Search Results (104)

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Daoist robes in the Ming Dynasty literature underwent a marked transformation from exclusive religious vestments to widespread secular attire. Originally confined to Daoist priests and sacred rites, these garments began to appear in everyday work, entertainment, and ceremonies across social strata. Drawing on a hand-coded corpus of novels that yields robe related passages, and by analyzing textual references from Ming novels, Daoist canonical works, and visual artifacts, and applying clothing psychology and semiotic theory, this study elucidates how Daoist robes were re-coded as secular fashion symbols. For example, scholar-officials donned Daoist robes to convey moral prestige, laborers adopted them to signal upward mobility, and merchants donned them to impersonate the educated elite for commercial gain. By integrating close textual reading with cultural theory, the article advances a three-stage model, sacred uniform, ritual costume, and secular fashion, that clarifies the semantic flow of Daoist robes. In weddings and funerals, many commoners flaunted Daoist robes despite sumptuary laws, using them to assert honor and status. These adaptations reflect both the erosion of Daoist institutional authority and the dynamic process of identity construction through dress in late Ming society. Our interdisciplinary analysis highlights an East Asian perspective on the interaction of religion and fashion, offering historical insight into the interplay between religious symbolism and sociocultural identity formation. Full article

With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article

In recent years, high-altitude cities with low emissions in western China have exhibited an upward trend in surface ozone (O₃). Based on observations and reanalysis data, this study analyzed the evolutionary characteristics and pollution mechanisms of ozone in Xining and quantified the impact of stratospheric intrusion. The results indicated that an upward trend in summer O₃ was observed in Xining. A total of 29 ozone exceedance days were found. Potential exceedance days (>150 and >140 μg/m³) showed substantial increases from 2022 to 2023. Using a stratospheric intrusion to surface (SITS) event identification algorithm, 42 events were found in Xining, with an average duration of 8.4 h. Spring exhibited the highest event frequency (13 events) and longest average duration. SITS events contributed an average of 19.7% to surface ozone, significantly exacerbating local exceedance risks. A typical ozone pollution episode from 25 July to 3 August 2021 was analyzed. The peak O₃ reached 170 μg/m³. Elevated temperature, intensified radiation, and unfavorable meteorological conditions synergistically promoted local photochemical ozone production and accumulation. Notably, a SITS event was simultaneously detected, elevating surface ozone by 24%, which confirmed that stratospheric intrusion was the main cause of pollution. Full article

A cyclonic ocean eddy (COE) exhibited an extraordinarily prolonged response to sequential typhoons Hinnamnor (1 September 2022) and Muifa (11 September 2022), reaching its peak strength 20 days post-typhoon (1 October 2022), almost double the typical 7–14-day latency for mesoscale eddies. In this study, we use a functional analysis apparatus, namely the multiscale window transform (MWT) and the MWT-based theory of canonical transfer and multiscale energetics analysis, to investigate the dynamics underlying this phenomenon. The original fields, which are obtained from HYCOM reanalysis data, are initially decomposed into three parts in three different scale windows, respectively, with the eddy-scale window (or COE window) lying in between. By examining the evolution of eddy kinetic energy (EKE), the response can be divided into two stages. From the energetic diagnosis, the COE’s response is not only visible at the surface but was even strengthened through interactions between the subsurface and surface, with vertical transport playing a crucial role. This response can be categorized into two stages: The energetics of the long-delayed response is in the first stage due to the storage of the eddy-scale available potential energy (EAPE) from the high-frequency scale window, where the typhoon injects energy through an inverse canonical transfer. The resulting EAPE is transported downward to the sub-surface. In the second stage, the subsurface EKE is carried upward to the surface via pressure work, leading to an explosive growth of the COE. These findings illuminate the significance of subsurface–surface interactions in modulating long-delayed eddy responses. Full article

This paper presents a detailed analysis of the energy dynamics of the Mediterranean tropical-like cyclone, Medicane Ianos, by using a moist static energy (MSE) budget framework. Medicanes are hybrid cyclonic systems that share characteristics of both extratropical and tropical cyclones, making their classification and prediction challenging. Using high-resolution ERA5 reanalysis data, we analyzed the life cycle of Ianos, which is one of the strongest recorded medicanes, employing the vertically integrated MSE spatial variance budget to quantify the contributions of different energy sources to the cyclone’s development. The chosen study area was approximately 2500² km², covering the entire track of the cyclone. The budget was calculated after tracking Ianos and applying Hart phase space analysis to assess the cyclone phases. The results show that the MSE budget can reveal that the cyclone development was driven by a delicate balance between convection and dynamical factors. The interplay between vertical and horizontal advection, in particular the upward transport of moist air and the lateral inflow of warm, moist air and cold, dry air, was a key mechanism driving the evolution of Ianos, followed by surface fluxes and radiative feedback. By analyzing what process contributes most to the increase in MSE variance, we concluded that Ianos can be assimilated in the tropical framework within a radius of 600 km around the cyclone center, but only during its intense phase. In this way, the budget can contribute as a diagnostic tool to the ongoing debate regarding medicanes classification. Full article

