Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance o...Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.展开更多
Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of grid...Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.展开更多
By making use of Empirical Orthogonal Function (EOF) analysis the spatial and temporal variability was investigated in January over the period of 1950 to 2000 in Pakistan. The analysis is based on the combination of g...By making use of Empirical Orthogonal Function (EOF) analysis the spatial and temporal variability was investigated in January over the period of 1950 to 2000 in Pakistan. The analysis is based on the combination of ground observed mean monthly temperature data and National Centre for Environmental Prediction (NCEP) reanalysis data of sea level pressure (SLP) and 500-hPa fields. The results reasonably reveal that the variation in January temperature have links with global teleconnections at SLP and 500-hPa pressure heights. The analysis shows variability at interannual to interdecadal time scale. The interannual variation is more prominent than the interdecadal signal of temperature anomaly. The simulated coefficient patterns show reasonable variation with regional detail from south (north) to north (south) in the study area. The study could be useful as baseline information for climate change studies in Pakistan.展开更多
Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inv...Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.A...In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.展开更多
Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wil...Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.展开更多
Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including at...Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.展开更多
As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wet...As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Maize(Zea mays)is highly susceptible to waterlogging stress,which reduces both the yield and quality of this important crop.However,the molecular mechanism governing waterlogging tolerance is poorly understood.In this...Maize(Zea mays)is highly susceptible to waterlogging stress,which reduces both the yield and quality of this important crop.However,the molecular mechanism governing waterlogging tolerance is poorly understood.In this study,we identify a waterlogging-and ethylene-inducible gene ZmEREB179 that encodes an ethylene response factor(ERF)localized in the nucleus.Overexpression of ZmEREB179 in maize increases the sensitivity to waterlogging stress.Conversely,the zmereb179 knockout mutants are more tolerant to waterlogging,suggesting that ZmEREB179 functions as a negative regulator of waterlogging tolerance.A transcriptome analysis of the ZmEREB179-overexpressing plants reveals that the ERF-type transcription factor modulates the expression of various stress-related genes,including ZmEREB180.We find that ZmEREB179 directly targets the ZmEREB180 promoter and represses its expression.Notably,the analysis of a panel of 220 maize inbred lines reveals that genetic variations in the ZmEREB179 promoter(Hap2)are highly associated with waterlogging resistance.The functional association of Hap2 with waterlogging resistance is tightly co-segregated in two F2 segregating populations,highlighting its potential applications in breeding programs.Our findings shed light on the involvement of the transcriptional cascade of ERF genes in regulating plant-waterlogging tolerance.展开更多
Nutrient uptake status dominates phytoplankton biomass and community structure in the Southern Ocean during austral summer,yet how nutrient utilization variability responds to phytoplankton community succession is sti...Nutrient uptake status dominates phytoplankton biomass and community structure in the Southern Ocean during austral summer,yet how nutrient utilization variability responds to phytoplankton community succession is still unclear,partly due to lack of data spanning the entire summer.In this study,nitrate,phosphate,and silicate combined with temperature,salinity,and apparent oxygen utilization(AOU)were analyzed along 45°E in the Cosmonaut Sea during December 2019,January 2021,and February 2022.The variations in nutrient utilization in the euphotic layer were studied using biogeochemical tracers,and seasonal nutrient depletion was also estimated.The results showed that nutrient distribution varied significantly from December to February.Significant positive correlations were observed for nitrate and silicate concentrations with salinity and AOU,indicating that nutrient distributions were mainly influenced by water mass and phytoplankton production.Increasing∆[N*]and decreasing∆[Si*]in the upper 50 m were observed south of 63.5°S from December to February,which possibly contributed to a progressive shift in dominant phytoplankton population from Phaeocystis antarctica to diatoms.The seasonal nutrient depletion generally increased from December to February.Moreover,the consumption of silicate substantially increased compared to nitrate,indicating that the abundance of diatoms was increasing with time during the austral summer.Our observations suggest that nutrient utilization status is closely related to phytoplankton community structure in the euphotic layer of the Cosmonaut Sea.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
