Search Results (1,263)

Substantial uncertainties remain in climate model simulations of tropical cyclones (TCs), particularly those associated with internal climate variability. While the influence of the El Niño–Southern Oscillation (ENSO) on interannual TC variability is well established, the contribution of the Interdecadal Pacific Oscillation (IPO) to decadal-scale uncertainty is less well constrained. Although models generally reproduce IPO-related variations in tropical cyclone genesis frequency (TCGF) over the eastern North Pacific, large discrepancies persist across the broader North Pacific basin. Clarifying the role of IPO in modulating TCGF uncertainty is therefore essential for improving decadal TC projections. In this study, we analyzed a large ensemble of historical simulations from the MRI-AGCM within the d4PDF (Database for Policy Decision Making for Future Climate Change) framework. Empirical orthogonal function (EOF) analysis is applied to IPO-composited fields to identify the leading modes of intermember (100 members *60 y, 6000 times) simulation uncertainty on a decadal-scale. The results reveal that state-of-the-art models exhibit robust and spatially coherent uncertainty structures in TCGF under different IPO phases. Two leading modes are identified: (1) a South China Sea mode, closely associated with systematic precipitation biases, and (2) a zonal dipole mode between the eastern and western North Pacific, linked to the equatorward propagation of Arctic Oscillation (AO)-related variability. Misrepresentation of AO variability is found to contribute substantially to biases in simulated TCGF patterns. Comparisons with observational datasets further support the proposed mechanisms. These findings highlight the importance of improving the representation of precipitation processes and extratropical–tropical teleconnections in climate models, which is critical for enhancing the reliability of decadal predictions of North Pacific TC activity. Full article

To investigate the relationship between fishery resources community structure (mainly fish and crustaceans) and environmental factors in the artificial reef waters off Nanri Island, surveys were conducted in November 2021, 2022, and 2023. Shannon–Wiener diversity index (H′), Margalef richness index (D), Pielou evenness index (J′), and resource density index (RD) were employed to characterize the community structure. From 2021 to 2023, DO and petroleum hydrocarbons exhibited significant interannual variation (p < 0.05), whereas DIP, DIN, and SS showed highly significant interannual variation (p < 0.01). Spatially, DO, COD, DIN, and petroleum hydrocarbons varied more than other factors. Both diversity and richness indices rose over the study period, with mean H′ rising from 1.693 to 1.942 and mean D from 2.107 to 2.474. The evenness index (J′) declined in 2022 but then increased to 0.787. In contrast, the resource density index (RD) dropped sharply in 2022 (107.2) and partially recovered in 2023 (155.4), though it remained below the 2021 level (218.5). Redundancy analysis revealed that five environmental variables (DIP, DIN, petroleum hydrocarbons, SS, and DO) primarily shaped the fishery resource community structure in the artificial reef area. This study provided reference data for artificial reef management and sustainable fishery development. Full article

To identify upland cotton varieties with consistently high yields and stable performance across variable growing seasons in Xinjiang, we evaluated yield data for 11 varieties over 4 consecutive years (2022–2025). Among the tested varieties, 02 achieved the highest average yield (10.85 kg per plot). Variety ZMBH1939 showed the most stable yield across years (coefficient of variation = 0.1557). Analysis of variance showed that variety, year, and their interaction significantly affected yield (p < 0.01 for all). Further evaluation using two complementary multi-environment trial models (AMMI and GGE) revealed consistent findings: 02 and FC190 were high-yielding but moderately stable; W21 and TH02 showed moderate yield with good stability; and XLM108 combined high yield potential with excellent stability. The control variety Z49 (CK) exhibited good stability but only moderate yield. Among the four trial years, 2023 was the most representative and discriminatory environment, making it ideal for screening superior varieties. Exploratory analysis of climatic covariates suggested that accumulated temperature (≥10 °C) may be associated with interannual yield variation (R² = 0.464), and low precipitation was linked to stronger environmental discrimination. However, given the limited number of environments (n = 4), these findings are preliminary and hypothesis-generating rather than confirmatory. This study provides a framework for understanding climate-driven yield variation in regional cotton trials and identifies promising germplasm (notably XLM108 and 02) for further breeding and promotion. Validation in multi-location or longer-term trials is required before drawing definitive conclusions. Full article

