Effective conservation relies on robust assessments;however,the lack of waterbird data in the Yellow River Basin(YRB)has led to an underestimation of key habitat significance.This study addressed this gap by evaluatin...Effective conservation relies on robust assessments;however,the lack of waterbird data in the Yellow River Basin(YRB)has led to an underestimation of key habitat significance.This study addressed this gap by evaluating YRB wetland conservation importance using waterbirds as indicators and applying Ramsar,Important Bird Areas(IBA),and East Asian-Australasian Flyway(EAAF)criteria.We integrated coordinated surveys with citizen science data,creating a framework that tackles data deficiencies along the under-monitored Central Asian Flyway(CAF).Our analysis identified 75 priority wetlands,supporting 15 threatened species and 49 exceeding global/flyway 1%thresholds,highlighting the basin's biodiversity.We observed strong seasonal habitat use,with high-altitude wetlands vital for breeding and migration,and the Yellow River Delta providing year-round refuge.This research also provided data to refine Baer's Pochard population estimates.Alarmingly,one-third of the identified priority areas,primarily rivers and lakes,remain unprotected.To address this,we recommend systematic surveys,enhanced protected areas,OECMs,and targeted wetland restoration.This study underscores the YRB's role in regional conservation and provides essential data for adaptive management,particularly emphasizing the CAF's importance.展开更多
Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycli...Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycling,and reuse in different industries.Currently,a large portion of tailings are managed through the tailing storage facilities(TSF)where these tailings undergo hydro-thermal-mechanical stresses with seasonal cycles which are not comprehensively understood.This study presents an investigative study to evaluate the performance of control and cement-stabilized copper MT under the influence of seasonal cycles,freeze-thaw(F-T)and wet-dry(W-D)conditions,representing the seasonal variability in the cold and arid regions.The control and cement-stabilized MT samples were subjected to a maximum of 12 F-T and 12 W-D cycles and corresponding micro-and-macro behavior was investigated through scanning electron microscope(SEM),volumetric strain(εvT,wet density(r),moisture content loss,and unconfined compressive strength(UCS)tests.The results indicated the vulnerability of Copper MT to 67%and 75%strength loss reaching residual states with 12 F-T and 8 W-D cycles,respectively.Whereas the stabilized MT retained 39%-55%and 16%-34%strength with F-T and W-D cycles,demonstrating increased durability.This research highlights the impact of seasonal cycles and corresponding strength-deformation characteristics of control and stabilized Copper MT in cold and arid regions.展开更多
The Himalayan monal(Lophophorus impejanus),Nepal’s national bird,is a protected species facing significant conservation challenges.Understanding the distribution and habitat preferences of the Himalayan monal(HM)is c...The Himalayan monal(Lophophorus impejanus),Nepal’s national bird,is a protected species facing significant conservation challenges.Understanding the distribution and habitat preferences of the Himalayan monal(HM)is crucial for its conservation.This study was conducted in the Langtang National Park(LNP),Nepal using the route census method during both winter(November/December 2022)and summer(June 2023)seasons to examine the seasonal variation in HM’s elevational distribution and habitat preference.Further,we assessed their conservation threats by conducting a semi-structured questionnaire survey with the local residents.During the winter period,the HMs preferred grassland habitats,while in the summer,their preference shifted to shrubland and barren area.HM abundance was negatively associated with the Normalized Differential Vegetation Index(NDVI)and the shortest distance from the survey trails in the winter.The HMs actively avoided areas with high anthropogenic pressure.In the summer,they showed a wider elevational range up to 4400 m above sea level(a.s.l.),with a higher sighting frequency between 3600 and 3900 m a.s.l.The questionnaire survey of the local residents revealed that anthropogenic pressure such as poaching and free-ranging livestock grazing are the major threats to the species in the study area.This study provides valuable insight into the complex habitat preferences and critical threats faced by the HMs in LNP and underscores the urgent need for targeted conservation action.展开更多
Halocarbons play a vital role in ozone depletion and global warming,and are regulated by the Montreal Protocol(MP)and its amendments.China has been identified as an important contributor to the halocarbon emissions,bu...Halocarbons play a vital role in ozone depletion and global warming,and are regulated by the Montreal Protocol(MP)and its amendments.China has been identified as an important contributor to the halocarbon emissions,but the regional sources of halocarbons in China are not yet well comprehended.To investigate the characteristics,emissions,and source profiles,this study conducted a field campaign in Xiamen,a coastal city in southeastern China.Higher enhancements were found in the unregulated halocarbons(CH_(3)Cl,CH_(2)Cl_(2),CHCl_(3))than in the MP eliminated species(CCl_(4),CH_(3)Br)and theMP controlled species(HCFCs,HFCs).Many of the measured halocarbons varied seasonally and regionally,depending on the anthropogenic sources and atmospheric transport.Backward trajectory analysis showed that the air masses from inland were polluted over Shandong,Hebei,and northern Fujian in the cold season,while the air masses fromthe sea in the warm season were clean.Different air masses in two seasons were associated with the halocarbon patterns in the study area.Industrial activities,especially solvent usage,were the primary sources of halocarbons.The emission hot spots in Fujian Province were concentrated in Sanming,Fuzhou,and Xiamen,and the unregulated halocarbons made the largest contribution.This study provides an insight for a deep understanding of the characteristics and potential sources of halocarbons,and for strengthened management of halocarbons in China.