This study examines in-situ temperature profiles in three representative sections,namely,the Dalian-Chengshantou(DC),the Chengshantou-Changsangot(CC),and the 36°N,to delineate the interannual variations of the Ye...This study examines in-situ temperature profiles in three representative sections,namely,the Dalian-Chengshantou(DC),the Chengshantou-Changsangot(CC),and the 36°N,to delineate the interannual variations of the Yellow Sea Cold Water Mass(YSCWM)and investigate their potential connections,along with forcing factors,across different regions.The findings reveal the fol-lowing insights:1)The YSCWM experiences warming trends at DC,CC,and the western segment of the 36°N,revealing correspond-ing minimum temperature rates of 0.021℃/yr,0.043℃/yr,and 0.063℃/yr,respectively.Conversely,the eastern portion of the 36°N displays a slight cooling trend,resulting in a pronounced zonal disparity in long-term temperature trends.2)The changes in the YSCWM are closely linked to the atmospheric wind patterns.Notably,the weakening of northerly winds during winter corresponds to the rise in YSCWM temperature,which is accompanied by a westward shift in the cold core of the 36°N section.3)Correlation analysis with factors such as the Arctic Oscillation(AO),Pacific Decadal Oscillation(PDO),and El Niño-Southern Oscillation(EN-SO),etc.,indicates that changes in large-scale climate systems influence the spatiotemporal variations of the YSCWM,resulting in seasonal differences.展开更多
Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation...Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation,along with the construction of a high-precision mobile gravity network covering Chinese mainland,have positioned temporal gravity variations(GVs)as an important tool for clarifying the signal characteristics and dynamic mechanisms of crustal sources.Reportedly,crustal mass transfer,which is affected by stress state and structural environment,alters the characteristics of the regional gravity field,thus serving as an indicator for locations of moderate to strong earthquakes and a seismology-independent predictor for regions at risk for strong earthquakes.Therefore,quantitatively tracking time-varying gravity is of paramount importance to enhance the effectiveness of earthquake prediction.In this study,we divided the areas effectively covered by the terrestrial mobile gravity network in the Sichuan-Yunnan region into small grids based on the latest observational data(since 2018)from the network.Next,we calculated the 1-and 3-year GVs and gravity gradient indicators(amplitude of analytic signal,AAS;total horizontal derivative,THD;and amplitude of vertical gradient,AVG)to quantitatively characterize variations in regional time-varying gravity field.Next,we assessed the effectiveness of gravity field variations in predicting earthquakes in the Sichuan-Yunnan region using Molchan diagrams constructed for gravity signals of 13 earthquakes(M≥5.0;occurred between 2021 and 2024)within the terrestrial mobile gravity network.The results reveal a certain correspondence between gravity field variations and the locations of moderate and strong earthquakes in the Sichuan-Yunnan region.Furthermore,the 3-year AAS and AVG outperform the 3-year THD in predicting subsequent seismic events.Notably,the AAS and AVG showed large probability gains prior to the M_(S)6.8 Luding earthquake,indicating their potential for earthquake prediction.展开更多
Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These su...Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.展开更多
Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of t...Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of the seasonal variations in the MHWs in the ECS remains limited.This study investigates the characteristics of MHWs in the ECS in summer and winter using high-resolution oceanic reanalysis.Our analyses reveal distinct spatial patterns of BMHWs in these seasons.During summer,the Taiwan Warm Current plays a crucial role in transporting warm water northward,potentially leading to intense BMHWs on the central ECS shelf.These BMHW events usually occur independently of surface warming due to strong stratification in summer.Conversely,winter BMHWs are more prevalent in coastal regions under the influence of coastal currents and typically feature consistent warming from surface to bottom with a deepened mixed layer.These findings inform the coherent vertical structure and driving mechanisms of MHWs in the ECS,which are essential for predicting and managing these extreme events in the future.展开更多
