistance to less developed counterparts, aiding them in navigating climate‐related challenges, devising adaptive strategies and facilitating their implementation. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large‐scale drivers in the agro‐ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation‐dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade−1) and cold spells (−1.28 days·decade−1). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation‐dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper‐level jet by a high‐pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea‐surface temperature, low sea‐level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate‐induced disasters occurring in various AEZs of Pakistan. [ABSTRACT FROM AUTHOR]

According to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristics such as precipitation, air temperature, humidity, wind, sunshine, cloud cover, and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from the Ethiopian Meteorological Institute. Download canESM2 (Canadian Second Generation Earth System Model). The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. The SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987 to 2018 was considered as a base period, whereas the period from 2019 to 2100 was considered as future periods. Historically, from 1987 to 2018, there was an overall increase in the mean annual minimum and maximum temperatures by 0.016 °C and 0.048 °C, respectively, with a little decrease in the average annual rainfall (up to 0.685 mm). The highest increment of maximum temperature recorded in October month up to + 2.7 °C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for the RCP4.5 scenario and 66% (2073–2100) for the RCP4.5 (representative concentration pathway 4.5) scenario in the Belg (February to May). Precipitation decreases in the Kiremt (June to September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategies. [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Introduction: Climate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking. Methods: This paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN). Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method. Results: As a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity. Discussion: The developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events have led to a surge in natural hazards in Pakistan that have escalated into humanitarian disasters. While remittances are often central to the livelihood of the most vulnerable, research has produced limited knowledge regarding the role of remittances in time of disaster. This paper investigates the reaction of Pakistani migrants to five major natural disasters via remittances from 1972 to 2023. Using an event‐study methodology, the paper compares the responses of remittances in different host countries namely Gulf Cooperation Council (GCC), Europe & the UK and North America. We provide evidence that remittances intensify in response to climate‐induced disasters, thus representing a reactive mechanism in time of hardship. The response is significant two (one) months after the events for GCC (Europe & the UK and North America) and tends to dissipate 5 months from the disaster occurrence (except for GCC). The intensity and the persistence of remittances' responsiveness (abnormal returns and volatility) depends on the nature of disasters, host countries' features and the economic conditions of migrants. Our findings highlight the need for further understanding the role of remittances as a coping mechanism in the face of environmental hazards to better inform policies geared toward disaster risk reduction. [ABSTRACT FROM AUTHOR]

Global warming can inhibit chlorophyll-based solar energy capturing of phytoplankton by decreasing nutrient supply through upwelling. However, species with proton-pump rhodopsin (PPR) can independently convert solar energy to cope with nutrient limitation. Besides prokaryotes, PPR has been documented in dinoflagellates and some species of other algal lineages, and its potential role in compensating for the deficiency of phosphorus has been demonstrated in dinoflagellates. However, PPR has not been studied in the coral reef endosymbiotic Symbiodiniaceae. Here, we report a PPR in Breviolum minutum (BmR). Both phylogenetic analysis and structure prediction results indicate that BmR resembles eukaryotic proton-pump rhodopsins, phylogenetically affiliated with the subgroup xanthorhodopsins. BmR contains the critical residues for proton pumping, retinal binding, and spectrum tuning for green absorption. To explore BmR's potential roles in responding to phosphorus limitation, we cultured B. minutum under different phosphorus conditions, and monitored physiological and BmR's transcriptional responses. Phosphorus limitation caused decreases in B. minutum population growth and photosynthesis efficiency. Meanwhile, our quantitative PCR showed that BmR expression was strongly upregulated under phosphorus limitation, showing a strong positive correlation with alkaline phosphatase activity and a negative correlation with photosynthetic efficiency. Our findings demonstrate that proton-pump rhodopsin occurs in Symbiodiniaceae and BmR has the potential to provide supplementary energy to support cell basal metabolisms when photosynthesis of B. minutum is impaired by phosphorus limitation, thereby enabling corals to better weather climate change. [ABSTRACT FROM AUTHOR]