Cooling water systems of coastal nuclear power plants in China are frequently threatened by blockages caused by marine organisms. However, long-term studies on macrobenthic community dynamics and their associations with environmental factors are scarce, limiting the precise prevention of such blockage risks. This study conducted quantitative monitoring of macrobenthos and synchronous measurement of water environmental factors at 24 sampling stations in three functional areas (water intake, harbor basin, and drainage outlet) adjacent to the Northeast Fujian NPP from 2018 to 2024. Community structure characteristics were analyzed using the Shannon–Wiener and Margalef indices. The Grappler Method Risk Index (GMRI) was employed to screen species at risk of blocking cooling water systems, and the Mantel test and random forest models were applied to explore the associations between the macrobenthic community and environmental factors. A total of 161 macrobenthic species were identified. Polychaetes (71 species, accounting for 44.1%) were the absolute dominant group, followed by crustaceans (35 species) and Mollusks (30 species). The interannual fluctuation range of the polychaete proportion was 41.1–57.8%, reaching a peak in 2023. There were significant differences in community structure among different areas (PERMANOVA, p < 0.05), with the largest inter-regional difference in 2024 (R² = 0.36). The annual average number of species (9 species), density (155.25 ind./m²), and biomass (29.58 g/m²) in the drainage outlet were higher than those in the water intake and harbor basin. The GMRI identified Protankyra bidentata (spiny sea cucumber, GMRI values of 50.67% to 64.98% from 2019 to 2023) and Actiniaria sp. (sea anemone, a GMRI value of 54.63% in 2021) as medium-risk species for cooling water system blockage, while most other organisms were classified as low risk or extremely low risk. The Mantel test and random forest analysis confirmed that nitrogen nutrients (NO₃⁻) and phosphorus (PO₄³⁻) were significantly positively correlated with the polychaete community. Furthermore, NO₃⁻ and NH₄⁺ each explained 13.66% of the variation in the diversity index (H′), serving as key factors driving community structure. This study demonstrates the co-dominance of thermal and nutrient drivers in shaping macrobenthic communities over a multi-year scale, and identifies specific, morphologically suited taxa as potential blockage risks. The findings provide a scientific basis for targeted risk-species monitoring and support the integration of long-term ecological data into NPP cooling water system security management. Full article

El Niño–Southern Oscillation (ENSO) has been identified as a key factor influencing grapevine phenology and harvest timing in South America. Nevertheless, few long-term analyses have explored its varietal impacts in hyper-arid viticultural regions. The goal was to evaluate the effect of ENSO phases on harvest dates and bioclimatic indices in different grapevine varieties cultivated in Northern Chile. The results revealed that Muscat of Alexandria showed little variation in harvest timing across ENSO phases. In contrast, harvest time in Thompson Seedless was delayed under La Niña events, being strongly correlated with the Maximum Spring Temperature Summation (SON_max) Index. Moscatel Rosada and Flame Seedless showed non-statistical significance and high variability on harvest dates. El Niño phases were consistently warmer than La Niña events that showed markedly greater interannual variability on harvest dates and bioclimatic index values. The strength of correlations was improved when the bioclimatic indices were recalculated over adjusted seasonal windows, underscoring the need for phenology-based rather than calendar-based approaches. These results provide new evidence of the heterogeneous responses of table and Pisco grapevine varieties to ENSO events in the hyper-arid regions of Northern Chile, underscoring the varietal differences in sensitivity to early-season climatic anomalies. Full article