展开更多
A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellu...Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellular vesicles(EVs)containing vital regulatory molecules,such as microRNAs(miRNAs).These miRNAs are pivotal in facilitating commu-nication within the follicles through diverse signaling and information transfer forms.EV-coupled miRNA signaling is implicated to be associated with ovarian function,follicle and oocyte growth and response to various environmen-tal insults.Herein,we investigated how seasonal variations directly influence the ovulatory and anovulatory states of ovarian follicles and how are they associated with follicular fluid EV-coupled miRNA dynamics in horses.Results Ultrasonographic monitoring and follicular fluid aspiration of preovulatory follicles in horses during the ano-vulatory(spring:non-breeding)and ovulatory(spring,summer,and fall:breeding)seasons and subsequent EV isola-tion and miRNA profiling identified significant variation in EV-miRNA cargo content.We identified 97 miRNAs with dif-ferential expression among the groups and specific clusters of miRNAs involved in the spring transition(miR-149,-200b,-206,-221,-328,and-615)and peak breeding period(including miR-143,-192,-451,-302b,-100,and let-7c).Bioinformatic analyses showed enrichments in various biological functions,e.g.,transcription factor activity,transcrip-tion and transcription regulation,nucleic acid binding,sequence-specific DNA binding,p53 signaling,and post-trans-lational modifications.Cluster analyses revealed distinct sets of significantly up-and down-regulated miRNAs associ-ated with spring anovulatory(Cluster 1)and summer ovulation–the peak breeding season(Clusters 4 and 6).Conclusions The findings from the current study shed light on the dynamics of FF-EV-coupled miRNAs in relation to equine ovulatory and anovulatory seasons,and their roles in understanding the mechanisms involved in seasonal shifts and ovulation during the breeding season warrant further investigation.展开更多
The symbiotic association between reef-building corals and Symbiodiniaceae is pivotal for coral reef ecosystems,yet remains susceptible to environmental factors.Currently,there is a dearth of research examining season...The symbiotic association between reef-building corals and Symbiodiniaceae is pivotal for coral reef ecosystems,yet remains susceptible to environmental factors.Currently,there is a dearth of research examining seasonal fluctuations in coral-associated Symbiodiniaceae communities.In this study,we investigated the seasonal dynamics of Symbiodiniaceae communities associated with coral species in the Luhuitou coral reef using high-throughput sequencing techniques and SymPortal analytical framework.The results indicated that the genus Cladocopium exhibited dominance(averaging 82%),followed by Durusdinium(18%)and Breviolum(0.01%)within the examined coral species.Among the 521 Symbiodiniaceae ITS2 sequence types,C15 emerged as the prevalent type(13.24%),trailed by C3u(9.51%)and D1(8.57%).Interestingly,Symbiodiniaceae communities varied among different coral species.Pocillopora damicornis displayed a predominant association with Durusdinium,while Porites lutea,Goniastrea retiformis,Montipora truncata,Montipora aequituberculata,and Acropora divaricata were entirely dominated by the genus Cladocopium(100%),showcasing distinct host specificity.In the cases of Hydnophora exesa,Acropora latistella,Acropora digitifera,and seawater,both Cladocopium and Durusdinium were concurrently detected.Moreover,the diversity of Symbiodiniaceae associated with P.damicornis,P.lutea,G.retiformis,M.truncata,M.aequituberculata,and A.digitifera exhibited significant variations across different seasons.Notably,the results revealed that the alterations in Symbiodiniaceae community compositions were primarily driven by nutrient concentrations and seawater temperature.The network analysis of Symbiodiniaceae revealed the dominant Symbiodiniaceae types C15,C17f,C3u,C3,and D4 were exclusive.This study provided the seasonal variation characteristics of Symbiodiniaceae communities among different coral species,which may be a potential adaptive mechanism to environmental conditions.展开更多
Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the nume...Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.展开更多
