The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha...The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.展开更多
Seasonal cycles are essential components of weather and climatic systems. This study utilized observational data from a meteorological station on an island in the South China Sea from 1961 to 2020, along with ERA5 rea...Seasonal cycles are essential components of weather and climatic systems. This study utilized observational data from a meteorological station on an island in the South China Sea from 1961 to 2020, along with ERA5 reanalysis data, to explore the variations in seasonal cycles and thermal comfort characteristics on the island. The observational data revealed that the onset of summer on the island occurred earlier each year, whereas the onset of autumn was gradually delayed,leading to an increase in the duration of summer. Urbanization had played an important role in elevating local temperatures and extending the duration of summer. The thermal comfort index exhibited a clear upward trend annually, reflecting a shift towards warmer and less comfortable conditions due to urbanization. From 1961 to 2020, the annual average thermal comfort index indicated that 36 years(60%) were characterized by hot discomfort, and 24 years(40%) were within the comfortable range. The number of comfortable days per year on the island exhibited a declining trend. Urbanization markedly influenced the thermal comfort levels on the island, contributing to an annual increase in the number of hot discomfort days. However, the reanalysis data did not reflect the actual observed changes in the comfort characteristics on the island.展开更多
The effect of antibacterial adhesive on the biological corrosion resistance of mortar in seawater environment was studied by means of scanning electron microscope,thermogravimetric analysis,X-ray diffraction,Fourier t...The effect of antibacterial adhesive on the biological corrosion resistance of mortar in seawater environment was studied by means of scanning electron microscope,thermogravimetric analysis,X-ray diffraction,Fourier transform infrared spectroscopy,and ultra-depth microscope.The results show that the antibacterial adhesive can effectively inhibit the growth of sulfur-oxidizing bacteria in seawater,hinder their metabolism to produce biological sulfate,and reduce the formation of destructive product gypsum.The mineral composition and thermal analysis showed that the peak value of plaster diffraction peak and the mass loss of plaster dehydration in antibacterial adhesive group were significantly lower than those in blank group(without protective coating group).In addition,the electric flux of chloride ions(>400 C)in the blank group of mortar samples was higher than that in the antibacterial adhesive group(<200 C),indicating that the antibacterial adhesive can effectively reduce the permeability of chloride ions in mortar,and thus hinder the Cl-erosion in seawater.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
The first shipborne ozone soundings(0–30 km) campaign in the South China Sea was conducted from 22 May to 15 June 2023, aiming to better investigate the ozone vertical structure over the South China Sea. Results show...The first shipborne ozone soundings(0–30 km) campaign in the South China Sea was conducted from 22 May to 15 June 2023, aiming to better investigate the ozone vertical structure over the South China Sea. Results show that ozone concentrations in the boundary layer over the South China Sea are higher than those at tropical marine sites. Balloon measurements revealed finer ozone lamina structures that satellite and reanalysis data could not reproduce. Notably, ozone in the upper troposphere(~13.5 km) decreased significantly due to transport by a tropical cyclone, while it increased slightly in the middle troposphere. These measurements provide valuable insights into ozone's chemical structure and support the need for long-term monitoring of the vertical evolution of ozone from the surface to the middle stratosphere over oceanic regions.展开更多
We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent law...We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent laws of concrete mix design.Four models,including random forest,Cat Boost,XGBoost,and deep neural network,were trained.The experimental results demonstrate that the XGBoost model performs the best in predicting the strength of sea sand concrete.Its R^(2)value reached 0.9999,and evaluation indexes such as MAPE,RMSE,MAE,and MSE are superior to those of other models.The principal component analysis(PCA)was conducted to visually analyze the structure and distribution of the original feature data,and Pearson correlation coefficient analysis and Shapley additive explanation(SHAP)were utilized to explore the impact of input characteristics on the strength of sea sand concrete.SHAP analysis is more conducive to revealing the nonlinear effects of various characteristics on the model prediction results,especially that particle size of stone has significant impacts on the strength of sea sand concrete.In addition,experimental verification was carried out to confirm the accuracy of the optimized training model.These findings offer some insights for the future design and application of sea sand in high-performance marine and coastal infrastructure.展开更多
Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-ins...Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.展开更多
Debate has persisted over whether the metamorphic basement of the Zhoushan Islands,easternmost Cathaysia Block,is Precambrian.Here,representative metamorphic rocks from the Qushan Islands were investigated using petro...Debate has persisted over whether the metamorphic basement of the Zhoushan Islands,easternmost Cathaysia Block,is Precambrian.Here,representative metamorphic rocks from the Qushan Islands were investigated using petrography,mineral chemistry,phase equilibria modeling and SHRIMP zircon U-Pb dating to constrain their metamorphic evolution and tectonic significance.Both the pelitic granulites(garnet-kyanite-perthite-biotite-quartz)and the mafic granulites(garnet-clinopyroxene-amphibole-plagioclase-quartz)reached high-pressure granulite-facies conditions of 1.2-1.4 GPa/820-900℃,and recorded three metamorphic stages along a clockwise P-T path with post-peak isothermal decompression.This trajectory indicated rapid exhumation of thickened continental crust during collisional orogeny.Metamorphic ages of 254±3 Ma,262±4 Ma and 259±3 Ma were obtained for mafic granulite,pelitic granulite and marble,respectively,and were consistent with the emplacement age of 259±4 Ma for a pegmatite vein.Detrital zircons in metasediments spanned 2706-330 Ma,which constrained the latest deposition to~330 Ma;thus represented mid-Paleozoic sediment metamorphosed during the late Paleozoic rather than Precambrian basement.We conclude that the Indosinian tectonothermal event in the Cathaysia Block had originated from late Paleozoic-early Mesozoic collisional orogeny between the South China Plate to the north and the Indochina Block to the south.展开更多
The Yellow Sea and Bohai Sea are among the global shelf seas susceptible to typhoons every year.Using observations and high-resolution numerical simulations,the current study investigates the dramatic changes in tempe...The Yellow Sea and Bohai Sea are among the global shelf seas susceptible to typhoons every year.Using observations and high-resolution numerical simulations,the current study investigates the dramatic changes in temperature and ocean heat content(OHC)of the Yellow Sea and Bohai Sea caused by Super Typhoon Maysak in early September 2020,which is representative of northward/northeastward-bypassing typhoons with centers just to the east of the study area.Temperature shows spatially coherent cooling in the upper mixed layer but warming in the subsurface layer in the majority of the offshore waters,due to wind-enhanced vertical mixing.In lower layers from the thermocline to sea bottom,temperature experiences significant warming in northeastern coastal waters of the Shandong Peninsula and in regions just off the Subei Shoal,but significant cooling in western coastal waters of the Korean Peninsula and southern coastal waters of the Shandong Peninsula.Significant temperature warming/cooling in lower layers is caused by coastal downwelling/upwelling.The total OHC of the study area decreases rapidly during Typhoon Maysak(2020)’s passage,which is generated comparably by latent heat loss at the sea surface and southward heat advection out of the study area at the southern boundary.Reduced shortwave radiation contributes positively but secondarily to the decreasing OHC during the first day.A numerical experiment suggests that Typhoon Maysak(2020)-induced OHC decline could have greatly affected the regional climate evolution in the following seasons.More studies are needed to fully understand the impacts of typhoons on regional climate changes in shelf seas at different time scales.展开更多
Predicting Antarctic sea ice is of substantial academic and practical significance.However,current prediction models,including deep learning(DL)-based models,show notable bias in the marginal ice zone.In this study,we...Predicting Antarctic sea ice is of substantial academic and practical significance.However,current prediction models,including deep learning(DL)-based models,show notable bias in the marginal ice zone.In this study,we developed a pure data-driven DL model for predicting the Antarctic austral summer monthly-to-seasonal sea ice concentration(SIC)by incorporating a novel hybrid sea ice edge constraint loss function(HybridLoss).The model is referred to as ASICNet.Independent testing based on the last five years(2019–23)demonstrates that ASICNet with HybridLoss achieves significantly higher skill metrics than without,with a reduced mean absolute error of 0.021 from 0.022,a reduced integrated ice edge error of 1.714×10^(6)from 1.794×10^(6)km^(2),but an increased pattern correlation coefficient of 0.40 from 0.38,although both ASICNet versions outperform dynamical and statistical models.Furthermore,enhanced heat maps were developed to interpret the predictability sources of sea ice within DL-based models,and the results suggest that the predictability of Antarctic sea ice is attributable to factors like the Antarctic Dipole(ADP),Amundsen Sea Low(ASL),and Southern Ocean sea surface temperature(SST),as revealed in previous studies.Thus,ASICNet is an efficient tool for austral summer Antarctic SIC prediction.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Diff...The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.展开更多
