Mean Climatic Characteristics in High Northern Latitudes in an Ocean-Sea Ice-Atmosphere Coupled Model

Emphasizing the model's ability in mean climate reproduction in high northern latitudes, results from an ocean-sea ice-atmosphere coupled model are analyzed. It is shown that the coupled model can simulate the main characteristics of annual mean global sea surface temperature and sea level pressure well, but the extent of ice coverage produced in the Southern Hemisphere is not large enough. The main distribution characteristics of simulated sea level pressure and temperature at 850 hPa in high northern latitudes agree well with their counterparts in the NCEP reanalysis dataset, and the model can reproduce the Arctic Oscillation (AO) mode successfully. The simulated seasonal variation of sea ice in the Northern Hemisphere is rational and its main distribution features in winter agree well with those from observations. But the ice concentration in the sea ice edge area close to the Eurasian continent in the inner Arctic Ocean is much larger than the observation. There are significant interannual variation signals in the simulated sea ice concentration in winter in high northern latitudes and the most significant area lies in the Greenland Sea, followed by the Barents Sea. All of these features agree well with the results from observations.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

毛竹ICE基因家族的全基因组鉴定及低温胁迫下的表达模式分析

【目的】对毛竹Phyllostachys edulis ICE基因家族进行鉴定及分析,找出响应毛竹抗寒关键家族成员,研究毛竹ICE基因的生物学功能、响应低温胁迫的分子机制及遗传转化,为提高毛竹抗寒性奠定理论基础。【方法】利用生物信息学方法分析毛竹ICE基因家族成员,并对4、0、−2℃低温处理0(对照)、0.5、1.0、24.0、48.0 h的毛竹生理指标和ICE基因的表达模式进行分析。【结果】共鉴定了4个毛竹ICE基因。保守结构域和多重序列比对分析表明:PeICE基因结构高度相似。系统发育关系及启动子顺式作用元件分析显示:PeICE基因与水稻Oryza sativa亲缘关系更近,同时存在大量与非生物胁迫相关的顺式作用元件。活性氧自由基(ROS)染色发现随着处理时间增长,ROS染色逐渐加深,但是其0℃处理24.0 h、−2℃处理1.0 h后染色逐渐减弱。脯氨酸(Pro)质量摩尔浓度、超氧化物歧化酶(SOD)活性显示:4和0℃条件下,Pro质量摩尔浓度和SOD活性整体增加,但−2℃时低于对照。过氧化物酶(POD)活性显示:在3个低温处理下均增加。ICE基因表达模式分析发现:4、0℃处理时PeICE表达量整体增加,且都以PeICE3增量最明显;而−2℃处理下PeICE整体表达量水平低于对照。【结论】随着温度降低和处理时间增强,毛竹受到的损伤不断增强,其内酶活系统以及ICE基因积极响应低温胁迫,其中,PeICE3对低温胁迫最为敏感,但在−2℃时,ICE基因表达量并未增加,推测该基因家族响应了寒冷胁迫而非冷冻胁迫。

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

Joint Probability Analysis and Prediction of Sea Ice Conditions in Liaodong Bay

Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.

Preface to the Special Topic on Ocean, Sea Ice and Northern Hemisphere Climate:In Remembrance of Professor Yongqi GAO's Key Contributions

This special issue commemorates the life work of Prof. Yongqi GAO who passed away in July 2021, his time cut short by illness. He had many great achievements, but still much more to contribute. The seven articles in this special issue are from research areas where he contributed, and they illustrate how his close colleagues are continuing his work.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

Application of Ice Pigging in a Drinking Water Distribution System:Impacts on Pipes and Bulk Water Quality

Ice pigging is an emerging technique for pipe cleaning in drinking water distribution systems.However,substantial confusion and controversy exist on the potential impacts of ice pigging on bulk water quality.This study monitored the microstructural features and composition of sediments and microbial community structures in bulk water in eight multimaterial Chinese networks.Chloride concentration analysis demonstrated that separate cleaning of pipes with different materials in complex networks could mitigate the risk of losing ice pigs and degrading water quality.The microstructural and trace element characterization results showed that ice pigs would scarcely disturb the inner surfaces of long-used pipes.The bacterial richness and diversity of bulk water decreased significantly after ice pigging.Furthermore,correlations were established between pipe service age,temperature,and chloride and total iron concentrations,and the 15 most abundant taxa in bulk water,which could be used to guide practical ice pigging operations.