Quinoa (Chenopodium quinoa Willd.) has gained worldwide recognition for its nutritional values, adaptability to diverse environments, and genetic diversity. This review explores the current understanding of quinoa tolerance to environmental stress, focusing on drought, salinity, heat, heavy metals, and UV-B radiation. Although drought and salinity have been extensively studied, other stress factors remain underexplored. The ever-increasing incidence of abiotic stress, exacerbated by unpredictable weather patterns and climate change, underscores the importance of understanding quinoa's responses to these challenges. Global gene banks safeguard quinoa's genetic diversity, supporting breeding efforts to develop stress-tolerant varieties. Recent advances in genomics and molecular tools offer promising opportunities to improve stress tolerance and increase the yield potential of quinoa. Transcriptomic studies have shed light on the responses of quinoa to drought and salinity, yet further studies are needed to elucidate its resilience to other abiotic stresses. Quinoa's ability to thrive on poor soils and limited water resources makes it a sustainable option for land restoration and food security enterprises. In conclusion, quinoa is a versatile and robust crop with the potential to address food security challenges under environmental constraints. [ABSTRACT FROM AUTHOR]

Accessibility to rigorous scientific information to promote risk mitigation measures by citizens is crucial, especially in the context of climate change and extreme weather events. This study focuses on the perception of flood risk and the implementation of mitigation strategies by residents in drylands urban sprawl areas. Risk perception, defined as the subjective assessment of the likelihood and potential consequences of flooding, is a key element of mitigation. While many studies have explored the link between risk perception and behaviour, this research addresses gaps in understanding how public information affects these perceptions and actions. In areas of rapid urban expansion, where regulation often overlooks environmental features, the lack of adequate information poses significant barriers to effective risk mitigation. This research reveals that although residents claim to understand flooding, their descriptions often indicate a lack of understanding of the phenomenon. This 'passive optimism' could be mitigated by providing rigorous information and specific technical recommendations. This study highlights the disconnect between residents' awareness of flood risks and the inadequacy of preventive measures, underlining the importance of targeted information and collaboration between the scientific community, government sectors, and local populations. [ABSTRACT FROM AUTHOR]

The article explores the impact of climate change on South Asian countries and their efforts to address climate issues. It highlights the vulnerability of countries like Bangladesh, India, Pakistan, Nepal, and Afghanistan to climate change. The 28th Conference of the Parties (COP-28) held in Dubai made important decisions on climate finance, resilience, and practical solutions. Each South Asian country faces specific policy challenges and objectives, such as Bangladesh's focus on loss and damage funding, India's efforts to reduce coal reliance, Pakistan's emphasis on climate finance and resilience, Nepal's call for support, and Afghanistan's limited participation due to political circumstances. The article recommends inclusive and gender-responsive climate adaptation policies, empowering local governments, addressing financial vulnerabilities, strengthening existing programs, and promoting climate awareness and education. COP-28 emphasized the need to move away from fossil fuels and introduced a dedicated fund for addressing loss and damage. [Extracted from the article]

Contrasting the extensive research on summer atmospheric rivers (ARs) in the Antarctic Peninsula (AP), winter AR impacts are less understood. This study examines a unique warming event from 1 to 3 July 2023, using in situ winter observations and ERA5 reanalysis. On 2 July, Frei station experienced an extreme warm event with a temperature of 2.7°C and a significant rise in the freezing level, coinciding with winter rainfall. A pressure dipole pattern over the AP, with contrasting circulations over Bellingshausen and Weddell Seas, facilitated an AR, carrying warm, humid air initially from South America/Atlantic and then the southeast Pacific. This shift resulted in anomalous water stable isotope composition in precipitation. Trends suggest a strengthening winter pressure dipole, associated with increased AR frequency and higher temperatures in northern AP. These findings highlight the importance of winter observations in exploring AR impacts, bridging knowledge gaps about winter AR behaviors. Plain Language Summary: The Antarctic Peninsula is increasingly witnessing climate extremes during summer, while the understanding of such extremes in winter remains limited. Our study explores a significant warming event in the northern Peninsula in early July 2023, utilizing recent winter in situ observations and atmospheric analysis. On 2 July, an exceptional temperature of 2.7°C was recorded, significantly altering the atmospheric freezing level and causing rainfall instead of snow. Our analysis revealed a unique atmospheric pattern around the Peninsula, characterized by varying air movements over the Bellingshausen and Weddell Seas. This pattern facilitated an "atmospheric river," a flow of warm and moist air from lower latitudes, including continental South America/Atlantic and the southeast Pacific, as reflected in the precipitation's water stable isotope composition. This event is indicative of a potential trend toward more frequent and severe occurrences, emphasizing the urgent need for comprehensive winter research in the Antarctic Peninsula to understand the broader implications of extreme weather events and climate change. Key Points: Early July 2023 had extreme winter warmth with rainfall in the northern Antarctic Peninsula, favored by an atmospheric riverIn recent decades, strengthened winter pressure dipole has directed northerly warm and moist air, resulting in warm events in the regionWe highlight the need for expanded winter research in the Antarctic Peninsula to better understand climate change and extreme weather events [ABSTRACT FROM AUTHOR]