Arctic sea ice is an essential component of the climate system and plays an important role in global climate change.This study calculates the volume flux through Fram Strait(FS)and the sea ice volume in the Greenland ...Arctic sea ice is an essential component of the climate system and plays an important role in global climate change.This study calculates the volume flux through Fram Strait(FS)and the sea ice volume in the Greenland Sea(GS)from 1979 to 2022,and analyzes trends before and after 2000.In addition,the contributions of advection and local processes to sea ice volume variations in the GS during different seasons are compared.The influence of the surface air temperature(SAT)and the sea surface temperature(SST)on sea ice volume variations is discussed,as well as the impact of atmospheric circulation on sea ice.Results indicate no significant trend in the sea ice volume flux through FS from 1979 to 2022.However,the sea ice volume in the GS exhibited a notable decreasing trend.Compared with the period of 1979-2000,the sea ice volume decreasing trend accelerated significantly during the period of 2001-2022.During winter,ice advection from the central Arctic Ocean exert a strong influence on the sea ice volume variations in the GS,whereas during summer,local processes,including the interactions with the atmosphere and ocean,as well as the dynamic process of sea ice itself,exert a considerable impact.The sea ice volume in the GS declined rapidly after 2000.Furthermore,the effects of local processes on sea ice have intensified,with the SST exerting a stronger influence on the sea ice volume variations in the GS than the SAT.The positive Arctic oscillation and dipole anomaly are important drivers for the transport of Arctic sea ice to the GS.The Winter North Atlantic oscillation intensifies ocean heat content,affecting sea ice in the GS.展开更多
Antarctic coastal polynyas play a vital role in atmosphere-ocean interactions and local ecosystems.This study investigates the interannual variability of springtime coastal polynyas over the Ross Sea based on satellit...Antarctic coastal polynyas play a vital role in atmosphere-ocean interactions and local ecosystems.This study investigates the interannual variability of springtime coastal polynyas over the Ross Sea based on satellite-retrieved sea-ice concentration(SIC)data from 1992 to 2021.Firstly,the springtime coastal polynya areas display large interannual variability as well as a positive trend of about 2000 km^(2)(10 yr)^(-1) over the 30 years.Secondly,based on composite analysis,in spring,we find that a deepened Amundsen Sea Low(ASL)induces stronger meridional winds over the eastern Ross Sea,leading to stronger sea-ice advection and expansion of coastal polynya areas.This is accompanied by more solar radiation absorption in early summer(about 16 W m^(2)),resulting in upper-ocean warming(~0.4℃)and significant sea-ice loss in late summer(~50%SIC).Additionally,the physical processes are validated by 500-year piControl simulations of a state-of-the-art Earth system model.Based on the same composite analysis,the results show that the sea-ice decline is consistent with the deepening of the ASL and the increase of the meridional sea-ice advection of the preceding spring,which is highly consistent with that of observations.This further confirms the circulations-polynyas-sea-ice physical linkages.Since the springtime ASL is strongly modulated by the tropical Pacific variability and the stratospheric polar vortex,changes in the polynya areas of the Ross Sea can be traced back to remote regions.展开更多
Sea ice and snow are the most sensitive and important crucial components of the global climate system,affecting the global climate by modulating the energy exchange between the ocean and the atmosphere.The sea near Zh...Sea ice and snow are the most sensitive and important crucial components of the global climate system,affecting the global climate by modulating the energy exchange between the ocean and the atmosphere.The sea near Zhongshan Station in Antarctica is covered by landfast sea ice,with snow depth influenced by both thermal factors and wind.This region frequently experiences katabatic winds and cyclones from the westerlies,leading to frequent snow blowing events that redistribute the snow and affects its depth,subsequently impacting the thermodynamic growth of sea ice.This study utilized the one-dimensional thermodynamic model ICEPACK to simulate landfast sea ice thickness and snow depth near Zhongshan Station in 2016.Two parameterization schemes for snow blowing,the Bulk scheme,and the ITDrdg(ITD/ridges)scheme are evaluated for their impact on snow depth.The results show that simulations using snow blowing schemes more closely align with observed results,with the ITDrdg scheme providing more accurate simulations,evidenced by root mean square errors of less than 10 cm for both snow depth and sea ice thickness.Snow blowing also impacts the thermodynamic growth of sea ice,particularly bottom growth.The sea ice bottom increases by 9.0 cm using the ITDrdg scheme compared to simulations without the snow blowing,accounting for 12.5%of total sea ice bottom growth.Furthermore,snow blowing process also influences snow ice formation,highlighting its primary role in affecting snow depth.Continued field observations of snow blowing are necessary to evaluate and improve parameterization schemes.展开更多
