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.

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.

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）.

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.

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）.