BACKGROUND To investigate whether seasonal differences in ambient temperature affect the incidence of early postoperative cognitive dysfunction(POCD)among elderly patients undergoing laparoscopic surgery in tropical r...BACKGROUND To investigate whether seasonal differences in ambient temperature affect the incidence of early postoperative cognitive dysfunction(POCD)among elderly patients undergoing laparoscopic surgery in tropical regions.Additionally,it explored the perioperative risk factors associated with early POCD following abdominal laparoscopic surgery.AIM To investigate the influence of seasonal differences in ambient temperature on POCD of elderly patients METHODS A total of 125 patients aged≥65 years from Hainan Province,China,who underwent laparoscopic surgery under general anesthesia with tracheal intubation,were enrolled. All patients completed the Mini-Mental State Examination one day before surgery and onpostoperative days 1, 3, and 7. A decline of ≥ 2 points from baseline was considered indicative of cognitivedysfunction. Serum levels of S100 calcium binding protein B and neuron-specific enolase were measured usingenzyme-linked immunosorbent assay at three time points: Preoperatively, immediately after extubation, and 24hours postoperatively. Perioperative clinical data were collected to identify potential risk factors for POCD.Propensity score matching (PSM) was performed (1:1, caliper = 0.03), resulting in 41 matched patient pairs betweenwinter and summer groups.RESULTSAfter PSM, baseline characteristics including age, gender, body mass index, education level, comorbidities, andsurgical variables were well balanced between groups. There were no significant differences in the incidence ofPOCD on postoperative days 1, 3, and 7 between patients undergoing laparoscopic surgery in winter vs summer.However, multivariable logistic regression revealed that surgical duration (day 1, P value = 0.049), advanced ageand elevated creatinine (day 3, P value = 0.044, P value = 0.008), and hypoalbuminemia (day 3, P value = 0.042;day7, P value = 0.015) were independently associated with early POCD.CONCLUSIONAmbient temperature differences between winter and summer in tropical regions did not significantly affect theincidence of early POCD in elderly patients undergoing laparoscopic surgery. Nonetheless, age, longer surgicalduration, elevated creatinine, and hypoalbuminemia emerged as key risk factors. These findings underscore theimportance of perioperative optimization to reduce the risk of POCD in elderly patients, regardless of seasonaltemperature variations.展开更多
Seasonal variation in phytoplankton composition influences the pathways and efficiency of energy flow,reshaping the structure of the trophic pyramid in the Ross Sea.However,field investigation of grazing processes pre...Seasonal variation in phytoplankton composition influences the pathways and efficiency of energy flow,reshaping the structure of the trophic pyramid in the Ross Sea.However,field investigation of grazing processes presents challenges that hinder our understanding of energy pathways.This study aims to provide insights into energy flow using a three-dimensional ecosystem model applied to the Ross Sea.By analyzing the simulation results,the role of the seasonal phytoplankton succession,specifically the shift from dominance by Phaeocystis antarctica to diatoms,in energy allocation is explored.The short-lived spring bloom of P.antarctica mainly fuels microzooplankton,creating a brief food chain where energy transfers primarily among smaller plankton.In contrast,the subsequent summer bloom of diatoms,which persists longer,provides nearly half of the total phytoplankton energy loss(via ingestion and mortality)to larger mesozooplankton.Our findings indicate that phytoplankton succession in the Ross Sea extends the bloom duration,particularly for diatoms,thereby facilitating energy transfer to higher trophic levels and improving overall energy utilization.This suggests that phytoplankton succession,an ecological strategy adapted to iron-deficient environments in the Ross Sea,explains why the colder region in front of the Ross Ice Shelf is significantly more productive than the northern areas,ultimately favored by top predators.展开更多
To investigate the seasonal characteristics in air pollution in Chengdu,a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summ...To investigate the seasonal characteristics in air pollution in Chengdu,a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter,respectively.The results showed that,apart from O_(3),the concentrations of other pollutants(CO,NO_(2),SO_(2),PM_(2.5)and PM_(10))were significantly higher in winter than in summer.All single particle aerosols were divided into seven categories:biomass burning(BB),coal combustion(CC),Dust,vehicle emission(VE),K mixedwith nitrate(K-NO_(3)),Kmixed with sulfate and nitrate(K-SN),and K mixedwith sulfate(K-SO_(4))particles.The highest contributions in both seasons were VE particles(24%).The higher contributions of K-SO_(4)(16%)and K-NO_(3)(10%)particles occurred in summer and winter,respectively,as a result of their different formation mechanisms.S-containing(KSO_(4)and K-SN),VE,and BB particles caused the evolution of pollution in both seasons,and they can be considered as targets for future pollution reduction.The mixing of primary sources particles(VE,Dust,CC,and BB)with secondary components was stronger in winter than in summer.In summer,as pollution worsens,the mixing of primary sources particles with 62[NO_(3)]−weakened,but themixing with 97[HSO_(4)]−increased.However,in winter,the mixing state of particles did not exhibit an obvious evolution rules.The potential source areas in summer were mainly distributed in the southern region of Sichuan,while in winter,besides the southern region,the contribution of the western region cannot be ignored.展开更多