Marine heat waves(MHWs), characterized by extreme warm sea surface temperature events, frequently occur in Chinese marginal seas. However, the seasonal variation and joint distribution of MHWs in the Bohai Sea have no...Marine heat waves(MHWs), characterized by extreme warm sea surface temperature events, frequently occur in Chinese marginal seas. However, the seasonal variation and joint distribution of MHWs in the Bohai Sea have not been fully described. Therefore, we conducted a systematic investigation of MHWs in this region. Our findings indicate that the frequency of MHW is low during winter, with long duration and weak intensity, while in summer, it is opposite, being high in the frequency, and shorter but stronger. Notably, in summer, the Laizhou and Liaodong bays exhibit a relatively long total day of MHWs compared to other areas in the Bohai Sea. Furthermore, our analysis of the joint distribution of MHWs, considering both duration and intensity, reveals significant seasonal variations. To provide practical insights for marine ranching, we have also investigated time series of MHWs at several specific stations and computed the correlation coefficients between MHW intensity and potential influential factors. Results suggest that sea surface height, cloud cover, wind stress, and wind stress curl are significantly correlated with MHW intensity, although these relationships vary geographically and seasonally. Overall, these findings elucidate the seasonal variation and potential influential factors of MHWs in the Bohai Sea and offer insights for decision-making and planning in marine ranching.展开更多
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008202)the National Natural Science Foundation of China(No.42406019)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202353066)。
文摘The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.
基金National Natural Science Foundation of China(U21A6001,42475077)Innovation Platform for Academicians of Hainan Province。
文摘Seasonal cycles are essential components of weather and climatic systems. This study utilized observational data from a meteorological station on an island in the South China Sea from 1961 to 2020, along with ERA5 reanalysis data, to explore the variations in seasonal cycles and thermal comfort characteristics on the island. The observational data revealed that the onset of summer on the island occurred earlier each year, whereas the onset of autumn was gradually delayed,leading to an increase in the duration of summer. Urbanization had played an important role in elevating local temperatures and extending the duration of summer. The thermal comfort index exhibited a clear upward trend annually, reflecting a shift towards warmer and less comfortable conditions due to urbanization. From 1961 to 2020, the annual average thermal comfort index indicated that 36 years(60%) were characterized by hot discomfort, and 24 years(40%) were within the comfortable range. The number of comfortable days per year on the island exhibited a declining trend. Urbanization markedly influenced the thermal comfort levels on the island, contributing to an annual increase in the number of hot discomfort days. However, the reanalysis data did not reflect the actual observed changes in the comfort characteristics on the island.
基金Funded by the National Natural Science Foundation of China(Nos.52278269,52278268,52178264)Tianjin Outstanding Young Scholars Science Fund Project(No.22JCJQJC00020)Key Project of Tianjin Natural Science Foundation(No.23JCZDJC00430)。
文摘The effect of antibacterial adhesive on the biological corrosion resistance of mortar in seawater environment was studied by means of scanning electron microscope,thermogravimetric analysis,X-ray diffraction,Fourier transform infrared spectroscopy,and ultra-depth microscope.The results show that the antibacterial adhesive can effectively inhibit the growth of sulfur-oxidizing bacteria in seawater,hinder their metabolism to produce biological sulfate,and reduce the formation of destructive product gypsum.The mineral composition and thermal analysis showed that the peak value of plaster diffraction peak and the mass loss of plaster dehydration in antibacterial adhesive group were significantly lower than those in blank group(without protective coating group).In addition,the electric flux of chloride ions(>400 C)in the blank group of mortar samples was higher than that in the antibacterial adhesive group(<200 C),indicating that the antibacterial adhesive can effectively reduce the permeability of chloride ions in mortar,and thus hinder the Cl-erosion in seawater.
