The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data

The research on sea ice resources is the academic base of sea ice exploitation in the Bohai Sea. According to the ice-water spectrum differences and the correlation between ice thickness and albedo, this paper comes up with a sea ice thickness inversion model based on the NOAA/AVHRR data. And then a sea ice resources quantity （SIQ） time series of Bohai Sea is established from 1987 to 2009. The results indicate that the average error of inversion sea ice thickness is below 30%. The maximum sea ice resources quantity is about 6×109 m3 and the minimum is 1.3×109 m3. And a preliminary analysis has been made on the errors of the estimate of sea ice resources quantity （SIQ）.

Sea ice thickness analyses for the Bohai Sea using MODIS thermal infrared imagery

Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009-2010 was investigated in this paper using MODIS night-time thermal infrared imagery. The cloud cover in the imagery was masked out manually. Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation. Weather forcing data was from the European Centre for Medium-Range Weather Forecasts （ECMWF） analyses. The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms. The overall bias and the root mean square error of the MODIS ice thickness are -1.4 cm and 3.9 cm, respectively. The MODIS results under cold conditions （air temperature 〈 -10~C） also agree with the estimated ice growth from Lebedev and Zubov models. The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature, in particular when the sea ice is relatively thin. It is less sensitive to the wind speed. Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.

Sea ice thickness estimation in the Bohai Sea using geostationary ocean color imager data

A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models （Lebedev and Zubov）, and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation （r2〉0.66） with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part Ⅰ: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.

Application of an ice-ocean coupled model to Bohai Sea ice simulation

The HAMSOM(Hamburg Shelf Ocean Model),a high-resolution regional ice-ocean coupled model,was applied to investigate the seasonal evolution of Bohai Sea ice for winter 2015/2016.HAMSOM was initialized with monthly climatological temperature and salinity data from WOA13 and driven by hourly meteorological data obtained from the NCEP above the sea surface and tides at the open boundary.The ice model used here is a modifi ed Hibler-type dynamic-thermodynamic sea ice model based upon viscous-plastic rheology.The ice extent,concentration,area,thickness,length of ice season as well as the distance between the top of Liaodong Bay(North China)and the outer ice edge line were simulated and compared with the observed data.Three types of modeling experiments were carried out to investigate the eff ects of wind,tide,and both wind and tide on Bohai Sea ice.The results show that wind,as both a dynamic and a thermodynamic factor,has a signifi cant impact on the ice thickness,ice area,and ice-freezing and ice breakup dates as well as the ice velocity,while tides are a dynamic factor that infl uences only the ice velocity.During the severe ice period,the wind speed intensity increased by 25%,the average ice thickness thickened by approximately 4.0 cm in Liaodong Bay,approximately 2.1 cm in Bohai Bay and approximately 2.5 cm in Laizhou Bay,and the total ice coverage area and total ice actual area increased by about 2×104 km 2 and 1.4×104 km 2,respectively.While the tidal amplitude intensity increased by 25%,the average ice velocity increased by approximately 0.1 m/s.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Winter sea ice albedo variations in the Bohai Sea of China

Sea ice conditions in the Bohai Sea of China are sensitive to large-scale climatic variations. On the basis of CLARA-A1-SAL data, the albedo variations are examined in space and time in the winter（December, January and February） from 1992 to 2008 in the Bohai Sea sea ice region. Time series data of the sea ice concentration（SIC）, the sea ice extent（SIE） and the sea surface temperature（SST） are used to analyze their relationship with the albedo. The sea ice albedo changed in volatility appears along with time, the trend is not obvious and increases very slightly during the study period at a rate of 0.388% per decade over the Bohai Sea sea ice region.The interannual variation is between 9.93% and 14.50%, and the average albedo is 11.79%. The sea ice albedo in years with heavy sea ice coverage, 1999, 2000 and 2005, is significantly higher than that in other years; in years with light sea ice coverage, 1994, 1998, 2001 and 2006, has low values. For the monthly albedo, the increasing trend（at a rate of 0.988% per decade） in December is distinctly higher than that in January and February. The mean albedo in January（12.90%） is also distinctly higher than that in the other two months. The albedo is significantly positively correlated with the SIC and is significantly negatively correlated with the SST（significance level 90%）.

