Atmospheric drivers of post-monsoon and winter thunderstorms in Central India

The rise in frequency of severe weather events cause significant socio-economic challenges in the Indian sub-continent. Understanding the causative mechanisms driving these heavy rainfall events is still unclear. Using the state-of-the-art C-band Doppler weather radar at Bhopal and reanalysis data, this study explored the storm-scale characteristics and the driving mechanisms of thunderstorms that occurred on 17 October 2021 (post-monsoon thunderstorm) and 06 January 2022 (winter thunderstorm) over Central India. The radar reflectivity exceeds 45 dBZ, indicating intense convection during thunderstorms. The thunderstorm associated with post-monsoon season is deeper (top heights beyond 12 km) compared to winter thunderstorm. The disdrometer observations showed that the winter thunderstorm is very intense reaching upto 158 mm/h with a short duration of about 30-min. Among the two thunderstorms, the post-monsoon thunderstorm is associated with a mesoscale convective system. The atmospheric water vapour transport from the surrounding oceans (Bay of Bengal and Arabian Sea) is a potential contributor to moisture advection. However, the vertical extent of the storm is regulated by the moistening of midtroposphere prior to the thunderstorm. The cyclonic circulation induced by the low-pressure system plays a major role in the vertical development of the thunderstorm during post-monsoon season. In contrast, the horizontal mass convergence between cold air from high latitudes and warm, moist air from the Bay of Bengal is the key to thunderstorm development for the winter case. The present findings would have profound significance in improving the simulations of these heavy rainfall events by the regional climate models.

Rapid changes in warm and cold extremes in recent decades and their future projections for India

Assessing changes in temperature extremes is essential for understanding the impacts of global warming on agriculture and public health in a populous and developing country like India. This study analyses trends in warm and cold extremes using surface observations (1980-2020) and Coupled Model Intercomparison Project Phase 6 (CMIP6) data. We find a significant increase in warm days (2-7 days/decade) and warm nights (2-8 days/decade) across India, particularly in the Northeast (NE), Northwest, and Peninsular (IP) regions during 1980-2020. Concurrently, cold days (-3 to -1 days/decade) and cold nights (-1 to -5 days/decade) have decreased in the same period. The frequency of warm days (3 ± 0.8 days/dec) has increased at a slightly higher rate than that of warm nights (2 ± 1 days/dec), which is consistent with the decrease in cold days (-1.8 ± 0.8 days/dec) and cold nights (-2±0.8 days/dec) in 1980-2020, and it is three-fold in NE and IP (-2 to -7 days/dec) in winter. Future projections under the high emission SSP5-8.5 scenario indicate a five-fold increase in warm days and nights, alongside a four-to six-fold reduction in cold days and nights by 2080-2100. Our analysis unfolds the severe warming in India and its potential to trigger more extreme weather events, regional climate change, and associated natural disasters such as frequent heat waves. These changes can have substantial implications for crop yields, heat stress, and energy demand.

Asymmetry in the intensity and frequency of extreme high temperature during daytime and nighttime in China in the summer 2022

This study reveals the spatiotemporal evolution pattern of 2022 summertime heatwave in China, which experienced the strongest extreme heat event in east-central China since 1961, and investigates its impact on natural environment. Four extreme high temperature (EHT) indicators combining intensity/frequency and daytime/nighttime are employed to analyze the EHTs and their anomalies. Additionally, four environmental variables directly influenced by EHTs, namely soil moisture, drought index, vegetation index and fire burned area, are utilized to characterize the impact of EHTs on the environment. The results show that the average intensity of EHT in the summer of 2022 is 39.99 °C during the day and 20.54 °C at night, and the average frequency is 14.6 % during the day and 17.7 % at night. In particular, the EHT intensity is very high during the day in the Northwest China, while the EHT frequency is high during the daytime in the Qinghai-Tibet Plateau and during the nighttime in South China. The daytime EHT intensity is higher than the nighttime, but the nighttime EHT frequency is higher than the daytime. Overall, EHTs have increased in frequency and intensity, in the context of 2017-2021, with daytime and nighttime intensities increasing by 0.67 °C and 0.78 °C, and frequencies increasing by 4.5 % and 7.6 %, respectively. EHTs are increasing significantly during the day in Qinghai-Tibet Plateau and during the night in South China, in contrast, a significant portion of North and Northeast China is decreasing, especially during the day, with more than half of the region experiencing a decrease. During different months of summer, the spatial distribution of the intensity of EHT is basically invariable, with high frequency values starting in places such as northern Xinjiang and the North China Plain, then shifting to the Qinghai-Tibet Plateau and the southern coastal areas during the daytime, and also to the Northeast at night in addition to that, and finally converging in the Sichuan Basin during the daytime, and also converging to the Qinghai-Tibet Plateau during the nighttime. Notably, there is an asymmetry between daytime and nighttime EHTs, with daytime extreme heat peaks being highest in July but nighttime highest in August, and with intensity and frequency increasing more at night than during the day. Furthermore, daytime EHT intensity had the largest effect on environmental factors in general, and its interaction with nighttime EHT intensity to form an intensity-based day-night composite of EHT that has a significant effect on environmental factors. However, nighttime EHT intensity was the most influential indicator of spatial heterogeneity for the area burned by fire, and the nighttime intensity and frequency composite EHTs had the greatest effect on fire. The results of this study contribute to a deeper understanding of the evolution model of EHTs and their impacts on environmental factors in China in the summer of 2022, and provide a scientific basis and reference for future responses to EHTs.

Climate risk and green innovation-ESG disconnect: Firm-level evidence from China

The inability to reliably quantify firms' climate change exposure has become a primary obstacle preventing academics from thoroughly investigating climate impacts on micro-organizations. In this study, we construct firm-level climate risk indicators using hand-collected data on meteorological factors and investigate whether and how climate risk affected the paradoxical relation between corporate green innovation and its' environmental, social, and governance (ESG) scores on the basis of the Chinese context. We document that the climate risk is significantly positively (or negatively) related to the negative (or positive) green innovation-ESG disconnect, implying that climate risk enhances the loose-coupling motives between green innovation and ESG for addressing internal efficiency and external legitimacy conflict. The above disconnect effect of climate risk, namely green innovation as compliance means and ESG as ends fail to complement each other as comparative advantages, is less pronounced for private-owned enterprises, firms with high corporate governance quality, and those with powerful CEOs. Furthermore, the disconnect effect of climate risk results in severe corporate performance volatility and diminishes future growth potential. Overall, this study contributes to the literature on climate risk at the micro level and offers initial evidence that climate risk causes means and ends cannot be mutually justified by demonstrating the green innovation-ESG disconnect, which has conducted few empirical research so far.

Extreme cold reduces seedling establishment, but native species appear more susceptible than non-native species

PREMISE

Extreme-cold events are increasingly recognized as one of the most damaging aspects of climate change in northern temperate ecosystems. However, little data exists describing how native and non-native species may respond to these extreme events, especially as seeds. We used a greenhouse experiment to test how extreme cold reduces seedling establishment in seven woody species common to eastern North America. We hypothesized that the effects of extreme cold depend on provenance (native vs. non-native) and chilling period.