Situated at the southernmost limits of the late Pleistocene Eurasian permafrost zone, the loess–paleosol sequences of the Lower Volga region contain numerous traces of cryogenesis. Cryogenic features are represented by thin vertical wedges in loess and paleosols, and involutions and wedges in alluvial deposits. Here we describe and interpret four stages of cryogenesis during the late Pleistocene, based on analysis of cryogenic structure morphology, morphoscopy of quartz grains, and micromorphology of subaerial sediments, in addition to calculation of the Cryogenic Weathering Index and a new luminescence chronology derived from published ages. These stages differ in type and distribution of cryostructures and formed in different paleogeographic conditions. Stage I, dated 95–90 ka (Marine Isotope Stage [MIS] 5b), is characterized by the existence of continuous permafrost in northern part of the Lower Volga valley. Stage II (75–70 ka, MIS 5a/MIS 4) is characterized by dry and cold conditions and widespread permafrost. During stage III (52–45 ka, MIS 3b/c), the permafrost was thin and of sporadic distribution. Stage IV (37–35 ka, MIS 3a) is characterized by thin and rare sporadic permafrost. The processes of cryogenic transformation of sediments in the region during these stages took place under both permafrost and seasonal frost conditions. The results obtained significantly improve current understanding of the extent of the permafrost in the south of the East European Plain during the late Pleistocene. [ABSTRACT FROM AUTHOR]

Drylands are unique among terrestrial ecosystems in that they have a significant proportion of primary production facilitated by non‐vascular plants such as colonial cyanobacteria, moss, and lichens, i.e., biocrusts, which occur on and in the surface soil. Biocrusts inhabit all continents, including Antarctica, an increasingly dynamic continent on the precipice of change. Here, we describe in‐situ field surveying and sampling, remote sensing, and modeling approaches to assess the habitat suitability of biocrusts in the Lake Fryxell basin of Taylor Valley, Antarctica, which is the main site of the McMurdo Dry Valleys Long‐Term Ecological Research Program. Soils suitable for the development of biocrusts are typically wetter, less alkaline, and less saline compared to unvegetated soils. Using random forest models, we show that gravimetric water content, electrical conductivity, and snow frequency are the top predictors of biocrust presence and biomass. Areas most suitable for the growth of dense biocrusts are soils associated with seasonal snow patches. Using geospatial data to extrapolate our habitat suitability model to the whole basin predicts that biocrusts are present in 2.7 × 105 m2 and contain 11–72 Mg of aboveground carbon, based on the 90% probability of occurrence. Our study illustrates the synergistic effect of combining field and remote sensing data for understanding the distribution and biomass of biocrusts, a foundational community in the carbon balance of this region. Extreme weather events and changing climate conditions in this region, especially those influencing snow accumulation and persistence, could have significant effects on the future distribution and abundance of biocrusts and therefore soil organic carbon storage in the McMurdo Dry Valleys. [ABSTRACT FROM AUTHOR]

