Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with em...Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with empirical orthogonal function(EOF)decomposition to forecast Arctic pentad-mean SIC,where each month is divided into six pentad-means–the first five each span five days,and the last encompasses the remaining days,which may vary in length.The models were trained on SIC data from 1989 to2018 and tested from 2019 to 2023,with lead times ranging from 1 to 12 pentad-means.Model skill was evaluated based on SIC spatial patterns,sea ice area(SIA),and the sea ice edge in September from 2019 to 2023.The moving-averaged 2-m temperature helps reduce the long short-term memory model's error in the Beaufort and Chukchi Seas.Based on the models'scores for each EOF time series,weighted ensemble prediction results were obtained.These results outperform two benchmark models across all lead times.In addition,the ensemble prediction better reproduces the seasonal cycle of the SIA,with relative errors ranging from 1.04%to 3.85%.The predicted September ice edge closely matches observations,with binary accuracy consistently above 90%.Forecast models show the lowest errors in the central Arctic,while relatively higher errors appear in the Barents and Kara Seas.展开更多
The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie poin...The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie point(STP).However,due to pronounced spatial heterogeneity in seawater and sea ice properties across the Arctic,the use of an STP often leads to regionally biased.To address this limitation,this study proposes a multi-tie point(MTP)sea ice thickness retrieval method based on SMOS brightness temperature and sea ice concentration time series.Multiple seawater and sea ice tie-point values are identified through point-by-point time series analysis,quality control,and statistical hypothesis testing,allowing spatial variability in radiometric properties to be explicitly considered.The MTP-based retrieval is applied to Arctic freeze-up conditions.Validation against independent SMOS thin sea ice thickness products shows that the MTP approach yields significantly reduced bias and root mean square error compared with the conventional STP method,with statistically significant improvements confirmed by paired t-tests.While retrieval accuracy stabilizes beyond a certain number of tie points,the preprocessing cost associated with tie-point selection increases substantially.Considering both accuracy and efficiency,the MTP framework provides a practical and robust approach for large-scale Arctic thin sea ice thickness retrieval and enables improved characterization of regional freezing processes and maximum ice thickness.展开更多
Prydz Bay,East Antarctica,is a critical region for studying ocean–sea ice–ice shelf interactions and their role in the global climate system.This review synthesizes the advancements in numerical modeling of physical...Prydz Bay,East Antarctica,is a critical region for studying ocean–sea ice–ice shelf interactions and their role in the global climate system.This review synthesizes the advancements in numerical modeling of physical oceanographic processes in Prydz Bay,highlighting the evolution from early one-dimensional thermodynamic models to contemporary high-resolution,three-dimensional coupled ocean–sea ice–ice shelf frameworks.We discuss key milestones in understanding processes such as frazil ice dynamics and its impact on the basal mass balance of the Amery Ice Shelf,the pathways and mechanisms of Modified Circumpolar Deep Water intrusions,and the dynamic influences of large icebergs on regional circulation.Despite significant progress,challenges remain in integrating multi-component interactions and achieving long-term,high-resolution climate projections.Future efforts should focus on developing fully coupled models that incorporate atmosphere–ocean–sea ice–ice shelf–iceberg interactions,supported by enhanced observational networks and improved computational efficiency.This review underscores the importance of continued modeling advancement to better predict the responses of Antarctic ice shelves and polar climate to global change.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
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.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
Using nine ice-tethered buoys deployed across the marginal ice zone(MIZ)and pack ice zone(PIZ)north of the Laptev Sea during the expedition of the Multidisciplinary drifting Observatory for the Study of Arctic Climate...Using nine ice-tethered buoys deployed across the marginal ice zone(MIZ)and pack ice zone(PIZ)north of the Laptev Sea during the expedition of the Multidisciplinary drifting Observatory for the Study of Arctic Climate(MOSAiC)in 2019-2020,we characterized the spatiotemporal variations in sea ice kinematics and deformation between October 2019 and July 2020 in the Transpolar Drift(TPD).From October to November,the buoys were in the upstream area of the TPD;spatial variations of deformation rates were significantly correlated with initial ice thickness(R=−0.84,P<0.05).From December 2019 to March 2020,the buoys were in the high Arctic and the ice cover was consolidated;heterogeneity in ice kinematics as measured across the buoys reduced by 65%.From April to May 2020,the buoys were in the downstream TPD;amplified spatial variations in ice kinematics were observed.This is because two buoys had drifted over the shallow waters north of Svalbard earlier;trajectory-stretching exponents derived from the data from these two buoys indicate deformation rates(10.6 d^(−1))that were about twice those in the deep basin(4.2 d^(−1)).By June 2020,a less consolidated ice pack and enhanced tidal forcing in the Fram Strait MIZ resulted in ice deformation with a semi-diurnal power spectral density of>0.25 d^(−1),which is about 1.5 times that in PIZ.Therefore,in both the upstream and downstream regions of the TPD,the transition between the MIZ and the PIZ contributes to the spatial and seasonal variations of sea ice motion and deformation.The results from this study can be used to support the characterization of the momentum balance and influencing factors during the ice advection along the TPD,which is a crucial corridor for Arctic sea ice outflow to the north Atlantic Ocean.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