The phenomenon of'bamboo-like'thin interlayers developed in rock salt is one of the most prominent features of Paleogene salt-bearing strata in eastern China,where centimeter-thick rock salts appear separately...The phenomenon of'bamboo-like'thin interlayers developed in rock salt is one of the most prominent features of Paleogene salt-bearing strata in eastern China,where centimeter-thick rock salts appear separately,forming rhythmic units.At present,detailed analyses of these rhythms of rock salt are still limited,which directly affects the achievement of comprehensive and in-depth understanding of the developmental laws pertaining to this kind of saline lake.Therefore,we selected the typical rhythmic'bamboo-like'rock salts of the Shizhai Depression in Jiangsu Province as the research subject.Through careful observation of rock salts in hand samples and detailed petrographic and mineralogical analyses,we analyzed the hydrogen and oxygen isotopic compositions,homogenization temperatures and chemical compositions of individual fluid inclusions in halite crystals.Early-stage rhythmic deposition was a product of continental saline lake evolution in winter or spring,late-stage rhythmic deposition being the product of evolution in the summer.The seasonal evolution of the halite sequences was determined and two brine enrichment events were identified.In addition,the quiet saline lake environment with concentrated brine represented by rock salt was more likely to precipitate potassium.This study provides a new reference for the evolution of both Paleogene climate and saline lakes in eastern China.展开更多
Ice shelves are important passageways for ice sheets flowing into the ocean.Through iceberg calving and basal melting,ice shelves exert considerable influence on the mass balance of the Antarctic Ice Sheet and glacier...Ice shelves are important passageways for ice sheets flowing into the ocean.Through iceberg calving and basal melting,ice shelves exert considerable influence on the mass balance of the Antarctic Ice Sheet and glacier stability.The Ross Ice Shelf(RIS),the largest body of floating ice on Earth,plays an essential role in any changes in the mass balance of the Antarctic Ice Sheet.The long-term elevation change trend of RIS has been calculated with multiple satellite altimetry in previous studies.However,the seasonal variations were less revealed.Based on crossover analysis and indirect observation adjustments,this study proposed a new method for constructing seasonal records for surface elevation changes in the RIS using ICESat laser altimetry data from 2003 to 2009.The results showed that surface elevation changes exhibited seasonal variations with fluctuations over 20 cm,and the seasonal change characteristics were closely related to the temperature.Interannual variations in RIS surface elevation decreased from 2003 to2009 at a rate of 2 cm/yr.From March 2003 to April 2007,the surface elevation decreased at 3.7 cm/yr;however,after April 2007,the surface elevation increased at 5.5 cm/yr.The more recent stages of surface elevation growth have been influenced by reductions in the summer basal melt,which is related to the decreases in ocean heat content.展开更多
Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from ...Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from stroke.Since 1990,China has reported the highest global stroke burden,with 2.19 million deaths and 45.9 million DALYs recorded in 2019~([2]).展开更多
This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)perio...This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.展开更多
Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouri...Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.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
Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the au...Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of a...Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.展开更多
基金The Science and Technology Basic Resources Survey Project,No.2021FY101002Wetland Protection and Restoration in China Funded by the Palson Institute and Laoniu Foundation,UNDP-GEF Flyway Project,No.PIMS ID:6110。
文摘Effective conservation relies on robust assessments;however,the lack of waterbird data in the Yellow River Basin(YRB)has led to an underestimation of key habitat significance.This study addressed this gap by evaluating YRB wetland conservation importance using waterbirds as indicators and applying Ramsar,Important Bird Areas(IBA),and East Asian-Australasian Flyway(EAAF)criteria.We integrated coordinated surveys with citizen science data,creating a framework that tackles data deficiencies along the under-monitored Central Asian Flyway(CAF).Our analysis identified 75 priority wetlands,supporting 15 threatened species and 49 exceeding global/flyway 1%thresholds,highlighting the basin's biodiversity.We observed strong seasonal habitat use,with high-altitude wetlands vital for breeding and migration,and the Yellow River Delta providing year-round refuge.This research also provided data to refine Baer's Pochard population estimates.Alarmingly,one-third of the identified priority areas,primarily rivers and lakes,remain unprotected.To address this,we recommend systematic surveys,enhanced protected areas,OECMs,and targeted wetland restoration.This study underscores the YRB's role in regional conservation and provides essential data for adaptive management,particularly emphasizing the CAF's importance.