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.
基金supported by the National Natural Science Foundation of China (Grant Nos.42394121,41675040)the Guangzhou Science and Technology Planning Program (202201010482)。
文摘The first shipborne ozone soundings(0–30 km) campaign in the South China Sea was conducted from 22 May to 15 June 2023, aiming to better investigate the ozone vertical structure over the South China Sea. Results show that ozone concentrations in the boundary layer over the South China Sea are higher than those at tropical marine sites. Balloon measurements revealed finer ozone lamina structures that satellite and reanalysis data could not reproduce. Notably, ozone in the upper troposphere(~13.5 km) decreased significantly due to transport by a tropical cyclone, while it increased slightly in the middle troposphere. These measurements provide valuable insights into ozone's chemical structure and support the need for long-term monitoring of the vertical evolution of ozone from the surface to the middle stratosphere over oceanic regions.
基金Funded by the Chongqing Natural Science Foundation Project(No.cstc202ljcyj-msxmX0725)。
文摘We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent laws of concrete mix design.Four models,including random forest,Cat Boost,XGBoost,and deep neural network,were trained.The experimental results demonstrate that the XGBoost model performs the best in predicting the strength of sea sand concrete.Its R^(2)value reached 0.9999,and evaluation indexes such as MAPE,RMSE,MAE,and MSE are superior to those of other models.The principal component analysis(PCA)was conducted to visually analyze the structure and distribution of the original feature data,and Pearson correlation coefficient analysis and Shapley additive explanation(SHAP)were utilized to explore the impact of input characteristics on the strength of sea sand concrete.SHAP analysis is more conducive to revealing the nonlinear effects of various characteristics on the model prediction results,especially that particle size of stone has significant impacts on the strength of sea sand concrete.In addition,experimental verification was carried out to confirm the accuracy of the optimized training model.These findings offer some insights for the future design and application of sea sand in high-performance marine and coastal infrastructure.
基金supported by the Khalifa University of Science and Technology internal grants(Nos.2021-CIRA-109,2020-CIRA-007,and 2020-CIRA-024).
文摘Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.
基金supported by the National Natural Science Foundation of China(42072223)Geological Survey project(DD20221649,DD20231429).
文摘Debate has persisted over whether the metamorphic basement of the Zhoushan Islands,easternmost Cathaysia Block,is Precambrian.Here,representative metamorphic rocks from the Qushan Islands were investigated using petrography,mineral chemistry,phase equilibria modeling and SHRIMP zircon U-Pb dating to constrain their metamorphic evolution and tectonic significance.Both the pelitic granulites(garnet-kyanite-perthite-biotite-quartz)and the mafic granulites(garnet-clinopyroxene-amphibole-plagioclase-quartz)reached high-pressure granulite-facies conditions of 1.2-1.4 GPa/820-900℃,and recorded three metamorphic stages along a clockwise P-T path with post-peak isothermal decompression.This trajectory indicated rapid exhumation of thickened continental crust during collisional orogeny.Metamorphic ages of 254±3 Ma,262±4 Ma and 259±3 Ma were obtained for mafic granulite,pelitic granulite and marble,respectively,and were consistent with the emplacement age of 259±4 Ma for a pegmatite vein.Detrital zircons in metasediments spanned 2706-330 Ma,which constrained the latest deposition to~330 Ma;thus represented mid-Paleozoic sediment metamorphosed during the late Paleozoic rather than Precambrian basement.We conclude that the Indosinian tectonothermal event in the Cathaysia Block had originated from late Paleozoic-early Mesozoic collisional orogeny between the South China Plate to the north and the Indochina Block to the south.
基金supported by the National Key Research and Development Program of China(Grant Nos.2022YFF0801400 and 2021YFF0704002)the Shandong Provincial Natural Science Foundation(Grant No.ZR2024LQX002)the National Science Foundation of China(Grant No.42176016).