As the Arctic undergoes rapid warming and sea ice continues to decline,Ocean-to-Ice Heat Flux(OIHF)plays a crucial role in regulating sea ice dynamics.This study investigates the seasonal variations in ocean-to-ice he...As the Arctic undergoes rapid warming and sea ice continues to decline,Ocean-to-Ice Heat Flux(OIHF)plays a crucial role in regulating sea ice dynamics.This study investigates the seasonal variations in ocean-to-ice heat flux in north of the Fram Strait using the regional Arctic Ocean/sea ice reanalysis product from 1991 to 2020.The analysis reveals that the OIHF exhibits significant seasonal variability,with a pronounced peak during winter in north of the Fram Strait,driven by inflows of Atlantic water.Warm Atlantic water intrusion begins in October,reaches its peak in January and February,and results in a delayed increase in OIHF,with maximum flux observed 2−3 months later.These results highlight the significant role of Atlantic water inflows in influencing Arctic sea ice dynamics and emphasize the need for further investigation into the coupled ocean-atmosphere processes that govern seasonal fluctuations in OIHF.展开更多
Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration...Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration of negative ions and their influencing factors in Xinfeng County were analyzed.The results show that the concentration of negative ions was the highest in summer,followed by spring;it was lower in autumn and the lowest in winter.In terms of diurnal variations,it was higher in the early morning and night,and lower in the noon and afternoon,which was closely related to the diurnal variations of human activities and meteorological conditions.The factors that affect the concentration of negative ions in the air are more complex.Besides meteorological factors,vegetation,altitude,human activities and other factors should be considered.展开更多
基金funded by the National Key R&D Program of China(Grant No.2021YFD1500200)National Natural Science Foundation of China(Grant No.42077149)+4 种基金China Postdoctoral Science Foundation(Grant No.2019M660782)National Science and Technology Basic Resources Survey Program of China(Grant No.2019FY101300)Doctoral research start-up fund project of Liaoning Provincial Department of Science and Technology(Grant No.2021-BS-136)China Scholarship Council(201908210132)Young Scientific and Technological Talents Project of Liaoning Province(Grant Nos.LSNQN201910 and LSNQN201914)。
文摘Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.
文摘Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.
文摘By making use of Empirical Orthogonal Function (EOF) analysis the spatial and temporal variability was investigated in January over the period of 1950 to 2000 in Pakistan. The analysis is based on the combination of ground observed mean monthly temperature data and National Centre for Environmental Prediction (NCEP) reanalysis data of sea level pressure (SLP) and 500-hPa fields. The results reasonably reveal that the variation in January temperature have links with global teleconnections at SLP and 500-hPa pressure heights. The analysis shows variability at interannual to interdecadal time scale. The interannual variation is more prominent than the interdecadal signal of temperature anomaly. The simulated coefficient patterns show reasonable variation with regional detail from south (north) to north (south) in the study area. The study could be useful as baseline information for climate change studies in Pakistan.
基金supported by the Opening Foundation of the State Key Laboratory Breeding Base of Desertification and Aeolian Sand Disaster Combating,Gansu Desert Control Research Institute (GSDC201503)the National Natural Science Foundation of China (41271024,31260129,31360204)+1 种基金the Program for Innovative Research Group of Gansu Province,China (1506RJIA155)Lanzhou University for providing Arc GIS technical support in the data processing
文摘Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金This work is partly supported by the National Key Research and Development Program of China(Grant No.2020YFB1805403)the National Natural Science Foundation of China(Grant No.62032002)the 111 Project(Grant No.B21049).
文摘In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.
基金funded by the National Natural Science Foundation of China(grant no.32270238 and 31870311).
文摘Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.
基金supported by the Basic Science Center Project of the National Natural Science Foundation of China(42388102)the National Natural Science Foundation of China(42174030)+2 种基金the Special Fund of Hubei Luojia Laboratory(220100020)the Major Science and Technology Program for Hubei Province(2022AAA002)the Fundamental Research Funds for the Central Universities of China(2042022dx0001 and 2042023kfyq01)。
文摘Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.
文摘As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the science and technology major program of Hubei Province(2022ABA001)the National Key Research and Development Program of Shandong Province(2022CXPT014)the Knowledge Innovation Program of Wuhan-Shugung Project(2023020201020413).