The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with heatwaves. Additionally, this study examines the role of different land cover types in modulating heatwave impacts, employing turbulent flux observations from micrometeorological towers. The interaction between heatwaves and droughts is further explored using the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture data, highlighting the amplifying role of water stress through land–atmosphere feedbacks. The results reveal a statistically significant upward trend in LST-derived thermal anomalies, with the 2022 heatwave identified as the most extreme event, when approximately 75% of Europe experienced strong positive anomalies. On average, 91% of heatwave episodes identified in reanalysis-based air temperature records coincided with LST-defined anomaly events, confirming LST as a robust proxy for heatwave detection. Flux tower observations show that, during heatwaves, evergreen coniferous and mixed forests predominantly enhance sensible heat fluxes (mean anomalies during midday of 74 W/m² and 62 W/m², respectively), while grasslands exhibit increased latent heat flux (89 W/m²). Notably, under extreme compound heat–drought conditions, this pattern reverses for grassed sites due to rapid soil moisture depletion. Overall, the findings underscore the combined influence of surface temperature and drought in driving extreme heat events and introduce a novel, multi-source approach that integrates satellite, reanalysis, and ground-based data to assess heatwave dynamics across scales. Full article

This study examines exceptional maximum temperature (T_max) events in mainland Portugal (1980–2024) using ERA5-Land reanalysis data at 1012 locations. To assess changes in the occurrence and temperature excess of exceptional events across two 22-year subperiods (or phases), percentile-based thresholds were adopted. An inventive severity heatmap is used to illustrate exceptional T_max changes between the two phases, which constitutes an addition to climate change research. Locations are categorised in the heatmap according to whether they experienced (i) more occurrences and more temperature excess, (ii) more occurrences but less temperature excess, (iii) fewer occurrences but more temperature excess, or (iv) fewer occurrences and less temperature excess. From the historical (1980–2002) to the contemporary (2002–2024) phase, results indicate a significant increase in the severity of extreme heat, particularly in central and southern Portugal, with over 90% of locations exhibiting a rise in exceptional event occurrences. While the Student’s t-test indicated significant differences in both occurrence and temperature excess between the phases, Sen’s slope estimation showed steady upward trends. The results point to crucial regions in the interior and southern Portugal that have warmed the most, posing growing threats to agriculture, human health, and water resources. Although slight cooling trends were observed in a few northern and central coastal regions, the overall pattern highlights an increase in extreme heat. This research is particularly relevant given the recent changes in exceptional T_max identified in Portugal, aligning with broader climate change patterns and trends. Full article

Introduction: Human papillomavirus (HPV) genotyping is critical for preventing and managing HPV-related health issues, including cancers. This study re-evaluates HPV genotype trends in Iran to inform prevention strategies. Materials and Methods: A cross-sectional analysis of HPV genotyping data from individuals tested at the ACECR Khorasan Razavi molecular laboratory in Iran (2016–2022) was conducted, with a forecast of genotype trends through 2027. Results: Among 5009 female patients, 40.4% tested positive for HPV (mean age: 32 ± 8.77 years), with a significant upward trend in positivity over time (tau = 0.905, p = 0.0069). HPV 6, 11, 16, 31, 53, and 54 showed significant increases (p < 0.01), while HPV 66, 84, 67, and 35 exhibited notable trends (p < 0.05). HPV 18 and 33 had marginal trends (p = 0.065, p = 0.052), and HPV 68, 70, and 82 remained stable. Linear regression indicated a non-significant decline in low-risk HPV cases (R = 0.703, p = 0.078) and negligible change in high-risk cases (R = 0.052, p = 0.912). Forecasts predicted increases in HPV 84, 54, 43, 42, and 26, with HPV 6 projected to decrease significantly. HPV 44, 73, and 33 were expected to remain stable. Conclusion: While low-risk HPV cases may decline, the trend lacks statistical significance, and high-risk HPV cases show no change. These findings underscore the need for targeted prevention strategies in Iran, particularly for high-risk genotypes, to reduce the burden of HPV-related cancers. Further research is essential to validate these trends and refine public health interventions. Full article