Droughts are complex extreme phenomena that severely impact regional development and water availability. Although the influence of interannual and decadal macroclimatic patterns, such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on precipitation alteration is widely recognized, current water management systems lack multivariate predictive approaches to anticipate their phases with sufficient operational lead time. This study developed a predictive framework to project ENSO and PDO phases, establishing an optimal temporal window to forecast drought-triggering conditions. Using monthly historical records, teleconnections were evaluated through cross-correlation and Granger causality. Subsequently, Vector Autoregression (VAR) models and machine learning algorithms (Random Forest) were implemented to project anomalies and classify climatic phases. The Granger causality test demonstrated that ENSO variations statistically precede PDO phase shifts, establishing an optimal forecasting window of three to four months. The VAR model exhibited robust joint explanatory capacity for a continuous four-month projection, while the Random Forest algorithm achieved a predictive accuracy of 52.2% specifically for categorical phase classification at a three-month lead time. It is concluded that this lagged interaction allows for reliable mathematical anticipation, providing an essential analytical framework for exploring regional hydrological dynamics and supporting local preventive water management. Full article

The Meiyu season over the Yangtze–Huaihe River Basin exhibits pronounced interannual variability and directly reflects the persistence of the East Asian summer rainband. This study examined the relationship between the preceding April sea ice anomaly of the Barents–Kara seas and Meiyu length during 1979–2023 based on CN05.1 precipitation, ERA5, HadISST sea ice concentration datasets, and Indo-Pacific SST index. A statistically significant inverse relationship was identified between the interannual Meiyu Length and the preceding April Barents–Kara seas sea ice anomaly, with the strongest signal located over the core Barents–Kara seas sector and a filtered Barents–Kara seas sea ice index–Meiyu length index correlation coefficient of −0.662. Composite and regression analyses demonstrated that reduced interannual April Barents–Kara seas sea ice concentration is associated with a downstream Rossby-wave-like upper-tropospheric circulation pattern, leading to a clearer upper-level potential vorticity band and an intensified westerly jet that generates increased convergence over the Yangtze–Huaihe River Basin. Additionally, the north-low–south-high circulation contrast over the East Asian–western North Pacific sector during years with a longer Meiyu period, associated with an interannual reduction in the Barents–Kara seas sea ice index, contributes to enhanced moisture convergence and convection that drive stronger ascent over the Yangtze–Huaihe River Basin, favoring a more persistent Meiyu rainband and a longer Meiyu period. Full article

Background: This study aims to measure Greenhouse Gas (GHG) emission fluxes at the soil–air and water–air interfaces in urban wetlands on the Qinghai–Tibet Plateau and identify the primary controlling factors. The objective is to elucidate the key drivers of carbon and nitrogen processes at different interface levels in wetlands within high-altitude urban settings, thereby providing a scientific basis for accurately estimating their contribution to greenhouse gas emissions. Results: In the wetlands of Xining City, with the exception of soil pH, bulk density, and moisture content (which showed no significant change over time), all other soil physicochemical properties differed significantly among the three wetlands and among the sampling periods (p < 0.05). Soil moisture content was less affected by variations across different wetlands and over time, and differences in soil physicochemical properties among different wetlands were small (p > 0.05). Significant differences were observed in the spatiotemporal variations in the physicochemical properties of water bodies in Xining’s wetlands (p < 0.05), although water pH and total organic carbon (TOC) were less affected by the interaction between different wetlands and time periods. There were no significant differences in the bulk density and moisture content of wetland sediments in Xining over time (p > 0.05), while all other physicochemical indicators of sediments showed significant differences (p < 0.05). The physicochemical properties of sediments were influenced by both different wetland types and different time periods. GHG fluxes at the water–air interface in Xining wetlands were greater than those at the soil–air interface; overall, GHG emissions from both interfaces acted as “sources.” Seasonal variations in wetland GHG emissions were pronounced, with emission peaks occurring from June to August. The study found that the primary soil factor influencing GHG emissions at the soil–air interface was total phosphorus (TP), while the primary sediment factors affecting emissions at the water–air interface were TP and nitrate nitrogen (NO₃⁻-N), and the primary water factor was TOC. The interannual cumulative emissions from both interfaces in the wetland totaled 705.88 g·m⁻². GHG emissions from the soil–air and water–air interfaces contributed 47.88% and 52.12%, respectively, to the global warming potential (GWP) of the wetland, while methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) contributed 32.55%, 62.33%, and 5.12%, respectively, to the GWP. Conclusions: Investigating the GHG emission patterns in Xining’s wetlands and identifying the primary factors influencing these emissions provides a scientific basis for the protection and restoration of these wetlands. This is of great significance for safeguarding the ecological security of Xining’s wetlands as well as the overall ecological security of high-altitude wetlands. Full article