文摘Maize(Zea mays)is highly susceptible to waterlogging stress,which reduces both the yield and quality of this important crop.However,the molecular mechanism governing waterlogging tolerance is poorly understood.In this study,we identify a waterlogging-and ethylene-inducible gene ZmEREB179 that encodes an ethylene response factor(ERF)localized in the nucleus.Overexpression of ZmEREB179 in maize increases the sensitivity to waterlogging stress.Conversely,the zmereb179 knockout mutants are more tolerant to waterlogging,suggesting that ZmEREB179 functions as a negative regulator of waterlogging tolerance.A transcriptome analysis of the ZmEREB179-overexpressing plants reveals that the ERF-type transcription factor modulates the expression of various stress-related genes,including ZmEREB180.We find that ZmEREB179 directly targets the ZmEREB180 promoter and represses its expression.Notably,the analysis of a panel of 220 maize inbred lines reveals that genetic variations in the ZmEREB179 promoter(Hap2)are highly associated with waterlogging resistance.The functional association of Hap2 with waterlogging resistance is tightly co-segregated in two F2 segregating populations,highlighting its potential applications in breeding programs.Our findings shed light on the involvement of the transcriptional cascade of ERF genes in regulating plant-waterlogging tolerance.
基金The National Polar Special Program“Impact and Response of Antarctic Seas to Climate Change”under contract Nos IRASCC 01-01-02 and IRASCC 02-02the National Key Research and Development Program of China under contract No.2022YFE0136500+1 种基金the National Natural Science Foundation of China(NSFC)under contract Nos 41976228,42276255 and 42176227the Scientific Research Fund of the Second Institute of Oceanography under contract Nos JG2011,JG2211 and JG2013.
文摘Nutrient uptake status dominates phytoplankton biomass and community structure in the Southern Ocean during austral summer,yet how nutrient utilization variability responds to phytoplankton community succession is still unclear,partly due to lack of data spanning the entire summer.In this study,nitrate,phosphate,and silicate combined with temperature,salinity,and apparent oxygen utilization(AOU)were analyzed along 45°E in the Cosmonaut Sea during December 2019,January 2021,and February 2022.The variations in nutrient utilization in the euphotic layer were studied using biogeochemical tracers,and seasonal nutrient depletion was also estimated.The results showed that nutrient distribution varied significantly from December to February.Significant positive correlations were observed for nitrate and silicate concentrations with salinity and AOU,indicating that nutrient distributions were mainly influenced by water mass and phytoplankton production.Increasing∆[N*]and decreasing∆[Si*]in the upper 50 m were observed south of 63.5°S from December to February,which possibly contributed to a progressive shift in dominant phytoplankton population from Phaeocystis antarctica to diatoms.The seasonal nutrient depletion generally increased from December to February.Moreover,the consumption of silicate substantially increased compared to nitrate,indicating that the abundance of diatoms was increasing with time during the austral summer.Our observations suggest that nutrient utilization status is closely related to phytoplankton community structure in the euphotic layer of the Cosmonaut Sea.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
文摘This study examines in-situ temperature profiles in three representative sections,namely,the Dalian-Chengshantou(DC),the Chengshantou-Changsangot(CC),and the 36°N,to delineate the interannual variations of the Yellow Sea Cold Water Mass(YSCWM)and investigate their potential connections,along with forcing factors,across different regions.The findings reveal the fol-lowing insights:1)The YSCWM experiences warming trends at DC,CC,and the western segment of the 36°N,revealing correspond-ing minimum temperature rates of 0.021℃/yr,0.043℃/yr,and 0.063℃/yr,respectively.Conversely,the eastern portion of the 36°N displays a slight cooling trend,resulting in a pronounced zonal disparity in long-term temperature trends.2)The changes in the YSCWM are closely linked to the atmospheric wind patterns.Notably,the weakening of northerly winds during winter corresponds to the rise in YSCWM temperature,which is accompanied by a westward shift in the cold core of the 36°N section.3)Correlation analysis with factors such as the Arctic Oscillation(AO),Pacific Decadal Oscillation(PDO),and El Niño-Southern Oscillation(EN-SO),etc.,indicates that changes in large-scale climate systems influence the spatiotemporal variations of the YSCWM,resulting in seasonal differences.
基金funded by the National Key R&D Program of China(Nos.2023YFE0101800 and 2023YFC 3007305)National Natural Science Foundation of China(Nos.42004069 and 42204093)Special Fund of the Institute of Geophysics,China Earthquake Administration(No.DQJB24X24).