基金the W.M.Keck Center for Nano-Scale Imaging in the Department of Chemistry and Biochemistry at the University of Arizona(Grant No.RRID:SCR_022884),with funding from the W.M.Keck Foundation Grant.
文摘Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycling,and reuse in different industries.Currently,a large portion of tailings are managed through the tailing storage facilities(TSF)where these tailings undergo hydro-thermal-mechanical stresses with seasonal cycles which are not comprehensively understood.This study presents an investigative study to evaluate the performance of control and cement-stabilized copper MT under the influence of seasonal cycles,freeze-thaw(F-T)and wet-dry(W-D)conditions,representing the seasonal variability in the cold and arid regions.The control and cement-stabilized MT samples were subjected to a maximum of 12 F-T and 12 W-D cycles and corresponding micro-and-macro behavior was investigated through scanning electron microscope(SEM),volumetric strain(εvT,wet density(r),moisture content loss,and unconfined compressive strength(UCS)tests.The results indicated the vulnerability of Copper MT to 67%and 75%strength loss reaching residual states with 12 F-T and 8 W-D cycles,respectively.Whereas the stabilized MT retained 39%-55%and 16%-34%strength with F-T and W-D cycles,demonstrating increased durability.This research highlights the impact of seasonal cycles and corresponding strength-deformation characteristics of control and stabilized Copper MT in cold and arid regions.
基金an MSc thesis research grant from the Zoological Society of London(ZSL)Nepal.RCK’s effort was supported in part by the Office of Research Infrastructure Programs(ORIP)of the National Institutes of Health through grant number P51OD010425 to the Washington National Primate Research Center,USA。
文摘The Himalayan monal(Lophophorus impejanus),Nepal’s national bird,is a protected species facing significant conservation challenges.Understanding the distribution and habitat preferences of the Himalayan monal(HM)is crucial for its conservation.This study was conducted in the Langtang National Park(LNP),Nepal using the route census method during both winter(November/December 2022)and summer(June 2023)seasons to examine the seasonal variation in HM’s elevational distribution and habitat preference.Further,we assessed their conservation threats by conducting a semi-structured questionnaire survey with the local residents.During the winter period,the HMs preferred grassland habitats,while in the summer,their preference shifted to shrubland and barren area.HM abundance was negatively associated with the Normalized Differential Vegetation Index(NDVI)and the shortest distance from the survey trails in the winter.The HMs actively avoided areas with high anthropogenic pressure.In the summer,they showed a wider elevational range up to 4400 m above sea level(a.s.l.),with a higher sighting frequency between 3600 and 3900 m a.s.l.The questionnaire survey of the local residents revealed that anthropogenic pressure such as poaching and free-ranging livestock grazing are the major threats to the species in the study area.This study provides valuable insight into the complex habitat preferences and critical threats faced by the HMs in LNP and underscores the urgent need for targeted conservation action.
基金supported by the National Natural Science Foundation of China(Nos.42030707,72394404)the International Partnership Program of the Chinese Academy of Sciences(No.121311KYSB20190029)the Fundamental Research Fund for the Central Universities(Nos.20720210083,20720210082).
文摘Halocarbons play a vital role in ozone depletion and global warming,and are regulated by the Montreal Protocol(MP)and its amendments.China has been identified as an important contributor to the halocarbon emissions,but the regional sources of halocarbons in China are not yet well comprehended.To investigate the characteristics,emissions,and source profiles,this study conducted a field campaign in Xiamen,a coastal city in southeastern China.Higher enhancements were found in the unregulated halocarbons(CH_(3)Cl,CH_(2)Cl_(2),CHCl_(3))than in the MP eliminated species(CCl_(4),CH_(3)Br)and theMP controlled species(HCFCs,HFCs).Many of the measured halocarbons varied seasonally and regionally,depending on the anthropogenic sources and atmospheric transport.Backward trajectory analysis showed that the air masses from inland were polluted over Shandong,Hebei,and northern Fujian in the cold season,while the air masses fromthe sea in the warm season were clean.Different air masses in two seasons were associated with the halocarbon patterns in the study area.Industrial activities,especially solvent usage,were the primary sources of halocarbons.The emission hot spots in Fujian Province were concentrated in Sanming,Fuzhou,and Xiamen,and the unregulated halocarbons made the largest contribution.This study provides an insight for a deep understanding of the characteristics and potential sources of halocarbons,and for strengthened management of halocarbons in China.