Thermodynamic and dynamic processes(TDP)significantly modulate the rapid variability of Arctic sea ice,with complex interactions between them.This study quantifies the Arctic sea ice budget of volume from 1989 to 2021...Thermodynamic and dynamic processes(TDP)significantly modulate the rapid variability of Arctic sea ice,with complex interactions between them.This study quantifies the Arctic sea ice budget of volume from 1989 to 2021 using data from NSIDC and PIOMAS.Results show that thermodynamic processes dominate seasonal Arctic sea ice budget variation,covering 40%of the sea ice zone,strongest at the margins and in the seasonal ice zone.Dynamic processes play a relay role,contributing less than half of that from thermodynamic processes.Their influence is strongest in winter and weakest in summer,closely linked to sea ice drift circulation.TDP exhibit opposite seasonal cycles,with thermodynamic processes inversely correlated with sea ice volume changes.Dynamic processes are most negatively correlated with thermodynamic processes when they precede by 21 d.After strong thermodynamic processes,dynamic processes become more pronounced,peaking 76 d later,indicating a seasonal coupled effect where dynamic processes sustain and amplify the sea ice changes initiated by thermodynamic processes.Significant long-term trends in TDP are identified.Thermodynamic processes have increased over the past three decades,particularly in June to July and October to November.Dynamic processes decreases from June to August but increases in September.This study enhances understanding of the complex interplay between TDP modulate Arctic sea ice changes and highlights potential decadal trends under climate change.展开更多
Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preced...Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation deficit.Further analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April,the dry conditions in Northwest China tend to be alleviated.The increase of sea ice in the Greenland Sea can excite a meridional circulation,which causes sea surface temperature(SST)anomalies in the North Atlantic via the sea-air interaction,manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic,but negative SST anomalies over the west of the Azores.This abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China,leading to abnormal cyclones in Northwest China.Consequently,Northwest China experiences a more humid climate than usual.展开更多
As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice E...As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.展开更多
As one of the strongest convection bands in the Southern Hemisphere,the South Pacific Convergence Zone(SPCZ)substantially influences the variabilities in the atmospheric circulation and Antarctic climate.In this study...As one of the strongest convection bands in the Southern Hemisphere,the South Pacific Convergence Zone(SPCZ)substantially influences the variabilities in the atmospheric circulation and Antarctic climate.In this study,it is revealed that the intensity of the SPCZ can change the characteristics of sea ice in the West Antarctica during austral autumn,which is significantly independent of the El Niño-Southern Oscillation(ENSO).Observational and numerical results suggest that a stronger-than-usual SPCZ can generate a poleward-propagating Rossby wave train along a great circular route and induce a weakening of the Amundsen Sea Low(ASL)near West Antarctica,which may somewhat offset the teleconnections exerted by ENSO.These changes in the strength and zonal extent of ASL is noticeable and robustly lead to a tripole response of sea-ice perturbations in the Ross,Amundsen,and Weddell Seas.We find that the wind-driven dynamical processes determine the local sea-ice changes,while the influence from thermodynamic processes is trivial.This research underscores the need to consider the SPCZ variability for a comprehensive understanding of sea-ice changes in West Antarctica on interannual timescales.展开更多
The melting of seasonal sea ice in Antarctica plays a pivotal role in the region’s carbon cycle,influencing global carbon storage and the exchange of carbon between the atmosphere and the ocean.However,the impact of ...The melting of seasonal sea ice in Antarctica plays a pivotal role in the region’s carbon cycle,influencing global carbon storage and the exchange of carbon between the atmosphere and the ocean.However,the impact of variability in the timing of seasonal sea ice retreat on the flux and composition of sinking particulate matter remains to be elucidated.In this study,we deployed sediment traps in Prydz Bay during the austral summers of 2019/2020 and 2020/2021,noting that sea ice melting occurred approximately one and a half months earlier in the former summer compared to the latter.We analyzed sediment trap data,which included total mass