METHODS

Following chilling periods of 80, 100, or 120 days, seeds experienced a false-spring with temperatures at 15°C for one week; half of the seeds in each dormancy treatment group experienced a two-day extreme-cold event (-13.9°C) while the rest returned to mild winter temperatures (4°C).

RESULTS

Extreme-cold events universally decreased seedling establishment, but non-native species had four times greater survival in the extreme-cold treatment (mean ± s.e.: 0.108 ± 0.024) compared to native species (0.024 ± 0.018). Furthermore, native seeds were increasingly susceptible to extreme-cold damage following a 120-day chilling period, whereas non-native seeds were able to resist extreme cold equally following all chilling periods.

CONCLUSIONS

These results suggest that in eastern North America, cold resistance could be a trait facilitating the success of non-native species. The introduction of non-native species may synergize with climate change to alter community composition, which could have important consequences for forest biodiversity in the Anthropocene.

Climate variability and warming in Coastal Odisha: assessing interannual temperature trends and impacts

Coastal Odisha, strategically positioned between the Bay of Bengal and a vast inland landmass, is highly susceptible to climate change and variability impacts. This study comprehensively examines interannual temperature variability and its relationship with mean seasonal temperature using 54 years of homogenised surface meteorological data (1969-2022) from seven WMO-standard meteorological stations. To assess inter-annual variability in association with mean seasonal temperature and precipitation, a difference of standard deviations (SD) of two periods (e.g. 1969-1996 and 1995-2022) along with the relation between running 28-year values of SD and means of seasonal temperature for the period from 1969 to 2022, F-ratio test along with slope and correlation values have been conducted. By comparing two distinct periods (1969-1996 and 1995-2022), the analysis reveals significant climatic shifts, including rising mean temperatures and increased interannual variability at most stations, but some of the stations show an increase in mean temperature with decreased or fluctuating variability. These trends are linked to a heightened frequency of extreme weather events, cascading impacts on agriculture, water resources, health, and infrastructure. Notably, the findings identify localised patterns where warming is accompanied by increased or reduced variability, offering nuanced insights into the region's complex dynamics. Explicitly noted the limitations of prior studies (e.g. low station density, short temporal scales, lack of focus on interannual variability),this study contributes novel insights to climate research in Odisha by employing long-term, high-resolution station-level data and advanced homogenisation techniques. The relationship between temperature and interannual variability is unique and the inverse relationship between the mean temperature and the variance of temperature is disrupted at most of the stations in coastal Odisha. Its findings hold critical implications for future research as a foundation for regional climate modelling, extreme event prediction, and studying climate variability's socio-economic impacts. Actionable recommendations, including targeted disaster risk reduction measures, adaptive agricultural practices, and resilient infrastructure planning, underscore policy relevance. By addressing gaps in regional climate knowledge, this work provides a roadmap for integrating localised climate insights into state-level and national climate adaptation frameworks, ensuring preparedness for the evolving climate challenges in coastal Odisha.

Cost-effective adaptations increase rice production while reducing pollution under climate change

Rice is a major source of greenhouse gas (GHG) and nitrogen pollution. While best management practices have been developed to enhance the sustainability of rice production under current climates, their adaptability and efficacy under future climate scenarios remain uncertain. Here we evaluated 49 best management practices across global grid cells of rice-producing areas in terms of increasing rice production, reducing GHG emissions and minimizing nitrogen pollution under future climate conditions. Optimal climate adaptation measures were assigned to each grid cell. We show that implementing the proposed adaptation strategy could increase global rice production by 36% while reducing GHG emissions and nitrogen losses by 23% and 32%, respectively. This approach could lead to a global benefit of US$117 billion for food supply, resource saving, climate mitigation and environmental protection, with total implementation costs of US$13 billion. Establishing practical and cost-effective adaptation strategies is critical for the sustainable development of the global agricultural system in the face of climate challenges.

Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation

Thailand is under threat from climate change, where extreme climate events are expected to intensify and increase in the coming decades. The objective is to assess extreme drought and rainfall events in Thailand based on climate modeling through an ensemble for future projections of extreme climate indices. The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. The projections were based on three scenarios: historical (20th century); intermediate forcing (RCP 4.5) and high forcing (RCP 8.5). The results of the climate indices pointed to significant regional differences in Thailand. Historically, the CDD indicated 35 consecutive dry days in the northern (N) and northeastern (NE) parts of Thailand, whereas the southern region showed CDD values of fewer than 10 consecutive dry days. In the R4.5 scenario, a meridional pattern emerged in CDD, increasing from east (E) to west (W). In the R8.5 scenario, the number of consecutive dry days increased across the entire country. The WSDI stood out in both the R4.5 and R8.5 scenarios, with an increase in the duration of warm spells in Thailand. The CSU did not perform satisfactorily in the scenarios adopted. Historically, the CWD indicated consecutive wet days in the N and NE, whereas in the R4.5 and R8.5 scenarios, this was observed only in the Central and Southern regions. Historically, the maximum number of consecutive rainy days varied in the NE and South via WW. In the R4.5 and R8.5 scenarios, there was a significant increase in the maximum number of consecutive rainy days across Thailand. Projections based on climate indices indicate that Thailand needs to adopt mitigation measures across its regions to neutralize the impacts of extreme drought and rainfall events on socioeconomic sectors, particularly in tourism, industry, agricultural production, and food security for its population.

Heatstroke characteristics and meteorological conditions in Hefei, China: thresholds and driving factors

BACKGROUND

Due to climate change and rapid urbanization, the frequency of heatwave events in East China has increased considerably since the 21st century, which has a considerable influence on human health, such as heatstroke. However, few studies have been conducted in this region on the relationship between heatstroke and meteorological conditions. To address this point, this study aimed to analyze the characteristics of heatstroke and their relationship with meteorological conditions in Hefei, China.

METHODS

The 2008-2022 heatstroke data from Hefei Center for Disease Control and Prevention was used. The relationship between heatstroke and meteorological conditions was discussed by statistical methods, such as correlation analysis, cluster analysis and linear regression analysis.

RESULTS

The number of heatstroke cases fluctuated upward from 36 cases in 2008 to 1051 cases in 2022, with 71.5% of all cases for males and females accounting for 28.5%. The highest frequency of heatstroke occurrence was found to be concentrated in the middle age group (40-59 years old). According to the statistical analysis, air temperature and relative humidity were the most important meteorological factors that influenced the occurrence of heatstroke. Then a threshold system of meteorological factors for heatstroke was established by utilizing the relationship: daily average temperature (T) ≥ 30 °C & daily average relative humidity (RH) ≤ 80% and daily maximum temperature (Tmax) ≥ 35 °C & daily minimum relative humidity (RHmin) ≤ 65%. The threshold in group outbreak areas was stricter than it in high incidence areas. Furthermore, the Pacific Subtropical High (PSH) was found to be the primary climatic factor that determined the occurrence of heatstroke occurrence on a seasonal scale. In addition, significant differences in heatstroke risk were found among different groups of people. Heatstroke risk was substantially higher in males than in females due to larger opportunities for outdoor labor. The reduced physical fitness of elderly people raised the risk of heatstroke more than other age groups in extremely high temperatures.