文摘The Yellow Sea and Bohai Sea are among the global shelf seas susceptible to typhoons every year.Using observations and high-resolution numerical simulations,the current study investigates the dramatic changes in temperature and ocean heat content(OHC)of the Yellow Sea and Bohai Sea caused by Super Typhoon Maysak in early September 2020,which is representative of northward/northeastward-bypassing typhoons with centers just to the east of the study area.Temperature shows spatially coherent cooling in the upper mixed layer but warming in the subsurface layer in the majority of the offshore waters,due to wind-enhanced vertical mixing.In lower layers from the thermocline to sea bottom,temperature experiences significant warming in northeastern coastal waters of the Shandong Peninsula and in regions just off the Subei Shoal,but significant cooling in western coastal waters of the Korean Peninsula and southern coastal waters of the Shandong Peninsula.Significant temperature warming/cooling in lower layers is caused by coastal downwelling/upwelling.The total OHC of the study area decreases rapidly during Typhoon Maysak(2020)’s passage,which is generated comparably by latent heat loss at the sea surface and southward heat advection out of the study area at the southern boundary.Reduced shortwave radiation contributes positively but secondarily to the decreasing OHC during the first day.A numerical experiment suggests that Typhoon Maysak(2020)-induced OHC decline could have greatly affected the regional climate evolution in the following seasons.More studies are needed to fully understand the impacts of typhoons on regional climate changes in shelf seas at different time scales.
基金jointly supported by the National Natural Science Foundation of China(Grant No.42376250)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070402).
文摘Predicting Antarctic sea ice is of substantial academic and practical significance.However,current prediction models,including deep learning(DL)-based models,show notable bias in the marginal ice zone.In this study,we developed a pure data-driven DL model for predicting the Antarctic austral summer monthly-to-seasonal sea ice concentration(SIC)by incorporating a novel hybrid sea ice edge constraint loss function(HybridLoss).The model is referred to as ASICNet.Independent testing based on the last five years(2019–23)demonstrates that ASICNet with HybridLoss achieves significantly higher skill metrics than without,with a reduced mean absolute error of 0.021 from 0.022,a reduced integrated ice edge error of 1.714×10^(6)from 1.794×10^(6)km^(2),but an increased pattern correlation coefficient of 0.40 from 0.38,although both ASICNet versions outperform dynamical and statistical models.Furthermore,enhanced heat maps were developed to interpret the predictability sources of sea ice within DL-based models,and the results suggest that the predictability of Antarctic sea ice is attributable to factors like the Antarctic Dipole(ADP),Amundsen Sea Low(ASL),and Southern Ocean sea surface temperature(SST),as revealed in previous studies.Thus,ASICNet is an efficient tool for austral summer Antarctic SIC prediction.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
基金supported by the United Kingdom(UK)Darwin Initiative(28-003).
文摘The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.
基金The National Key Research and Development Program of China under contract Nos 2021YFC2803303 and 2021YFC2803302the National Natural Science Foundation of China under contract No.42171133the Fundamental Research Funds for the Central Universities,China,under contract No.2042022dx0001.
文摘Arctic sea ice is an essential component of the climate system and plays an important role in global climate change.This study calculates the volume flux through Fram Strait(FS)and the sea ice volume in the Greenland Sea(GS)from 1979 to 2022,and analyzes trends before and after 2000.In addition,the contributions of advection and local processes to sea ice volume variations in the GS during different seasons are compared.The influence of the surface air temperature(SAT)and the sea surface temperature(SST)on sea ice volume variations is discussed,as well as the impact of atmospheric circulation on sea ice.Results indicate no significant trend in the sea ice volume flux through FS from 1979 to 2022.However,the sea ice volume in the GS exhibited a notable decreasing trend.Compared with the period of 1979-2000,the sea ice volume decreasing trend accelerated significantly during the period of 2001-2022.During winter,ice advection from the central Arctic Ocean exert a strong influence on the sea ice volume variations in the GS,whereas during summer,local processes,including the interactions with the atmosphere and ocean,as well as the dynamic process of sea ice itself,exert a considerable impact.The sea ice volume in the GS declined rapidly after 2000.Furthermore,the effects of local processes on sea ice have intensified,with the SST exerting a stronger influence on the sea ice volume variations in the GS than the SAT.The positive Arctic oscillation and dipole anomaly are important drivers for the transport of Arctic sea ice to the GS.The Winter North Atlantic oscillation intensifies ocean heat content,affecting sea ice in the GS.