flux(TMF),particulate organic carbon(POC),biogenic silica(BSi),particulate inorganic carbon,and lithogenic particle(Litho)fluxes,as well as the stable isotopesδ^(13)C andδ^(15)N of particulate organic matter(POM).Additionally,we incorporated remote sensing data on sea ice concentration and chlorophyll a.This dramatic delay in sea ice melting timing could result in a significant increase in TMF,BSi and POC fluxes in the summer of 2020/2021 compared to 2019/2020.Elevated BSi fluxes and more ^(13)C-depleted POC in the austral summer of 2020/2021 suggest that the delayed melting of sea ice may have stimulated the productivity of centric diatoms.Furthermore,the higher BSi/POC ratio and more negativeδ^(15)N values of POM,along with a reduced presence of krill in the traps,indicate a diminished grazing pressure from zooplankton,which collectively enhanced the sedimentation efficiency of POC during the austral summer of 2020/2021.These findings highlight the critical role of sea ice melting timing in regulating productivity,flux and composition of sinking particulate matter in the Prydz Bay ecosystem,with significant implications for carbon cycling in polar oceans.展开更多
This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),...This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),and the El Niño-Southern Oscillation(ENSO),along with the total sea ice condition during the preceding spring,serve as precursor signals of February sea ice extent(SIE).These climate modes interact,energizing the Pacific-South American pattern(PSA),which deepens and shifts the Amundsen Sea Low(ASL)westward in spring.This pattern generates a dipole sea ice anomaly characterized by an increase in sea ice in the northern Ross Sea but a decrease in ice in the Bellingshausen and northern Weddell Seas.However,as the season transitions into summer,the ASL exerts a pronounced delayed effect,contributing to widespread sea ice loss across West Antarctica.Strong southerly winds on the western flank of the ASL push sea ice away from the inner Ross Sea,exposing coastal waters that absorb solar radiation,thereby accelerating ice melt through positive ice-albedo feedback.Simultaneously,northwesterly winds on the eastern flank transport warm air toward the Bellingshausen and northern Weddell Seas,intensifying ice loss in these regions.Furthermore,the active PSA is accompanied by a tripole sea surface temperature pattern characterized by warming in the Weddell Sea,which promotes continued ice melt.The co-occurrence of an exceptionally positive SAM,a La Niña,and a strong negative IOD during spring 2022,combined with lower-than-normal total spring SIE,ultimately contributed to the record-low Antarctic SIE observed in February 2023.展开更多
Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through...Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.展开更多
In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Baye...In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.展开更多
A three-dimensional coupled sea ice-ice shelf-ocean numerical model is developed for the Prydz Bay,Antarctica,using the Regional Ocean Modeling System with a grid resolution of approximately 2 km.The influence of the ...A three-dimensional coupled sea ice-ice shelf-ocean numerical model is developed for the Prydz Bay,Antarctica,using the Regional Ocean Modeling System with a grid resolution of approximately 2 km.The influence of the grounding giant iceberg D15 on the distribution of sea ice and polynyas in the Prydz Bay is analyzed through two numerical experiments.Iceberg D15,grounded off the western edge of the West Ice Shelf(WIS),obstructs the southwestward transport of sea ice along the east coast of Prydz Bay,causing sea ice to accumulate to the east of the iceberg and form multi-year fast ice.Grounding of Iceberg D15 also decreases sea ice coverage off its south edge and creates ice-free openings in spring near Davis Station and Zhongshan Station,facilitating the accessibility of vessels to the research stations.These simulated sea ice patterns closely match current satellite observations.When Iceberg D15 is removed,the previously blocked sea ice north of the iceberg,which moved westward,shifts southwesterly along the coastline,leading to a reduction in sea ice thickness during winter and spring,as well as lower sea ice concentrations in spring across large areas north and west of the iceberg.In contrast,the sea ice thickness increases considerably southwest of the WIS,extending to the front of the Amery Ice Shelf during seasons covered by sea ice.The increase in sea ice concentration can also extend to as far as 75°E in spring.Without Iceberg D15,which previously contributed to the ice barrier of Barrier Polynya(BP),the shape of BP changes,the area of BP and Davis Polynya(DP)decreases,and the polynya off the northwest edge of the WIS near 83°E expands.These polynya patterns are much similar to the satellite remote sensing observations before Iceberg D15 was grounded.From April to October,the total area of BP and DP decreases by 2.83×10^(4)km^(2)(60%)and 2.20×10^(3)km^(2)(20%),respectively,while the total sea ice production decreases by 4.11×10^(10)m^(3)(66%)and 1.52×10^(10)m^(3)(52%)compared to the experiment with iceberg.These results indicate the substantial effects of grounding giant icebergs on the spatio-temporal distribution of sea ice,the area of polynyas,and sea ice production.High-resolution Antarctic coastal numerical models,typically with grid scales of kilometers,are sufficient to represent large icebergs,and adding the grounding giant icebergs is crucial for producing realistic simulations of sea ice and polynyas.展开更多