Advances in ice avalanches on the Tibetan Plateau

As some of the greatest natural disasters in the cryosphere,ice avalanches(IAs)seriously threaten lives and cause catastrophic damage to the resource environment,but a comprehensive overview of the state of knowledge on IAs remains lacking.We summarized 63 IAs on the Tibetan Plateau(TP)since the 20th century,of which,over 20 IAs occurred after the 21st century.The distributions of IAs are mainly concentrated in the southeastern and northwestern TP,and the occurrence time of IAs is mostly concentrated from July to September.We highlight recent advances in mechanical properties and genetic mechanisms of IAs and emphasize that temperature,rainfall,and seismicity are the inducing factors.The failure modes of IAs are summarized into 6 categories by examples:slip pulling type,slip toppling type,slip breaking type,water level collapse type,cave roof collapse type,and wedge failure type.Finally,we deliver recommendations concerning the risk assessment and prediction of IAs.The results provide important scientific value for addressing climate change and resisting glacier-related hazards.

Wintertime Arctic Sea-Ice Decline Related to Multi-Year La Niña Events

Arctic sea ice has undergone a significant decline in the Barents-Kara Sea(BKS)since the late 1990s.Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes(mainly referring to La Niña events).The occurrence of multi-year La Niña(MYLA)events has increased significantly in recent decades,and their impact on Arctic sea ice needs to be further explored.In this study,we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña(OTLA)years.The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years.This is because MYLA events tend to be accompanied by a warm Arctic-cold continent pattern with a barotropic high pressure blocked over the Urals region.Consequently,more frequent northward atmospheric rivers intrude into the BKS,intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice.However,in the early winter of OTLA years,a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere,which obstructs the atmospheric rivers to the south of Iceland.We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes.Considering the rapid climate changes in the past,more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

Some recent advances in remote sensing-based monitoring of changes in the Greenland Ice Sheet

The mass balance of the Greenland Ice Sheet(GrIS)plays a crucial role in global sea level change.Since the 1960s,remote sensing missions have been providing extensive and continuous observation data for change monitoring of the GrIS.In this paper,we present our recent research results from remote sensing-based GrIS change monitoring.First,historical satellite data are processed and used to fill data gaps and are combined with existing partial maps,completing an ice velocity map of the GrIS from the 1960s to 1980s.This map provides valuable data for estimating the historical mass balance of Greenland.Second,the monthly gravimetry-based mass balance of the GrIS from 2002 to 2020 is estimated by combining Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow On(GRACE-FO)data.It is found that the GrIS has lost a total mass of approximately 4443±75 Gt during this period.Third,based on Global Land Ice Measurements from Space(GLIMS),an updated Greenland glacier inventory is achieved utilizing data collected between 2006 and 2020.This inventory provides more detailed and up-to-data glacier boundaries of Greenland.Overall,these advances provide essential data support for estimating the mass balance of the GrIS,contributing to the advancement of research on global sea level change.

The sudden ocean warming and its potential influences on earlyfrozen landfast ice in the Prydz Bay, East Antarctica

The ocean conditions beneath the ice cover play a key role in understanding the sea ice mass balance in the polar regions.An integrated high-frequency ice-ocean observation system,including Acoustic Doppler Velocimeter,Conductivity-Temperature-Depth Sensor,and Sea Ice Mass Balance Array(SIMBA),was deployed in the landfast ice region close to the Chinese Zhongshan Station in Antarctica.A sudden ocean warming of 0.14℃(p<0.01)was observed beneath early-frozen landfast ice,from(−1.60±0.03)℃during April 16-19 to(−1.46±0.07)℃during April 20-23,2021,which is the only significant warming event in the nearly 8-month records.The sudden ocean warming brought a double rise in oceanic heat flux,from(21.7±11.1)W/m^(2) during April 16-19 to(44.8±21.3)W/m^(2) during April 20-23,2021,which shifted the original growth phase at the ice bottom,leading to a 2 cm melting,as shown from SIMBA and borehole observations.Simultaneously,the slowdown of ice bottom freezing decreased salt rejection,and the daily trend of observed ocean salinity changed from+0.02 d^(-1) during April 16-19,2021 to+0.003 d^(-1) during April 20-23,2021.The potential reasons are increased air temperature due to the transit cyclones and the weakened vertical ocean mixing due to the tide phase transformation from semi-diurnal to diurnal.The high-frequency observations within the ice-ocean boundary layer enhance the comprehensive investigation of the ocean’s influence on ice evolution at a daily scale.