基金supported by the National Key R&D Program of China(Grant No.2021YFC2802504)the National Outstanding Youth Grant(Grant No.41925027)+1 种基金the National Natural Science Foundation of China(Grant No.42206251)the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311021008).
文摘Antarctic coastal polynyas play a vital role in atmosphere-ocean interactions and local ecosystems.This study investigates the interannual variability of springtime coastal polynyas over the Ross Sea based on satellite-retrieved sea-ice concentration(SIC)data from 1992 to 2021.Firstly,the springtime coastal polynya areas display large interannual variability as well as a positive trend of about 2000 km^(2)(10 yr)^(-1) over the 30 years.Secondly,based on composite analysis,in spring,we find that a deepened Amundsen Sea Low(ASL)induces stronger meridional winds over the eastern Ross Sea,leading to stronger sea-ice advection and expansion of coastal polynya areas.This is accompanied by more solar radiation absorption in early summer(about 16 W m^(2)),resulting in upper-ocean warming(~0.4℃)and significant sea-ice loss in late summer(~50%SIC).Additionally,the physical processes are validated by 500-year piControl simulations of a state-of-the-art Earth system model.Based on the same composite analysis,the results show that the sea-ice decline is consistent with the deepening of the ASL and the increase of the meridional sea-ice advection of the preceding spring,which is highly consistent with that of observations.This further confirms the circulations-polynyas-sea-ice physical linkages.Since the springtime ASL is strongly modulated by the tropical Pacific variability and the stratospheric polar vortex,changes in the polynya areas of the Ross Sea can be traced back to remote regions.
基金The National Natural Science Foundation of China under contract Nos 42306255 and 41976217the National Key R&D Program of China under contract No.2018YFA0605903。
文摘Sea ice and snow are the most sensitive and important crucial components of the global climate system,affecting the global climate by modulating the energy exchange between the ocean and the atmosphere.The sea near Zhongshan Station in Antarctica is covered by landfast sea ice,with snow depth influenced by both thermal factors and wind.This region frequently experiences katabatic winds and cyclones from the westerlies,leading to frequent snow blowing events that redistribute the snow and affects its depth,subsequently impacting the thermodynamic growth of sea ice.This study utilized the one-dimensional thermodynamic model ICEPACK to simulate landfast sea ice thickness and snow depth near Zhongshan Station in 2016.Two parameterization schemes for snow blowing,the Bulk scheme,and the ITDrdg(ITD/ridges)scheme are evaluated for their impact on snow depth.The results show that simulations using snow blowing schemes more closely align with observed results,with the ITDrdg scheme providing more accurate simulations,evidenced by root mean square errors of less than 10 cm for both snow depth and sea ice thickness.Snow blowing also impacts the thermodynamic growth of sea ice,particularly bottom growth.The sea ice bottom increases by 9.0 cm using the ITDrdg scheme compared to simulations without the snow blowing,accounting for 12.5%of total sea ice bottom growth.Furthermore,snow blowing process also influences snow ice formation,highlighting its primary role in affecting snow depth.Continued field observations of snow blowing are necessary to evaluate and improve parameterization schemes.
基金Supported by the National Natural Science Foundation of China (Nos. 92358302, 42227901)。
文摘Marine heat waves(MHWs), characterized by extreme warm sea surface temperature events, frequently occur in Chinese marginal seas. However, the seasonal variation and joint distribution of MHWs in the Bohai Sea have not been fully described. Therefore, we conducted a systematic investigation of MHWs in this region. Our findings indicate that the frequency of MHW is low during winter, with long duration and weak intensity, while in summer, it is opposite, being high in the frequency, and shorter but stronger. Notably, in summer, the Laizhou and Liaodong bays exhibit a relatively long total day of MHWs compared to other areas in the Bohai Sea. Furthermore, our analysis of the joint distribution of MHWs, considering both duration and intensity, reveals significant seasonal variations. To provide practical insights for marine ranching, we have also investigated time series of MHWs at several specific stations and computed the correlation coefficients between MHW intensity and potential influential factors. Results suggest that sea surface height, cloud cover, wind stress, and wind stress curl are significantly correlated with MHW intensity, although these relationships vary geographically and seasonally. Overall, these findings elucidate the seasonal variation and potential influential factors of MHWs in the Bohai Sea and offer insights for decision-making and planning in marine ranching.