With the accelerating effects of global warming,changes in Arctic sea ice extent(SIE)have become a focal point of research.However,due to its spatial heterogeneity and the complexity of its evolution,understanding the...With the accelerating effects of global warming,changes in Arctic sea ice extent(SIE)have become a focal point of research.However,due to its spatial heterogeneity and the complexity of its evolution,understanding the mechanisms driving sea ice remains a significant challenge.This study systematically examines the spatiotemporal variability of Arctic SIE and its coupling mechanisms with atmospheric-oceanic dynamic processes based on passive microwave satellite observations and atmospheric reanalysis datasets.The findings show that during the period from 1979 to 2022(44 a),the SIE exhibited an annual change rate of(−4.36±0.30)×10^(4)km^(2).The most significant decline was observed in summer[(−7.39±0.48)×10^(4)km^(2)/a].In contrast,the decrease in winter sea ice concentration(SIC)was primarily observed in the Barents Sea and Kara Sea.Meanwhile,persistent SIC retreat was observed across most of the Arctic during spring,summer and autumn.To quantify the contributions of environmental factors,the study employs multiple approaches,which reveal that sea surface temperature is the most influential factor.Furthermore,meteorological statistical methods are used to investigate how climate patterns regulate SIC by influencing Arctic atmospheric circulation.These findings highlight the intricate interactions among Arctic atmosphere,ocean,SIE and climate patterns,providing a theoretical framework and scientific basis for understanding the evolution of SIE.展开更多
The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyz...The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyze heat fluxes profile through the ice from ice surface down to basal ice-ocean interface using the data measured by 11 thermistor stringbased ice mass balance buoys(IMBs)between September and December 2018 in the Pacific sector of Arctic Ocean.The conductive heat fluxes gradually decreased from surface downward through the lower ice layers due to the thermal inertia and energy storage in the brine pockets.At the ice bottom,the oceanic heat flux decreased from(5.9±1.3)W/m^(2)in mid-September to(1.8±0.8)W/m^(2)by the end of December in response to the decreasing of available absorbed solar radiation regulated by the latitude and sea ice concentration.The initial ice thicknesses can explain the onset of ice basal growth by 44.8%(R^(2)).From 15 September to the average onset of ice basal growth by 13 November,the accumulated heat fluxes released from the ice surface to the atmosphere,caused by the cooling of the ice layer,and from the ocean to the ice bottom were estimated as 25.73 MJ/m^(2),6.49 MJ/m^(2),and 20.30 MJ/m^(2),respectively.The latter two components mainly play the roles in buffering the onset of ice basal growth.展开更多
Sea ice exhibits complex mechanical properties,and no unified constitutive model currently exists.This study establishes an elastoplastic sea ice constitutive model based on non-ordinary state-based Peridynamics(PD)an...Sea ice exhibits complex mechanical properties,and no unified constitutive model currently exists.This study establishes an elastoplastic sea ice constitutive model based on non-ordinary state-based Peridynamics(PD)and the TsaiWu yield criterion,applying force state calculations to sea ice collisions while mitigating zero energy modes.A Fortran program implements the elastic-plastic constitutive equation of PD to simulate spherical ice-steel plate collisions.The program's accuracy in simulating sea ice collisions is validated through comparison with finite element results.Using the established model,this study simulates collisions between vertical structures and layer ice,analyzing the effects of impact velocity,vertical structure size,and critical elongation on sea ice load.The findings demonstrate positive correlations between collision force and impact velocity,vertical structure size,and critical elongation.At high velocities,impact significantly affects collision force,primarily following a quadratic function,while vertical structure effects exhibit a linear relationship.展开更多
基金supported by the National Key Research and Development Program(No.2023YFC2809101)the Laoshan Laboratory Technology Innovation Project(No.LSKJ202202301)。
文摘Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with empirical orthogonal function(EOF)decomposition to forecast Arctic pentad-mean SIC,where each month is divided into six pentad-means–the first five each span five days,and the last encompasses the remaining days,which may vary in length.The models were trained on SIC data from 1989 to2018 and tested from 2019 to 2023,with lead times ranging from 1 to 12 pentad-means.Model skill was evaluated based on SIC spatial patterns,sea ice area(SIA),and the sea ice edge in September from 2019 to 2023.The moving-averaged 2-m temperature helps reduce the long short-term memory model's error in the Beaufort and Chukchi Seas.Based on the models'scores for each EOF time series,weighted ensemble prediction results were obtained.These results outperform two benchmark models across all lead times.In addition,the ensemble prediction better reproduces the seasonal cycle of the SIA,with relative errors ranging from 1.04%to 3.85%.The predicted September ice edge closely matches observations,with binary accuracy consistently above 90%.Forecast models show the lowest errors in the central Arctic,while relatively higher errors appear in the Barents and Kara Seas.