Characteristics of the sea ice reflectance spectrum polluted by oil spills based on field experiments in the Bohai Sea

Oil spilled on the sea ice surface in the Bohai Sea of China is studied through the field measurements of the reflectance of a simulated sea ice-oil film mixed pixel. The reflection characteristics of sea ice and oil film are also analyzed. It is found that the mixed pixel of sea ice and oil film is a linear mixed pixel. The means of extracting sea ice pixels containing oil film is presented using a double-band ratio oil-film sea-ice index（DROSI） and a normalized difference oil-film sea-ice index（NDOSI） through the analysis of the reflectance curves of the sea iceoil film pixel for different ratios of oil film. The area proportion of the oil film in the sea ice-oil film pixel can be accurately estimated by the average reflectance of the band of 1 610–1 630 nm, and the volume of the spilled oil can be further estimated. The method of the sea ice-oil film pixel extraction and the models to estimate the proportion of oil film area in the sea ice-oil film pixel can be applied to the oil spill monitoring of the ice-covered area in the Bohai Sea using multispectral or hyperspectral remote sensing images in the shortwave infrared band（1 500–1 780 nm）.

The long-term trend of Bohai Sea ice in different emission scenarios

Based on a coupled ocean-sea ice model,this study investigates how changes in the mean state of the atmosphere in different CO2 emission scenarios (RCP 8.5,6.0,4.5 and 2.6) may affect the sea ice in the Bohai Sea,China,especially in the Liaodong Bay,the largest bay in the Bohai Sea. In the RCP 8.5 scenario,an abrupt change of the atmospheric state happens around 2070. Due to the abrupt change,wintertime sea ice of the Liaodong Bay can be divided into 3 periods: a mild decreasing period (2021–2060),in which the sea ice severity weakens at a near-constant rate;a rapid decreasing period (2061–2080),in which the sea ice severity drops dramatically;and a stabilized period (2081–2100). During 2021–2060,the dates of first ice are approximately unchanged,suggesting that the onset of sea ice is probably determined by a cold-air event and is not sensitive to the mean state of the atmosphere. The mean and maximum sea ice thickness in the Liaodong Bay is relatively stable before 2060,and then drops rapidly in the following decade. Different from the RCP 8.5 scenario,atmospheric state changes smoothly in the RCP 6.0,4.5 and 2.6 scenarios. In the RCP 6.0 scenario,the sea ice severity in the Bohai Sea weakens with time to the end of the twenty-first century. In the RCP 4.5 scenario,the sea ice severity weakens with time until reaching a stable state around the 2070s. In the RCP 2.6 scenario,the sea ice severity weakens until the 2040s,stabilizes from then,and starts intensifying after the 2080s. The sea ice condition in the other bays of the Bohai Sea is also discussed under the four CO_(2) emissions scenarios. Among atmospheric factors,air temperature is the leading one for the decline of the sea ice extent. Specific humidity also plays an important role in the four scenarios. The surface downward shortwave/longwave radiation and meridional wind only matter in certain scenarios,while effects from the zonal wind and precipitation are negligible.

Study on the Exploitation of Bohai Sea Ice as Freshwater Resources

Water shortage is a major issue in northern China. With the transfer of China＇s economic center from south to north, Circum-Bohai Economic Circle has gradually formed and developed as the third pole of China＇s economy after Yangtze River Delta and Pearl River Delta, the problem of water has inevitably become more and more serious. South-to-North Water Diversion and Sea Water Desalination are helpful to solve this dilemma, but some difficulties appeared during the course of the two projects. By contrast, sea ice desalination can be considered as a third way to alleviate freshwater crisis in Bohai Rim. Firstly, the amount of sea ice in Bohai Sea is as much as 1.27 billion m3 even in normal years. Then the salinity of sea ice is only 1/4 to 1/7 as that of sea water. And furthermore, desalinized water of sea ice can meet national water quality standard for drinking. Purposely use the abundant sea ice resources as water source for industry, agriculture and living is very likely to open a new path for the solution of freshwater shortage, which can promote rapid and healthy development of Circum-Bohai Economic Circle.