As one of the globally significant agricultural cultural heritages, Longji Terraced Fields in Longsheng, Guangxi, China, attract numerous tourists. This study aims to describe the weather phenomena and climate change characteristics of Longji Terraced Fields in recent years to reveal their impact on the tourism economy. Utilizing meteorological station data and considering the actual situation in Longsheng, Guilin, the existing models for evaluating tourism climate comfort are improved. The tourism climate comfort of Longji Terraced Fields from 2002 to 2022 is discussed. The results show that the improved model can better reflect the local situation. The results show that the current Holiday Climate Index and Modified Climate Index for Tourism are not suitable for evaluating the Longji Terraces. Adjustments were made to these indices to account for the high annual precipitation and relative humidity of Longsheng. Combining extensive questionnaire surveys, it was found that the improved evaluation model better reflects tourists' perceptions of climate comfort. Analysis indicates that when the modified model value is above 70, tourist satisfaction exceeds 80%. The most comfortable tourism periods for the Longji Terraces are August, September, and October, while the least comfortable periods are January, February, and March. This study helps to understand the seasonal variations in tourism climate comfort at Longji Terraced Fields and provides a scientific basis for local tourism industry responses to climate change, thereby increasing tourism revenue. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates the factors influencing local populations of Aedes albopictus, a highly invasive mosquito species known for its role in spreading vector-borne diseases. Aimed at enhancing vector control effectiveness, the research focused on understanding the interplay between meteorological elements, human activity, and pest management efforts in determining mosquito abundance at local scales. Conducted over a study season at a botanical garden, the investigation utilized weekly BG-Trap data to observe adult Ae. albopictus dynamics. Results indicated a significant seasonal trend affected by temperature and modulated by rainfall events responsible for the observed bimodal abundance pattern. Additionally, nearby stagnant water and human presence were positively linked to mosquito abundance, while larvicide effectiveness varied with the preceding rainfall levels and application timing. Given the potential for global warming to shift weather patterns and increase the frequency of extreme events, these findings underscore the importance of integrating fine-scale ecological parameters with broader climatic trends in mosquito abatement strategies. This comprehensive approach could enhance the efficacy of interventions and mitigate the impact of mosquito-borne illnesses. Mosquito species, including the Asian tiger mosquito, can transmit disease-causing pathogens such as dengue, Zika, and chikungunya, with their population dynamics influenced by a variety of factors including climate shifts, human activity, and local environmental conditions. Understanding these dynamics is vital for effective control measures. Our study, conducted in Jardí Botanic Marimurtra from May to November 2021, monitored Ae. albopictus activity using BG-Traps and investigated larval control effects. We employed Generalized Linear Mixed Models to analyze variables like weather, human presence, and larvicidal control on adult mosquito abundance. Adults of Ae. albopictus exhibited a seasonal pattern influenced by temperature but with bimodal peaks linked to cumulative rainfall. Proximity to stagnant water and visitor influx directly affected mosquito captures. Additionally, the effectiveness of larvicide treatments depended on interactions between preceding rainfall levels and treatment timing. Our research emphasizes the significance of studying vector ecology at local scales to enhance the efficacy of control programs and address the escalating burden of vector-borne diseases. Considering the impacts of extreme weather events and climate shifts is essential for the development of robust vector control strategies. Furthermore, our distinct findings serve as a prime illustration of utilizing statistical modeling to gain mechanistic insights into ecological patterns and processes. [ABSTRACT FROM AUTHOR]

The challenge of feeding an additional 2 billion people by 2050 is one of the most pressing issues of our generation. The required changes in the current food system must be achieved while reducing the negative environmental impacts of current farming practices on our climate and biodiversity and avoiding deforestation. This formidable challenge must be overcome in a projected climate that is more variable and where extreme weather events are increasingly common. While the green revolution, agricultural land expansion and agrotechnological innovations have significantly increased crop productivity over the last 50 years, the gains in the yields of most major crops have reached a plateau. Moreover, global hunger remains at a record high. Climate change‐associated changes in weather patterns have decreased the yields of major crops. Further land expansion is impossible without severe trade‐offs with biodiversity and climate change mitigation. Wars and pandemics are currently severely disrupting the global agri‐food system, increasing prices and exacerbating food insecurity, with the world's poorest suffering the most. The climate change–social instability nexus will continue to cause additional stress to the agri‐food system. Here, we consider the inequities in the current food system, highlighting the weak interconnection among research, policy and societal action that is hindering mitigation and adaptation efforts. We argue that improved interconnections among research, policy, governance and societal action will unlock the potential to achieve food security while supporting climate change mitigation targets. Our analysis includes specific strategies such as strengthening small‐scale farmers, promoting fair trade practices and reducing food waste to achieve these goals. [ABSTRACT FROM AUTHOR]