基金supported by the National Key Research and Development Program of China(Grant nos.2023YFC2809103,2024YFC2813505)the Fundamental Research Funds for the Central Universities(Grant nos.2042025kf0083,2042025gf0014)the Antarctic Zhongshan Ice and Space Environment National Observation and Research Station(Grant no.ZSNORS-20252702).
文摘The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie point(STP).However,due to pronounced spatial heterogeneity in seawater and sea ice properties across the Arctic,the use of an STP often leads to regionally biased.To address this limitation,this study proposes a multi-tie point(MTP)sea ice thickness retrieval method based on SMOS brightness temperature and sea ice concentration time series.Multiple seawater and sea ice tie-point values are identified through point-by-point time series analysis,quality control,and statistical hypothesis testing,allowing spatial variability in radiometric properties to be explicitly considered.The MTP-based retrieval is applied to Arctic freeze-up conditions.Validation against independent SMOS thin sea ice thickness products shows that the MTP approach yields significantly reduced bias and root mean square error compared with the conventional STP method,with statistically significant improvements confirmed by paired t-tests.While retrieval accuracy stabilizes beyond a certain number of tie points,the preprocessing cost associated with tie-point selection increases substantially.Considering both accuracy and efficiency,the MTP framework provides a practical and robust approach for large-scale Arctic thin sea ice thickness retrieval and enables improved characterization of regional freezing processes and maximum ice thickness.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant nos.SML2021SP306,SML2023SP201)the National Key R&D Program of China(Grant no.2024YFF0506603)+1 种基金the National Natural Science Foundation of China(Grant no.42576020)Guangdong Basic and Applied Basic Research Foundation,China(Grant nos.2024A1515012717,2026A1515012241).
文摘Prydz Bay,East Antarctica,is a critical region for studying ocean–sea ice–ice shelf interactions and their role in the global climate system.This review synthesizes the advancements in numerical modeling of physical oceanographic processes in Prydz Bay,highlighting the evolution from early one-dimensional thermodynamic models to contemporary high-resolution,three-dimensional coupled ocean–sea ice–ice shelf frameworks.We discuss key milestones in understanding processes such as frazil ice dynamics and its impact on the basal mass balance of the Amery Ice Shelf,the pathways and mechanisms of Modified Circumpolar Deep Water intrusions,and the dynamic influences of large icebergs on regional circulation.Despite significant progress,challenges remain in integrating multi-component interactions and achieving long-term,high-resolution climate projections.Future efforts should focus on developing fully coupled models that incorporate atmosphere–ocean–sea ice–ice shelf–iceberg interactions,supported by enhanced observational networks and improved computational efficiency.This review underscores the importance of continued modeling advancement to better predict the responses of Antarctic ice shelves and polar climate to global change.
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supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
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 Key Research and Development Program of China(Grant no.2021YFC2803304)the National Natural Science Foundation of China(Grant nos.52192691 and 52192690)the Program of Shanghai Academic/Technology Research Leader(Grant no.22XD1403600).
文摘Using nine ice-tethered buoys deployed across the marginal ice zone(MIZ)and pack ice zone(PIZ)north of the Laptev Sea during the expedition of the Multidisciplinary drifting Observatory for the Study of Arctic Climate(MOSAiC)in 2019-2020,we characterized the spatiotemporal variations in sea ice kinematics and deformation between October 2019 and July 2020 in the Transpolar Drift(TPD).From October to November,the buoys were in the upstream area of the TPD;spatial variations of deformation rates were significantly correlated with initial ice thickness(R=−0.84,P<0.05).From December 2019 to March 2020,the buoys were in the high Arctic and the ice cover was consolidated;heterogeneity in ice kinematics as measured across the buoys reduced by 65%.From April to May 2020,the buoys were in the downstream TPD;amplified spatial variations in ice kinematics were observed.This is because two buoys had drifted over the shallow waters north of Svalbard earlier;trajectory-stretching exponents derived from the data from these two buoys indicate deformation rates(10.6 d^(−1))that were about twice those in the deep basin(4.2 d^(−1)).By June 2020,a less consolidated ice pack and enhanced tidal forcing in the Fram Strait MIZ resulted in ice deformation with a semi-diurnal power spectral density of>0.25 d^(−1),which is about 1.5 times that in PIZ.Therefore,in both the upstream and downstream regions of the TPD,the transition between the MIZ and the PIZ contributes to the spatial and seasonal variations of sea ice motion and deformation.The results from this study can be used to support the characterization of the momentum balance and influencing factors during the ice advection along the TPD,which is a crucial corridor for Arctic sea ice outflow to the north Atlantic Ocean.