Using surface observations, ADTD lightning data, and radar reflectivity from April-September 2022–2024 in the Xi’an terminal area, this study classified severe convective events into four categories: ordinary thunderstorms, short-duration heavy precipitation, convective wind gust, and hail events. Their temporal variability, spatial distribution, life cycle characteristics, and propagation pathways were systematically analyzed. The results reveal significant differences among convective event types across multiple temporal and spatial scales. Convective wind gust events exhibited the strongest interannual variability, with a decrease of 44% from 2023 to 2024. Hail events occurred relatively infrequently, totaling only 16 cases from 2022 to 2024. Seasonally, convective wind gusts were concentrated in April-May, while ordinary thunderstorms and short-duration heavy precipitation events mainly occurred in July–August. Most events initiated during the afternoon and intensified toward evening, with short-duration heavy precipitation events showing a bimodal diurnal variation. Ordinary thunderstorms were dominated by short-lived events lasting 30–60 min, whereas heavy precipitation, convective wind gust, and hail events were primarily associated with long-lived convective systems exceeding 180 min. Spatially, severe convective weather generally initiated in the western part of the terminal area and propagated eastward. Lightning activity was more concentrated in the southeastern sector, indicating greater impacts on the SHX waypoint. Propagation paths were predominantly oriented toward the east-northeast. Full article

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the performance of multiple lentil genotypes, alongside optimal seeding densities and growing seasons, through a series of field experiments conducted over five years. In addition, a sensory evaluation was performed on 12 selected genotypes to assess consumer-relevant quality traits. The findings revealed substantial variability in yield among genotypes, ranging from 0.9 to 1.6 t/ha; however, interannual variation exerted a more pronounced influence, with yields fluctuating between 0.1 and 2.0 t/ha. Notably, autumn-sown lentils achieved yields of up to 2.7 t/ha in three out of four growing seasons, even among genotypes lacking full winter-hardiness, indicating significant production potential under appropriate management conditions. Optimal plant densities were identified within the range of 180–240 plants/m². From an economic standpoint, higher seeding densities appear justifiable, as the increased seed costs are offset by corresponding gains in yield. Since intercropping of lentils with oats did not negatively affect grain yield nor the thousand kernel weight, the benefits of this cropping system are highlighted. Sensory analysis demonstrated statistically significant differences in attributes such as mealiness and juiciness, leading to the classification of genotypes into three distinct sensory clusters. Despite these differences, overall sensory variation was relatively limited, suggesting that genotype selection may be guided primarily by agronomic performance, climatic adaptability, and winter-hardiness, as well as by market preferences for seed colour and size. Collectively, these results highlight the potential of autumn sowing as a viable strategy to enhance lentil production and reduce the risk of crop failure in Swiss agricultural systems. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