CONCLUSIONS

A meteorological threshold system had been established to forecast heatstroke occurrence in a short-term time, and a key climate driving factor of heatstroke was found for long-term heatstroke prediction in Hefei. These findings could facilitate the disease control department to take preventive and control measures to reduce the impact of heatstroke on human health and society.

Modelling climate change and aridity for climate impact studies in semi-arid regions: The case of Giba basin, northern Ethiopia

of long-term and future climate variability is crucial for impact assessment studies in drought-prone areas like the Giba basin in northern Ethiopia. This study has applied the statistical downscaling model (SDSM) and (De Martonne and Pinna combinative) aridity index methods to evaluate the climate system of the Giba basin. Historical data (1961-2019) from seven meteorological stations and global grided data were used for future climate projections (2020-2100) under the three emission scenarios (RCPs 2.6, 4.5, and 8.5) for the three-time horizons (2040s, 2060s, and 2080s). Analysis of results showed that rainfall and temperature projection on a monthly and/or seasonal basis has more significance than on an annual basis for impact studies particularly, in areas where irrigation practices are common like in the Giba basin. Seasonal projection of rainfall in the basin showed a slightly decreasing trend during the spring season (MAM), and a significant increment in the main rainy season (JJA) under all scenarios and for the whole projection year. On an annual basis, a maximum increase of rainfall, up to +285 mm/year and +298 mm/year was expected to increase at Abyi Adi and Mekelle Obs stations, respectively, under RCP 8.5 in the 2080s. Temperature projection showed a consistent rise throughout the basin that ranges from a minimum increase of Tmax by +0.29 °C in the 2040s (RCP 2.6) at Mekelle Obs station to a maximum increase of Tmin by +2.35 °C in the 2080s (RCP8.5) at Abyi Adi station. In general, it is observed that the rate of increment of projected Tmin was more than that of Tmax in all stations in the Giba basin, which showed a continuous contraction of the gap between Tmin and Tmax, hence, the prevalence of global warming. This has led to a considerable increment of aridity till the end of the 21st century. Hence, the implementation of locally-suited climate change resilient strategies is crucial to enhance the sustainability of the ecosystem and ensure food security in the basin.

Heavy rainfalls in Poland and their hyetographs

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions

Gross Primary Productivity (GPP) is a crucial indicator of the carbon fixed by plants through photosynthesis, playing a vital role in understanding and managing ecological and environmental processes. However, global warming, characterized by elevated temperatures, water shortage, and increased drought stress, has significantly impacted GPP. Various GPP products based on different algorithms and input data have been developed, but their performance under extreme climatic conditions remains unverified. This study evaluated the consistency and accuracy of eight global GPP products from 2003 to 2014 using flux towers data. The results show that GPP products performed well under overall conditions, with an average correlation coefficient (R2) of 0.604, and Penman-Monteith-Leuning-version-2 (PMLv2) showed the best performance (R2 = 0.664). However, under extreme climatic conditions like high temperature, high vapor pressure deficit (VPD), and drought, the accuracy significantly dropped (R2 = 0.3), with Global-dataset-of-solar-induced-chlorophyll-fluorescence (GOSIF) being the most affected. Accuracy was lower in croplands (CRO) and grasslands (GRA). To enhance accuracy under extreme climatic conditions, GPP products were used as inputs to a Convolutional Neural Network (CNN) based on ECMWF-Reanalysis-5th-Generation (ERA5) meteorological data and compared with random forests (RF). Four GPP products significantly contributed to the model, with a cumulative contribution of 80.3 %. Under extreme climatic conditions, CNN significantly improved the estimation accuracy of GPP and outperformed RF. The optimal values for R2 and the root mean square error (RMSE) were 0.905 (increase by at least 201.7 %) and 7.708 gC m-2 8d-1 (decrease by at least 50.7 %). The model also performed well at 20 independent validation sites (R2 = 0.783). This study offers a method to improve GPP estimation under extreme climatic conditions, unrestricted by time and space.

Diurnal temperature range and cardiopulmonary health in Taiwan: Evaluating impacts, thresholds, and vulnerable groups

The health impacts of the diurnal temperature range (DTR), which may be affected by climate change, have received little attention. The objectives of this study were (1) to evaluate the association of DTR and cardiopulmonary outcomes, (2) to select the proper thresholds for a DTR warning system, and (3) to identify vulnerable groups. The weather and health records in Taiwan from 2000 to 2019, with a maximum DTR of 12.8 °C, were analyzed using generalized additive models. The health outcomes included cardiovascular (CVD) and respiratory disease (RD) categories and several sub-categories, such as ischemic heart disease, stroke, pneumonia, asthma, and chronic obstructive pulmonary disease. The results showed that the associations of DTR and cardiopulmonary outcomes were as significant as, and sometimes even stronger than, those of the daily maximum temperature and daily minimum apparent temperature in the warm and cold seasons, respectively. The significant association began at DTR of 6 °C, lower than previously reported. The identified DTR warning thresholds were 8.5 and 11 °C for the warm and cold seasons, respectively. DTR is statistically significantly associated with a 5-36% and a 9-20% increase in cardiopulmonary emergency and hospitalized cases in the warm season with a 1 °C increase above 8.5 °C, respectively. In the cold season, DTR is significantly associated with 7-41%, 4-30%, and 36-100% increases in cardiopulmonary emergency, hospitalized, and mortality with a 1 °C increase above 11 °C, respectively. People with hypertension, hyperglycemia, and hyperlipidemia had even higher risks. Vulnerable age and sex groups were identified if they had a lower DTR-health threshold than the general population, which can be integrated into a warning system. In conclusion, DTR may be increased on a local or city scale under climate change; a DTR warning system and vulnerable group identification may be warranted in most countries for health risk reduction.

User-tailored sub-selection of climate model ensemble members for impact studies

Climate model ensembles serve as an input to all impact studies that use sector-specific models (e.g., hydrological, ecological, crop models, fire hazard) at regional or local scales. These models require regionally scaled climate information to simulate the potential environmental and socio-economic sectoral impacts of climate change. Such simulations are based on comprehensive multi-member climate ensembles derived from the bias-adjusted and downscaled global and regional climate models. Due to limited computational resources, users of climate scenarios often can only include a small number of the ensemble members in their calculations, and therefore they often select them at random. A pre-selection of meaningful, consistent and case-specific members is therefore desired by the climate data users. In this work, we aim to fill this gap and present a novel user-tailored procedure for sub-selecting ensemble members for a variety of applications. Our method is based on the ranking of the climate change signal (CCS) calculated for a set of climate indices (e.g., mean temperature or number of hot days). Based on the CCS strength, three ensemble members representing the strongest, weakest, and median CCS are selected for each application. We also demonstrate the robustness of our approach in a specific hydrological impact model framework. Providing a systematic procedure not only assists impact modelers in selecting appropriate members, but also improves the consistency and comparability of different impact studies.