文摘Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation,along with the construction of a high-precision mobile gravity network covering Chinese mainland,have positioned temporal gravity variations(GVs)as an important tool for clarifying the signal characteristics and dynamic mechanisms of crustal sources.Reportedly,crustal mass transfer,which is affected by stress state and structural environment,alters the characteristics of the regional gravity field,thus serving as an indicator for locations of moderate to strong earthquakes and a seismology-independent predictor for regions at risk for strong earthquakes.Therefore,quantitatively tracking time-varying gravity is of paramount importance to enhance the effectiveness of earthquake prediction.In this study,we divided the areas effectively covered by the terrestrial mobile gravity network in the Sichuan-Yunnan region into small grids based on the latest observational data(since 2018)from the network.Next,we calculated the 1-and 3-year GVs and gravity gradient indicators(amplitude of analytic signal,AAS;total horizontal derivative,THD;and amplitude of vertical gradient,AVG)to quantitatively characterize variations in regional time-varying gravity field.Next,we assessed the effectiveness of gravity field variations in predicting earthquakes in the Sichuan-Yunnan region using Molchan diagrams constructed for gravity signals of 13 earthquakes(M≥5.0;occurred between 2021 and 2024)within the terrestrial mobile gravity network.The results reveal a certain correspondence between gravity field variations and the locations of moderate and strong earthquakes in the Sichuan-Yunnan region.Furthermore,the 3-year AAS and AVG outperform the 3-year THD in predicting subsequent seismic events.Notably,the AAS and AVG showed large probability gains prior to the M_(S)6.8 Luding earthquake,indicating their potential for earthquake prediction.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)National Natural Science Foundation of China(42475003)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP209)。
文摘Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.
基金The National Natural Science Foundation of China under contract No.42030410the Laoshan Laboratory under contract Nos LSKJ202202404 and LSKJ202202403+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science and TechnologyJiangsu Innovation Research Group under contract No.JSSCTD202346Jiangsu Funding Program for Excellent Postdoctoral Talent under contract No.2023ZB690。
文摘Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of the seasonal variations in the MHWs in the ECS remains limited.This study investigates the characteristics of MHWs in the ECS in summer and winter using high-resolution oceanic reanalysis.Our analyses reveal distinct spatial patterns of BMHWs in these seasons.During summer,the Taiwan Warm Current plays a crucial role in transporting warm water northward,potentially leading to intense BMHWs on the central ECS shelf.These BMHW events usually occur independently of surface warming due to strong stratification in summer.Conversely,winter BMHWs are more prevalent in coastal regions under the influence of coastal currents and typically feature consistent warming from surface to bottom with a deepened mixed layer.These findings inform the coherent vertical structure and driving mechanisms of MHWs in the ECS,which are essential for predicting and managing these extreme events in the future.
基金The National Natural Science Foundation of China under contract Nos 42406259,42175172,and 41975134the Science Foundation for Youths of Hunan Province(Category C)under contract No.2025JJ60240.
文摘As the Arctic undergoes rapid warming and sea ice continues to decline,Ocean-to-Ice Heat Flux(OIHF)plays a crucial role in regulating sea ice dynamics.This study investigates the seasonal variations in ocean-to-ice heat flux in north of the Fram Strait using the regional Arctic Ocean/sea ice reanalysis product from 1991 to 2020.The analysis reveals that the OIHF exhibits significant seasonal variability,with a pronounced peak during winter in north of the Fram Strait,driven by inflows of Atlantic water.Warm Atlantic water intrusion begins in October,reaches its peak in January and February,and results in a delayed increase in OIHF,with maximum flux observed 2−3 months later.These results highlight the significant role of Atlantic water inflows in influencing Arctic sea ice dynamics and emphasize the need for further investigation into the coupled ocean-atmosphere processes that govern seasonal fluctuations in OIHF.
文摘Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration of negative ions and their influencing factors in Xinfeng County were analyzed.The results show that the concentration of negative ions was the highest in summer,followed by spring;it was lower in autumn and the lowest in winter.In terms of diurnal variations,it was higher in the early morning and night,and lower in the noon and afternoon,which was closely related to the diurnal variations of human activities and meteorological conditions.The factors that affect the concentration of negative ions in the air are more complex.Besides meteorological factors,vegetation,altitude,human activities and other factors should be considered.