Nearshore mariculture is a major contributor to the supply of “blue food”; however, its rapid expansion in bay systems has intensified sea-space competition and environmental pressures, underscoring the need for accurate and long-term monitoring. This study used multitemporal Sentinel-2 imagery processed using Google Earth Engine (GEE) to develop an automated identification framework for raft and cage aquaculture along the coast of Fujian, China, from 2017 to 2024. Three widely used classifiers—U-Net, DeepLabV3+, and random forest (RF)—were comparatively evaluated. Of these methods, U-Net had the most stable overall performance under optically complex nearshore conditions and was, therefore, used for province-scale mapping. Based on the U-Net-derived maps, the spatiotemporal evolution of mariculture was quantified. The results showed that mariculture in Fujian exhibited a persistent bay-oriented, dual-core clustering pattern, with major hotspots concentrated in Ningde and Zhangzhou. In the 2024 winter–summer comparison, raft aquaculture displayed a clear seasonal contrast, characterized by expansion in winter and contraction in summer, whereas cage aquaculture showed relatively smaller seasonal variation. Interannually, the mariculture system shifted from a mixed cage–raft configuration toward the dominance of raft aquaculture, accompanied by a spatial redistribution of mapped aquaculture density from inner nearshore waters toward bay mouths and more open waters. Overall, in this study, we demonstrate the potential of deep learning-enabled Sentinel-2 remote sensing for monitoring nearshore mariculture structures and provide mode-specific observational evidence for marine spatial planning, environmental risk management, and sustainable mariculture development in nearshore waters and semi-enclosed bay systems. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

Three locally calibrated Indices of Biotic Integrity (IBIs) based on macroinvertebrates (B-IBI), zooplankton (Z-IBI), and phytoplankton (P-IBI) were developed to characterize relative aquatic ecological condition at impaired sites in Dongjiang Lake, a deep reservoir-type lake in China, during 2021–2023. Using synchronous monitoring data, candidate metrics for the three biotic groups were screened and assembled by integrating taxonomic diversity, community composition, pollution-tolerance attributes, trophic indicators, and functional feeding groups. Metric values were standardized using a linear transformation, and site conditions were classified using a unified five-class grading scheme under the present local calibration framework. A total of 327 taxonomic units (species or morphospecies) were recorded across the three biotic groups, indicating relatively high biodiversity in the study area. Under the present locally calibrated framework, most impaired sites were classified within the moderate-to-good range, with clear interannual variation and spatial heterogeneity. Upstream and downstream sections showed greater fluctuations in IBI classes than the lake area. The macroinvertebrate-based IBI was more sensitive to long-term and cumulative habitat disturbance, whereas the zooplankton- and phytoplankton-based IBIs responded more rapidly to short-term variation in nutrients and water quality. Together, these results indicate that multitaxon IBIs can provide complementary information on relative ecological condition within Dongjiang Lake and may support ecological zoning, pollutant management, and restoration prioritization in similar deep reservoir-type lake systems. Full article

Lake color is an intuitive indicator reflecting the ecological and physicochemical status of lakes and is of great value for both ecological monitoring and environmental assessment. However, the types, spatiotemporal variations, and driving mechanisms of global lake color phenology remain unclear. In this study, we systematically analyzed the color phenology of 975 global lakes based on Landsat remote sensing data from 1984 to 2021. The results indicate that lake color phenology can be categorized into six types, including the perennial green type, evergreen type, and seasonal patterns (spring green, summer green, autumn green, and winter green). Approximately 43.9% of the lakes are classified as the evergreen type, mainly concentrated in the Southern Hemisphere. Further research reveals notable spatial differences in the change in lake color phenology: about 69.4% of lakes in the Southern Hemisphere exhibit relatively stable phenological patterns (frequency of changes within the study area ≤ 2), while approximately 64.4% in the Northern Hemisphere show phenological variations. This dynamic disparity is closely related to lake attributes (area, water depth, elevation) as well as external climatic and watershed conditions (precipitation, wind speed, vegetation). Our findings contribute to developing the interannual patterns of lake color into a novel ecological indicator, thereby advancing the dynamic monitoring and assessment of global lake status. Full article