Projection of land use and land cover changes based on land change modeler and integrating both land use land cover and climate change on the hydrological response of Big Creek Lake Watershed, South Alabama

Changing land use/land cover (LULC) and climate substantially affect the hydrological components of a watershed. This study explored the future impact of the hydrological responses due to the changing LULC and climate on the Big Creek Lake watershed in Alabama, USA, from 2021 to 2050 using the Soil and Water Assessment Tool (SWAT). Five climate model datasets were used under the moderate scenario (Representative Concentrative Pathways 4.5) and the extreme scenario (Representative Concentrative Pathways 8.5), and the datasets were downscaled and bias-corrected. In addition, changing the LULC of five categories was predicted by Cellular Automata Markov (CA- Markov). With these data combined with the elevation (Digital Elevation Model), soils, and weather data, the SWAT model was calibrated and validated for the studied watershed to quantify how climate change will affect streamflow, nitrogen, and phosphorus. Our results indicate streamflow will increase due to the 50-acre increase in urban LULC. As streamflow increases, the percolation, surface runoff, lateral flow, groundwater flow, and water yield will also increase because the streamflow impacts these hydrological components. Moreover, the increase rate in streamflow is the same for all the components for January, February, and March. Therefore, there is a strong correlation between these months. On the contrary, evaporation will be high in May, June, and July because of the increasing temperature and streamflow. However, the changes in the water hydrological parameters and total nitrogen and phosphorus will be more intense in RCP8.5 than in RCP4.5.

The impact of intra-annual temperature fluctuations on agricultural temperature extreme events and attribution analysis in mainland China

Daily temperature variations, which tend to exhibit non-constant and non-linear patterns, are often characterized by intra-annual fluctuations that cause severe and frequent extreme temperature events that have an enormous impact on agricultural production. However, the quantitative relationship between intra-annual temperature fluctuations and extreme agricultural temperatures remains unclear. We aimed to investigate intra-annual temperature fluctuation changes based on daily meteorological data in nine agricultural regions across China from 1960 to 2022 and quantify the impact of temperature fluctuations on extreme agricultural temperatures during crop growth periods. Moreover, an attribution analysis of intra-annual temperature fluctuations was performed using climate indicators. Main results showed: (1) intra-annual temperature fluctuations in each region exhibited a certain decrease, and the spatial distribution showed a significant decreasing trend from north to south. (2) Intra-annual temperature fluctuations have moderately exacerbated extreme agricultural hot and cold events during growth periods, which have brought serious challenges to agriculture owing to advances in phenology and unsynchronized rain heat compared to climate warming. The proportion of positive correlations between temperature fluctuations and extreme temperatures was much larger than that of the negative correlations in nearly all of China (percentage of stations: 20.5 %), and the negative correlation was concentrated only in southern China (percentage of stations: 3.7 %). (3) Hydrothermal coupling and elevation moderately affected intra-annual temperature fluctuations. Average temperature, relative humidity, and elevation had negative correlations with intra-annual temperature fluctuations, the correlation coefficients (R) were - 0.62, -0.42 and - 0.12, respectively; however, reference crop evapotranspiration (ET0) exhibited a positive correlation (R: 0.29), and all reached highly significant levels (P < 0.01). Climate indicators mainly affected intra-annual temperature fluctuations in eastern China. Specifically, the El Niño-Southern Oscillation (ENSO), unstable Asia Polar Vortex, and increased Western Pacific Subtropical High have enhanced temperature fluctuations. The deepened East Asian Trough and Arctic Oscillation of the negative phase weakened the intra-annual temperature fluctuations. This investigation highlights the crucial function of temperature fluctuation in intensifying extreme temperature occurrences and provides a more reasonable scientific foundation for extreme event prediction and agricultural planning.

A historical analysis of eco-environmental changes in hydrologically regulated lakes

Human have a long history of implementing hydrological regulations in lakes. With the rapid increase in global population since the mid-20s, the anthropogenic pressure on freshwater resources has intensified, which would result in abrupt change in lake ecosystems. A meta-analysis study had been conducted involving 36 lakes of different types and latitudes from 39 study sites around the world. These lakes were subject to different hydrological regulations between 1900 and 2017. Hydrological regulations tended to result in a more stable water level and poorer water quality. In addition, a turning point occurred in 1956 when the response time interval (RTI)-the time between hydrological regulation implementation and significant ecological change in the lake-decreased significantly since, which coincided with a surge in population size, water and fertilizer usage since the mid-20s. The results of principal component analysis (PCA) emphasized the influence of anthropogenic pressure on the resilience of lakes. The rapid increase of anthropogenic pressure after the mid-20s limited the buffering capacity of large lakes. As the global population continues to grow and water stress is exacerbated by climate change, human may be pressured to implement even larger-scale lake hydrological regulation projects that will likely cause rapid and long-lasting deterioration of these aquatic ecosystems. Environmental resilience analysis combined with multi-indicators is necessary for the monitoring and management of eco-environment changes in regulated lakes.

The effect of climate change and temperature extremes on Aedes albopictus populations: a regional case study for Italy

The Asian tiger mosquito, Aedes albopictus, has spread widely throughout Italy since its introduction, with significant public health implications. We examine how decadal temperature trends and sub-monthly heatwave events affect its climate-driven geographical distribution and temporal dynamics using a new regional-scale dynamical Aedes model. The model is calibrated using [Formula: see text] years of ovitrap data for Emilia-Romagna and reproduces the vector seasonality and, to a lesser extent, its inter-annual variability. Simulated vector density hotspots overlap with densely populated areas in Rome, Milan, Naples, Foggia, Catania, Palermo, Lecce, Cagliari, Genoa, Turin and large urban centres in Emilia-Romagna. Lower risk is simulated over the Central Apennine mountains and the Alps. At decadal time scale, we simulate a lengthening of the active mosquito season by 0.5-3 weeks per decade, with the vector becoming homodynamic in southern Italy. Depending on the climatic setting, heatwaves can increase or reduce vector populations and, in some locations, can temporarily decrease mosquito populations. Such decreases can be followed by a population rebound and overshoot. Given the model's skill in reproducing key spatio-temporal Ae. albopictus features, there is potential to develop an early warning system to inform control efforts at a national scale.

Effect of High Nighttime Temperatures on Growth, Yield, and Quality of Two Wheat Cultivars During the Whole Growth Period

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period.

Transcriptome analysis reveals key regulatory networks and genes involved in the acquisition of cold stress memory in pepper seedlings

Temperature is an important limiting factor in the counter-seasonal cultivation of pepper. Currently, there are no studies on transcriptomic analysis of 'cold stress memory' in pepper. In this study, in order to understand the mechanism of 'cold stress memory' in pepper (Capsicum annuum L.), seedlings were subjected to the following treatments: normal temperature treatment (P0), the first cold treatment for 3 days (P3), the recovery temperature treatment for 3 days (R3), and another cold treatment for 3 days (RP3). The results showed that P3 plants wilted the most, RP3 the second and R3 the least. Leaf reactive oxygen species (ROS) and electrolyte leakage were the most in P3, the second in RP3 and the least in R3. In addition, RP3 had the highest accumulation of zeaxanthin, violaxanthin and β-cryptoxanthin, followed by P3, and R3 had the least. These results suggest that pepper seedlings are characterized by 'cold stress memory'. Transcriptomics was used to analyze the key genes and transcription factors involved in the biosynthesis of zeaxanthin, violaxanthin and β-cryptoxanthin during the formation of 'cold stress memory'. This study provides candidate genes and transcription factors for an in-depth study of the cold tolerance mechanism in pepper.