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
基金Southern Illinois University,Carbondale,ILMinistry of Higher Education&Scientific Research,Baghdad,Iraq+2 种基金NIFA-USDA Hatch project accession#1016077(Multistate#W4171)USDAARS project 6066-31000-015-00DNIH MS-IDeA network of Biomedical Research Excellence award 5P20GMI03476-19.GMI received a PhD scholarship from the Ministry of Higher Education&Scientific Research,Baghdad,Iraq.
文摘Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellular vesicles(EVs)containing vital regulatory molecules,such as microRNAs(miRNAs).These miRNAs are pivotal in facilitating commu-nication within the follicles through diverse signaling and information transfer forms.EV-coupled miRNA signaling is implicated to be associated with ovarian function,follicle and oocyte growth and response to various environmen-tal insults.Herein,we investigated how seasonal variations directly influence the ovulatory and anovulatory states of ovarian follicles and how are they associated with follicular fluid EV-coupled miRNA dynamics in horses.Results Ultrasonographic monitoring and follicular fluid aspiration of preovulatory follicles in horses during the ano-vulatory(spring:non-breeding)and ovulatory(spring,summer,and fall:breeding)seasons and subsequent EV isola-tion and miRNA profiling identified significant variation in EV-miRNA cargo content.We identified 97 miRNAs with dif-ferential expression among the groups and specific clusters of miRNAs involved in the spring transition(miR-149,-200b,-206,-221,-328,and-615)and peak breeding period(including miR-143,-192,-451,-302b,-100,and let-7c).Bioinformatic analyses showed enrichments in various biological functions,e.g.,transcription factor activity,transcrip-tion and transcription regulation,nucleic acid binding,sequence-specific DNA binding,p53 signaling,and post-trans-lational modifications.Cluster analyses revealed distinct sets of significantly up-and down-regulated miRNAs associ-ated with spring anovulatory(Cluster 1)and summer ovulation–the peak breeding season(Clusters 4 and 6).Conclusions The findings from the current study shed light on the dynamics of FF-EV-coupled miRNAs in relation to equine ovulatory and anovulatory seasons,and their roles in understanding the mechanisms involved in seasonal shifts and ovulation during the breeding season warrant further investigation.
文摘The symbiotic association between reef-building corals and Symbiodiniaceae is pivotal for coral reef ecosystems,yet remains susceptible to environmental factors.Currently,there is a dearth of research examining seasonal fluctuations in coral-associated Symbiodiniaceae communities.In this study,we investigated the seasonal dynamics of Symbiodiniaceae communities associated with coral species in the Luhuitou coral reef using high-throughput sequencing techniques and SymPortal analytical framework.The results indicated that the genus Cladocopium exhibited dominance(averaging 82%),followed by Durusdinium(18%)and Breviolum(0.01%)within the examined coral species.Among the 521 Symbiodiniaceae ITS2 sequence types,C15 emerged as the prevalent type(13.24%),trailed by C3u(9.51%)and D1(8.57%).Interestingly,Symbiodiniaceae communities varied among different coral species.Pocillopora damicornis displayed a predominant association with Durusdinium,while Porites lutea,Goniastrea retiformis,Montipora truncata,Montipora aequituberculata,and Acropora divaricata were entirely dominated by the genus Cladocopium(100%),showcasing distinct host specificity.In the cases of Hydnophora exesa,Acropora latistella,Acropora digitifera,and seawater,both Cladocopium and Durusdinium were concurrently detected.Moreover,the diversity of Symbiodiniaceae associated with P.damicornis,P.lutea,G.retiformis,M.truncata,M.aequituberculata,and A.digitifera exhibited significant variations across different seasons.Notably,the results revealed that the alterations in Symbiodiniaceae community compositions were primarily driven by nutrient concentrations and seawater temperature.The network analysis of Symbiodiniaceae revealed the dominant Symbiodiniaceae types C15,C17f,C3u,C3,and D4 were exclusive.This study provided the seasonal variation characteristics of Symbiodiniaceae communities among different coral species,which may be a potential adaptive mechanism to environmental conditions.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42122034,42075043,42330609)the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0103)+2 种基金Key Talent Project in Gansu and Central Guidance Fund for Local Science and Technology Development Projects in Gansu(No.24ZYQA031)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021427)West Light Foundation of the Chinese Academy of Sciences(xbzg-zdsys-202215)。
文摘Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.