Asian Mega-Deltas (AMDs) are important food baskets and contribute significantly to global food security. However, these areas are extremely susceptible to the consequences of climate change, such as rising temperatures, sea-level rise, water deficits/surpluses and saltwater intrusion. This study focused on maize crop suitability mapping and yield assessment in two major AMDs: the Ganges Delta, spanning parts of northeast India and Bangladesh, and the Mekong Delta across Vietnam and Cambodia. We investigated the historical climate reanalysis AgERA datasets and climate projections from the Coupled Model Intercomparison Phase 6 (CMIP6) for the periods 2040–2070 and 2070–2100 using PyAEZ-based modeling to estimate maize yields for periods in the near (2050s) and far future (2100s). Province-level yield estimates were validated against statistics reported by the governments of the respective countries. Model performance varied across regions, with R² values ranging from 0.07 to 0.94, MAE from 0.67 t·ha⁻¹ (14.2%) to 1.56 t·ha⁻¹ (20.7%) and RMSE from 0.62 t·ha⁻¹ (14.6%) to 1.74 t·ha⁻¹ (23.1%) in the Ganges Delta, and R² values from 0.23 to 0.85, MAE from 0.37 t·ha⁻¹ (12.8%) to 2.7 t·ha⁻¹ (27.2%) and RMSE from 0.45 t·ha⁻¹ (15.9%) to 1.76 t·ha⁻¹ (30.9%) in the Mekong Delta. The model performed comparatively better in the Indian region of the Ganges Delta than in the Bangladeshi region, where some yield underestimation was observed not accurately capturing the increasing upward trend in reported yields over time. Similarly, yields were underestimated in some provinces of the Mekong Delta since 2008. This may be attributed to improved management practices and the model’s inability to fully capture high-input management systems. There are also limitations related to the downscaling of CMIP6 data; the yield estimated using the downscaled CMIP6 data has small variability under rainfed and irrigated conditions. Despite these limitations, the modeling approach effectively identified vulnerable regions for maize production under future climate scenarios. Additionally, maize crop suitability zones were delineated, providing critical insights for planning and policy design to support climate adaptation in these vulnerable regions. Full article

Introduction: The COVID-19 pandemic has disrupted antibiotic use; easing public health measures may alter infection presentations and antibiotic prescribing in primary care. The study investigated post-pandemic antibiotic utilisation trends in primary care. Methods: A multi-centre, retrospective cohort study was conducted across seven public primary care clinics in Western Singapore, which included all patients prescribed oral antibiotics between 2022 and 2023. Descriptive statistics were used to visualise the prevalence and conditions of the prescribed antibiotics. Antibiotic quality was evaluated using the WHO’s AWaRe (access, watch, reserve) classification. Antibiotic use was quantified using the number of items dispensed per 1000 inhabitants (NTI), defined daily doses (DDD) per 1000 inhabitants per day (DID), and DDD per 100 visits. Segmented regression analysis was applied to monthly prescriptions to assess the utilisation trends. Results: Antibiotic prescription rates increased significantly, from 3.5% in 2022 to 4.0% in 2023 (p = 0.001), with a 9.5% relative increase (38,920 prescriptions for 1,112,574 visits to 42,613 prescriptions for 1,063,646 visits). Respiratory conditions drove the increase in antibiotics use, with a 68.3% rise in prescriptions, with upper respiratory tract infections being the most common diagnosis for antibiotic prescriptions (n = 9296 prescriptions in 2023), with a steady monthly upward trend. Access group antibiotics accounted for >90% of prescriptions. The most antibiotics were prescribed for acne, with 36,304 DDD per 100 visits in 2023. Both NTI and DID significantly increased in 2022, largely contributed by a >100% increase in Watch group antibiotic use. Total antibiotic NTI dipped slightly in 2023, with a stable trend in both NTI and DID for all antibiotics. Conclusions: The post-COVID-19 pandemic surge in the antibiotic prescription rate for respiratory conditions and Watch group antibiotic use highlight the need for targeted stewardship interventions. Optimising acne treatment and diagnosis coding are key strategies to further reduce unnecessary prescriptions. Full article

Understanding the impact of human activities on regional water resources is essential for sustainable basin management. This study examines long-term terrestrial water storage anomalies (TWSA) in the Three Gorges Reservoir Area (TGRA) over two decades, from 2003 to 2023. The analysis utilizes data from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission (GRACE-FO), complemented by Global Land Data Assimilation System (GLDAS) models and ECMWF Reanalysis v5 (ERA5) datasets. The research methodically explores the comparative contributions of natural factors and human activities to the region’s hydrological dynamics. By integrating the GRACE Drought Severity Index (GRACE-DSI), this study uncovers the dynamics of droughts during extreme climate events. It also reveals the pivotal role of the Three Gorges Dam (TGD) in mitigating these events and managing regional water resources. Our findings indicate a notable upward trend in TWSA within the TGRA, with an annual increase of 0.93 cm/year. This trend is largely due to the effective regulatory operations of TGD. The dam effectively balances the seasonal distribution of water storage between summer and winter and substantially reduces the adverse effects of extreme droughts on regional water resources. Further, the GRACE-DSI analysis underscores the swift recovery of TWSA following the 2022 drought, highlighting TGD’s critical role in responding to extreme climatic conditions. Through correlation analysis, it was found that compared with natural factors (correlation 0.62), human activities (correlation 0.91) exhibit a higher relative contribution to TWSA variability. The human-induced contributions were derived from the difference between GRACE and GLDAS datasets, capturing the combined effects of all human activities, including the operations of the TGD, agricultural irrigation, and urbanization. However, the TGD serves as a key regulatory facility that significantly influences regional water resource dynamics, particularly in mitigating extreme climatic events. This study provides a scientific basis for water resource management in the TGRA and similar large reservoir regions, emphasizing the necessity of integrating the interactions between human activities and natural factors in basin management strategies. Full article