This article delves into the cultural and meteorological significance of Groundhog Day, tracing its roots back to the Christian holiday of Candlemas and its connection to the pagan holiday Imbolc. It explores the folklore surrounding Groundhog Day and its supposed ability to predict the arrival of spring, emphasizing that these predictions are not scientifically accurate but can stimulate discussions about weather and climate. The article also discusses the historical and cultural origins of the Groundhog Day tradition and its diffusion from Europe to America. It raises questions about what constitutes a "fair" Candlemas and the paradox of global warming in relation to the Groundhog Day predictions, highlighting the role of atmospheric circulation and climate patterns in understanding the folklore. [Extracted from the article]

Environmental factors resulting from climate change and air pollution are risk factors for many chronic conditions including dementia. Although research has shown the impacts of air pollution in terms of cognitive status, less is known about the association between climate change and specific health-related outcomes of older people living with dementia. In response, we outline a scoping review protocol to systematically review the published literature regarding the evidence of climate change, including temperature and weather variability, on health-related quality of life, morbidity, mobility, falls, the utilization of health resources, and mortality among older adults living with dementia. This scoping review will be guided by the framework proposed by Arksey and O'Malley. Electronic search (Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science) using relevant subject headings and synonyms for two concepts (older people with dementia, weather/ climate change). No publication date or other restrictions will be applied to the search strategy. No language restriction will be applied in order to understand the impact of non-English studies in the literature. Eligible studies must include older adults (65+years) with dementia living in the community and investigate the impacts of climate change and/or weather on their health-related quality of life, morbidity, mobility, falls, use of health resources and mortality. Two independent reviewers will screen abstracts and select those for a full-text review, perform these reviews, select articles for retention, and extract data from them in a standardized manner. This data will then be synthesized and interpreted. OSF registration: DOI: 10.17605/OSF.IO/YRFM8. [ABSTRACT FROM AUTHOR]

We validate the multiyear potential predictability of wintertime heavy precipitation potential in East Asia by combining initialized decadal hindcasts of the global climate model and large ensemble simulations from a high‐resolution global atmospheric model. By analyzing a set of initialized hindcasts, the major predictive components of sea surface temperature (SST) variability beyond interannual timescales are identified as high‐latitudes multidecadal variability and the so‐called trans‐basin variability (TBV). A set of 100 ensemble simulations using a high‐resolution atmospheric model showed a significantly large signal‐to‐noise ratio for the wintertime heavy precipitation potential in East Asia, which is closely related to the TBV. When the SST around the maritime continent is higher, the anomalously low pressure in the northwestern Pacific enhances low‐level cold air transport due to the winter monsoon. Consequently, the resultant weaker baroclinicity in the lower atmosphere reduces storm activity and wintertime heavy precipitation potential in East Asia. Plain Language Summary: Statistical changes in extreme weather and climate events have attracted much attention as potential sources of natural disasters. Numerical experiments to obtain huge ensembles of multiyear predictions with initialization using a high‐resolution model represent a direct approach but a difficult solution for the computational resources available currently. Here, we validated the multiyear potential predictability of wintertime heavy precipitation potential in East Asia, by combining initialized decadal hindcasts of the global climate model and large ensemble simulations from a high‐resolution global atmospheric model. By analyzing a set of initialized hindcasts, we identified one of the major predictive components of sea surface temperature (SST) variability beyond interannual timescales as the so‐called trans‐basin variability (TBV). A set of 100 ensemble simulations using a high‐resolution atmospheric model show a significantly large signal‐to‐noise ratio for the wintertime heavy precipitation potential in East Asia, which is closely related to the TBV. When the SST around the maritime continent is higher, the anomalously low pressure in the northwestern Pacific enhances low‐level cold air transport due to the winter monsoon. Consequently, the resultant weaker baroclinicity in the lower atmosphere reduces storm activity and wintertime heavy precipitation potential in East Asia. Key Points: Multiyear potential predictability is validated for the storm activity and wintertime heavy precipitation potential in East AsiaWe combine initialized decadal hindcasts of global climate model with large ensembles of high‐resolution atmospheric simulationTrans‐basin variability as a predictive climate mode controls the precipitation potential through sea surface temperature changes around the maritime continent [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