Increased offspring size and reduced gestation length in an ectothermic vertebrate under a worst-case climate change scenario

As global temperatures continue to rise, understanding the impacts of warming environments has become increasingly important. Temperature is especially relevant for ectothermic organisms which depend upon consistent and predictable annual temperature cycles for reproduction and development. However, additional research is required in this area to elucidate the potential impacts of climate change on future generations. To understand how projected increases in environmental temperatures may impact reproductive outcomes within natural populations of ectothermic vertebrates, we manipulated minimum ambient temperatures during gestation in Red-sided garter snakes (Thamnophis sirtalis parietalis). Wild snakes were collected in the Interlake region of Manitoba, Canada during their spring mating season and allowed to mate in controlled conditions. For the duration of gestation, mated females were placed into one of two ambient thermal conditions: temperatures emulating those found in the species' natural habitat or temperatures with a consistent 5 °C increase to match end-of-century climate change projections. We recorded observations for each litter and all neonates resulting from controlled mating trials. We observed no difference in litter sizes or birth rates between thermal conditions. However, we observed a significant reduction in gestation length and significant increase to neonate body mass and body condition associated with increased ambient temperatures. These results suggest that increased minimum temperatures during gestation may confer reproductive benefits for the northern populations of this species even under the most extreme current modeled warming predictions. We discuss the broader implications of this effect, including possible negative ecological outcomes.

Observation-constrained projections reveal longer-than-expected dry spells

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10-26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42-44% greater, on average, than 'mid-range' or 'high-end' future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land-atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Thermal tolerance plasticity and dynamics of thermal tolerance in Eublepharis macularius: Implications for future climate-driven heat stress

The intensity and duration of heat waves, as well as average global temperatures, are expected to increase due to climate change. Heat waves can cause physiological stress and reduce fitness in animals. Species can reduce overheating risk through phenotypic plasticity, which allows them to raise their thermal tolerance limits over time. This mechanism could be important for ectotherms whose body temperatures are directly influenced by available environmental temperatures. Geckos are a large, diverse group of ectotherms that vary in their thermal habitats and times of daily activity, which could affect how they physiologically adjust to heat waves. Data on thermal physiology are scarce for reptiles, with only one study in geckos. Understanding thermal tolerance and plasticity, and their relationship, is essential for understanding how some species are able to adjust or adapt to changing temperatures. In this study, we estimated thermal tolerance and plasticity, and their interaction, in the crepuscular gecko, Eublepharis macularius, a species that is emerging as a model for reptile biology. After estimating basal thermal tolerance for 28 geckos, thermal tolerance was measured for each individual a second time at several timepoints (3, 6, or 24 h) to determine thermal tolerance plasticity. We found that thermal tolerance plasticity (1) does not depend on the basal thermal tolerance of the organism, (2) was highest after 6 h from initial heat shock, and (3) was negatively influenced by individual body mass. Our findings contribute to the increasing body of work focused on understanding the influence of biological and environmental factors on thermal tolerance plasticity in organisms and provide phenotypic data to further investigate the molecular basis of thermal tolerance plasticity in organisms.

Tissue- and time-dependent metabolite profiles during early grain development under normal and high night-time temperature conditions

BACKGROUND

Wheat grain development in the first few days after pollination determines the number of endosperm cells that influence grain yield potential and is susceptible to various environmental conditions, including high night temperatures (HNTs). Flag leaves and seed-associated bracts (glumes, awn, palea, and lemma) provide nutrients to the developing seed. However, the specific metabolic roles of these tissues are uncertain, especially their dynamics at different developmental stages and the time in a day. Tissue- and time-dependent metabolite profiling may hint at the metabolic roles of tissues and the mechanisms of how HNTs affect daytime metabolic status in early grain development.

RESULTS

The metabolite profiles of flag leaf, bract, seed (embryo and endosperm), and entire spike were analyzed at 12:00 (day) and 23:00 (night) on 2, 4, and 6 days after fertilization under control and HNT conditions. The metabolite levels in flag leaves and bracts showed day/night oscillations, while their behaviors were distinct between the tissues. Some metabolites, such as sucrose, cellobiose, and succinic acid, showed contrasting oscillations in the two photosynthetic tissues. In contrast, seed metabolite levels differed due to the days after fertilization rather than the time in a day. The seed metabolite profile altered earlier in the HNT than in the control condition, likely associated with accelerated grain development caused by HNT. HNT also disrupted the day/night oscillation of sugar accumulation in flag leaves and bracts.

CONCLUSIONS

These results highlight distinct metabolic roles of flag leaves and bracts during wheat early seed development. The seed metabolite levels are related to the developmental stages. The early metabolic events in the seeds and the disruption of the day/night metabolic cycle in photosynthetic tissues may partly explain the adverse effects of HNT on grain yield.

Mitochondrial Protein-Coding Gene Expression in the Lizard Sphenomorphus incognitus (Squamata:Scincidae) Responding to Different Temperature Stresses

In the context of global warming, the frequency of severe weather occurrences, such as unexpected cold spells and heat waves, will grow, as well as the intensity of these natural disasters. Lizards, as a large group of reptiles, are ectothermic. Their body temperatures are predominantly regulated by their environment and temperature variations directly impact their behavior and physiological activities. Frequent cold periods and heat waves can affect their biochemistry and physiology, and often their ability to maintain their body temperature. Mitochondria, as the center of energy metabolism, are crucial for maintaining body temperature, regulating metabolic rate, and preventing cellular oxidative damage. Here, we used RT-qPCR technology to investigate the expression patterns and their differences for the 13 mitochondrial PCGs in Sphenomorphus incognitus (Squamata:Scincidae), also known as the brown forest skink, under extreme temperature stress at 4 °C, 8 °C, 34 °C, and 38 °C for 24 h, compared to the control group at 25 °C. In southern China, for lizards, 4 °C is close to lethal, and 8 °C induces hibernation, while 34/38 °C is considered hot and environmentally realistic. Results showed that at a low temperature of 4 °C for 24 h, transcript levels of ATP8, ND1, ND4, COI, and ND4L significantly decreased, to values of 0.52 ± 0.08, 0.65 ± 0.04, 0.68 ± 0.10, 0.28 ± 0.02, and 0.35 ± 0.02, respectively, compared with controls. By contrast, transcript levels of COIII exhibited a significant increase, with a mean value of 1.86 ± 0.21. However, exposure to 8 °C for 24 h did not lead to an increase in transcript levels. Indeed, transcript levels of ATP6, ATP8, ND1, ND3, and ND4 were significantly downregulated, to 0.48 ± 0.11, 0.68 ± 0.07, 0.41 ± 0.08, 0.54 ± 0.10, and 0.52 ± 0.07, respectively, as compared with controls. Exposure to a hot environment of 34 °C for 24 h led to an increase in transcript levels of COI, COII, COIII, ND3, ND5, CYTB, and ATP6, with values that were 3.3 ± 0.24, 2.0 ± 0.2, 2.70 ± 1.06, 1.57 ± 0,08, 1.47 ± 0.13, 1.39 ± 0.56, and 1.86 ± 0.12, respectively, over controls. By contrast, ND4L exhibited a significant decrease (to 0.31 ± 0.01) compared with controls. When exposed to 38 °C, the transcript levels of the 13 PCGs significantly increased, ranging from a 2.04 ± 0.23 increase in ND1 to a 6.30 ± 0.96 rise in ND6. Under two different levels of cold and heat stress, the expression patterns of mitochondrial genes in S. incognitus vary, possibly associated with different strategies employed by this species in response to low and high temperatures, allowing for rapid compensatory adjustments in mitochondrial electron transport chain proteins in response to temperature changes. Furthermore, this underscores once again the significant role of mitochondrial function in determining thermal plasticity in reptiles.