文摘BACKGROUND To investigate whether seasonal differences in ambient temperature affect the incidence of early postoperative cognitive dysfunction(POCD)among elderly patients undergoing laparoscopic surgery in tropical regions.Additionally,it explored the perioperative risk factors associated with early POCD following abdominal laparoscopic surgery.AIM To investigate the influence of seasonal differences in ambient temperature on POCD of elderly patients METHODS A total of 125 patients aged≥65 years from Hainan Province,China,who underwent laparoscopic surgery under general anesthesia with tracheal intubation,were enrolled. All patients completed the Mini-Mental State Examination one day before surgery and onpostoperative days 1, 3, and 7. A decline of ≥ 2 points from baseline was considered indicative of cognitivedysfunction. Serum levels of S100 calcium binding protein B and neuron-specific enolase were measured usingenzyme-linked immunosorbent assay at three time points: Preoperatively, immediately after extubation, and 24hours postoperatively. Perioperative clinical data were collected to identify potential risk factors for POCD.Propensity score matching (PSM) was performed (1:1, caliper = 0.03), resulting in 41 matched patient pairs betweenwinter and summer groups.RESULTSAfter PSM, baseline characteristics including age, gender, body mass index, education level, comorbidities, andsurgical variables were well balanced between groups. There were no significant differences in the incidence ofPOCD on postoperative days 1, 3, and 7 between patients undergoing laparoscopic surgery in winter vs summer.However, multivariable logistic regression revealed that surgical duration (day 1, P value = 0.049), advanced ageand elevated creatinine (day 3, P value = 0.044, P value = 0.008), and hypoalbuminemia (day 3, P value = 0.042;day7, P value = 0.015) were independently associated with early POCD.CONCLUSIONAmbient temperature differences between winter and summer in tropical regions did not significantly affect theincidence of early POCD in elderly patients undergoing laparoscopic surgery. Nonetheless, age, longer surgicalduration, elevated creatinine, and hypoalbuminemia emerged as key risk factors. These findings underscore theimportance of perioperative optimization to reduce the risk of POCD in elderly patients, regardless of seasonaltemperature variations.
基金The National Natural Science Foundation of China under contract No.41941008the National Key Research and Development Program of China under contract No.2023YFC3107702.
文摘Seasonal variation in phytoplankton composition influences the pathways and efficiency of energy flow,reshaping the structure of the trophic pyramid in the Ross Sea.However,field investigation of grazing processes presents challenges that hinder our understanding of energy pathways.This study aims to provide insights into energy flow using a three-dimensional ecosystem model applied to the Ross Sea.By analyzing the simulation results,the role of the seasonal phytoplankton succession,specifically the shift from dominance by Phaeocystis antarctica to diatoms,in energy allocation is explored.The short-lived spring bloom of P.antarctica mainly fuels microzooplankton,creating a brief food chain where energy transfers primarily among smaller plankton.In contrast,the subsequent summer bloom of diatoms,which persists longer,provides nearly half of the total phytoplankton energy loss(via ingestion and mortality)to larger mesozooplankton.Our findings indicate that phytoplankton succession in the Ross Sea extends the bloom duration,particularly for diatoms,thereby facilitating energy transfer to higher trophic levels and improving overall energy utilization.This suggests that phytoplankton succession,an ecological strategy adapted to iron-deficient environments in the Ross Sea,explains why the colder region in front of the Ross Ice Shelf is significantly more productive than the northern areas,ultimately favored by top predators.
基金supported by the Basic Research Cultivation Support Plan of Southwest Jiaotong University(No.2682023ZTPY016)the Natural Science Foundation of Sichuan Province(No.2022NSFSC0982)the National Natural Science Foundation of China(Nos.U23A2030,42205100,and 41805095).
文摘To investigate the seasonal characteristics in air pollution in Chengdu,a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter,respectively.The results showed that,apart from O_(3),the concentrations of other pollutants(CO,NO_(2),SO_(2),PM_(2.5)and PM_(10))were significantly higher in winter than in summer.All single particle aerosols were divided into seven categories:biomass burning(BB),coal combustion(CC),Dust,vehicle emission(VE),K mixedwith nitrate(K-NO_(3)),Kmixed with sulfate and nitrate(K-SN),and K mixedwith sulfate(K-SO_(4))particles.The highest contributions in both seasons were VE particles(24%).The higher contributions of K-SO_(4)(16%)and K-NO_(3)(10%)particles occurred in summer and winter,respectively,as a result of their different formation mechanisms.S-containing(KSO_(4)and K-SN),VE,and BB particles caused the evolution of pollution in both seasons,and they can be considered as targets for future pollution reduction.The mixing of primary sources particles(VE,Dust,CC,and BB)with secondary components was stronger in winter than in summer.In summer,as pollution worsens,the mixing of primary sources particles with 62[NO_(3)]−weakened,but themixing with 97[HSO_(4)]−increased.However,in winter,the mixing state of particles did not exhibit an obvious evolution rules.The potential source areas in summer were mainly distributed in the southern region of Sichuan,while in winter,besides the southern region,the contribution of the western region cannot be ignored.