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 Research and Development Program of China(Grant no.2019YFA0607004)the National Natural Science Foundation of China(Grant nos.42430411,42075024,42205029 and 42230405)。
文摘Thermodynamic and dynamic processes(TDP)significantly modulate the rapid variability of Arctic sea ice,with complex interactions between them.This study quantifies the Arctic sea ice budget of volume from 1989 to 2021 using data from NSIDC and PIOMAS.Results show that thermodynamic processes dominate seasonal Arctic sea ice budget variation,covering 40%of the sea ice zone,strongest at the margins and in the seasonal ice zone.Dynamic processes play a relay role,contributing less than half of that from thermodynamic processes.Their influence is strongest in winter and weakest in summer,closely linked to sea ice drift circulation.TDP exhibit opposite seasonal cycles,with thermodynamic processes inversely correlated with sea ice volume changes.Dynamic processes are most negatively correlated with thermodynamic processes when they precede by 21 d.After strong thermodynamic processes,dynamic processes become more pronounced,peaking 76 d later,indicating a seasonal coupled effect where dynamic processes sustain and amplify the sea ice changes initiated by thermodynamic processes.Significant long-term trends in TDP are identified.Thermodynamic processes have increased over the past three decades,particularly in June to July and October to November.Dynamic processes decreases from June to August but increases in September.This study enhances understanding of the complex interplay between TDP modulate Arctic sea ice changes and highlights potential decadal trends under climate change.
基金supported by the National Natural Science Foun-dation of China [grant numbers 41991281 and 42005028]。
文摘Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation deficit.Further analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April,the dry conditions in Northwest China tend to be alleviated.The increase of sea ice in the Greenland Sea can excite a meridional circulation,which causes sea surface temperature(SST)anomalies in the North Atlantic via the sea-air interaction,manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic,but negative SST anomalies over the west of the Azores.This abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China,leading to abnormal cyclones in Northwest China.Consequently,Northwest China experiences a more humid climate than usual.
基金supported by the National Natural Science Foundation China(Grant No.42176222).
文摘As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.
基金supported by the National Natural Science Foundation of China(Grant No.42375024).
文摘As one of the strongest convection bands in the Southern Hemisphere,the South Pacific Convergence Zone(SPCZ)substantially influences the variabilities in the atmospheric circulation and Antarctic climate.In this study,it is revealed that the intensity of the SPCZ can change the characteristics of sea ice in the West Antarctica during austral autumn,which is significantly independent of the El Niño-Southern Oscillation(ENSO).Observational and numerical results suggest that a stronger-than-usual SPCZ can generate a poleward-propagating Rossby wave train along a great circular route and induce a weakening of the Amundsen Sea Low(ASL)near West Antarctica,which may somewhat offset the teleconnections exerted by ENSO.These changes in the strength and zonal extent of ASL is noticeable and robustly lead to a tripole response of sea-ice perturbations in the Ross,Amundsen,and Weddell Seas.We find that the wind-driven dynamical processes determine the local sea-ice changes,while the influence from thermodynamic processes is trivial.This research underscores the need to consider the SPCZ variability for a comprehensive understanding of sea-ice changes in West Antarctica on interannual timescales.
基金The National Key Research and Development Program of China under contract No.2022YFE0136500the Scientific Research Fund of the Second Institute of Oceanography,Ministry of Natural Resources,under contract Nos JG2212 and JG2211+2 种基金the National Natural Science Foundation of China under contract Nos 42276255,41976228,and 42176227the National Polar Special Program“Impact and Response of Antarctic Seas to Climate Change”under contract Nos IRASCC 01-01-02 and IRASCC 02-02the China Scholarship Council under contract No.201704180017.