Temporal and spatial aggregation of rainfall extremes over India under anthropogenic warming

India experienced several unprecedented floods in the recent decades. The increase in the extreme rainfall events (EREs) is the primary cause for these floods, manifesting its societal impacts. The daily downscaled and bias corrected (DBC) Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall and sea surface temperature (SST) are prepared for the Indian region and are utilized to examine the characteristics of EREs. The DBC products capture the characteristic features of EREs for the baseline period, which inspired us to assess the EREs over India in CMIP6 future projections. Consistent with the observations, DBC product shows ~ 8% of Indian land found to experienced extremely heavy rainfall associated with the long duration EREs in the baseline period. However, area and extreme rainfall thresholds are projected to increase by about 18(13)% and 58(50)%, respectively in the far future under SSP5-8.5 (SSP2-4.5) emission scenario relative to the baseline period. A two-fold-65(62)% increase in long-duration EREs compared to the short-duration EREs and substantial warming ~ 2.4(2.9) oC of Indian Ocean SSTs in the far future under SSP5-8.5 (SSP2-4.5) emission scenario compared to baseline period are reported. These findings may provide fundamental insights to formulate national climate change adaptation policies for the EREs.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics of the Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the data were subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03 days/year and decrease in consecutive wet days (CWD) at 0.02 days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5 °C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Ecological responses of squamate reptiles to nocturnal warming

Nocturnal temperatures are increasing at a pace exceeding diurnal temperatures in most parts of the world. The role of warmer nocturnal temperatures in animal ecology has received scant attention and most studies focus on diurnal or daily descriptors of thermal environments' temporal trends. Yet, available evidence from plant and insect studies suggests that organisms can exhibit contrasting physiological responses to diurnal and nocturnal warming. Limiting studies to diurnal trends can thus result in incomplete and misleading interpretations of the ability of species to cope with global warming. Although they are expected to be impacted by warmer nocturnal temperatures, insufficient data are available regarding the night-time ecology of vertebrate ectotherms. Here, we illustrate the complex effects of nocturnal warming on squamate reptiles, a keystone group of vertebrate ectotherms. Our review includes discussion of diurnal and nocturnal ectotherms, but we mainly focus on diurnal species for which nocturnal warming affects a period dedicated to physiological recovery, and thus may perturb activity patterns and energy balance. We first summarise the physical consequences of nocturnal warming on habitats used by squamate reptiles. Second, we describe how such changes can alter the energy balance of diurnal species. We illustrate this with empirical data from the asp viper (Vipera aspis) and common wall lizard (Podarcis muralis), two diurnal species found throughout western Europe. Third, we make use of a mechanistic approach based on an energy-balance model to draw general conclusions about the effects of nocturnal temperatures. Fourth, we examine how warmer nights may affect squamates over their lifetime, with potential consequences on individual fitness and population dynamics. We review quantitative evidence for such lifetime effects using recent data derived from a range of studies on the European common lizard (Zootoca vivipara). Finally, we consider the broader eco-evolutionary ramifications of nocturnal warming and highlight several research questions that require future attention. Our work emphasises the importance of considering the joint influence of diurnal and nocturnal warming on the responses of vertebrate ectotherms to climate warming.

HYADES - A Global Archive of Annual Maxima Daily Precipitation

Time series of annual maxima daily precipitation are crucial for understanding extreme precipitation behavior and its shifts toward nonstationarity with global warming. Extreme precipitation insight assists hydraulic infrastructure design, water resource management, natural hazard prevention, and climate change adaptation. However, not even a third of the records are of sufficient length, and the number of active stations keeps decreasing. Herein, we present HYADES: archive of yearly maxima of daily precipitation records, a global dataset derived from the Global Historical Climatology Network database of daily records (GHCN-Daily). The HYADES dataset contains records from 39 206 stations (heterogeneously distributed worldwide) with record lengths varying from 16 to 200 years between 1805 and 2023. HYADES was extracted through a methodology designed to accurately capture the true maxima even in the presence of missing values within the records. The method's thresholds were determined and evaluated through Monte Carlo simulations. Our approach demonstrates a 96.73% success rate in detecting the true maxima while preserving time series statistical properties of interest (L-moments and temporal monotonic trend).

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Will trees or grasses profit from changing rainfall regimes in savannas?

Increasing rainfall variability is widely expected under future climate change scenarios. How will savanna trees and grasses be affected by growing season dry spells and altered seasonality and how tightly coupled are tree-grass phenologies with rainfall? We measured tree and grass responses to growing season dry spells and dry season rainfall. We also tested whether the phenologies of 17 deciduous woody species and the Soil Adjusted Vegetation Index of grasses were related to rainfall between 2019 and 2023. Tree and grass growth was significantly reduced during growing season dry spells. Tree growth was strongly related to growing season soil water potentials and limited to the wet season. Grasses can rapidly recover after growing season dry spells and grass evapotranspiration was significantly related to soil water potentials in both the wet and dry seasons. Tree leaf flushing commenced before the rainfall onset date with little subsequent leaf flushing. Grasses grew when moisture became available regardless of season. Our findings suggest that increased dry spell length and frequency in the growing season may slow down tree growth in some savannas, which together with longer growing seasons may allow grasses an advantage over C3 plants that are advantaged by rising CO2 levels.

Photosynthesis in newly developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming

We examined photosynthetic traits of pre-existing and newly developed flag leaves of four wheat genotypes grown in controlled-environment experiments. In newly developed leaves, acclimation of the maximum rate of net CO2 assimilation (An) to warm nights (i.e. increased An) was associated with increased capacity of Rubisco carboxylation and photosynthetic electron transport, with Rubisco activation state probably contributing to increased Rubisco activity. Metabolite profiling linked acclimation of An to greater accumulation of monosaccharides and saturated fatty acids in leaves; these changes suggest roles for osmotic adjustment of leaf turgor pressure and maintenance of cell membrane integrity. By contrast, where An decreased under warm nights, the decline was related to lower stomatal conductance and rates of photosynthetic electron transport. Decreases in An occurred despite higher basal PSII thermal stability in all genotypes exposed to warm nights: Tcrit of 45-46.5 °C in non-acclimated versus 43.8-45 °C in acclimated leaves. Pre-existing leaves showed no change in An-temperature response curves, except for an elite heat-tolerant genotype. These findings illustrate the impact of night-time warming on the ability of wheat plants to photosynthesize during the day, thereby contributing to explain the impact of global warming on crop productivity.