Climate change plays a significant role in altering the behavior of large-scale atmospheric systems, particularly the subtropical high-pressure systems relevant to the climate of Iran. This study investigates the impact of climate change on the subtropical high-pressure system over Iran by utilizing ERA5 reanalysis data and CORDEX projections. Focusing on future projections (2022–2063) under RCP4.5 and RCP8.5 scenarios, the analysis reveals substantial shifts in the position and intensity of the subtropical high when comparing the high-pressure center between currently observed data and the projected scenarios. The center of the high-pressure system exhibits a northward migration, particularly pronounced in August; a consistent upward trend in geopotential height, analyzed using the Kendall trend method, is observed, indicating a strengthening of the high-pressure system. This intensification leads to a westward and northward expansion of the summer high-pressure cell. Consequently, this study anticipates the emergence of more pronounced cyclonic circulations at higher latitudes (>38° N) in the future. These findings suggest that climate change will substantially alter the behavior of the subtropical high over Iran, impacting regional weather patterns and potentially leading to climate anomalies. Full article

In order to understand the tectonic control on shale oil migration and accumulation, samples of the Daanzhai Member of the Lower Jurassic Ziliujing Formation from the well core in a tectonically stable area and upright anticline outcrop were selected for total organic carbon (TOC) content analysis, rock pyrolysis, fluorescence scanning, and scanning electron microscopy. The results show the following: (1) In the tectonically stable area, the TOC of shale oil reservoirs is positively correlated with S₁, and a high OSI interval usually occurs in high-TOC shales. The oil content of the limestone lamina decreases with an increasing distance from black shale. The vertical migration of shale oil into or across the lamina is not obvious and is mainly micro-scale. (2) The migration pathway includes a lamina interface, shell–clay interface, calcite cleavage, feldspar or calcite dissolution pores, and quartz or kaolinite intergranular pores. Large-scale shale oil migration time occurs at the peak of oil generation. (3) In the area of strong tectonic deformation, the formation of fractures in limestone further promotes the migration of oil from shale into the lamina. (4) The re-migration of shale oil during the uplift and deformation period involves three processes: upward migration in a clay matrix, then entry and migration along the limestone–shale interface from the lateral pinch-out points of the lamina, migration into the lamina joints, and then short diffusion into the limestone. (5) The migration of shale oil in the Daanzhai shale was controlled by the history of hydrocarbon generation and tectonic deformation and occurred in several stages. Full article

Large-scale and long-term landslide susceptibility assessments are crucial for revealing the patterns of landslide risk variation and for guiding the formulation of disaster prevention and mitigation policies at the national level. This study, through the establishment of a global dynamic landslide susceptibility model, uses the multi-dimensional analysis strategy and studies the development trend of China’s large-scale landslide susceptibility. First, a global landslide dataset consisting of 8023 large-scale landslide events triggered by rainfall and earthquakes between 2001 and 2020 was constructed based on the GEE (Google Earth Engine) platform. Secondly, a global dynamic landslide susceptibility model was developed using the ResNet18 (18-layer residual neural network) DL (deep learning) framework, incorporating both dynamic and static LCFs (landslide conditioning factors). The model was utilized to generate sequential large-scale landslide susceptibility maps for China from 2001 to 2022. Finally, the MK (Mann–Kendall) test was used to investigate the change trends in the large-scale landslide susceptibility of China. The results of the study are as follows. (1) The ResNet18 model outperformed SVMs (support vector machines) and CNNs (convolutional neural networks), with an AUC value of 0.9362. (2) SHAP (Shapley Additive Explanations) analyses revealed that precipitation played an important factor in the occurrence of landslides in China. In addition, profile curvature, NDVI, and distance to faults are thought to have a significant impact on landslide susceptibility. (3) The large-scale landslide susceptibility trends in China are complex and varied. Particular emphasis should be placed on Southwest China, including Chongqing, Guizhou, and Sichuan, which exhibit high landslide susceptibility and notable upward trends, and also consider Northwest China, including Shaanxi and Shanxi, which have high susceptibility but decreasing trends. These results provide valuable insights for disaster prevention and mitigation in China. Full article