文摘The melting of seasonal sea ice in Antarctica plays a pivotal role in the region’s carbon cycle,influencing global carbon storage and the exchange of carbon between the atmosphere and the ocean.However,the impact of variability in the timing of seasonal sea ice retreat on the flux and composition of sinking particulate matter remains to be elucidated.In this study,we deployed sediment traps in Prydz Bay during the austral summers of 2019/2020 and 2020/2021,noting that sea ice melting occurred approximately one and a half months earlier in the former summer compared to the latter.We analyzed sediment trap data,which included total mass flux(TMF),particulate organic carbon(POC),biogenic silica(BSi),particulate inorganic carbon,and lithogenic particle(Litho)fluxes,as well as the stable isotopesδ^(13)C andδ^(15)N of particulate organic matter(POM).Additionally,we incorporated remote sensing data on sea ice concentration and chlorophyll a.This dramatic delay in sea ice melting timing could result in a significant increase in TMF,BSi and POC fluxes in the summer of 2020/2021 compared to 2019/2020.Elevated BSi fluxes and more ^(13)C-depleted POC in the austral summer of 2020/2021 suggest that the delayed melting of sea ice may have stimulated the productivity of centric diatoms.Furthermore,the higher BSi/POC ratio and more negativeδ^(15)N values of POM,along with a reduced presence of krill in the traps,indicate a diminished grazing pressure from zooplankton,which collectively enhanced the sedimentation efficiency of POC during the austral summer of 2020/2021.These findings highlight the critical role of sea ice melting timing in regulating productivity,flux and composition of sinking particulate matter in the Prydz Bay ecosystem,with significant implications for carbon cycling in polar oceans.
基金supported by the National Natural Science Foundation of China (Grant Nos.42288101 and 42375045)
文摘This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),and the El Niño-Southern Oscillation(ENSO),along with the total sea ice condition during the preceding spring,serve as precursor signals of February sea ice extent(SIE).These climate modes interact,energizing the Pacific-South American pattern(PSA),which deepens and shifts the Amundsen Sea Low(ASL)westward in spring.This pattern generates a dipole sea ice anomaly characterized by an increase in sea ice in the northern Ross Sea but a decrease in ice in the Bellingshausen and northern Weddell Seas.However,as the season transitions into summer,the ASL exerts a pronounced delayed effect,contributing to widespread sea ice loss across West Antarctica.Strong southerly winds on the western flank of the ASL push sea ice away from the inner Ross Sea,exposing coastal waters that absorb solar radiation,thereby accelerating ice melt through positive ice-albedo feedback.Simultaneously,northwesterly winds on the eastern flank transport warm air toward the Bellingshausen and northern Weddell Seas,intensifying ice loss in these regions.Furthermore,the active PSA is accompanied by a tripole sea surface temperature pattern characterized by warming in the Weddell Sea,which promotes continued ice melt.The co-occurrence of an exceptionally positive SAM,a La Niña,and a strong negative IOD during spring 2022,combined with lower-than-normal total spring SIE,ultimately contributed to the record-low Antarctic SIE observed in February 2023.
基金The National Key Research and Development Program of China under contract No.2021YFC2803301the National Natural Science Foundation of China under contract Nos 41976212 and 41830105the Natural Science Foundation of Jiangsu Province under contract No.BK20210193.
文摘Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.
基金supported by the National Natural Science Foundation of China(No.42030406)。
文摘In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.
基金The National Natural Science Foundation of China under contract Nos 41976217 and 42306249the National Key Research and Development Program of China under contract No.2018YFA0605701.
文摘A three-dimensional coupled sea ice-ice shelf-ocean numerical model is developed for the Prydz Bay,Antarctica,using the Regional Ocean Modeling System with a grid resolution of approximately 2 km.The influence of the grounding giant iceberg D15 on the distribution of sea ice and polynyas in the Prydz Bay is analyzed through two numerical experiments.Iceberg D15,grounded off the western edge of the West Ice Shelf(WIS),obstructs the southwestward transport of sea ice along the east coast of Prydz Bay,causing sea ice to accumulate to the east of the iceberg and form multi-year fast ice.Grounding of Iceberg D15 also decreases sea ice coverage off its south edge and creates ice-free openings in spring near Davis Station and Zhongshan Station,facilitating the accessibility of vessels to the research stations.These simulated sea ice patterns closely match current satellite observations.When Iceberg D15 is removed,the previously blocked sea ice north of the iceberg,which moved westward,shifts southwesterly along the coastline,leading to a reduction in sea ice thickness during winter and spring,as well as lower sea ice concentrations in spring across large areas north and west of the iceberg.In contrast,the sea ice thickness increases considerably southwest of the WIS,extending to the front of the Amery Ice Shelf during seasons covered by sea ice.The increase in sea ice concentration can also extend to as far as 75°E in spring.Without Iceberg D15,which previously contributed to the ice barrier of Barrier Polynya(BP),the shape of BP changes,the area of BP and Davis Polynya(DP)decreases,and the polynya off the northwest edge of the WIS near 83°E expands.These polynya patterns are much similar to the satellite remote sensing observations before Iceberg D15 was grounded.From April to October,the total area of BP and DP decreases by 2.83×10^(4)km^(2)(60%)and 2.20×10^(3)km^(2)(20%),respectively,while the total sea ice production decreases by 4.11×10^(10)m^(3)(66%)and 1.52×10^(10)m^(3)(52%)compared to the experiment with iceberg.These results indicate the substantial effects of grounding giant icebergs on the spatio-temporal distribution of sea ice,the area of polynyas,and sea ice production.High-resolution Antarctic coastal numerical models,typically with grid scales of kilometers,are sufficient to represent large icebergs,and adding the grounding giant icebergs is crucial for producing realistic simulations of sea ice and polynyas.