Role of the Greenland Sea ice anomaly in the late-spring drought over Northwest China

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.

The in situ observation of modelled sea ice drift characteristics in the Bohai Sea

Sea ice drift is mainly controlled by ocean currents, local wind, and internal ice stress. Information on sea ice motion, especially in situ synchronous observation of an ice velocity, a current velocity, and a wind speed, is of great significance to identify ice drift characteristics. A sea ice substitute, the so-called "modelled ice", which is made by polypropylene material with a density similar to Bohai Sea ice, is used to complete a free drift experiment in the open sea. The trajectories of isolated modelled ice, currents and wind in the Bohai Sea during non-frozen and frozen periods are obtained. The results show that the currents play a major role while the wind plays a minor role in the free drift of isolated modelled ice when the wind is mild in the Bohai Sea. The modelled ice drift is significantly affected by the ocean current and wind based on the ice–current–wind relationship established by a multiple linear regression. The modelled ice velocity calculated by the multiple linear regression is close to that of the in situ observation, the magnitude of the error between the calculated and observed ice velocities is less than12.05%, and the velocity direction error is less than 6.21°. Thus, the ice velocity can be estimated based on the observed current velocity and wind speed when the in situ observed ice velocity is missing. And the modelled ice of same thickness with a smaller density is more sensitive to the current velocity and the wind speed changes. In addition, the modelled ice drift characteristics are shown to be close to those of the real sea ice, which indicates that the modelled ice can be used as a good substitute of real ice for in situ observation of the free ice drift in the open sea, which helps solve time availability, safety and logistics problems related to in situ observation on real ice.

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.

Sustainable observations of landfast sea ice in the cryosphere:a case study at the Liaodong Bay,Bohai Sea,North China

Sustainable monitoring of sea ice is crucial for better understanding air-ice-ocean interactions and identifying new processes.However,it is an expensive process particularly for the polar cryosphere environment.The seasonal ice-covered sea area can be used as a test bed for cryosphere-related process studies due to convenient access and conduction of field work,and the seasonal regime variation of the Arctic sea ice resulting from climate changes.In this paper,a small landfast sea ice monitoring program has been carried out for four consecutive seasons at Jiangjunshi Port,the Bohai Sea,North China,analyzing the temperature and salinity of air,ice and ocean and discussing the influence on mechanical properties.The effect of air temperature on sea ice temperature is focused.During low-temperature periods,the maximum correlation coefficient between air temperature and ice temperature,along with temperature fluctuation within ice,decreases as ice depth increases.Ice salinity was measured using ice core sampling and ice crumb sampling,with ice crumb salinity twice larger compared to ice core sampling when the ice temperature is−3℃.Ice salinity variations with ice temperature and the salinity profiles were fitted.Analysis of the profiles of under-ice seawater salinity reveals the presence of a high-salinity layer near the bottom of sea ice during the initial stage of sea ice growth.Based on the dynamic changes in sea ice temperature and sea ice salinity,this study evaluates the mechanical properties of sea ice,with the fitting determination coefficients of the obtained parameterized formulas significantly better than those reported in current research.

A Sea Ice Recognition Algorithm in Bohai Based on Random Forest

As an important maritime hub,Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter,which greatly affects the socio-economic and development of the region.Therefore,this paper uses FY-4A(a weather satellite)data to study sea ice in the Bohai Sea.After processing the data for land removal and cloud detection,it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions.The random forests classification algorithm is introduced in sea ice identification,which can achieve a certain effect of sea ice classification recognition under cloud cover.Under non-clear sky conditions,the results of Bohai Sea ice identification based on random forests have been improved,and the algorithm can effectively identify Bohai Sea Ice and can improve the accuracy of sea ice identification,which lays a foundation for the accuracy and stability of sea ice identification.It realizes sea ice identification in the Bohai Sea and provides data support and algorithm support for marine climate forecasting related departments.

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.

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.

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.