基金supported by the Natural Science Foundation of Jiangxi Province,China(Grant No.20242BAB20130)the Basic Research Funds Program of the Chinese Academy of Geological Sciences(Grant No.YYWF201607)the National Natural Science Foundation of China(Grant No.41902064)。
文摘The phenomenon of'bamboo-like'thin interlayers developed in rock salt is one of the most prominent features of Paleogene salt-bearing strata in eastern China,where centimeter-thick rock salts appear separately,forming rhythmic units.At present,detailed analyses of these rhythms of rock salt are still limited,which directly affects the achievement of comprehensive and in-depth understanding of the developmental laws pertaining to this kind of saline lake.Therefore,we selected the typical rhythmic'bamboo-like'rock salts of the Shizhai Depression in Jiangsu Province as the research subject.Through careful observation of rock salts in hand samples and detailed petrographic and mineralogical analyses,we analyzed the hydrogen and oxygen isotopic compositions,homogenization temperatures and chemical compositions of individual fluid inclusions in halite crystals.Early-stage rhythmic deposition was a product of continental saline lake evolution in winter or spring,late-stage rhythmic deposition being the product of evolution in the summer.The seasonal evolution of the halite sequences was determined and two brine enrichment events were identified.In addition,the quiet saline lake environment with concentrated brine represented by rock salt was more likely to precipitate potassium.This study provides a new reference for the evolution of both Paleogene climate and saline lakes in eastern China.
基金supported by the National Key Research and Development Program of China under grant numbers 2023YFC2809103 and 2024YFC2813505the National Natural Science Foundation of China under the grant number 41706216+2 种基金the Fundamental Research Funds for the Central Universities under grant numbers 2042022kf1204,2042022kf1069,2042023gf0012,2042022dx0001the Hubei Provincial Natural Science Foundation of China under grant number 2022CFB081the State Key Laboratory of Geodesy and Earth's Dynamics,Innovation Academy for Precision Measurement Science and Technology under grant number SKLGED2023-2-6。
文摘Ice shelves are important passageways for ice sheets flowing into the ocean.Through iceberg calving and basal melting,ice shelves exert considerable influence on the mass balance of the Antarctic Ice Sheet and glacier stability.The Ross Ice Shelf(RIS),the largest body of floating ice on Earth,plays an essential role in any changes in the mass balance of the Antarctic Ice Sheet.The long-term elevation change trend of RIS has been calculated with multiple satellite altimetry in previous studies.However,the seasonal variations were less revealed.Based on crossover analysis and indirect observation adjustments,this study proposed a new method for constructing seasonal records for surface elevation changes in the RIS using ICESat laser altimetry data from 2003 to 2009.The results showed that surface elevation changes exhibited seasonal variations with fluctuations over 20 cm,and the seasonal change characteristics were closely related to the temperature.Interannual variations in RIS surface elevation decreased from 2003 to2009 at a rate of 2 cm/yr.From March 2003 to April 2007,the surface elevation decreased at 3.7 cm/yr;however,after April 2007,the surface elevation increased at 5.5 cm/yr.The more recent stages of surface elevation growth have been influenced by reductions in the summer basal melt,which is related to the decreases in ocean heat content.
基金Supported by Qingdao Key Medical and Health Discipline Project(2025060)Qingdao Municipal Science and Technology Special Program for the Public(23-2-8-smjk-18-nsh)+1 种基金Shandong Public Health Association Program(No.SGWXH202303)Qingdao Outstanding Health Professional Development(2020-2022.2022-2024)。
文摘Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from stroke.Since 1990,China has reported the highest global stroke burden,with 2.19 million deaths and 45.9 million DALYs recorded in 2019~([2]).
文摘This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.
文摘Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.
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jointly supported by the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 316323005]the Guangdong Basic and Applied Basic Research Foundation[grant numbers 2023A1515010741 and 2024B1515020035]the Science and Technology Planning Project of Guangdong Province[grant number 2023B1212060019]。
文摘Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Development Plan in Jilin Province of China(20230203135SF)+1 种基金National Natural Science Foundation of China(41875119)Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。
文摘Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.