基金The National Natural Science Foundation of China under contract Nos 42430101,42274006,42192535 and 42104084.
文摘With the accelerating effects of global warming,changes in Arctic sea ice extent(SIE)have become a focal point of research.However,due to its spatial heterogeneity and the complexity of its evolution,understanding the mechanisms driving sea ice remains a significant challenge.This study systematically examines the spatiotemporal variability of Arctic SIE and its coupling mechanisms with atmospheric-oceanic dynamic processes based on passive microwave satellite observations and atmospheric reanalysis datasets.The findings show that during the period from 1979 to 2022(44 a),the SIE exhibited an annual change rate of(−4.36±0.30)×10^(4)km^(2).The most significant decline was observed in summer[(−7.39±0.48)×10^(4)km^(2)/a].In contrast,the decrease in winter sea ice concentration(SIC)was primarily observed in the Barents Sea and Kara Sea.Meanwhile,persistent SIC retreat was observed across most of the Arctic during spring,summer and autumn.To quantify the contributions of environmental factors,the study employs multiple approaches,which reveal that sea surface temperature is the most influential factor.Furthermore,meteorological statistical methods are used to investigate how climate patterns regulate SIC by influencing Arctic atmospheric circulation.These findings highlight the intricate interactions among Arctic atmosphere,ocean,SIE and climate patterns,providing a theoretical framework and scientific basis for understanding the evolution of SIE.
基金The National Key Research and Development Program of China under contract No.2021YFC2803300the National Natural Science Foundation of China under contract No.42325604+1 种基金the Ministry of Industry and Information Technology of China under contract No.CBG2N21-2-1Program of Shanghai Academic/Technology Research Leader under contract No.22XD1403600.
文摘The thaw-freezing transition period is crucial to determine the initial sea ice status prior to the freezing season.The heat and mass balance at ice-ocean interface is the major driving process.In this study,we analyze heat fluxes profile through the ice from ice surface down to basal ice-ocean interface using the data measured by 11 thermistor stringbased ice mass balance buoys(IMBs)between September and December 2018 in the Pacific sector of Arctic Ocean.The conductive heat fluxes gradually decreased from surface downward through the lower ice layers due to the thermal inertia and energy storage in the brine pockets.At the ice bottom,the oceanic heat flux decreased from(5.9±1.3)W/m^(2)in mid-September to(1.8±0.8)W/m^(2)by the end of December in response to the decreasing of available absorbed solar radiation regulated by the latitude and sea ice concentration.The initial ice thicknesses can explain the onset of ice basal growth by 44.8%(R^(2)).From 15 September to the average onset of ice basal growth by 13 November,the accumulated heat fluxes released from the ice surface to the atmosphere,caused by the cooling of the ice layer,and from the ocean to the ice bottom were estimated as 25.73 MJ/m^(2),6.49 MJ/m^(2),and 20.30 MJ/m^(2),respectively.The latter two components mainly play the roles in buffering the onset of ice basal growth.
基金financially supported by the Aeronautical Science Foundation of China(Grant No.023M031077001)Shandong Provincial Natural Science Foundation(Grant No.ZR2022QE092)the Open Fund Project of Key Laboratory of Ocean Observation Technology,MNR(Grant No.2022klootA03)。
文摘Sea ice exhibits complex mechanical properties,and no unified constitutive model currently exists.This study establishes an elastoplastic sea ice constitutive model based on non-ordinary state-based Peridynamics(PD)and the TsaiWu yield criterion,applying force state calculations to sea ice collisions while mitigating zero energy modes.A Fortran program implements the elastic-plastic constitutive equation of PD to simulate spherical ice-steel plate collisions.The program's accuracy in simulating sea ice collisions is validated through comparison with finite element results.Using the established model,this study simulates collisions between vertical structures and layer ice,analyzing the effects of impact velocity,vertical structure size,and critical elongation on sea ice load.The findings demonstrate positive correlations between collision force and impact velocity,vertical structure size,and critical elongation.At high velocities,impact significantly affects collision force,primarily following a quadratic function,while vertical structure effects exhibit a linear relationship.