Significantly wetter or drier future conditions for one to two thirds of the world's population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world's current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century's end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

Exponential increases in high-temperature extremes in North America

Global warming in the 21st century will alter the frequency of extreme climatic events, such as high-temperature anomalies and "heat waves". Observations of extreme high temperatures during recent decades have detected upward trends in their frequency of occurrence, and recent state-of-the-art Global Climate Models (GCMs), e.g., Climate Model Intercomparison Projects (CMIPs), notably CMIP5 and CMIP6, have predicted acceleration of temperature trends and high-temperature events by 2100 under projected greenhouse-gas emission scenarios. Hence, the 21st century is expected to experience substantial shifts in the occurrence of extreme events, where present-day, extreme-but-rare high-temperature events will become common during the summer months. The increasing frequency of extreme heat may affect the health and resiliency of social, biological, and infrastructure systems in many regions worldwide, underscoring the need for accurate and reliable long-term assessments of climatic change across global and regional scales. So far, many investigations of high-temperature extremes have been carried out under end-point scenarios, e.g., by comparing GCM-projected changes in the frequency of high-temperature extremes expected in the late 21st century to the late 20th century. In this study, we use extreme value theory and decades of observations of high-temperature extremes at thousands of meteorological stations across North America to investigate continuous shifts in the frequency of extreme high-temperature events due to projected local warming trends. We find that the odds of exceedance of 50-year extreme high-temperature events increases exponentially with increases in mean local temperature. At a majority of the stations studied here, a local mean temperature increase of 0.5-1 [Formula: see text]C can double the odds of exceedance of 50-year extreme high-temperature events. Based on time-dependent temperature projections, the odds of exceedance of 50-year extreme high-temperature events doubles approximately every 20 years (or sooner) for [Formula: see text] 96% of the stations. Moreover, we find that, for [Formula: see text] 80% of the stations in North America, investigated here, the 50-year extreme high-temperature events will be exceeded annually before 2100.

Ecosystem feedbacks constrain the effect of day-to-day weather variability on land-atmosphere carbon exchange

Whole-ecosystem interactions and feedbacks constrain ecosystem responses to environmental change. The effects of these constraints on responses to climate trends and extreme weather events have been well studied. Here we examine how these constraints respond to changes in day-to-day weather variability without changing the long-term mean weather. Although environmental variability is recognized as a critical factor affecting ecological function, the effects of climate change on day-to-day weather variability and the resultant impacts on ecosystem function are still poorly understood. Changes in weather variability can alter the mean rates of individual ecological processes because many processes respond non-linearly to environmental drivers. We assessed how these individual-process responses to changes in day-to-day weather variability interact with one another at an ecosystem level. We examine responses of arctic tundra to changes in weather variability using stochastic simulations of daily temperature, precipitation, and light to drive a biogeochemical model. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates in our model. However, responses of some processes (e.g., respiration) were inconsistent with expectations because ecosystem feedbacks can moderate, or even reverse, direct process responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere; less variability led to higher carbon sequestration on land. The magnitude of modeled ecosystem response to weather variability was comparable to that predicted for the effects of climate mean trends by the end of the century.

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020

Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning.

Variation characteristics of extreme climate events in Southwest China from 1961 to 2017

Climate change is increasing the intensity of extreme climate events. Significant impacts of extreme climate events on human society and ecosystem have occurred in many places of the world, for example, Southwest China (SWC). In this study, the daily temperature and precipitation data from 438 meteorological stations are used to analyze the variation characteristics of extreme climate events in the SWC from 1961 to 2017. The annual extreme warm events show a significant increasing trend at 99% confidence level at most stations, and a few stations with a decreasing trend are mainly located in the southern Sichuan Province, the northern Yunnan Province and the western Guizhou Province. Meanwhile, the annual extreme cold events show a significant decreasing trend at 99% confidence level at most stations, and a few stations with an increasing trend are mainly distributed in the Sichuan Basin. Both the annual extreme heavy precipitation indexes and rainstorm indexes show nonsignificant increasing trends, but they differ greatly in the spatial distribution. These indexes in the western Tibet, Chongqing and most parts of Guizhou show significant increasing trends at 95% confidence level, while those in the central Sichuan and southeastern Yunnan show significant decreasing trends. The percentage of extreme heavy precipitation shows a significant increasing trend at 99% confidence level, especially in the northeastern Sichuan, the central-eastern Guizhou and the central Yunnan. Overall, under the background of global warming, the extreme warm events in SWC increase significantly from 1961 to 2017, and the extreme cold events decrease significantly. The variation trends of extreme precipitation events differ greatly in different regions, and the percentage of extreme heavy precipitation increases significantly.

Assessment of Spatio-temporal variability of climate in the lower Gangetic alluvial plain

Global climate change is a cause of concern as extreme events have intensified in recent years, with increased floods and droughts also reported in the Lower Gangetic Plain (LGP). Assessments from Regional Climatic Models (RCM) cannot capture the local climate variability necessary for devising an action plan for climate risk mitigation. The present study aims to fill this gap by assessing the long-term local-scale climate variability using Modified Mann-Kendall (MMK) and Centroidal Day (CD) shifts for the Patna district, which can be extended to other districts in the LGP. The time series is split into two halves to account for changes due to urbanization in recent three to four decades. CD analysis shows a forward shift in the monsoonal and annual rainfall in recent decades. The variability in total rainfall has become more pronounced post-1985 during monsoon, postmonsoon, and winter seasons. An increase of 64.53 mm (18.9%) in surface runoff and a strong correlation between built-up area and precipitation further cement the role of urbanization in local climate change. Despite a 5.74% decrease in monsoonal rainfall, a 3.51 mm/day increase in rainfall intensity is observed during the monsoon. The implications of these variabilities have posed new challenges for the agricultural production and management of water resources and the interactions between groundwater and surface runoff.

Extreme precipitation patterns in the Asia-Pacific region and its correlation with El Niño-Southern Oscillation (ENSO)

In the Asia-Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990-2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June-August (JJA), and by precipitation frequency in December-February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March-May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions

Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non-stressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water-related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.

An Observation-Based Dataset of Global Sub-Daily Precipitation Indices (GSDR-I)

Precipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks.