Climate change and the resultant environmental deterioration signify one of the most challenging problems facing humankind in the 21st century. The origins of climate change are multifaceted and rooted in anthropogenic activities, resulting in increasing greenhouse gases in the environment and leading to global warming and weather drifts. Extremophilic fungi, characterized by their exceptional properties to survive extreme habitats, harbor great potential in mitigating climate change effects. This review provides insight into the potential applications of extremophilic fungi in climate change mitigation strategies. They are able to metabolize organic biomass and degrade carbon compounds, thereby safely sequestering carbon and extenuating its release into the environment as noxious greenhouse gases. Furthermore, they possess extremozymes, which break down recalcitrant organic species, including lignocellulosic biomass and hydrocarbons. Enzymatic machinery equips these extremophilic fungi to perform the bioremediation of polluted environments. Extremophilic fungi can also be exploited for various biological interventions, such as biofuels, bioplastics, and other bioprocessing applications. However, these fungi characterize a valued but underexplored resource in the arsenal of climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of - 0.13 and - 1.05 ms-1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 ms-1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 ms-1 , with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 ms-1 and between 7 and 13.3 ms-1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 ms-1 and an error of ∼ 1.5 ms-1 , a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (- 0.18 ± 1.52 ms-1) than in the winter (0.63 ± 2.07 ms-1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets. [ABSTRACT FROM AUTHOR]

This document provides a summary of a hackathon event organized by the Ministry of Justice (MoJ) and Aon, a reinsurance company, to explore the social impacts of climate change. The hackathon focused on developing consistent scenarios for future climate risk, understanding uncertainties in European windstorm modeling, and providing real-time context for extreme weather events. The teams developed tools and methodologies to assess the severity and potential impact of extreme events on insured losses. The collaboration between the MoJ, Aon, and participants from various institutions aims to further research and develop papers and grant proposals in these areas. [Extracted from the article]

This article explores the role of fossil organic carbon (FOC) weathering in global biogeochemical cycles. FOC is a unique type of organic carbon that has accumulated and fossilized in the Earth's lithosphere over time. Recent studies have shown that FOC is easily reacted, mobilized, and transformed under surface conditions, releasing CO2 and impacting global biogeochemical cycles. However, there are challenges in understanding FOC weathering, including limited data for calibrating its kinetics and the influence of factors like rock types, tectonic activities, geomorphic processes, and climate. The article suggests that future research should focus on studying FOC weathering in different environments and developing proxies to reconstruct past weathering. It also emphasizes the importance of understanding the connection between FOC weathering and the biogeochemical cycles of other nutrient elements. [Extracted from the article]

The health-care system and particularly renal replacement therapy has a significant carbon footprint adding to global warming and extreme weather conditions. Improving sustainability has become the focus of national and international working groups. Many reviews underline the need for improvement of sustainability in nephrology, in particular dialysis, and provide recommendations on how to reduce waste, energy, and water consumption. However, how to implement these recommendations, and where to start, is not always clear. This paper summarizes discussions within the 'working group on sustainable nephrology' of the Swiss Society of Nephrology. We do not provide a detailed review of the topic but instead present a practical 10-point action plan to help health-care workers in nephrology make a start and improve the carbon footprint of their dialysis centres. We emphasize the importance of ongoing research, cooperation, and dialogue, and welcome additional ideas from the wider renal community. [ABSTRACT FROM AUTHOR]

Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models. Plain Language Summary: Accurate risk assessment of extreme weather events in a changing climate requires high‐resolution data, which is expensive for climate models to produce. Thus, there arises a need for downscaling (super‐resolution) methods. We show that by using simplified physics and statistics to prime generative adversarial learning, one can reconstruct observed high‐resolution mid‐latitude extreme rainfall fields from low‐resolution climate models with high fidelity. As a result, uncertainty quantifying downscaling (25:1 ratio) has become feasible, overcoming limitations of extant dynamical and statistical downscaling methods. Key Points: We leverage data, physics, and machine learning to develop an approach to downscale rainfallPriming a two‐step generative adversarial learning with simplified physics and statistics and applying optimal estimation‐based bias correction overcomes common limitations of conventional downscaling strategiesThe proposed downscaling method enables the rapid and robust estimation of extreme rainfall hazards [ABSTRACT FROM AUTHOR]