Humulus lupulus L. Strobilus In Situ Photosynthesis and Respiration Temperature Responses

The primary metabolism and respiration of the hop strobilus has not been quantified in response to daily temperature fluctuations. The objective of this study was to assess strobilus gas exchange, specifically the response to temperature fluctuations. Hop strobilus were measured under controlled environment conditions to assess the organ's contribution to carbon assimilation and respiration during the maturation phase. Strobilus-specific daily carbon budgets were estimated in response to temperature fluctuation. The optimal temperature for net carbon gain occurred at 15.7 °C. Estimated strobilus carbon uptake decreased approximately 41% per 5 °C increase in temperature above 20 °C. Daily temperatures within 10-27 °C resulted in a net positive strobilus daily carbon balance, whereas temperature increases beyond 27 °C increasingly exhaust strobilus carbon reserves. The Q10 temperature coefficient (the rate respiration increases every 10 °C rise in temperature) approximately doubled per 10 °C rise in temperature from 7-40 °C (1.94-2) with slightly reduced values at lower temperatures. In conclusion, we show that photosynthetically active bracts maintain a positive strobilus carbon balance at moderate temperatures and as mean daily temperatures progressively exceed 27 °C, strobilus net carbon reserves are precipitously exhausted due to ever-increasing respiration rates.

Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km² in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management.

Elevated [CO2] negatively impacts C4 photosynthesis under heat and water stress without penalizing biomass

Elevated [CO2] (eCO2) and water stress reduce leaf stomatal conductance (gs), which may affect leaf thermoregulation during heat waves (heat stress). Two sorghum lines, with different leaf width were grown in a glasshouse at a mean day temperature of 30 °C, under different [CO2] and watering levels, and subjected to heat stress (43 °C) for 6 d at the start of the reproductive stage. We measured leaf photosynthetic and stomatal responses to light transients before harvesting the plants. Photosynthesis at growth conditions (Agrowth) and biomass accumulation were enhanced by eCO2 under control conditions. Heat stress increased gs, especially in wider leaves, and reduced the time constant of stomatal opening (kopen) at ambient [CO2] but not eCO2. However, heat stress reduced photosynthesis under water stress and eCO2 due to increased leaf temperature and reduced evaporative cooling. eCO2 prevented the reduction of biomass under both water and heat stress, possibly due to improved plant and soil water status as a result of reduced gs. Our results suggest that the response of the C4 crop sorghum to future climate conditions depends on the trade-off between low gs needed for high water use efficiency and drought tolerance, and the high gs needed for improved thermoregulation and heat tolerance under an eCO2 future.

Climate extremes drive the phenology of a dominant species in meadow steppe under gradual warming

Plant phenology in terrestrial ecosystems, especially in the Northern Hemisphere, is expected to change owing to the projected increasing frequency and intensity of climate extremes in the context of global warming. Although such changes under mean climate change have been extensively reported in the literature, little is known about the impacts of climate extremes. In this study, climatic changes and their effects on plant phenology were characterized using long-term climatic and phenological data from the start and end of the growing season (SOS and EOS, respectively) from 2005 to 2020 for Stipa baicalensis, a dominant species in a temperate meadow steppe. The results showed that the temperature, including the mean and minimum temperatures, and extreme warm indices significantly increased; however, annual precipitation, and the frequency of extreme cold and precipitation events decreased. The SOS of S. baicalensis was initially earlier and later, whereas the EOS trended to be delayed. However, the growing season (LOS) was slightly prolonged. Compared with the indices under mean temperature, the pre-season (before SOS or EOS) minimum temperature dominantly affected SOS and EOS, whereas the mean and extreme precipitation slightly affected them. Furthermore, the findings showed that plant phenology responded to extreme temperatures quicker and stronger than mean temperatures. This study provides insight into how key extreme climatic factors could affect plant phenophases and improve and refine the phenological model. This could also be useful in enhancing grassland ecosystem management and sustainable development.

Effects of Post Anthesis High Temperature Stress on Carbon Partitioning and Starch Biosynthesis in a Spring Wheat (Triticum aestivum L.) Adapted to Moderate Growth Temperatures

This study investigates carbon partitioning in the developing endosperm of a European variety of spring wheat subjected to moderately elevated daytime temperatures (27°C/16°C day/night) from anthesis to grain maturity. Elevated daytime temperatures caused significant reductions in both fresh and dry weights and reduced starch content of harvested grains compared to plants grown under a 20°C/16°C day/night regime. Accelerated grain development caused by elevated temperatures was accounted for by representing plant development as thermal time (°CDPA). We examined effects of high temperature stress (HTS) on uptake and partitioning of [U-14C]-sucrose supplied to isolated endosperms. HTS caused reduced sucrose uptake into developing endosperms from the second major grain filling stage (approximately 260°CDPA) up to maturity. Enzymes involved in sucrose metabolism were unaffected by HTS, whereas key enzyme activities involved in endosperm starch deposition such as ADP-glucose pyrophosphorylase and soluble isoforms of starch synthase were sensitive to HTS throughout grain development. HTS caused a decrease in other major carbon sinks such as evolved CO2, ethanol-soluble material, cell walls and protein. Despite reductions in labelling of carbon pools caused by HTS, the relative proportions of sucrose taken up by endosperm cells allocated to each cellular pool remain unchanged, except for evolved CO2, which increased under HTS and may reflect enhanced respiratory activity. The results of this study show that moderate temperature increases in some temperate wheat cultivars can cause significant yield reductions chiefly through three effects: reduced sucrose uptake by the endosperm, reduced starch synthesis, and increased partitioning of carbon into evolved CO2.

Trends and Spatio-Temporal Variability of Summer Mean and Extreme Precipitation across South Korea for 1973-2022

Climate change has altered the frequency, intensity, and timing of mean and extreme precipitation. Extreme precipitation has caused tremendous socio-economic losses, and severe impacts on human life, livelihood, and ecosystems. In recent years, heavy rainfall events occurred during the boreal summer (June-to-August) frequently and sporadically over South Korea. Given that its severity, a call for an urgent investigation of summer extreme rainfall is needed. Although many previous studies have addressed daily extreme precipitation, hourly extreme rainfall still needs to be thoroughly investigated. Therefore, in this study, we investigated the trends, spatio-temporal variability, and long-term variations in mean and extreme precipitation over South Korea during the boreal summertime using daily and hourly observational data through various analysis methods. During the past 50 years (1973-2022), there has been a notable escalation in maximum hourly precipitation, although the boreal summer mean precipitation has increased only marginally. Regionally, an increase in mean and extreme rainfall occurred in the northern part of the central region and the southern coast of the Korean peninsula. Moreover, the increase in intensity and frequency of extreme precipitation as well as in dry day have contributed more to the total summer precipitation in recent years. Our findings provide scientific insights into the progression of extreme summer precipitation events in South Korea.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13143-023-00323-7.

Climate change and pregnancy complications: From hormones to the immune response

Pregnant women are highly vulnerable to adverse environments. Accumulating evidence highlights that increasing temperatures associated with the ongoing climate change pose a threat to successful reproduction. Heat stress caused by an increased ambient temperature can result in adverse pregnancy outcomes, e.g., preterm birth, stillbirth and low fetal weight. The pathomechanisms through which heat stress interferes with pregnancy maintenance still remain vague, but emerging evidence underscores that the endocrine system is severely affected. It is well known that the endocrine system pivotally contributes to the physiological progression of pregnancy. We review – sometimes speculate - how heat stress can offset hormonal dysregulations and subsequently derail other systems which interact with hormones, such as the immune response. This may account for the heat-stress related threat to successful pregnancy progression, fetal development and long-term children’s health.